Drug-induced liver injury (DILI) accounts for 20-40% of all instances of clinical hepatic failure and is a common reason for withdrawal of an approved drug or discontinuation of a potentially new drug from clinical/nonclinical development. Numerous individual risk factors contribute to the susceptibility to human DILI and its severity that are either compound-and/or patient-specific. Compound-specific primary mechanisms linked to DILI include: cytotoxicity, reactive metabolite formation, inhibition of bile salt export pump (BSEP), and mitochondrial dysfunction. Since BSEP is an energydependent protein responsible for the efflux of bile acids from hepatocytes, it was hypothesized that humans exposed to drugs that impair both mitochondrial energetics and BSEP functional activity are more sensitive to more severe manifestations of DILI than drugs that only have a single liability factor. As annotated in the United States National Center for Toxicological Research Liver Toxicity Knowledge Base (NCTR-LTKB), the inhibitory properties of 24 Most-DILI-, 28 Less-DILI-, and 20 No-DILIconcern drugs were investigated. Drug potency for inhibiting BSEP or mitochondrial activity was generally correlated across human DILI concern categories. However, drugs with dual potency as mitochondrial and BSEP inhibitors were highly associated with more severe human DILI, more restrictive product safety labeling related to liver injury, and appear more sensitive to the drug exposure (Cmax) where more restrictive labeling occurs. Conclusion: These data affirm that severe manifestations of human DILI are multifactorial, highly associated with combinations of drug potency specifically related to known mechanisms of DILI (like mitochondrial and BSEP inhibition), and, along with patient-specific factors, lead to differences in the severity and exposure thresholds associated with clinical DILI. (HEPATOLOGY 2014;60:1015-1022

Biomarkers are increasingly used in drug development to aid scientific and clinical decisions regarding the progress of candidate and marketed therapeutics. Biomarkers can improve the understanding of diseases as well as therapeutic and off-target effects of drugs. Early implementation of biomarker strategies thus promises to reduce costs and time-to-market as drugs proceed through increasingly costly and complex clinical development programs. The 2003 American Association of Pharmaceutical Sciences/Clinical Ligand Assay Society Biomarkers Workshop (Salt Lake City, UT, USA, October 24-25, 2003) addressed key issues in biomarker research, with an emphasis on the validation and implementation of biochemical biomarker assays, covering from preclinical discovery of efficacy and toxicity biomarkers through clinical and postmarketing implementation. This summary report of the workshop focuses on the major issues discussed during presentations and open forums and noted consensus achieved among the participants on topics from nomenclature to best practices. For example, it was agreed that because reliable and accurate data provide the basis for sound decision making, biomarker assays must be validated in a manner that enables the creation of such data. The nature of biomarker measurements often precludes direct application of regulatory guidelines established for clinical diagnostics or drug bioanalysis, and future guidance on biomarker assay validation should therefore be adaptable enough that validation criteria do not stifle creative biomarker solutions.

A computational approach is described that can predict the VD(ss) of new compounds in humans, with an accuracy of within 2-fold of the actual value. A dataset of VD values for 384 drugs in humans was used to train a hybrid mixture discriminant analysis-random forest (MDA-RF) model using 31 computed descriptors. Descriptors included terms describing lipophilicity, ionization, molecular volume, and various molecular fragments. For a test set of 23 proprietary compounds not used in model construction, the geometric mean fold-error (GMFE) was 1.78-fold (+/-11.4%). The model was also tested using a leave-class out approach wherein subsets of drugs based on therapeutic class were removed from the training set of 384, the model was recast, and the VD(ss) values for each of the subsets were predicted. GMFE values ranged from 1.46 to 2.94-fold, depending on the subset. Finally, for an additional set of 74 compounds, VD(ss) predictions made using the computational model were compared to predictions made using previously described methods dependent on animal pharmacokinetic data. Computational VD(ss) predictions were, on average, 2.13-fold different from the VD(ss) predictions from animal data. The computational model described can predict human VD(ss) with an accuracy comparable to predictions requiring substantially greater effort and can be applied in place of animal experimentation.

Hepatotoxicity remains a major challenge in drug development. Although alanine aminotransferase (ALT) remains the gold standard biomarker of liver injury, alternative biomarker strategies to better predict the potential for severe drug-induced liver injury (DILI) are essential. In this study, we evaluated the utility of glutamate dehydrogenase (GLDH), purine nucleoside phosphorylase (PNP), malate dehydrogenase (MDH), and paraxonase 1 (PON1) as indicators of liver injury in cohorts of human subjects, including healthy subjects across age and gender, subjects with a variety of liver impairments, and several cases of acetaminophen poisoning. In the healthy subjects, levels of GLDH and MDH were not affected by age or gender. Reference ranges for GLDH and MDH in healthy subjects were 1-10 and 79-176U/L, respectively. In contrast, the levels of PON1 and PNP were not consistent across cohorts of healthy subjects. Furthermore, GLDH and MDH had a strong correlation with elevated ALT levels and possessed a high predictive power for liver injury, as determined by ROC analysis. In contrast, PON1 and PNP did not detect liver injury in our study. Finally, evaluation of patients with acetaminophen-induced liver injury provided evidence that both GLDH and MDH might have utility as biomarkers of DILI in humans. This study is the first to evaluate GLDH, MDH, PON1, and PNP in a large number of human subjects and, and it provides an impetus for prospective clinical studies to fully evaluate the diagnostic value of GLDH and MDH for detection of liver injury.

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Within the drug development industry the assessment of abuse potential for novel molecules involves the generation and review of data from multiple sources, ranging from in-vitro binding and functional assays through to in-vivo nonclinical models in mammals, as well as collection of information from studies in humans. This breadth of data aligns with current expectations from regulatory agencies in both the USA and Europe. To date, there have been a limited number of reviews on the predictive value of individual models within this sequence, but there has been no systematic review on how each of these models contributes to our overall understanding of abuse potential risk. To address this, we analyzed data from 100 small molecules to compare the predictive validity for drug scheduling status of a number of models that typically contribute to the abuse potential assessment package. These models range from the assessment of in-vitro binding and functional profiles at receptors or transporters typically associated with abuse through in-vivo models including locomotor activity, drug discrimination, and self-administration in rodents. Data from subjective report assessments in humans following acute dosing of compounds were also included. The predictive value of each model was then evaluated relative to the scheduling status of each drug in the USA. In recognition of the fact that drug scheduling can be influenced by factors other than the pharmacology of the drug, we also evaluated the predictive value of each assay for the outcome of the human subjective effects assessment. This approach provides an objective and statistical assessment of the predictive value of many of the models typically applied within the pharmaceutical industry to evaluate abuse potential risk. In addition, the impact of combining information from multiple models was examined. This analysis adds to our understanding of the predictive value of each model, allows us to critically evaluate the benefits and limitations of each model, and provides a method for identifying opportunities for improving our assessment and prediction of abuse liability risk in the future.