Dennis O. Scott

To assess the utility of Extended Clearance Classification System (ECCS) in understanding absorption, distribution, metabolism, and elimination (ADME) attributes and enabling victim drug-drug interaction (DDI) predictions.

The purpose of this work is to develop a mathematical model of energy balance and body weight regulation that can predict species-specific response to common pre-clinical interventions. To this end, we evaluate the ability of a previously published mathematical model of mouse metabolism to describe changes in body weight and body composition in rats in response to two short-term interventions. First, we adapt the model to describe body weight and composition changes in Sprague-Dawley rats by fitting to data previously collected from a 26-day caloric restriction study. The calibrated model is subsequently used to describe changes in rat body weight and composition in a 23-day cannabinoid receptor 1 antagonist (CB1Ra) study. While the model describes body weight data well, it fails to replicate body composition changes with CB1Ra treatment. Evaluation of a key model assumption about deposition of fat and fat-free masses shows a limitation of the model in short-term studies due to the constraint placed on the relative change in body composition components. We demonstrate that the model can be modified to overcome this limitation, and propose additional measurements to further test the proposed model predictions. These findings illustrate how mathematical models can be used to support drug discovery and development by identifying key knowledge gaps and aiding in the design of additional experiments to further our understanding of disease-relevant and species-specific physiology.

Because of the importance of intracellular unbound drug concentrations in the prediction of in vivo concentrations that are determinants of drug efficacy and toxicity, a number of assays have been developed to assess in vitro unbound concentrations of drugs. Here we present a rapid method to determine the intracellular unbound drug concentrations in cultured cells, and we apply the method along with a mechanistic model to predict concentrations of metformin in subcellular compartments of stably transfected human embryonic kidney 293 (HEK293) cells. Intracellular space (ICS) was calculated by subtracting the [³H]-inulin distribution volume (extracellular space, ECS) from the [¹⁴C]-urea distribution volume (total water space, TWS). Values obtained for intracellular space (mean ± S.E.M.; μl/10⁶ cells) of monolayers of HEK cells (HEK-empty vector [EV]) and cells overexpressing human organic cation transporter 1 (HEK-OCT1), 1.21± 0.07 and 1.25±0.06, respectively, were used to determine the intracellular metformin concentrations. After incubation of the cells with 5 µM metformin, the intracellular concentrations were 26.4 ± 7.8 μM and 268 ± 11.0 μM, respectively, in HEK-EV and HEK-OCT1. In addition, intracellular metformin concentrations were lower in high K⁺ buffer (140 mM KCl) compared with normal K⁺ buffer (5.4 mM KCl) in HEK-OCT1 cells (54.8 ± 3.8 μM and 198.1 ± 11.2 μM, respectively; P < 0.05). Our mechanistic model suggests that, depending on the credible range of assumed physiologic values, the positively charged metformin accumulates to particularly high levels in endoplasmic reticulum and/or mitochondria. This method together with the computational model can be used to determine intracellular unbound concentrations and to predict subcellular accumulation of drugs in other complex systems such as primary cells.

In this work, we leverage a mathematical model of the underlying physiochemical properties of tissues and physicochemical properties of molecules to support the development of hepatoselective glucokinase activators. Passive distribution is modeled via a Fick-Nernst-Planck approach, using in vitro experimental data to estimate the permeability of both ionized and neutral species. The model accounts for pH and electrochemical potential across cellular membranes, ionization according to Henderson-Hasselbalch, passive permeation of the neutral species using Fick’s law, and passive permeation of the ionized species using the Nernst-Planck equation. The mathematical model of the physiochemical system allows derivation of a single set of parameters governing the distribution of drug molecules across multiple conditions both in vitro and in vivo. A case study using this approach in the development of hepatoselective glucokinase activators via organic anion-transporting polypeptide–mediated hepatic uptake and impaired passive distribution to the pancreas is described. The results for these molecules indicate the permeability penalty of the ionized form is offset by its relative abundance, leading to passive pancreatic exclusion according to the Nernst-Planck extension of Fickian passive permeation. Generally, this model serves as a useful construct for drug discovery scientists to understand subcellular exposure of acids or bases using specific physiochemical properties.