•DILIrank contains the largest number of drugs (N=1036) ranked by their risk for causing DILI.•The existing drug labeling-based DILI annotation was enhanced by weighing evidence of causality.•Drugs ...were classified as verified vMost-, vLess-, vNo-DILI-concern, leaving out terminology ‘Ambiguous DILI-concern’ drugs.•DILIrank is invaluable for the development of predictive models using emerging technologies.
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.
Drug-induced liver injury (DILI) presents a significant challenge to drug development and regulatory science. The FDA's Liver Toxicity Knowledge Base (LTKB) evaluated >1000 drugs for their likelihood ...of causing DILI in humans, of which >700 drugs were classified into three categories (most-DILI, less-DILI, and no-DILI). Based on this dataset, we developed and compared 2-class and 3-class DILI prediction models using the machine learning algorithm of Decision Forest (DF) with Mold2 structural descriptors. The models were evaluated through 1000 iterations of 5-fold cross-validations, 1000 bootstrapping validations and 1000 permutation tests (that assessed the chance correlation). Furthermore, prediction confidence analysis was conducted, which provides an additional parameter for proper interpretation of prediction results. We revealed that the 3-class model not only had a higher resolution to estimate DILI risk but also showed an improved capability to differentiate most-DILI drugs from no-DILI drugs in comparison with the 2-class DILI model. We demonstrated the utility of the models for drug ingredients with warnings very recently issued by the FDA. Moreover, we identified informative molecular features important for assessing DILI risk. Our results suggested that the 3-class model presents a better option than the binary model (which most publications are focused on) for drug safety evaluation.
Persistent organic pollutants (POPs) present in foods have been a major concern for food safety due to their persistence and toxic effects. To ensure food safety and protect human health from POPs, ...it is critical to achieve a better understanding of POP pathways into food and develop strategies to reduce human exposure. POPs could present in food in the raw stages, transferred from the environment or artificially introduced during food preparation steps. Exposure to these pollutants may cause various health problems such as endocrine disruption, cardiovascular diseases, cancers, diabetes, birth defects, and dysfunctional immune and reproductive systems. This review describes potential sources of POP food contamination, analytical approaches to measure POP levels in food and efforts to control food contamination with POPs.
Drug‐induced liver injury (DILI) is a major public health concern, and improving its prediction remains an unmet challenge. Recently, we reported the Rule‐of‐2 (RO2) and found lipophilicity (logP ≥3) ...and daily dose ≥100 mg of oral medications to be associated with significant risk for DILI; however, the RO2 failed to estimate grades of DILI severity. In an effort to develop a quantitative metrics, we analyzed the association of daily dose, logP, and formation of reactive metabolites (RM) in a large set of Food and Drug Administration‐approved oral medications and found factoring RM into the RO2 to highly improve DILI prediction. Based on these parameters and by considering n = 354 drugs, an algorithm to assign a DILI score was developed. In univariate and multivariate logistic regression analyses the algorithm (i.e., DILI score model) defined the relative contribution of daily dose, logP, and RM and permitted a quantitative assessment of risk of clinical DILI. Furthermore, a clear relationship between calculated DILI scores and DILI risk was obtained when applied to three independent studies. The DILI score model was also functional with drug pairs defined by similar chemical structure and mode of action but divergent toxicities. Specifically, for drug pairs where the RO2 failed, the DILI score correctly identified toxic drugs. Finally, the model was applied to n = 159 clinical cases collected from the National Institutes of Health's LiverTox database to demonstrate that the DILI score correlated with the severity of clinical outcome. Conclusions: Based on daily dose, lipophilicity, and RM, a DILI score algorithm was developed that provides a scale of assessing the severity of DILI risk in humans associated with oral medications. (Hepatology 2016;64:931‐940)
Abstract
Animal studies are unavoidable in evaluating chemical and drug safety. Generative Adversarial Networks (GANs) can generate synthetic animal data by learning from the legacy animal study ...results, thus may serve as an alternative approach to assess untested chemicals. AnimalGAN, a GAN method to simulate 38 rat clinical pathology measures, was developed with significant robustness even for the drugs that vary significantly from these used during training, both in terms of chemical structure, drug class, and the year of FDA approval. AnimalGAN showed comparable results in hepatotoxicity assessment as using the real animal data and outperformed 12 conventional quantitative structure-activity relationship approaches. Using AnimalGAN, a virtual experiment of 100,000 rats ranked hepatotoxicity of three structurally similar drugs in a similar trend that has been observed in human population. AnimalGAN represented a significant step with artificial intelligence towards the global effort in replacement, reduction, and refinement (3Rs) of animal use.
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•This drug list provides access to the largest number of drugs classified for human hepatotoxicity.•DILIst (DILI severity and toxicity) is the result of augmentation of five DILI ...related large literature datasets (N > 350 drugs) using a statistical approach.•It contains the information for 1279 drugs of which 768 were DILI positives and 511 were DILI negatives.
Drug-induced liver injury (DILI) is of significant concern to drug development and regulatory review because of the limited success with existing preclinical models. For developing alternative methods, a large drug list is needed with known DILI severity and toxicity. We augmented the DILIrank data set annotated using US Food and Drug Administration (FDA) drug labeling) with four literature datasets (N >350 drugs) to generate the largest drug list with DILI classification, called DILIst (DILI severity and toxicity). DILIst comprises 1279 drugs, of which 768 were DILI positives (increase of 65% from DILIrank), whereas 511 were DILI negatives (increase of 65%). The investigation of DILI positive–negative distribution across various therapeutic categories revealed the most and least frequent DILI categories. Thus, we consider DILIst to be an invaluable resource for the community to improve DILI research.
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.
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.
Combinatorial drug therapy can improve the therapeutic effect and reduce the corresponding adverse events.
strategies to classify synergistic vs. antagonistic drug pairs is more efficient than ...experimental strategies. However, most of the developed methods have been applied only to cancer therapies. In this study, we introduce a novel method, XGBoost, based on five features of drugs and biomolecular networks of their targets, to classify synergistic vs. antagonistic drug combinations from different drug categories. We found that XGBoost outperformed other classifiers in both stratified fivefold cross-validation (CV) and independent validation. For example, XGBoost achieved higher predictive accuracy than other models (0.86, 0.78, 0.78, and 0.83 for XGBoost, logistic regression, naïve Bayesian, and random forest, respectively) for an independent validation set. We also found that the five-feature XGBoost model is much more effective at predicting combinatorial therapies that have synergistic effects than those with antagonistic effects. The five-feature XGBoost model was also validated on TCGA data with accuracy of 0.79 among the 61 tested drug pairs, which is comparable to that of DeepSynergy. Among the 14 main anatomical/pharmacological groups classified according to WHO Anatomic Therapeutic Class, for drugs belonging to five groups, their prediction accuracy was significantly increased (odds ratio < 1) or reduced (odds ratio > 1) (Fisher's exact test,
< 0.05). This study concludes that our five-feature XGBoost model has significant benefits for classifying synergistic vs. antagonistic drug combinations.
Structural variants (SVs) are a major source of human genetic diversity and have been associated with different diseases and phenotypes. The detection of SVs is difficult, and a diverse range of ...detection methods and data analysis protocols has been developed. This difficulty and diversity make the detection of SVs for clinical applications challenging and requires a framework to ensure accuracy and reproducibility. Here, we discuss current developments in the diagnosis of SVs and propose a roadmap for the accurate and reproducible detection of SVs that includes case studies provided from the FDA-led SEquencing Quality Control Phase II (SEQC-II) and other consortium efforts.
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