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

FOR PHARMA & LIFE SCIENCES

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.

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EXECUTIVE SUMMARYLiver 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.

INTRODUCTIONDrug toxicity is a leading cause for dismissing lead compounds during drug development and a frequent reason for the withdrawal of drugs from clinical trials and subsequently from the market.i, ii Liver toxicity is the major type of drug-induced toxicity due to the primary role of the liver in metabolizing foreign chemicals and clearing them from the body.

Drug metabolism in liver cells typically occurs in three phases: oxidation by membrane-associated cytochrome P450 enzymes in hepatocytes, conjugation with various hydrophilic anionic groups causing drug inactivation, and the discharge of conjugated and inactivated forms of the drug into bile that empties into the intestine for excretion from the body1.

When a drug or its metabolite inhibits one or more enzymes involved in its metabolism by the liver, the drug may accumulate in the liver and cause one or more types of liver toxicity. Cholestasis is a common type of liver toxicity characterized by the inability to secrete bile. Once patients stop taking the drug causing cholestasis, most will recover although it can take many months.

Severe cases, however, may lead to liver failure2. A partial list of medications known to cause cholestasis includes:iii

• Antibiotics such as ampicillin and other penicillins

• Anabolic steroids

• Oral contraceptives

• Chlorpromazine

• Cimetidine

• Estradiol

• Imipramine

• Prochlorperazine

• Terbinafine

• Tolbutamide

In this article, we use drug-induced cholestasis as a case study to demonstrate how knowledge networks composed of drug-target relationships can be used in combination with biological association networks to build a proposed mechanistic model of drug toxicity. Such a model might be used to predict potential toxicity of a drug candidate early in the development pipeline and reduce attrition in the clinical trial stages, to develop biomarkers of drug toxicity, and even to predict individual risk of drug-induced toxicities.

Bringing new, effective and profitable drugs to market.

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A POWERFUL TOOL FOR PATHWAY ANALYSISA large collection of research articles on cholestasis already exists, but the challenge is sifting through all those papers manually to piece together key results and relationships from the various studies into a single, coherent disease model for drug induced cholestasis. The process of pathway analysis can help researchers make sense of the vast amount of published results that are scattered across dozens or hundreds of journal articles. Pathway analysis systematically processes published scientific papers, extracts and gathers relevant information about molecular interactions into a database, and then provides tools to mine that information. This systematic approach helps researchers ensure a more comprehensive survey of the published information, which in turn gives them greater confidence in the interpretation of their experimental data. For this case study, we used Elsevier’s Pathway Studio to review virtually all of the published information about the process of cholestasis, find common features among cholestasis-inducing drugs, and by applying knowledge of the biology, we could propose an in-silico model for drug-induced cholestasis.

Creating a draft model of drug-Induced cholestasisWe 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

metabolites that induced cholestasis and manually removed them from the final result since this model was focused on drug-induced cholestasis—an example of how researchers can customize search results based on their own knowledge.

We hypothesized that drugs induce cholestasis through common mechanisms; therefore, we started searching for proteins downstream of at least two different drugs known to induce cholestasis to start the model building process.

Pathway Studio enabled us to build a pathway and apply a filter to hone in on individual proteins, complexes, and protein functional-classes. We also hypothesized that drugs might cause cholestasis by inhibiting off-target proteins and, therefore, we filtered for relationships with negative regulatory effects. Manual inspection of the result-ing drug-target relationships revealed that many cholestasis-inducing drugs were anticancer drugs that shared com-mon therapeutic targets irrelevant to cholestasis. To identify off-target proteins potentially relevant to cholestasis, we extended the search to capture proteins linked to all types of cholestasis – rather than only drug-induced cholestasis - with positive or unknown regulatory effects.

This Underpinning Pathway Studio® is Elsevier’s proprietary text mining technology that extracts facts from full-text articles in the more than 2,000 biomedical journals, as well as PubMed abstracts.

Armed with Pathway Studio, biologists can rapidly access facts extracted not only from abstracts, but also from all the full-text articles in the Elsevier corpus. Having access to the full text of articles and an industry-leading text-mining tool gives researchers a measurable advantage in terms of rendering a complete picture of the genes and proteins involved in the biology of a disease or response to a drug.

