FOR PHARMA & LIFE SCIENCES

WHITEPAPER Modeling Drug-Induced Liver Toxicity: A Case Study for Pathway Analysis

BRINGING NEW, EFFECTIVE AND PROFITABLE DRUGS TO MARKET This case study demonstrates how pathway analysis can be used to create complex, predictive mechanistic models of biological processes, providing novel insights to researchers and helping them direct the course of their studies. Bringing new, effective and profitable drugs to market.

EXECUTIVE SUMMARY Liver toxicity is a key reason why new drugs fail in clinical trials, or once they are in broader use. Drug-induced cholestasis is a common form of liver toxicity, yet currently there is no model or test to predict which drugs may induce cholestasis in patients. This case study demonstrates how pathway analysis can be used to create complex, predictive mechanistic models of biological processes, providing novel insights to researchers and helping them direct the course of their studies.

INTRODUCTION Drug toxicity is a leading cause for Severe cases, however, may lead to liver dismissing lead compounds during drug failure2. A partial list of medications development and a frequent reason for the known to cause cholestasis includes:iii withdrawal of drugs from clinical trials and subsequently from the market.i, ii Liver • Antibiotics such as ampicillin and toxicity is the major type of drug-induced other penicillins toxicity due to the primary role of the liver • Anabolic in metabolizing foreign chemicals and • Oral contraceptives clearing them from the body. • Chlorpromazine Drug in liver cells typically • Cimetidine occurs in three phases: oxidation by • Estradiol membrane-associated P450 in hepatocytes, conjugation with • Imipramine various hydrophilic anionic groups causing • Prochlorperazine drug inactivation, and the discharge of conjugated and inactivated forms of • Terbinafine the drug into bile that empties into the • Tolbutamide intestine for excretion from the body1. In this article, we use drug-induced cholestasis as a case study to When a drug or its metabolite inhibits demonstrate how knowledge networks one or more enzymes involved in its composed of drug-target relationships metabolism by the liver, the drug may can be used in combination with accumulate in the liver and cause one or biological association networks to build more types of liver toxicity. Cholestasis a proposed mechanistic model of drug is a common type of liver toxicity toxicity. Such a model might be used characterized by the inability to secrete to predict potential toxicity of a drug bile. Once patients stop taking the drug candidate early in the development causing cholestasis, most will recover pipeline and reduce attrition in the clinical although it can take many months. trial stages, to develop biomarkers of drug toxicity, and even to predict individual risk of drug-induced toxicities.

2 A POWERFUL TOOL FOR PATHWAY ANALYSIS This Underpinning Pathway Studio® A large collection of research articles metabolites that induced cholestasis and is Elsevier’s proprietary text mining on cholestasis already exists, but the manually removed them from the final technology that extracts facts from challenge is sifting through all those result since this model was focused on full-text articles in the more than papers manually to piece together drug-induced cholestasis—an example 2,000 biomedical journals, as well as key results and relationships from the of how researchers can customize search PubMed abstracts. various studies into a single, coherent results based on their own knowledge. disease model for drug induced Armed with Pathway Studio, cholestasis. The process of pathway We hypothesized that drugs induce biologists can rapidly access facts analysis can help researchers make cholestasis through common extracted not only from abstracts, but sense of the vast amount of published mechanisms; therefore, we started also from all the full-text articles in results that are scattered across dozens searching for proteins downstream of the Elsevier corpus. Having access or hundreds of journal articles. Pathway at least two different drugs known to to the full text of articles and an analysis systematically processes induce cholestasis to start the model industry-leading text-mining tool published scientific papers, extracts building process. gives researchers a measurable and gathers relevant information about advantage in terms of rendering a molecular interactions into a database, Pathway Studio enabled us to build complete picture of the and and then provides tools to mine that a pathway and apply a filter to hone proteins involved in the biology of a information. This systematic approach in on individual proteins, complexes, disease or response to a drug. helps researchers ensure a more and protein functional-classes. We also comprehensive survey of the published hypothesized that drugs might cause information, which in turn gives them cholestasis by inhibiting off-target greater confidence in the interpretation proteins and, therefore, we filtered for of their experimental data. For this relationships with negative regulatory case study, we used Elsevier’s Pathway effects. Manual inspection of the result- Studio to review virtually all of the ing drug-target relationships revealed published information about the process that many cholestasis-inducing drugs of cholestasis, find common features were anticancer drugs that shared com- among cholestasis-inducing drugs, and mon therapeutic targets irrelevant to by applying knowledge of the biology, cholestasis. To identify off-target proteins we could propose an in-silico model for potentially relevant to cholestasis, we drug-induced cholestasis. extended the search to capture proteins linked to all types of cholestasis – rather Creating a draft model of drug-Induced than only drug-induced cholestasis - with cholestasis positive or unknown regulatory effects. We began by selecting the term “cholestasis” in Pathway Studio’s knowledgebase and searched for small molecule entities associated with positive regulation upstream of the cholestasis starting point. By inspecting the sentences extracted from the literature by Pathway Studio for some of these upstream entities, we identified several

