Evidence for a crucial modulating role of the sodium channel in the QTc prolongation related to antipsychotics.

Blockade of the cardiac hERG channel is recognized as the main mechanism underlying the QT prolongation induced by many classes of drugs, including antipsychotics. However, antipsychotics interact with a variety of other pharmacological targets that could also modulate cardiac function. The present study aims to identify those key factors involved in the QT prolongation induced by antipsychotics. The interactions of 28 antipsychotics were measured on a variety of pharmacological targets. Binding affinity (K(i)), functional channel blockade (IC50), and the corresponding ratios to total and free plasma drug concentration were compared with the corrected QT changes (QTc) associated with the therapeutic use of these drugs by multivariable linear regression analysis to determine the best predictors of QTc. Besides confirming hERG as the primary predictor of QTc, all analyses consistently show the concomitant involvement of Na(V)1.5 channel as modulating factor of the QTc related to hERG blockade. In particular, the hERG/Na(V)1.5 ratio explains the 57% of the overall QTc variability associated with antipsychotics. Since it is known that inhibition of late I Na could offset the dysfunctional effects of hERG blockade, we hypothesize the inhibition of late I(Na) as a crucial compensatory mechanism of the QTc associated with antipsychotics and hence an important factor to consider concomitantly with hERG blockade to appraise the arrhythmogenic risk of these drugs more accurately.

A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities.

As indicated in ICH M7 draft guidance, in silico predictive tools including statistically-based QSARs and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. To address this need, we developed and validated a QSAR model to predict Salmonella t. mutagenicity (Ames assay outcome) of pharmaceutical impurities using Prous Institute's Symmetry(SM), a new in silico solution for drug discovery and toxicity screening, and the Mold2 molecular descriptor package (FDA/NCTR). Data was sourced from public benchmark databases with known Ames assay mutagenicity outcomes for 7300 chemicals (57% mutagens). Of these data, 90% was used to train the model and the remaining 10% was set aside as a holdout set for validation. The model's applicability to drug impurities was tested using a FDA/CDER database of 951 structures, of which 94% were found within the model's applicability domain. The predictive performance of the model is acceptable for supporting regulatory decision-making with 84±1% sensitivity, 81±1% specificity, 83±1% concordance and 79±1% negative predictivity based on internal cross-validation, while the holdout dataset yielded 83% sensitivity, 77% specificity, 80% concordance and 78% negative predictivity. Given the importance of having confidence in negative predictions, an additional external validation of the model was also carried out, using marketed drugs known to be Ames-negative, and obtained 98% coverage and 81% specificity. Additionally, Ames mutagenicity data from FDA/CFSAN was used to create another data set of 1535 chemicals for external validation of the model, yielding 98% coverage, 73% sensitivity, 86% specificity, 81% concordance and 84% negative predictivity.