METHODS FOR TREATMENT OR PROPHYLAXIS OF KIDNEY OR LIVER DYSFUNCTION

TECHNICAL FIELD

The invention relates to methods useful for treatment or prophylaxis of liver or kidney dysfunction commonly associated with parenteral nutrition, short bowel syndrome and intestinal failure. More particularly, the invention relates to methods of using of a GLP-2 peptide, or analogs thereof, for the treatment or prophylaxis of liver or kidney dysfunction commonly associated with parenteral nutrition, short bowel syndrome and intestinal failure.

BACKGROUND

Intestinal failure is a condition caused “by the critical reduction of functional gut mass below the minimal amount necessary for adequate digestion and absorption to satisfy body nutrient and fluid requirements” (Goulet et al., Curr. Op. Org. Trans., 14:256, 2009). This condition is often caused by short bowel syndrome (SBS), although it can also result from other insults on the gut (Thompson et al., J. Am. Coll. Surg. 201:85, 2005). Intestinal failure can be managed to some extent through parenteral nutrition (PN): the provision of nutrition intravenously as opposed to through the gastrointestinal tract (Klein, Gastroenterology 121:970, 2002).

In patients with SBS, a process called intestinal adaptation, in which the short bowel increases its absorption to compensate for the lost tissue, sometimes restores enough gut function that the patient can cease PN after limited treatment (Welters et al., ANZ J. Surg. 72:229, 2002). However, other individuals depend on long term or chronic PN as a source of nutrition. The availability of PN to patients with intestinal failure is limited, however, by PN-associated liver and kidney dysfunction.

PN-associated liver disease (PNALD) occurs to some degree in most patients receiving long-term PN (Cavicchi et al., Ann. Int. Med. 132:525, 2000) (Salvino R, J. Parenter Enteral Nutr. 30:202, 2006). In some cases it can be progressive resulting in hepatic failure necessitating liver transplantation (Buchman et al., Hepatology 43:9, 2006) (Chan et al., Surgery 126:28, 1999). Although the frequency of severe, irreversible liver injury from PNALD varies depending on circumstances, some reports suggest that it will ultimately occur in more than 50% of long-term PN recipients. Furthermore, PN-associated liver dysfunction impairs the process of intestinal adaptation in short bowel patients, which may necessitate more PN (Goulet et al., Curr. Op. Org. Trans., 14:256, 2009).

PNALD pathogenesis and progression factors are incompletely understood, and reliable parameters to identify patients at high risk for progression are lacking (Fulford et al., Nutr. Clin. Pract. 19:274, 2004) (Buchman, Gastroenter. 130:S5, 2006). However, SBS due to massive intestinal resection has been identified as one of the risk factors of PNALD, as interruption in enterohepatic circulation causes alterations in bile acid metabolism and excretion.

Another important and serious complication in long-term PN patients is renal dysfunction, manifested by a progressive decrease in creatinine clearance. In a series of long term PN patients, creatinine clearance was found to decrease on average 3.5% per year with a follow-up of 10 years or greater (Buchman et al., J. Parenter Enteral Nutr. 17:438, 1993). The decrease could not be ascribed to advancing age, nephrotoxic drug use, nutritional status, amino acid content of PN or septicemia episodes. Similarly creatinine clearance was found to be reduced in children receiving long term PN, with the degree of impairment directly proportional to the duration of PN (Moukarzel et al., J. Pediatr. 119:864, 1991). No evidence for tubular dysfunction, nephrocalcinosis or obstructive uropathy was found in the children studied.

Unfortunately, patients with SBS may be dependent on long-term PN and therefore have limited options if liver or renal dysfunction or failure develops. Patients who develop irreversible and severe reductions in renal function may progress to renal failure, necessitating chronic dialysis or renal transplantation. Those patients with SBS who develop irreversible liver injury and failure, may be referred for intestine or combined intestine-liver transplantation (Buchman et al., Gastroenterololgy 124:1111, 2003) (Keller et al., Best Prac. Res. Clin Gastroenterol. 18: 977, 2004) (Chungfat et al., J. Amer. Coll. Surg. 205:755, 2007).

Recent developments in PN formulations may provide some relief from PNALD. In several small studies, substitution of all or some of the plant based lipids for fish based lipids in PN formulations resulted in an increase in liver function in individuals receiving PN (Antébi et al., 2004, Mertes et al., Ann. Nutr. Metab. 50:253, 2006). However, the long term safety and efficacy of these formulations have not been established (Wiles and Woodward, Curr. Op. Clin. Nutr. Metab. Care 21:265, 2009), further underscoring the need for additional treatments for PNALD.

BRIEF DESCRIPTION

The present invention provides methods for treating kidney or liver dysfunction in individuals. In some instances, the methods are used with individuals receiving parenteral nutrition, experiencing short bowel syndrome, or suffering from intestinal failure. The methods disclosed comprise the step of administering to an individual a GLP-2 peptide or a GLP-2 peptide analog in an amount effective to treat liver or kidney disease.

This invention also provides methods for prophylaxis against kidney or liver dysfunction in individuals. In some instances, the methods are used with individuals receiving parenteral nutrition, experiencing short bowel syndrome, or suffering from intestinal failure. The methods disclosed comprise administering to an individual a GLP-2 peptide, or a GLP-2 peptide analog in an amount effective for prophylaxis of liver or kidney disease.

In one embodiment, the invention is directed to a method of treating impaired liver function in an individual experiencing intestinal failure, short bowel syndrome, or parenteral nutrition, comprising the step of administering to an individual having impaired liver function one or more of a GLP-2 peptide or a GLP-2 peptide analog in an amount effective to cause improvement in liver function.

In one such embodiment, the GLP-2 peptide or the GLP-2 peptide analog is administered at a dose of between about 0.001 mg/kg/day and about 10 mg/kg/day. In another such embodiment, the GLP-2 peptide analog is teduglutide (SEQ ID NO.:4). In another such embodiment, teduglutide is administered at a dose of between about 0.05 mg/kg/day and about 0.1 mg/kg/day.

In one such embodiment, the improvement in liver function is observed within about four weeks after the beginning of administering the GLP-2 peptide or GLP-2 peptide analog to the individual.

In one such embodiment, the improvement in liver function is monitored by the use of one or more diagnostic biomarkers. In another such embodiment, the diagnostic biomarkers are selected from the group consisting of: bilirubin, gamma glutamyl transferase, alanine transaminase, aspartate aminotransferase, alkaline phosphatase and albumin.

In another such embodiment, the individual level of bilirubin, gamma glutamyl transferase, alanine transaminase, aspartate aminotransferase or alkaline phosphatase, if selected, decreases at least about 5 percent. In another such embodiment, the level of albumin, if selected, increases at least about 5 percent.

In a separate embodiment, the invention is directed to a method for prophylaxis against impairment of liver function in an individual experiencing intestinal failure, short bowel syndrome, or parenteral nutrition, comprising the step of administering to an individual one or more of a GLP-2 peptide or a GLP-2 peptide analog in an amount effective for prophylaxis against impaired liver function.

In one such embodiment, the prophylaxis against impaired liver function is observed within about four weeks after the beginning of administering the GLP-2 peptide or GLP-2 peptide analog to the individual.

In one such embodiment, the prophylaxis against impaired liver function is monitored by the use of one or more diagnostic biomarkers. In another such embodiment, the diagnostic biomarkers are selected from the group consisting of: bilirubin, gamma glutamyl transferase, alanine transaminase, aspartate aminotransferase, alkaline phosphatase and albumin.

In another such embodiment, the individual level of bilirubin, gamma glutamyl transferase, alanine transaminase, aspartate aminotransferase or alkaline phosphatase, if selected, increases, if at all, less than about 10 percent. In another such embodiment, the level of albumin, if selected, decreases, if at all, less than about 10 percent.

In a separate embodiment, the invention is drawn to a method of treating impaired kidney function in an individual experiencing intestinal failure, short bowel syndrome, or parenteral nutrition, comprising the step of administering to an individual having impaired kidney function one or more of a GLP-2 peptide or a GLP-2 peptide analog in an amount effective to cause improvement in kidney function.

In one such embodiment, the improvement in kidney function is observed within about four weeks after the beginning of administering the GLP-2 peptide or GLP-2 peptide analog to the individual.

In one such embodiment, the improvement in kidney function is monitored by the use of one or more diagnostic biomarkers. In another such embodiment, the diagnostic biomarkers are selected from the group consisting of: urea nitrogen, creatinine and glomerular filtration rate. In another such embodiment, the individual level of urea nitrogen, or creatinine, if selected, decreases at least about 5 percent. In another such embodiment, the level of glomerular filtration rate, if selected, increases at least about 5 percent.

In a separate embodiment, the invention is drawn to a method for prophylaxis against impairment of kidney function in an individual experiencing intestinal failure, short bowel syndrome, or parenteral nutrition, comprising administering to an individual one or more of a GLP-2 peptide or a GLP-2 peptide analog in an amount effective for prophylaxis against impaired kidney function.

In one such embodiment, the prophylaxis against impaired kidney function is observed within about four weeks after the beginning of administering the GLP-2 peptide or GLP-2 peptide analog to the individual.

In one such embodiment, the prophylaxis against impaired kidney function is monitored by the use of one or more diagnostic biomarkers. In another such embodiment, the diagnostic biomarkers are selected from the group consisting of: urea nitrogen, creatinine and glomerular filtration rate. In another such embodiment, the individual level of urea nitrogen, or creatinine, if selected, increases, if at all, less than about 5 percent. In another such embodiment, the level of glomerular filtration rate, if selected, decreases, if at all, less than about 5 percent.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph which depicts the average change from baseline over time of alanine transaminase (ALT) in subjects treated with varying doses of teduglutide or placebo.

FIG. 2 is a graph which depicts the average change from baseline over time of aspartate aminotransferase (AST) in subjects treated with varying doses of teduglutide or placebo.

FIG. 3 is a graph which depicts the average change from baseline over time of total bilirubin in subjects treated with varying doses of teduglutide or placebo.

FIG. 4 is a graph which depicts the average change from baseline over time of alkaline phosphatase (ALP) in subjects treated with varying doses of teduglutide or placebo.

FIG. 5 is a graph which depicts the average change from baseline over time of ALT in subjects treated with varying doses of teduglutide or placebo, where the subjects had abnormal baseline values of ALT.

FIG. 6 is a graph which depicts the average change from baseline over time of ALT in subjects treated with varying doses of teduglutide or placebo, where the subjects had normal baseline values of ALT.

FIG. 7 is a graph which depicts the average change from baseline over time of AST in subjects treated with varying doses of teduglutide or placebo, where the subjects had abnormal baseline values of AST.

FIG. 8 is a graph which depicts the average change from baseline over time of AST in subjects treated with varying doses of teduglutide or placebo, where the subjects had normal baseline values of AST.

FIG. 9 is a graph which depicts the average change from baseline over time of creatinine in subjects treated with varying doses of teduglutide or placebo.

FIG. 10 is a graph which depicts the average change from baseline over time of urea nitrogen in subjects treated with varying doses of teduglutide or placebo.

FIG. 11 is a graph which depicts the average change from baseline over time of GFR in subjects treated with varying doses of teduglutide or placebo.

FIG. 12 is a graph which depicts the percent of individuals from groups receiving varying doses of teduglutide or placebo that experience a decrease in ALP greater than 5% over time.

FIG. 13 is a graph which depicts the percent of individuals from groups receiving varying doses of teduglutide or placebo that experience a decrease in ALT greater than 5% over time.

FIG. 14 is a graph which depicts the percent of individuals from groups receiving varying doses of teduglutide or placebo that experience an increase in ALP greater than 5% over time.

FIG. 15 is a graph which depicts the percent of individuals from groups receiving varying doses of teduglutide or placebo that experience an increase in ALT greater than 5% over time.

FIG. 16 is a graph which depicts the percent of individuals from groups receiving varying doses of teduglutide or placebo that experience a decrease in GFR greater than 5% over time.

FIG. 17 is a graph which depicts the average change from baseline of ALT in individuals treated with placebo or 0.05 mg/kg/day teduglutide.

FIG. 18 is a graph which depicts the average percent change from baseline of ALT in the data depicted in FIG. 17.

FIG. 19 is a graph which depicts the average change from baseline of AST in individuals treated with placebo or 0.05 mg/kg/day teduglutide

FIG. 20 is a graph which depicts the average percent change from baseline of AST in the data depicted in FIG. 19.

FIG. 21 is a graph which depicts the average change from baseline of albumin in individuals treated with placebo or 0.05 mg/kg/day teduglutide

FIG. 22 is a graph which depicts the average percent change from baseline of albumin in the data depicted in FIG. 21.

FIG. 23 is a graph which depicts the average change from baseline of ALP in individuals treated with placebo or 0.05 mg/kg/day teduglutide

FIG. 24 is a graph which depicts the average percent change from baseline of ALP in the data depicted in FIG. 23.

FIG. 25 is a graph which depicts the average change from baseline of bilirubin in individuals treated with placebo or 0.05 mg/kg/day teduglutide

FIG. 26 is a graph which depicts the average percent change from baseline of bilirubin in the data depicted in FIG. 25.

FIG. 27 is a graph which depicts the average change from baseline of gamma glutamyl transferase (GGT) in individuals treated with placebo or 0.05 mg/kg/day teduglutide

FIG. 28 is a graph which depicts the average percent change from baseline of GGT in the data depicted in FIG. 27.

DETAILED DESCRIPTION

It will be readily understood that the embodiments, as generally described herein, are exemplary. The following more detailed description of various embodiments is not intended to limit the scope of the present disclosure, but is merely representative of various embodiments. Moreover, the order of the steps or actions of the methods disclosed herein may be changed by those skilled in the art without departing from the scope of the present disclosure. In other words, unless a specific order of steps or actions is required for proper operation of the embodiment, the order or use of specific steps or actions may be modified.

DEFINITIONS

Unless specifically defined otherwise, the technical terms, as used herein, have their normal meaning as understood in the art. The following terms are specifically defined with examples for the sake of clarity.

The term “GLP-2 peptide” and the term “GLP-2” refer herein to the various naturally produced forms of GLP-2, particularly the mammalian forms, e.g., rat GLP2, ox GLP-2, porcine GLP-2, bovine GLP-2, guinea pig GLP-2, hamster GLP-2 and human GLP-2, the sequences of which have been reported by many authors including Buhl et al in J. Biol. Chem., 263:8621, 1988, which is hereby incorporated by reference in its entirety.

