Patent Application
20210130899
The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Sep. 25, 2017, is named 39663495_SL.txt and is 204,800 bytes in size.
Corticotropin-releasing hormone (CRH or corticotropin-releasing factor/CRF) is pivotal in modulating the activity of the hypothalamic-pituitary-adrenal (HPA) axis during stress, stress-response and stress-adaptation, as well as in inflammation. CRH is a 41 aa peptide hormone derived from a 196-amino acid pre-prohormone, produced in the hypothalamus and transported in small vessels to the pituitary from which the peripheral stress hormone corticotropin (also known as adrenocorticotropic hormone/ACTH) is released which, in turn, induces secretion of cortisol from the adrenal gland. CRH containing nerve fibers also project to areas in the CNS implicated in behavioral adaptation to stress, including the amygdala, being implied in fear and anxiety, the prefrontal cortex and the hippocampus. Persistent stress is hypothesized to result in anxiety, depressive symptoms and other stress-related disorders in patients with inherited or acquired vulnerability. Among those patients, antagonists of CRH would appear to be the ideally tailored therapy. The effects of CRH in the brain, where CRH acts like a neurotransmitter, are conveyed via the type 1 CRH receptor (CRHR1, or CRF-R1), which mediates a variety of endocrine, behavioural, and autonomic stress-responses (Heinrichs and Koob, J Pharmacol Exp Ther. 2004 November; 311(2):427-40), including, but not being limited to, psychiatric conditions such as anxiety disorders and major depression (Holsboer and Ising, Eur J Pharmacol 2008, 583(2-3):350-7; Koob and Zorilla, Neuropsychopharmacology 2012, 37(1):308-9). In murine models, CRHR1 deletions displayed less depression-related behaviors, while CRH overexpression in the CNS lead to an increase of several behaviors that can, within certain limitations, be extrapolated to human depression.
The World Health Organization (WHO) considers depression as one of the top ten causes of morbidity and mortality, with a lifetime prevalence for depression ranging, e.g., from 12-16% in Germany. Depressive disorders account for a worldwide number of over one million suicides annually, and create a significant burden on costs in health care, work leave, disability pension, early retirement, loss of productivity of workers, by far surmounting direct costs such as inpatient and outpatient treatments. Finally, depression also multiplies the risk for other conditions such as cardiovascular disease, diabetes and neurodegenerative disorders.
Significant effort has been focused on the development of inhibitors of neuropeptide receptor ligands as drugs for psychiatric diseases and related conditions, including CRHR1 antagonists for the treatment of anxiety and depression (Griebel and Holsboer, Nature Reviews Drug Discovery 2012, 11:462-478). However, essentially all randomized controlled trials using CRHR1 antagonists in humans produced negative results, which has lead several originators to stall CRHR1 antagonist development, see Williams, Expert Opin Ther Pat 2013, 23(8):1057-68.
The present invention rests in part on the recognition that several of these earlier trials testing CRHR1 antagonist only failed to show statistically relevant effects due to the lack of appropriate patient stratification and selection according to their individual, underlying pathophysiology. In other words, a CRHR1 antagonist can only be effective in pathologies where the underlying causality is dominated by CRH over-activity or excessive CRH secretion. In the absence of CRH over-activity, a CRHR1-antagonist is not likely to have any significant effect.
Methods and algorithms for predicting an ACTH response to CRHR1 antagonists using the dex/CRH test in patients with depressive symptoms and/or anxiety symptoms, as well as a set of genotypes of single nucleotide polymorphisms (SNPs) for use in such methods and algorithms, have been described in WO 2013/160315 (A2). Correspondingly, CRHR1 antagonists for use in the treatment of depressive symptoms and/or anxiety symptoms in patients having CRH over-activity have been described in WO 2013/160317 (A2), wherein CRH over-activity is detected by determining the status of the same set of genotypes of SNPs as in WO 2013/160315 (A2). However, there remains a need for improved methods of predicting the treatment response to CRHR1 antagonists. In particular, there is a strong need to provide a direct prediction of clinical response in subjects treated with a treatment with a CRHR1 antagonist, e.g., in subjects having depressive symptoms or anxiety symptoms, or another stress-related condition mediated by CRHR1. A particularly useful CRHR1 antagonist is SSR-125543 or a pharmaceutically acceptable salt thereof.
The present invention rests on additional evidence unknown in the prior art, according to which many polymorphisms are present in essentially all relevant nodes of the CRH/CRHR1 signaling chain. It is, thus, an object of the present invention to provide a method of treatment, wherein a particularly useful set of genomic DNA polymorphisms is used for predicting a central CRH over-activity and/or a clinical response to treatment with SSR-125543, in particular in, but not being limited to, patients with anxiety symptoms or depressive symptoms. Thus, the present invention provides improved methods of treatment comprising SSR-125543.
The present invention is based, at least in part, on the recognition of polymorphism genotypes, including, but not being limited to, single nucleotide polymorphism (SNP) genotypes that are predictive of a subject's clinical responsiveness or non-responsiveness to treatment with a corticotropin releasing hormone receptor type 1 (CRHR1) antagonist. Specifically, the presence or absence of one or more of the polymorphism genotypes disclosed in Table 2 herein can be used to predict the likelihood that a given subject will or will not respond to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. The set and subsets of polymorphism genotypes, compositions, and methods described herein are thus useful in selecting appropriate treatment modalities (e.g., a treatment with SSR-125543 or a non-CRHR1 antagonist) for a subject having a condition treatable by SSR-125543 or a pharmaceutically acceptable salt thereof.
Thus, in a first aspect, the invention provides a method of treating a condition which is treatable by SSR-125543 or a pharmaceutically acceptable salt thereof in a subject in need thereof, comprising administering an effective amount of SSR-125543 or a pharmaceutically acceptable salt thereof to the subject, wherein the subject has been predicted to respond, or has an increased likelihood of responding, to a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. In one embodiment, the method of treating comprises predicting a treatment response of a subject to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, wherein predicting comprises: providing a biological sample obtained from the subject, and detecting the presence or absence of one or more polymorphism genotypes in the biological sample, wherein the one or more polymorphism genotypes comprise: (a) at least one polymorphism genotype selected from the group consisting of rs34169260 (A/G), rs796287 (A/C), rs56149945 (A/G), rs6190 (T/C), rs7179092 (T/C), rs7614867 (A/G), rs920640 (T/C), rs7167722 (T/C), rs920638 (T/C), rs7165629 (T/C), rs80049044 (T/A), rs16941058 (A/G), rs112015971 (A/G), rs10894873 (T/C), rs117455294 (T/G), rs1170303 (T/C), rs16940681 (C/G), rs968519 (T/C), rs28381866 (T/C), rs79320848 (T/G), rs114653646 (T/G), rs2589496 (T/C), rs10482650 (A/G), rs17614642 (A/G), rs73200317 (T/C), rs1380146 (T/A), rs735164 (T/C), rs730976 (T/G), rs55934524 (T/G), rs4570614 (A/G), rs4458044 (C/G), rs77850169 (A/G), rs35339359 (A/G), rs34800935 (T/C), rs72945439 (T/C), rs113959523 (A/G), rs116798177 (A/G), rs11247577 (T/G), rs75869266 (T/C), rs74372553 (T/C), rs11691508 (A/G), rs6493965 (A/G), rs4869476 (T/C), rs3730170 (T/C), rs2145288 (A/C), rs2935752 (A/C), rs146512400 (A/G), rs62057097 (T/C), rs115061314 (T/C), rs34113594 (T/G), rs61751173 (A/G), rs74338736 (A/C), rs10851726 (T/C), rs4610906 (T/C), rs59485211 (T/C), rs7060015 (T/G), rs75710780 (T/G), rs6520908 (T/C), rs487011 (T/G), rs1383699 (A/C), rs67516871 (A/G), rs114106519 (T/C), rs7220091 (A/G), rs12489026 (A/G), rs876270 (T/C), rs4968161 (T/C), rs62056907 (A/G), rs2235013 (T/C), rs16878812 (A/G), rs6549407 (A/G), rs28381848 (A/G), rs79723704 (A/C), rs72814052 (A/G), rs10152908 (T/C), rs172769 (A/C), rs78596668 (T/C), rs73307922 (T/C), rs3842 (A/G), rs7210584 (A/C), rs62402121 (T/C), rs55709291 (A/G), rs72747088 (A/G), rs929610 (G/C), rs6766242 (T/C), rs1468552 (G/C), rs78838114 (T/C), rs62489862 (T/C), rs894342 (A/G), rs58882373 (T/C), rs3811939 (A/G), rs6984688 (T/G), rs1018160 (T/C), rs76602912 (A/G), rs80067508 (A/G), rs74888440 (T/C), rs12481583 (T/C), rs66794218 (A/G), rs16946701 (A/G), rs75726724 (A/G), rs67959715 (T/A), rs11871392 (T/G), rs2044070 (A/G), rs77612799 (T/C), rs6743702 (T/C), rs616870 (T/C), rs79590198 (A/G), rs75715199 (A/G), rs13087555 (T/C), rs4869618 (T/C), rs117397046 (A/G), rs8042817 (A/G), rs2258097 (T/C), rs2260882 (C/G), rs532996 (A/G), rs11747040 (T/C), rs10034039 (T/G), rs116909369 (A/G), rs79134986 (A/G), rs117615688 (T/C), rs8032253 (T/C), rs12818653 (T/A), rs4587884 (A/C), rs77122853 (T/C), rs117615061 (T/C), rs74682905 (A/G), rs2257468 (T/C), rs2032582 (T/G), rs2235015 (T/G), rs2729794 (T/C), rs77549514 (A/G), rs74790420 (A/C), rs73129579 (T/C), rs12913346 (A/C), rs117560908 (T/C), rs72747091 (A/G), rs2935751 (A/G), rs4331446 (A/G), rs7523266 (T/C), rs7648662 (T/C), rs117034065 (A/G), rs4836256 (T/C), rs80238698 (T/C), rs3730173 (T/C), rs11687884 (T/C), rs72693005 (T/C), rs2589476 (T/C), rs9813396 (T/C), rs10482667 (A/G), rs72784444 (A/G), rs75074511 (T/C), rs7951003 (A/G), rs79584784 (A/G), rs2214102 (T/C), rs28811003 (A/G), rs6100261 (A/T), rs77152456 (A/G), rs66624622 (T/G), rs140302965 (A/G), rs11653269 (T/C), rs74405057 (A/G), rs7121 (A/G), rs16977818 (A/C), rs12490095 (T/C), rs118003903 (A/G), rs62377761 (T/C), P1_M_061510_6_34_M (−/CACTTACCTTCTTTGTGCCACAGTTTCCCTATCTAAAACAC AAGGTTATCAGTTATCAACATCTCTTGGGATTGTGAGGACTAAAGTAATGCACATAA AG), rs375115639 (−/AAATTACCCTGTTAGGTTTCAATGAAACACCTTTTCTCTTGTAACA AACATCTCCTCC AAGCTAGAATTTCAAAACAG), rs1002204 (A/C), rs10062367 (A/G), rs10482642 (A/G), rs10482658 (A/G), rs1053989 (A/C), rs10851628 (T/C), rs10947562 (T/C), rs11069612 (A/G), rs11071351 (T/C), rs11091175 (A/G), rs11638450 (T/C), rs11715827 (T/G), rs11745958 (T/C), rs11834041 (A/G), rs1202180 (T/C), rs12054781 (A/G), rs12539395 (A/G), rs12720066 (T/G), rs1279754 (A/C), rs12872047 (T/C), rs12876742 (A/C), rs12917505 (A/G), rs13066950 (T/G), rs13229143 (C/G), rs1383707 (T/C), rs1441824 (T/C), rs1652311 (A/G), rs17064 (T/A), rs17100236 (A/G), rs17149699 (A/G), rs1724386 (A/G), rs17250255 (A/G), rs17327624 (T/G), rs17616338 (A/G), rs17687796 (A/G), rs17740874 (T/C), rs17763104 (T/C), rs1880748 (T/C), rs1882478 (A/G), rs1944887 (T/C), rs2028629 (A/G), rs2143404 (A/G), rs2173530 (T/C), rs220806 (T/C), rs2235047 (A/C), rs2242071 (A/G), rs2257474 (T/C), rs2295583 (A/T), rs234629 (T/C), rs234630 (A/G), rs2436401 (A/G), rs258750 (T/C), rs2589487 (T/C), rs28364018 (T/G), rs28381774 (T/C), rs28381784 (A/G), rs2963155 (A/G), rs3133622 (T/G), rs32897 (T/C), rs33388 (A/T), rs3730168 (T/C), rs3735833 (T/G), rs3777747 (A/G), rs3786066 (T/C), rs3798346 (T/C), rs3822736 (A/G), rs389035 (T/C), rs3924144 (A/G), rs4148737 (T/C), rs4148749 (G/C), rs417968 (T/C), rs4458144 (T/C), rs4515335 (T/C), rs4728699 (A/G), rs4758040 (A/G), rs4812040 (A/G), rs4912650 (T/G), rs4957891 (T/C), rs5906392 (A/G), rs6026561 (T/C), rs6026565 (T/A), rs6026567 (A/G), rs6026593 (A/G), rs6092704 (T/G), rs6100260 (A/G), rs6128461 (T/C), rs6415328 (T/C), rs6610868 (T/C), rs6686061 (A/C), rs6730350 (T/G), rs6746197 (T/C), rs6963426 (T/C), rs7121326 (T/C), rs7721799 (A/G), rs7787082 (T/C), rs7799592 (A/C), rs796245 (T/C), rs809482 (A/C), rs8125112 (T/C), rs919196 (A/G), rs920750 (T/C), rs9332385 (A/G), rs930473 (T/G), rs9324921 (A/C), rs9348979 (A/G), rs9571939 (A/C), and rs9892359 (T/C); (b) at least one polymorphism genotype being in linkage disequilibrium with any one of the polymorphism genotypes of (a); or (c) a combination of (a) and (b); and predicting the treatment response from the presence or absence of the one or more polymorphism genotypes of (a), (b), or (c). In a preferred embodiment, the method of treating comprises predicting a treatment response of a subject to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, wherein predicting comprises: providing a biological sample obtained from the subject, and detecting the presence or absence of one or more polymorphism genotypes in the biological sample, wherein the one or more polymorphism genotypes comprise: (a) at least one polymorphism genotype selected from the group consisting of rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G) as disclosed in Table 2, optionally (b) in combination with at least one polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2 and predicting the treatment response from the presence or absence of the one or more polymorphism genotypes of (a), optionally in combination with (b).
