Ricardo E. Paxson

Model-based design approaches in drug discovery: a parallel to traditional engineering approaches

Model-based design (MBD) has been successfully applied in the automotive, chemical, and aerospace industries. Here we discuss the possible application of engineering-based MBD approaches to drug discovery. One of the biggest challenges in drug discovery is the high attrition rate of new drugs in development: Many promising candidates prove ineffective or toxic in animal or human testing. More often than not, these failures are the result of a poor understanding of the molecular mechanisms of the biological systems they target. Recent advances in biological systems modeling make MBD an attractive approach to improve drug development. We elaborate on the view that the pharmaceutical industry should be able to use MBD to design new drugs more effectively. There are significant differences between drug discovery and traditional engineering that lead to specific MBD requirements. We delineate those differences and introduce suggestions to overcome them.

gPKPDSim: a SimBiology®-based GUI application for PKPD modeling in drug development

Modeling and simulation (M&S) is increasingly used in drug development to characterize pharmacokinetic-pharmacodynamic (PKPD) relationships and support various efforts such as target feasibility assessment, molecule selection, human PK projection, and preclinical and clinical dose and schedule determination. While model development typically require mathematical modeling expertise, model exploration and simulations could in many cases be performed by scientists in various disciplines to support the design, analysis and interpretation of experimental studies. To this end, we have developed a versatile graphical user interface (GUI) application to enable easy use of any model constructed in SimBiology® to execute various common PKPD analyses. The MATLAB®-based GUI application, called gPKPDSim, has a single screen interface and provides functionalities including simulation, data fitting (parameter estimation), population simulation (exploring the impact of parameter variability on the outputs of interest), and non-compartmental PK analysis. Further, gPKPDSim is a user-friendly tool with capabilities including interactive visualization, exporting of results and generation of presentation-ready figures. gPKPDSim was designed primarily for use in preclinical and translational drug development, although broader applications exist. gPKPDSim is a MATLAB®-based open-source application and is publicly available to download from MATLAB® Central™. We illustrate the use and features of gPKPDSim using multiple PKPD models to demonstrate the wide applications of this tool in pharmaceutical sciences. Overall, gPKPDSim provides an integrated, multi-purpose user-friendly GUI application to enable efficient use of PKPD models by scientists from various disciplines, regardless of their modeling expertise.

Applications of sensitivity analysis for drug discovery and development in the ErbB receptor network

We are using quantitative biochemical network models of receptor signaling to yield significant improvements in drug design and decision-making throughout the development of our therapeutics. As an example, we have developed a model of the ErbB receptor network that includes ErbB1-4, ligand-receptor binding of different ErbB ligands, receptor trafficking, and intracellular signal transfer leading to ERK and Akt phosphorylation. The model is first trained using quantitative experimental data and then our in silico predictions are verified by an independent set of experiments.