3 The set of identified proteins linked We then began building a cholestasis to cholestasis in the literature was model from 21 proteins directly involved compared to the set of common targets in metabolism, irrespective of cholestatic drugs, and the proteins at of their method of regulation. The the intersection of those groups were proteins in the model could be classified chosen for further analysis. After manual as hepatocyte bile acid importers or curation and removing highly generic exporters, or enzymes for bile acid and uninformative functional classes synthesis and conjugation (Figure 1). The (e.g., cytokines and monooxygenases), model is consistent with the current view we identified 58 proteins linked to of bile acid circulation that postulates cholestasis and inhibited by at least two that hepatocytes preferentially import cholestatic drugs. damaged, unconjugated bile acids and export repaired conjugated bile acids3, while maintaining levels of circulating bile by synthesizing it from .

transport cholesterol metabolism cholesterol cholesterol export

ABCA1

BA import BA export Slco1a1 ABCB11

SLC22A8 BA conjugation ABCB1 acetate- Slco1a4 HMGCR HSD11B1 CYP3A4 UGT1A1 CoA ... CYP7A1 CYP27A1 ABCC4

SLCO1B1 ABCC1 BA synthesis SLC10A1 xenobiotic -transp... SLC22A1

ABCG2 SLCO1B3

bile acid transpor t bile acids bile acid metabolism

Figure 1. A screenshot of the pathway generated by Pathway Studio showing 21 proteins (red ovals and amber hexagons) chosen for a draft of the drug-induced cholestasis model. (See Figure 2.)

4 Expanding the draft model by bile acids5, 7. FXR and PXR activate Sub-network enrichment analysis The next step was to find transcription the expression of proteins involved in (SNEA)4 is a statistical method factors upstream from the proteins bile acid conjugation and secretion, but developed for Pathway Studio to in the model using another function at the same time repress expression of identify sub-networks in the global of Pathway Studio. The top seven genes involved in bile acid import and knowledge network that are enriched sub-network enrichment (SNE) seeds synthesis (Figure 2). If there is an excess with entities selected by the user. The included six nuclear receptors and of bile, FXR and PXR induce conjugation biological interpretation of SNEA hepatic nuclear factor-1 (HNF1A) of bile acids, thereby increasing their results depends on the type of the in the following order of statistical secretion while suppressing bile acid global knowledge chosen for analysis. significance: PXR, FXR, CAR, PPARA, synthesis and import. If there is a deficit We used the option “Expression RXR, HNF1A, and SHP. HNF1A of bile, decreased FXR and PXR activities targets” that allows for identification functions in liver growth and derepress synthesis and import of bile of major expression regulators differentiation and, therefore, we acids, while down-regulating conjugation upstream of the userselected proteins. excluded it from the list of bile acid and secretion. Based on this regulation homeostasis regulators. Reviews of the profile, FXR and PXR likely act as principal supporting evidence revealed that only internal sensors and regulators of bile FXR and PXR could be directly activated acid concentration in hepatocytes.