GLP-2 peptides include peptides that conform to the general formula represented below as SEQ ID NO:1:

R1-[Y]m-His-Ala-Asp-Gly-Ser-Phe-Ser-Asp-Glu-Met-

Asn-Thr-aa1-Leu-Ala-aa2-Leu-Ala-aa3-Arg-Asp-Phe-

Ile-Asn-Trp-Leu-aa4-aa5-Thr-Lys-Ile-Thr-Asp-[X]-

n-R2

wherein aa refers to an amino acid residue that is synthetic or genetically encoded, and;

aa1 is a neutral/polar/large/nonaromatic residue such as Ile or Val;

aa2 is a neutral/polar residue such as Asn or Ser;

aa3 is a neutral residue such as Ala or Thr;

aa4 is a neutral/polar/large/nonaromatic residue such as Ile or Leu;

aa5 is a neutral or basic residue such as Gin or His;

X is Arg, Lys, Arg-Lys or Lys-Lys;

Y is Arg or Arg-Arg;

m is 0 or 1;

n is 0 or 1;

R1 is H or an N-terminal blocking group; and

R2 is OH or a C-terminal blocking group SEQ ID NO:1.

The “blocking groups” represented by R1 and R2 are chemical groups that are routinely used to confer biochemical stability and resistance to digestion by exopeptidase. Suitable N-terminal protecting groups include, for example, C_1-5alkanoyl groups such as acetyl. Also suitable as N-terminal protecting groups are amino acid analogs lacking the amino function. Suitable C-terminal protecting groups include groups which form ketones or amides at the carbon atom of the C-terminal carboxyl, or groups which form esters at the oxygen atom of the carboxyl. Ketone and ester-forming groups include alkyl groups, particularly branched or unbranched C_1-5alkyl groups, e.g. methyl, ethyl and propyl groups, while amide-forming groups include amino functions such as primary amine, or alkylamino functions, e.g. mono-C_1-5-alkylamino and di-C_1-5alkylamino groups such as methylamino, ethylamino, dimethylamino, diethylamino, methylethylamino and the like. Amino acid analogs are also suitable for protecting the C-terminal end of the present compounds, for example, decarboxylated amino acid analogs such as agmatine.

GLP-2 peptides are known in the art, and are further disclosed in U.S. Pat. No. 5,990,077, which is hereby incorporated by reference in its entirety.

The term “GLP-2 peptide analog” and the term “GLP-2 analog” refer herein to a peptide that incorporates an amino acid substitution at one or more sites within a GLP-2 peptide “background”, which is either a mammalian GLP-2 species per se, or is a variant of a mammalian GLP-2 species in which the C-terminus and/or the N-terminus has been altered by addition of one or two basic residues, or has been modified to incorporate a blocking group of the type used conventionally in the art of peptide chemistry to protect peptide termini from undesired biochemical attack and degradation in vivo. Thus, GLP-2 peptide analogs incorporate an amino acid substitution in the context of any mammalian GLP-2 species, including but not limited to human GLP-2, bovine GLP-2, rat GLP-2, degu GLP-2, ox GLP-2, porcine GLP-2, guinea pig GLP-2 and hamster GLP-2, the sequences of which have been reported by many authors, including Buhl et al, J. Biol. Chem., 1988, 263(18):8621, which is hereby incorporated by reference. The GLP-2 analogs disclosed herein only include peptides that when administered in an effective dose to individuals experiencing one or more of parenteral nutrition, short bowel syndrome or intestinal failure, demonstrate at least one of the following properties: prophylaxis of liver dysfunction, prophylaxis of kidney dysfunction, treatment of liver dysfunction, or treatment of kidney dysfunction. The disclosure provided herein, and the knowledge available in the art would allow a person skilled in the art to determine which of GLP-2 peptides or GLP-2 analogs would retain at least one of the said properties. For example, the disclosure provided herein provides guidance regarding the design of GLP-2 peptides and GLP-2 peptide analogs. Furthermore, the guidance provided herein demonstrate how to determine, using diagnostic biomarkers, whether a GLP-2 peptide or a GLP-2 analog have one of the properties of prophylaxis of liver dysfunction, prophylaxis of kidney dysfunction, treatment of liver dysfunction, or treatment of kidney dysfunction. In this light, the following examples of GLP-2 analogs are provided.

In some instances, GLP-2 peptide analogs according to the present invention include peptides that conform to the sequence of the general formula presented below as SEQ ID NO:2:

R1-(Y1)m-X1-X2-X3-X4-Ser5-Phe6-Ser7-Asp8-(P1)-

Leu14-Asp15-Asn16-Leu17-Ala18-X19-X20-Asp21-

Phe22-(P2)-Trp25-Leu26-Ile27-Gln-28-Thr29-Lys30-

(P3)-(Y2)n-R2,

wherein

X1 is His or Tyr

X2 is Ala or any other amino acid conferring on said analog resistance to dipeptidyl peptidase IV enzyme;

X3 is Asp or Glu;

X4 is Gly or Ala;

P1 is Glu-X10-Asn-Thr-Ile or Tyr-Ser-Lys-Tyr (SEQ ID NO:3);

X10 is Met or an oxidatively stable amino acid;

X19 is Ala or Thr;

X20 is Arg, Lys, His or Ala;

P2 is Ile-Asn, Ile-Ala or Val-Gln;

P3 is a covalent bond, or is Ile, Ile-Thr or Ile-Thr-Asp;

R1 is H or an N-terminal blocking group;

R2 is OH or a C-terminal blocking group;

Y1 is one or two basic amino acids selected from the group Arg, Lys, and His;

Y2 is one or two basic amino acids selected from the group Arg, Lys, and His; and

m and n, independently, are 0 or 1;

wherein at least one of X1, X2, X3, X4, P1, X10, X19, X20, P2 and P3 is other than a wild type, mammalian GLP-2 residue; and

wherein when the peptide is administered in an effective dose to individuals experiencing one or more of parenteral nutrition, short bowel syndrome or intestinal failure, the GLP-2 analog has at least one of the following properties: prophylaxis of liver dysfunction, prophylaxis of kidney dysfunction, treatment of liver dysfunction, or treatment of kidney dysfunction.

GLP-2 peptide analogs may be analogs of full length GLP-2, i.e., GLP-2(1-33), and P3 is accordingly the sequence Ile-Thr-Asn. Alternatively, the GLP-2 analogs may be C-terminally truncated, to yield GLP-2(1-32) forms in which P3 is Ile-Thr, or GLP-2(1-31) forms in which P3 is Ile, or GLP-2(1-30) forms in which P3 is a covalent bond.

Furthermore, in certain embodiments, GLP-2 analogs may incorporate desired amino acid substitutions into a “background” which is an N-terminally or C-terminally modified form of a mammalian GLP-2 peptide. Such analogs are represented according to SEQ ID NO.:2 as those in which R1 constitutes an N-terminal blocking group, and/or when m is 1 then Y1 is one or two basic amino acids such as Arg or Lys; and/or R2 is a C-terminal blocking group; and/or when n is 1 then Y2 is independently, one or two basic amino acids such as Arg or Lys.

The “blocking groups” represented by R1 and R2 are chemical groups that are routinely used in the art of peptide chemistry to confer biochemical stability and resistance to digestion by exopeptidase. Suitable N-terminal protecting groups include, for example, O_1-5alkanoyl groups such as acetyl. Also suitable as N-terminal protecting groups are amino acid analogs lacking the amino function. Suitable C-terminal protecting groups include groups which form ketones or amides at the carbon atom of the C-terminal carboxyl, or groups which form esters at the oxygen atom of the carboxyl. Ketone and ester-forming groups include alkyl groups, particularly branched or unbranched C_1-5alkyl groups, e.g., methyl, ethyl and propyl groups, while amide-forming groups include amino functions such as primary amine, or alkylamino functions, e.g., mono-O_1-5alkylamino and di-O_1-5alkylamino groups such as methylamino, ethylamino, dimethylamino, diethylamino, methylethylamino and the like. Amino acid analogs are also suitable for protecting the C-terminal end of the present compounds, for example, decarboxylated amino acid analogs such as agmatine.

GLP-2 analogs can alternately be generated using standard techniques of peptide chemistry according to the guidance provided herein. Particularly preferred analogs for use in the invention are those based upon the sequence of human GLP-2 (SEQ ID NO: 4) wherein one or more amino acid residues are conservatively substituted for another amino acid residue, and wherein when the peptide is administered in an effective dose to individuals experiencing one or more of parenteral nutrition, short bowel syndrome or intestinal failure, the GLP-2 analog has at least one of the following properties: prophylaxis of liver dysfunction, prophylaxis of kidney dysfunction, treatment of liver dysfunction, or treatment of kidney dysfunction.

Conservative substitutions in any naturally occurring GLP-2, preferably the human GLP-2 sequence, are defined as exchanges of any member of the following five groups for another member of the same group:

I.

Ala, Ser, Thr, Pro, Gly

II.

Asn, Asp, Glu, Gln

III.

His, Arg, Lys

IV.

Met, Leu, Ile, Val, Cys

V.

Phe, Tyr, Trp.

In certain embodiments, GLP-2 analogs may be created by changing an amino acid residue in one mammalian GLP-2 to the corresponding amino acid in another mammalian GLP-2 peptide. Wild-type mammalian GLP-2 residues which occur at a specific position are determined by aligning the sequences of GLP-2's isolated from different mammalian species and comparing the sequence to the human sequence, reproduced below, for convenience (SEQ ID NO:3):

His-Ala-Asp-Gly-Ser-Phe-Ser-Asp-Glu-Met-Asn-Thr-

Ile-Leu-Asp-Asn-Leu-Ala-Ala-Arg-Asp-Phe-Ile-Asn-

Trp-Leu-Ile-Gln-Thr-Lys-Ile-Thr-Asp

The amino acid residues which, for purposes of this application, are known to vary at specific positions in wild type mammalian GLP-2s are the following (according to the notation of SEQ ID NO.:2): position X13, which may be Ile or Val; position X16, which may be Asn or Ser; position X19, which may be Alanine or Threonine; position X20, which may be Arg or Lys; position X27, which may be Ile or Leu; and position X28, which may be Gln or His.

GLP-2 analogs also include peptides with non-conservative substitutions of amino acids in any vertebrate GLP-2 sequence, provided that the non-conservative substitutions occur at amino acid positions known to vary in GLP-2 isolated from different species. Such non-conserved residue positions are readily determined by aligning all known vertebrate GLP-2 sequences. For example, Buhl et al., J. Biol. Chem., 1988, 263(18):8621, compared the sequences of human, porcine, rat, hamster, guinea pig, and bovine GLP-2's, and found that positions 13, 16, 19, 27 and 28 according to SEQ ID NO.:3 were non-conserved (position numbers refer to the analogous position in the human GLP-2 sequence). Nishi and Steiner, Mol. Endocrinol., 1990, 4:1192-8, found that an additional position corresponding to residue 20 of SEQ ID NO.:3 also varied in degu, a rodent species indigenous to South America. Thus, under this standard, the amino acid positions which vary in mammals and which preferably may be substituted with non-conservative residues are, according to the positions of SEQ ID NO.:3, positions 13, 16, 19, 20, 27, and 28. The additional amino acid residues which vary in vertebrates and which also may be substituted with non-conserved residues occur at positions 2, 5, 7, 8, 9, 10, 12, 17, 21, 22, 23, 24, 26, 29, 30, 31, 32, and 33 in SEQ ID NO.:3.

Alternatively, non-conservative substitutions may be made at any position by alanine-scanning provided that when the resulting peptide is administered in an effective dose to individuals experiencing one or more of parenteral nutrition, short bowel syndrome or intestinal failure, the GLP-2 analog has at least one of the following properties: prophylaxis of liver dysfunction, prophylaxis of kidney dysfunction, treatment of liver dysfunction, or treatment of kidney dysfunction. The technique of alanine scanning mutagenesis is described by Cunningham and Wells, Science, 1989, 244:1081, and incorporated herein by reference in its entirety. Since most GLP-2 sequences consist of only approximately 33 amino acids (and in human GLP-2 alanine already occurs at four positions), one of skill in the art could easily test an alanine analog at each remaining position for one or more of the effects of prophylaxis of liver dysfunction, prophylaxis of kidney dysfunction, treatment of liver dysfunction, or treatment of kidney dysfunction, as taught in Example 1 below.

One particular GLP-2 analog, teduglutide, is particularly useful because it is a dipeptidyl peptidase IV resistant GLP-2 analog with the peptide sequence:

(SEQ ID NO: 4)

His-Gly-Asp-Gly-Ser-Phe-Ser-Asp-Glu-Met-Asn-Thr-

Ile-Leu-Asp-Asn-Leu-Ala-Ala-Arg-Asp-Phe-Ile-Asn-

Trp-Leu-Ile-Gln-Thr-Lys-Ile-Thr-Asp

GLP-2 analogs are known in the art, and are further disclosed in U.S. Pat. No. 5,789,379, U.S. Pat. No. 5,834,428, U.S. Pat. No. 6,184,201, United States patent application publication number 20030162703 and United States patent application publication number 20060105954, all of which are hereby incorporated by reference in their entirety.

The term “diagnostic biomarker” refers herein to any measurable state of an organism, wherein measurement of that state is useful in diagnosing or determining the progression or regression of one or more diseases, or in determining the level of function of particular body systems or organs. Diagnostic biomarkers are well known and commonly used in the art, and one skilled in the art can readily choose a diagnostic biomarker to diagnose or follow the course of a particular disease, or to determine the level of function of particular body systems. Several examples of diagnostic biomarkers are given below for the purpose of illustration only.

Albumin is a diagnostic biomarker that can be used to assay liver function or dysfunction. Because albumin is synthesized in the liver, albumin levels may decrease in individuals experiencing liver dysfunction.

Bilirubin is a diagnostic biomarker frequently used to assess the function of the liver. Because bilirubin is cleared from the body through the liver, increased levels of bilirubin in an individual are associated with decreased liver function. The level of bilirubin is often measured by testing urine or blood using methods well known and commonly used in the art.

Gamma glutamyl transferase (GGT), alkaline phosphatase (ALP), alanine transaminase (ALT), and aspartate aminotransferase (AST) are also diagnostic biomarkers used to assess the function of the liver. These enzymes are highly concentrated in liver cells, and damage to liver cells releases these enzymes into the blood. Elevated levels of these enzymes, as determined by assaying an individual's blood using known methods, are indicative of liver damage and decreased liver function.

