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NOVEL IN SILICO MODELS FOR ADMET USING RAPID ESTIMATES OF QUANTUM CHEMICAL DESCRI

Award Number: R43CA130388
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 07/01/2007
PERIOD OF PERFORMANCE END DATE: 12/31/2007

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NOVEL IN SILICO MODELS FOR ADMET USING RAPID ESTIMATES OF QUANTUM CHEMICAL DESCRI - DESCRIPTION (provided by applicant): New empirical descriptors to replace ab initio calculations will be generated to enable development of improved Ultra High Throughput (UHT, >200,000 compounds/hour) in silico models for Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties of molecules from their structures. Partial atomic charges, atomic and molecular reactivities, pKa, and related descriptors will be generated using computer models trained from a set of about 500 molecules for which most of the ab initio geometries have been optimized. This approach has been demonstrated by a preliminary model for partial atomic charges, which improved existing models for pKa and several ADMET properties. Phase I will complete the development of the charge model, implement atomic and molecular reactivity descriptors, and develop improved models for pKa as well as for a number of ADMET properties. Phase II will expand this work by developing additional models for ADMET properties using in-house and publicly available data sources. The models and new descriptors will be embodied in a commercially available software program already used by industry, government, and academic organizations to provide rapid and accurate estimation of ADMET properties, to help prioritize drug candidates for synthesis and screening. This project seeks to develop empirical quantum level descriptors for characterizing molecules, and to employ them in improved computer models to predict Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties of molecules from their structures. These improved models will enable industry and government agencies to rapidly identify compounds with poor ADMET properties, thereby eliminating wasted experiments, and contributing to reduced risks to test subjects, reduced failed clinical trials, and safer, less costly therapies for patients.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2007 ( Subtotal = $99,998 )
2007	2007	SIMULATIONS PLUS INC	42505 10TH STREET WEST	LANCASTER	CA	93534	LOS ANGELES	USA	Cancer Treatment Research	000	1	6/20/2007	NEW	$99,998
														Subtotal = $99,998

Grand Total All Awards = $99,998

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NOVEL IN SILICO MODELS FOR ADMET USING RAPID ESTIMATES OF QUANTUM CHEMICAL DESCRI

Award Number: R43CA130388

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 07/01/2007

PERIOD OF PERFORMANCE END DATE: 12/31/2007

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