Toward predictive models for drug-induced liver injury in humans: are we there yet?
| Autor: | Jürgen Borlak, Minjun Chen, Lillian Tong, Hong Fang, Halil Bisgin, Weida Tong, Huixiao Hong |
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| Rok vydání: | 2014 |
| Předmět: |
Drug
medicine.medical_specialty Drug-Related Side Effects and Adverse Reactions media_common.quotation_subject Clinical Biochemistry Quantitative Structure-Activity Relationship Pharmacology Models Biological Toxicogenetics Drug withdrawal Drug Discovery medicine Animals Humans In patient Intensive care medicine media_common Liver injury business.industry Mechanism (biology) Biochemistry (medical) Computational Biology medicine.disease Biomarker (cell) Drug development Pharmaceutical Preparations Chemical and Drug Induced Liver Injury Toxicogenomics business Biomarkers |
| Zdroj: | Biomarkers in medicine. 8(2) |
| ISSN: | 1752-0371 |
| Popis: | Drug-induced liver injury (DILI) is a frequent cause for the termination of drug development programs and a leading reason of drug withdrawal from the marketplace. Unfortunately, the current preclinical testing strategies, including the regulatory-required animal toxicity studies or simple in vitro tests, are insufficiently powered to predict DILI in patients reliably. Notably, the limited predictive power of such testing strategies is mostly attributed to the complex nature of DILI, a poor understanding of its mechanism, a scarcity of human hepatotoxicity data and inadequate bioinformatics capabilities. With the advent of high-content screening assays, toxicogenomics and bioinformatics, multiple end points can be studied simultaneously to improve prediction of clinically relevant DILIs. This review focuses on the current state of efforts in developing predictive models from diverse data sources for potential use in detecting human hepatotoxicity, and also aims to provide perspectives on how to further improve DILI prediction. |
| Databáze: | OpenAIRE |
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