In another embodiment, the method of treating comprises detecting a polymorphism genotype associated with a treatment response of a subject to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, the detecting comprising providing a biological sample obtained from the subject, and detecting the presence or absence of one or more polymorphism genotypes in the biological sample, wherein the one or more polymorphism genotypes comprise: (a) at least one polymorphism genotype selected from the group consisting of rs34169260 (A/G), rs796287 (A/C), rs56149945 (A/G), rs6190 (T/C), rs7179092 (T/C), rs7614867 (A/G), rs920640 (T/C), rs7167722 (T/C), rs920638 (T/C), rs7165629 (T/C), rs80049044 (T/A), rs16941058 (A/G), rs112015971 (A/G), rs10894873 (T/C), rs117455294 (T/G), rs1170303 (T/C), rs16940681 (C/G), rs968519 (T/C), rs28381866 (T/C), rs79320848 (T/G), rs114653646 (T/G), rs2589496 (T/C), rs10482650 (A/G), rs17614642 (A/G), rs73200317 (T/C), rs1380146 (T/A), rs735164 (T/C), rs730976 (T/G), rs55934524 (T/G), rs4570614 (A/G), rs4458044 (C/G), rs77850169 (A/G), rs35339359 (A/G), rs34800935 (T/C), rs72945439 (T/C), rs113959523 (A/G), rs116798177 (A/G), rs11247577 (T/G), rs75869266 (T/C), rs74372553 (T/C), rs11691508 (A/G), rs6493965 (A/G), rs4869476 (T/C), rs3730170 (T/C), rs2145288 (A/C), rs2935752 (A/C), rs146512400 (A/G), rs62057097 (T/C), rs115061314 (T/C), rs34113594 (T/G), rs61751173 (A/G), rs74338736 (A/C), rs10851726 (T/C), rs4610906 (T/C), rs59485211 (T/C), rs7060015 (T/G), rs75710780 (T/G), rs6520908 (T/C), rs487011 (T/G), rs1383699 (A/C), rs67516871 (A/G), rs114106519 (T/C), rs7220091 (A/G), rs12489026 (A/G), rs876270 (T/C), rs4968161 (T/C), rs62056907 (A/G), rs2235013 (T/C), rs16878812 (A/G), rs6549407 (A/G), rs28381848 (A/G), rs79723704 (A/C), rs72814052 (A/G), rs10152908 (T/C), rs172769 (A/C), rs78596668 (T/C), rs73307922 (T/C), rs3842 (A/G), rs7210584 (A/C), rs62402121 (T/C), rs55709291 (A/G), rs72747088 (A/G), rs929610 (G/C), rs6766242 (T/C), rs1468552 (G/C), rs78838114 (T/C), rs62489862 (T/C), rs894342 (A/G), rs58882373 (T/C), rs3811939 (A/G), rs6984688 (T/G), rs1018160 (T/C), rs76602912 (A/G), rs80067508 (A/G), rs74888440 (T/C), rs12481583 (T/C), rs66794218 (A/G), rs16946701 (A/G), rs75726724 (A/G), rs67959715 (T/A), rs11871392 (T/G), rs2044070 (A/G), rs77612799 (T/C), rs6743702 (T/C), rs616870 (T/C), rs79590198 (A/G), rs75715199 (A/G), rs13087555 (T/C), rs4869618 (T/C), rs117397046 (A/G), rs8042817 (A/G), rs2258097 (T/C), rs2260882 (C/G), rs532996 (A/G), rs11747040 (T/C), rs10034039 (T/G), rs116909369 (A/G), rs79134986 (A/G), rs117615688 (T/C), rs8032253 (T/C), rs12818653 (T/A), rs4587884 (A/C), rs77122853 (T/C), rs117615061 (T/C), rs74682905 (A/G), rs2257468 (T/C), rs2032582 (T/G), rs2235015 (T/G), rs2729794 (T/C), rs77549514 (A/G), rs74790420 (A/C), rs73129579 (T/C), rs12913346 (A/C), rs117560908 (T/C), rs72747091 (A/G), rs2935751 (A/G), rs4331446 (A/G), rs7523266 (T/C), rs7648662 (T/C), rs117034065 (A/G), rs4836256 (T/C), rs80238698 (T/C), rs3730173 (T/C), rs11687884 (T/C), rs72693005 (T/C), rs2589476 (T/C), rs9813396 (T/C), rs10482667 (A/G), rs72784444 (A/G), rs75074511 (T/C), rs7951003 (A/G), rs79584784 (A/G), rs2214102 (T/C), rs28811003 (A/G), rs6100261 (A/T), rs77152456 (A/G), rs66624622 (T/G), rs140302965 (A/G), rs11653269 (T/C), rs74405057 (A/G), rs7121 (A/G), rs16977818 (A/C), rs12490095 (T/C), rs118003903 (A/G), rs62377761 (T/C), P1_M_061510_6_34_M (−/CACTTACCTTCTTTGTGCCACAGTTTCCCTATCTAAAACACAAGGTTATCAGTTATC AACATCTCTTGGGATTGTGAGGACTAAAGTAATGCACATAAAG), rs375115639 (−/AAATTACCCTGTTAGGTTTCAATGAAACACCTTTTCTCTTGTAACAAACATCTCCTC CA AGCTAGAATTTCAAAACAG), rs1002204 (A/C), rs10062367 (A/G), rs10482642 (A/G), rs10482658 (A/G), rs1053989 (A/C), rs10851628 (T/C), rs10947562 (T/C), rs11069612 (A/G), rs11071351 (T/C), rs11091175 (A/G), rs11638450 (T/C), rs11715827 (T/G), rs11745958 (T/C), rs11834041 (A/G), rs1202180 (T/C), rs12054781 (A/G), rs12539395 (A/G), rs12720066 (T/G), rs1279754 (A/C), rs12872047 (T/C), rs12876742 (A/C), rs12917505 (A/G), rs13066950 (T/G), rs13229143 (C/G), rs1383707 (T/C), rs1441824 (T/C), rs1652311 (A/G), rs17064 (T/A), rs17100236 (A/G), rs17149699 (A/G), rs1724386 (A/G), rs17250255 (A/G), rs17327624 (T/G), rs17616338 (A/G), rs17687796 (A/G), rs17740874 (T/C), rs17763104 (T/C), rs1880748 (T/C), rs1882478 (A/G), rs1944887 (T/C), rs2028629 (A/G), rs2143404 (A/G), rs2173530 (T/C), rs220806 (T/C), rs2235047 (A/C), rs2242071 (A/G), rs2257474 (T/C), rs2295583 (A/T), rs234629 (T/C), rs234630 (A/G), rs2436401 (A/G), rs258750 (T/C), rs2589487 (T/C), rs28364018 (T/G), rs28381774 (T/C), rs28381784 (A/G), rs2963155 (A/G), rs3133622 (T/G), rs32897 (T/C), rs33388 (A/T), rs3730168 (T/C), rs3735833 (T/G), rs3777747 (A/G), rs3786066 (T/C), rs3798346 (T/C), rs3822736 (A/G), rs389035 (T/C), rs3924144 (A/G), rs4148737 (T/C), rs4148749 (G/C), rs417968 (T/C), rs4458144 (T/C), rs4515335 (T/C), rs4728699 (A/G), rs4758040 (A/G), rs4812040 (A/G), rs4912650 (T/G), rs4957891 (T/C), rs5906392 (A/G), rs6026561 (T/C), rs6026565 (T/A), rs6026567 (A/G), rs6026593 (A/G), rs6092704 (T/G), rs6100260 (A/G), rs6128461 (T/C), rs6415328 (T/C), rs6610868 (T/C), rs6686061 (A/C), rs6730350 (T/G), rs6746197 (T/C), rs6963426 (T/C), rs7121326 (T/C), rs7721799 (A/G), rs7787082 (T/C), rs7799592 (A/C), rs796245 (T/C), rs809482 (A/C), rs8125112 (T/C), rs919196 (A/G), rs920750 (T/C), rs9332385 (A/G), rs930473 (T/G), rs9324921 (A/C), rs9348979 (A/G), rs9571939 (A/C), and rs9892359 (T/C); (b) at least one polymorphism genotype being in linkage disequilibrium with any one of the polymorphism genotypes of (a); or (c) a combination of (a) and (b). In one embodiment, the method further comprises predicting the treatment response from the presence or absence of the polymorphism genotypes of (a), (b), or (c). In a preferred embodiment, the method of treating comprises detecting a polymorphism genotype associated with a treatment response of a subject to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, the detecting comprising providing a biological sample obtained from the subject, and detecting the presence or absence of one or more polymorphism genotypes in the biological sample, wherein the one or more polymorphism genotypes comprise: (a) at least one genotype selected from the group consisting of rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G) as disclosed in Table 2, optionally (b) in combination with at least one polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2. In a further preferred embodiment, the method further comprises predicting the treatment response from the presence or absence of the polymorphism genotypes of (a), optionally in combination with (b).
In another aspect, SSR-125543 or a pharmaceutically acceptable salt thereof for use in treating a condition which is treatable by a CRHR1 antagonist in a subject in need thereof is provided, wherein the subject has been predicted to respond, or has an increased likelihood of responding, to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, as determined by the step of predicting a treatment response described above. In another aspect, SSR-125543 or a pharmaceutically acceptable salt thereof for use in treating a condition which is treatable by a CRHR1 antagonist in a subject in need thereof is provided, wherein the subject has been predicted to respond, or has an increased likelihood of responding, to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, as determined by the step of detecting a polymorphism genotype associated with a treatment response of a subject to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. In embodiments, when a favorable treatment predictive response is determined based on a polymorphism(s) described herein, then the subject can be administered a dosage of treatment that is less than the amount of a dosage of treatment required by an individual not having the polymorphism(s).
The above aspects of the invention can be put into practice in any one of the following embodiments.
In one embodiment, providing a biological sample comprises extraction and/or purification of nucleic acids such as DNA or RNA, in particular genomic DNA from the subject's sample. In one embodiment, the detecting step can comprise amplification of nucleic acids extracted and/or purified from the sample obtained from the subject, and optionally clean-up of amplified products. The detecting step can further comprise fragmentation of amplified nucleic acids, or labelling of amplified nucleic acids.
In one embodiment, the detecting step can further comprise specific hybridization of at least one polynucleotide to a nucleic acid comprising at least one polymorphism genotype selected from the group disclosed in Table 2 herein. Hybridization can be achieved by mixing and heating the at least one polynucleotide and the sample nucleic acid to a temperature at which denaturation occurs, e.g., at about 90-95° C. and subsequent incubation at a temperature at which hybridization occurs, e.g., at about 45-55° C. in buffer conditions suitable for specific hybridization. In one embodiment the polynucleotide is labelled. The polynucleotide can be a primer or probe. Specifically, in some embodiments, the detecting step comprises a method selected from the group consisting of allele-specific oligonucleotide (ASO)-dot blot analysis, primer extension assays, iPLEX polymorphism/SNP genotyping, dynamic allele-specific hybridization (DASH) genotyping, the use of molecular beacons, tetra primer ARMS PCR, a flap endonuclease invader assay, an oligonucleotide ligase assay, PCR-single strand conformation polymorphism (SSCP) analysis, quantitative real-time PCR assay, polymorphism/SNP microarray based analysis, restriction enzyme fragment length polymorphism (RFLP) analysis, targeted resequencing analysis and/or whole genome sequencing analysis.
In one embodiment, the predicting step comprises: (a) determining whether the subject will respond, or has an increased likelihood of responding to the treatment with SSR-125543 or a pharmaceutically acceptable salt thereof; and/or (b) determining whether the subject will not respond, or has a decreased likelihood of responding to the treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. The determining step may further comprise, but is not limited to, one or more statistical analysis methods selected from the group consisting of artificial neural network learning, decision tree learning, decision tree forest learning, linear discriminant analysis, non-linear discriminant analysis, genetic expression programming, relevance vector machines, linear models, generalized linear models, generalized estimating equations, generalized linear mixed models, the elastic net, the lasso support vector machine learning, Bayesian network learning, probabilistic neural network learning, clustering, and regression analysis. The predicting step may also comprise providing a value indicative of the subject being responsive, or having an increased likelihood of responding to the treatment with SSR-125543 or a pharmaceutically acceptable salt thereof; and/or providing a value indicative of the subject being non-responsive, or having a decreased likelihood of responding to the treatment with SSR-125543 or a pharmaceutically acceptable salt thereof.
In one embodiment, the one or more polymorphism genotypes comprise at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 15, at least 19, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, or all (a) polymorphism genotypes selected from the group consisting of the polymorphism genotypes disclosed in Table 2, (b) polymorphism genotypes being in linkage disequilibrium with the polymorphism genotypes disclosed in Table 2; or (c) a combination of (a) and (b). In a further preferred embodiment, the one or more polymorphism genotypes comprise at least two, at least three, at least four or all polymorphism genotypes selected from the group consisting of rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G) as disclosed in Table 2, optionally in combination with at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 11, at least 12, at least 13, at least 14, at least 15, polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2.
In a specific embodiment, the one or more polymorphism genotypes comprise (a) at least two polymorphism genotypes selected from the group consisting of the polymorphism genotypes disclosed in Table 2, (b) at least two polymorphism genotypes being in linkage disequilibrium with the polymorphism genotypes disclosed in Table 2; or (c) a combination of (a) and (b). Exemplary sets of at least two polymorphism genotypes useful in the methods of the invention are disclosed in Table 5. Therefore, the specific combinations of at least two polymorphism genotypes disclosed in Table 5 are used in specific embodiments of the invention, while further combinations of at least two polymorphism genotypes are expressly contemplated. Preferred combinations of at least two polymorphism genotypes are selected from the group of polymorphism genotypes selected from rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G) as disclosed in Table 2, optionally in combination with at least one polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2.
