Artificial intelligence is changing the way businesses operate and innovate across a huge range of industries. Perhaps nowhere is this more true than in the pharmaceutical industry, where AI promises to help revolutionize the way drugs are developed.
In this article we’re exploring the Important Role AI Plays in the Future of Pharmaceuticals. AI is predicted to play an important role in the future of pharmaceuticals and the industry as a whole.
Though pharmaceutical companies routinely report profits that are eye-watering to the average individual, the fact is that profits have been declining steadily for the past decade.
According to a report from Deloitte, R&D returns have declined from roughly 10% in 2010 to 1.8% in 2019. The average cost to bring a drug to market has increased from $1.2 billion to close to $2 billion during the same timeframe — an increase of 67%. New drugs that do make it to market are generating less revenue on average than those introduced in the past.
As more generic drugs have come onto the market, issues of price competition have become a factor for big pharma companies. Increasingly, regulation and efforts to rein in drug costs are topics of focus in U.S. politics.
Artificial intelligence has a critical role to play in the future of pharmaceuticals. Four key areas where AI is having an impact on the pharma industry include:
Specialty drugs, either those designed to treat rare diseases or those targeted to specific populations (i.e.; precision medicine) are a major area of opportunity for pharma companies. However, such drugs are generally both more difficult and more expensive to develop. By their very nature they also target smaller groups, so the size of the market for such drugs is limited. In the past this has meant that it’s been fairly cost-prohibitive to focus on developing specialty drugs.
AI can be used to identify molecules and compounds that may be able to be used in the treatment of rare diseases. This can help point researchers to specific areas of focus and reduce risks associated with launching new trials.
AI is exceptional when it comes to sifting and interpreting large sets of data. One way AI can be put to use in pharma is in helping to identify new uses for existing drugs. AI can find patterns in large datasets that may not be obvious to human researchers. In other words, AI can find connections we didn’t even know to look for and, in so doing, identify new uses for drugs that are already on the market.
As discussed earlier, bringing a new drug to market is an incredibly costly endeavor. Even worse, years of research and hundreds of millions of dollars of investment in clinical trials are by no means a guarantee of success. In fact, only about 1 in 10 trials are successful, but it’s very difficult to know which ones will fail until years of work have already been poured into development.
AI can help boost trial success rates by identifying the most promising potential treatments from the outset. Transferring some of the onerous early stage trial-and-error work that goes into drug development from human researchers to machines is a means of more quickly pinpointing the best treatments to focus on, reducing time wasted on less viable options.
With the average cost of drug development reaching $2 billion and the time to bring a new drug to market clocking in at upwards of a decade, pharma is focusing on ways to alleviate these burdens.
Already we’re seeing the power of AI to have an impact in this regard. In January, 2020 a British biotech company called Exscientia, in partnership with Japanese pharma company Sumitomo Dainippon Pharma, announced that they had used AI to invent a new molecule to be used in a drug for the treatment of OCD. The treatment is moving to the clinical trial phase — a process that took 12 months as opposed to the usual 5 years.
The past decade has been a challenging one in the pharmaceutical field as development costs have grown while the average value of a new drug has declined. Artificial intelligence is one of the most promising technologies being brought to bear on the industry with the potential to improve trial success rates, identify the most viable areas of focus, and reduce both the time and cost involved in drug development.