Abstract
Drug-induced liver injury (DILI) is a significant concern in drug development due to the poor concordance between preclinical and clinical findings of liver toxicity. We hypothesized that the DILI types (hepatotoxic side effects) seen in the clinic can be translated into the development of predictive in silico models for use in the drug discovery phase. We identified 13 hepatotoxic side effects with high accuracy for classifying marketed drugs for their DILI potential. We then developed in silico predictive models for each of these 13 side effects, which were further combined to construct a DILI prediction system (DILIps). The DILIps yielded 60–70% prediction accuracy for three independent validation sets. To enhance the confidence for identification of drugs that cause severe DILI in humans, the “Rule of Three” was developed in DILIps by using a consensus strategy based on 13 models. This gave high positive predictive value (91%) when applied to an external dataset containing 206 drugs from three independent literature datasets. Using the DILIps, we screened all the drugs in DrugBank and investigated their DILI potential in terms of protein targets and therapeutic categories through network modeling. We demonstrated that two therapeutic categories, anti-infectives for systemic use and musculoskeletal system drugs, were enriched for DILI, which is consistent with current knowledge. We also identified protein targets and pathways that are related to drugs that cause DILI by using pathway analysis and co-occurrence text mining. While marketed drugs were the focus of this study, the DILIps has a potential as an evaluation tool to screen and prioritize new drug candidates or chemicals, such as environmental chemicals, to avoid those that might cause liver toxicity. We expect that the methodology can be also applied to other drug safety endpoints, such as renal or cardiovascular toxicity.
Highlights
Drug-induced liver injury (DILI) poses a significant challenge to medical and pharmaceutical professionals as well as regulatory agencies
The rationale behind this study is that the side effects observed in clinical trial and post-marketing surveillance can be translated into a screening system for use in drug discovery
As a proof-of-concept study, we developed an in silico system based on 13 hepatotoxic side effects to predict drug-induced liver injury (DILI), which is one of the most frequent causes of drug failure in clinical trial and withdrawal from post-marketing application, and one of the most difficult clinical endpoints to predict from preclinical studies
Summary
Drug-induced liver injury (DILI) poses a significant challenge to medical and pharmaceutical professionals as well as regulatory agencies. It is the leading cause of acute liver failure, which has a high mortality rate (30%) as treatment is limited due to the availability of livers for transplantation [1]. DILI information and guidance for pharmaceutical industries has been released by regulatory agencies such as the U.S Food and Drug Administration (FDA) (http://www.fda.gov/downloads/ Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ UCM174090.pdf), European Medicines Agency (EMA) Canada (http://www.hc-sc.gc.ca/dhp-mps/alt_formats/pdf/consultation/drug-medic/draft_ebauche_hepatotox_guide_ld-eng.pdf), highlighting both the significance and difficulties in DILI research. In the FDA, the Critical Path Initiative identified DILI as a key area of focus in a concerted effort to broaden the agency’s knowledge for better evaluation tools and safety biomarkers (http://www.fda.gov/ScienceResearch/SpecialTopics/ RegulatoryScience/ucm228131.htm)
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