Translational pharmacokinetic-pharmacodynamic modeling of QTc effects in dog and human.

INTRODUCTION: Preclinical assessment of the heart rate corrected QT interval (QTc) is an important component of the cardiovascular safety evaluation in drug discovery. Here we aimed to quantify the translational relationship between QTc prolongation and shortening in the conscious telemetered dog and humans by a retrospective pharmacokinetic-pharmacodynamic (PKPD) analysis. METHODS: QTc effects of 2 proprietary compounds and 2 reference drugs (moxifloxacin and dofetilide) were quantified in conscious dogs and healthy volunteers via a linear and Emax pharmacokinetic-pharmacodynamic models. The translational relationship was quantified by correlating the QTc response from dog and human at matching free drug concentrations. RESULTS: A consistent translational relationship was found at low delta-QTc intervals indicating that a QTc change of 2.5-8 ms in dog would correspond to a 10 ms change in human. DISCUSSION: The translational relationship developed here can be used to predict the QTc liability in human using preclinical dog data. It could therefore help protect the health of human volunteers, for example by appropriate clinical study design and dose selection, as well as improve future decision-making and help reduce compound attrition due to changes in QT interval.

Has inhibition of Aß production adequately been tested as therapeutic approach in mild AD? A model-based meta-analysis of γ-secretase inhibitor data.

PURPOSE: To date, γ-secretase inhibition is the most frequently studied mechanism of reducing Aß in clinical trials with as yet no therapeutic success for AD patients, as measured by the slowing down of cognitive decline or an improvement in cognitive function. The aims of this investigation were to evaluate whether the amyloid hypothesis has been tested clinically, and to explore whether preclinical data are predictive of clinical Aß effects. METHODS: A model-based-meta analysis on Aß levels and drug exposure over time was performed on published and in-house (pre-)clinical data with γ-secretase inhibitors (GSIs; semagacestat, avagacestat, begacestat, PF-3074014, and MK0752). RESULTS: The clinical data available did not show any significant or robust reduction of CNS Aß over time at dose levels intended for AD patients. In contrast, these doses resulted in an average increase in plasma Aß levels over a 24-h interval. A general agreement between preclinical and clinical data was found and allowed for interspecies extrapolations. CONCLUSIONS: More substantially, CNS Aß-lowering drugs are needed to test whether inhibition of Aß production is efficacious in mild AD. Predictions based on preclinical data could assist in the selection of drug candidates and trial design.

Application of data mining and visualization techniques for the prediction of drug-induced nausea in man.

The therapeutic value of many drugs can be limited by gastrointestinal (GI) adverse effects such as nausea and vomiting. Nausea is a subjective human sensation, hence little is known about preclinical biomarkers that may accurately and effectively predict its presence in man. The aim of this analysis was to use informatics and data-mining tools to identify plausible preclinical GI effects that may be associated with nausea and that could be of potential use in its prediction. A total of 86 marketed drugs were used in this analysis, and the main outcome was a confirmation that nausogenic and non-nausogenic drugs can be clearly separated based on their preclinical GI observations. Specifically, combinations of common preclinical GI effects (vomiting, diarrhea, and salivary hypersecretion) proved to be strong predictors. The model was subsequently validated with a subset of 20 blinded proprietary small molecules and successfully predicted clinical outcome in 90% of cases. This investigation demonstrated the feasibility of data-mining approaches to facilitate discovery of novel, plausible associations that can be used to understand drug-induced adverse effects.