cyclosporine Nitogenin Erythromycin Silybin Glyburide Pirinixic acid ciprofloxacin methimazole Flutamide clozapine Digoxin AI3-17297 Griseofulvin troglita... Clarithromycin Cholerebic bezafibrate methylprednisolone BA canalicular ursodiol Cimetex BA import propylthiouracil BA conjugation secretion

glycocholate BA synthesis BA-AA BA-AA NTCP bile acids taurocholate cholesterol BA-R OATP1 UGT1A1 phospholip BSEP bilirubin ids Butazolidine sod CYP7A1 CYP27A1 Kesscocide ium ORCT1 MXR monensin CYP3A4 Tacrolimus OATP8 Imidazole MPR4 Trabectedin isoflurane OATP2 Phomin sulindac MDR1 Colchicine Slco1a4 Sonazine Zantac

captopri ABCA1 glutathione disu Ezetimibe l SLC22A8 lfide

ABCC1

PXR FXR TPA

SHP

FGF19

Indicates autocrine repression of bile synthesis

gamma-alpha-carboxyet hyodesoxycholat all-trans ret phosphatidylchol hyocholate hyl hydroxychroman be e inoic acid KNG1 CAMP sulfur L-glutamate ine ta-D-glucoside

Indicates PXR activation Indicates FXR activation

Figure 2. A draft cholestasis model generated by Pathway Studio depicting the proteins that are directly involved in bile acid (BA) circulation and metabolism and are inhibited by cholestatic drugs. The model shows BA homeostatic circulation through the hepatocyte. Liver cells preferentially import damaged, unconjugated BAs, and they secrete into the bile duct repaired BAs conjugated with the amino acids taurine and glycine (BA-AA ). BAs also can be modified by anionic chemical groups (BA-R) such as glucoronate or sulfate, which also promote their excretion from hepatocytes. Thirty-seven drugs (in green ovals) that are explained by the draft model are shown next to their respective targets.

Risk

5 To propagate information about bile acid Expanding the model using protein concentration throughout the entire liver, functional similarity an autocrine signaling process is most likely A “paralog” network was created in Pathway used. To find secreted hormones involved Studio by importing paralog pairs of all in autocrine signaling, we inspected human proteins. Paralogs were calculated expression targets downstream of FXR and from the BLAST output generated by the PXR using a function for adding common BLASTp program, running the human expression targets. Among the 17 targets proteome against itself as described shared by FXR and PXR, we found only previously8. All paralog pairs were then FGF19 to be secreted according to protein imported into the Pathway Studio database annotations from the literature. Publications under a new relationship type labeled retrieved by Pathway Studio that support “Paralog”, and annotated with sequence PXR -> FGF19 expression regulation similarity. We used this paralog network to suggested that FGF19 is used by liver as identify functional homologs of proteins a principal signal for suppression of bile involved in bile acid circulation, using acid synthesis5. Statements supporting the a 50% similarity cutoff. This approach FXR->FGF19 relationship revealed that the added following the proteins: SLC10A2 SHP transcription factor is the principal as the homolog of SLC10A1 transporter; mediator of FGF19 autocrine signaling SLCO1A2 and SLCO4C1 as homologs that suppresses expression of CYP7A1 and of SLCO1B1; CYP7B1 and CYP8B1 as CYP27A1—two enzymes catalyzing rate- homologs of CYP7A1; ABCB5 as homologs limiting steps in bile acid synthesis6, 7. of ABCB11/BSEP; ABCC3, ABCC6, and ABCC2 as homologs of ABCC1. Subsequent The initial draft cholestasis disease model inspection of the protein annotations from shown in Figure 2 contains an FGF19 the literature confirmed the roles of these indirect autocrine loop and an incomplete proteins in bile acid transport or synthesis. metabolic pathway for bile acid synthesis, To add even more transporters to the but no details about which bile acids directly model we found all proteins regulating activate PXR and FXR. To enhance the the bile acid transport Cell Process in predictive power of the model generated Pathway Studio, and then selected proteins by Pathway Studio, we added detailed annotated as transporters. This approach molecular mechanisms behind these added OSTalpha and OSTbeta transporters indirect relationships and added more to the model. These two proteins have a proteins directly involved in bile acid sequence similarity below 50% to other bile circulation and metabolism. Even though transporters and therefore were missed by this expanded model contains proteins that the initial sequence similarity search. are not directly targeted by the cholestasis- inducing drugs, we hypothesize that the inhibition of these proteins may also cause cholestasis because they are involved in bile acid circulation and metabolism, along with other known targets.