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The set of identified proteins linked to cholestasis in the literature was compared to the set of common targets of cholestatic drugs, and the proteins at the intersection of those groups were chosen for further analysis. After manual curation and removing highly generic and uninformative functional classes (e.g., cytokines and monooxygenases), we identified 58 proteins linked to cholestasis and inhibited by at least two cholestatic drugs.

We then began building a cholestasis model from 21 proteins directly involved in bile acid metabolism, irrespective of their method of regulation. The proteins in the model could be classified as hepatocyte bile acid importers or exporters, or enzymes for bile acid synthesis and conjugation (Figure 1). The model is consistent with the current view of bile acid circulation that postulates that hepatocytes preferentially import damaged, unconjugated bile acids and export repaired conjugated bile acids3, while maintaining levels of circulating bile by synthesizing it from cholesterol.

UGT1A1HMGCR CYP3A4

bile acid metabolism

ABCG2

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Slco1a1

SLCO1B3

SLCO1B1

ABCA1

ABCB11

ABCB1

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SLC22A8

Slco1a4 CYP7A1

SLC10A1

SLC22A1

CYP27A1acetate-CoA ...

BA synthesis

BA conjugation

BA import BA export

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.)

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Risk

Expanding the draft modelThe next step was to find transcription factors upstream from the proteins in the model using another function of Pathway Studio. The top seven sub-network enrichment (SNE) seeds included six nuclear receptors and hepatic nuclear factor-1 (HNF1A) in the following order of statistical significance: PXR, FXR, CAR, PPARA, RXR, HNF1A, and SHP. HNF1A functions in liver growth and differentiation and, therefore, we excluded it from the list of bile acid homeostasis regulators. Reviews of the supporting evidence revealed that only FXR and PXR could be directly activated

by bile acids5, 7. FXR and PXR activate the expression of proteins involved in bile acid conjugation and secretion, but at the same time repress expression of genes involved in bile acid import and synthesis (Figure 2). If there is an excess of bile, FXR and PXR induce conjugation of bile acids, thereby increasing their secretion while suppressing bile acid synthesis and import. If there is a deficit of bile, decreased FXR and PXR activities derepress synthesis and import of bile acids, while down-regulating conjugation and secretion. Based on this regulation profile, FXR and PXR likely act as principal internal sensors and regulators of bile acid concentration in hepatocytes.

BA-AA

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ciprofloxacin

bezafibrate

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methimazole

clozapine

monensin

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captopril

methylprednisolone

Indicates PXR activation Indicates FXR activation

BA synthesis

BA conjugationBA importBA canalicular

secretion

Indicates autocrinerepression of bile synthesis

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.

Sub-network enrichment analysis (SNEA)4 is a statistical method developed for Pathway Studio to identify sub-networks in the global knowledge network that are enriched with entities selected by the user. The biological interpretation of SNEA results depends on the type of the global knowledge chosen for analysis. We used the option “Expression targets” that allows for identification of major expression regulators upstream of the userselected proteins.

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To propagate information about bile acid concentration throughout the entire liver, an autocrine signaling process is most likely used. To find secreted hormones involved in autocrine signaling, we inspected expression targets downstream of FXR and PXR using a function for adding common expression targets. Among the 17 targets shared by FXR and PXR, we found only FGF19 to be secreted according to protein annotations from the literature. Publications retrieved by Pathway Studio that support PXR -> FGF19 expression regulation suggested that FGF19 is used by liver as a principal signal for suppression of bile acid synthesis5. Statements supporting the FXR->FGF19 relationship revealed that the SHP transcription factor is the principal mediator of FGF19 autocrine signaling that suppresses expression of CYP7A1 and CYP27A1—two enzymes catalyzing rate-limiting steps in bile acid synthesis6, 7.

The initial draft cholestasis disease model shown in Figure 2 contains an FGF19 indirect autocrine loop and an incomplete metabolic pathway for bile acid synthesis, but no details about which bile acids directly activate PXR and FXR. To enhance the predictive power of the model generated by Pathway Studio, we added detailed molecular mechanisms behind these indirect relationships and added more proteins directly involved in bile acid circulation and metabolism. Even though this expanded model contains proteins that 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.

