Patent Application
20120276577
The present invention relates to a method for determining the susceptibility of a cell strain to a compound intended for controlling the growth of said cell strain.
Testing the susceptibility of cell strains, such as tumour cell strains or microorganism strains, to drugs is a crude challenge, in particular in view of the increasing prevalence of drug resistance.
Indeed, besides cancer cells, resistances to drugs have notably been evidenced in bacteria, protozoan parasites, and fungi, such as yeasts and filamentous fungi. This has notably been evidenced in the case of the determination of the susceptibility of Candida albicans to fluconazole (FCZ). Candida albicans is the leading cause of invasive candidiasis, a major hospital-acquired infection. Fluconazole, an azole derivative agent, is one of the main first-line therapy alternatives. Azole resistant strains have emerged, possibly as a consequence of the use of azole-based antifungal agents in iterative and long-term therapies.
In vitro susceptibility testing is essential both for epidemiologic surveillance, e.g. to detect the emergence of resistant-microorganisms, and to adapt therapy for a given patient.
Susceptibility of cell strains to drugs is usually determined following the well-known broth microdilution methods as gold standards tests. These methods are based on growth inhibition and involve the determination of the Minimal Inhibitory Concentration (MIC). Such methods are notably recommended by the European Committee on Antibiotic Susceptibility Testing (EUCAST) and the Clinical Laboratory Standards Institute (CLSI) (Rodriguez-Tudela. et al. J Clin Microbiol 45, 109-111 (2007); Espinel-Ingroff et al. J Clin Microbiol 43, 3884-3889 (2005)).
For the EUCAST methodology, the MIC endpoint, for example for fluconazole susceptibility testing, is determined as the drug concentration inducing a 50% growth inhibition (IC50) with respect to the control as measured after 24 h of growth with a spectrophotometer. For the CLSI methodology, MIC endpoints are defined visually as the point at which there is prominent reduction in growth in the sample as compared to the control after 48 h of incubation. This visual end-point correlates with 50% growth inhibition (Rex at al. Clin Microbiol Rev 14, 643-658 (2001)).
Both reference methods are robust and reliable, though they remain time-consuming and thus inadequate for routine determination in an hospital context (Revankar et al. J Clin Microbiol 36, 153-156 (1998); Lass-Florl at al. Antimicrob Agents Chemother 52, 3637-3641 (2008)).
Thus, to circumvent this drawback, some commercial assays such as the E-Test (AB-Biodisk) or YeastOne Panel (Trek Diagnostic) have been proposed that can be used widely and easily in clinical microbiology labs. Overall, they have been favourably compared with the reference methods, while results with some pairs of microorganism-drugs do not exactly correlate with the results of the standards. Moreover, they still remain quite long to carry out and reading of the assays may be particularly difficult. This is particularly the case when testing C. albicans isolates against fluconazole, since it frequently leads to a trailing phenomenon, defined by a low-level growing of the colonies even over increasing concentrations of the drug. More recently, this so-called paradoxical effect has also been described for some Candida isolates when tested against ecchinocandin drugs.
The patent application US 2008/0009029 describes a method of determination of bacterial resistance to the ampicillin antibiotic. To measure the bacterial resistance to antibiotics, the protein profiles of bacteria are measured after cultivation in media containing the antibiotics. However, the teaching of US 2008/0009029 is limited to the measurement of microbial (bacterial) growth in the presence of antibiotics. This patent does not give any insight into the possible measurement of fungal growth in the presence of antifungal drugs. It does not either teach the determination of the minimal concentration of drug inducing a detectable change in mass spectrometry spectra.
Given these limitations, there is a clear need for the development of an equally robust method for determining the susceptibility of a fungus such as a yeast to drugs, with faster turn-around times and where endpoints determination is objective.
There is also a need in the art for a method for quantifying the resistance of a cell strain to a drug, e.g. determining the minimal concentration of drug inducing a detectable change in mass spectrometry spectra.