6 Expanding the model by adding bile acid expression relationship we identified metabolism pathway FGFR4 receptor, small heterodimer We also expanded the cholestasis model by partner (SHP), and the JNK pathways as adding the bile acid metabolism pathway, intermediate steps for FGF19 signaling10, which is already present in the metabolic 11, 12. To find other members of the FGF19 pathway collection in Pathway Studio. It signaling pathway we built a model of was copied “as is” to extend the cholestasis the FGF19-FGFR4 regulome9 by finding model. CYP7A1, CYP27A1 and CYP3A4 are all proteins downstream of either the only enzymes of bile acid synthesis FGF19 or FGFR4 in the “Regulation”, inhibited by cholestasisinducing drugs. It “DirectRegulation”, “ProtModification” or is known that both CYP7A1 and CYP27A1 “Binding” networks contained in Pathway catalyze rate limiting steps in the bile acid Studio. Signal transduction proteins synthesis6, 7. from the FGF19-FGFR4 regulome were connected using the physical interactions Expanding the model by reconstructing found in the “Binding”, “DirectRegulation”, FGF19 autocrine signaling pathway in “ProtModification” networks. The gap hepatocytes between JNK signaling and FGFR4 adaptor Since FGF19 is a secreted ligand, it needs proteins was closed by copying conserved signaling pathways consisting of receptor signaling blocks from the FGFR1->RUNX2 and signal transduction molecules to canonical pathway contained in the Elsevier propagate signals repressing the expression signaling pathway collection: the GRB2- of bile acid metabolic enzymes inside SOS1- RAS block was added to show the hepatocytes. Because the FGF19 pathway RAF kinase activation process, and gamma was not described in the literature, and PLC-PKC block was added to describe JNK1 was not available in the Elsevier canonical (MAPK8) activation. FGFR1 is a functional pathway collection, we reconstructed this homolog of FGFR4. The resulting model pathway from scratch using the Pathway produced by Pathway Studio is consistent Studio knowledgebase. By inspecting with experimental observations from references supporting the FGF19->CYP7A1 95,395 publications (Figure 3).

7 PXR FXR

FGF19

FGFR4

PLCG1

PTPN11 SHC1 diac... IP3 FRS2

RASGRF1 GRB2

ITPR1 Ca2+ SOS1

RASGRP1 PKC Ras

RAF1

MAP2K2 MAP2K1 MAPK8

MAPK1 MAPK3 RPS6KB1

CREB1

JUN FOS

HNF4A SHP

FXR PXR

CYP27A1 CYP7A1 CYP8B1

Figure 3. Output from Pathway Studio—consistent with experimental observations from 95,395 publications—shows the predicted FGF19 signaling pathway repressing expression of for BA synthesis. Proteins from the FGF19–FGFR4 regulome are highlighted in green. These proteins are reported as being activated by either FGF19 or FGFR4. Other proteins were added to the pathway because of their similarity to the canonical FGFR1 signaling pathway available from the Elsevier signaling pathway collection in the Pathway Studio database. They represent conserved signaling blocks that exist in multiple signal transduction pathways. HN F4A was added to the pathway because it is the major transcriptional regulator of BA synthesis enzymes.