Expanding the model using protein functional similarityA “paralog” network was created in Pathway Studio by importing paralog pairs of all human proteins. Paralogs were calculated from the BLAST output generated by the BLASTp program, running the human proteome against itself as described previously8. All paralog pairs were then imported into the Pathway Studio database under a new relationship type labeled “Paralog”, and annotated with sequence similarity. We used this paralog network to identify functional homologs of proteins involved in bile acid circulation, using a 50% similarity cutoff. This approach added following the proteins: SLC10A2 as the homolog of SLC10A1 transporter; SLCO1A2 and SLCO4C1 as homologs of SLCO1B1; CYP7B1 and CYP8B1 as homologs of CYP7A1; ABCB5 as homologs of ABCB11/BSEP; ABCC3, ABCC6, and ABCC2 as homologs of ABCC1. Subsequent inspection of the protein annotations from the literature confirmed the roles of these proteins in bile acid transport or synthesis. To add even more transporters to the model we found all proteins regulating the bile acid transport Cell Process in Pathway Studio, and then selected proteins annotated as transporters. This approach added OSTalpha and OSTbeta transporters to the model. These two proteins have a sequence similarity below 50% to other bile transporters and therefore were missed by the initial sequence similarity search.

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Expanding the model by adding bile acid metabolism pathwayWe also expanded the cholestasis model by adding the bile acid metabolism pathway, which is already present in the metabolic pathway collection in Pathway Studio. It was copied “as is” to extend the cholestasis model. CYP7A1, CYP27A1 and CYP3A4 are the only enzymes of bile acid synthesis inhibited by cholestasisinducing drugs. It is known that both CYP7A1 and CYP27A1 catalyze rate limiting steps in the bile acid synthesis6, 7.

Expanding the model by reconstructing FGF19 autocrine signaling pathway in hepatocytesSince FGF19 is a secreted ligand, it needs signaling pathways consisting of receptor and signal transduction molecules to propagate signals repressing the expression of bile acid metabolic enzymes inside hepatocytes. Because the FGF19 pathway was not described in the literature, and was not available in the Elsevier canonical pathway collection, we reconstructed this pathway from scratch using the Pathway Studio knowledgebase. By inspecting references supporting the FGF19->CYP7A1

expression relationship we identified FGFR4 receptor, small heterodimer partner (SHP), and the JNK pathways as intermediate steps for FGF19 signaling10,

11, 12. To find other members of the FGF19 signaling pathway we built a model of the FGF19-FGFR4 regulome9 by finding all proteins downstream of either FGF19 or FGFR4 in the “Regulation”, “DirectRegulation”, “ProtModification” or “Binding” networks contained in Pathway Studio. Signal transduction proteins from the FGF19-FGFR4 regulome were connected using the physical interactions found in the “Binding”, “DirectRegulation”, “ProtModification” networks. The gap between JNK signaling and FGFR4 adaptor proteins was closed by copying conserved signaling blocks from the FGFR1->RUNX2 canonical pathway contained in the Elsevier signaling pathway collection: the GRB2-SOS1- RAS block was added to show the RAF kinase activation process, and gamma PLC-PKC block was added to describe JNK1 (MAPK8) activation. FGFR1 is a functional homolog of FGFR4. The resulting model produced by Pathway Studio is consistent with experimental observations from 95,395 publications (Figure 3).

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Figure 3. Output from Pathway Studio—consistent with experimental observations from 95,395 publications—shows the predicted FGF19 signaling pathway repressing expression of enzyme 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.

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Figure 4. The Pathway Studio–generated cholestasis model expanded with neutral BA biosynthesis pathway. When viewing this article online, users may zoom in this page to see model details.

The final result of this work is the fully expanded cholestasis model shown in Figure 4. This model contains 51 proteins, including members of 21 functional classes representing enzymatic steps in bile acid synthesis, and 57 chemicals, including unconjugated and conjugated bile acids, and intermediate steps of bile acid synthesis. The expanded model includes 20 proteins from the draft cholestasis model that are known targets of cholestasis-inducing drugs.