It is therefore an object of the present invention to provide such a method.
The present invention arises from the unexpected finding, by the inventors, that the protein composition of a C. albicans strain changes reproducibly in response to a particular drug concentration to which it is subjected, and that this variation in protein composition can be evidenced by mass spectrometry. Besides, the inventors have also shown that the values obtained for the minimal concentration of drug inducing a detectable change in mass spectrometry spectra of a protein extract of C. albicans are approximately equal (by two dilutions) with the minimal inhibitory concentrations determined for C. albicans using a standard method (CLSI).
The present invention thus relates to a method for determining the susceptibility of a cell strain to a compound intended for controlling the growth of said cell strain, comprising:
growing the cell strain in a first compound-free culture medium and in at least a second culture medium comprising the compound at a test concentration;
obtaining mass-spectrometry spectra for a protein extract of the cell strain grown in the first culture medium and for a protein extract of the cell strain grown in the second culture medium;
comparing the mass spectrometry spectra;
As intended herein, the expression “cell strain” relates to any kind of eukaryotic or prokaryotic cell strain. In particular, where the cell strain is an eukaryotic cell strain it can be from a pluricellular or an unicellular organism. As will be clear to one of skill in the art, the unicellular organism can notably such that it develops into a pluricellular organism. Preferably, the cell strain is a tumour cell strain or a microorganism strain. Preferably, the cell strain is a microorganism strain selected from the group constituted of a bacterial strain, a fungus strain, such as a filamentous fungus, in particular of the Ascomycota (e.g. of the Aspergillus or Fusarium genus) and Zygomycota phyla, or a yeast strain, in particular of the Ascomycota and Basiodiomycota phyla, a protozoan strain, and an algae strain.
In a most preferred embodiment, the cell strain is a yeast strain, in particular selected from group consisting of a Candida strain, a Saccharomyces strain, a Debatyomyces strain, a Pichia strain, a Geotrichum strain, a Cryptococcus strain, a Fisiobasidiella strain, and a Trichosporon strain. Most preferably the cell strain is a Candida strain. Besides, among yeasts of the Candida genus, it is preferred that the cell strain is a Candida strain selected from a Candida albicans strain, a Candida glabrala strain, a Candida tropicalis strain, a Candida parapsilosis strain, a Candida kefyr strain, a Candida krusei strain, a Candida dubliniensis strain, a Candida guillermondii strain and a Candida lusitaniae strain, and particularly preferred that the cell strain is a Candida albicans strain.
As intended herein, the term “compound for controlling the growth of said cell strain” relates to a compound liable to kill cells of the cell strain or to inhibit, partially or totally, the growth of cells of the cell strain. Thus, the compound may notably be an anti-tumour compound, an antibiotic or antibacterial compound, or an antifungal compound. However, it is preferred that the compound is an antifungal compound selected from an azole compound, an echinochandin compound, such as caspofungin, micafungin, or anidulafungin, a polyene compound, such as amphotericin B or nystatin, and anti-metabolites, such as flucytosine. Preferably, the antifungal compound is an azole compound selected from the group constituted of fluconazole, voriconazole, posaconazole, isavuconazole, ravuconazole, ketoconazole, and itraconazole. Most preferably the compound is fluconazole.
As intended herein the expression “determining the susceptibility of a cell strain” relates to determining whether, and within what measure, the compound as defined above kills or inhibits the growth of cells of the cell strain. In particular, “determining the susceptibility of a cell strain” relates to determining the minimal concentration of the compound which yields a detectable difference in the mass spectrometry spectra.
As intended herein, “mass spectrometry” relates to any method enabling determining the m/z ratio of one or more molecules, such as proteins, within a sample, such as a protein extract as defined above, wherein m represents the mass and z the charge of said molecules. Mass spectrometry as defined above can be carried out by any one of the numerous mass spectrometry methods known in the art, such as Matrix-Assisted Laser Desorption/Ionisation Time-Of-Flight (MALDI-TOF) mass spectrometry, or Surface-Enhanced Laser Desorption/Ionisation Time-Of-Flight (SELDI-TOF). However, it preferred that mass spectrometry as defined above is carried out by MALDI-TOF.
