Data-Backed Probability of Technical and Regulatory Success: Here’s How AI Supports Critical Decisions in Drug Development

Drug development is a slow and risky business, now even more so than in the past. Research shows that the average cost of developing a drug from discovery to market entry is about $2.6B and takes roughly 12 years. You will be hard-pressed to find any other products - except nuclear power plants - with similar timelines and costs.

What makes this even more challenging is that only about 12% of all drugs in clinical trials are approved by the FDA. This makes drug development a very long game with odds of success roughly that of a row bet in roulette.

No wonder biopharma companies are looking to decrease development time and cost and improve the probability of success. The industry is increasingly looking to AI to help achieve those goals – and bring novel treatments to patients faster.

Identify the Winners Early

Drug development, of course, isn't roulette. And "winning" or "losing" FDA approval is not based on chance but rather a large number of factors such as efficacy and safety data generated in clinical trials, the mechanism of action or the therapeutic area.?

For drug developers, this means that the earlier they can identify promising drug candidates with a higher likelihood to succeed, the sooner they can zero in on development efforts. The reverse is also true. Discontinuing a drug candidate with a low probability of technical and regulatory success (PTRS) early saves potentially hundreds of millions and frees up resources to advance more promising candidates.

Calculating PTRS is a critical process at multiple steps in drug development. Making the right decision is especially important in phase transitions where companies need to decide which drug candidates they take forward and whether to continue (or discontinue) a specific drug development program.

Drug developers have established approaches to evaluate PTRS, such as historical estimates, input from external experts, and statistical analyses. Still, the low drug approval rates show significant room for improvement. Innovation and new approaches are essential to augment current processes, reduce bias, and incorporate more data-backed decisions supplemented by artificial intelligence (AI). AI should be viewed as a powerful tool in one's toolbelt capable of improving current PTRS assessments.

AI – the Perfect Tool for the Job

Crunching massive amounts of data and detecting trends and patterns is precisely what AI/ML excels at, which makes it the perfect tool for the job and further enhances current approaches to PTRS.

Decades of detailed information about many aspects of drug development are available in disparate databases. Information such as clinical trial design, clinical trial outcomes, regulatory data, information about drug biology, and the companies sponsoring the trials can be pulled and curated. This will serve as an unbiased foundation on which to base PTRS assessments.

But how can an individual properly synthesize and pull all this disparate data together?

AI, a pattern recognition machine in the simplest of terms, is fit for this job. It operates similarly to the human brain in that AI algorithms learn by "seeing." That learning enables AI to identify patterns and detect previously hidden connections. While humans are very good at pattern recognition in their own right, the amount of data that needs to be considered for PTRS assessments far exceeds the human processing power.

Since 2017, we've been curating data from a multitude of different data sources as well as developing, selecting, honing, and training a variety of advanced AI models with the biomedical expertise needed.

Industry-wide, we are only scratching the surface when using AI in drug discovery. Strategically applying it to critical decision-making points like phase transitions generates actionable insights and gives drug developers a powerful tool to bring life-changing drugs to market faster.

If you want to learn more about how we can work together, visit intelligencia.ai .

References

"Deloitte pharma study: Drop-off in returns on R&D investments – sharp decline in peak sales per asset ," Deloitte, published January 23. 2023, Accessed October 23, 2023.

"Fast to first-in-human: Getting new medicines to patients more quickly ," McKinsey, published February 10, 2023, Accessed July 10, 2023.

Why 90% of clinical drug development fails and how to improve it ? Acta Pharm Sin B. 2022 Jul; 12(7): 3049–3062

PhRMA Research and Development Policy Framework . Published Sept. 2021. Accessed October 23, 2023.

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