Discovery and development of a drug from early to late stages on average cost 2 billion dollars, and require many different types of simulation and modeling tools in order to help scientists to better understand the drug disposition mechanisms and predict possible outcomes within various experimental settings. Importantly, because modeling approach involves assumptions and reductions of certain components while new concepts and knowledge should validate or replace the old ones, the approach should be built on the fundamentally strong algorithms and modules that can be easily refined, rather than curve-fitting and using fudge factors. Here, we will develop a mechanistic modeling platform to make better predictions in pharmacokinetics and cellular drug disposition for drug-like molecules in human using primarily in vitro data. We use mathematical approach to simulate drug clearance in a well-defined in vitro system constructed with our patented technology that allows us to tailor the expression of drug transporters in MDCK cell monolayers, and measure intrinsic parameters of individual component of transcellular drug transport. Thus our novelty comes from measuring and incorporating the true, intrinsic kinetic parameters at the site of action int the model as opposed to apparent observations. The models for drug-drug interaction, as well as transporter-metabolizing enzyme interplay which affects the temporal dynamics of disposition will undergo a series of rigorous validation processes tailored to each specific example (rosuvastatin-rifampicin, metformin-cimetidine, and erythromycin-CYP3A4 metabolite). Each models will incorporate new concepts, such as, intracellular unbound concentration, intrinsic efflux kinetic parameters (Vmax,intrinsic and Km,intrinsic), and steady-state concentration-dependent SLC transporters generally discussed among International Transporter Consortium and regulatory agencies. Although selected test models represent the liver and renal transporter-mediated disposition, the intrinsic parameters and our in vitro expression systems together provide fundamental building blocks in the scope of systems pharmacology, allowing future modification to other scenarios and upgrades (e.g. brain or intestine, etc.), and promote application of modeling and simulation in the field of science and biotechnology business.
Success of this project will benefit public health by facilitating discovery and development of drugs allowing scientists to make more informed decisions. Particularly, this will help scientists to better understand the drug disposition mechanisms and allow more information on possible outcomes to assist clinicians to make important decisions during drug development and prescription stages. This research will deliver precision medicine one step closer to us with better knowledge dealing with drug clearance, adverse drug reactions, as well as pharmacogenomics variances. Through promoting better prediction tool so that other scientists can utilize it in various research will enhance the speed and efficiency of te drug discovery and development process, and the betterment of health worldwide.
| Hosey, Chelsea M; Benet, Leslie Z (2015) Predicting the extent of metabolism using in vitro permeability rate measurements and in silico permeability rate predictions. Mol Pharm 12:1456-66 |
