An acute rat in vivo screening model to predict compounds that alter blood glucose and/or insulin regulation

Abstract

IntroductionDrug-induced glucose dysregulation and insulin resistance have been associated with weight gain and potential induction and/or exacerbation of diabetes mellitus in the clinic suggesting they may be safety biomarkers when developing antipsychotics. Glucose and insulin have also been suggested as potential efficacy biomarkers for some oncology compounds. The objective of this study was to qualify a medium throughput rat in vivo acute Intravenous Glucose Tolerance Test (IVGTT) for predicting compounds that will induce altered blood glucose and/or insulin levels. MethodsAcute and sub-chronic studies were performed to qualify an acute IVGTT model. Double cannulated male rats (Han–Wistar and Sprague–Dawley) were administered vehicle, olanzapine, aripiprazole or other compounds at t=−44min for acute studies and at time=−44min on the last day of dosing for sub-chronic studies, treated with dextrose (time=0min; i.v.) and blood collected using an automated Culex® system for glucose and insulin analysis (time=−45, −1, 2, 10, 15, 30, 45, 60, 75, 90, 120, 150 and 180min). ResultsOlanzapine significantly increased glucose and insulin area under the curve (AUC) values while aripiprazole AUC values were similar to control, in both acute and sub-chronic studies. All atypical antipsychotics evaluated were consistent with literature references of clinical weight gain. As efficacy biomarkers, insulin AUC but not glucose AUC values were increased with a compound known to have insulin growth factor-1 (IGF-1) activity, compared to control treatment. DiscussionThese studies qualified the medium throughput acute IVGTT model to more quickly screen compounds for 1) safety — the potential to elicit glucose dysregulation and/or insulin resistance and 2) efficacy — as a surrogate for compounds affecting the glucose and/or insulin regulatory pathways. These data demonstrate that the same in vivo rat model and assays can be used to predict both clinical safety and efficacy of compounds.