Translational Models and Tools to Reduce Clinical Trials and Improve Regulatory Decision Making for QTc and Proarrhythmia Risk (ICH E14/S7B Updates).

After multiple drugs were removed from the market secondary to drug-induced torsade de pointes (TdP) risk, the International Council for Harmonisation (ICH) released guidelines in 2005 that focused on the nonclinical (S7B) and clinical (E14) assessment of surrogate biomarkers for TdP. Recently, Vargas et al. published a pharmaceutical-industry perspective making the case that "double-negative" nonclinical data (negative in vitro hERG and in vivo heart-rate corrected QT (QTc) assays) are associated with such low probability of clinical QTc prolongation and TdP that potentially all double-negative drugs would not need detailed clinical QTc evaluation. Subsequently, the ICH released a new E14/S7B Draft Guideline containing Questions and Answers (Q&As) that defined ways that double-negative nonclinical data could be used to reduce the number of "Thorough QT" (TQT) studies and reach a low-risk determination when a TQT or equivalent could not be performed. We review the Vargas et al. proposal in the context of what was contained in the ICH E14/S7B Draft Guideline and what was proposed by the ICH E14/S7B working group for a "stage 2" of updates (potential expanded roles for nonclinical data and details for assessing TdP risk of QTc-prolonging drugs). Although we do not agree with the exact probability statistics in the Vargas et al. paper because of limitations in the underlying datasets, we show how more modest predictive value of individual assays could still result in low probability for TdP with double-negative findings. Furthermore, we expect that the predictive value of the nonclinical assays will improve with implementation of the new ICH E14/S7B Draft Guideline.

A general procedure to select calibration drugs for lab-specific validation and calibration of proarrhythmia risk prediction models: An illustrative example using the CiPA model.

INTRODUCTION: In response to the ongoing shift of the regulatory cardiac safety paradigm, a recent White Paper proposed general principles for developing and implementing proarrhythmia risk prediction models. These principles included development strategies to validate models, and implementation strategies to ensure a model developed by one lab can be used by other labs in a consistent manner in the presence of lab-to-lab experimental variability. While the development strategies were illustrated through the validation of the model under the Comprehensive In vitro Proarrhythmia Assay (CiPA), the implementation strategies have not been adopted yet. METHODS: The proposed implementation strategies were applied to the CiPA model by performing a sensitivity analysis to identify a subset of calibration drugs that were most critical in determining the classification thresholds for proarrhythmia risk prediction. RESULTS: The selected calibration drugs were able to recapitulate classification thresholds close to those calculated from the full list of CiPA drugs. Using an illustrative dataset it was shown that a new lab could use these calibration drugs to establish its own classification thresholds (lab-specific calibration), and verify that the model prediction accuracy in the new lab is comparable to that in the original lab where the model was developed (lab-specific validation). DISCUSSION: This work used the CiPA model as an example to illustrate how to adopt the proposed model implementation strategies to select calibration drugs and perform lab-specific calibration and lab-specific validation. Generic in nature, these strategies could be generally applied to different proarrhythmia risk prediction models using various experimental systems under the new paradigm.

Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative.

The International Council on Harmonization (ICH) S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are pro-arrhythmic. In response, the Comprehensive In Vitro Proarrhythmia Assay (CiPA) was proposed that integrates multi-ion channel pharmacology data in vitro into a human cardiomyocyte model in silico for proarrhythmia risk assessment. Previously, we reported the model optimization and proarrhythmia metric selection based on CiPA training drugs. In this study, we report the application of the prespecified model and metric to independent CiPA validation drugs. Over two validation datasets, the CiPA model performance meets all pre-specified measures for ranking and classifying validation drugs, and outperforms alternatives, despite some in vitro data differences between the two datasets due to different experimental conditions and quality control procedures. This suggests that the current CiPA model/metric may be fit for regulatory use, and standardization of experimental protocols and quality control criteria could increase the model prediction accuracy even further.

Comprehensive In Vitro Proarrhythmia Assay (CiPA) Update from a Cardiac Safety Research Consortium / Health and Environmental Sciences Institute / FDA Meeting.

A Cardiac Safety Research Consortium / Health and Environmental Sciences Institute / FDA-sponsored Think Tank Meeting was convened in Washington, DC, on May 21, 2018, to bring together scientists, clinicians, and regulators from multiple geographic regions to evaluate progress to date in the Comprehensive In Vitro Proarrhythmia Assay (CiPA) Initiative, a new paradigm to evaluate proarrhythmic risk. Study reports from the 4 different components of the CiPA paradigm (ionic current studies, in silico modeling to generate a Torsade Metric Score, human induced pluripotent stem cell-derived ventricular cardiomyocytes, and clinical ECG assessments including J-Tpeakc) were presented and discussed. This paper provides a high-level summary of the CiPA data presented at the meeting.

17α-Estradiol and genistein inhibit high fat diet induced prostate gene expression and prostate growth in the rat.

PURPOSE: High dietary fat and low phytoestrogen intake are associated with prostate cancer development and progression. Our previous study showed that exposure to a high fat diet significantly increased prostate 5α-reductase-2 mRNA and prostate growth in the rat. In the current experiments we determined the effects of genistein and 17α-estradiol on the modulation of dietary fat induced prostate 5α-reductase-2 and insulin-like growth factor-1 gene expression, and prostate growth. MATERIALS AND METHODS: At weaning male ACI/Seg rats (Harlan® Sprague-Dawley®) were fed a low or a high fat diet, with or without genistein or 17α-estradiol for 2, 4 or 10 weeks. The prostate was dissected and weighed. We determined the levels of prostate 5α-reductase-2 mRNA, insulin-like growth factor-1 mRNA, dihydrotestosterone, and plasma insulin-like growth factor-1, dihydrotestosterone and testosterone. RESULTS: Two-week exposure to a high fat diet significantly increased prostate insulin-like growth factor-1 mRNA without significant changes in plasma insulin-like growth factor-1, which was blocked by genistein and 17α-estradiol. Genistein but not 17α-estradiol also inhibited prostate 5α-reductase-2 mRNA and intraprostatic dihydrotestosterone induced by the high fat diet at 2 weeks. Genistein and 17α-estradiol completely blocked high fat diet induced prostate growth at 10 weeks of dietary treatment. However, neither genistein nor 17α-estradiol had any significant effect when co-administered with the low fat diet. CONCLUSIONS: Results indicate that genistein and 17α-estradiol can inhibit dietary fat induced changes in prostate 5α-reductase-2 and insulin-like growth factor-1 gene expression, and prostate growth in the rat. This may be beneficial to prevent dietary fat associated prostate diseases such as prostate cancer.