The expression “mass spectrometry spectra” relate to recordings of the m/z ratios and optionally the quantities of the various molecules, in particular proteins, contained in the protein extracts submitted to mass spectrometry. Usually, mass spectrometry spectra are graphs representing signal intensity (corresponding to the quantity of molecule) as a function of the m/z ratio. The association of signal intensity to an m/z ratio defines a peak. As will be clear to one of skill in the art “a mass spectrometry spectrum” as intended herein can be either obtained from one recording or be the mean of a plurality of recordings.
As intended herein, “mass spectrometry spectra are detectably different” in particular if a detectable difference in intensity of m/z ratio can be established. The man skilled in the art knows how to establish that two spectra present detectable differences, in particular using exact permutation tests based on Spearman rank correlation coefficients, such as described in “Design and Analysis of DNA Microarray Investigations”, R M Simon et al, SPRINGER, 2003, in particular on pages 68 and 123.
Numerous procedures are known in the art for extracting proteins from cells and one of skill in the art knows how to adapt them depending on the type of cell strain. Accordingly, any one of these extraction methods can be used in the method of the invention. However, it is preferred, within the frame of the method according to the invention, that the protein extract is obtained by an ethanol treatment of grown cells followed by a treatment with a mixture of formic acid and acetonitrile, in particular where the cell strain is a yeast strain, more particularly a Candida strain.
As intended herein, the expression “culture medium” relates to any medium liable to sustain the growth of cells of the cell strain. Preferably, the culture medium as defined above is a minimum medium. Preferably also, the medium is a liquid medium. As will be clear to one of skill in the art, the composition of the compound-free culture medium and the culture medium comprising the compound at a test concentration should preferably identical except for said compound.
The cell strain can be grown for any amount of time provided it is sufficient for the compound to induce significant changes in the protein content of the cell strain. However, so that the method is carried out as quickly as possible, it is preferred that the amount of time for growing the cells is the minimal time for the compound to induce significant changes in the protein content of the cell strain. Thus, the cell strain is grown during less than 24 hours, more preferably during less than 20 hours, and most preferably during about 15 hours.
In an embodiment of the above-defined method, the cell strain is grown in several culture media with increasing test concentrations of the compound, and the minimal concentration of the compound yielding a mass-spectrometry spectrum detectably different from the mass-spectrometry spectrum obtained from the cell-extract of the microorganism strain grown in the compound-free culture medium is determined.
As intended herein the “minimal concentration of the compound yielding a mass-spectrometry spectrum significantly different from the mass-spectrometry spectrum obtained from the cell-extract of the microorganism strain grown in the compound-free culture medium” is also called the minimal profile (i.e. mass spectrometry spectrum) change concentration (MPCC). Advantageously, the inventors have shown that for a given cell strain and compound the MPCC and the MIC are correlated.
Preferably, determining the minimal concentration comprises:
obtaining a mass spectrometry spectrum from a protein extract of the cell strain grown in the culture medium having the highest test concentration of the several culture media;
comparing the mass spectrometry spectra obtained from protein extracts of the several culture media to (i) the mass spectrometry spectrum from a protein extract of the cell strain grown in the culture medium having the highest test concentration and (ii) the mass spectrometry spectrum from the compound-free culture medium;
selecting the mass spectrometry spectrum obtained from a protein extract of the several culture media with the lowest compound concentration and which presents more similarity with the mass spectrometry spectrum (i) than with the mass spectrometry spectrum (ii);
the lowest compound concentration being the minimal concentration to be determined.
Determining that a mass spectrometry spectrum presents more similarity, or shares more resemblance, with a first spectrum than with a second spectrum can be routinely determined by one of skill in the art, in particular using a similarity measure based on Spearman rank correlation coefficients.