Examples of diagnostic biomarkers useful to assess kidney function include urea nitrogen, creatinine and glomerular filtration rate. Urea nitrogen and creatinine are both molecules that are cleared from the body in large part through the kidneys. Thus, higher levels of the urea nitrogen and creatinine diagnostic biomarkers in an individual's serum, as assayed using known methods, are associated with decreased liver function.

Glomerular filtration rate (GFR) is a measurement of the filtration capacity of an individual's kidneys. A higher GFR is associated with increased liver function. This diagnostic biomarker may be calculated through any method where the filtration capacity of the kidneys is measured. For example, inulin, a polysaccharide, is sometimes injected into an individual's plasma at a known concentration, and filtration of inulin into the individual's urine by the kidneys is monitored. Alternatively, GFR is often estimated by a formula well known in the art, which incorporates an individual's age, mass, and creatinine level in the serum.

Because many diagnostic biomarkers are associated with more than one disease or body system or organ function, often a panel of tests involving multiple diagnostic biomarkers will be ordered by a medical professional to provide a clearer assessment of a particular disease state or the level of function of a particular body system. For example, when liver function is in question, a liver function panel is often ordered wherein two or more of the diagnostic biomarkers useful in assessing liver function are ordered.

Methods

The methods described herein are methods for treatment or prophylaxis of liver or kidney disease in individuals experiencing one or more of: parenteral nutrition, intestinal failure or short bowel syndrome. In these methods, GLP-2 or GLP-2 peptide analogs are administered to individuals. A researcher may determine whether a particular GLP-2 peptide or GLP-2 analog has a prophylactic effect against kidney or liver disease by administering the peptide or analog to individuals in danger of developing kidney or liver disease (e.g. individuals experiencing one or more of: parenteral nutrition, intestinal failure or short bowel syndrome.) The researcher would then determine, using diagnostic biomarkers, whether the individuals thus treated are less likely to develop liver or kidney dysfunction.

Likewise, a researcher can determine whether a particular GLP-2 peptide or analog may be used to treat kidney or liver disease by administering the peptide or analog to individuals who have kidney or liver disease. The researcher would then determine, using diagnostic biomarkers, whether the individuals thus treated show improvement in liver or kidney function.

The specific therapeutic regimens used to assess whether a molecule has a desired effect are well known in the art. A researcher faced with the task of determining whether a particular GLP-2 peptide or analog may be used for treatment or prophylaxis of kidney or liver disease would choose the appropriate regimen to make this determination.

Delivery methods and formulations useful for administering peptides to individuals are well known in the art, and a skilled person would be able to determine the suitability of any particular method of delivery of a peptide to an individual for particular circumstances. For the purposes of illustration only, the following examples of methods and formulations for administering peptides to individuals are provided.

Peptides may be administered to individuals orally, however, actions of the digestive system will generally greatly reduce the bioavailability of the peptide. In order to increase peptide oral bioavailability, peptides may be administered in formulations containing enzyme inhibitors, or the peptides may be administered as part of a micelle, nanoparticle or emulsion in order to protect the peptide from digestive activity.

Peptides may also be administered by means of an injection. The peptides may be injected subcutaneously, intramuscularly, or intravenously. Further disclosure regarding methods of administering peptides through injection is found in U.S. Pat. No. 5,952,301, which is hereby incorporated by reference in its entirety.

Peptides may further be administered by pulmonary delivery. A dry powder inhalation system may be used, wherein peptides are absorbed through the tissue of the lungs, allowing delivery without injection, while bypassing the potential reduction in bioavailability seen with oral administration (see Onoue et al., Expert Op. on Therapeutic Patents 18:429, 2008, which is hereby incorporated by reference).

A typical human dose of a GLP-2 peptide would be from about 10 μg/kg body weight/day to about 10 mg/kg/day, preferably from about 50 μg/kg/day to about 5 mg/kg/day, and most preferably from about 100 μg/kg/day to about 1 mg/kg/day. As the GLP-2 analogs can be from about 10 to even about 100 times more potent than GLP-2, a typical dose of such a GLP-2 analog may be lower, for example, from about 100 ng/kg body weight/day to 1 about mg/kg/day, preferably from about 1 μg/kg/day to about 500 μg/kg/day, and even more preferably from about 1 μg/kg/day to about 100 μg/kg/day.

In one aspect of the invention, a GLP-2 peptide, or a GLP-2 peptide analog may be used in a method to treat liver dysfunction. In one embodiment of a method to treat liver dysfunction, one or more of a GLP-2 peptide or a GLP-2 peptide analog are administered to an individual having impaired liver function in an amount sufficient to cause improvement of liver function, wherein the individual is experiencing one or more of the following: intestinal failure, short bowel syndrome or parenteral nutrition.

In several embodiments of the method to treat liver dysfunction of the invention, the GLP-2 peptide analog is teduglutide (SEQ ID NO.:4). In another embodiment, teduglutide is administered at a dose in the range of from about 0.001 mg/kg/day to about 10 mg/kg/day, from about 0.01 mg/kg/day to about 1 mg/kg/day, from about 0.05 mg/kg/day to about 0.2 mg/kg/day, from about 0.001 mg/kg/day to about 0.01 mg/kg/day, from about 0.01 mg/kg/day to about 0.1 mg/kg/day, from about 0.1 mg/kg/day to about 1 mg/kg/day, or from about 1 mg/kg/day to about 10 mg/kg/day.

In some embodiments, improvement in liver function may be observed in a time frame of less than one, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or more than twelve weeks after the beginning of administration of one or more of GLP-2 peptide or GLP-2 peptide analog to the individual with liver dysfunction.

In another embodiment of a method to treat liver dysfunction, increased liver function is determined through the use of at least one diagnostic biomarker. In some such methods, the diagnostic biomarker used is selected from the group consisting of: bilirubin, alanine transaminase, aspartate aminotransferase, and alkaline phosphatase. In another embodiment, at least one of the tested biomarkers decreases by at least 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 200, 500, or 1000 percent after treatment with GLP-2 or GLP-2 analog.

In another aspect of the invention, GLP-2 peptide, or a GLP-2 peptide analog may be used in a method for prophylaxis against liver dysfunction. In one embodiment of a method for prophylaxis against liver dysfunction, one or more of a GLP-2 peptide or a GLP-2 peptide analog are administered to an individual in an amount sufficient for prophylaxis against liver dysfunction, wherein the individual is experiencing one or more of the following: intestinal failure, short bowel syndrome or parenteral nutrition.

In another embodiment of a method for prophylaxis against liver dysfunction, the GLP-2 peptide analog is teduglutide (SEQ ID NO:4). In another embodiment, teduglutide is administered at a dose in the range of from about 0.001 mg/kg/day to about 10 mg/kg/day, from about 0.01 mg/kg/day to about 1 mg/kg/day, from about 0.05 mg/kg/day to about 0.2 mg/kg/day, from about 0.001 mg/kg/day to about 0.01 mg/kg/day, from about 0.01 mg/kg/day to about 0.1 mg/kg/day, from about 0.1 mg/kg/day to about 1 mg/kg/day, or from about 1 mg/kg/day to about 10 mg/kg/day.

In another embodiment, prophylaxis against liver dysfunction occurs in a time frame of less than one, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or more than twelve weeks after the beginning of administration of one or more of GLP-2 peptide or GLP-2 peptide analog to the individual.

In another embodiment of a method for prophylaxis against liver dysfunction, liver function is monitored through the use of diagnostic biomarkers. In another embodiment, the diagnostic biomarkers used are selected from the group consisting of: bilirubin, alanine transaminase, aspartate aminotransferase, and alkaline phosphatase.

In another embodiment, at least one of the tested biomarkers increases, if at all, by less than 0.1, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90 or 100 percent after treatment with GLP-2 or GLP-2 analog.

In another aspect of the invention, GLP-2 peptide, or a GLP-2 peptide analog may be used in a method to treat kidney dysfunction. In one embodiment of a method to treat kidney dysfunction, one or more of a GLP-2 peptide or a GLP-2 peptide analog are administered to an individual having impaired kidney function in an amount sufficient to cause improvement of liver function, wherein the individual is experiencing one or more of the following: intestinal failure, short bowel syndrome or parenteral nutrition.

In another embodiment of a method to treat kidney dysfunction, the GLP-2 peptide analog is teduglutide SEQ ID NO.:4). In another embodiment, teduglutide is administered at a dose in the range of from about 0.001 mg/kg/day to about 10 mg/kg/day, from about 0.01 mg/kg/day to about 1 mg/kg/day, from about 0.05 mg/kg/day to about 0.2 mg/kg/day, from about 0.001 mg/kg/day to about 0.01 mg/kg/day, from about 0.01 mg/kg/day to about 0.1 mg/kg/day, from about 0.1 mg/kg/day to about 1 mg/kg/day, or from about 1 mg/kg/day to about 10 mg/kg/day.

In another embodiment, improvement in kidney function occurs in a time frame of less than one, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or more than twelve weeks after the beginning of administration of one or more of GLP-2 peptide or GLP-2 peptide analog to the individual with kidney dysfunction.

In another embodiment of a method to treat kidney dysfunction, increased kidney function is determined through the use of diagnostic biomarkers. In another embodiment, the diagnostic biomarkers used are selected from the group consisting of: urea nitrogen, creatinine and glomerular filtration rate. In another embodiment, one or more of the individual level of urea nitrogen, or creatinine, if selected, decreases at least 5, about 5, 10, 20, 50, 100, 200, 500, 1000 or more than 1000 percent after treatment with GLP-2 or GLP-2 analog or the level of glomerular filtration rate, if selected, increases by at least 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 200, 500, or 1000 percent after treatment with GLP-2 or GLP-2 analog.

In another aspect of the invention, a GLP-2 peptide, or a GLP-2 peptide analog may be used in a method for prophylaxis against kidney dysfunction. In one embodiment of a method for prophylaxis against kidney dysfunction, one or more of a GLP-2 peptide or a GLP-2 peptide analog are administered to an individual in an amount sufficient for prophylaxis against liver dysfunction, wherein the individual is experiencing one or more of the following: intestinal failure, short bowel syndrome or parenteral nutrition.

In another embodiment of a method for prophylaxis against kidney dysfunction, the GLP-2 peptide analog is teduglutide (SEQ ID NO.:4). In another embodiment, teduglutide is administered at a dose in the range of from about 0.001 mg/kg/day to about 10 mg/kg/day, from about 0.01 mg/kg/day to about 1 mg/kg/day, from about 0.05 mg/kg/day to about 0.2 mg/kg/day, from about 0.001 mg/kg/day to about 0.01 mg/kg/day, from about 0.01 mg/kg/day to about 0.1 mg/kg/day, from about 0.1 mg/kg/day to about 1 mg/kg/day, or from about 1 mg/kg/day to about 10 mg/kg/day.

In another embodiment, prophylaxis of kidney dysfunction occurs in a time frame of less than one, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or more than twelve weeks after the beginning of administration of one or more of GLP-2 peptide or GLP-2 peptide analog to the individual.

In another embodiment of a method for prophylaxis against kidney dysfunction, kidney function is monitored through the use of diagnostic biomarkers. In another embodiment, the diagnostic biomarkers used are selected from the group consisting of: urea nitrogen, creatinine and glomerular filtration rate. In another embodiment, one or more of the individual level of urea nitrogen, or creatinine, if selected, increases, if at all, by less than 0.1, 0.5, 1, 2, 3, 4 or 5 percent after treatment with GLP-2 or GLP-2 analog or the level of glomerular filtration rate, if selected, decreases, if at all, by less than 0.1, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90 or 100 percent after treatment with GLP-2 or GLP-2 analog.

The specific examples included hereafter are for illustrative purposes only and are not to be considered as limiting to this disclosure.

Example 1

Human patients with short bowel syndrome were divided into three groups: a control group (16 total individuals) to receive placebo, an experimental group (35 total individuals) to receive teduglutide at 0.05 mg/kg/day throughout the study, and an experimental group (32 total individuals) to receive teduglutide at 0.1 mg/kg/day throughout the study. Patients were treated by subcutaneous injection of placebo or the appropriate dose of teduglutide. As individuals with short bowel syndrome are likely to experience associated liver and kidney disease, known biomarkers of liver and kidney function were monitored every four weeks, for 24 weeks. The liver biomarkers tested included total bilirubin, ALT and AST and ALP. The kidney biomarkers tested included urea nitrogen, creatinine and GFR. Before administration of placebo or teduglutide, diagnostic biomarker levels were tested to establish a baseline for each individual.

To visualize the effect of GLP-2 on the levels of the diagnostic biomarkers, the average change from baseline was calculated for the placebo and teduglutide treated groups for each visit after a baseline was established. The average change from baseline of each group is depicted in FIGS. 1-7. The numerical figures for each diaanostic biomarker at each time point are depicted in Table 1.