8 e

The final result of this work is the g) e e fully expanded cholestasis model Co A taurine aurochenodeoxycholate,NADPH:oxyg n (6-alpha-hydroxylatin

shown in Figure 4. This model T H+ taurohyocholat taurochenodeoxycholat NADP H

contains 51 proteins, including NADP + l O2 H2 O e

members of 21 functional classes e sulfat e 3',5'-ADP amino acid N-choloyltransferase

representing enzymatic steps 3'-phosphoadenyly chenodeoxycholoyl-CoA in bile acid synthesis, and 57 glycochenodeoxycholat A ferase glycochenodeoxycholate 7-sulfat e Co A bile chemicals, including unconjugated L-glycine e propionyl-Co e propionyl-CoA glycochenodeoxycholate sulfotrans conjugation d

and conjugated bile acids, and C-acyltransferas C choloyl-CoA glycocholat Co A Co A BSEP intermediate steps of bile acid e e taurochenodeoxycholat l-Co A propanoyl-CoA synthesis. The expanded model oA H+ NADH NADH H+ 3alpha,7alpha,12alpha-trihy 3alpha,7alpha-dihydroxy-5 roxy-5beta-24-oxocholestanoy beta-24-oxocholestanoyl- - A dehydrogenas includes 20 proteins from the draft n MPR 2 NAD+ NAD+ amino acid N-choloyltransferas cholestasis model that are known e 3-hydroxyacyl-Co oyl-CoA glycochenodeoxycholate A hydratas

targets of cholestasis-inducing drugs. 5beta-cholestanoyl-CoA 3alpha,7alpha,24-trihydroxy trahydroxy-5beta-cholesta 3alpha,7alpha,12alpha,24-te n h taurine H2O H2O MPR 4 A e A ta-cholest-24-enoyl-Co 3alpha,7alpha,12alpha-trihydroxy-5be oyl-Co Co A 24-enoyl-Co e roxy-5beta-cholest- 3alpha,7alpha-dihyd oxidas e acce... acce... 3alpha,7alpha,12alpha-tri ydroxy-5beta-cholest-24-e - y d glycocholat MPR 3

Figure 4. The Pathway Studio–generated A acc... reduce reduced acc... e A ta-cholestanoyl-CoA cholestasis model expanded with neutral BA e oyl-Co 3-alpha,7-alpha,12-alpha-trihydroxy-5-b taurocholat

biosynthesis pathway. When viewing this article -5beta-cholestanoyl-Co MDR3 2-epimeras (25S)-3alpha,7alpha-dihydrox rihydroxy-5beta-cholestan-26 (25S)-3alpha,7alpha,12alpha-T t online, users may zoom in this page to see - e A A 2-methylacyl-CoA Co A model details. PP i P oyl-Co lph a taurocholat AT P AT holestanoyl-Co OS Ta AMP rihydroxy-5beta-cholestan-26 (25R)-3alpha,7alpha,12alpha- AM P PP i PP i lestanate-CoA (25R)-3alpha,7alpha-dihydroxy-5beta-c cholic thiokinase Co A n cholic thiokinase 3alpha,7alpha-dihydroxy-5beta-cho AMP P P Co A cholic thiokinase AT a CoA AT AM P OSTbet a at e PP i ci d e H+ 3a,7a-dihydroxycoprosta trihydroxycholestanoic NADH N... N... NADH H+ H2 O MXR H2 O H+ H+ O2 O2 bile repair H2 O NAD+ NAD+ NADP H H2 O THAL-NAD oxidoreductase NADP H cholestanetriol 26-hydroxylas aldehyde dehydrogenase (NAD+) l SH P an-26-al n-26-a MDR1 e e H+ bile export bile repair 3,7,12-trihydroxycholesta NADH NADH HNF4 A 3alpha,7alpha-dihydroxy-5beta-cholest H+ t l NAD+ TEHC-NAD oxidoreductase NAD+ alcohol dehydrogenas cholestanetriol 26-hydroxylas ro l e cholestane-3,7,26-trio cholestane-3,7,12,27-te N... H2 O FX R FGF1 9 NADP + H2 O O2 NADP H e e O2 H+ PX R CYP3A4 H+ cholestanetriol 26-hydroxylas NADP H l pha-diol 12alpha-hydroxylas a 5beta-cholestane-3alpha,7al H+ NADP + NADP H H2 O trihydroxycoprostan dihydroxycoprostane NADP + Cholestane-3,7,12,25-tetrol N... NADP H cholestero ) O2 NAD+ H+ bile synthesis H2 O a H+ H+ se (B-specific O2 H+ NADH NADP H NADP H 3-alpha-hydroxysteroid dehydrogen se NADP + stan-3-one -cholestan-3-one l H2O cholesterol 7-alpha-monooxygen 7alpha,12alpha-dihydroxy-5beta NADP + 7alpha-hydroxy-5beta-chole NADP + se H+ H+ NAD+ a 7alpha-hydroxycholestero NADP H NADP H NADH e delta4-3-oxosteroid 5beta-reducta H2 O N... NADP H O2 H+ en-3-one -dehydrogenase p e e 7-hydroxy-4-cholesten-3-on tor 7alpha,12alpha-dihydroxycholest-4- cholest-5-ene-3beta,7alpha-diol 3bet SH P reduced acce se ha-hydroxylas e chenodeoxycholat 7alpha-hydroxycholest-4-en-3-one 12alp acceptor PX R 3-oxo-5alpha- 4-dehydrogena cholat e ha-cholestan-3-on 7alpha,12alpha-dihydroxy-5alp TP 1 TP 8 TP 2 OA ORCT1 OA NTCP OA bile import