In another specific embodiment, the one or more polymorphism genotypes comprise (a) at least four polymorphism genotypes selected from the group consisting of the polymorphism genotypes disclosed in Table 2, (b) at least four polymorphism genotypes being in linkage disequilibrium with the polymorphism genotypes disclosed in Table 2, or (c) a combination of (a) and (b). Exemplary sets of at least four polymorphism genotypes useful in the methods of the invention are disclosed in Table 6. Therefore, the specific combinations of at least four polymorphism genotypes disclosed in Table 6 are used in specific embodiments of the invention, while further combinations of at least four polymorphism genotypes are expressly contemplated. Preferred combinations of at least four polymorphism genotypes are selected from the group of polymorphism genotypes selected from rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G) as disclosed in Table 2, optionally in combination with at least one polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2.
In another specific embodiment, the one or more polymorphism genotypes comprise (a) at least eight polymorphism genotypes selected from the group consisting of the polymorphism genotypes disclosed in Table 2, (b) at least eight polymorphism genotypes being in linkage disequilibrium with the polymorphism genotypes disclosed in Table 2, or (c) a combination of (a) and (b). Exemplary sets of at least eight polymorphism genotypes useful in the methods of the invention are shown in Table 7. Therefore, the specific combinations of at least eight polymorphism genotypes disclosed in Table 7 are used in specific embodiments of the invention, while further combinations are expressly contemplated. Preferred combinations of at least eight polymorphism genotypes are selected from the group of polymorphism genotypes selected from rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G) as disclosed in Table 2, in combination with at least four polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2.
In another embodiment, the one or more polymorphism genotypes comprise (a) at least 16 polymorphism genotypes selected from the group consisting of the polymorphism genotypes disclosed in Table 2, (b) at least 16 polymorphism genotypes being in linkage disequilibrium with the polymorphism genotypes disclosed in Table 2, or (c) a combination of (a) and (b). Preferred combinations of at least 16 polymorphism genotypes are selected from the group of polymorphism genotypes selected from rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G) as disclosed in Table 2, in combination with at least 12 polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2. In another embodiment, the one or more polymorphism genotypes comprise (a) at least 32 polymorphism genotypes selected from the group consisting of the polymorphism genotypes disclosed in Table 2, (b) at least 16 polymorphism genotypes being in linkage disequilibrium with the polymorphism genotypes disclosed in Table 2, or (c) a combination of (a) and (b). In another embodiment, the one or more polymorphism genotypes comprise at least 150 polymorphism genotypes selected from the group consisting of the polymorphism genotypes disclosed in Table 2, (b) at least 16 polymorphism genotypes being in linkage disequilibrium with the polymorphism genotypes disclosed in Table 2, or (c) a combination of (a) and (b). In another embodiment, the one or more polymorphism genotypes comprise all polymorphism genotypes disclosed in Table 2.
In some embodiments, the method can include detecting the presence or absence of (a) one or more of the polymorphism genotypes disclosed in Tables 2, 5, 6, or 7, (b) one or more polymorphism genotypes being in linkage disequilibrium with the polymorphism genotypes disclosed in Tables 2, 5, 6, or 7, or (c) a combination of (a) and (b), predicting that the subject will respond, or is likely to respond to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof and selecting a treatment with a CRHR1 agent for the subject. The method can further include administering SSR-125543 or a pharmaceutically acceptable salt thereof to the subject. In the preferred embodiment of the invention, the method can include detecting the presence or absence of (a) one or more of the polymorphism genotypes selected from the group consisting in rs2028629 (A/G), rs6026567 (A/G), rs11715827 (T/G) and rs2044070 (A/G) as disclosed in Table 2, optionally (b) in combination with at least one of the polymorphism genotypes selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2, predicting that the subject will respond, or is likely to respond to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof and selecting a treatment with a CRHR1 agent for the subject. The method can further include administering SSR-125543 or a pharmaceutically acceptable salt thereof to the subject.
In some embodiments, the predicting step can include creating a record indicating that the subject will respond, or is likely to respond to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. The record can be created on a computer readable medium.
In some embodiments, the method can include detecting the presence or absence of (a) one or more of any of the polymorphism genotypes disclosed in Tables 2, 5, 6 or 7, (b) one or more polymorphism genotypes being in linkage disequilibrium with the polymorphism genotypes disclosed in Tables 2, 5, 6, or 7, or (c) a combination of (a) and (b), predicting that the subject will not respond, or is not likely to respond to a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof and selecting a treatment with treatment with a non-CRHR1 antagonist for the subject. The method can further include administering the treatment with the non-CRHR1 antagonist to the subject. In the preferred embodiment of the invention, the method can include detecting the presence or absence of (a) one or more of the polymorphism genotypes selected from the group consisting in rs2028629 (A/G), rs6026567 (A/G), rs11715827 (T/G) and rs2044070 (A/G) as disclosed in Table 2, optionally (b) in combination with at least one of the polymorphism genotypes selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2, predicting that the subject will not respond, or is not likely to respond to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof and selecting a treatment with treatment with a non-CRHR1 antagonist for the subject. The method can further include administering the treatment with the non-CRHR1 antagonist to the subject.
In some embodiments, the method can include creating a record indicating that the subject will not respond, or is not likely to respond to a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. The record can be created on a computer readable medium.
In one embodiment, the subject is a mammal. Preferably, in all aspects of the invention, the subject is human.
In one embodiment, the subject has a condition which is treatable by a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, as described herein. The condition can be characterized, caused or accompanied by CRH overproduction or over-activity. The condition can be characterized, caused or accompanied by ACTH overproduction or over-activity. The condition can be characterized, caused or accompanied by over-activity of the Hypothalamic-pituitary-adrenal (HPA) axis.
In another embodiment, the subject has and/or the treatment is a treatment of a condition selected from the group consisting of anxiety symptoms, generalized anxiety disorder, panic, phobias, obsessive-compulsive disorder, post-traumatic stress disorder, sleep disorders such as insomnia, hypersomnia, narcolepsy, idiopathic hypersomnia, excessive amounts of sleepiness, lack of alertness, lack of attentiveness, absentmindedness and/or lack of or aversion to movement or exercise, sleep disorders induced by stress, pain perception such as fibromyalgia, mood disorders such as depressive symptoms, including major depression, single episode depression, recurrent depression, child abuse induced depression, mood disorders associated with premenstrual syndrome, and postpartum depression, dysthymia, bipolar disorders, cyclothymia, chronic fatigue syndrome, stress-induced headache, eating disorders such as anorexia and bulimia nervosa, hemorrhagic stress, stress-induced psychotic episodes, endocrine disorders involving ACTH overproduction, ACTH over-activity, e.g., adrenal disorders, including, but not limited to congenital adrenal hyperplasia (CAH), euthyroid sick syndrome, syndrome of inappropriate antidiarrhetic hormone (ADH), obesity, infertility, head traumas, spinal cord trauma, ischemic neuronal damage (e.g., cerebral ischemia such as cerebral hippocampal ischemia), excitotoxic neuronal damage, epilepsy, senile dementia of the Alzheimers type, multi-infarct dementia, amyotrophic lateral sclerosis, chemical dependencies and addictions (e.g., dependencies on alcohol, nicotine, cocaine, heroin, benzodiazepines, or other drugs), drug and alcohol withdrawal symptoms, hypertension, tachycardia, congestive heart failure, osteoporosis, premature birth, and hypoglycaemia, inflammatory disorders such as rheumatoid arthritis and osteoarthritis, pain, asthma, psoriasis and allergies, irritable bowel syndrome, Crohn's disease, spastic colon, post-operative ileus, ulcer, diarrhea, stress-induced fever, human immunodeficiency virus (HIV) infections, neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and Huntington's disease, gastrointestinal diseases, stroke, stress induced immune dysfunctions, muscular spasms, urinary incontinence.
In a specific embodiment, the subject has and/or the treatment is a treatment of depressive symptoms, anxiety symptoms or both depressive symptoms and anxiety symptoms. In another specific embodiment, the subject has and/or the treatment is a treatment of depressive disorder, anxiety disorder or both depressive disorder and anxiety disorder. In another specific embodiment, the subject has and/or the treatment is a treatment of a sleep disorder.
In contrast to the prior art, the present invention identifies sets of polymorphisms indicative of a clinical response in subjects which are in need of a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. Therefore, in all aspects of the invention, the treatment response to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof is preferably a clinical response. Generally, the clinical response can be a prevention, alteration, alleviation or complete remission of a clinical parameter in any of the above conditions. In particular, the clinical response can be a prevention, alteration, alleviation or complete remission of depressive symptoms and/or anxiety symptoms, or a decrease in adverse effects resulting from the treatment.
In some embodiments, the clinical response is a prevention, alteration, alleviation or complete remission of depressive symptoms, as determined using a scale selected from the group consisting of the Hamilton Depression Rating Scale (HAM-D), the Beck Depression Inventory (BDI), the Montgomery-Asberg Depression Scale (MADRS), the Geriatric Depression Scale (GDS), and/or the Zung Self-Rating Depression Scale (ZSRDS).
In some embodiments, the clinical response is a prevention, alteration, alleviation or complete remission of anxiety symptoms, as determined using a scale selected from the group consisting of Hamilton Anxiety Rating Scale (HAM-A) and/or the State-Trait Anxiety Rating Scale (STAI).
Any of the methods described herein can further include a step of prescribing a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof or non-CRHR1 antagonist (the choice of which depends upon the outcome of the predictive methods described herein) for the subject.
In all aspects, the sample obtained from the subject can comprise any type of cells containing genomic DNA. Specifically, the sample can be, e.g., a buccal sample, a blood sample, a tissue sample, a formalin-fixed, paraffin-embedded tissue sample, or a hair follicle.
In all embodiments of the invention, the CRHR1 antagonist is SSR-12554.
The term “comprise” or “comprising” as used herein is to be construed as “containing” or “including” and does generally not exclude other elements or steps, but encompasses the term “consisting of” as an optional, specific embodiment. Thus, a group defined as comprising a certain number of embodiments, is also to be construed as a disclosure of a group which optionally consists only of these embodiments. Where an indefinite or a definite article is used when referring to a singular noun such as “a” or “an” or “the”, it includes a plural form of that noun unless specifically stated. Vice versa, when the plural form of a noun is used it refers also to the singular form. For example, when polymorphism genotypes are mentioned, this is also to be understood as a single polymorphism genotype.
Furthermore, the terms “first”, “second”, “third” or “(a)”, “(b)”, “(c)” and the like in the description and in the claims are used for distinguishing between elements and not necessarily for describing a sequential or chronological order. It is to be understood that the terms so used can be interchangeable under appropriate circumstances and that the embodiments of the invention described herein are capable of operation in other sequences than described or illustrated herein.
“Corticotropin releasing hormone” or “CRH” is used synonymously to the term “corticotropin releasing factor” or “CRF” herein, and refers to the known human 41 aa peptide or its mammalian homologues. The term “corticotropin releasing hormone receptor 1” or “CRHR1” refers to the receptor which binds to CRH and is used synonymously to the term “corticotropin-releasing factor receptor 1”, or CRF-R1, or CRFR-1 herein.
A “CRHR1 antagonist”, as used herein, refers to SSR-125543 or a pharmaceutically acceptable salt thereof, a compound capable of binding directly or indirectly to CRHR1 so as to modulate the receptor mediated activity. SSR-12554, as used herein, is used synonymous to SSR-125543A, 4-(2-chloro-4-methoxy-5-methylphenyl)-N(2-cyclopropyl-1-(3-fluoro-4-methyl phenyl)ethyl)-5-methyl-N-(2-propynyl)-1,3-thiazol-2-amine, or specifically 4-(2-chloro-4-methoxy-5-methylphenyl)-N-[(1S)-2-cyclopropyl-1-(3-fluoro-4-methylphenyl)ethyl]-5-methyl-N-prop-2-ynyl-1,3-thiazol-2-amine and encompasses any pharmaceutically acceptable salt thereof. The CRHR1 mediated activity may be exerted on a downstream target within the signalling pathway of CRHR1. A “downstream target” may refer to a molecule such as an endogenous molecule (e.g. peptide, protein, lipid, nucleic acid or oligonucleotide), that is regulated by CRHR1 directly or indirectly, comprising direct or indirect modulation of the activity and/or expression level and/or localization, degradation or stability of the downstream target. SSR-125543 is shown in the following table.
In one aspect, the present invention provides a method of treating a condition which is treatable by SSR-125543 or a pharmaceutically acceptable salt thereof in a subject in need thereof, comprising administering an effective amount of SSR-125543 or a pharmaceutically acceptable salt thereof to the subject, wherein the subject has been predicted to respond, or has an increased likelihood of responding, to a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. The method of treating can comprise the step of predicting a treatment response of a subject (such as a human patient) to a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof.
Specifically, predicting a treatment response of a subject to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, comprises providing a biological sample obtained from the subject, detecting the presence or absence of one or more polymorphism genotypes in the biological sample, wherein the one or more polymorphism genotypes comprise: (a) at least one polymorphism genotype selected from the group consisting of the polymorphism genotypes disclosed in Table 2, preferably at least one polymorphism genotypes selected from the group consisting of rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G), optionally in combination with at least one polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C), (b) at least one polymorphism genotype being in linkage disequilibrium with any one of the polymorphism genotypes of (a); or (c) a combination of (a) and (b), and predicting the treatment response from the presence or absence of the one or more polymorphism genotypes of (a), (b), or (c). In a preferred aspect, the present invention provides a method of treating a condition characterized, caused or accompanied by CRH overproduction or over-activity, comprising administering an effective amount of SSR-125543 or a pharmaceutically acceptable salt thereof to a subject in need of such a treatment, wherein the subject has been predicted to respond, or has an increased likelihood of responding, to a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, wherein the treatment response has been predicted by detecting the presence or absence of one or more polymorphism genotypes in a biological sample from the subject, wherein the one or more polymorphism genotypes comprise: (a) at least one polymorphism genotype selected from the group consisting of rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G), optionally (b) in combination with at least one polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C). In another aspect, the present invention provides a method of treating a subject having a likelihood of a positive response to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, comprising detecting the presence or absence of one or more polymorphism genotypes in a biological sample from a subject, wherein the one or more polymorphism genotypes comprise: (a) at least one polymorphism genotype selected from the group consisting of rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G), optionally (b) in combination with at least one polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C), and administering to the subject determined to have the one or more polymorphism genotypes and an increased likelihood of being responsive, an effective amount of SSR-125543 or a pharmaceutically acceptable salt thereof.
