2026 ASA Biopharmaceutical Section Distance Learning Series
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Committee Chair 2026
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Ji Young Kim
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Takeda
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Member (2026)
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Aida Yazdanparast
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AbbVie
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Member (2026)
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Arinjita Bhattacharyya
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Merck
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March: Challenges and opportunities in Neuroscience - Updates from the ASA BIOP SWG of Neuroscience
Presenters: Mandy Jin (AbbVie), Jianchang Lin (Takeda), and Co-leads of the SWG of NS
Date: March 27, 2026 11am-12pm ET
Registration Link: www.ticketleap.events/tickets/asabiop/...
Abstract:
This ASA webinar will present updates from the ASA BIOP Scientific Working Group for Neuroscience, emphasizing both the unique challenges and promising opportunities in modern neuroscience research. Attendees will gain insight into the group's efforts to address the complexity of neuroscience, including the development of advanced statistical and data science methodologies for clinical research. The session will highlight innovative study methods and designs that improve research efficiency, and discuss how artificial intelligence and machine learning (AI/ML) can transform the clinical trial design and analysis as well as interpretation of large-scale neuroscience datasets. An interactive Q&A segment will foster collaborative dialogue, encouraging practical discussions on overcoming current obstacles and leveraging emerging technologies to advance the field. This session will include an introduction to the ASA BIOP SWG for Neuroscience by Mandy Jin, an update on statistical methods in Neuroscience subteam by Jia Jia (AbbVie) and Hui Yang (Astellas), an update on Innovative study designs in Neuroscience subteam by Bo Lu (OSU) and Inna Perevozskaya (BMS), an update on AI/ML in Neuroscience subteam by Xiaodong Luo (Sanofi) and Yixin Fang (AbbVie). And the session will conclude with a Q&A led by Jianchang Lin.
February: A Bayesian Approach to Kinetic Modeling of Accelerated Stability Studies and Shelf Life Determination for Packaged Drug Products
Authors: Joris Chau, Hans Coppenolle, Yimer Kifle, Stan Altan
Link to materials:
Abstract:
Kinetic modeling of accelerated stability data serves an important purpose in the development of small molecule solid dose pharmaceutical products, providing support for shelf life claims and expediting the development path to clinical investigations. In this context, a Bayesian kinetic modeling framework is considered, accommodating different types of nonlinear kinetics with temperature and humidity dependent rates of degradation and accounting for the humidity conditions of the micro-environment within the packaging to predict the shelf life. In comparison to kinetic modeling based on nonlinear least-squares regression, the Bayesian approach allows for interpretable posterior inference, heteroscedastic error modeling, and the opportunity to include prior information based on historical data or expert knowledge. While both frameworks perform comparably for high-quality data from well-designed studies, the Bayesian approach provides additional robustness when the data are sparse or less well behaved. This is illustrated through several case studies of both real and simulated data.
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