Background. Ventricular tachycardia reentry circuits in chronic infarct scars can contain slow conduction zones, which are difficult to distinguish from bystander areas adjacent to the circuit during catheter mapping. This study developed criteria for identifying reentry circuit sites using computer simulations. These criteria then were tested during catheter mapping in humans to predict sites at which

This white paper provides a summary of a scientific proposal presented at a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/Food and Drug Administration-sponsored Think Tank, held at Food and Drug Administration's White Oak facilities, Silver Spring, MD, on July 23, 2013, with the intention of moving toward consensus on defining a new paradigm in the field of cardiac safety in which proarrhythmic risk would be primarily assessed using nonclinical in vitro human models based on solid mechanistic considerations of torsades de pointes proarrhythmia. This new paradigm would shift the emphasis from the present approach that strongly relies on QTc prolongation (a surrogate marker of proarrhythmia) and could obviate the clinical Thorough QT study during later drug development. These discussions represent current thinking and suggestions for furthering our knowledge and understanding of the public health case for adopting a new, integrated nonclinical in vitro/in silico paradigm, the Comprehensive In Vitro Proarrhythmia Assay, for the assessment of a candidate drug's proarrhythmic liability, and for developing a public-private collaborative program to characterize the data content, quality, and approaches required to assess proarrhythmic risk in the absence of a Thorough QT study. This paper seeks to encourage multistakeholder input regarding this initiative and does not represent regulatory guidance.

Background-Despite the established efficacy of statins, many patients do not achieve recommended LDL cholesterol (LDL-C) goals. Contributing factors may be inadequate dosing, increased risk for adverse effects with high-dose monotherapy, and increased potential for intolerance and adverse effects with combinations of available agents. Methods and Results-In a double-blind study, 628 patients with baseline LDL-C 145 to 250 mg/dL and triglycerides Յ350 mg/dL were randomly assigned to receive 1 of the following for 12 weeks: ezetimibe ( Coadministration of ezetimibe provided a significant additional 12% LDL-C reduction, 3% HDL-C increase, 8% triglyceride reduction, and 10% hs-CRP reduction versus atorvastatin alone. Ezetimibe plus atorvastatin provided LDL-C reductions of 50% to 60%, triglyceride reductions of 30% to 40%, and HDL-C increases of 5% to 9%, depending on atorvastatin dose. LDL-C reductions with ezetimibe plus 10 mg atorvastatin (50%) and 80 mg atorvastatin alone (51%) were similar. Conclusions-Ezetimibe plus atorvastatin was well tolerated, with a safety profile similar to atorvastatin alone and to placebo. When coadministered with atorvastatin, ezetimibe provided significant incremental reductions in LDL-C and triglycerides and increases in HDL-C. Coadministration of ezetimibe and atorvastatin offers a well-tolerated and highly efficacious new treatment option for patients with hypercholesterolemia. (Circulation. 2003;107:2409-2415.)

The International Council for Harmonisation revised the E14 guideline through the questions and answers process to allow concentration-QTc (C-QTc) modeling to be used as the primary analysis for assessing the QTc interval prolongation risk of new drugs. A well-designed and conducted QTc assessment based on C-QTc modeling in early phase 1 studies can be an alternative approach to a thorough QT study for some drugs to reliably exclude clinically relevant QTc effects. This white paper provides recommendations on how to plan and conduct a definitive QTc assessment of a drug using C-QTc modeling in early phase clinical pharmacology and thorough QT studies. Topics included are: important study design features in a phase 1 study; modeling objectives and approach; exploratory plots; the pre-specified linear mixed effects model; general principles for model development and evaluation; and expectations for modeling analysis plans and reports. The recommendations are based on current best modeling practices, scientific literature and personal experiences of the authors. These recommendations are expected to evolve as their implementation during drug development provides additional data and with advances in analytical methodology.