Feb. 12, 2007 It now costs more than $800 million to develop a new drug. But what if pharmaceutical companies had a way to predict which experimental drugs will ultimately get FDA approval, giving them the confidence to invest money in them, and which drugs will ultimately fail, allowing them to cut their losses early?
In the February issue of Nature Reviews Drug Discovery, researchers from the Children's Hospital Boston Informatics Program (CHIP) present a forecasting model that may increase the efficiency of drug R&D and save hundreds of millions of dollars per new drug. They also argue that more data sharing by the drug industry -- particularly of "negative" data -- would greatly improve the accuracy of forecasting and benefit industry and patients alike, allowing more medical discoveries to be brought to the bedside.
Asher Schachter, MD, MMSc, MS, and Marco Ramoni, PhD, both of CHIP, constructed a Bayesian network model to calculate the probability that a given new drug would pass successfully through Phase III trials and receive New Drug Application (NDA) approval. Their approach differs from convention in modeling populations of drugs rather than populations of patients. They used publicly available safety and efficacy data for about 500 successful and failed new drugs, broken down by therapeutic category, then confirmed the validity of their model by testing it with a group of cancer drugs whose fates are already known.
To gauge the model's potential economic impact, Schachter and Ramoni then performed a pharmaco-economic analysis in collaboration with Stan Finkelstein, MD, Senior Research Scientist at the MIT Sloan School of Management. This analysis, using summary data on industry-reported expenditures and revenues, indicated that application of the model would reduce mean capitalized expenditures by an average of $283 million per successful new drug (from $727 to $444), and increase revenues by an average of $160 million per Phase III trial (from $347 to $507 million) during the drug's first seven years on the market.
Schachter, also a pediatric nephrologist at Children's Hospital Boston, believes that more data sharing by the pharmaceutical industry would enable the industry to learn more from its own failures. "There's a tendency in the industry to bury data on failed drugs and forget about them," Schachter says. "We hope our model will add fuel to efforts to show that data-sharing could be beneficial to everybody."
Such efforts include legislation introduced in the Senate last year (S3807) that would establish a clinical trial registry database that would report the results of later-stage clinical trials, both good and bad.
In their report in Nature Reviews Drug Discovery, Schachter and Ramoni also argue that more accurate clinical forecasting would eliminate unsafe investigational new drugs; avoid subjecting patients to unnecessary drug trials; reduce the cost of prescription drugs for consumers; and empower the industry to take risks on truly innovative new drugs, so that more get to market.
The need for pharmaceutical industry involvement in early trials is especially acute for pediatric drugs, Schachter adds. Companies are reluctant to conduct clinical trials in children, fearing a negative impact on marketability. Instead, doctors often resort to giving adult drugs to children off-label, outside the context of a controlled, safety-monitored study.
For more information on the model and related issues, visit: http://phorecaster.com.
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