Minjun Chen

FDA-approved drug labeling for the study of drug-induced liver injury §

Drug-induced liver injury (DILI) is a leading cause of drugs failing during clinical trials and being withdrawn from the market. Comparative analysis of drugs based on their DILI potential is an effective approach to discover key DILI mechanisms and risk factors. However, assessing the DILI potential of a drug is a challenge with no existing consensus methods. We proposed a systematic classification scheme using FDA-approved drug labeling to assess the DILI potential of drugs, which yielded a benchmark dataset with 287 drugs representing a wide range of therapeutic categories and daily dosage amounts. The method is transparent and reproducible with a potential to serve as a common practice to study the DILI of marketed drugs for supporting drug discovery and biomarker development.

High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury.

Drug‐induced liver injury (DILI) is a leading cause of drug failure in clinical trials and a major reason for drug withdrawals from the market. Although there is evidence that dosages of ≥100 mg/day are associated with increased risk for hepatotoxicity, many drugs are safe at such dosages. There is an unmet need to predict risk for DILI more reliably, and lipophilicity might be a contributing factor. We analyzed the combined factors of daily dose and lipophilicity for 164 US Food and Drug Administration–approved oral medications and observed high risk for hepatotoxicity (odds ratio [OR], 14.05; P < 0.001) for drugs given at dosages ≥100 mg/day and octanol‐water partition coefficient (logP) ≥3. This defined the “rule‐of‐two.” Similar results were obtained for an independent set of 179 oral medications with 85% of the rule‐of‐two positives being associated with hepatotoxicity (OR, 3.89; P < 0.01). Using the World Health Organizations Anatomical Therapeutic Chemical classification system, the rule‐of‐two performed best in predicting DILI in seven therapeutic categories. Among 15 rule‐of‐two positives, 14 were withdrawn from hepatotoxic drugs, and one was over‐the‐counter medication labeled for liver injury. We additionally examined drug pairs that have similar chemical structures and act on the same molecular target but differ in their potential for DILI. Again, the rule‐of‐two predicted hepatotoxicity reliably. Finally, the rule‐of‐two was applied to clinical case studies to identify hepatotoxic drugs in complex comedication regimes to further demonstrate its use. Conclusion: Apart from dose, lipophilicity contributes significantly to risk for hepatotoxicity. Applying the rule‐of‐two is an appropriate means of estimating risk for DILI compared with dose alone. (HEPATOLOGY 2013)

A Decade of Toxicogenomic Research and Its Contribution to Toxicological Science

Toxicogenomics enjoyed considerable attention as a ground-breaking addition to conventional toxicology assays at its inception. However, the pace at which toxicogenomics was expected to perform has been tempered in recent years. Next to cost, the lack of advanced knowledge discovery and data mining tools significantly hampered progress in this new field of toxicological sciences. Recently, two of the largest toxicogenomics databases were made freely available to the public. These comprehensive studies are expected to stimulate knowledge discovery and development of novel data mining tools, which are essential to advance this field. In this review, we provide a concise summary of each of these two databases with a brief discussion on the commonalities and differences between them. We place our emphasis on some key questions in toxicogenomics and how these questions can be appropriately addressed with the two databases. Finally, we provide a perspective on the future direction of toxicogenomics and how new technologies such as RNA-Seq may impact this field.

ArrayTrack: An FDA and Public Genomic Tool

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarrays, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data, and the availability of functional information for data interpretation. At the FDAs National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack, that is also used in the routine review of genomic data submitted to the FDA. ArrayTrack stores a full range of information related to DNA microarrays and clinical and non-clinical studies as well as the digested data derived from proteomics and metabonomics experiments. In addition, ArrayTrack provides a rich collection of functional information about genes, proteins, and pathways drawn from various public biological databases for facilitating data interpretation. Many data analysis and visualization tools are available with ArrayTrack for individual platform data analysis, multiple omics data integration, and integrated analysis of omics data with study data. Importantly, gene expression data, functional information, and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. Using ArrayTrack, users can select an analysis method from the ArrayTrack tool box, apply the method to selected microarray data, and the analysis of results can be directly linked to individual gene, pathway, and Gene Ontology analysis. ArrayTrack is publicly available online ( http://www.fda.gov/nctr/science/centers/toxicoinformatics/ArrayTrack/index.htm ) and the prospective user can also request a local installation version by contacting the authors.

Toward predictive models for drug-induced liver injury in humans: are we there yet?

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.

Genomic indicators in the blood predict drug-induced liver injury

Genomic biomarkers for the detection of drug-induced liver injury (DILI) from blood are urgently needed for monitoring drug safety. We used a unique data set as part of the Food and Drug Administration led MicroArray Quality Control Phase-II (MAQC-II) project consisting of gene expression data from the two tissues (blood and liver) to test cross-tissue predictability of genomic indicators to a form of chemically induced liver injury. We then use the genomic indicators from the blood as biomarkers for prediction of acetaminophen-induced liver injury and show that the cross-tissue predictability of a response to the pharmaceutical agent (accuracy as high as 92.1%) is better than, or at least comparable to, that of non-therapeutic compounds. We provide a database of gene expression for the highly informative predictors, which brings biological context to the possible mechanisms involved in DILI. Pathway-based predictors were associated with inflammation, angiogenesis, Toll-like receptor signaling, apoptosis, and mitochondrial damage. The results show for the first time and support the hypothesis that genomic indicators in the blood can serve as potential diagnostic biomarkers predictive of DILI.

Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure.

The U.S. Tox21 and EPA ToxCast program screen thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors, then used supervised machine learning to predict in vivo hepatotoxic effects. A set of 677 chemicals was represented by 711 in vitro bioactivity descriptors (from ToxCast assays), 4,376 chemical structure descriptors (from QikProp, OpenBabel, PaDEL, and PubChem), and three hepatotoxicity categories (from animal studies). Hepatotoxicants were defined by rat liver histopathology observed after chronic chemical testing and grouped into hypertrophy (161), injury (101) and proliferative lesions (99). Classifiers were built using six machine learning algorithms: linear discriminant analysis (LDA), Naïve Bayes (NB), support vector machines (SVM), classification and regression trees (CART), k-nearest neighbors (KNN), and an ensemble of these classifiers (ENSMB). Classifiers of hepatotoxicity were built using chemical structure descriptors, ToxCast bioactivity descriptors, and hybrid descriptors. Predictive performance was evaluated using 10-fold cross-validation testing and in-loop, filter-based, feature subset selection. Hybrid classifiers had the best balanced accuracy for predicting hypertrophy (0.84 ± 0.08), injury (0.80 ± 0.09), and proliferative lesions (0.80 ± 0.10). Though chemical and bioactivity classifiers had a similar balanced accuracy, the former were more sensitive, and the latter were more specific. CART, ENSMB, and SVM classifiers performed the best, and nuclear receptor activation and mitochondrial functions were frequently found in highly predictive classifiers of hepatotoxicity. ToxCast and ToxRefDB provide the largest and richest publicly available data sets for mining linkages between the in vitro bioactivity of environmental chemicals and their adverse histopathological outcomes. Our findings demonstrate the utility of high-throughput assays for characterizing rodent hepatotoxicants, the benefit of using hybrid representations that integrate bioactivity and chemical structure, and the need for objective evaluation of classification performance.

The Liver Toxicity Knowledge Base: A Systems Approach to a Complex End Point

Drug‐induced liver injury (DILI) is a major concern in public health management, drug development, and regulatory implementation. The Liver Toxicity Knowledge Base (LTKB) was developed with the specific aim of enhancing our understanding of DILI. It seeks to achieve improvement in DILI prediction through integrated analysis of diverse sources of drug‐elicited data. The project has also produced a centralized resource of data as well as predictive models that will be useful for research and drug regulation.

DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans.

This paper provides the largest, revised drug reference list annotated and ranked by the risk for developing hepatotoxicity in humans (DILIrank). We created the new DILIrank list by complementing the previously used drug-labeling information together with existing evidence of clinical causality assessments. High-throughput methods are powerful tools to develop predictive models for assessing drug-induced liver injury (DILI). However, the development of predictive models requires a drug reference list with an accurate annotation of DILI risk in humans. We previously developed a DILI annotation schema based on information curated from the US Food and Drug Administration (FDA)-approved drug labeling for 287 drugs. In this article, we refine the schema by weighing the evidence of causality (i.e., a verification process to evaluate a drug as the cause of DILI) and generate a data set that ranks the DILI risk (DILIrank) in humans for 1036 FDA-approved drugs, providing the largest annotated data set of such drugs in the public domain.Copyright

High Daily Dose and Being a Substrate of Cytochrome P450 Enzymes are Two Important Predictors of Drug-induced Liver Injury

Drug-induced liver injury (DILI) is complicated and difficult to predict. It has been observed that drugs with extensive hepatic metabolism have a higher likelihood of causing DILI. Cytochrome P450 (P450) enzymes are primarily involved in hepatic metabolism. Identifying the associations of DILI with drugs that are P450 substrates, inhibitors, or inducers will be extremely helpful to clinicians during the decision-making process of caring for a patient suspected of having DILI. We collected metabolism data on P450 enzymes for 254 orally administered drugs in the Liver Toxicity Knowledge Base Benchmark Dataset with a known daily dose, and applied logistic regression to identify these associations. We revealed that drugs that are substrates of P450 enzymes have a higher likelihood of causing DILI [odds ratio (OR), 3.99; 95% confidence interval (95% CI), 2.07–7.67; P < 0.0001], which is dose-independent, and drugs that are P450 inhibitors have a higher likelihood of generating DILI only when they are administered at high daily doses (OR, 6.03; 95% CI, 1.32–27.5; P = 0.0098). However, drugs that are P450 inducers are not observed to be associated with DILI (OR, 1.55; 95% CI, 0.65–3.68; P = 0.3246). Our findings will be useful in identifying the suspected medication as a cause of liver injury in clinical settings.