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APPLICATIONS OF THE CHOLESTASIS MODEL GENERATED BY PATHWAY STUDIOImplications for drug developmentWe used Pathway Studio to generate a model that provides a potential explanation why cholestasis is one of the most common drug-induced toxicities. The liver uses the same molecular machinery to maintain bile acid homeostasis and to detoxify food. The physiologic role of CYP3A4 and other cytochrome P450 enzymes is clearing the liver of toxic bile acids and other chemicals produced by bacterial flora or ingested with food14. If a drug cannot withstand metabolization by liver P450 enzymes (called the first-pass effect), its effective bloodstream concentration drops soon after digestion, preventing the drug from reaching the target tissue at an effective dose. Because CYP3A4 is the major enzyme degrading drugs, there is a great need to avoid the firstpass effect bias in drug development by selecting compounds that do not bind to, or only reversibly bind to CYP3A4 without being metabolized by it. This second possibility effectively implies that many drugs withstand the firstpass effect because they are competitive CYP3A4 inhibitors.

To avoid accumulation of a nondegradable drug in the liver and to ensure the drug can reach its target tissue, drug molecules must be exported by transporters back into the gut. Most drugs or their metabolites are 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

acid exporters and multidrug resistance proteins belong to one family of ABC cassette glycoproteins that use ATP energy to transport various chemicals out of a cell. Therefore, drugs capable of inhibiting MDR1 are likely to inhibit BSEP, albeit with a different affinity. MDR1 and its paralogs are often overexpressed in cancer cells20, leading to drug resistance in cancer. The pursuit for better anticancer drugs leads to the selection of chemicals inhibiting these MDR proteins in order to achieve greater effectiveness.

The ABC proteins have two translocation and two nucleotide-binding domains. Most drugs bind to the translocation domain, but some can also bind to the nucleotide-binding domain18, 19. The increased interest of the pharmaceutical industry in kinase inhibitors designed to bind ATP-binding domain in proteins kinases can potentially increase the number of drugs capable of binding to the nucleotide-binding domain of ABC transporters. Our cholestasis model suggests that former and current drug development is biased towards creating MDR and CYP3A inhibitors, thus increasing these drugs’ cholestatic potential. There are 387 drugs known to inhibit MDR1 and 404 drugs known to inhibit CYP3A4 in the Pathway Studio database (as of April 2012), supporting this point.

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Biomarkers for drug-induced cholestasisFXR protein senses and regulates bile acid concentration15. PXR appears to function in vitamin E metabolism32, but has a promiscuous ligand-binding domain16, suggesting that PXR also protects the liver from various toxic products generated by bacteria or ingested with food. Activated PXR inhibits bile acid synthesis globally in the liver because of the inhibition of HNF4A, and up-regulation of SHP via FGF19–FHFR4 pathway40. This process may allow the liver to refocus for drug degradation and clearance. However, if a drug blocks bile acid secretion, the accumulating bile hyperactivates FXR in a cell that already has hyperactivated PXR. Activation of FXR further inhibits bile acid synthesis locally through overexpression of SHP, and 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

within each individual24. Thus, the combination of FGF19 and FXR-specific biomarkers may indicate activation of FXR caused by an individual’s diet or genotype rather than by drug toxicity. Therefore, to allow specific detection of drug-induced cholestasis, a third biomarker specific for PXR is necessary. Several PXR-specific biomarkers were found in the Pathway Studio knowledgebase shown on Figure 2: vitamin E metabolite gamma-alphacarboxyethyl hydroxychroman beta-Dglucoside25, bilirubin37, hyodeoxycholate and hyocholate38, and retinoic acid (RA)39. In summary, our model suggests that a panel consisting of three blood and seven urine biomarkers may be combined to accurately assess cholestatic risk in patients during drug therapy (Figure 2).

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Estimating cholestatic risk for drug targetsTable 1 illustrates how the data in Pathway Studio allows a straightforward calculation of cholestatic risk associated with off-target inhibition by a drug. To find drugs inhibiting each transporter, we added small molecules upstream of the transporter in Pathway Studio database in the negative regulation and direct regulation networks. Using another tool in Pathway Studio, we then intersected inhibitors with drugs known to induce cholestasis to calculate cholestatic risk.

