Biotech’s Secret to Surviving Economic Challenges? It’s All in the Data & AI
Contributed Commentary by Dr. Jia Chen, Medidata
September 6, 2024 | Data has always been crucial in the life sciences industry, but in today's fiercely competitive landscape, its significance has skyrocketed. Amidst dwindling funding and mounting market pressures, biopharmaceutical companies need to better harness the power of information to design and accelerate clinical trials and timelines. Success is measured by the tangible difference these therapies make in patients' lives. However, while the average cost of developing a new drug has surged, hovering around $2.3 billion, the success rate for these drugs reaching patients is still only around 10.8%. Behind these statistics lie countless stories of patients waiting for life-saving treatments, hoping for a breakthrough that could alleviate their suffering.
Aside from the financial challenges, constantly evolving regulatory requirements add further levels of complexity and pressure to the drug development process. Against this backdrop, efficient trial design and execution are paramount to ensure timelines are met and within budget.
One of the more exciting developments is the use of generative AI to produce synthetic data. By employing algorithms trained on existing and extensive datasets, this emerging and innovative technology can generate anonymized datasets that ensure patient privacy while providing valuable insights. This approach allows researchers to simulate drug interactions on digital models of disease, accelerating the evaluation of therapies and ensuring that promising treatments can reach real patients faster. Moreover, synthetic data supports the creation of precision medicines, specifically designed to provide benefit to targeted patient groups.
Clinical trial solutions that leverage integrated platforms and AI for smart trial design can help sponsors and study sites make sense of an intricate ecosystem of data, including insights from electronic health records, lab, and sensor sources. This represents a critical step to ensuring data integrity, security, and compliance with regulatory standards and further reinforces the validity of trial results.
Today, the patient is at the center of all innovations across the healthcare industry. AI plays a pivotal role in this shift, enhancing the design of clinical protocols to ensure treatments are safer and more effective. It is imperative to have the ability to predict how patients will most likely respond to a particular treatment or their risk of experiencing adverse effects. Predictive technologies can also be used to diversify patient recruitment by identifying sites with the right patient mix for trials. This improves not only the accuracy and depth of scientific research but also access to new treatments for underserved populations.
As the sector grapples with economic tailwinds, clinical trial solutions are effectively providing new opportunities for collaboration across the biopharmaceutical sector, making it possible to share historical datasets with broader users. For example, clinical developers of Chimeric antigen receptor T (CAR-T) cell therapies, a promising new therapeutic approach to treat cancer, continue to face challenges, including a lack of relevant data. Where this information was previously locked in individual studies, advancements in technology are making it more accessible for researchers to draw insights from the volume, depth, and richness of historical trial data.
Intelligent technologies will continue to evolve and be integrated across the full spectrum of trial solutions, providing a connected experience for patients and shifting the paradigm to offer a more comprehensive understanding of data – both within and beyond the confines of traditional trial settings.
With the biopharmaceutical sector facing numerous challenges, companies can now leverage emerging technologies, including AI, to find efficiencies throughout the study process and utilize better insights to bring life-changing treatments to patients sooner.
Dr. Jia Chen is a Senior Director at Medidata, part of Dassault Systèmes. She leads the strategy and growth of synthetic data solutions leveraging generative AI. Prior to Medidata, she successfully led the launch of new products serving millions of users and held leadership roles in product management, innovation incubators, strategic partnerships and AI client centers at IBM corporate headquarters as well as in emerging markets. She was named as one of the top 35 innovators (TR35) by MIT Tech Review, and one of the top 26 tech women innovators at IBM. Dr. Chen received her Ph.D. in Physics from Yale University and holds 45 patents. She’s currently the chair of the Yale Graduate School Alumni Association Board, and a member of the Yale Alumni Association Board of Governors. She can be reached at Jia.CHEN@3ds.com.