- Utilizing AI and machine learning can help at every stage of the drug discovery process.
- The following is a preview of an Insider Intelligence Health report, AI in Drug Discovery and Development.
- Do you work in the Health industry? Get business insights on the latest tech innovations, market trends, and your competitors with data-driven research.
The pharmaceutical industry has been slow-moving when it comes to adopting digital health technology, and pharma companies overall have taken a long time to implement AI and machine learning strategies — making broad-scale digital transformation difficult.
There is ample opportunity for drug discovery and development, but it relies on the ability of companies to implement advanced health tech into everyday strategies.
While the healthcare industry is rapidly adopting digital tech, the pharma industry is lagging on digital maturity, and any measures even early movers are taking to catch up are patchworked due to a lack of strategy and digital-focused leadership.
AI & Machine Learning in the Drug Development Process
An incredible amount of time and money goes into drug development — bringing a drug to market costs about $2.8 billion dollars over 12+ years, according to Taconic Biosciences’ tally.
Utilizing AI and machine learning can help at every stage of the drug discovery process. Healthcare AI startups were able to raise over $2 billion in Q3 2020, and those using AI to streamline the drug making process were the recipients of some of the heftiest sums compared with startups deploying the tech in other healthcare segments.
AI in Drug Discovery (Phase I)
The drug discovery process ranges from reading and analyzing already existing literature, to testing the ways potential drugs interact with targets. According to Insider Intelligence’ AI in Drug Discovery and Development report, AI could curb drug discovery costs for companies by as much as 70%.
AI in Preclinical Development (Phase 2)
The preclinical development phase of drug discovery involves testing potential drug targets on animal models. Utilizing AI during this phase could help trials run smoothly and enable researchers to more quickly and successfully predict how a drug might interact with the animal model.
AI in Clinical Trials (Phase 3)
After making it through the preclinical development phase, and receiving approval from the FDA, researchers begin testing the drug with human participants. Overall, this is a four-phase process and usually considered the longest and most expensive stage in the drug making journey.
AI can facilitate participant monitoring during clinical trials—generating a larger set of data more quickly—and aid in participant retention by personalizing the trial experience.
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Pharma Investments in AI
Big tech investments in pharma are at an all time high. Specifically, big tech firms with a broad range of AI and cloud solutions make valuable partners to drugmakers, which have varied needs when it comes to AI.
For example, Moderna leverages Amazon’s AWS cloud platform to speed up its drug development process. And while Moderna has recently made headlines as a top contestant in the race to develop a coronavirus vaccine, the company should also be recognized for its success in developing a cancer vaccine in just 40 days while leaning on AWS.
Moderna is just one example of the many pharma companies taking advantage of Big Tech’s growing interest in the digital health industry. And Insider Intelligence expects Big Tech to continue using their AI brawn to forge pharma tie-ups.
Here are the companies analyzed in the report:
- Eli Lilly
- Litmus Health
- Recursion Pharmaceuticals