TABLE 1

Selected Lab Tests Results

Placebo
0.05
0.10
p-value
p-value
p-value

Lab Tests
Visits
(N = 16)
(N = 35)
(N = 32)
Placebo vs. 0.05
Placebo vs. 0.10
0.05 vs. 0.10

ALT (SGPT)

N
2.0 Baseline
15
34
32

Mean (SD)

35.8 (19.06)
45.9 (33.41)
50.2 (35.70)
0.2817
0.1519
0.6171

Median

32.0
32.0
43.0

Range

16, 69
14, 137
12, 174

N
3.0 DW4
16
31
32

Mean (SD)

38.0 (22.45)
30.1 (14.08)
38.6 (23.30)
0.1453
0.9332
0.0860

Median

31.0
27.0
30.0

Range

14, 80
11, 68
10, 105

N
4.0 DW8
16
30
30

Mean (SD)

43.2 (28.57)
37.3 (23.71)
34.1 (19.74)
0.4618
0.2111
0.5682

Median

32.0
31.5
28.5

Range

15, 111
12, 107
9, 93

N
5.0 DW12
16
28
29

Mean (SD)

38.3 (24.61)
34.6 (24.30)
40.4 (22.11)
0.6307
0.7744
0.3521

Median

31.0
26.0
35.0

Range

13, 92
11, 125
11, 102

N
6.0 DW16
15
29
30

Mean (SD)

37.5 (21.79)
39.5 (39.44)
39.7 (27.97)
0.8577
0.7943
0.9836

Median

34.0
28.0
30.0

Range

14, 81
10, 219
8, 137

N
7.0 DW20
16
28
29

Mean (SD)

36.4 (23.76)
29.7 (13.35)
41.3 (29.36)
0.2344
0.5755
0.0624

Median

31.5
26.0
29.0

Range

11, 93
12, 61
9, 104

N
8.0 DW 24
16
27
29

Mean (SD)

33.4 (17.87)
31.7 (26.63)
42.2 (27.28)
0.8211
0.2499
0.1484

Median

27.5
20.0
37.0

Range

14, 66
8, 134
9, 120

ALT (SGPT)

Change from

Baseline

N
3.0 DW4
15
30
32

Mean (SD)

2.3 (10.92)
−14.9 (26.39)
−11.6 (22.98)
0.0202
0.0325
0.5931

Median

2.0
−5.0
−6.5

Range

−20, 32
−100, 13
−69, 29

N
4.0 DW8
15
29
30

Mean (SD)

2.9 (10.88)
−9.8 (30.38)
−15.7 (26.87)
0.1273
0.0140
0.4317

Median

3.0
−4.0
−8.0

Range

−12, 30
−103, 49
−126, 13

N
5.0 DW12
15
27
29

Mean (SD)

4.1 (15.87)
−12.7 (29.55)
−10.6 (30.82)
0.0483
0.0920
0.7945

Median

1.0
−10.0
−11.0

Range

−22, 41
−83, 70
−107, 61

N
6.0 DW16
15
28
30

Mean (SD)

1.7 (13.23)
−3.2 (24.76)
−10.0 (35.59)
0.4798
0.2244
0.4013

Median

−1.0
−2.5
−10.0

Range

−20, 30
−56, 82
−124, 87

N
7.0 DW20
15
27
29

Mean (SD)

2.3 (11.96)
−16.0 (28.73)
−8.2 (37.70)
0.0235
0.2979
0.3906

Median

1.0
−5.0
−9.0

Range

−21, 26
−107, 12
−130, 77

N
8.0 DW 24
15
26
29

Mean (SD)

−1.3 (12.07)
−13.5 (24.40)
−7.3 (32.62)
0.0785
0.5010
0.4281

Median

−1.0
−5.0
−4.0

Range

−25, 27
−70, 45
−133, 60

AST (SGOT)

N
2.0 Baseline
15
34
32

Mean (SD)

33.6 (13.27)
37.9 (21.78)
41.1 (21.79)
0.4851
0.2249
0.5478

Median

28.0
28.5
36.5

Range

20, 55
12, 106
17, 98

N
3.0 DW4
16
29
31

Mean (SD)

33.7 (14.08)
29.1 (19.23)
33.5 (17.00)
0.4077
0.9777
0.3460

Median

33.0
27.0
28.0

Range

14, 59
11, 121
16, 96

N
4.0 DW8
16
30
30

Mean (SD)

35.6 (17.71)
33.5 (18.56)
29.3 (12.52)
0.7046
0.1636
0.3084

Median

28.0
28.0
28.0

Range

17, 69
14, 88
12, 63

N
5.0 DW12
16
28
28

Mean (SD)

32.9 (15.73)
29.2 (15.48)
36.4 (20.41)
0.4531
0.5512
0.1401

Median

25.0
26.5
31.5

Range

18, 72
15, 95
15, 118

N
6.0 DW16
15
29
29

Mean (SD)

33.5 (15.64)
29.3 (12.05)
33.0 (16.71)
0.3374
0.9237
0.3481

Median

29.0
25.0
30.0

Range

18, 70
14, 74
14, 70

N
7.0 DW20
16
28
28

Mean (SD)

34.3 (17.59)
26.1 (7.02)
34.9 (19.82)
0.0344
0.9277
0.0326

Median

27.5
25.0
30.0

Range

16, 74
14, 44
14, 105

N
8.0 DW 24
16
27
29

Mean (SD)

29.8 (11.89)
27.7 (10.23)
35.3 (18.69)
0.5420
0.2915
0.0658

Median

27.0
25.0
32.0

Range

13, 56
12, 55
11, 96

AST (SGOT)

Change from

Baseline

N
3.0 DW4
15
28
31

Mean (SD)

−0.7 (7.85)
−5.5 (24.09)
−7.3 (15.21)
0.4624
0.1265
0.7362

Median

−2.0
−4.0
−5.0

Range

−11, 19
−50, 96
−44, 18

N
4.0 DW8
15
29
30

Mean (SD)

0.1 (10.20)
−3.2 (19.88)
−10.3 (16.56)
0.5463
0.0309
0.1414

Median

2.0
−3.0
−5.0

Range

−26, 16
−45, 63
−62, 11

N
5.0 DW12
15
27
28

Mean (SD)

−0.2 (12.58)
−5.9 (19.28)
−3.3 (21.39)
0.3146
0.6157
0.6379

Median

−4.0
−8.0
−3.5

Range

−23, 25
−42, 70
−43, 68

N
6.0 DW16
15
28
29

Mean (SD)

−0.1 (8.29)
−4.3 (11.75)
−6.6 (22.80)
0.2280
0.2973
0.6411

Median

−3.0
−1.5
−4.0

Range

−11, 17
−39, 18
−56, 49

N
7.0 DW20
15
27
28

Mean (SD)

1.5 (11.13)
−8.0 (14.96)
−4.9 (24.96)
0.0366
0.3480
0.5794

Median

−4.0
−4.0
−2.5

Range

−15, 26
−61, 6
−62, 55

N
8.0 DW 24
15
26
29

Mean (SD)

−3.8 (12.49)
−6.8 (14.29)
−4.8 (23.34)
0.5069
0.8748
0.7153

Median

0.0
−4.5
−3.0

Range

−42, 9
−45, 22
−66, 46

CREATININE

(RATE BLANKED)

N
2.0 Baseline
16
34
32

Mean (SD)

84.9 (27.44)
80.8 (33.61)
82.8 (19.70)
0.6716
0.7627
0.7686

Median

77.5
67.5
80.0

Range

44, 141
37, 177
49, 115

N
3.0 DW4
16
31
32

Mean (SD)

88.3 (26.81)
84.8 (30.01)
81.0 (21.62)
0.6962
0.3136
0.5647

Median

87.5
80.0
83.0

Range

44, 135
42, 152
44, 128

N
4.0 DW8
16
30
31

Mean (SD)

87.6 (32.96)
81.9 (27.34)
84.2 (21.41)
0.5392
0.6710
0.7239

Median

79.0
71.0
86.0

Range

44, 177
40, 155
53, 124

N
5.0 DW12
16
28
30

Mean (SD)

92.1 (34.00)
86.8 (29.41)
86.4 (25.37)
0.5846
0.5189
0.9577

Median

80.0
80.0
84.5

Range

53, 177
44, 169
53, 143

N
6.0 DW16
15
29
30

Mean (SD)

85.5 (30.37)
82.1 (29.95)
84.2 (21.85)
0.7243
0.8699
0.7591

Median

78.0
79.0
87.5

Range

44, 142
35, 155
44, 124

N
7.0 DW20
16
28
29

Mean (SD)

89.1 (28.96)
85.6 (28.77)
86.6 (20.44)
0.6962
0.7367
0.8741

Median

83.5
77.5
88.0

Range

53, 144
42, 150
57, 133

N
8.0 DW 24
16
27
29

Mean (SD)

88.2 (25.60)
82.8 (31.57)
82.0 (22.35)
0.5646
0.4058
0.9189

Median

80.0
80.0
81.0

Range

47, 133
35, 155
44, 130

CREATININE

(RATE BLANKED)

Change from

Baseline

N
3.0 DW4
16
30
32

Mean (SD)

3.4 (11.35)
1.2 (12.01)
−1.8 (10.49)
0.5483
0.1207
0.2966

Median

2.5
1.5
−0.5

Range

−26, 18
−38, 18
−23, 21

N
4.0 DW8
16
29
31

Mean (SD)

2.6 (24.64)
−1.1 (15.51)
1.2 (10.40)
0.5360
0.7851
0.4946

Median

0.0
0.0
0.0

Range

−44, 80
−43, 27
−27, 22

N
5.0 DW12
16
27
30

Mean (SD)

7.2 (21.95)
1.8 (16.71)
2.8 (12.77)
0.3703
0.3946
0.8024

Median

3.5
8.0
6.5

Range

−17, 80
−60, 19
−36, 36

N
6.0 DW16
15
28
30

Mean (SD)

4.3 (14.49)
−1.6 (13.06)
0.6 (11.86)
0.1860
0.3693
0.5097

Median

4.0
0.0
0.0

Range

−18, 44
−47, 21
−26, 27

N
7.0 DW20
16
27
29

Mean (SD)

4.2 (9.11)
0.7 (13.11)
2.6 (12.05)
0.3501
0.6531
0.5636

Median

6.5
0.0
0.0

Range

−13, 18
−27, 18
−36, 21

N
8.0 DW 24
16
26
29

Mean (SD)

3.3 (14.89)
−2.9 (14.03)
−2.0 (13.18)
0.1864
0.2315
0.8032

Median

3.5
−0.5
0.0

Range

−26, 35
−49, 20
−37, 23

GFR

N
2.0 Baseline
16
34
32

Mean (SD)

80.8 (29.31)
84.9 (31.25)
76.7 (26.52)
0.6584
0.6333
0.2577

Median

77.4
78.7
74.4

Range

29, 133
31, 194
34, 159

N
3.0 DW4
16
31
32

Mean (SD)

77.1 (29.04)
86.1 (36.99)
80.3 (25.98)
0.3989
0.6987
0.4704

Median

72.2
77.0
75.6

Range

31, 130
40, 221
31, 137

N
4.0 DW8
16
29
31

Mean (SD)

80.6 (37.65)
89.1 (39.16)
77.9 (25.63)
0.4874
0.7684
0.1930

Median

76.0
81.6
76.9

Range

32, 191
38, 233
37, 138

N
5.0 DW12
16
27
30

Mean (SD)

76.8 (32.54)
85.7 (39.48)
77.3 (25.57)
0.4503
0.9527
0.3399

Median

74.2
76.5
68.7

Range

29, 161
35, 218
34, 126

N
6.0 DW16
14
29
30

Mean (SD)

84.0 (38.95)
89.8 (38.02)
78.3 (23.12)
0.6440
0.5493
0.1655

Median

79.0
84.1
77.5

Range

30, 194
43, 222
32, 125

N
7.0 DW20
16
28
29

Mean (SD)

78.0 (34.24)
83.2 (32.77)
77.3 (26.45)
0.6183
0.9402
0.4547

Median

73.5
74.0
76.1

Range

28, 163
37, 191
35, 147

N
8.0 DW 24
16
27
29

Mean (SD)

76.3 (24.48)
89.7 (41.28)
82.4 (27.05)
0.2468
0.4593
0.4356

Median

77.4
78.1
81.1

Range

29, 133
43, 223
40, 130

GFR Change from

Baseline

N
3.0 DW4
16
30
32

Mean (SD)

−3.7 (7.92)
−1.4 (12.80)
3.6 (11.81)
0.5087
0.0316
0.1212

Median

−2.3
−0.6
3.1

Range

−22, 8
−52, 21
−41, 26

N
4.0 DW8
16
28
31

Mean (SD)

−0.1 (18.84)
−0.1 (19.13)
0.6 (10.87)
0.9992
0.8710
0.8617

Median

−0.4
1.1
0.2

Range

−26, 58
−73, 38
−23, 32

N
5.0 DW12
16
26
30

Mean (SD)

−3.9 (13.27)
−3.1 (12.18)
0.2 (15.54)
0.8358
0.3743
0.3887

Median

−2.3
−5.0
−0.5

Range

−26, 28
−21, 27
−47, 50

N
6.0 DW16
14
28
30

Mean (SD)

−1.1 (20.00)
0.8 (12.08)
1.1 (13.73)
0.7123
0.6722
0.9161

Median

−4.2
0.9
1.1

Range

−20, 61
−21, 31
−34, 32

N
7.0 DW20
16
27
29

Mean (SD)

−2.8 (11.98)
−2.0 (16.60)
−0.6 (16.16)
0.8765
0.6345
0.7386

Median

−6.1
0.9
1.5

Range

−19, 30
−52, 29
−44, 51

N
8.0 DW 24
16
26
29

Mean (SD)

−4.4 (17.25)
3.7 (12.81)
4.6 (16.73)
0.0884
0.0950
0.8311

Median

−0.0
1.7
2.3

Range

−51, 22
−13, 40
−29, 53

ALKALINE

PHOSPHATASE

N
2.0 Baseline
16
34
32

Mean (SD)

162.1 (118.63)
183.6 (95.32)
158.9 (78.50)
0.4952
0.9118
0.2567

Median

116.0
163.0
143.0

Range

53, 502
54, 444
58, 376

N
3.0 DW4
16
31
32

Mean (SD)

156.9 (91.52)
159.5 (90.91)
147.5 (74.98)
0.9263
0.7072
0.5707

Median

143.5
139.0
124.0

Range

53, 361
47, 463
64, 362

N
4.0 DW8
16
30
31

Mean (SD)

173.6 (149.49)
152.2 (84.05)
139.2 (75.84)
0.5359
0.2988
0.5290

Median

127.5
134.0
114.0

Range

53, 602
40, 410
52, 314

N
5.0 DW12
16
28
30

Mean (SD)

156.6 (97.09)
143.5 (73.92)
145.9 (83.34)
0.6170
0.6983
0.9069

Median

143.0
130.5
115.0

Range

45, 377
38, 376
42, 405

N
6.0 DW16
15
29
30

Mean (SD)

149.5 (87.48)
168.2 (128.08)
137.7 (81.33)
0.6152
0.6567
0.2777

Median

119.0
134.0
108.5

Range

52, 386
52, 667
49, 400

N
7.0 DW20
16
28
29

Mean (SD)

145.7 (80.23)
144.2 (76.09)
137.1 (82.59)
0.9508
0.7367
0.7369

Median

116.5
136.5
107.0

Range

65, 336
38, 363
52, 389

N
8.0 DW 24
16
26
29

Mean (SD)

138.8 (84.49)
169.6 (204.22)
136.7 (85.50)
0.5704
0.9374
0.4304

Median

116.0
131.0
105.0

Range

69, 400
38, 1139
41, 430

ALKALINE

PHOSPHATASE

Change from

Baseline

N
3.0 DW4
16
30
32

Mean (SD)

−5.2 (44.57)
−24.8 (45.66)
−11.3 (32.19)
0.1696
0.5863
0.1838

Median

−2.0
−16.0
−10.5

Range

−158, 44
−194, 51
−86, 65

N
4.0 DW8
16
29
31

Mean (SD)

11.5 (42.54)
−24.0 (64.47)
−21.2 (41.41)
0.0547
0.0146
0.8369

Median

2.0
−20.0
−20.0

Range

−87, 100
−209, 149
−110, 118

N
5.0 DW12
16
27
30

Mean (SD)

