Predicting the cost and pace of pharmacogenomic advances: an evidence-based study

• Published in: Clinical chemistry (Baltimore, Md.) Md.), 2013-04, Vol.59 (4), p.649-657
• Main Authors: Arnaout, Ramy, Buck, Thomas P, Roulette, Paulvalery, Sukhatme, Vikas P
• Format: Article
• Language: English
• Subjects: Anticoagulants; Drug dosages; Drug therapy; Evidence-Based Medicine; Failure; Genomics; Health Care Costs; Investments; Pharmaceutical industry; Pharmacogenetics; Prescription drugs; Presidential elections; Return on investment
• ISSN: 0009-9147; 1530-8561
• DOI: 10.1373/clinchem.2012.199455

Adverse outcomes associated with prescription drug use are common and costly. Many adverse outcomes can be avoided through pharmacogenomics: choosing and dosing of existing drugs according to a person's genomic variants. Finding and validating associations between outcomes and genomic variants and developing guidelines for avoiding drug-related adverse outcomes will require further research; however, no data-driven estimates yet exist for the time or money required for completing this research. We identified examples of associations between adverse outcomes and genomic variants. We used these examples to estimate the time and money required to identify and confirm other associations, including the cost of failures, and to develop and validate pharmacogenomic dosing guidelines for them. We built a Monte Carlo model to estimate the time and financial costs required to cut the overall rate of drug-related adverse outcomes by meaningful amounts. We analyzed the model's predictions for a broad range of assumptions. Our model projected that the development of guidelines capable of cutting overall drug-related adverse outcomes by 25%-50% with current approaches will require investment of single-digit billions of dollars and take 20 years. The model forecasts a pump-priming phase of 5-7 years, which would require expenditures of hundreds of millions of dollars, with little apparent return on investment. The single most important parameter was the extent to which genomic variants cause adverse outcomes. The size of the labor force was not a limiting factor. A "50 000 Pharmacogenomes Project" could speed progress. Our approach provides a template for other areas of genomic research.