The Coming Leap in AI-based Drug Discovery

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The Coming Leap in AI-based Drug Discovery

By 2022, the global pharmaceutical industry will generate $1.1 trillion in sales and spend an estimated $182 billion on R&D.  While these numbers are enormous, they still represent a huge cost-benefit pay-off for mankind in terms of saving lives and dramatically improving our quality of life.  And, for consumers, that pay-off will become even greater in the near future because so many “expensive medications” are about to “come off patent,” permitting the sale of inexpensive generic versions.  That shift puts great pressure on the pharmaceutical industry to discover and commercialize new and better treatments for diseases that will help billions of people and earn them hundreds of billions of dollars. 

In the December 2017 issue, we looked at how new “body-on-a-chip technology promises to accelerate commercialization and reduce development costs by ensuring that only drug candidates with a minimum likelihood of side-effects and maximum probability of efficacy, move into stage-one and stage-two clinical trials. 

At the same time, the discovery portion of the R&D cycle is on the verge of a dramatic increase in both effectiveness and productivity, enabled by the Artificial Intelligence.  As highlighted in the November 2017 issue of Trends, the application of AI to drug discovery and regulatory approval will not only deliver more drugs to market sooner, but will dramatically reduce the cost of those drugs.

In this issue, we’ll examine some of the specific drug discovery solutions that are already emerging and consider what we can expect to see within the coming decade.

One particularly high-leverage opportunity lies in using computers to identify ways of repurposing existing drug compounds for new diseases.  A computer program for doing just that, called DrugPredict, was recently developed by researchers at the Case Western Reserve University School of Medicine.  It matches existing data about FDA-approved drugs to diseases, and predicts potential drug efficacy.

In a recent study published in the journal Oncogene, DrugPredict was tested on epithelial ovarian cancer, which is the fifth leading cause of cancer deaths in women; it kills approximately 14,000 women annually in the United States. Available therapies are only moderately successful, with more than 70 percent of women dying within five years of diagnosis...

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