AI-generated in silico data in patent applications

The impact of AI-generated in silico data on pharma patent applications

In silico data generated using AI platforms can identify existing medication candidates and match them with diseases and conditions that do not yet have a cure much quicker and more reliably than a human will ever be able to do. However, it raises issues about the patentability of those computer-assisted drug innovations. One of the most well-known examples of drug repurposing is Viagra®, which proved tremendously effective for treating erectile dysfunction when clinical trials for a new drug originally aimed at fighting angina showed certain side effects.

Repurposing approved drugs, or drugs that have stalled in clinical trials, has really taken off in the past few years. It is not surprising to see why: getting a new drug to market typically takes 13–15 years and between US$2 billion and US$3 billion on average; for repurposed drugs, the time and costs associated with R&D go down to ∼6 years and the cost is ∼US$300 million [1]. Getting it right could bring a cure or reprieve to patients, not to mention a potential financial windfall to the new patent holder. However, identifying drugs that show efficacy is more easily said than done – human predictions are often based on a hunch, and people get it wrong more frequently than they get it right.

Artificial intelligence (AI) and machine learning (ML) have the potential to transform the way in which drugs are identified for repurposing. However, there is a gap between current patent laws and computer-assisted drug innovations. As AI evolves and the accuracy of predictions improves, patent law might need to be updated – along a similar vein to copyright laws, which were brought into the 21st century to include computer-generated imagery. The pharmaceutical industry and the legal profession will need to work together to solve some key issues for which there are no easy answers.

Continue reading this article on ScienceDirect here.

 References

1 N. Nosengo
Can you teach old drugs new tricks?
Nature, 534 (2016), pp. 314-316
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