This project was started as part of a joint project of the CADD Group with several groups at the Department of Defense (DoD), with the title Computational platforms for transforming small molecules into investigational new drugs. The projects lead PI on the DoD side was Dr. S. Anders Wallqvist, Tri-Service Biotechnology High-Performance Computing Software Applications Institute for Force Health Protection (BHSAI), Telemedicine and Advanced Technology Research Center (TATRC), U.S. Army Medical Research and Materiel Command (USAMRMC), 2405 Whittier Drive, Suite 200, Frederick, MD 217602. Other participating groups are at the Department of Biochemistry, Walter Reed Army Institute of Research (WRAIR), and the Department of Cell Biology and Biochemistry, U.S. Army Medical Research Institute for Infectious Diseases (USAMRIID).
The aim of the overall project is to integrate three fundamental aspects of the preclinical drug development phase, i.e., structure-based drug design, analysis and prediction of pharmacological data, and the prediction of adverse and off-target effects, in particular those related to drug metabolization, from chemical structures. The most important aspect of Dr. Pugliese's work concerned metabolism and metabolites. The work having effectively started in early 2010, Dr. Pugliese worked on implementing a resource for successful prediction of metabolism and metabolites of drug-like small molecules as part of our computer-aided drug design capabilities, until his departure from NCI for a permanent position in June, 2011. The first phase of this project, consisting of canvassing the field for predictive computer tools as well as data sets that can be used to test these tools and develop (better) predictive models, was successfully completed. Both commercial and free resources have been compiled or acquired. A comparison and benchmark study with appropriate publication is in the works. In this part of the project, we focused on (prediction of) metabolic stability data such as half-life values in Human Liver Microsome or Human Hepatocyte assays. While the initial tests and application of these resources were done in the context of pathogens of interest to DoD, the general capability of predicting metabolic stability, metabolization profile and specific metabolites of a small molecule is applicable to all types of drug development, and therefore is very useful in the development of anti-cancer therapeutics aiming at molecular targets of high interest to NCI, as well as in, e.g., NCI's anti-HIV drug design projects.
|Zakharov, Alexey V; Varlamova, Ekaterina V; Lagunin, Alexey A et al. (2016) QSAR Modeling and Prediction of Drug-Drug Interactions. Mol Pharm 13:545-56|
|Mansouri, Kamel; Abdelaziz, Ahmed; Rybacka, Aleksandra et al. (2016) CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023-33|
|Tarasova, Olga A; Urusova, Aleksandra F; Filimonov, Dmitry A et al. (2015) QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors. J Chem Inf Model 55:1388-99|
|Zakharov, Alexey V; Peach, Megan L; Sitzmann, Markus et al. (2014) A new approach to radial basis function approximation and its application to QSAR. J Chem Inf Model 54:713-9|
|Zakharov, Alexey V; Peach, Megan L; Sitzmann, Markus et al. (2014) QSAR modeling of imbalanced high-throughput screening data in PubChem. J Chem Inf Model 54:705-12|
|Zakharov, Alexey V; Peach, Megan L; Sitzmann, Markus et al. (2012) Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes. Future Med Chem 4:1933-44|
|Peach, Megan L; Zakharov, Alexey V; Liu, Ruifeng et al. (2012) Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software. Future Med Chem 4:1907-32|