It now costs over $800 million to develop a new drug. And what's worse, there is no guarantee of success.
What if drug companies could predict which new drugs will get FDA approval and which will fail? Researchers have developed an analytical model that could save hundreds of millions of dollars per new drug, and urge more data sharing by the drug industry to improve forecasting, so that more medical discoveries can be brought to the bedside. If pharmaceutical companies had a way to predict which experimental drugs will ultimately get FDA approval, it would give them the confidence to invest more money in them. If they knew which drugs will ultimately fail, they could 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.
Domain: Bio-IT-Cybernetics convergence
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