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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July:
(1) Spatially Aware Plate Layouts (SAPL): An R Shiny Tool Integrating Experimental Constraints into Randomization for Optimal Plate Designs
Date: July 10, 2026, 11am-12pm EDT
Registration Link: events.ticketleap.com/tickets/asabiop/...
Presenter:
Beate Ehrhardt, Mathematical Innovation Research Fellow at the Institute for Mathematical Innovation at the University of Bath
Abstract:
Multi well plates underpin high throughput experimentation across biochemistry, pharmacology, and molecular biology, yet spatial artefacts-such as edge effects and local clustering-can introduce systematic biases that compromise reproducibility and statistical power. Traditional layout strategies, whether manually designed or based on naive randomization, often fail to address these spatial dependencies effectively.
We introduce SAPL (Spatially Aware Plate Layouts), a controlled randomization framework that merges rigorous experimental design principles with the spatial structure of common plate formats. In contrast to full randomization, which may inadvertently concentrate critical conditions or controls, SAPL actively enforces spatial balance by managing factors such as control dispersion, edge related susceptibility, and row/column structure. This approach enhances the reliability of normalization procedures and strengthens downstream statistical analyses.
By integrating spatial awareness into the randomization procedure, SAPL helps improve the reproducibility, robustness, and translational value of preclinical data. To facilitate widespread use, SAPL is available as an open access, user friendly R Shiny tool requiring no programming expertise. It reduces the time of generating a 384 well plate from 1 day to 15min. The framework has been rigorously evaluated in collaboration with scientists at AstraZeneca and the Functional Genomics Screening Lab across diverse experimental settings.
(2) Title: Efficiency+: Advancing Clinical Trial Operations through Statistical Innovation
Date: July 24, 2026, 11am-12pm EDT
Registration Link: events.ticketleap.com/tickets/asabiop/...
Presenters: Fei Chen (J&J), Guohui Wu (Regeneron), Leann Goldstein (Amgen), Roy,Dr.,Dooti (MED BDS) (Boehringer Ingelheim), Christi Kleoudis (AstraZeneca), Palanikumar Ravindran (BMS), Vlad Anisimov (Amgen), Clara Cali Mella (Bayer)
Most clinical trial statisticians have probably never been asked: How many vials of drug should we ship to a site in Brazil next month? Or: Should we activate five more sites in Japan to stay on track for enrollment? These are clinical operations questions, but they are fundamentally statistical problems: delayed enrollment, drug supply waste, underperforming sites, and many more. Yet statisticians have historically lacked a dedicated community to share methods, compare approaches, and develop best practices in this space. Formed in 2025 under the ASA Biopharmaceutical Section, the Efficiency+ Scientific Working Group (SWG) was created to fill that gap, uniting statisticians, data scientists, and clinical operations professionals from over a dozen pharmaceutical and biotech companies.
In this webinar, Efficiency+ members will provide a comprehensive update on the group's first year of activities and its vision for the future.
Agenda
- Introduction to Efficiency+ (Fei Chen, Johnson & Johnson, 8 min)
Overview of the group's mission, its four topic workstreams - patient recruitment monitoring and forecasting/site selection, dynamic trial monitoring and data quality, study design and operations impact, and clinical supply chain - and key achievements since the July 2025 kickoff.
- Reading Group Highlights (Guohui Wu, Regeneron, 8 min)
A tour of recent literature and invited talks covered by the Efficiency+ reading group, highlighting emerging methods and open research questions in clinical trial operations.
- Workstream Reports (Workstream leads, 8 min each)
Each of the four topic sub-team leads will report on their workstream's activities, ongoing research, and planned deliverables - including progress toward a special journal issue and a shared clinical trial simulation engine:
o Patient Recruitment Monitoring & Forecasting / Site Selection - Clara Cali Mella (Bayer) & Vlad Anisimov (Amgen)
o Dynamic Trial Monitoring and Data Quality - Palanikumar Ravindran (BMS)
o Study Design and Operations Impact - Dooti Roy (Boehringer Ingelheim)
o Clinical Supply Chain - Christi Kleoudis (AstraZeneca)
- Community and Outreach (Leanne Goldstein, Amgen)
Social media engagement, conference presence for the remainder of 2026 and into 2027 (IBC, JSM, RISW, SCOPE Europe), website and GitHub resources, and how to get involved.