We found that MDR1 inhibition has a relatively low risk of inducing cholestasis (12%), as compared to BSEP or ABCB4 inhibition (39% and 50%, respectively). BSEP inhibition risk is expected to be higher because BSEP is the major bile exporter that has a fivefold higher affinity towards bile acid than MDR113.

Predicting cholestatic risk for new compoundsThe cholestasis model we created with Pathway Studio suggests that the cholestatic potential of a particular compound depends on that compound’s ability to inhibit bile acid export and induce PXR activity at the same time. PXR activation can follow inhibition of the CYP3A enzymes, which are known to metabolize more than half of all drugs. According to the information in the literature, PXR regulates 23 different cytochrome p450 enzymes, including CYP2C9 which metabolizes more than 10% of all drugs.

One way to predict cholestatic risk for a new compound is to perform virtual docking simulations first against BSEP, MDR1, and other bile acid exporters, and then against cytochrome p450 enzymes 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.

Transporter Specificity Number of InhibitorsNumber of InhibitorsKnown to InduceCholestasis

Cholestatic Risk

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 glucoronidated bile acids

44 10 0.227

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 glucoronidated bile acids

44 4 0.091

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.

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Another way to assess cholestatic potential is by monitoring intracellular bile concentration in response to compound treatment, because FXR activation must be concomitant with PXR activation to indicate cholestatic risk. A recently developed in vitro assay using cultured rat hepatocytes to monitor the accumulation of intrahepatocellular bile30 can also be used for measuring activation of FXR and PXR. The activity of these transcription factors in response to a drug can be calculated using the subnetwork enrichment analysis function on microarray gene expression data31.

Workflow for using the cholestasis model in personalized medicineOur cholestasis model provides a foundation for evaluating a patient’s predisposition to cholestasis. Knowing this predisposition is important when a cholestatic drug is prescribed. This model predicts that patients with common single nucleotide polymorphisms (SNPs) leading to attenuated bile acid exporter activity, or over-activating PXR or FXR, may develop cholestasis at lower drug doses that are normally tolerated by non-predisposed patients. Such predisposed patients can be prescribed additional anti-cholestatic drugs to minimize the risk of adverse reactions.

CONCLUSIONThe process of drug metabolism in the body is both highly complex and highly regulated, and affects the usefulness of every ingested drug. Improving our understanding of these complex molecular interactions and processes can have profound effects on the drug development process, by identifying potentially problematic compounds earlier in the development process, before clinical trials, and before a drug is administered to millions of people. Pathway analysis – the study of these interactions – can provide valuable insights into the process of drug metabolism early in this process.

Unfortunately the information about these metabolic processes is scattered across hundreds or thousands of journal articles, making it very difficult for any single researcher to gather, read, and understand the information in order

to construct a comprehensive model. Software applications like Elsevier’s Pathway Studio® can provide the missing piece to help reconstruct these complex puzzles. Pathway Studio is a powerful decision-support solution that helps researchers integrate research findings described in scientific publications into a searchable knowledgebase. This knowledgebase is populated using Elsevier’s proprietary natural language processing (NLP)-based text-mining technology, which can extract structured information (e.g., gene and protein names, functions, interactions) from unstructured data (the text content of scientific articles). Unlike manually curated databases, Pathway Studio gives users direct access to the underlying evidence—sentences extracted from journal articles and abstracts that describe specific results or relationships—so that each researcher can independently validate and determine the applicability of the facts to his or her own work. The applications of Pathway Studio for experimental data analysis and literature exploration are limited only by the imagination of the researcher.

In this case study we used Pathway Studio in a step-wise manner to generate a very complex disease model that could be used to reduce risk in early-stage drug development, personalize treatment for individuals at elevated risk of drug-associated toxicities, and identify biomarkers of potential toxicity. This cholestasis model was first presented at Biomarker world Congress, Philadelphia in February 2009, along with its prediction of fGf19 as a biomarker for drug-induced cholestasis. The research article by Schaap et al that experimentally confirms fGf19 as biomarker for cholestasis was published in April 200914, providing independent validation of this cholestasis model, and of the value of creating in Silico models. Since 2009, nine additional papers have been published, including two so far in 2014,that support the role of fGf19 as an autocrine regulator of bile acid turnover in liver.

AcknowledgmentFigures reprinted with permission from Bentham Science Publishers

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