A “subject”, as used herein, can generally be any mammal, in which one or more polymorphism genotypes as disclosed in Table 2, in particular one or more polymorphism genotypes selected from the group consisting of rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G), optionally in combination with at least one polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C), or polymorphism genotypes being in linkage disequilibrium with any one of the polymorphism genotypes of Table 2 are conserved or homologous. In particular, the term “subject” includes a human subject, and any model organism such as mice, rats, cats, dogs, simians, cattle. Preferably, the subject is a human subject.
A “treatment with SSR-125543 or a pharmaceutically acceptable salt thereof”, as used herein, refers to the treatment of a condition in the subject which can be treated by administration of SSR-125543 or a pharmaceutically acceptable salt thereof, as is made plausible herein or in the prior art. “Conditions treatable with SSR-125543 or a pharmaceutically acceptable salt thereof”, as used herein, are conditions which can generally be treated by administration of SSR-125543 or a pharmaceutically acceptable salt thereof and/or are commonly characterized, caused or accompanied by CRH over-activity, by ACTH over-activity and/or by over-activity of the Hypothalamic-pituitary-adrenal (HPA) axis.
The term “CRH over-activity” is used herein synonymously to the terms “CRH system over-activity”, “CRH hyperactivity”, “CRH hyperdrive” or “central CRH hyperdrive”. An indication for CRH over-activity may be an increase in activity or concentration of CRH or of one or several molecules downstream of the CRHR1 receptor, that are activated or whose concentration is increased based on the activation of CRHR1 receptor upon CRH binding, for instance, but not being limited to, ACTH. A further indication for CRH over-activity may be a decrease in activity or concentration of one or several molecules downstream of the CRHR1 receptor, that are inactivated or whose concentration is decreased resulting from the activation of CRHR1 receptor upon CRH binding. For instance, the concentrations or activities of adrenocorticotrophin (ACTH) and/or cortisol can be used for determining a value indicative for CRH over-activity. The CRH over-activity in each patient may be determined by a CRH test as described in Holsboer et al., N Engl J Med. 1984; 311(17):1127, or by a combined dexamethasone suppression/CRH stimulation test (dex/CRH test) as described in Heuser et al., J Psychiatr Res 1994, 28(4):341-56; both incorporated herein by reference in their entirety.
In particular, conditions which can be treated using SSR-125543 or a pharmaceutically acceptable salt thereof in a subject comprise, but are not limited to, behavioural disorders, neuropsychiatric disorders, mood disorders, neurological disorders, neurodegenerative disorders, endocrine disorders, inflammatory or stress-induced immune disorders, CRH-related cardiovascular diseases or metabolic diseases. Specifically, such conditions comprise anxiety symptoms, generalized anxiety disorder, panic, phobias, obsessive-compulsive disorder, post-traumatic stress disorder, sleep disorders such as insomnia, hypersomnia, narcolepsy, idiopathic hypersomnia, excessive amounts of sleepiness, lack of alertness, lack of attentiveness, absentmindedness and/or lack of or aversion to movement or exercise, sleep disorders induced by stress, pain perception such as fibromyalgia, mood disorders such as depressive symptoms, including major depression, single episode depression, recurrent depression, child abuse induced depression, mood disorders associated with premenstrual syndrome, and postpartum depression, dysthymia, bipolar disorders, cyclothymia, chronic fatigue syndrome, stress-induced headache, eating disorders such as anorexia and bulimia nervosa, hemorrhagic stress, stress-induced psychotic episodes, endocrine disorders involving ACTH overproduction, ACTH over-activity, e.g., adrenal disorders, including, but not limited to congenital adrenal hyperplasia (CAH), euthyroid sick syndrome, syndrome of inappropriate antidiarrhetic hormone (ADH), obesity, infertility, head traumas, spinal cord trauma, ischemic neuronal damage (e.g., cerebral ischemia such as cerebral hippocampal ischemia), excitotoxic neuronal damage, epilepsy, senile dementia of the Alzheimers type, multi-infarct dementia, amyotrophic lateral sclerosis, chemical dependencies and addictions (e.g., dependencies on alcohol, nicotine, cocaine, heroin, benzodiazepines, or other drugs), drug and alcohol withdrawal symptoms, hypertension, tachycardia, congestive heart failure, osteoporosis, premature birth, and hypoglycaemia, inflammatory disorders such as rheumatoid arthritis and osteoarthritis, pain, asthma, psoriasis and allergies, irritable bowel syndrome, Crohn's disease, spastic colon, post-operative ileus, ulcer, diarrhea, stress-induced fever, human immunodeficiency virus (HIV) infections, neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and Huntington's disease, gastrointestinal diseases, stroke, stress induced immune dysfunctions, muscular spasms, urinary incontinence. In a specific embodiment, the subject has and/or the treatment is a treatment of depressive symptoms, anxiety symptoms or both depressive symptoms and anxiety symptoms. The depressive and/or anxiety symptoms can be symptoms of a depressive disorder, an anxiety disorder or both a depressive disorder and anxiety disorder. In another specific embodiment, the subject has and/or the treatment is a treatment of a sleep disorder.
A “treatment response”, as used herein, generally refers to any measurable response specific for the treatment with SSR-125543 or a pharmaceutically acceptable salt thereof and/or the condition being treated, during and/or shortly after treatment as compared to before said treatment. Generally, the treatment response can be a biological response or a clinical response. A biological response would include, for example, any alteration in CRH over-activity, as defined above.
Preferably, according to the invention, the treatment response is a clinical treatment response. A “clinical treatment response”, as used herein, refers to a prevention, alteration, alleviation or complete remission, as measured by the alteration in severity and/or frequency of relapse of individual symptoms and/or the mean change on a diagnostic marker or scale of any type commonly used in assessing clinical responses in the conditions described herein, see, for instance, Harrison's Principles of Internal Medicine, 18th ed./editors Longo et al., Mcgraw-Hill Publ. Comp, NY, US (2011), as incorporated herein by reference in its entirety. A clinical treatment response can also include an alteration, increase or decrease in adverse effects resulting from the treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. Predicting a clinical response, or lack thereof, is expressly distinguished from predicting merely biological responses, since a clinical response is to be seen as target variable directly linked to treatment success, or failure, respectively. Therefore, while biological responses can also be predicted by the methods described herein, the methods of the invention are particularly suited for predicting a clinical response, as defined above.
In preferred embodiments, the clinical response can be a prevention, alteration, alleviation or complete remission of depressive symptoms and/or anxiety symptoms. In preferred embodiments, the clinical response can be a prevention, alteration, alleviation or complete remission of a sleep disorder. Depressive symptoms comprise, but are not limited to, low mood, low self-esteem, loss of interest or pleasure, psychosis, poor concentration and memory, social isolation, psychomotor agitation/retardation, thoughts of death or suicide, significant weight change (loss/gain), fatigue, and feeling of worthlessness. The depressive symptoms can last for weeks to lifelong with periodic reoccurring depressive episodes. For the diagnosis of the depression mode (e.g. moderate or severe depression) the Hamilton Depression Rating Scale (HAM-D) (Hamilton, J Neurol Neurosurg Psychiatry, 1960) may be used. In addition or alternatively, the depression mode may be also rated by alternative scales as the Beck Depression Inventory (BDI), the Montgomery-Asberg Depression Scale (MADRS), the Geriatric Depression Scale (GDS), and/or the Zung Self-Rating Depression Scale (ZSRDS). Therefore, in some embodiments, the clinical response is a prevention, alteration, alleviation or complete remission of depressive symptoms as determined using a scale selected from the group consisting of HAM-D, BDI, MADRS, GDS, ZSRDS.
Anxiety symptoms comprise, but are not limited to, panic disorders, generalized anxiety disorder, phobias and posttraumatic stress disorder, avoidance behavior which may lead to social isolation, physical ailments like tachycardia, dizziness and sweating, mental apprehension, stress and/or tensions. The severity of anxiety symptoms ranges from nervousness and discomfort to panic and terror in subjects. Anxiety symptoms may persist for several days, weeks, or even months and years, if not suitably treated. The severity of anxiety symptoms may be measured by the Hamilton Anxiety Rating Scale (HAM-A) and/or the State-Trait Anxiety Rating Scale (STAI). Therefore, in some embodiments, the clinical response is a prevention, alteration, alleviation or complete remission of anxiety symptoms as determined using a scale selected from the group consisting of HAM-A and STAI. Sleep disorders comprise, but are not limited to, insomnia, hypersomnia, narcolepsy, idiopathic hypersomnia, excessive amounts of sleepiness, lack of alertness, lack of attentiveness, absentmindedness and/or lack of or aversion to movement or exercise, sleep disorders induced by stress
“Alteration”, as used herein, refers to any change in a clinical response as defined above. “Alleviation”, as used herein, refers to any amelioration in a clinical response, including partial amelioration of one or more symptoms, temporary disappearance of one or more symptoms, wherein relapse is not excluded, as well as complete remission of one or more symptoms. “Complete remission” refers to disappearance of all manifestations and symptoms of a disease to be treated, as described herein.
The present method of treatment identifies sets of polymorphism genotypes that are predictive for the treatment response of a subject to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. Thus, the presence of one or more of these polymorphism genotypes can be used to predict the likelihood of responding or not responding to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof in a subject.
The term “polymorphism”, as used herein, refers to a sequential variation of a genomic allele at the same locus within a population of subjects and having a certain frequency in the population, including deletions/insertions (designated “[−/I]” herein), point mutations and translocations. The term “polymorphism”, as used herein, in particular includes, but is not limited to, single nucleotide polymorphisms (SNPs). For instance, as used herein, the term “polymorphism” can also include polymorphic deletions, or insertions, respectively, of more than one nucleotide. The term “single nucleotide polymorphism” or “SNP” is well understood by the skilled person and refers to a point mutation of a genomic allele at the same locus within a population of subjects and having a certain frequency in a population. The term “genotype”, as used herein, encompasses one or both genomic alleles at the same locus of a subject. The term “polymorphism genotype” or “SNP genotype”, as used herein, refers to the presence of a polymorphism or SNP within the genotype of a subject, either in one or both genomic alleles at the same locus. The allele being present in the majority of the population, is also referred to herein as wild-type allele or major allele. As used herein, this state is defined as the “absence of one or more polymorphism genotypes”. The nucleotide being present in the minority of the population is also referred to herein as the variation, point mutation, mutated nucleotide or minor allele. As used herein this state is defined as “presence of one or more polymorphism genotype”. For instance, P_ID 1 as identified in Table 2 below, (rs34169260, TOP, [A/G]) exhibits a variation to nucleotide G instead of the wild-type nucleotide A. Typically, a polymorphism or SNP genotype occurs in a certain percentage of a population, for example in at least 5% or at least 10% of a population. In other words, the minor allele frequency (MAF) is equal or higher than about 0.05 or about 0.10 (MAF>0.05 or MAF>0.10).
Theoretically, a wild-type allele could be mutated to three alternative nucleotides. However, a mutation to a first nucleotide within germline cells of an individual which persists within a population occurs very rarely. The chance of the same nucleotide being mutated to yet another nucleotide and again persisting within a population is virtually non-existent and can be therefore neglected. Therefore, as used herein, a certain nucleotide position in the genome of an individual can only have the above two states, namely the wild-type state (absence of a polymorphism genotype from both alleles of a single subject) and the mutated state (presence of a polymorphism genotype in one or both alleles of a single subject). The presence of a polymorphism genotype in both alleles may have a higher predictive value than the presence of a polymorphism genotype in one allele only, the other allele comprising a wild-type genotype. The presence or absence of a polymorphism genotype on one or two alleles may be associated with an algorithm for predicting the treatment response to the CRHR1 antagonist as described herein.
Sets of polymorphism genotypes useful in predicting a treatment response are disclosed in Table 2. Table 2 provides a consecutively numbered identifier (P_ID) for internal reference, an rs-identifier (rs_ID), as commonly known in polymorphism databases such as NCBI's dbSNP or NCBI's Blast, the polymorphism (P, indicated in bold and defined as [wild-type/variation]), the strand designation (Str, see, e.g., Illumina Inc. “TOP/BOT” Strand and “A/B” Allele—A guide to Illumina's method for determining Strand and Allele for the GOLDENGATE and INFINIUM Assays”, Technical Note, © 2006; illumina.com/documents/products/technotes/technote_topbot.pdf; incorporated by reference herein in its entirety), specific probe sequences for the respective allele in humans (AlleleA Probe, see also SEQ ID NOs: 275-548), a human chromosomal identifier (Chr), and a reference to the sequence of the genomic flanking sequence in humans (TopGenomicSequence), as disclosed in SEQ ID NOs: 1-274. A person skilled in the art is able to derive the exact position and polymorphism genotype sequence from the rs-nomenclature identified in Table 2 from suitable database entries and associated information systems, e.g. the NCBI's Single Nucleotide Polymorphism database (dbSNP; ncbi.nlm.nih.gov/SNP/), or the NCBI's standard nucleotide BLAST database (blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch), even where the nomenclature, or the surrounding sequence elements were subject to alterations over time. The NCBI's databases which are well known and commonly used by the skilled person, allow information extraction of the exact polymorphic site (“the nucleotide associated with the SNP”) for any of the polymorphism genotypes identified in Table 2, either by using the AlleleA Probe information (via the NCBI's nucleotide BLAST database) or by entering the rs-identifiers (re_ID) information of Table 2 (via the NCBI's dbSNP database). In the approach using the nucleotide blast database (blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch) the skilled person can extract for each of the polymorphism genotypes of Table 2 the relevant AlleleA Probe sequence information (column 4 of Table 2) and enter the sequence in the database search filed choosing the database search set “human genomic plus transcripts”. The obtained search result provides the respective Primary Assembly information of the specific genomic sequence (under the section “sequence producing significant alignment”). From the link to the Primary Assembly the genomic position of the polymorphic site on the respective chromosome can be exactly identified. The skilled person is aware that the polymorphic site is positioned one base after the last base of the AlleleA Probe as identified in Table 2. For instance, P_ID 146 as identified in Table 2 below, (re2589476, [T/C]) exhibit an AlleleA Probe sequence being CTCCTCATTATTCGCTTCTGCTGTAACTGCACCTATGGTAACCCAGGTGC. By denoting the variable nucleotide as Y the sequence including the variable nucleotide is given as the same sequence with a “Y” at the end following the terminal “GC”. This searching approach works regardless of Top or Bottom assignments, as known by the skilled in the art. Further, the skilled person is able to recognize if the information is presented in the sequence as given or on the complementary strand. In the alternative approach using the dbSNP database (ncbi.nlm.nih.gov/SNP/) the skilled person can enter in the search field of the database the SNP rs_ID name of the polymorphism genotype as identified in column 2 of Table 2 and can, thus, run the search for obtaining the relevant information on the polymorphic position on the respective chromosome. Finally, the skilled person will also know that for sequences such as P_ID 166 which are common structural variants (SV), a specialized database for SVs (NCBI's dbVar database; ncbi.nlm.nih.gov/dbvar/variants/nssv16186739/) is used for the relevant information. Also from these databases, a person skilled in the art will know to genotype the polymorphism form the available information based on his routine work. Further, the polymorphism information P as indicated in Table 2 also provides information which of the alleles, i.e. the wild-type allele or the mutation variant, is associated with a positive prediction that the subject will respond or is likely to respond to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. In particular, for each of the polymorphism genotypes identified in Table 2 below, in the column P, the allele which is highlighted by underlining, represents the allele which is predictive for a positive treatment response or an increased likelihood of positive treatment response with SSR-125543 or a pharmaceutically acceptable salt thereof.