−5.5 (39.84)
−39.4 (48.95)
−13.1 (55.43)
0.0239
0.6319
0.0637

Median

−0.5
−24.0
−16.5

Range

−133, 43
−204, 21
−129, 146

N
6.0 DW16
15
28
30

Mean (SD)

10.1 (22.36)
−12.3 (69.90)
−21.3 (59.79)
0.2363
0.0571
0.6004

Median

4.0
−20.5
−20.5

Range

−19, 68
−127, 266
−141, 196

N
7.0 DW20
16
27
29

Mean (SD)

−16.4 (103.44)
−36.9 (49.89)
−18.5 (63.67)
0.3863
0.9328
0.2370

Median

6.5
−25.0
−23.0

Range

−396, 59
−171, 59
−176, 194

N
8.0 DW 24
16
25
29

Mean (SD)

−23.3 (86.39)
−11.6 (166.86)
−18.9 (67.08)
0.7973
0.8497
0.8296

Median

−1.0
−29.0
−21.0

Range

−325, 48
−200, 738
−171, 235

TOTAL

BILIRUBIN

N
2.0 Baseline
16
32
32

Mean (SD)

10.8 (8.01)
11.2 (9.10)
12.8 (21.85)
0.8711
0.7258
0.7101

Median

9.5
9.0
9.0

Range

3, 38
3, 44
3, 130

N
3.0 DW4
16
28
31

Mean (SD)

11.1 (8.10)
10.4 (9.48)
9.0 (5.65)
0.8041
0.3210
0.5124

Median

8.5
7.0
7.0

Range

4, 36
3, 46
3, 34

N
4.0 DW8
16
27
29

Mean (SD)

13.4 (17.06)
10.8 (8.86)
9.4 (9.80)
0.5139
0.3305
0.5975

Median

8.0
8.0
7.0

Range

3, 75
3, 36
3, 56

N
5.0 DW12
16
25
29

Mean (SD)

11.5 (9.47)
9.4 (6.65)
10.3 (11.39)
0.4177
0.7319
0.7284

Median

9.5
7.0
7.0

Range

3, 39
3, 26
3, 65

N
6.0 DW16
15
27
28

Mean (SD)

11.5 (7.50)
10.0 (8.90)
9.8 (9.14)
0.5831
0.5368
0.9306

Median

9.0
7.0
7.5

Range

4, 31
3, 43
4, 53

N
7.0 DW20
16
26
29

Mean (SD)

10.1 (5.35)
9.7 (6.78)
10.4 (10.13)
0.8444
0.9162
0.7727

Median

8.5
9.0
7.0

Range

4, 22
3, 31
3, 56

N
8.0 DW 24
16
23
27

Mean (SD)

10.7 (5.31)
10.0 (6.28)
11.3 (12.28)
0.7060
0.8522
0.6385

Median

10.0
8.0
9.0

Range

4, 23
4, 29
3, 67

TOTAL

BILIRUBIN

Change from

Baseline

N
3.0 DW4
16
26
31

Mean (SD)

0.3 (3.11)
−0.8 (4.31)
−4.0 (17.41)
0.3556
0.3370
0.3770

Median

1.0
0.0
−1.0

Range

−5, 6
−17, 7
−96, 4

N
4.0 DW8
16
25
29

Mean (SD)

2.6 (17.05)
−0.4 (4.78)
−3.8 (13.91)
0.4109
0.1812
0.2504

Median

0.0
−1.0
−1.0

Range

−24, 61
−8, 14
−74, 5

N
5.0 DW12
16
24
29

Mean (SD)

0.8 (7.59)
−1.8 (5.75)
−2.9 (12.93)
0.2359
0.3128
0.7088

Median

0.0
−1.0
0.0

Range

−11, 25
−20, 11
−65, 16

N
6.0 DW16
15
26
28

Mean (SD)

0.6 (6.90)
−1.3 (9.00)
−3.6 (14.89)
0.4828
0.3040
0.4928

Median

0.0
−1.0
−0.5

Range

−17, 17
−32, 17
−77, 6

N
7.0 DW20
16
25
29

Mean (SD)

−0.6 (4.73)
−0.8 (5.69)
−2.7 (14.42)
0.9375
0.5762
0.5257

Median

0.5
0.0
0.0

Range

−16, 5
−14, 10
−74, 12

N
8.0 DW 24
16
22
27

Mean (SD)

−0.1 (5.12)
−0.5 (7.11)
−2.4 (12.73)
0.8184
0.4805
0.5357

Median

0.0
0.0
0.0

Range

−15, 7
−22, 9
−63, 9

UREA NITROGEN

N
2.0 Baseline
16
34
32

Mean (SD)

5.8 (1.64)
5.7 (2.38)
6.2 (2.16)
0.9176
0.5347
0.4226

Median

5.9
4.9
6.1

Range

3, 8
3, 12
2, 13

N
3.0 DW4
16
31
32

Mean (SD)

6.1 (1.63)
5.3 (2.09)
6.4 (2.19)
0.1835
0.5914
0.0378

Median

6.3
4.9
6.3

Range

3, 10
3, 11
4, 13

N
4.0 DW8
16
30
31

Mean (SD)

5.9 (1.43)
5.3 (1.58)
6.4 (2.00)
0.2159
0.4397
0.0291

Median

5.7
5.2
6.4

Range

4, 8
2, 10
3, 12

N
5.0 DW12
16
28
30

Mean (SD)

5.9 (1.84)
4.8 (1.69)
6.3 (2.08)
0.0552
0.5380
0.0049

Median

5.9
4.4
5.9

Range

3, 9
2, 8
3, 13

N
6.0 DW16
15
29
30

Mean (SD)

5.9 (1.24)
5.0 (1.73)
6.0 (1.86)
0.0627
0.8182
0.0240

Median

6.2
4.4
6.1

Range

4, 8
3, 9
3, 12

N
7.0 DW20
16
28
29

Mean (SD)

5.9 (1.48)
5.0 (1.80)
6.3 (1.91)
0.1319
0.4434
0.0145

Median

6.1
5.2
6.0

Range

3, 9
2, 9
4, 13

N
8.0 DW 24
16
27
29

Mean (SD)

5.9 (2.28)
5.0 (1.88)
6.1 (1.98)
0.1694
0.8151
0.0471

Median

5.8
4.9
5.9

Range

3, 11
1, 9
2, 10

UREA NITROGEN

Change from

Baseline

N
3.0 DW4
16
30
32

Mean (SD)

0.3 (1.33)
−0.4 (2.16)
0.3 (1.22)
0.2366
0.8974
0.1392

Median

−0.1
−0.4
0.2

Range

−2, 3
−6, 8
−2, 3

N
4.0 DW8
16
29
31

Mean (SD)

0.2 (1.34)
−0.4 (2.02)
0.2 (1.20)
0.3700
0.8901
0.1896

Median

0.2
−0.3
0.1

Range

−2, 3
−7, 3
−2, 3

N
5.0 DW12
16
27
30

Mean (SD)

0.2 (1.65)
−0.8 (1.86)
0.1 (1.47)
0.0898
0.8891
0.0439

Median

0.6
−0.5
0.0

Range

−4, 3
−8, 2
−2, 4

N
6.0 DW16
15
28
30

Mean (SD)

0.3 (1.17)
−0.5 (2.13)
−0.2 (1.47)
0.1815
0.2664
0.5093

Median

0.2
−0.3
0.0

Range

−2, 2
−7, 4
−4, 3

N
7.0 DW20
16
27
29

Mean (SD)

0.1 (1.54)
−0.6 (1.94)
−0.0 (1.60)
0.2128
0.8544
0.1896

Median

0.4
−0.3
−0.1

Range

−3, 3
−6, 3
−2, 4

N
8.0 DW 24
16
26
29

Mean (SD)

0.1 (2.41)
−0.6 (2.02)
−0.2 (1.87)
0.2765
0.5756
0.4506

Median

0.1
−0.7
0.3

Range

−4, 6
−7, 5
−4, 4

With regards to liver biomarkers, FIG. 1 shows that on average in the groups receiving teduglutide, the level of ALT decreased by about 10 U/L, whereas on average in the group receiving placebo, the level of ALT increased slightly compared to baseline. This reduction in ALT seen in the groups receiving teduglutide was maintained for the entire duration of the study. Similar reductions were seen for AST and total bilirubin (FIGS. 2 and 3 respectively). In the groups receiving teduglutide, the average level of ALP also decreased relative to baseline for the entire period of the study, but in the last two weeks of observation, the average level of the control group also decreased relative to baseline (see FIG. 4). Since several conditions besides liver dysfunction can affect ALP (see Corathers, Pediatrics in Review 27:382, 2006), it is unclear what caused this decrease in the placebo group. However, taken together, this panel of diagnostic biomarkers supports a conclusion that teduglutide helps alleviate liver dysfunction in individuals with short bowel syndrome.

The efficacy of teduglutide in treating liver dysfunction was further tested by sorting the data into two groups: individuals with abnormal baseline values of liver diagnostic biomarkers; and individuals with normal baseline values of liver diagnostic biomarkers. The mean diagnostic biomarker change from baseline for these two groups is shown for ALT (FIGS. 5 and 6) and AST (FIGS. 7 and 8). After treatment with teduglutide, statistically significant improvements for both ALT and AST biomarkers were observed in patients with abnormal baseline values. These changes were significant when compared to baseline levels for the group, and when compared to placebo (see FIGS. 5 and 7).

With regards to kidney biomarkers, FIG. 9 shows the average change versus baseline for creatinine. On average, the creatinine level in the group receiving placebo increased relative to baseline, whereas the creatinine levels in the groups receiving teduglutide remained stable or decreased slightly. Similar results are seen in the levels of urea nitrogen, and in the GFR of short bowel patients (see FIGS. 10 and 11).

To further confirm these results, the change in each biomarker for each individual patient was sorted based on whether the biomarker increased more than 5%, decreased more than 5%, or remained within plus or minus 5% from baseline at each time point throughout the course of the study. The raw data for this sorting is reproduced in Table 2 below.

TABLE 2

Selected Lab Tests Change from Baseline

Lab Tests
Week
Range
Placebo
teduglutide 0.05
teduglutide 0.1

ALKALINE
4
−5%<= and
3(18.8%)
6(17.1%)
4(12.5%)

PHOS-

<=5%

PHATASE

<−5%
6(37.5%)
21(60.0%)
17(53.1%)

>5%
7(43.8%)
3(8.6%)
11(34.4%)

8
−5%<= and
3(18.8%)
2(5.7%)
2(6.3%)

<=5%

<−5%
5(31.3%)
21(60.0%)
24(75.0%)

>5%
8(50.0%)
6(17.1%)
5(15.6%)

12
−5%<= and
6(37.5%)
2(5.7%)
1(3.1%)

<=5%

<−5%
6(37.5%)
23(65.7%)
19(59.4%)

>5%
4(25.0%)
2(5.7%)
10(31.3%)

16
−5%<= and
5(31.3%)
2(5.7%)
2(6.3%)

<=5%

<−5%
3(18.8%)
20(57.1%)
21(65.6%)

>5%
7(43.8%)
6(17.1%)
7(21.9%)

20
−5%<= and
3(18.8%)
1(2.9%)
1(3.1%)

<=5%

<−5%
4(25.0%)
22(62.9%)
19(59.4%)

>5%
9(56.3%)
4(11.4%)
9(28.1%)

24
−5%<= and
4(25.0%)
0(0.0%)
4(12.5%)

<=5%

<−5%
7(43.8%)
21(60.0%)
20(62.5%)

>5%
5(31.3%)
4(11.4%)
5(15.6%)

ALT (SGPT)
4
−5%<= and
2(12.5%)
0(0.0%)
2(6.3%)

<=5%

<−5%
5(31.3%)
22(62.9%)
20(62.5%)

>5%
8(50.0%)
8(22.9%)
10(31.3%)

8
−5%<= and
1(6.3%)
4(11.4%)
2(6.3%)

<=5%

<−5%
6(37.5%)
16(45.7%)
20(62.5%)

>5%
8(50.0%)
9(25.7%)
8(25.0%)

12
−5%<= and
1(6.3%)
4(11.4%)
2(6.3%)

<=5%

<−5%
7(43.8%)
19(54.3%)
20(62.5%)

>5%
7(43.8%)
4(11.4%)
7(21.9%)

16
−5%<= and
0(0.0%)
0(0.0%)
2(6.3%)

<=5%

>−5%
8(50.0%)
16(45.7%)
19(59.4%)

>5%
7(43.8%)
12(34.3%)
9(28.1%)

20
−5%<= and
3(18.8%)
1(2.9%)
2(6.3%)

<=5%

<−5%
6(37.5%)
17(48.6%)
19(59.4%)

>5%
6(37.5%)
9(25.7%)
8(25.0%)

24
−5%<= and
1(6.3%)
4(11.4%)
0(0.0%)

<=5%

<−5%
8(50.0%)
18(51.4%)
18(56.3%)

>5%
6(37.5%)
4(11.4%)
11(34.4%)

AST (SGOT)
4
−5%<= and
3(18.8%)
2(5.7%)
4(12.5%)

<=5%

<−5%
7(43.8%)
21(60.0%)
17(53.1%)

>5%
5(31.3%)
5(14.3%)
10(31.3%)

8
−5%<= and
1(6.3%)
4(11.4%)
4(12.5%)

<=5%

<−5%
6(37.5%)
16(45.7%)
21(65.6%)

>5%
8(50.0%)
9(25.7%)
5(15.6%)

12
−5%<= and
0(0.0%)
3(8.6%)
3(9.4%)

<=5%

<−5%
8(50.0%)
19(54.3%)
15(46.9%)

>5%
7(43.8%)
5(14.3%)
10(31.3%)

16
−5%<= and
1(6.3%)
6(17.1%)
3(9.4%)

<=5%

<−5%
8(50.0%)
14(40.0%)
18(56.3%)

>5%
6(37.5%)
8(22.9%)
8(25.0%)

20
−5%<= and
0(0.0%)
4(11.4%)
3(9.4%)

<=5%

<−5%
9(56.3%)
17(48.6%)
16(50.0%)

>5%
6(37.5%)
6(17.1%)
9(28.1%)

24
−5%<= and
4(25.0%)
3(8.6%)
3(9.4%)

<=5%

<−5%
6(37.5%)
17(48.6%)
15(46.9%)

>5%
5(31.3%)
6(17.1%)
11(34.4%)

CREATININE
4
−5%<= and
5(31.3%)
9(25.7%)
13(40.6%)

