Quantitative Integration of Biological, Pharmacokinetic, and Medical Imaging Data for Organ-Wide Dose-Response Predictions

The central nervous system (CNS) is the most difficult target for drug delivery therapies. Despite datasets available describing physiological, biochemical, cellular, and metabolic properties of the CNS, the development of infusion therapies still faces major delivery challenges. There is a need for the integration of data obtained from different experimental modalities to design molecular therapies. In this paper, we propose a novel mathematical method for the integration of datasets to generate useful dosing criteria for infusion therapies. A case study is used to demonstrate the design of gene silencing therapies to down regulate NMDA receptors in the spinal cord for chronic pain management. Based on experimentally derived kinetics for short interfering RNA (siRNA) and magnetic resonance images, the biodistribution and pharmacokinetics of siRNAs were predicted for different infusion modes. This adaptable, multiscale computational platform enables the prediction of dose-response on an organ-wide level. The quantitative integration of valuable datasets with engineering precision is expected to accelerate the clinical implementation of novel therapeutics.