Further useful combinations of more than one polymorphism genotype are disclosed in Tables 5, 6, and 7 below, which all refer to the consecutively numbered, internal polymorphism-identifier (P_ID) of Table 2 to specify the genotype identity.
For the purposes of the present invention, the one or more polymorphism genotypes described above may be represented, for instance, within a nucleic acid of a length of, e.g., 1 nt, 2 nt, 3 nt, 4 nt, 5 nt, 10 nt, 15 nt, 20 nt, 25 nt, 30 nt, 35 nt, 40 nt, 45 nt, 50 nt, 60 nt, 70 nt, 80 nt, 90 nt, 100 nt, 200 nt, 300 nt, 400 nt, 500 nt, 1000 nt, 2000 nt, or more or any length in between these lengths. The nucleic acid may be any nucleic acid molecule, e.g. a DNA molecule, e.g., a genomic DNA molecule or a cDNA molecule, an RNA molecule, or a derivative thereof. The one or more polymorphism genotypes may further be represented by translated forms of the nucleic acid, e.g. a peptide or protein, as long as the polymorphic modification leads to a corresponding modification of the peptide or protein. Corresponding information is readily available in the art, e.g., from databases such as the NCBI dbSNP repository or the NCBI Genbank.
The polymorphism genotypes as described herein may be present on both strands of genomic DNA or its derivatives, i.e. on the chromosomal/genomic 5′→3′ strand and/or the chromosomal/genomic 3′→5′ strand. For example, a polymorphism can be present on the 5′→3′ strand as A, it is present on the 3′→5′ strand as T and vice versa. Also the insertion or deletion of bases may be detected on both DNA strands, with correspondence as defined above. For analytic purposes, the strand identity may be defined, or fixed, or may be chosen at will, e.g. in dependence on factors such the availability of binding elements, GC-content etc. Furthermore, for more universally applicable designation, a polymorphism genotype may be defined on both strands at the same time, or using the commonly known designations, such as the “probe/target”-designation, the “plus(+)/minus(−)”-designation, the “TOP/BOT”-designation or the “forward/reverse”-designation, as described in Nelson et al., Trends Genet. 2012, 28(8):361-3, or Illumina Inc. “TOP/BOT” Strand and “A/B” Allele—A guide to Illumina's method for determining Strand and Allele for the GOLDENGATE and INFINIUM Assays”, Technical Note, © 2006; illumina.com/documents/products/technotes/technote_topbot.pdf, both incorporated by reference herein in their entirety. For the sake of unambiguity in polymorphism genotype designation, e.g., the “TOP/BOT”-designation can be used to identify the polymorphism genotypes in Table 2 above. In the alternative, the probe sequence or the genomic flanking sequences can be used to identify the polymorphism genotypes in Table 2 above.
A “polymorphic site” or “polymorphic variant” as used herein relates to the position of a polymorphism or SNP as described herein within the genome or portion of a genome of a subject, or within a genetic element derived from the genome or portion of a genome of a subject.
“Linkage disequilibrium” as used herein refers to co-inheritance of two or more alleles at frequencies greater than would be expected from the separate frequencies of occurrence of each allele in the corresponding control population. The expected frequency of occurrence of two or more alleles that are inherited independently is the population frequency of the first allele multiplied by the population frequency of the second allele. Alleles or polymorphisms that co-occur at expected frequencies are said to be in linkage equilibrium. Polymorphisms in linkage disequilibrium with a polymorphism of Table 2 can be identified by methods known to one skilled in the art. For example, Devlin and Risch (Genomics 1995, 29(2):311-22; incorporated herein by reference in its entirety) provide guidance for determining the parameter delta (also referred to as “r”) as a standard measure of the linkage disequilibrium. Gabriel et al. (Science 2002, 296(5576):2225-9; incorporated herein by reference in its entirety) provides instructions for finding the maximal r2 value in populations for disease gene mapping. Further, Carlson et al. (Am J Hum Genet 2004; 74(1): 106-120) disclose methods for selecting and analyzing polymorphisms based on linkage disequilibrium for disease gene association mapping. Stoyanovich and Pe'er (Bioinformatics, 2008, 24(3):440-2; incorporated herein by reference in its entirety) show that polymorphisms in linkage disequilibrium with identified polymorphisms have virtually identical response profiles. Currently, several databases provide datasets that can be searched for polymorphisms in strong linkage disequilibrium, which can be accessed by the following addresses: 1000.genomes.org, hapmap.org, broadinsitute.org/mpg/snap. An example workflow for determining polymorphisms in linkage disequilibrium to a specific polymorphism is outlined in Uhr et al. (Neuron 2008, 57(2):203-9; incorporated herein by reference in its entirety). Preferably, the linkage disequilibrium referred to herein is strong linkage disequilibrium. “Strong linkage disequilibrium”, as used herein, means that the polymorphism is in linkage disequilibrium with an r2 higher than 0.7 or higher than 0.8 in the tested population or an ethnically close reference population with the identified polymorphism.
A “sample obtained from a subject” as used herein may be any sample any biological sample comprising a bodily fluid, cell, tissue, or fraction thereof, which includes analyte biomolecules of interest such as nucleic acids (e.g., DNA or RNA). For instance, the sample obtained from the subject can be a buccal sample, a blood sample, plasma, serum, semen, sputum, cerebral spinal fluid, tears, a tissue sample, a formalin-fixed, paraffin-embedded tissue sample, or a hair follicle. Such samples are routinely collected, processed, preserved and/or stored by methods well known in the art. A biological sample can be further fractionated, if desired, to a fraction containing particular cell types. If desired, a sample can be a combination of samples from a subject such as a combination of a tissue and fluid sample.
In some embodiments, the subject's nucleic acid or DNA is extracted, isolated, and/or purified from the sample by any method commonly known in the art prior to polymorphism and/or SNP genotyping analysis. The term “isolated nucleic acid molecule”, as used herein, refers to a nucleic acid entity, e.g. DNA, RNA etc, being substantially free of other biological molecules, such as, proteins, lipids, carbohydrates, other nucleic acids or other material, such as cellular debris and growth media. Generally, the term “isolated” is not intended to refer to the complete absence of such material, or to the absence of water, buffers, or salts, unless they are present in amounts which substantially interfere with the steps of detecting and/or predicting. In alternative embodiments, detection of one or more polymorphism genotypes may also be performed by using a non-extracted, non-isolated or non-purified sample. In some embodiments, DNA amplification by any suitable method known in the art is used prior to the detection of one or more polymorphism genotypes.
The term “detecting the presence or absence of one or more polymorphism/SNP genotypes” is used herein synonymously to a “polymorphism/SNP genotyping analysis” and refers to a step of determining in one or several patients the presence or absence of at least one polymorphism/SNP genotype, typically several polymorphism/SNP genotypes, or all polymorphism/SNP genotypes disclosed in Table 2, or, in some embodiments, all (known) polymorphism/SNP genotypes of the human genome, including endogenous and exogenous regions. In a preferred embodiment, the term refers to a step of determining in one or several patients the presence or absence of at least one polymorphism/SNP genotype selected from the group consisting of rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026507 (A/G) as disclosed in Table 2, optionally in combination with one or more polymorphism/SNP genotypes selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2. In particular, detecting the presence or absence of one or more polymorphism genotypes as used herein may not be limited to the CRHR1 gene or to genes of the CRH pathway, but can encompass a genome-wide screening for polymorphism genotypes.
A detection step or polymorphism/SNP genotyping analysis can be performed by any suitable method known in the art. Such methods include, but are not limited to, PCR-related methods using polymorphism/SNP-specific primers and/or probes, a primer extension reaction, polymorphism/SNP microarrays analysis, sequencing analysis, mass spectrometry, 5′-nuclease assays, allele specific hybridization, high-throughput/multiplex variants of these techniques or combinations thereof, as described in the prior art, for example in Rampal, DNA Arrays: Methods and Protocols (Methods in Molecular Biology) 2010; Graham & Hill, DNA Sequencing Protocols (Methods in Molecular Biology) 2001; Schuster, Nat. Methods, 2008 and Brenner, Nat. Biotech., 2000; Mardis, Annu Rev Genomics Hum Genet., 2008, which are incorporated herein by reference. Genome-wide arrays can be purchased from different suppliers such as Illumina or Affymetrix. For primer selection, multiplexing and assay design, and the mass-extension for producing primer extension products the MassARRAY Assay Designer software may be used using the sequences presented in Table 2 as input. The MassARRAY Typer 3.4 software may be used for genotype calling.
For example, the presence or absence of a polymorphism genotype can be detected by determining the nucleotide sequence at the respective locus and may be carried out by allele-specific oligonucleotide (ASO)-dot blot analysis, primer extension assays, iPLEX polymorphism/SNP genotyping, dynamic allele-specific hybridization (DASH) genotyping, the use of molecular beacons, tetra primer ARMS PCR, a flap endonuclease invader assay, an oligonucleotide ligase assay, PCR-single strand conformation polymorphism (SSCP) analysis, quantitative real-time PCR assay, polymorphism/SNP microarray based analysis, restriction enzyme fragment length polymorphism (RFLP) analysis, targeted resequencing analysis and/or whole genome sequencing analysis. In some embodiments, any of the methods described herein can comprise the determination of the haplotype for two copies of the chromosome comprising the polymorphism genotypes identified herein.
In another example, genomic DNA isolated from a biological sample can be amplified using PCR as described above. The amplicons can be detectably-labeled during the amplification (e.g., using one or more detectably labeled dNTPs) or subsequent to the amplification. Following amplification and labeling, the detectably-labeled-amplicons are then contacted with a plurality of polynucleotides, containing one or more of a polynucleotide (e.g., an oligonucleotide) being capable of specifically hybridizing to a corresponding amplicon containing a specific polymorphism, and where the plurality contains many probe sets each corresponding to a different, specific polymorphism. Generally, the probe sets are bound to a solid support and the position of each probe set is predetermined on the solid support. The binding of a detectably-labeled amplicon to a corresponding probe of a probe set indicates the presence of a nucleic acid containing the polymorphism so amplified in the biological sample. Suitable conditions and methods for detecting a polymorphism or SNP using nucleic acid arrays are further described in, e.g., Lamy et al. (2006) Nucleic Acids Research 34(14): e100; European Patent Publication No. 1234058; U.S. Publication Nos. 2006/0008823 and 2003/0059813; and U.S. Pat. No. 6,410,231; the disclosures of each of which are incorporated herein by reference in their entirety.
In yet another example, MALDI-TOF (matrix-assisted laser desorption ionization time of flight) mass spectrometry on the Sequenom platform (San Diego, USA) may be used to detect one or more polymorphism genotypes.
Polynucleotides for use in detection of one or more of the polymorphism genotypes disclosed in Tables 2, 5, 6 or 7 are capable of specifically hybridizing to nucleic acids comprising said one or more polymorphism genotypes and can comprise the nucleic acid sequences of the polymorphism genotypes themselves, including up and/or downstream, flanking sequences, e.g., as hybridization polynucleotide probes or primers (e.g., for amplification or reverse transcription). “Capable of specifically hybridizing”, as used herein, refers to capability of hybridization under stringent conditions in any one of the methods of detection involving hybridization disclosed herein, as known to one skilled in the art. In that sense, primers and probes useful in such detection methods are particular polynucleotides capable of specifically hybridizing.
Primers or probes should contain a sequence of sufficient length and complementarity to a corresponding polymorphism locus to specifically hybridize with that locus under suitable hybridization conditions. For example, the polymorphism probes can include at least one (e.g., at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least 10, at least 11, at least 12, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 55 or more) nucleotides 5′ or 3′ to the polymorphism of interest. The polymorphic site of each probe (i.e., the polymorphism region) is generally flanked on one or both sides by sequence that is common among the different alleles. In specific embodiments, the polynucleotides capable of specifically hybridizing to the polymorphism genotypes are selected from the group consisting of the polynucleotides disclosed as “AlleleA Probe” in Table 2. The term “primer” may denote an oligo- or polynucleotide that acts as an initiation point of nucleotide synthesis under conditions in which synthesis of a primer extension product complementary to a nucleic acid strand is induced. The term “probe” may denote an oligonucleotide that is capable of specifically hybridizing to a target nucleic acid under suitable conditions, e.g., stringent conditions suitable for allele-specific hybridization. Primers and probes can be designed such are suitable for discriminating between wild-type allele or mutated allele of the position of a polymorphism to be analyzed, as described, e.g., by Coleman, and Tsongalis, Molecular Diagnostics: For the Clinical Laboratorian, 2007; Weiner et al. Genetic Variation: A Laboratory Manual, 2010, which are incorporated herein by reference.
Any of the methods of detecting a polymorphism can, optionally, be performed in multiplex formats that allow for rapid preparation, processing, and analysis of multiple samples, see above.
The detected polymorphism genotypes may be represented by values 0, 1 or 2. The value “0” may indicate that the polymorphism is present on none of the two homologous chromosomes, or in no allele, or is absent. The value “1” may indicate that the polymorphism is present on one of the two homologous chromosomes, or in one allele, or that the polymorphism genotype is heterozygous. The value “2” may indicate that the polymorphism is present on both homologous chromosomes, or in both alleles, or that the polymorphism genotype is homozygous.