(RATE

<=5%

BLANKED)

<−5%
3(18.8%)
7(20.0%)
12(37.5%)

>5%
8(50.0%)
14(40.0%)
7(21.9%)

8
−5%<= and
7(43.8%)
8(22.9%)
10(31.3%)

<=5%

<−5%
4(25.0%)
11(31.4%)
8(25.0%)

>5%
5(31.3%)
10(28.6%)
13(40.6%)

12
−5%<= and
4(25.0%)
4(11.4%)
4(12.5%)

<=5%

<−5%
4(25.0%)
7(20.0%)
9(28.1%)

>5%
8(50.0%)
16(45.7%)
17(53.1%)

16
−5%<= and
2(12.5%)
12(34.3%)
8(25.0%)

<=5%

<−5%
5(31.3%)
8(22.9%)
10(31.3%)

>5%
8(50.0%)
8(22.9%)
12(37.5%)

20
−5%<= and
4(25.0%)
8(22.9%)
9(28.1%)

<=5%

<−5%
3(18.8%)
9(25.7%)
7(21.9%)

>5%
9(56.3%)
10(28.6%)
13(40.6%)

24
−5%<= and
3(18.8%)
9(25.7%)
9(28.1%)

<=5%

<−5%
5(31.3%)
8(22.9%)
11(34.4%)

>5%
8(50.0%)
9(25.7%)
9(28.1%)

GFR
4
−5%<= and
6(37.5%)
9(25.7%)
11(34.4%)

<=5%

<−5%
7(43.8%)
13(37.1%)
7(21.9%)

>5%
3(18.8%)
8(22.9%)
14(43.8%)

8
−5%<= and
6(37.5%)
7(20.0%)
13(40.6%)

<=5%

<−5%
5(31.3%)
9(25.7%)
9(28.1%)

>5%
5(31.3%)
12(34.3%)
9(28.1%)

12
−5%<= and
4(25.0%)
5(14.3%)
10(31.3%)

<=5%

<−5%
8(50.0%)
15(42.9%)
11(34.4%)

>5%
4(25.0%)
6(17.1%)
9(28.1%)

16
−5%<= and
4(25.0%)
10(28.6%)
6(18.8%)

<=5%

<−5%
8(50.0%)
7(20.0%)
12(37.5%)

>5%
2(12.5%)
11(31.4%)
12(37.5%)

20
−5%<= and
3(18.8%)
7(20.0%)
10(31.3%)

<=5%

<−5%
10(62.5%)
10(28.6%)
11(34.4%)

>5%
3(18.8%)
10(28.6%)
8(25.0%)

24
−5%<= and
6(37.5%)
8(22.9%)
9(28.1%)

<=5%

<−5%
6(37.5%)
8(22.9%)
6(18.8%)

>5%
4(25.0%)
10(28.6%)
14(43.8%)

TOTAL
4
−5%<= and
1(6.3%)
6(17.1%)
3(9.4%)

BILIRUBIN

<=5%

<−5%
6(37.5%)
12(34.3%)
16(50.0%)

>5%
9(56.3%)
8(22.9%)
12(37.5%)

8
−5%<= and
4(25.0%)
4(11.4%)
6(18.8%)

<=5%

<−5%
7(43.8%)
13(37.1%)
16(50.0%)

>5%
5(31.3%)
8(22.9%)
7(21.9%)

12
−5%<= and
4(25.0%)
4(11.4%)
6(18.8%)

<=5%

<−5%
6(37.5%)
14(40.0%)
14(43.8%)

>5%
6(37.5%)
6(17.1%)
9(28.1%)

16
−5%<= and
4(25.0%)
3(8.6%)
8(25.0%)

<=5%

<−5%
5(31.3%)
14(40.0%)
14(43.8%)

>5%
6(37.5%)
9(25.7%)
6(18.8%)

20
−5%<= and
3(18.8%)
6(17.1%)
6(18.8%)

<=5%

<−5%
5(31.3%)
12(34.3%)
14(43.8%)

>5%
8(50.0%)
7(20.0%)
9(28.1%)

24
−5%<= and
5(31.3%)
4(11.4%)
6(18.8%)

<=5%

<−5%
4(25.0%)
8(22.9%)
11(34.4%)

>5%
7(43.8%)
10(28.6%)
10(31.3%)

UREA
4
−5%<= and
3(18.8%)
7(20.0%)
7(21.9%)

NITROGEN

<=5%

<−5%
6(37.5%)
16(45.7%)
10(31.3%)

>5%
7(43.8%)
7(20.0%)
15(46.9%)

8
−5%<= and
2(12.5%)
5(14.3%)
7(21.9%)

<=5%

<−5%
6(37.5%)
14(40.0%)
9(28.1%)

>5%
8(50.0%)
10(28.6%)
15(46.9%)

12
−5%<= and
1(6.3%)
4(11.4%)
3(9.4%)

<=5%

<−5%
6(37.5%)
17(48.6%)
13(40.6%)

>5%
9(56.3%)
6(17.1%)
14(43.8%)

16
−5%<= and
3(18.8%)
6(17.1%)
12(37.5%)

<=5%

<−5%
5(31.3%)
12(34.3%)
9(28.1%)

>5%
7(43.8%)
10(28.6%)
9(28.1%)

20
−5%<= and
2(12.5%)
7(20.0%)
6(18.8%)

<=5%

<−5%
6(37.5%)
13(37.1%)
12(37.5%)

>5%
8(50.0%)
7(20.0%)
11(34.4%)

24
−5%<= and
2(12.5%)
2(5.7%)
2(6.3%)

<=5%

<−5%
7(43.8%)
7(48.6%)
14(43.8%)

>5%
7(43.8%)
7(20.0%)
13(40.6%)

For illustration, FIGS. 15 and 16 show a compilation of the data from Table 2 for ALP and ALT (liver disease biomarkers) over the course of the study. FIG. 15 shows that within 4 weeks of the beginning of treatment, roughly 60% of the individuals in the groups receiving teduglutide experienced a decrease in ALP greater than 5% from baseline, whereas the percentage of individuals from the placebo group experiencing a similar decrease is much smaller. A similar dataset for ALT confirms that, in general, groups receiving teduglutide had a greater fraction of individuals experiencing a decrease of more than 5% in liver biomarkers associated with liver disease than groups receiving placebo, beginning at 4 weeks after the commencement of treatment.

By comparing the percent of individuals experiencing an increase in biomarkers associated with disease between groups, it is possible to determine whether GLP-2 can be effective in prophylaxis as well as treatment of liver and kidney disease associated with SBS, PN and intestinal failure.

FIG. 17 compares the percent of individuals in the group receiving placebo who experienced an increase of greater than 5% in ALP to the percent of individuals experiencing the same increase in the groups receiving teduglutide. FIG. 18 shows the same type of data for ALT. These graphs show that, in general, the percent of individuals receiving placebo who experienced an increase in biomarkers associated with liver disease is greater than the percent of individuals receiving teduglutide who experienced the same increase. These results support the conclusion that teduglutide is an effective prophylactic against increases in biomarkers associated with liver dysfunction when administered to individuals with SBS.

A similar prophylactic effect is seen for kidneys in FIG. 19. FIG. 19 compares the percent of the placebo group experiencing a decrease in GFR (which is linked with decreased kidney function) to the percent of individuals experiencing the same decrease in the groups receiving teduglutide. In both groups receiving teduglutide, the percent of the group experiencing a decrease in GFR greater than 5% is always less than the percent of the group receiving placebo that experience the same decrease.

Example 2

To further confirm the results of Example 1, a further study was conducted involving more individuals and additional diagnostic biomarkers. Human patients with short bowel syndrome were divided into two groups: a control group (43 total individuals) to receive placebo, and an experimental group (42 total individuals) to receive teduglutide at 0.05 mg/kg/day throughout the study. Patients were treated by subcutaneous injection of placebo or the appropriate dose of teduglutide. Known biomarkers of liver function were monitored every four weeks, for 24 weeks. The liver biomarkers tested included albumin, gamma glutamyl transferase, total bilirubin, ALT and AST and ALP. Before administration of placebo or teduglutide, diagnostic biomarker levels were tested to establish a baseline for each individual.

To determine the effect of GLP-2 on the levels of the diagnostic biomarkers, the average level of each biomarker was measured for the placebo and teduglutide treated groups for each visit. Furthermore, the average change from baseline was calculated using the established baseline values. The average level and average change from baseline for each biomarker of each group is given in Table 3. Examples of the changes observed are provided in FIGS. 17 through 28. For FIGS. 17-28, p-values were calculated using the t-test.

TABLE 3

Selected Lab Test Results

Teduglutide 0.05

Placebo (N = 43)
mg/kg/d (N = 42)

Change

Change

Observed
from
Observed
from

Parameter/Visit
Statistic
Value
Baseline
Value
Baseline

Albumin (g/L)

Baseline
n
43

42

Mean (SD)
41.8
(4.1)

42.4
(3.2)

Median
42

42.5

Min, Max
31, 51

35, 48

Week 2
n
43
43
40
40

Mean (SD)
41.0
(4.2)
−0.8
(3.0)
41.5
(3.0)
−0.9
(2.0)

Median
41
−1
42
−1

Min, Max
32, 49
−12, 6
35, 47
−5, 3

Week 4
n
43
43
40
40

Mean (SD)
41.1
(3.9)
−0.7
(2.1)
41.7
(3.0)
−0.7
(2.6)

Median
42
−1
42
−1

Min, Max
30, 47
−6, 3
34, 47
−7, 5

Week 8
n
41
41
40
40

Mean (SD)
41.4
(4.7)
−0.6
(2.4)
41.7
(3.2)
−0.7
(2.1)

Median
42
−1
42
−1

Min, Max
31, 54
−5, 7
36, 48
−5, 4

Week 12
n
33
33
34
34

Mean (SD)
40.6
(4.1)
−1.2
(1.9)
41.2
(3.2)
−0.9
(2.4)

Median
41
−1
41
−1

Min, Max
33, 50
−6, 4
35, 47
−7, 5

Week 16
n
40
40
38
38

Mean (SD)
40.8
(4.3)
−1.3
(2.1)
41.7
(3.4)
−0.7
(2.1)

Median
41.5
−1
42
−0.5

Min, Max
31, 48
−7, 3
32, 49
−5, 3

Week 20
n
40
40
38
38

Mean (SD)
40.7
(4.2)
−1.3
(2.5)
41.8
(4.0)
−0.6
(2.4)

Median
41
−1.5
43
−1

Min, Max
32, 50
−6, 5
33, 49
−8, 4

Week 24
n
39
39
38
38

Mean (SD)
40.1
(4.4)
−1.7
(2.4)
41.9
(3.2)
−0.4
(2.3)

Median
40
−2
42
0

Min, Max
31, 49
−6, 4
33, 48
−8, 3

Endpoint
n
43
43
42
42

Mean (SD)
40.3
(4.4)
−1.5
(2.5)
41.9
(3.2)
−0.5
(2.3)

Median
41
−2
42
0

Min, Max
31, 49
−6, 4
33, 48
−8, 3

Alkaline

Phosphatase (U/L)

Baseline
n
43

42

Mean (SD)
151.8
(85.3)

134.7
(66.3)

Median
124

112

Min, Max
40, 506

50, 351

Week 2
n
43
43
40
40

Mean (SD)
145.7
(84.0)
−6.1
(28.1)
123.7
(54.7)
−12.5
(23.9)

Median
121
−1
104
−8

Min, Max
62, 547
−94, 41
53, 298
−76, 27

Week 4
n
42
42
40
40

Mean (SD)
148.8
(82.2)
−1.6
(27.1)
115.8
(52.7)
−20.5
(26.2)

Median
128
−1.5
101
−15.5

Min, Max
60, 474
−102, 59
54, 307
−89, 34

Week 8
n
40
40
40
40

Mean (SD)
152.3
(80.2)
−0.7
(44.7)
112.0
(56.4)
−24.3
(26.9)

Median
130
4
94
−18.5

Min, Max
59, 365
−141, 104
46, 341
−105, 19

Week 12
n
33
33
34
34

Mean (SD)
150.5
(86.5)
−5.1
(29.0)
108.6
(54.7)
−27.2
(33.9)

Median
122
−6
98
−23

Min, Max
49, 483
−64, 67
52, 304
−127, 25

Week 16
n
40
40
38
38

Mean (SD)
144.5
(65.1)
−7.8
(54.1)
114.2
(55.9)
−24.5
(37.5)

Median
129.5
−3
102
−22.5

Min, Max
49, 320
−224, 116
46, 311
−104, 71

Week 20
n
40
40
38
38

Mean (SD)
141.5
(67.7)
−10.9
(45.9)
109.8
(52.1)
−28.6
(37.6)

Median
125
−5.5
94.5
−25.5

Min, Max
54, 364
−142, 90
54, 303
−117, 77

Week 24
n
39
39
38
38

Mean (SD)
143.3
(71.6)
−4.9
(46.4)
106.2
(59.6)
−29.2
(34.8)

Median
124
−1
88
−24.5

Min, Max
54, 383
−137, 94
49, 328
−122, 33

Endpoint
n
43
43
42
42

Mean (SD)
145.2
(70.6)
−6.6
(46.6)
106.4
(56.9)
−28.3
(34.9)

Median
129
−1
90.5
−22

Min, Max
54, 383
−137, 94
49, 328
−122, 33

ALT (U/L)

Baseline
n
43

42

Mean (SD)
44.2
(35.0)

43.0
(29.1)

Median
36

33

Min, Max
12, 219

11, 143

Week 2
n
43
43
40
40

Mean (SD)
45.8
(43.9)
1.6
(17.0)
35.2
(24.7)
−8.9
(18.4)

Median
33
−2
27.5
−5.5

Min, Max
14, 289

−25, 70
9, 142
−58, 48

Week 4
n
43
43
40
40

Mean (SD)
51.6
(46.6)
7.4
(24.6)
34.9
(21.2)
−9.2
(14.0)

Median
39
2
29
−7

Min, Max
14, 280
−30, 92
8, 104
−44, 29

Week 8
n
41
41
40
40

Mean (SD)
51.2
(51.8)
7.6
(25.2)
33.9
(24.1)
−10.2
(17.3)

Median
34
3
26
−5.5

Min, Max
16, 324
53, 105
9, 138
−65, 20

Week 12
n
33
33
34
34

Mean (SD)
50.6
(59.3)
4.1
(24.7)
33.6
(19.6)
−9.8
(18.7)

