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.

Crystallization is a major separation process in the pharmaceutical industry. Most crystallizations are performed batchwise, but there is great incentive for converting them to continuous operations. This paper investigates the modeling, simulation, and optimization of a special antisolvent plug-flow crystallizer: the multisegmented, multiaddition plug-flow crystallizer (MSMA-PFC). The MSMA-PFC accepts multiple antisolvent flows along its length, permitting finer control of supersaturation. A steady-state population balance equation was applied for tracking the crystal size distribution, and a mass balance equation was used to track the depletion of dissolved solute (flufenamic acid). A multiobjective optimization framework was applied to determine the antisolvent flow rates into each segment that simultaneously maximize the average crystal size, and minimize the coefficient of variation. The set of coupled differential equations was solved, depending on circumstance, with either the method-of-moments (MOM), or the high-resolution finite-volume (FV) method. The significant nonconvexity in the objective functions motivated the use of the nondominated sorting genetic algorithm (NSGA-II) to calculate the Pareto frontiers for the two competing objectives. It was found that the optimal antisolvent profile provides better product crystals, compared to the cases with equal additions of antisolvent in 1−4 injection points by keeping the total amount of antisolvent the same. The sensitivity of the Pareto frontier to variation in the growth and nucleation kinetic parameters was investigated. In addition, a novel simultaneous design and control (SDC) approach was proposed, based on the optimization of the full crystallizer design, over not only antisolvent profile, but also the number of injections and total crystallizer length, providing the best crystallization design that can allow optimal product performance in conjunction with the multiaddition control approach.

This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/ US Food and Drug Administration-sponsored Think Tank Meeting on May 22, 2018. The meeting was convened to evaluate the progress in the development of a new cardiac safety paradigm, the Comprehensive in Vitro Proarrhythmia Assay (CiPA). The opinions regarding these principles reflect the collective views of those who participated in the discussion of this topic both at and after the breakout session. Although primarily discussed in the context of in silico models, these principles describe the interface between experimental input and model-based interpretation and are intended to be general enough to be applied to other types of nonclinical models for proarrhythmia assessment. This document was developed with the intention of providing a foundation for more consistency and harmonization in developing and validating different models for proarrhythmia risk prediction using the example of the CiPA paradigm.In July 2013, a Think Tank jointly sponsored by Cardiac Safety Research Consortium (CSRC), Health and Environmental Sciences Institute (HESI), and the US Food and Drug Administration (FDA) proposed a new cardiac safety paradigm, Comprehensive in Vitro Proarrhythmia Assay (CiPA). CiPA uses a new mechanistic, model-informed approach to predict the risk of Torsade de Pointes (TdP), a rare but potentially lethal form of ventricular tachycardia that can be induced by drugs and lead to sudden death. 1 Since its inception, global stakeholders including regulatory agencies (the FDA, European Medicines Agency, Health Canada, and the Japan Pharmaceuticals and Medical Devices Agency), industry, and academia have assembled various