The term “predicting a treatment response from the presence or absence of the one or more polymorphism genotypes”, as used herein, generally refers to a prediction step that provides a reasonably high prediction performance by associating the presence or absence of a polymorphism genotype with a treatment response. Similarly, the term “polymorphism genotype associated with a treatment response of a subject to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof”, as used herein, generally refers to a polymorphism genotype being predicted to be associated with a treatment response with a reasonably high prediction performance. Specifically, the predicting step may comprise determining whether the subject will respond, or has an increased likelihood of responding to the treatment with SSR-125543 or a pharmaceutically acceptable salt thereof; and/or (b) determining whether the subject will not respond, or has a decreased likelihood of responding to the treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. This is generally achieved herein by associating the presence or absence of the one or more polymorphism genotypes as a variable with a value indicative for treatment response within an algorithm for predicting a treatment response to a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, which is commonly a computer-implemented algorithm. The evaluation of the performance of the algorithm may depend on the problem the algorithm is applied for. If the algorithm is used to identify patients that are likely to respond to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, the performance is preferably expressed by a high accuracy and/or sensitivity and/or precision. If patients should be identified which are likely not to respond to the treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, specificity and/or negative predictive value can be statistical measures to describe the performance of the prediction algorithm. For optimizing the prediction performance of the predicting step, a step of determining and/or optimizing the algorithm by a machine-learning method in a first subset of the test set and testing the prediction performance in an second independent subset of the test set may be carried out and repeated based on different numbers and groups of polymorphism genotypes, until the desired prediction performance is reached. Specifically, the algorithm for predicting may comprise a classification function (also known as binary classification test), which can comprise one or more statistical analysis methods and/or machine learning methods which are available to one of skill in the art. Specifically, statistical analysis methods and/or machine learning methods to be used in the invention may be selected from the group consisting of artificial neural network learning, decision tree learning, decision tree forest learning, linear discriminant analysis, non-linear discriminant analysis, genetic expression programming, relevance vector machines, linear models, generalized linear models, generalized estimating equations, generalized linear mixed models, the elastic net, the lasso support vector machine learning, Bayesian network learning, probabilistic neural network learning, clustering, and regression analysis, e.g., as described and exemplified herein. Statistical methods and/or machine learning methods from the group mentioned above may exist in different variants, especially applying or not applying prior and posterior weights in the analysis leading to solutions which may be applicable in different settings and may lead to models with more or less explanatory variables. The results of single methods may be used in a method called “ENSEMBLE learning” in which the results of several single analysis with one of the methods mentioned above are combined to arrive at a better prediction using either simply majority vote or using one of the machine learning algorithms with the results of the single analyses again as input to that specific algorithm.
In an exemplary embodiment of the method of the invention, the number of minor alleles for both polymorphism rs74888440 (P1) and rs9813396 (P2) is coded as a numeric variable, which can take one of the following values: 0, 1 or 2, denoting the two variables thus created as V1 for rs74888440 and V2 for rs9813396. Each subject is designated a value of the predictive quantitative variable PQV such that PQV=0.3205619+(0.2923413*V1)+(0.2362708*V2)+(−0.0104643*V1*V2). Depending on whether a subject's PQV is above or below a value of 0.5, that person is then predicted to not to respond, or to have a decreased likelihood of responding to a treatment with a CRHR1 antagonist (if PQV<=0.5), or to respond, or to have an increased likelihood of responding to a treatment with a CRHR1 antagonist (if PQV>0.5). For example, a subject who has no minor alleles at either of the two polymorphisms (homozygous for the common allele at both loci, such that V1=V2=0) is designated a PQV of 0.3205619 and is consequently predicted to be a non-responder. In another example, a subject who is heterozygous at P1 (V1=1) and homozygous for P2 (V2=2) is then designated a PQV of (0.3205619)+(0.2923413*1)+(0.2362708*2)+(−0.0104643*1*2)=1.064516 and is, in consequence, predicted to be a responder. In this example, a sensitivity of 0.6285714 and a specificity of 0.6626506 is reached.
In another exemplary embodiment of the method of the invention, the number of minor alleles for both SNPs rs74888440 (P1) and rs220806 (P2) is coded as a numeric variable, which can take one of the following values: 0, 1 or 2, denoting the two variables thus created as V1 for rs74888440 and V2 for rs220806. Each subject is designated a value of the predictive quantitative variable PQV such that PQV=0.539548+(0.460452*V1)+(−0.1765537*V2)+(−0.1567797*V1*V2). Depending on whether a subject's PQV is above or below a value of 0.5, that subject is then predicted to not to respond, or to have a decreased likelihood of responding to a treatment with a CRHR1 antagonist (if PQV<=0.5), or to respond, or to have an increased likelihood of responding to a treatment with a CRHR1 antagonist (if PQV>0.5). For example, a subject who has no minor alleles at either of the two SNPs (homozygous for the common allele at both loci, such that V1=V2=0) is designated a PQV of 0.539548 and is consequently predicted to be a responder. In another example, a subject who is heterozygous at SNP1 (V1=1) and homozygous for SNP2 (V2=2) is then designated a PQV of (0.539548)+(0.460452*1)+(−0.1765537*2)+(−0.1567797*1*2)=0.3333333 and is, in consequence, predicted to be a non-responder. In this example, a sensitivity of 0.6857143 and a specificity of 0.626506 is reached.
In a similar manner, one of skill in the art, having the polymorphisms of Table 2 and the additional information above at hand, will readily derive suitable methods, combinations of methods, parameters and/or coefficients such as those exemplified herein, for use in the methods of the invention, achieving a high performance of prediction.
Preferably, the prediction of the treatment response is made with a high accuracy, sensitivity, precision, specificity and/or negative predictive value.
“Accuracy”, “sensitivity”, “precision”, “specificity” and “negative predictive value” are exemplary statistical measure of the performance of the prediction algorithm. In the following, examples are given for determining the performance of the prediction algorithm.
As used herein, accuracy may be calculated as (number of true positives+number of true negatives)/(number of true positives+number of false positives+number of true negatives+number of false negatives), e.g., (number of patients correctly diagnosed as responding to CRHR1 antagonist+number of patients correctly diagnosed as not responding to CRHR1 antagonist)/(number of patients correctly diagnosed as responding to CRHR1 antagonist+number of patients wrongly diagnosed as responding to CRHR1 antagonist+number of patients correctly diagnosed as not responding to CRHR1 antagonist+number of patients wrongly diagnosed as not responding to CRHR1 antagonist). In some embodiments, the accuracy of prediction is higher than 50%, at least 60%, at least 70%, at least 80% or at least 90%.
As used herein, sensitivity may be calculated as (true positives)/(true positives+false negatives), e.g., (number of patients correctly diagnosed as responding to CRHR1 antagonist)/(number of patients correctly diagnosed as responding to CRHR1 antagonist+number of patients wrongly diagnosed as not responding to CRHR1 antagonist). In some embodiments, the sensitivity of prediction is higher than 50%, at least 60%, at least 70%, at least 80% or at least 90%.
As used herein, precision (also referred to as positive predictive value) may be calculated as (true positives)/(true positives+false positives), e.g.: (number of patients correctly diagnosed as responding to CRHR1 antagonist)/(number of patients correctly diagnosed as responding to CRHR1 antagonist+number of patients wrongly diagnosed as responding to CRHR1 antagonist). In some embodiments, the precision of prediction is higher than 50%, at least 60%, at least 70%, at least 80% or at least 90%.
As used herein, specificity is calculated as (true negatives)/(true negatives+false positives), e.g.: (number of patients correctly diagnosed as not responding to CRHR1 antagonist)/(number of patients correctly diagnosed as not responding to CRHR1 antagonist+number of patients wrongly diagnosed as responding to CRHR1 antagonist). In some embodiments, the specificity of prediction is higher than 50%, at least 60%, at least 70%, at least 80%, at least 85% or at least 90%.
As used herein, negative predictive value is calculated as (true negatives)/(true negatives+false negatives), e.g.: (number of patients correctly diagnosed as not responding to CRHR1 antagonist)/(number of patients correctly diagnosed as not responding to CRHR1 antagonist+number of patients wrongly diagnosed as not responding to CRHR1 antagonist). In some embodiments, the negative predictive value is higher than 50%, at least 60%, at least 70%, at least 80% or at least 90%.
Other statistical measures useful for describing the performance of the prediction algorithm are geometric mean of sensitivity and specificity, geometric mean of positive predictive value and negative predictive value, F-measure and area under ROC curve, and the positive and negative likelihood ratios, the false discovery rate and Matthews correlation coefficient. These measures and method for their determination are well known in the art.
In general, a prediction algorithm with high sensitivity may have low specificity and vice versa. For the purposes of the present invention, it is generally preferable that the prediction algorithm is based on a number of polymorphism genotypes selected from Table 2 sufficient to achieve a sensitivity and specificity of higher than 50% each, optionally at least 60% each, at least 70% each, at least 80% each, or at least 90% each. In a preferred embodiment of the present invention, the prediction algorithm is based on a number of polymorphism genotypes selected from the group consisting of rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G) from Table 2, optionally in combination with one or more polymorphism genotypes selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2.
For a prediction whether a patient will respond, or has an increased likelihood of responding to a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, the prediction algorithm may be based on a number of polymorphisms sufficient to achieve a prediction sensitivity and/or precision of higher than 50%, optionally at least 60%, at least 70%, at least 80%, or at least 90%.
For the prediction whether the subject will not respond, or has a decreased likelihood of responding to a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof, the prediction algorithm may be based on a number of polymorphisms sufficient to achieve a prediction specificity and/or negative predictive value of higher than 50%, optionally at least 60%, at least 70%, at least 80%, at least 85% or at least 90%.
For a prediction whether a patient responds to a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof or not, the prediction algorithm may be based on a number of polymorphisms sufficient to achieve sensitivity and/or precision and/or specificity and/or negative predictive value of higher than 50%, optionally at least 60%, at least 70%, at least 80%, or at least 90%.
Based on the disclosure of the present invention, in particular of the highly useful set of polymorphism genotypes disclosed in Table 2, the skilled person in the art is enabled to employ the statistical analysis methods and/or machine-learning methods disclosed herein and to identify suitable parameters for further improving the prediction performance, as defined above. The whole statistical work-flow can be automated by the use of an algorithm as described above, implemented and/or stored on a machine-readable medium, e.g., implemented and/or stored on a computer.
Typically, at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 19, at least 20, at least 30, at least 50, at least 100, at least 100, at least 200 or all polymorphism genotypes disclosed in Table 2 are used for predicting the treatment response to SSR-125543 or a pharmaceutically acceptable salt thereof. In a very preferred embodiment of the invention, at least 1, at least 2, at least 3 or at least 4 polymorphism genotypes selected from the group consisting of rs11715827 (T/G), rs2044076 (A/G), rs2028629 (A/G) and rs6026567 (A/G) as disclosed in Table 2 are used for predicting the treatment response to SSR-125543 or a pharmaceutically acceptable salt thereof, optionally in combination with at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14 or all polymorphism genotypes selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2.
Using various such polymorphism genotype sets and statistical analysis methods as described above, the present invention consistently achieves a high predictive performance in directly predicting a clinical response. For instance, Example 1 describes a study with clinical data from 300 enrolled patients, wherein 150 polymorphism genotypes were used for predicting the clinical treatment response of subjects to a treatment with SSR-125543. Therein, a sensitivity of about 78% and a specificity of about 73% was observed, which is considered to reflect a superior reliability in predicting both true positive responses and true negative responses. Further, Example 2 provides examples of minimal subsets of only one, two, four or eight polymorphism genotypes selected from the group of polymorphism genotypes disclosed in Table 2, achieving a performance of predicting a clinical treatment response with values for specificity and sensitivity which are still higher than 60%, or even higher than 70%. Predictive performance in terms of sensitivity and specificity can be further increased to at least 75% each, e.g., by including specific combinations of 32 polymorphism genotypes, as is also shown in Example 2. Further, Example 3 describes an example of a specific set of the four polymorphism genotypes rs2028629 (A/G), rs6026567 (A/G), rs17715827 (T/G) and rs2044070 (A/G) selected from the group of polymorphism genotypes disclosed in Table 2, for which a particular high performance of predicting an outcome of clinical treatment response of subjects to a treatment with SSR-125543 was observed. Predicted performance in terms of sensitivity and specificity was even increased by including to these four polymorphism genotypes combinations of at least one and preferably all polymorphism genotypes selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2, with values for sensitivity higher than 90% and values of specificity higher than 85%.
Furthermore, in patients with depressive symptoms and/or anxiety symptoms, another embodiment of the method of treatment, the step of predicting a treatment response as described above may be also accompanied by analyzing the rapid-eye-movement (REM) sleep, e.g. during night sleep of a patient in a sleep EEC. In some embodiments, an alteration in REM sleep may serve as an additional biomarker to identify subjects who would benefit from treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. REM sleep typically comprises a characteristic coincidence of nearly complete muscle atonia, a waking-like pattern of brain oscillations and rapid eye movements (REMs). The amount of REMs during consecutive REM sleep episodes is usually increasing throughout the night. Single and short REMs with low amplitude can be characteristic for initial parts of REM sleep. The amount of REMs, in particular within the first REM sleep episode, can be of clinical relevance. Recent clinical and animal data supports the correlation of REM density with an increased CRH activity. For example, Kimura et al. (Mol. Psychiatry, 2010) showed that mice overexpressing CRH in the forebrain exhibit constantly increased rapid eye movement (REM) sleep compared to wildtype mice. In addition, it could be shown that treatment with another CRHR1 antagonist, DMP696 could reverse the REM enhancement. Further, the polymorphism analysis and REM density analysis as described herein may be combined for predicting the response of patients with depressive symptoms and/or anxiety symptoms to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. The REM analysis may be carried out before, concomitant or after the polymorphism analysis. For example, the REM density analysis may be carried out on subjects that where identified by the polymorphism analysis as responding, or having an increased likelihood of responding to the treatment with SSR-125543 or a pharmaceutically acceptable salt thereof; or as not responding, or having a decreased likelihood of responding to the treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. The recording of a “sleep-EEG” (also referred to “polysomatic recordings”) may comprise electroencephalography (EEG), vertical and horizontal elecrooculography (EOG), electromyography (EMG) and/or electrocardiography (ECG). In EOG, muscle activities of right and left eye may be recorded by electrooculograms (one or typically two channels) in order to visualize the phasic components of REM sleep. “REM analysis” or “analyzing the rapid-eye-movement (REM)” may refer to a method comprising recoding of muscle activities of right and left eye by EOG and then analyzing the electrooculogram. The recognition of REM in the electrooculogram may be done manually, for example by standard guidelines Rechtschaffen and Kales, 1968, Bethesda, Md.: National Institute of Neurological Diseases and Blindness, incorporated herein by reference in its entirety.