More particularly, once the mass spectrometry spectra have been obtained from protein extracts of the several culture media, a mean spectrum can be determined. Then simple peak detection can be performed on this mean spectrum, and the final peak locations can be selected based on the mean intensity of the peak.
In a first step of statistical analysis, testing whether there is a difference between the extreme concentration spectra may be achieved. This may can be routinely determined by one of skill in the art performing an exact permutation test using Spearman rank correlation coefficient as a similar measure. Briefly, all the rank correlation coefficients between all the spectra may first be calculated. The mean of the intra-class rank correlations coefficients (IntraRCCM) and the mean of the inter-class rank correlation coefficients (InterRCCM) may then be determined.
The test criterion may be the ratio InterRCCM/IntraRCCM under the null hypothesis of no difference between class memberships, the criterion's expected value being 1. When class memberships are informative, interRCCM is lower than intraRCCM, and expected criterion values are lower than 1. The permutation test can be achieved by computing the distribution of the criterion for all the permutations of the class memberships.
Once the difference between extreme concentrations has been statistically proved, the minimal concentration at which a particular spectrum starts to differ significantly from the null control spectrum one may be determined. This can be achieved by computing for each concentration, the corresponding spectrum similarity with each spectrum from the two extreme concentrations, and by classifying it as “near of the null concentration” or “near of the maximum concentration” according to the similarity values, using the mean inter-class rank correlation coefficient (InterRCCM). The minimal profile change concentration (MPCC) is defined as the minimum concentration that is more similar to the maximum concentration than to the null one.
In a preferred embodiment, the invention relates to a device for implementing the above-defined method in which the cell strain is grown in several culture media with increasing test concentrations of the compound, and the minimal concentration of the compound yielding a mass-spectrometry spectrum detectably different from the mass-spectrometry spectrum obtained from the cell-extract of the microorganism strain grown in the compound-free culture medium is determined.
Such device comprises;
means for obtaining a mass-spectrometry spectrum for a protein extract of the yeast strain grown in a first compound-free culture medium and in several culture media with increasing test concentrations of the compound;
means for comparing the mass spectrometry spectra obtained for protein extracts of the several culture media with increasing test concentrations of the compound to (i) the mass spectrometry spectrum of a protein extract of the yeast strain grown in the culture medium having the highest assayed test concentration and (ii) the mass spectrometry spectrum of a protein extract of the yeast strain grown in the compound-free culture medium;
means for determining the minimal concentration by determining the mass spectrometry spectrum obtained from a protein extract of the several culture media with the lowest compound concentration and which presents more similarity with the mass spectrometry spectrum (i) than with the mass spectrometry spectrum (ii); the lowest compound concentration being the minimal concentration.
A device 1 for performing the data analysis is schematically illustrated in
The method according to the invention is realised by means of a software, the instructions of which are stored in memory 4 and are processed by CPU 2.
In a first step, data acquisition is performed. The mass spectrometer MS is plugged onto the Input/Output interface 12. The data corresponding to the spectrum obtained from a sample currently analysed with the mass spectrometer MS are transferred to device 1.
Once the transfer of a spectrum is completed, the user labels it with the value of the FCZ concentration of the class of the sample, and an integer between 1 and n to identify said sample in said class.
The spectrum is then stored in database 6 with an Id corresponding to said concentration and said integer.
After the n spectra for the c values of the FCZ concentration are acquired, device 1 is put in a pre-processing mode by the user.
In this pre-processing mode, peak extraction algorithm is performed. The average of the n×c spectrum is calculated. The base line of the average spectrum is determined. Finally, on the average spectrum, peaks are retained for a signal to noise ratio greater than 4 times the value of the base line. Only the peaks with a m/z ratio between 3000 and 20000 are retained for the statistical analysis. This leads to the extraction of a set of N characteristic m/z ratios where peaks occur.
