![]() ![]() Higher rates also diminish large pharma M&A intentions as costs rise. However, startups struggle in a high-interest rate environment as revenue from product sales are often years away. The synergy between the two spurs drug discovery. The biotech startup environment is a rich source of innovation that complements large pharma R&D efforts. Small molecules can be administered orally, making them more convenient for many patients, and they also are critical to treating many diseases. The result could be greater investment in biologics and less investment in small-molecule medicines.īoth biologics and small molecules are equally valuable. It basically eliminates incentives for pursuing new breakthroughs and uses for older medicines. For example, the Inflation Reduction Act contains what some have called a “pill penalty” as it establishes price setting after nine years for small-molecule drugs compared with 13 years for biologics. The unintended consequences of new pricing policies could diminish investment in promising R&D candidates. Unintended effects of pricing restrictions Strong data foundations and governance will be critical to prevent vulnerabilities as many companies move to operationalize AI across their enterprises. Alliances and organizations are emerging to help companies self-regulate. ![]() Twenty-five percent of our projects entail working with partners, which has doubled research productivity as measured by dollars spent per clinical candidate and doubled our first-in-human entries.Ĭoncerns about data quality, security, privacy, and trustworthiness have all threatened to slow the uptake of AI. ![]() We are in constant contact with the innovation ecosystem, adopting a drug discovery “without borders” strategy. We are increasing the number of clinical trials by 50% and, to date, have quadrupled our pipeline value between 20. ![]() AI learnings are highlighting key structural elements to guide design cycles, making them shorter and cheaper, and resulting in higher new molecular success rates. We are using advanced active learning approaches, improving AI model training, and requiring less data to train our models. We then create virtual patients to drive in silico clinical trials and, finally, genomics-based precision medicine will help us achieve patient stratification. Ninety percent of our disease targets are credentialed using single-cell genomics and 75% of small-molecule projects are enabled by AI and machine learning (ML) compound design. Our key AI models in small-molecule drug discovery are achieving more than 80% prediction accuracy–and they are constantly improving with the use of active learning. With AI, we are breaking new barriers to unlock previously undruggable targets and bring forward new therapies for patients who currently have no treatment options.Īt Sanofi, leveraging AI to empower drug discovery and development is having a major impact. AI and data analytics are driving breakthroughs that enable us to predict patient responses, increase the likelihood of clinical trial success, and determine individualized treatment plans for patients. In pharmaceutical research and development (R&D), AI is already delivering on its potential. ![]()
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