Structure Activity Relationships (SARs) Using a Structurally Diverse Drug Database: Validating Success of Predictor Tools

Malcolm J D'Souza; Fumie Koyoshi; Lynn M Everett

Structure Activity Relationships (SARs) Using a Structurally Diverse Drug Database: Validating Success of Predictor Tools

Pharm Rev. 2009 Sep-Oct;7(5):https://web.archive.org/web/20100125114948/http://www.pharmainfo.net/reviews/structure-activity-relationships-sars-using-structurally-diverse-drug-database-validating-su.

Authors

Malcolm J D'Souza¹, Fumie Koyoshi¹, Lynn M Everett²

Affiliations

¹ Department of Chemistry, Wesley College, 120 N. State Street, Dover, Delaware 19901-3875, USA.
² Department of Biology, Wesley College, 120 N. State Street, Dover, Delaware 19901-3875, USA.

PMID: 26478759
PMCID: PMC4605434

Abstract

ADME/Tox (absorption, distribution, metabolism, elimination and toxicity) technology is traditionally associated as a tool in the drug discovery process which is often used to predict the efficiency of drug adsorption, distribution, metabolic pathways, and elimination. For the past four years we have been involved in an effort to evaluate readily available Food and Drug Administration (FDA) consumer drug profiles and pharmacological data. Portable Document Format (PDF) data from drug profiles available on the FDA Drug Information website were used to create a searchable FDA Consumer Drug Database^© using Bio-Rad's KnowItAll^® platform which includes ADME/Tox in silico predictors. 14 pertinent pharmaceutical and pharmacological properties were collected for 75 structurally diverse consumer prescription drugs, and for several drugs, not all properties were completely populated. The major objective of this investigation was to validate the platforms prediction models for plasma protein binding (PPB) and bioavailability (BIO).

Keywords: (quantitative) structure activity relationship (Q)SAR; ADME/Tox; FDA consumer drug database©; KnowItAll®; chemical informatics; predictor tools.

Abstract

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