Median
37
2
27.5
−4

Min, Max
17, 339
−40, 120
11, 94
−62, 35

Week 16
n
40
40
38
38

Mean (SD)
52.8
(52.5)
8.6
(26.3)
37.5
(29.4)
−8.0
(20.8)

Median
33.5
2
26.5
−3

Min, Max
14, 296
−36, 84
13, 140
−64, 45

Week 20
n
40
40
38
38

Mean (SD)
44.8
(39.7)
0.7
(17.9)
34.8
(26.7)
−10.3
(17.8)

Median
35
0.5
28.5
−6.5

Min, Max
16, 253
−66, 52
5, 149
−55, 24

Week 24
n
39
39
38
38

Mean (SD)
42.4
(34.0)
0.6
(17.5)
28.5
(16.3)
−13.5
(17.8)

Median
35
0
23.5
−7.5

Min, Max
16, 212
−43, 65
7, 91
−60, 21

Endpoint
n
43
43
42
42

Mean (SD)
43.9
(34.1)
−0.3
(20.6)
31.0
(24.3)
−12.1
(17.6)

Median
35
−1
23.5
−4

Min, Max
16, 212
−66, 65
7, 149
−60, 21

AST (U/L)

Baseline
n
43

42

Mean (SD)
34.6
(21.1)

32.4
(16.0)

Median
30

30

Min, Max
12, 145

12, 99

Week 2
n
43
43
40
40

Mean (SD)
36.3
(26.9)
1.7
(13.4)
29.0
(14.8)
−3.6,
(8.4)

Median
29
0
24.5
−3

Min, Max
16, 186
−28, 41
11, 78
−21, 22

Week 4
n
43
43
40
40

Mean (SD)
41.0
(24.9)
6.3
(15.3)
29.1
(14.4)
−3.5
(10.7)

Median
34
3
26.5
−5

Min, Max
11, 148
−19, 59
13, 89
−31, 43

Week 8
n
41
41
40
40

Mean (SD)
39.6
(29.4)
5.1
(14.3)
27.5
(12.6)
−5.1
(10.5)

Median
30
2
23.5
−5

Min, Max
16, 191
−25, 46
8, 63
−46, 17

Week 12
n
33
33
34
34

Mean (SD)
39.1
(35.1)
3.5
(16.0)
28.4
(12.0)
−4.2
(11.9)

Median
29
0
25
−5

Min, Max
13, 201
−27, 58
9, 60
−39, 34

Week 16
n
40
40
38
38

Mean (SD)
39.7
(30.9)
4.8
(17.1)
29.8
(13.6)
−3.3
(10.8)

Median
31
1
26
−3

Min, Max
17, 186
−32, 54
9, 65
−42, 20

Week 20
n
40
40
38
38

Mean (SD)
35.7
(22.8)
0.7
(9.8)
28.4
(14.5)
−4.2
(8.4)

Median
29
−1
24.5
−2.5

Min, Max
15, 148
−18, 30
10, 77
−38, 10

Week 24
n
39
39
38
38

Mean (SD)
37.3
(28.7)
2.8
(23.4)
25.8
(12.2)
−5.8
(10.4)

Median
28
1
23
−5

Min, Max
17, 135
−31, 98
11, 73
−47, 14

Endpoint
n
43
43
42
42

Mean (SD)
38.3
(28.7)
3.7
(24.0)
27.0
(14.0)
−5.4
(9.9)

Median
28
1
24
−3

Min, Max
17, 135
−31, 98
11, 77
−47, 14

Bilirubin (umol/L)

Baseline
n
43

42

Mean (SD)
9.95
(7.80)

12.20
(9.80)

Median
8.5

9.05

Min, Max
1.7, 39.7

2.8, 57.4

Week 2
n
43
43
40
40

Mean (SD)
10.97
(9.82)
1.02
(4.28)
10.29
(9.46)
−2.12
(4.58)

Median
8.2
0.2
7.65
−1.85

Min, Max
3.4, 55.4
−8.9, 15.7
2.4, 57.4
−18.2, 8.6

Week 4
n
43
43
40
40

Mean (SD)
11.60
(10.62)
1.65
(4.67)
11.31
(8.52)
−1.10
(4.28)

Median
8.6
0.5
9.55
−1.5

Min, Max
1.7, 58.9
−6.8, 19.2
3.4, 54.0
−14.2, 9.0

Week 8
n
41
41
40
40

Mean (SD)
12.82
(13.19)
2.60
(7.01)
10.81
(9.74)
−1.60
(4.48)

Median
8.6
1.2
7.75
−0.95

Min, Max
3.4, 80.5
−5.2, 40.8
3.4, 61.8
−14.9, 6.8

Week 12
n
33
33
34
34

Mean (SD)
14.32
(22.81)
3.95
(16.41)
10.57
(9.72)
−1.54
(4.45)

Median
8.6
0
8.35
−0.05

Min, Max
1.7, 132.2
−5.0, 92.5
3.2, 57.8
−13.2, 5.2

Week 16
n
40
40
38
38

Mean (SD)
12.95
(18.10)
2.73
(12.44)
9.40
(5.31)
−3.41
(7.25)

Median
7.75
1.05
8.1
−1.75

Min, Max
3.4, 115.4
−8.6, 75.7
3.4, 27.4
−37.7, 5.2

Week 20
n
40
40
38
38

Mean (SD)
13.64
(17.22)
3.42
(11.28)
9.88
(6.38)
−2.76
(4.82)

Median
7.7
1
7.85
−1.7

Min, Max
3.4, 106.3
−6.8, 66.6
3.7, 31.9
−25.5, 2.8

Week 24
n
39
39
38
38

Mean (SD)
13.51
(14.75)
3.42
(8.68)
8.64
(6.11)
−3.68
(5.53)

Median
8.9
1.6
6.8
−2.35

Min, Max
2.9, 76.9
−7.6, 37.2
2.5, 31.3
−26.1, 3.3

Endpoint
n
43
43
42
42

Mean (SD)
13.26
(14.31)
3.30
(8.41)
8.87
(6.14)
−3.33
(5.40)

Median
8.6
1.6
6.95
−1.95

Min, Max
2.9, 76.9
−7.6, 37.2
2.5, 31.3
−26.1, 3.4

Gamma Glutamyl

Transferase (U/L)

Baseline
n
43

42

Mean (SD)
85.7
(77.9)

75.1
(68.5)

Median
61

38.5

Min, Max
12, 377

8, 255

Week 2
n
43
43
40
40

Mean (SD)
84.0
(81.5)
−1.7
(20.9)
84.6
(70.1)
7.4
(30.5)

Median
45
−1
52.5
2.5

Min, Max
11, 426
−72, 49
11, 279
−48, 117

Week 4
n
43
43
40
40

Mean (SD)
90.8
(94.9)
5.1
(28.0)
78.9
(71.6)
1.7
(30.2)

Median
53
1
53.5
−0.5

Min, Max
13, 461
−66, 99
12, 309
−88, 105

Week 8
n
41
41
40
40

Mean (SD)
89.7
(94.4)
3.2
(30.3)
71.5
(68.2)
−5.7
(34.5)

Median
57
0
46
−5.5

Min, Max
10, 481
−78, 104
6, 281
−145, 77

Week 12
n
33
33
34
34

Mean (SD)
90.0
(101.7)
−3.0
(42.1)
67.9
(60.4)
−8.1
(41.3)

Median
53
−5
51
−4

Min, Max
11, 556
−75, 179
8, 240
−162, 56

Week 16
n
40
40
38
38

Mean (SD)
88.3
(89.3)
0.0
(34.9)
72.4
(67.0)
−7.0
(40.7)

Median
57.5
0
54
−5.5

Min, Max
12, 503
−74, 126
6, 294
−161, 90

Week 20
n
40
40
38
38

Mean (SD)
89.8
(84.2)
1.5
(39.0)
70.1
(64.2)
−9.6
(37.5)

Median
53
−0.5
47.5
−8

Min, Max
16, 451
−79, 102
8, 261
−146, 57

Week 24
n
39
39
38
38

Mean (SD)
83.0
(83.8)
−3.2
(34.5)
60.4
(57.9)
−13.6
(36.9)

Median
45
0
40.5
−6

Min, Max
10, 461
−97, 84
7, 228
−144, 46

Endpoint
n
43
43
42
42

Mean (SD)
81.1
(80.6)
−4.6
(34.3)
62.3
(59.3)
−12.8
(37.0)

Median
46
−1
42
−6

Min, Max
10, 461
−97, 84
7, 228
−144, 46

Note:

Baseline is defined as the last assessment prior to the start of treatment. Endpoint is defined as the last assessment after the start of treatment.

As depicted in Table 3 and FIGS. 17 through 20 and 23 through 28, in general, the cohort receiving teduglutide experienced a lower level, on average, of the tested diagnostic biomarkers elevated in individuals with liver dysfunction than the cohort receiving placebo. Furthermore, depicted in FIGS. 21 and 22, as treatment progressed, the measured level of albumin became elevated in the cohort receiving teduglutide relative to the cohort receiving placebo. All of these data are consistent with increased liver function in the cohort receiving teduglutide.

To further confirm these results, the change in each biomarker for each individual patient was sorted based on whether the biomarker increased more than 10%, decreased more than 10%, or remained within plus or minus 10% from baseline at each time point throughout the course of the study. The data for this sorting is reproduced in Table 4 below. P values are provided, which were calculated for each distribution of biomarker changes for each week using the chi-squared test.

TABLE 4

Selected Lab Tests Change from Baseline

Placebo
0.05 mg/kg/d

Parameter/Visit
Statistic
(N = 43)
(N = 42)

Albumin

Week 2
m
43
40

>10% increase from
n (%)
2
(4.7%)
0

Baseline

Within 10% of Baseline
n (%)
37
(86.0%)
37
(92.5%)

>10% decrease from
n (%)
4
(9.3%)
3
(7.5%)

Baseline

p-value

0.622

Week 4
m
43
40

>10% increase from
n (%)
0
2
(5.0%)

Baseline

Within 10% of Baseline
n (%)
42
(97.7%)
35
(87.5%)

>10% decrease from
n (%)
1
(2.3%)
3
(7.5%)

Baseline

p-value

0.167

Week 8
m
41
40

>10% increase from
n (%)
1
(2.4%)
1
(2.5%)

Baseline

Within 10% of Baseline
n (%)
39
(95.1%)
38
(95.0%)

>10% decrease from
n (%)
1
(2.4%)
1
(2.5%)

Baseline

p-value

>0.999

Week 12
m
33
34

>10% increase from
n (%)
1
(3.0%)
1
(2.9%)

Baseline

Within 10% of Baseline
n (%)
31
(93.9%)
31
(91.2%)

>10% decrease from
n (%)
1
(3.0%)
2
(5.9%)

Baseline

p-value

>0.999

Week 16
m
40
38

>10% increase from
n (%)
0
0

Baseline

Within 10% of Baseline
n (%)
36
(90.0%)
36
(94.7%)

>10% decrease from
n (%)
4
(10.0%)
2
(5.3%)

Baseline

p-value

0.676

Week 20
m
40
38

>10% increase from
n (%)
1
(2.5%)
0

Baseline

Within 10% of Baseline
n (%)
34
(85.0%)
37
(97.4%)

>10% decrease from
n (%)
5
(12.5%)
1
(2.6%)

Baseline

p-value

0.151

Week 24
m
39
38

>10% increase from
n (%)
1
(2.6%)
0

Baseline

Within 10% of Baseline
n (%)
33
(84.6%)
36
(94.7%)

>10% decrease from
n (%)
5
(12.8%)
2
(5.3%)

Baseline

p-value

0.344

Endpoint
m
43
42

>10% increase from
n (%)
1
(2.3%)
0

Baseline

Within 10% of Baseline
n (%)
37
(86.0%)
40
(95.2%)

>10% decrease from
n (%)
5
(11.6%)
2
(4.8%)

Baseline

p-value

0.347

Alkaline Phosphatase

Week 2
m
43
40

>10% increase from
n (%)
4
(9.3%)
6
(15.0%)

Baseline

Within 10% of Baseline
n (%)
28
(65.1%)
19
(47.5%)

>10% decrease from
n (%)
11
(25.6%)
15
(37.5%)

Baseline

p-value

0.277

Week 4
m
42
40

>10% increase from
n (%)
8
(19.0%)
2
(5.0%)

Baseline

Within 10% of Baseline
n (%)
25
(59.5%)
16
(40.0%)

>10% decrease from
n (%)
9
(21.4%)
22
(55.0%)

Baseline

p-value

0.004**

Week 8
m
40
40

>10% increase from
n (%)
13
(32.5%)
2
(5.0%)

Baseline

Within 10% of Baseline
n (%)
15
(37.5%)
13
(32.5%)

>10% decrease from
n (%)
12
(30.0%)
25
(62.5%)

Baseline

p-value

0.001**

Week 12
m
33
34

>10% increase from
n (%)
8
(24.2%)
5
(14.7%)

Baseline

Within 10% of Baseline
n (%)
12
(36.4%)
6
(17.6%)

>10% decrease from
n (%)
13
(39.4%)
23
(67.6%)

Baseline

p-value

0.069

Week 16
m
40
38

>10% increase from
n (%)
11
(27.5%)
8
(21.1%)

Baseline

Within 10% of Baseline
n (%)
14
(35.0%)
7
(18.4%)

>10% decrease from
n (%)
15
(37.5%)
23
(60.5%)

Baseline

p-value

0.11

Week 20
m
40
38

>10% increase from
n (%)
10
(25.0%)
3
(7.9%)

Baseline

Within 10% of Baseline
n (%)
14
(35.0%)
12
(31.6%)

>10% decrease from
n (%)
16
(40.0%)
23
(60.5%)

Baseline

p-value

0.09

Week 24
m
39
38

>10% increase from
n (%)
13
(33.3%)
6
(15.8%)

Baseline

Within 10% of Baseline
n (%)
10
(25.6%)
9
(23.7%)

>10% decrease from
n (%)
16
(41.0%)
23
(60.5%)

Baseline

p-value

0.159

Endpoint
m
43
42

>10% increase from
n (%)
13
(30.2%)
6
(14.3%)

Baseline

Within 10% of Baseline
n (%)
13
(30.2%)
12
(28.6%)

>10% decrease from
n (%)
71
(39.5%)
24
(57.1%)

Baseline

p-value

0.148

ALT

Week 2
m
43
40

>10% increase from
n (%)
15
(34.9%)
7
(17.5%)

Baseline

Within 10% of Baseline
n (%)
11
(25.6%)
10
(25.0%)

