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Artificial intelligence could acclaim a new era for drug development however forethought continues about how revolutionary its touch will actually be
The recent deal from the Top 10 Pharma MNC ($43m deal with British Artificial intelligence firm) may signal a revolution for drug development that could accelerate the drug discovery process, helping patients in urgent need of specialized treatments. Also many other players looking at investments in the AI arena on the expectation those AI solutions are braced to shape new the biopharma industry.
In the similar manner that the financial sector has engaged physicists and mathematicians to originate prognostic software to tweak trading decisions, many professionals foretell that Life sciences industry will be particularly centripetal to Artificial intelligence (AI) solutions as its inherent stranding in science, design and invention.
Artificial intelligence (AI) for discovery, development and regulatory challenges
The punctilious nature of pharma research and development means that drug discovery – bringing a new drug tomarket once a lead compound has beendiscovered – can take more than 10 years.The development of a single molecule can often cost more than $1bn because of inefficiencies in the sampling process and the vast numbers of screenings that generally required – fewer than 1 in 10 molecules that enter the discovery phases actually fetch up being taken to market.
Currently, some pharma majors using AI solutions to filter out difficult-to-find molecules in the drug discovery phases to expedite the development of treatments in a number of therapeutic areas. It could result in significantly shorten development times and brings down the prices – as screenings will be mainly substituted by super computer and prognosticative algorithms or models.
Clinical stages could benefit from AI algorithms inrespects to clinical trial design, site selection and subjects enrolment. Adverse events (AE) can be better auspicated with the help of AI solutions. Machine learning (ML) and analytics can accelerate the examination of clinical trial data. These combinations could also advance Pharmacovigilance measures, further down the drug discovery process.
Also regulatory affairs could benefit from AI /ML algorithms in respects to Regulatory Intelligence function, where submission requirements across 120 plus markets for a diverse product portfolio demands for wise use of industry-leading knowledge management and license management resources. However, while novel technology options are essential components, it is not enough by itself. An industry-leading Regulatory Intelligence and regulatory information management capacity must be provided by professionals who understand:
- The value of the data and the definition of the information they are dealing,
- The needs of their internal stakeholders, who require employing that data,
- The predilections and uses of the company’s external stakeholders who will be impacted by submission of that data.
Also, Regulatory Intelligence function evolved in recent times in global expanded global market to inform regulatory strategies, influence external environment, to ensure compliance and still there is a long gap in the industry as the Regulatory Intelligence function is mostly manual in all companies either reviewing the new guidance/regulations or the impact on company. Subscription databases are good but don’t minimize manual efforts.
Even if you utilize both public and paid databases, with a diverse product portfolio in multiple countries, keeping up to date using manual efforts is very tough. Adding more people to Regulatory Intelligence and Policy department is not an answer as cost justification every month will be challenging
Machine reading of global online documents daily, classifying them appropriately, forming internal relationships for software programs to understand the “context” is one of the answers to this challenge.