Probabilistic modeling of the drug development domain: A Bayesian domain-knowledge application for pharmacovigilance

A recent analysis by the Tufts Center for the Study of Drug Development estimates that the cost of developing a single new chemical entity (NCE) into a successful therapeutic agent is $802 million. This figure is largely dependent on the expense of investigating NCEs that ultimately fail to be approved for use: between 70 - 90% of NCEs do not achieve New Drug Application (NDA) approval, and many of these failures are identified during the later, more costly phases of drug development. The exponential growth in the number of putative NCEs as a result of combinatorial chemistry and high-throughput screening has only confounded this problem by significantly increasing the number of early-phase NCEs under consideration for further costly development in human clinical trials. It is widely agreed upon that there are 3 major categories of reasons for drug failure: safety (toxicity), efficacy, and economics. This thesis is concerned with developing a Bayesian domain-knowledge probabilistic model (called Pharminator) to address the first two of these categories, with a goal of predicting clinical success of an NCE. Pharmacoeconomic modeling is a vastly different domain compared to Pharminator's clinical trial domain, and is beyond the scope of this thesis. While several clinical predictive models have been described in the literature over the past 10 years, the ongoing costly failure rate in drug development warrants developing more reliable predictors of NCE clinical success. The number of NDA approvals in 2002 fell to a 5-year low of 18, compared to 30, 35, 27, and 24 in 1998, 1999, 2000, and 2001 respectively, despite rapidly increasing numbers of NCEs as a result of high-throughput screening and combinatorial chemistry.

Department dc:contributor.department

Harvard University--MIT Division of Health Sciences and Technology.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2003

Author dc:creator

• Schachter, Asher Daniel, 1967-

Advisor dc:contributor.advisor

• Isaac S. Kohane.

• Harvard University--MIT Division of Health Sciences and Technology.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/32249

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/32249