Characterization of ascending dose canine telemetry model supports its use in E14/S7B QT integrated risk assessments.

INTRODUCTION: Nonclinical evaluation of the cardiovascular effects of novel chemical or biological entities (NCE, NBEs) is crucial for supporting first-in-human clinical trials. One important aspect of these evaluations is the assessment of potential QT/QTc prolongation risk, as drug-induced QT prolongation can have catastrophic effects. The recent publication of E14/S7B Q&As allows for the situational incorporation of nonclinical QTc data as part of an integrated risk assessment for a Thorough QT (TQT) waiver application provided certain best practice criteria are met. Recent publications provided detailed characterization of nonclinical QTc telemetry data collected from the commonly used Latin square study design. METHODS: To understand whether data from alternate telemetry study designs were sufficient to serve as part of the E14/S7B integrated risk assessment, we report the performance and translational sensitivity to identify clinical risk of QTc prolongation risk for an ascending dose telemetry design. RESULTS: The data demonstrated low variability in QTci interval within animals from day to day, indicating a well-controlled study environment and limited concern for uncontrolled effects across dosing days. Historical study variances of the ascending dose design with n = 4 subjects, measured by least significant difference (LSD) and root mean square error (RMSE) values, were low enough to detect a + 10 ms QTci interval change, and the median minimum detectable difference (MDD) for QTci interval changes was <10 ms. Furthermore, concentration-QTci (C-QTci) assessments to determine +10 ms QTci increases for known hERG inhibitors were comparable to clinical CC values listed in the E14/S7B training materials, supporting the use of the ascending dose design in an E14/S7B integrated risk assessment. DISCUSSION: These findings suggest that the ascending dose design can be a valuable tool in nonclinical evaluation of QT/QTc prolongation risk and the support of TQT waiver applications.

Daily electrical activity in the master circadian clock of a diurnal mammal.

Circadian rhythms in mammals are orchestrated by a central clock within the suprachiasmatic nuclei (SCN). Our understanding of the electrophysiological basis of SCN activity comes overwhelmingly from a small number of nocturnal rodent species, and the extent to which these are retained in day-active animals remains unclear. Here, we recorded the spontaneous and evoked electrical activity of single SCN neurons in the diurnal rodent Rhabdomys pumilio, and developed cutting-edge data assimilation and mathematical modeling approaches to uncover the underlying ionic mechanisms. As in nocturnal rodents, R. pumilio SCN neurons were more excited during daytime hours. By contrast, the evoked activity of R. pumilio neurons included a prominent suppressive response that is not present in the SCN of nocturnal rodents. Our modeling revealed and subsequent experiments confirmed transient subthreshold A-type potassium channels as the primary determinant of this response, and suggest a key role for this ionic mechanism in optimizing SCN function to accommodate R. pumilio's diurnal niche.

Data Assimilation Methods for Neuronal State and Parameter Estimation.

This tutorial illustrates the use of data assimilation algorithms to estimate unobserved variables and unknown parameters of conductance-based neuronal models. Modern data assimilation (DA) techniques are widely used in climate science and weather prediction, but have only recently begun to be applied in neuroscience. The two main classes of DA techniques are sequential methods and variational methods. We provide computer code implementing basic versions of a method from each class, the Unscented Kalman Filter and 4D-Var, and demonstrate how to use these algorithms to infer several parameters of the Morris-Lecar model from a single voltage trace. Depending on parameters, the Morris-Lecar model exhibits qualitatively different types of neuronal excitability due to changes in the underlying bifurcation structure. We show that when presented with voltage traces from each of the various excitability regimes, the DA methods can identify parameter sets that produce the correct bifurcation structure even with initial parameter guesses that correspond to a different excitability regime. This demonstrates the ability of DA techniques to perform nonlinear state and parameter estimation and introduces the geometric structure of inferred models as a novel qualitative measure of estimation success. We conclude by discussing extensions of these DA algorithms that have appeared in the neuroscience literature.