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Translating clinical findings into knowledge in drug safety evaluation--drug induced liver injury prediction system (DILIps).

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.

SUBMITTER: Liu Z

PROVIDER: S-EPMC3240589 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Translating clinical findings into knowledge in drug safety evaluation--drug induced liver injury prediction system (DILIps).

Liu Zhichao Z Shi Qiang Q Ding Don D Kelly Reagan R Fang Hong H Tong Weida W

PLoS computational biology 20111215 12

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 silic ...[more]

PMID: 22194678

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Project description:BackgroundCurrent approaches to identifying drug-drug interactions (DDIs), include safety studies during drug development and post-marketing surveillance after approval, offer important opportunities to identify potential safety issues, but are unable to provide complete set of all possible DDIs. Thus, the drug discovery researchers and healthcare professionals might not be fully aware of potentially dangerous DDIs. Predicting potential drug-drug interaction helps reduce unanticipated drug interactions and drug development costs and optimizes the drug design process. Methods for prediction of DDIs have the tendency to report high accuracy but still have little impact on translational research due to systematic biases induced by networked/paired data. In this work, we aimed to present realistic evaluation settings to predict DDIs using knowledge graph embeddings. We propose a simple disjoint cross-validation scheme to evaluate drug-drug interaction predictions for the scenarios where the drugs have no known DDIs.ResultsWe designed different evaluation settings to accurately assess the performance for predicting DDIs. The settings for disjoint cross-validation produced lower performance scores, as expected, but still were good at predicting the drug interactions. We have applied Logistic Regression, Naive Bayes and Random Forest on DrugBank knowledge graph with the 10-fold traditional cross validation using RDF2Vec, TransE and TransD. RDF2Vec with Skip-Gram generally surpasses other embedding methods. We also tested RDF2Vec on various drug knowledge graphs such as DrugBank, PharmGKB and KEGG to predict unknown drug-drug interactions. The performance was not enhanced significantly when an integrated knowledge graph including these three datasets was used.ConclusionWe showed that the knowledge embeddings are powerful predictors and comparable to current state-of-the-art methods for inferring new DDIs. We addressed the evaluation biases by introducing drug-wise and pairwise disjoint test classes. Although the performance scores for drug-wise and pairwise disjoint seem to be low, the results can be considered to be realistic in predicting the interactions for drugs with limited interaction information.

Project description:BackgroundDrug-induced liver injury (DILI) is a common adverse event, which compromises the safety of numerous drugs, poses a significant risk to patient health, and enhances healthcare expenditures. Many articles have been recently published on DILI related research, though no relevant scientometric study has been published yet. This scientometric study was aimed at comprehensively analyzing the knowledge base and emerging topics on DILI.MethodsThe articles and reviews related to DILI, published from 2010 to 2019 in the Web of Science Core Collection (WoSCC), were retrieved on March 15, 2020, using relevant keywords. Four different scientometric software (HistCite, VOSviewer, CiteSpace, and R-bibliometrix) was used to conduct this scientometric study.ResultsA total of 1,995 publications were retrieved (including 1,550 articles and 445 reviews) from 592 academic journals with 56,273 co-cited references in 10 languages by 2,331 institutions from 79 countries/regions. The majority of publications (n = 727, 36.44%) were published in the United States, and the University of North Carolina contributed the most publications (n = 89, 4.46%). The most productive academic journal on DILI was the Toxicological Sciences [n = 79, 3.96%; impact factor (IF) 2018 = 3.564], and Hepatology was the first co-cited journal (n = 7,383, IF 2018 = 14.971). Fontana RJ and Teschke R may have significant influence on DILI research, with more publications (n = 46; n = 39) and co-citations (n = 382; n = 945). Definition, incidence rate or clinical characteristics, etiology or pathogenesis (such as the character of the innate immune system, the regulation of cell-death pathways, and susceptible HLA-B*5701 genotype), identification of main drugs and causality assessment (criteria and methods) were the knowledge base for DILI research. Exploring the microscopic mechanism (such as the organelle dysfunction and cytotoxicity induced by drugs, and exploration of role of neutrophils in DILI using mouse models) and developed newer approaches to prevent DILI (such as the prospective HLA-B*5701 screening and in vitro approaches for assessing the potential risk of candidate drugs for DILI) were the recent major topics for DILI research.ConclusionThis scientometric study comprehensively reviewed the publications related to DILI during the past decade using quantitative and qualitative methods. This information would provide references for scholars, researching on DILI.

Dataset Information

Translating clinical findings into knowledge in drug safety evaluation--drug induced liver injury prediction system (DILIps).

Publications

Translating clinical findings into knowledge in drug safety evaluation--drug induced liver injury prediction system (DILIps).

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