CPHI Annual Report Predicts AI Transformation of All Drug Development Processes by 2026

A recent publishing of the first part of the CPHI Annual Report predicts substantial progress ahead for artificial intelligence (AI)’s use in pharma, despite the uproar surrounding ChatGPT and other AI programs. According to a press release, the implications go to the extent that within the next 10 years, more than 50% of approved drugs will involve AI in their development and/or manufacturing.

The findings come ahead of CPHI Barcelona held at Fira Barcelona from Oct. 24–26, 2023. The report features insights from 250 global pharma companies and is seen as a key indicator of the industry’s future growth prospects, according to the press release. This year is the first time in the report’s history that pharmaceutical “AI companies” (26%) have overtaken “late stage” (20%) and “early stage” (19%) biotechs as the industry’s most appealing investment option.

“The tools we have today to evaluate genetic material and protein structures are tremendous. As we gather data, the models we use for evaluation within AI will improve and our criteria and insight will become more refined, allowing us to design and direct evaluation models more efficiently,” said Bikash Chatterjee, CPHI Report expert and chief science officer, Pharmatech Associates, in a press release. “I would agree that we are very near to seeing AI discovered molecules getting approved today—definitely within the next two to five years. In the next decade, it is likely most drug therapies will be identified by some element of AI.”

In addition, the rate of change is accelerating, with 62% forecasting that the first fully AI drug discovered and developed therapy will be approved by FDA within the next five years, whereas 20% believe this can be achieved in under two years. By 2030, over 52% of new drugs approved will be discovered or developed using AI. According to the press release, costs are also predicted to be lower as clinical trial designs are improved alongside in silico modeling and manufacturing efficiencies, with clinical trial patient recruitment benefiting from the technology’s ability to analyze massive datasets.

AI’s role in target discovery and manufacturing optimization remained the most chosen application looking three years ahead; however, approximately 43% envision that it will help to build base regulatory submissions.

“Adoption will be fastest—initially—for drug discovery as there are no GMP [good manufacturing practice] or compliance requirements and, given the poor success rate of drugs that make it to market, going from nine out of 10 drugs failing to eight out of 10 drugs failing would be doubling of current success rates. Its potential impact cannot be overstated,” added Chatterjee in a press release.

Source: CPHI/Informa