The pre-processing algorithm then comprises the discretisation of each of the n×c spectra in database 6. A discretised spectrum is derived from each spectrum by reading the values of the m/z ratio for the N characteristic m/z ratios where peaks occur.
Finally, the pre-processing algorithm runs a rank list creation routine for associating a ranked spectrum to each discretised spectrum. Each coordinate of the discretised spectrum is replaced by its rank in the ordered list of the N m/z ratios of said discretised spectrum.
Each thus obtained ranked spectrum is stored in database 6 with the Id of the corresponding initial spectrum.
Then, device 1 is put in a statistical analysis mode where CPU 2 processes the following comparison algorithm:
The first step consists in selecting two classes of n spectra. These two classes are respectively the class corresponding to a null value of the FCZ concentration and the class corresponding to the maximum FCZ concentration. The corresponding 2n ranked spectra are retrieved from database 6 and stored into memory 4.
CPU 2 then calculates the n(2n−1) correlation rank coefficients, one coefficient for each possible pair of ranked spectra. The average of the correlation rank coefficients of the pairs of spectra from the same class (same concentration of FCZ) leads to the determination of the IntraRCCM parameter. The average of the correlation rank coefficients of the pairs of specta from different classes (different concentrations of FCZ) leads to the determination of the InterRCCM parameter.
Finally, CPU 2 computes the ratio interRCCM/IntraRCCM by dividing the InterRCCM parameter by the IntraRCCM parameter before comparing it with unity.
If the ratio interRCCM/IntraRCCM is equal to unity, it means that there is no difference between the two classes and the statistical analysis ends.
On the other hand, if ratio interRCCM/IntraRCCM is lower than 1 it means that there is a difference between the two classes with extreme concentration.
In this case, CPU 2 processes the following similarity algorithm:
For a class corresponding to an intermediary value of the FCZ concentration, a null interRCCM parameter is calculated between the intermediary class and the null concentration class and a maximum interRCCM parameter is calculated between the intermediary class and the maximum concentration class.
The intermediary class is said more similar to the maximum concentration class than to the null concentration class when the maximum interRCCM parameter is greater than the null interRCCM parameter.
At the end, the minimal profile change concentration MPCC is given by the smallest of the concentrations of the intermediary classes that are more similar to the maximum concentration class.
This MPCC value can be displayed on the LCD screen at the end of the processing. It is stored in database 6.
As a variant, other statistical analysis methods may be used.
The invention will be further described by the following non-limiting figures and Examples.
All experiments were performed using sequential Candida albicans clinical isolates. Eight groups of sequential related isolates (groups A to K) were characterized. All strains were isolated from HIV-positive patients with oropharyngeal candidasis that were treated mainly with FCZ. Each group contains one azole susceptible strain (FCZ MIC ranged from 0.125 to 8 μg/ml) and its related-sequential strains with higher FCZ MICs (ranging from 16 to 128 μg/ml) within the same MLST genotype. For each clinical isolate, MICs according to CLSI broth-microdilution methodology 1, and presence of ERG11 and TAC mutations, and/or CDR1/2 and MDR hyperexpression were determined previously 2 (Table 1). The ATCC 90028 C. albicans strain (MIC=0.25 μg/ml) was added as a reference strain.
Fluconazole (FCZ) pure powder (Sigma Chemical CO., Saint-Louis, Mo., USA) was dissolved in pure water. Serial dilutions of drug (concentration ranged from 256 to 0.25 μg/ml), made into RPMI 1640 medium (with glutamine and without bicarbonate, Invitrogen) buffered with MOPS (0.165M) (Sigma Chemical CO., Saint-Louis, Mo., USA) and adjusted to pH 7 with sodium hydroxide (5N), were dispensed in 600 μl aliquots into sterile 24-well flat-bottomed microtiter plates.