According to the invention, SSR-125543 or a pharmaceutically acceptable salt thereof is used in the method of treatment of the conditions which are treatable by SSR-125543 or a pharmaceutically acceptable salt thereof.
SSR-125543 or a pharmaceutically acceptable salt thereof may be administered as the raw chemical but the active ingredient is preferably formulated in a pharmaceutical composition suitable for administration by any convenient route, preferably in a form suitable for use in human medicine. The treatment can comprise any suitable route of administration, such as oral, buccal, parenteral, topical (including ophthalmic and nasal), depot or rectal administration or in a form suitable for administration by inhalation or insufflation (either through the mouth or nose) administration of SSR-125543 or a pharmaceutically acceptable salt thereof.
CRHR1 antagonists can be administered at any suitable efficacious dose, which one skilled in the art will readily adapt, e.g., to the specific condition to be treated. For many therapeutic indications as encompassed herein, a dose of about 1 mg to about 2000 mg per day, about 2 mg to about 1000 mg per day, about 5 mg to about 500 mg per day, about 10 mg to about 250 mg, or about 20 to about 100 mg daily will be efficacious. It will be appreciated that it may be necessary to make routine variations to the dosage, depending on the age and condition of the patient and the precise dosage will be ultimately at the discretion of the attendant physician or veterinarian. The dosage will also depend on the route of administration and the particular compound selected. Thus, for parenteral administration a daily dose will typically be in the range of 1 to about 100 mg, preferably 1 to 80 mg per day. For oral administration a daily dose will typically be within the range of 1 to 300 mg e.g. 1 to 100 mg of a CRHR1 antagonist. For instance, in treating depressive symptoms and/or anxiety symptoms, daily oral doses of about 10 mg, about 20 mg, or about 100 mg of SSR-125543 or a pharmaceutically acceptable salt thereof can be efficacious.
The disclosure further provides compositions comprising polynucleotides (e.g., probes), as well as kits and arrays for use in the detection step of the method of treatment. Polynucleotide compositions, kits, and arrays are useful in, e.g., detecting the presence of (a) one or more polymorphism genotypes as disclosed in Table 2, preferably one or more polymorphism genotypes selected from the group consisting of rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G) as disclosed in Table 2, optionally in combination with one or more polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2, (b) one or more polymorphism genotypes being in linkage disequilibrium with any one of the polymorphism genotypes of (a), or a combination of (a) and (b). The compositions, kits and arrays are further useful for predicting the treatment response of a subject to treatment with a CRHR1 antagonist.
The compositions, kits or arrays can include at least one polynucleotide capable of specifically hybridizing to a nucleic acid comprising: (a) at least one polymorphism genotype as disclosed in Table 2; preferably one or more polymorphism genotypes selected from the group consisting of rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G) as disclosed in Table 2, optionally in combination with one or more polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2, (b) at least one polymorphism genotype being in linkage disequilibrium with any one of the polymorphism genotypes of (a), or (c) a combination of (a) and (b). The at least one polynucleotide can comprise less than 100,000, less than 90,000, less than 80,000, less than 70,000, less than 60,000, less than 50,000, less than 40,000, less than 30,000, less than 20,000, less than 15,000, less than 10,000, less than 5,000, less than 4,000, less than 3,000, less than 2,000, less than 1,500, less than 1,000, less than 750, less than 500, less than 200, less than 100, or less than 50 different polynucleotides in total. Specifically, the compositions, kits or arrays can include at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least 10, at least 11, at least 12, at least 15, at least 20, or at least 30, or at least 50, or at least 100, or at least 200, or 274 polynucleotides capable of specifically hybridizing to each of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least 10, at least 11, at least 12, at least 15, at least 20, or at least 30, or at least 50, or at least 100, or at least 200, or 274 of (a) at least one polymorphism genotype as disclosed in Table 2, preferably one or more polymorphism genotypes selected from the group consisting of rs11715827 (T/G), rs2044070 (A/G), rs2028629 (A/G) and rs6026567 (A/G) as disclosed in Table 2, optionally in combination with one or more polymorphism genotype selected from the group consisting of rs17740874 (T/C), rs3811939 (A/G), rs1882478 (A/G), rs2235013 (T/C), rs2214102 (T/C), rs6415328 (T/C), rs77152456 (A/G), rs66794218 (A/G), rs2589476 (T/C), rs118003903 (A/G), rs11871392 (T/G), rs2589487 (T/C), rs74338736 (A/C), rs6026593 (A/G) and rs6520908 (T/C) as disclosed in Table 2; (b) at least one polymorphism genotype being in linkage disequilibrium with any one of the polymorphism genotypes of (a), or (c) a combination of (a) and (b).
A polynucleotide can include a coding sequence or non-coding sequence (e.g., exons, introns, or 5′ or 3′ regulatory sequences). The polynucleotide can also be single or double-stranded and of variable length. The length of one strand of a polynucleotide capable of specifically hybridizing to a nucleic acid comprising: (a) at least one a polymorphism genotype as disclosed in Table 2; (b) at least one polymorphism genotype being in linkage disequilibrium with any one of the polymorphism genotypes of (a), or (c) a combination of (a) and (b) can be about six nucleotides (e.g., about seven nucleotides, about eight nucleotides, about nine nucleotides, about 10 nucleotides, about 12 nucleotides, about 13 nucleotides, about 14 nucleotides, about 15 nucleotides, about 20 nucleotides, about 25 nucleotides, about 30 nucleotides, about 35 nucleotides, about 40 nucleotides, about 50 nucleotides, about 75 nucleotides, about 100 nucleotides, or about 150 or more nucleotides) in length. As is commonly known in the art, a longer polynucleotide often allows for higher stringency hybridization and wash conditions. The polynucleotide can be DNA, RNA, modified DNA or RNA, or a hybrid, where the nucleic acid contains any combination of deoxyribo- and ribo-nucleotides, and any combination of uracil, adenine, thymine, cytosine and guanine, as well as other bases such as inosine, xanthine, and hypoxanthine.
The polynucleotides can be attached to a solid support, e.g., a porous or non-porous material that is insoluble. The polynucleotides can be arranged in an array on the solid support, e.g., in a microarray. A solid support can be composed of a natural or synthetic material, an organic or inorganic material. The composition of the solid support on which the polynucleotide sequences are attached by either 5′ or 3′ terminal attachment generally depend on the method of attachment (e.g., covalent attachment). Suitable solid supports include, but are not limited to, plastics, resins, polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glasses, membranes, nylon, natural fibers such as silk, wool and cotton, or polymers. The material comprising the solid support can have reactive groups such as carboxy, amino, or hydroxyl groups, which are used for attachment of the polynucleotides. Polymeric solid supports can include, e.g., polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone, polyacrylonitrile, polymethyl methacrylate, polytetrafluoroethylene, butyl rubber, styrenebutadiene rubber, natural rubber, polyethylene, polypropylene, (poly)tetrafluoroethylene, (poly)vinylidenefluoride, polycarbonate, or polymethylpentene (see, e.g., U.S. Pat. No. 5,427,779, the disclosure of which is hereby incorporated by reference in its entirety). Alternatively, polynucleotides can be attached to the solid support without the use of such functional groups.
Arrays of polynucleotides can also be conjugated to solid support particles. Many suitable solid support particles are known in the art and illustratively include, e.g., particles, such as LUMINEX-type encoded particles, magnetic particles, and glass particles. Exemplary particles that can be used can have a variety of sizes and physical properties. Particles can be selected to have a variety of properties useful for particular experimental formats. For example, particles can be selected that remain suspended in a solution of desired viscosity or to readily precipitate in a solution of desired viscosity. Particles can be selected for ease of separation from sample constituents, for example, by including purification tags for separation with a suitable tag-binding material, paramagnetic properties for magnetic separation, and the like. Encoded particles can be used. Each particle includes a unique code (such as a bar code, luminescence code, fluorescence code, a nucleic acid code, and the like). Encoding can be used to provide particles for evaluating different nucleic acids in a single biological sample. The code is embedded (for example, within the interior of the particle) or otherwise attached to the particle in a manner that is stable through hybridization and analysis. The code can be provided by any detectable means, such as by holographic encoding, by a fluorescence property, color, shape, size, weight, light emission, quantum dot emission and the like to identify particle and thus the capture probes immobilized thereto. Encoding can also be the ratio of two or more dyes in one particle that is different than the ratio present in another particle. For example, the particles may be encoded using optical, chemical, physical, or electronic tags. Examples of such coding technologies are optical bar codes fluorescent dyes, or other means. The particle code can be a nucleic acid, e.g., a single stranded nucleic acid.
Different encoded particles can be used to detect or measure multiple nucleic acids (e.g., polymorphism genotypes or mRNAs) in parallel, so long as the encoding can be used to identify the polynucleotide (corresponding to an analyte nucleic acid) on a particular particle, and hence the presence or amount of the analyte nucleic acid (e.g., a polymorphism genotypes or mRNA from a biological sample) being evaluated. A sample can be contacted with a plurality of such coded particles. When the particles are evaluated, e.g., using a fluorescent scanner, the particle code is read as is the fluorescence associated with the particle from any probe used to evaluate modification of the intact substrate associated with the particles.
One exemplary platform utilizes mixtures of fluorescent dyes impregnated into polymer particles as the means to identify each member of a particle set to which a specific capture probe has been immobilized. Another exemplary platform uses holographic barcodes to identify cylindrical glass particles. For example, Chandler et al. (U.S. Pat. No. 5,981,180) describes a particle-based system in which different particle types are encoded by mixtures of various proportions of two or more fluorescent dyes impregnated into polymer particles. Soini (U.S. Pat. No. 5,028,545) describes a particle-based multiplexed assay system that employs time-resolved fluorescence for particle identification. Fulwyler (U.S. Pat. No. 4,499,052) describes an exemplary method for using particle distinguished by color and/or size. U.S. Publication Nos. 2004-0179267, 2004-0132205, 2004-0130786, 2004-0130761, 2004-0126875, 2004-0125424, and 2004-0075907 describe exemplary particles encoded by holographic barcodes.
U.S. Pat. No. 6,916,661 describes polymeric microparticles that are associated with nanoparticles that have dyes that provide a code for the particles. The polymeric microparticles can have a diameter of less than one millimeter, e.g., a size ranging from about 0.1 to about 1,000 micrometers in diameter, e.g., 3-25 μm or about 6-12 μm. The nanoparticles can have, e.g., a diameter from about 1 nanometer (nm) to about 100,000 nm in diameter, e.g., about 10-1,000 nm or 200-500 nm.
An “array”, as used herein, refers to a plurality of polynucleotides comprised in the composition or kit being immobilized at predetermined positions on a solid support such that each polynucleotide can be identified by its position.
The compositions, kits and arrays can be, but are not necessarily used in genome-wide genotyping analysis, but for efficient, low cost, and application-specific genotyping analysis, can be tailored to be used in the methods of treatment of the invention for detecting and/or predicting a treatment response to a treatment with a CRHR1 antagonist, as disclosed herein. Thus, the array of polynucleotides can have less than 100,000 (e.g., less than 90,000; less than 80,000; less than 70,000; less than 60,000; less than 50,000; less than 40,000; less than 30,000; less than 20,000; less than 15,000; less than 10,000; less than 5,000; less than 4,000; less than 3,000; less than 2,000; less than 1,500; less than 1,000; less than 750; less than 500, less than 200, less than 100, or less than 50) different polynucleotides.
The kits described above can, optionally, contain instructions for detecting the presence or absence of the at least one polymorphism genotype in a sample obtained from a subject. The kits can include one or more reagents for processing a biological sample. For example, a kit can include reagents for isolating mRNA or genomic DNA from a biological sample and/or reagents for amplifying isolated mRNA (e.g., reverse transcriptase, primers for reverse transcription or PCR amplification, or dNTPs) and/or genomic DNA. The kits can also, optionally, contain one or more reagents for detectably-labeling an mRNA, mRNA amplicon, genomic DNA or DNA amplicon, which reagents can include, e.g., an enzyme such as a Klenow fragment of DNA polymerase, T4 polynucleotide kinase, one or more detectably-labeled dNTPs, or detectably-labeled gamma phosphate ATP (e.g., 33P-ATP). The kits can include a software package for analyzing the results of, e.g., a microarray analysis. The kits described herein can also, optionally, include instructions for administering a CRHR1 antagonist where presence or absence of one or more polymorphism genotypes detectable by the plurality of polynucleotides or the array predicts that a subject will response to a CRHR1 antagonist.
The following are examples of the practice of the invention. They are not to be construed as limiting the scope of the invention in any way.
Based on basic science studies, the role of CRH was recognized as causal for signs and symptoms prevalent in depression, rendering blocking of CRH/CRHR1 signalling as a viable treatment option. Further clinical findings have found that CRH is elevated in a subgroup of patients with depression, where CRH causes core symptoms. Compound SSR-125543 has been developed elsewhere as a specific CRHR1 antagonist blocking the effect of CRH. A clinical trial evaluating the efficacy and tolerability of SSR-125543 in comparison to placebo and a standard antidepressant has been carried out previously without having predicted the treatment response according to the invention. However, based on additional studies (not published), it was recognized that among patients diagnosed with major depression, only a fraction of 20-30% has central CRH over-activity. Thus, a substantial fraction of non-stratified patients might not show a treatment response, in view of about 70-80% of patients treated with the CRHR1 antagonist not having a central CRH increase. Given the pharmacological specificity, only patients with central CRH-over-activity are likely to benefit from treatment with SSR-125543.