9 APPLICATIONS OF THE CHOLESTASIS MODEL GENERATED BY PATHWAY STUDIO Implications for drug development We used Pathway Studio to generate acid exporters and multidrug resistance a model that provides a potential proteins belong to one family of ABC explanation why cholestasis is one cassette glycoproteins that use ATP energy of the most common drug-induced to transport various chemicals out of a toxicities. The liver uses the same cell. Therefore, drugs capable of inhibiting molecular machinery to maintain bile MDR1 are likely to inhibit BSEP, albeit acid homeostasis and to detoxify food. with a different affinity. MDR1 and its The physiologic role of CYP3A4 and other paralogs are often overexpressed in cancer enzymes is clearing cells20, leading to drug resistance in the liver of toxic bile acids and other cancer. The pursuit for better anticancer chemicals produced by bacterial flora or drugs leads to the selection of chemicals ingested with food14. If a drug cannot inhibiting these MDR proteins in order to withstand metabolization by liver P450 achieve greater effectiveness. enzymes (called the first-pass effect), its effective bloodstream concentration The ABC proteins have two translocation drops soon after digestion, preventing the and two nucleotide-binding domains. Most drug from reaching the target tissue at drugs bind to the translocation domain, an effective dose. Because CYP3A4 is the but some can also bind to the nucleotide- major enzyme degrading drugs, there is binding domain18, 19. The increased interest a great need to avoid the firstpass effect of the pharmaceutical industry in kinase bias in drug development by selecting inhibitors designed to bind ATP-binding compounds that do not bind to, or only domain in proteins kinases can potentially reversibly bind to CYP3A4 without being increase the number of drugs capable of metabolized by it. This second possibility binding to the nucleotide-binding domain effectively implies that many drugs of ABC transporters. Our cholestasis withstand the firstpass effect because they model suggests that former and current are competitive CYP3A4 inhibitors. drug development is biased towards creating MDR and CYP3A inhibitors, To avoid accumulation of a thus increasing these drugs’ cholestatic nondegradable drug in the liver and potential. There are 387 drugs known to to ensure the drug can reach its target inhibit MDR1 and 404 drugs known to tissue, drug molecules must be exported inhibit CYP3A4 in the Pathway Studio by transporters back into the gut. database (as of April 2012), supporting Most drugs or their metabolites are this point. cleared from the liver via a family of multidrugresistant ABC transporters. MDR1/ ABCB1 is the major drug exporter linked to more than 1,000 drugs in the Pathway Studio database. It is also the second major exporter of conjugated bile acids after ABCB11/BSEP29. Bile