After cultivation during 48 h at 37° C. on Sabouraud agar medium, yeasts cells were transferred into RPMI 1640 medium (with glutamine but without bicarbonate, Invitrogen) buffered with MOPS (0.165M) (Sigma Chemical CO., Saint-Louis, Mo., USA), and adjusted to pH 7 with sodium hydroxide (5N). Then, 600 μl of RPMI with yeast (2.106 yeasts/imp were added to 600 μl of RPMI with FCZ into microtiter plates (final FCZ concentration ranged from 128 to 0.125 μg/ml), or in RPMI alone as negative control. Culture was performed in a 30° C. incubator for 15 hours, with continuous agitation.
Yeast extraction was performed as follows:
The supernatant was distributed (0.5 μl droplet) in duplicate on a MALDI AnchorChip sample slide (Bruker-Daltonics, Bremen, Germany), then air-dried. The α-cyano-4-hydroxycinnamic acid (CHCA) matrix (Bruker-Daltonics), prepared at a concentration of 50 mg/ml in 50% acetonitrile and 50% water with 0.1% TFA, was sonicated for 5 min before being spotted (0.5 μl) over the dried sample. A DH5a Escherichia coil protein extract (Bruker-Daltonics) was deposited on the calibration spot of the Anchorchip for external calibration. MALDI analysis were performed on a Bruker Autoflex I MALDI TOF mass spectrometer with a nitrogen laser (337 nm) operating in linear mode with delayed extraction (260 ns) at 20 kV accelerating voltage. Each spectrum was automatically collected in the positive ion mode as an average of 500 laser shots (50 laser shots at 10 different spot positions). Laser energy was set just above the threshold for ion production. Mass range between 3,000 and 20,000 m/z (ratio mass/charge) was selected to collect the signals with the AutoXecute tool of flexControl acquisition software (Version 2.4; Bruker-Daltonics). Only peaks with a signal/noise ratio >3 were considered. Spectra were eligible for further analysis when the peaks had a resolution better than 600. For each cultivation condition, we collected mass spectra from 3 biological replicates and 2 technical replicates.
Data obtained from the flex control acquisition software were a set of lists of mass/charge peaks and corresponding intensities, one list for each spectrum. For each FCZ concentration (11 FCZ concentrations and without FCZ), 6 spectra, obtained from 3 biological replicates tested in duplicate, were collected, so the whole initial set of data consisted of 72 spectra.
Alignment was performed using a method based on the mean spectrum, using an approach similar to that recommended by Coombes et al. (Coombes K R, Baggerly K A, and Morris J S: Pre-Processing Mass Spectrometry Data. Fundamentals of Data Mining in Genomics and Proteomics, W Dubitzky, M Granzow, and D Berrar, eds. Kluwer, Boston, 79-99 (2007)).
The mean spectrum of the 72 spots was computed, a simple peak detection algorithm was run on the mean spectrum, and a selection of the final peak locations was based on the mean intensity of the peak. For each spectrum, the peaks corresponding to the final peak locations were retained for the statistical analysis. An error of 0.05% of the mass/charge ratio was allowed in the two last steps.
At this stage, data consisted of 12 ordered subsets of 6 spectra, each subset corresponding to a concentration value, from 0 to 128 μg/ml. The first step of statistical analysis was aimed at testing whether there was a difference between the extreme concentration spectra.
An exact permutation test using Spearman rank correlation coefficient as a similar measure, based on the following computations, has been carried out:
The test criterion is the ratio InterRCCM/IntraRCCM Under the null hypothesis of no difference between class memberships, the criterion's expected value is 1. When class memberships are informative, interRCCM is lower than intraRCCM, and expected criterion values are lower than 1. The permutation test is achieved by computing the distribution of the criterion for all the permutations of the class memberships, and the exact p-value is the proportion of criteria lower or equal to the one corresponding to the observed criterion value.
