AI and the Disruption of Big Pharma

Right now there is a lot of focus on Big Pharma for obvious reasons, so it was with interest that I read an article in Forbes by Alex Zhavoronkov about the pharmaceutical industry’s rather slow approach to using artificial intelligence (AI) when it is already very much a facet of less life-and-death services, such as Netflix movie suggestions.

Significantly, Zhavoronkov says, “Experts suggest that the pharmaceutical industry remains one of the most inefficient industries, a last holdout against technological disruption.” It’s curious is it not, that a sector that is all about scientific advancement should be so behind the times?

According to the article, “the efficiency of the industry has been on the decline since the 1950s. Just as an example, it now costs over $2.6 Billion to bring a drug — or a New Molecular Entity (NME) — to the market.” It’s a high cost, and then there are the costs related to the failed drug trials to factor in as well. Eventually, you and I pay for it all.

But AI has potential to perform a role in small molecule drug discovery, and we need to understand its potential and its limitations, especially in relation to the way that the pharma giants have traditionally gone about finding new drugs.

Zhavoronkov comments: “The process of small molecule drug discovery includes several steps: the scientists form a disease hypothesis, identify a target, design a molecule and then conduct pre-clinical studies takes on average five years and may cost hundreds of millions of dollars.” Then it takes another five years of clinical development and testing phases, which mean more expenditure.

AI might look like the perfect solution to reduce costs, and with all the Big Data available, surely it can help pinpoint marketable drugs. But, Zhavoronkov says:“Despite the many advances in technology that have led to major disruptions including mobile and personal computing, the Internet, and genome sequencing, the cost to develop a drug is steadily increasing.”

The pharma sector apparently remains sceptical about its uses: “they prefer to incrementally develop internal capabilities across the entire spectrum of the drug discovery process instead of making big bets on specific enabling technologies.”

Zhavoronkov remains optimistic about the future of AI in pharmaceuticals, and identifies the key to its success as “a massive integration of the systems used to identify biological targets, systems that help design novel molecules, and systems that personalize the treatments, and predict the clinical trials outcomes.” As he concluded that “The recent COVID-19 pandemic demonstrated the impotence of today’s traditional and AI-powered approaches, as seen when attempts to repurpose other drugs to treat Covid-19 did not really produce any promising candidates, and a “lot more work needs to be done in AI and laboratory automation to significantly accelerate drug discovery.”

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