10 Biomarkers for drug-induced within each individual24. Thus, the cholestasis combination of FGF19 and FXR-specific FXR protein senses and regulates bile biomarkers may indicate activation of acid concentration15. PXR appears to FXR caused by an individual’s diet or function in E metabolism32, genotype rather than by drug toxicity. but has a promiscuous ligand-binding Therefore, to allow specific detection domain16, suggesting that PXR also of drug-induced cholestasis, a third protects the liver from various toxic biomarker specific for PXR is necessary. products generated by bacteria or Several PXR-specific biomarkers ingested with food. Activated PXR were found in the Pathway Studio inhibits bile acid synthesis globally in the knowledgebase shown on Figure liver because of the inhibition of HNF4A, 2: metabolite gamma- and up-regulation of SHP via FGF19– alphacarboxyethyl hydroxychroman FHFR4 pathway40. This process may allow beta-Dglucoside25, bilirubin37, the liver to refocus for drug degradation hyodeoxycholate and hyocholate38, and clearance. However, if a drug blocks and retinoic acid (RA)39. In summary, bile acid secretion, the accumulating bile our model suggests that a panel hyperactivates FXR in a cell that already consisting of three blood and seven has hyperactivated PXR. Activation of FXR urine biomarkers may be combined further inhibits bile acid synthesis locally to accurately assess cholestatic risk in through overexpression of SHP, and patients during drug therapy (Figure 2). globally through the increase in FGF1921.

This scenario suggests that FGF19 concentration is an indicator for autocrine down-regulation of bile acid synthesis. However, FGF19 levels naturally vary, and so do not differentiate between a healthy liver’s response due to PXR activation and aberrant FXR hyperactivation resulting from the inhibition of bile secretion by a drug. Therefore, inclusion of another biomarker specifically indicating FXR hyperactivation is necessary to add specificity. We found several FXRspecific biomarkers in the Pathway Studio database, as shown on Figure 2: kinogen 1 (KNG1)17, cathelicidin antimicrobial peptide (CAMP)33, sulfur34, phosphatidylcholine35, and glutamate36. FXR can be activated by all transretinoic acid and triglycerides21 due to its interaction with a retinoic acid receptor (RXR)22 or by peroxisome proliferatoractivated receptor gamma (PPAR– )23. The FXR level varies due to individual genetic variation, and daily

11 Estimating cholestatic risk for drug targets Predicting cholestatic risk for new Table 1 illustrates how the data in Pathway compounds Studio allows a straightforward calculation The cholestasis model we created with of cholestatic risk associated with off- Pathway Studio suggests that the cholestatic target inhibition by a drug. To find drugs potential of a particular compound depends inhibiting each transporter, we added small on that compound’s ability to inhibit bile molecules upstream of the transporter in acid export and induce PXR activity at Pathway Studio database in the negative the same time. PXR activation can follow regulation and direct regulation networks. inhibition of the CYP3A enzymes, which Using another tool in Pathway Studio, are known to metabolize more than half of we then intersected inhibitors with drugs all drugs. According to the information in known to induce cholestasis to calculate the literature, PXR regulates 23 different cholestatic risk. cytochrome p450 enzymes, including CYP2C9 which metabolizes more than 10% We found that MDR1 inhibition has a of all drugs. relatively low risk of inducing cholestasis (12%), as compared to BSEP or ABCB4 One way to predict cholestatic risk for inhibition (39% and 50%, respectively). a new compound is to perform virtual BSEP inhibition risk is expected to be higher docking simulations first against BSEP, because BSEP is the major bile exporter that MDR1, and other bile acid exporters, and has a fivefold higher affinity towards bile then against cytochrome p450 enzymes acid than MDR113. regulated by PXR. Pharmacophore models for virtual docking can be calculated based on known structures of mouse MDR126 and through homology modeling for human MDR1, BSEP, and other transporters. A pharmacophore model for CYP3A4 and other P450s has already been developed27, 28, 29.

