In a context of economic uncertainty, high interest rates, talent shortages and rising capital costs, artificial intelligence (AI) is the priority for many industries, governments and academic institutions. While there is a long list of challenges that the healthcare industry must overcome, there is reason to believe that through artificial intelligence we are on the cusp of a great era of breakthroughs that could fundamentally change the field of medicine.
In pharmaceutical research and development (R&D), artificial intelligence is already realizing its potential. Artificial intelligence and data analytics are driving discoveries that allow us to predict patient responses, increase the likelihood of clinical trial success, and determine personalized treatment plans for patients. With artificial intelligence, we are breaking down new barriers to unlock previously untreatable targets and advance new therapies for patients who currently have no treatment options.
At Sanofi, using artificial intelligence to power drug discovery and development is having a major impact. Our key AI models in small molecule drug discovery are achieving prediction accuracy greater than 80% and are constantly improving with the use of active learning. 90% of our disease targets are credited using single-cell genomics, and 75% of small molecule projects are enabled by AI and machine learning (ML)-based compound design. We will then create virtual patients to conduct in silico clinical trials, and ultimately genomics-based precision medicine will help us achieve patient stratification.
We are using advanced active learning approaches, improving AI model training and requiring less data to train our models. Insights gained from AI are highlighting key structural elements to guide design cycles, making them shorter and more cost-effective, and driving new, higher molecular success rates. We are increasing the number of clinical trials by 50% and, to date, we have quadrupled the value of our pipeline between 2019 and 2023.
We are in constant contact with the innovation ecosystem, adopting a “borderless” drug discovery strategy. Twenty-five percent of our projects involve collaboration with partners, which has doubled research productivity measured in dollars spent per clinical candidate and doubled our first-in-human enrollments.
Furthermore, the way we operate is profoundly changed. Decisions have moved from an annual retrospective reporting capability to a dynamic forward-looking decision intelligence approach, linking strategic choices with operational decisions and seeking to improve our feedback loop.
It is clear that we are at the crossroads of a major expansion of medical discovery, but to fully exploit AI, there are several challenges that will have a major impact on the pharmaceutical industry’s ability to unlock the potential.
AI regulation
Regional differences in regulation will drive restrictions on where AI can be employed, standards and what constitutes high-risk applications.
Concerns about data quality, security, privacy and reliability have all threatened to slow the adoption of AI. Alliances and organizations are emerging to help companies self-regulate.
Strong data foundations and governance will be key to preventing vulnerabilities as many companies move to operationalize AI across their enterprises.
Undesirable effects of price restrictions
Unintended consequences of new pricing policies could reduce investment in promising research and development candidates. For example, the Inflation Reduction Act contains what some have called a “pill penalty,” as it establishes pricing after nine years for small molecule drugs compared to 13 years for biologics. It basically eliminates incentives to pursue new discoveries and uses for older drugs. The result could be more investment in biologics and less investment in small-molecule drugs.
Both biologics and small molecules have the same value. Small molecules can be administered orally, making them more convenient for many patients, and they are also critical for the treatment of many diseases.
Access to capital for biotech startups
The biotech startup environment is a rich source of innovation that complements the pharmaceutical industry’s extensive research and development efforts. The synergy between the two stimulates drug discovery.
However, startups struggle in a high interest rate environment as revenue from product sales is often years away. Higher rates also decrease the merger and acquisition intentions of large pharmaceutical companies as costs rise.
In 2021, 111 biotech companies had IPOs in the United States. In 2023, only 20 have had IPOs. At the same time, pressure for biotech companies to merge or fail has increased. According to EY, half of biotech companies lack the liquidity to sustain operations for more than 18 months. Creating an attractive environment for biotechnology is critical to maintaining power in the R&D innovation machine.
Building trust with new models for clinical trial design
Patient trust benefits from decentralized clinical trial strategies that allow participation by those in different regions of the world. This, combined with designs that take into account the representation of the patient population most likely to benefit, particularly disadvantaged patients, and gathering information from these patients, can create greater patient acceptance of new therapies.
Decisions and actions on each of the above aspects will need to be made carefully, evaluating trade-offs to ensure you get the maximum impact from new innovations, insights and tools. By increasing collaboration with diverse stakeholders to identify obstacles and formulate solutions in these uncharted territories, we can facilitate faster discoveries.
Paul Hudson is the CEO of Sanofi.
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