Idiosyncratic adverse drug reactions (IADRs) in humans can result in a broad range of clinically significant toxicities leading to attrition during drug development as well as postlicensing withdrawal or labeling. IADRs arise from both drug and patient related mechanisms and risk factors. Drug related risk factors, resulting from parent compound or metabolites, may involve multiple contributory mechanisms including organelle toxicity, effects related to compound disposition, and/or immune activation. In the current study, we evaluate an in vitro approach, which explored both cellular effects and covalent binding (CVB) to assess IADR risks for drug candidates using 36 drugs which caused different patterns and severities of IADRs in humans. The cellular effects were tested in an in vitro Panel of five assays which quantified (1) toxicity to THLE cells (SV40 T-antigen-immortalized human liver epithelial cells), which do not express P450s, (2) toxicity to a THLE cell line which selectively expresses P450 3A4, (3) cytotoxicity in HepG2 cells in glucose and galactose media, which is indicative of mitochondrial injury, (4) inhibition of the human bile salt export pump, BSEP, and (5) inhibition of the rat multidrug resistance associated protein 2, Mrp2. In addition, the CVB Burden was estimated by determining the CVB of radiolabeled compound to human hepatocytes and factoring in both the maximum prescribed daily dose and the fraction of metabolism leading to CVB. Combining the aggregated results from the in vitro Panel assays with the CVB Burden data discriminated, with high specificity (78%) and sensitivity (100%), between 27 drugs, which had severe or marked IADR concern, and 9 drugs, which had low IADR concern, we propose that this integrated approach has the potential to enable selection of drug candidates with reduced propensity to cause IADRs in humans.

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When scientifically well-founded, the mechanistic basis of physiologically based pharmacokinetic (PBPK) models can help reduce the uncertainty and increase confidence in extrapolations outside the studied scenarios or studied populations. However, it is not always possible to establish mechanistically credible PBPK models. Requirements to establishing confidence in PBPK models, and challenges to meeting these requirements, are presented in this article. Parameter non-identifiability is the most challenging among the barriers to establishing confidence in PBPK models. Using case examples of small molecule drugs, this article examines the use of hypothesis testing to overcome parameter non-identifiability issues, with the objective of enhancing confidence in the mechanistic basis of PBPK models and thereby improving the quality of predictions that are meant for internal decisions and regulatory submissions. When the mechanistic basis of a PBPK model cannot be established, we propose the use of simpler models or evidence-based approaches.

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ABSTRACT:Simcyp, a population-based simulator, is widely used for evaluating drug-drug interaction (DDI) risks in healthy and disease populations. We compare the prediction performance of Simcyp with that of mechanistic static models using different types of inhibitor concentrations, with the aim of understanding their strengths/ weaknesses and recommending the optimal use of tools in drug discovery/early development. The inclusion of an additional term in static equations to consider the contribution of hepatic first pass to DDIs (AUCR hfp ) has also been examined. A second objective was to assess Simcyp's estimation of variability associated with DDIs. The data set used for the analysis comprises 19 clinical interactions from 11 proprietary compounds. Except for gut interaction parameters, all other input data were identical for Simcyp and static models. Static equations using an unbound average steady-state systemic inhibitor concentration (I sys ) and a fixed fraction of gut extraction and neglecting gut extraction in the case of induction interactions performed better than Simcyp (84% compared with 58% of the interactions predicted within 2-fold). Differences in the prediction outcomes between the static and dynamic models are attributable to differences in first-pass contribution to DDI. The inclusion of AUCR hfp in static equations leads to systematic overprediction of interaction, suggesting a limited role for hepatic first pass in determining inhibition-based DDIs for our data set. Our analysis supports the use of static models when elimination routes of the victim compound and the role of gut extraction for the victim and/or inhibitor in humans are not well defined. A fixed variability of 40% of predicted mean area under the concentration-time curve ratio is recommended.

1. We compared direct scaling, regression model equation and the so-called "Poulin et al." methods to scale clearance (CL) from in vitro intrinsic clearance (CL) measured in human hepatocytes using two sets of compounds. One reference set comprised of 20 compounds with known elimination pathways and one external evaluation set based on 17 compounds development in Merck (MS). 2. A 90% prospective confidence interval was calculated using the reference set. This interval was found relevant for the regression equation method. The three outliers identified were justified on the basis of their elimination mechanism. 3. The direct scaling method showed a systematic underestimation of clearance in both the reference and evaluation sets. The "Poulin et al." and the regression equation methods showed no obvious bias in either the reference or evaluation sets. 4. The regression model equation was slightly superior to the "Poulin et al." method in the reference set and showed a better absolute average fold error (AAFE) of value 1.3 compared to 1.6. A larger difference was observed in the evaluation set were the regression method and "Poulin et al." resulted in an AAFE of 1.7 and 2.6, respectively (removing the three compounds with known issues mentioned above). A similar pattern was observed for the correlation coefficient. Based on these data we suggest the regression equation method combined with a prospective confidence interval as the first choice for the extrapolation of human in vivo hepatic metabolic clearance from in vitro systems.