Once the difference between extreme concentrations has been statistically proved, the aim of the next step is to find the minimal concentration at which a particular spectrum starts to differ significantly from the null control spectrum one. This is achieved by computing for each concentration, the corresponding spectrum similarity with each from the two extreme concentrations, and by classifying it as “near of the null concentration” or “near of the maximum concentration” according to the similarity values. The similarity function used is the mean inter-class rank correlation coefficient (InterRCCM). The minimal profile change concentration (MPCC) is defined as the minimum concentration that is more similar to the maximum concentration than to the null one.
In order to obtain standardized fingerprints, the inventors first optimized the protocols. All experiments were carried out using the C. albicans susceptible reference strain, ATCC 90028 (Methods). The influence of starting inoculum, the concentration of yeast cells in the sample to be analyzed and thus the minimum culturing time required, on the quality and reproducibility of the fingerprint reproducibility were assessed. This allowed establishing that optimal results are obtained from cultures initiated with 106 yeasts/ml.
The inventors then determined the effect that varying concentrations of FCZ (serial dilution from 128 to 0.125 μg/ml) would have on C. albicans mass spectrometry fingerprint patterns (Methods).
The yeast cell pellets obtained from cultures grown for 15 h were subjected to acid extraction and the supernatants analyzed by MALDI-TOF MS. A typical result is presented in
Accordingly, these observations led the inventors to formulate a new endpoint, namely the minimal profile change concentration (MPCC), a value defined as the lowest drug (FCZ in the experiment above) concentration at which a mass spectrum profile change can be detected.
Besides, the inventors have further devised a novel statistical approach to calculate this value objectively (Methods), which is based on the mass and intensity of each peak in the fingerprints. In this statistical analysis, the discrepancies between the mass spectra at the two extreme conditions (128 μg/ml FCZ and no FCZ) are first defined. Then, the similarity to each of the two “extreme” spectra is statistically evaluated for the spectrum recorded at each of the different intermediate FCZ concentrations, to yield a classification of “nearer to the 128 μg/ml” or “nearer to the FCZ negative” spectrum.
The validity of the new methodology above was established as follows. The MPCC of the reference strain (C. albicans ATCC 90028, MIC=0.25 μg/ml) was determined, as well as that of sixteen C. albicans isolates with distinct drug resistance profiles. Eight are known to have low-MICs (range 0.125 to 8 μg/ml) and the other eight have high-MICs (range 16 to 128 μg/ml) to FCZ, these MICs having been determined by the CLSI standard method. In addition, the FCZ-resistant strains tested are representative of the different resistance mechanisms, mating types and clades 15 that are found in C. albicans (Table 1).
Comparison of MPCCs with the MICs for the 17 strains above (
Importantly, the MPCC determinations were concordant and accurate irrespective of the type of drug resistance mechanism (ERG11 mutations, TAG mutation, CDR1/2 or MDR hyper-expression), the mating type, or the clade to which the different strains tested belonged. These results not only validate the new methodology, but also offer strong indication of the robustness of the methodology present in the face of C. albicans strains with diverse genetic backgrounds.
As it stands, where a mass spectrometer is already available, the running costs for each sample analyzed by the method presented here is slightly less than 1 Euro, which compares quite favourably with the higher costs of all other methods. The diagnostic profile shift observed for FCZ does not vary with either the genetic background of the strain tested, nor the level or mechanism of the resistance to FCZ. Accordingly, it is likely that a characteristic profile would be associated with each of the different classes of drugs known to inhibit a given pathogen (e.g. triazoles echinocandins, polyene, and antimetabolites for C. albicans). The present methodology appears to be suitable for monitoring the emergence of resistance to drugs used against pathogenic organisms, including bacteria and eukaryotic cancer cells or pathogens such as Cryptosporidiurn and Plasmodium.
C. albicans
| Number | Date | Country | Kind |
|---|---|---|---|
| 09305601.8 | Jun 2009 | EP | regional |
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/EP2010/059101 | 6/25/2010 | WO | 00 | 3/28/2012 |