>10% decrease from
n (%)
17
(39.5%)
23
(57.5%)

Baseline

p-value

0.17

Week 4
m
43
40

>10% increase from
n (%)
20
(46.5%)
6
(15.0%)

Baseline

Within 10% of Baseline
n (%)
9
(20.9%)
8
(20.0%)

>10% decrease from
n (%)
14
(32.6%)
26
(65.0%)

Baseline

p-value

0.004**

Week 8
m
41
40

>10% increase from
n (%)
19
(46.3%)
7
(17.5%)

Baseline

Within 10% of Baseline
n (%)
9
(22.0%)
7
(17.5%)

>10% decrease from
n (%)
13
(31.7%)
26
(65.0%)

Baseline

p-value

0.006**

Week 12
m
33
34

>10% increase from
n (%)
11
(33.3%)
7
(20.6%)

Baseline

Within 10% of Baseline
n (%)
13
(39.4%)
10
(29.4%)

>10% decrease from
n (%)
9
(27.3%)
17
(50.0%)

Baseline

p-value

0.153

Week 16
m
40
38

>10% increase from
n (%)
17
(42.5%)
8
(21.1%)

Baseline

Within 10% of Baseline
n (%)
10
(25.0%)
10
(26.3%)

>10% decrease from
n (%)
13
(32.5%)
20
(52.6%)

Baseline

p-value

0.1

Week 20
m
40
38

>10% increase from
n (%)
16
(40.0%)
5
(13.2%)

Baseline

Within 10% of Baseline
n (%)
10
(25.0%)
10
(26.3%)

>10% decrease from
n (%)
14
(35.0%)
23
(60.5%)

Baseline

p-value

0.017*

Week 24
m
39
38

>10% increase from
n (%)
14
(35.9%)
4
(10.5%)

Baseline

Within 10% of Baseline
n (%)
13
(33.3%)
10
(26.3%)

>10% decrease from
n (%)
12
(30.8%)
24
(63.2%)

Baseline

p-value

0.007**

Endpoint
m
43
42

>10% increase from
n (%)
15
(34.9%)
5
(11.9%)

Baseline

Within 10% of Baseline
n (%)
14
(32.6%)
12
(28.6%)

>10% decrease from
n (%)
14
(32.6%)
25
(59.5%)

Baseline

p-value

0.017*

AST

Week 2
m
43
40

>10% increase from
n (%)
17
(39.5%)
5
(12.5%)

Baseline

Within 10% of Baseline
n (%)
14
(32.6%)
15
(37.5%)

>10% decrease from
n (%)
12
(27.9%)
20
(50.0%)

Baseline

p-value

0.014*

Week 4
m
43
40

>10% increase from
n (%)
19
(44.2%)
8
(20.0%)

Baseline

Within 10% of Baseline
n (%)
15
(34.9%)
6
(15.0%)

>10% decrease from
n (%)
9
(20.9%)
26
(65.0%)

Baseline

p-value

<.001**

Week 8
m
41
40

>10% increase from
n (%)
20
(48.8%)
8
(20.0%)

Baseline

Within 10% of Baseline
n (%)
11
(26.8%)
8
(20.0%)

>10% decrease from
n (%)
10
(24.4%)
24
(60.0%)

Baseline

p-value

0.003**

Week 12
m
33
34

>10% increase from
n (%)
11
(33.3%)
7
(20.6%)

Baseline

Within 10% of Baseline
n (%)
16
(48.5%)
6
(17.6%)

>10% decrease from
n (%)
6
(18.2%)
21
(61.8%)

Baseline

p-value

<.001**

Week 16
m
40
38

>10% increase from
n (%)
16
(40.0%)
7
(18.4%)

Baseline

Within 10% of Baseline
n (%)
12
(30.0%)
12
(31.6%)

>10% decrease from
n (%)
12
(30.0%)
19
(50.0%)

Baseline

p-value

0.085

Week 20
m
40
38

>10% increase from
n (%)
13
(32.5%)
7
(18.4%)

Baseline

Within 10% of Baseline
n (%)
15
(37.5%)
11
(28.9%)

>10% decrease from
n (%)
12
(30.0%)
20
(52.6%)

Baseline

p-value

0.122

Week 24
m
39
38

>10% increase from
n (%)
13
(33.3%)
6
(15.8%)

Baseline

Within 10% of Baseline
n (%)
14
(35.9%)
8
(21.1%)

>10% decrease from
n (%)
12
(30.8%)
24
(63.2%)

Baseline

p-value

0.019*

Endpoint
m
43
42

>10% increase from
n (%)
14
(32.6%)
6
(14.3%)

Baseline

Within 10% of Baseline
n (%)
16
(37.2%)
11
(26.2%)

>10% decrease from
n (%)
13
(30.2%)
25
(59.5%)

Baseline

p-value

0.018*

Bilirubin

Week 2
m
43
40

>10% increase from
n (%)
19
(44.2%)
7
(17.5%)

Baseline

Within 10% of Baseline
n (%)
12
(27.9%)
5
(12.5%)

>10% decrease from
n (%)
12
(27.9%)
28
(70.0%)

Baseline

p-value

<.001**

Week 4
m
43
40

>10% increase from
n (%)
18
(41.9%)
14
(35.0%)

Baseline

Within 10% of Baseline
n (%)
16
(37.2%)
7
(17.5%)

>10% decrease from
n (%)
9
(20.9%)
19
(47.5%)

Baseline

p-value

0.022*

Week 8
m
41
40

>10% increase from
n (%)
24
(58.5%)
10
(25.0%)

Baseline

Within 10% of Baseline
n (%)
10
(24.4%)
10
(25.0%)

>10% decrease from
n (%)
7
(17.1%)
20
(50.0%)

Baseline

p-value

0.002**

Week 12
m
33
34

>10% increase from
n (%)
14
(42.4%)
9
(26.5%)

Baseline

Within 10% of Baseline
n (%)
10
(30.3%)
10
(29.4%)

>10% decrease from
n (%)
9
(27.3%)
15
(44.1%)

Baseline

p-value

0.297

Week 16
m
40
38

>10% increase from
n (%)
21
(52.5%)
9
(23.7%)

Baseline

Within 10% of Baseline
n (%)
11
(27.5%)
5
(13.2%)

>10% decrease from
n (%)
8
(20.0%)
24
(63.2%)

Baseline

p-value

<.001**

Week 20
m
40
38

>10% increase from
n (%)
21
(52.5%)
8
(21.1%)

Baseline

Within 10% of Baseline
n (%)
6
(15.0%)
5
(13.2%)

>10% decrease from
n (%)
13
(32.5%)
25
(65.8%)

Baseline

p-value

0.007**

Week 24
m
39
38

>10% increase from
n (%)
22
(56.4%)
6
(15.8%)

Baseline

Within 10% of Baseline
n (%)
6
(15.4%)
6
(15.8%)

>10% decrease from
n (%)
11
(28.2%)
26
(68.4%)

Baseline

p-value

<.001**

Endpoint
m
43
42

>10% increase from
n (%)
24
(55.8%)
7
(16.7%)

Baseline

Within 10% of Baseline
n (%)
7
(16.3%)
8
(19.0%)

>10% decrease from
n (%)
12
(27.9%)
27
(64.3%)

Baseline

p-value

<.001**

*p <= 0.050;

**p <= 0.010.

Note:

Percentages are based upon m, defined as the number of subjects in the Safety Population who have both a baseline and post-baseline visit value for the associated parameter.

Note:

Baseline is defined as the last assessment prior to the start of treatment. Endpoints defined as the last assessment after the start of treatment.

Note:

The treatment comparison is based on an exact chi-square test.

These data confirm the results from Example 1, namely that, in general, an individual receiving teduglutide is more likely to experience a>10% decrease in liver biomarkers elevated in individuals with liver dysfunction than an individual receiving placebo alone. Conversely, an individual receiving teduglutide is also less likely to experience a>10% increase in biomarkers elevated in individuals with liver dysfunction than an individual receiving placebo alone. These results support the conclusion that teduglutide is useful to both treat and protect against liver dysfunction.

Example 3

In this example the data presented in Example 2 are analyzed to evaluate whether the beneficial effects of teduglutide on liver function tests are related to the reduction of parenteral nutrition volume.

Table 5 depicts the change in liver enzyme levels from baseline to week 24 in responders and non-responders. Responders are human patients with reduced parenteral nutrition, while non-responders are human patients without reduced parenteral nutrition. Human patients either received placebo or teduglutide at 0.05 mg/kg/day. Known biomarkers of liver function were monitored for 24 weeks. The liver biomarkers tested included ALP, ALT, AST, total bilirubin and GGTP.

TABLE 5

Change from Baseline to Week 24 in Liver Enzyme Levels

Lab Parameter (unit)
Statistic
Responder
Non-responder
Total

Placebo

ALKALINE PHOSPHATASE (U/L)
n
13
26
39

Mean (SD)
−7.62
(43.632)
−3.54
(48.529)
−4.90
(46.415)

Median
−7
2
−1

Min, Max
−101, 59.0
−137, 94.0
−137, 94.0

ALT (U/L)
n
13
26
39

Mean (SD)
0.54
(9.649)
0.58
(20.451)
0.56
(17.452)

Median
2
−1
0

Min, Max
−16.0, 17.0
−43.0, 65.0
−43.0, 65.0

AST (U/L)
n
13
26
39

Mean (SD)
−0.54
(5.285)
4.54
(28.423)
2.85
(23.371)

Median
−1
1
1

Min, Max
−9.0, 10.0
−31.0, 98.0
−31.0, 98.0

BILIRUBIN, TOTAL (μmol/L)
n
13
26
39

Mean (SD)
2.64
(7.458)
3.81
(9.344)
3.42
(8.679)

Median
1.7
0.4
1.6

Min, Max
−4.8, 25.7
−7.6, 37.2
−7.6, 37.2

GGTP (U/L)
n
13
26
39

Mean (SD)
−15.00
(29.858)
2.69
(35.681)
−3.21
(34.503)

Median
−5
3.5
0

Min, Max
−96.0, 28.0
−97.0, 84.0
−97.0, 84.0

Teduglutide 0.05 mg/kg/day

ALKALINE PHOSPHATASE (U/L)
n
27
11
38

Mean (SD)
−35.52
(33.942)
−13.82
(33.487)
−29.24
(34.815)

Median
−34
−7
−24.5

Min, Max
−122, 33.0
−107, 12.0
−122, 33.0

ALT (U/L)
n
27
11
38

Mean (SD)
−17.48
(19.380)
−3.82
(7.209)
−13.53
(17.815)

Median
−14
−3
−7.5

Min, Max
−60.0, 21.0
−23.0, 5.0
−60.0, 21.0

AST (U/L)
n
27
11
38

Mean (SD)
−7.30
(11.776)
−2.18
(4.119)
−5.82
(10.371)

Median
−7
−2
−5

Min, Max
−47.0, 14.0
−10.0, 4.0
−47.0, 14.0

BILIRUBIN, TOTAL (μmol/L)
n
27
11
38

Mean (SD)
−4.19
(5.998)
−2.43
(4.151)
−3.68
(5.531)

Median
−2.4
−1.7
−2.35

Min, Max
−26.1, 2.1
−11.1, 3.3
−26.1, 3.3

GGTP (U/L)
n
27
11
38

Mean (SD)
−18.41
(41.808)
−1.91
(16.861)
−13.63
(36.914)

Median
−10
3
−6

Min, Max
−144, 46.0
−45.0, 19.0
−144, 46.0

Table 6 depicts the change in liver enzyme levels (ALP, ALT, AST, total bilirubin and GGTP), as presented in Table 5, and stratifies them by treatment group (placebo versus teduglutide at 0.05 mg/kg/day) and responder status (responder versus non-responder).

TABLE 6

Analysis of Change from Baseline to Week 24 in Liver Enzyme Levels

Treatment

teduglutide 0.05

Lab Parameter (unit)
Statistic
Placebo
mg/kg/day
p-value

ALKALINE PHOSPHATASE (U/L)
LS Means (SE)
−5.58
(6.968)
−24.67
(7.338)
0.0632

95% CI
−19.47,
8.31
−39.29,
−10.04

ALT (U/L)
LS Means (SE)
0.56
(2.939)
−10.65
(3.095)
0.0105

95% CI
−5.30,
6.41
−16.82,
−4.48

AST (U/L)
LS Means (SE)
2.00
(3.099)
−4.74
(3.264)
0.1386

95% CI
−4.18,
8.18
−11.24,
1.77

BILIRUBIN, TOTAL (μmol/L)
LS Means (SE)
3.23
(1.251)
−3.31
(1.317)
0.0006

95% CI
0.73,
5.72
−5.93,
−0.68

GGTP (U/L)
LS Means (SE)
−6.15
(5.990)
−10.16
(6.308)
0.6467

95% CI
−18.09,
5.79
−22.73,
2.41

Responder Status

Lab Parameter (unit)
Statistic
Responder
Non-responder
p-value

ALKALINE PHOSPHATASE (U/L)
LS Means (SE)
−21.57
(6.925)
−8.68
(7.379)
0.2068

95% CI
−35.37,
−7.76
−23.38,
6.03

ALT (U/L)
LS Means (SE)
−8.47
(2.921)
−1.62
(3.112)
0.1128

95% CI
−14.29,
−2.65
−7.82,
4.58

AST (U/L)
LS Means (SE)
−3.92
(3.080)
1.18
(3.282)
0.2612

95% CI
−10.06,
2.22
−5.36,
7.72

BILIRUBIN, TOTAL (μmol/L)
LS Means (SE)
−0.77
(1.243)
0.69
(1.324)
0.4223

95% CI
−3.25,
1.70
−1.95,
3.33

GGTP (U/L)
LS Means (SE)
−16.70
(5.953)
0.39
(6.343)
0.0532

95% CI
−28.57,
−4.84
−12.25,
13.03

The data presented in this example show that the positive effect on liver function tests is an effect of teduglutide (difference from placebo), and that the major part of the effect is not mediated through the reduction of parenteral nutrition (difference between responders and non-responders).

It will be apparent to those having skill in the art that many changes may be made to the details of the above-described embodiments without departing from the underlying principles of the invention. The scope of the present invention should, therefore, be determined only by the following claims.

	Number	Date	Country
Parent	PCT/US11/36106	May 2011	US
Child	13673386		US

METHODS FOR TREATMENT OR PROPHYLAXIS OF KIDNEY OR LIVER DYSFUNCTION

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CROSS-REFERENCE TO RELATED APPLICATIONS

Provisional Applications (1)

Continuation in Parts (1)