Hypothesis Generation and Evaluation in Clinical Trial Design

We are developing and implementing novel applications of knowledge representation, ontology development and natural language processing to address issues within the pharmaceutical industry. It is well-documented that the pharmaceutical industry is experiencing significant difficulties in maintaining its historical record of drug approvals and financial achievement because of failures in moving compounds from the discovery pipeline to regulatory approval and commercial success. The critical link in this transition is the clinical trial, heavily regulated and monitored for patient safety and drug efficacy. At present, only about 9% of drugs entering clinical trials succeed in being approved by the regulatory bodies, and unfortunately, that, alone, does not guarantee commercial success as a significant number of approved drugs fail upon introduction into the marketplace. Our approach evaluates and refines the hypothesis upon which these trials are based, establishes a comprehensive approach to an early go/no go decision, identifies risk and improves the probability for success.