Here, a method of predicting a clinical treatment response (e.g., as measured by the HAM-D score) has been devised, which detects one or more polymorphism genotypes selected from the polymorphism genotypes disclosed in Table 2, using a chip containing probes specific for these polymorphism genotypes, allowing for identification of depressive patients being likely to respond to a treatment with SSR-125543. DNA samples obtained from 300 subjects enrolled in the earlier clinical trial, as mentioned above, were extensively analyzed by polymorphism genotyping. Using a machine-learning algorithm as described herein, polymorphism genotypes predictive of a response to SSR-125543 were identified, as disclosed in Table 2. Further, 150 or more polymorphism genotypes of this set were used to further “train” the algorithm, assisted by common machine-learning algorithms as described herein, and to test the prediction. Thus, having the set of useful polymorphism genotypes as disclosed in Table 2, at hand, a prediction algorithm can be readily devised, which provides superior prediction of a clinical response with high sensitivity and specificity. As is shown in Table 3, test predictions of a clinical response with a sensitivity of about 78% and a specificity of about 73% have been achieved.
To exclude the possibility that the polymorphism genotypes disclosed herein are merely identifying patients that are good responders to any kind of drug intervention, the performance of the method among patients treated with the standard antidepressant escitalopram used as comparator in the earlier clinical trial has also been tested. The sensitivity was 50%, and specificity was 43%, and thus insensitive and unspecific regarding prediction of response to a standard antidepressant, see Table 4. Therefore, the present method is to be considered highly specific for predicting the response to SSR-125543.
The above results were further challenged by considering a “lucky split” between the training and the testing cohort. Another 10.000 random splits were calculated which corroborated the initial result, achieving an odds ratio of 5, which indicates that chances of non-response are 5 times higher if the CRH genotyping analysis described herein predicts poor response. Transforming these findings into a time course curve where those depressed patients that where tested positive in the CRH genotyping analysis and treated with SSR-125543 were compared with patients treated by placebo resulted in a clear superiority of the investigational drug, see
To further evaluate the usefulness of the set of polymorphism genotypes provided in Table 2, further predictions have been tested using minimal subsets selected as prediction variables. As few as singular polymorphism genotypes selected from Table 2, as well as subsets of two, four or eight polymorphism genotypes selected from Table 2 proved useful in the method of predicting a clinical response, e.g., as measured by the HAM-D scale.
Treatment response to an anti-depressant therapy comprising SSR-125543 was predicted based on the same patient data of the earlier clinical trial and polymorphism genotyping set as described above, using statistical tools selected from the group consisting of random forests, support vector machines, neural networks, linear discriminant analyses, clustering methods such as k-nearest neighbours and their respective derivatives, linear models and their derivatives, as well as their combinations.
Surprisingly, even this univariate, bivariate, quadrivariate or octovariate analyses using combinations of polymorphism genotypes as disclosed in Tables 2, 5-7 herein, yielded clinical response predictions of a quality significantly better (i.e. both sensitivity and specificity>50%) than randomness, based on assessing the P-value of concordance between observed and predicted outcome in a 10-fold cross-validation procedure.
In particular, a total number of 78 singular polymorphism genotypes was identified with nominally significant P-values. Of those, 46 yielded a specificity and sensitivity of >50% each in predicting a clinical response. One singular polymorphism yielded both a sensitivity and specificity of higher than 60% each in predicting a clinical response.
Of all tested combinations of two of the univariate significantly predicting polymorphisms, 237 exhibited both sensitivity and specificity of at least 60% each in predicting a clinical response. Finally, a number of 46 tested combinations of two of the univariate significantly predicting polymorphism genotypes yielded a sensitivity and specificity beyond 65% each in predicting a clinical response, see Table 5.
In higher order analyses, using sets of four and eight polymorphism genotypes selected from the group disclosed in Table 2, a complete enumeration becomes unpractical (over a million combinations for the sets of four and over 1010 for the set of eight polymorphism genotypes). Therefore, randomly sampled sets (1000 combinations each) of such cardinalities k are presented herein.
For k=4, 72.1% of tested polymorphism genotype combinations yield a sensitivity and specificity of higher than 50% each, 20.5% of polymorphism genotype combinations yield a sensitivity and specificity of higher than 60% each, and 5.8% of polymorphism genotype combinations yield a sensitivity and specificity of higher than 65% each in predicting a clinical response. Two quadriavariate combinations even yield at least 70% in both sensitivity and specificity in predicting a clinical response, see Table 6.
For k=8, 93.3% of tested polymorphism genotype combinations yield a sensitivity and specificity of higher than 50% each, 32.6% of polymorphism genotype yield a sensitivity and specificity of higher than 60% each, 8.7% of polymorphism genotype combinations yield a sensitivity and specificity of 65% each, and, finally, 0.5% (5 combinations) of octovariate polymorphism genotype combinations yield a sensitivity and specificity at least 70% in sensitivity and specificity in predicting a clinical response, see Table 7.
For k=32, 99.9% of tested polymorphism genotype combinations yield a sensitivity and specificity of higher than 50% each in specificity and sensitivity, 98.9% of tested polymorphism genotype combinations yield a sensitivity and specificity of higher than 60% each, 72.8% of tested polymorphism genotype combinations yield a sensitivity and specificity of higher than 65% each, 15.6% of tested polymorphism genotype combinations yield a sensitivity and specificity of higher than 70% each in predicting a clinical response. Finally, some of the tested polymorphism genotype combinations (0.3%) even yield a sensitivity and specificity of higher than 75% each (data not shown).
As will be understood from the above explanations and data in Table 5, Table 6, and Table 7, even minimal subsets of polymorphism genotypes selected from the particularly useful set of polymorphism genotypes disclosed in Table 2 already allow for predictions of a clinical response significantly better than 50% (“coin-flip”). Therefore, while the present invention ideally aims at predicting the treatment response to SSR-125543 with sensitivity and specificity of at least 75% each, at least 80% each, at least 85% each, or even at least 90% each, methods of prediction using smaller subsets, e.g., of only one, two, four, or eight polymorphism genotypes selected from the group consisting of the polymorphism genotypes disclosed in Table 2 already provide a significant performance in predicting clinical responses. A subset of k=32 polymorphism genotypes already comprises combinations yielding a sensitivity and specificity of at least 75% each in predicting a clinical response. The predictive performance can be further increased by including, e.g., 150 polymorphism genotypes, as has been done in Example 1, 200 polymorphism genotypes, 250 polymorphism genotypes or all polymorphism genotypes as disclosed in Table 2.
To further evaluate the usefulness of specific set of polymorphism genotypes, a combination of 19 single nucleotide polymorphisms selected from the group polymorphism genotypes of Table 2 consisting of rs17740874, rs11715827, rs3811939, rs1882478, rs2235013, rs2214102, rs6415328, rs2044070, rs77152456, rs66794218, rs2589476, rs118003903, rs11871392, rs2589487, rs2028629, rs6026567, rs74338736, and rs6026593 has been found to be highly predictive for a clinical treatment response to a therapy comprising SSR-125543 or a pharmaceutically acceptable salt thereof.
Further, it was surprisingly found that of the above group of 19 SNPS, the four polymorphisms rs2028629, rs6026567, rs11715827 and rs2044070 as described in Table 2 show, considered on their own, significant evidence for being associated with a positive prediction of a response or a likelihood of response to a treatment with SSR-125543 or a pharmaceutically acceptable salt thereof. Interestingly and surprisingly, in all polymorphisms the allele G was found to be associated with a positive outcome, i.e. a good response or a good likelihood of response to the treatment.
The findings of prediction usefulness for these four polymorphism genotypes are described below. The data show that there is a significant evidence for association between response to treatment and each of the four polymorphism.
(i) rs2028629
rs2028629 (P_ID 208) is a polymorphism with the alleles A and G ([A/G]) as shown in Table 2. It was found that having at least one copy of the allele G (a person possessing genotypes AG or GG in contrast to the wild-type genotype AA) is positively associated with good response to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof.
This is significant at a p-value of 0.0298 by logistic regression. The estimated odds ratio by logistic regression for having genotypes AG or GG in contrast to genotype AA given as its natural logarithm is 0.8172, corresponding to a value of 2.26 on the original scale. Predicting treatment response with polymorphism rs2028629 a sensitivity of 0.557 and of specificity of 0.626 was obtained.
(ii) rs6026567
rs6026567 (P_ID 249) is a polymorphism with the alleles A and G ([A/G]) as shown in Table 2. It was found that there is a positive correlation between the number of alleles G with good response to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof.
This is significant at a p-value of 0.00129 by logistic regression. The estimated odds ratio by logistic regression per copy of allele G is 0.7795, corresponding to a value of 2.18 on the original scale. Predicting treatment response with polymorphism rs6026567 a sensitivity of 0.271 and of specificity of 0.904 was obtained.
(iii) rs11715827
rs11715827 (P_ID 179) is a polymorphism with the alleles G and T ([T/G]) as disclosed in Table 2. It was found that there is a positive correlation between the number of alleles G with good response to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof.
This is significant at a p-value of 0.00023 by logistic regression. The estimated odds ratio by logistic regression per copy of allele G is 1.2267, corresponding to a value of 3.41 on the original scale. Predicting treatment response with polymorphism rs11715827 a sensitivity of 0.771 and of specificity of 0.506 was obtained.
(iv) rs2044070
rs2044070 (P_ID 102) is a polymorphism with the alleles A and G ([A/G]) as disclosed in Table 2. It was found that there is a positive correlation between the number of alleles G with good response to treatment with SSR-125543 or a pharmaceutically acceptable salt thereof.
This is significant at a p-value of 0.000129 by logistic regression. The estimated odds ratio by logistic regression per copy of allele G is 0.9558, corresponding to a value of 2.60 on the original scale. Predicting treatment response with polymorphism rs2044070 a sensitivity of 0.786 and of specificity of 0.530 was obtained.
Advantageously, the predictive properties to a treatment response of each one of the polymorphism rs2028629, rs6026567, rs11715827 and rs2044070 can be increased by combination of all of the four polymorphism genotypes, with the prediction response of each polymorphism genotype acting additively.
Thus, using the set of all of the four polymorphism genotypes rs2028629, rs6026567, rs11715827 and rs2044070, it is possible to create a classifier based on these four SNPs alone using logistic regression applying 10-fold cross validation in the building of the model. Combination of the four polymorphisms (with “good response to treatment” as the target category) yielded a sensitivity of 0.700 and a specificity of 0.759. The estimated log-odds ratios for the number of G-alleles in a person are 0.2205 for rs2028629, 0.7258 for rs6026567, 0.8733 for rs11715827 and 0.8065 for rs2044070, with the SNPs acting additively, so no interaction needs to be assumed for these four SNPs and for this predictor. The intercept in this model is negative and estimated as −1.2703.
The optimal threshold (obtained by aiming at maximum accuracy of prediction in the 10-fold cross validation) was found to be 0.512945 with patients obtaining a value equal to or above this threshold predicted to sow good response to treatment.
To illustrate this by an example, a person is genotyped and found to have genotype AG for rs2028629, AA for rs6026567, TT for rs11715827 and GG for rs2044070. This translates into 1 copy of a G-allele for rs2028629, 0 copies for rs6026567, 0 copies for rs11715827 and 2 copies for rs2044070. The predicted quantity (PQ) for this patient then is calculated as:
−1.2703+1*0.2205+0*0.7258+0*0.8733+2*0.8065=0.5632
As PQ for this patient is 0.5632 and thus above the threshold we predict this patient to show good response.
In another example, another person is genotyped and found to have genotype AA for rs2028629, AG for rs6026567, TT for rs11715827 and AA for rs2044070. This translates into 0 copies of a G-allele for rs2028629, 1 copy for rs6026567, 0 copies for rs11715827 and 0 copies for rs2044070. The predicted quantity (PQ) for this patient then is calculated as:
−1.2703+0*0.2205+1*0.7258+0*0.8733+0*0.8065=−0.5445
As PQ for this patient is −0.5445 and thus below the threshold we predict this patient to show poor response or, in other words, to not show good response.
Moreover, it was also surprisingly found that the level of treatment prediction can be further increased by combination of the four SNPs (rs2028629, rs6026567, rs11715827 and rs2044070) with one or more or all of the polymorphism genotypes rs17740874, rs3811939, rs1882478, rs2235013, rs2214102, rs6415328, rs77152456, rs66794218, rs2589476, rs118003903, rs11871392, rs2589487, rs74338736 and rs6026593.
Accordingly, the level of performance (sensitivity of 0.700; specificity of 0.759) can be further increased by using one or more of these specific 19 SNPs or the total set. Here it was found that using a probabilistic neural network as originally described by Specht (Specht D F. Probabilistic neural networks and the polynomial adaline as complementary techniques for classification. IEEE Trans Neural Netw. 1990; 1(1):111-121, which is incorporated by reference) and following the idea of neuron reduction as described by Kusy and Kluska (Kusy M, Kluska J. Assessment of prediction ability for reduced probabilistic neural network in data classification problems. Soft Computing. 2017; 21:199-212, which is incorporated by reference) as well as allowing for a different value of the smoothing parameter per variable (SNP) as described by Kusy and Zajdel (Kusy M, Zajdel R. Probabilistic neural network training procedure based on Q (0)-learning algorithm in medical data classification. Applied Intelligence. 2014; 21:837-854, which is incorporated by reference) an improved prediction as measured by sensitivity and specificity in leave-one-out cross validation is obtained. Particularly, values of 0.914 for sensitivity and 0.880 for specificity (adjusted to three informative digits) have been obtained. The AUC on the ROC for this model is 0.923, the positive predictive value 0.865, and the negative predictive value 0.924. Accuracy is estimated at 0.895.
As shown in Table 5, test prediction of a clinical response with a sensitivity of up to 91% and a specificity of up to 88% have been achieved.
The foregoing exemplary embodiments are to be considered illustrative of, and not limiting to, the invention disclosed herein. It will be apparent to those skilled in the art that various modifications may be made without departing from the scope or spirit of the invention. Therefore, it will be appreciated that the scope of the present invention is primarily defined by the appended claims, and is not limited by the specific embodiments which have been presented as examples. All changes which come within the meaning and range of equivalency of the claims are intended to be encompassed.
This application is a continuation-in-part of U.S. application Ser. No. 15/562,466, filed Sep. 28, 2017, which claims the priority benefit of PCT/EP2016/057230, filed Apr. 1, 2016, which claims priority benefit of U.S. Provisional Application No. 62/141,881, filed Apr. 2, 2015. The entire contents of which are hereby incorporated by reference herein.
| Number | Date | Country | |
|---|---|---|---|
| 62141881 | Apr 2015 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 15562466 | Sep 2017 | US |
| Child | 17145776 | US |
