Drug safety assessment by machine learning models.

The evaluation of drug-induced Torsades de pointes (TdP) risks is crucial in drug safety assessment. In this study, we discuss machine learning approaches in the prediction of drug-induced TdP risks using preclinical data. Specifically, a random forest model was trained on the dataset generated by the rabbit ventricular wedge assay. The model prediction performance was measured on 28 drugs from the Comprehensive In Vitro Proarrhythmia Assay initiative. Leave-one-drug-out cross-validation provided an unbiased estimation of model performance. Stratified bootstrap revealed the uncertainty in the asymptotic model prediction. Our study validated the utility of machine learning approaches in predicting drug-induced TdP risks from preclinical data. Our methods can be extended to other preclinical protocols and serve as a supplementary evaluation in drug safety assessment.

Statistical learning in preclinical drug proarrhythmic assessment.

Torsades de pointes (TdP) is an irregular heart rhythm characterized by faster beat rates and potentially could lead to sudden cardiac death. Much effort has been invested in understanding the drug-induced TdP in preclinical studies. However, a comprehensive statistical learning framework that can accurately predict the drug-induced TdP risk from preclinical data is still lacking. We proposed ordinal logistic regression and ordinal random forest models to predict low-, intermediate-, and high-risk drugs based on datasets generated from two experimental protocols. Leave-one-drug-out cross-validation, stratified bootstrap, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The potential outlier drugs identified by our models are consistent with their descriptions in the literature. Our method is accurate, interpretable, and thus useable as supplemental evidence in the drug safety assessment.

Baseline selection in concentration-QTc modeling: impact on assay sensitivity.

The baseline selection in concentration-QTc (C-QTc) modeling is not well studied in the literature. Time-matched baseline and pre-dose baseline have been commonly used as a covariate in C-QTc modeling for parallel and crossover study, respectively. It has been showed that the C-QTc model using time-matched baseline has a low chance of showing assay sensitivity in parallel study. To better understand the impacts of baseline section in C-QTc, we examined the original and subsampled moxifloxacin and placebo data from more than 50 of TQT studies submitted to FDA with regard to assay sensitivity. Our analyses show that baseline selection (time-matched, pre-dose, average) has an impact on prediction from C-QTc modeling and the impact depends on study design (parallel, crossover). The impact to categorical table of ΔQTc is unlikely to alter the interpretation of the outlier category (ΔQTc>60) that corresponds to the regulatory concern. The results presented here can guide C-QTc study design as well as baseline selection in C-QTc modeling.

Assay sensitivity in "Hybrid thorough QT/QTc (TQT)" study.

A concurrent positive control should be included in a thorough QTc clinical trial to validate the study according to ICH E14 guidance. Some pharmaceutical companies have started to use "hybrid TQT" study to meet ICH E14 regulatory requirements since the release of ICH E14 Q&A (R3). The "hybrid TQT" study includes the same treatment arms (therapeutic and/or supratherapeutic dose of investigational drug, placebo, and positive control) with sample size less than traditional TQT studies, but use concentration-QTc (C-QTc) analysis as primary analysis and assay sensitivity analysis. To better understand the statistical characteristics of assay sensitivity with a commonly used positive control - Moxifloxacin - in "hybrid TQT" studies, we examined the original and subsampled moxifloxacin and placebo data from more than a hundred of TQT studies submitted to FDA. The assay sensitivity results are quite consistent between classical E14 analysis and C-QTc analysis using the original datasets. Performance of assay sensitivity in "hybrid TQT" studies using subsampled data depends on number of moxifloxacin subjects, study design (crossover design and parallel design), and C-QTc model. The results presented here can aid the design of future "hybrid TQT" studies.

Evaluation of dependent variable, time effect, covariates, and covariation structure in concentration-QTc modeling: A simulation study.

The revised ICH E14 Question and Answer (R3) document issued in December 2015 enables pharmaceutical companies to use concentration-QTc (C-QTc) modeling as the primary analysis for assessing QTc prolongation risk of new drugs. A new approach by including the time effect into the current C-QTc model is introduced. Through a simulation study, we evaluated performances of different C-QTc modeling with different dependent variables, covariates, and covariance structures. This simulation study shows that C-QTc models with ΔQTc being dependent variable without time effect inflate false negative rate and that fitting C-QTc models with different dependent variables, covariates, and covariance structures impacts the control of false negative and false positive rates. Appropriate C-QTc modeling strategies with good control of false negative rate and false positive rate are recommended.