Number of Inhibitors Transporter Specificity Number of Inhibitors Known to Induce Cholestatic Risk Cholestasis ABCB4/MDR3 phospholipids 4 2 0.5

ABCC3 organic anions 5 2 0.4

BSEP conjugated bile acids 23 9 0.391

ABCC2/Mrp2 organic anions, sulfated and 44 10 0.227 glucoronidated bile acids

ABCC4/Mrp4 organic anions 26 5 0.192

ABCG2 foreign 65 11 0.169

MDR1 foreign 323 38 0.117

ABCC1 organic anions, sulfated and 44 4 0.091 glucoronidated bile acids

Table 1. Risk of cholestasis associated with the inhibition of various bile acid transporters from the model. Risk is estimated as a proportion of drugs known to induce cholestasis among all known inhibitors for the transporter.

12 Another way to assess cholestatic to construct a comprehensive model. potential is by monitoring intracellular Software applications like Elsevier’s Pathway bile concentration in response to Studio® can provide the missing piece to compound treatment, because FXR help reconstruct these complex puzzles. activation must be concomitant with PXR Pathway Studio is a powerful decision- activation to indicate cholestatic risk. A support solution that helps researchers recently developed in vitro assay using integrate research findings described in cultured rat hepatocytes to monitor the scientific publications into a searchable accumulation of intrahepatocellular knowledgebase. This knowledgebase is bile30 can also be used for measuring populated using Elsevier’s proprietary activation of FXR and PXR. The activity natural language processing (NLP)-based of these transcription factors in response text-mining technology, which can extract to a drug can be calculated using the structured information (e.g., and subnetwork enrichment analysis function protein names, functions, interactions) on microarray data31. from unstructured data (the text content of scientific articles). Unlike manually curated Workflow for using the cholestasis model databases, Pathway Studio gives users in personalized medicine direct access to the underlying evidence— Our cholestasis model provides a sentences extracted from journal articles foundation for evaluating a patient’s and abstracts that describe specific results predisposition to cholestasis. Knowing or relationships—so that each researcher this predisposition is important when a can independently validate and determine cholestatic drug is prescribed. This model the applicability of the facts to his or her predicts that patients with common single own work. The applications of Pathway nucleotide polymorphisms (SNPs) leading Studio for experimental data analysis and to attenuated bile acid exporter activity, or literature exploration are limited only by the over-activating PXR or FXR, may develop imagination of the researcher. cholestasis at lower drug doses that are normally tolerated by non-predisposed In this case study we used Pathway patients. Such predisposed patients can be Studio in a step-wise manner to generate prescribed additional anti-cholestatic drugs a very complex disease model that to minimize the risk of adverse reactions. could be used to reduce risk in early- stage drug development, personalize CONCLUSION treatment for individuals at elevated risk The process of drug metabolism in the body of drug-associated toxicities, and identify is both highly complex and highly regulated, biomarkers of potential toxicity. This and affects the usefulness of every ingested cholestasis model was first presented at drug. Improving our understanding of Biomarker world Congress, Philadelphia these complex molecular interactions and in February 2009, along with its prediction processes can have profound effects on the of fGf19 as a biomarker for drug-induced drug development process, by identifying cholestasis. The research article by Schaap potentially problematic compounds earlier et al that experimentally confirms fGf19 as in the development process, before clinical biomarker for cholestasis was published trials, and before a drug is administered to in April 200914, providing independent millions of people. Pathway analysis – the validation of this cholestasis model, and study of these interactions – can provide of the value of creating in Silico models. valuable insights into the process of drug Since 2009, nine additional papers have metabolism early in this process. been published, including two so far in 2014,that support the role of fGf19 as an Unfortunately the information about autocrine regulator of bile acid turnover these metabolic processes is scattered in liver. across hundreds or thousands of journal articles, making it very difficult for any Acknowledgment single researcher to gather, read, and Figures reprinted with permission from understand the information in order Bentham Science Publishers

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