In
the U.S., drug companies spend more than $50 billion on R&D, while in
Europe spending surpasses €30 billion. To help lower costs and to reduce the
time taken for new drugs to hit the market, pharma is turning to machine
learning.
The pharmaceutical
regulatory environment is becoming more challenging, and drugs must go through
extensive testing before they hit the market. As a result, there are major
incentives for drug companies to reduce R&D spending in order to free up
funds for additional ventures and offer lower prices for their products.
By
adopting sophisticated data science, and machine learning, pharmaceutical
researchers can save money and time on R&D. On top of that, machine
learning technology provides new ways for drug companies to streamline nearly
every other aspect of their businesses.
To
outline how machine learning can be best applied, data science software company, Dataiku,
has recently released a whitepaper titled “How
Machine Learning is Transforming Pharmaceuticals”.
The
paper goes on to assess how big data and machine learning are transforming the
pharmaceutical industry. The focus is with how big data analytics can improve
the patient recruitment process. This involves accessing and searching cloud services,
where relevant databases contain terabytes of publicly- and privately-held data
to enable clinical trial recruitment with better precision.
Another
example is with Natural Language Processing, which speeds up hypothesis
testing. When scientists design a drug trial tend to use their own knowledge of
medical literature and previous studies. Now algorithms can screen decades of
different studies and trials.
A
third application is with data
science methods, which can assist scientists with identifying
patterns in public and private datasets far faster and more meaningfully. Such
analytics can also assist with developing personalized medicines, where drugs
can be customized to small populations of patients with particular genetic
profiles.
Posted by Dr. Tim Sandle, Pharmaceutical Microbiology
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