Attendees will leave with a clear picture of the state of statistical innovation in clinical trial operations and opportunities to contribute to or benefit from this growing community.
Target Audience: Statisticians, data scientists, and clinical operations professionals with interest in enrollment forecasting, site selection, trial monitoring, adaptive/platform trial design, or clinical supply chain optimization.
May: Exploring Biopharma Careers and Community with ASA BIOP
Date: May 28, 2026 7 - 8:30 pm
Link to materials: Slides
Abstract:
Whether you're already a member of the Biopharmaceutical Section (BIOP) within the American Statistical Association (ASA) or are just interested in drug development or a career in biopharma, this webinar has something to offer you! This 90‑minute session introduces BIOP, focusing on how graduate students and early career statisticians can get the most out of their BIOP membership. The webinar will feature a panel of statisticians working in different settings, including clinical and non‑clinical research across diverse organizations. They will share what their work looks like day‑to‑day, how teams collaborate, and how methods you learn in graduate school are used in practice.
We will walk through how to join ASA and BIOP and what membership offers right now. BIOP initiatives include support through student awards and scholarships, educational opportunities such as web‑based training and working groups, and career development resources with job postings, internship and fellowship announcements. You will also hear about networking opportunities, professional development, and support for attending conferences, plus access to BIOP community news, the BIOP podcast, and the Biopharma Report.
The session will also highlight one of the Section's flagship events: the ASA Biopharmaceutical Section Regulatory‑Industry Statistics Workshop, held every September in the Washington, DC area. The workshop features two days of sessions bringing together industry, academia, and regulators, preceded by a day of short courses – an excellent way for students to learn, network, and present.
April: From Data to Cure: Strategize Rare Disease & Precision Care Future Outlook of Real World Data Ecosystem in China Healthcare
Presenters: Dr. Luyan Dai, G-Plume Consulting
Date: April 24, 2026
Link to materials: Slides
Abstract:
This seminar talk focuses on the development of China's real-world data (RWD) ecosystem for rare diseases and precision care. Strong government-led policies and the National Medical Products Administration (NMPA)'s targeted efforts form the core driving force for industrial development. The Chinese government has initiated a unique data sovereignty model centered on government-authorized carriers and a Trusted Data Space (TDS) and built a multi-tier RWD platform system with privacy computing, multi-modal data governance and end-to-end clinical application capabilities. It also leverages diversified funding channels including government carrier funds and charitable funds to support the construction of research infrastructure, promote multi-center natural history studies, and build a decentralized ecological closed loop and national "1+N" collaboration network, realizing the transformation of RWD from passive collection to asset operation.
A series of supportive measures for rare disease and precision medicine innovation have been rolled out: it optimizes the regulatory review system for clinical research and innovative products, standardizes RWD application and clinical evidence generation, and endorses the integration of AI and Bayesian methodology applications. The construction of compliant cross-institutional data sharing mechanisms and trusted data infrastructure are supported to accelerate the translational application of precision medicine technologies such as gene diagnosis and targeted therapy, and guides the building of an industry win-win ecosystem linking hospitals, biotechs and research institutions, effectively reducing R&D risks and improving the efficiency of clinical trial development for rare diseases.
Driven by government policies and NMPA's regulatory support, China's healthcare ecosystem has achieved notable efficiency gains, including shortening clinical trial recruitment cycles, and achieving a significant reduction in R&D costs. The precision medicine methodologies developed for rare diseases are replicable to complex diseases such as tumors and neurological disorders, evolving from single-disease optimization to disease spectrum-based precision medical capabilities. With government-led institutional guarantees, a standardized RWD ecosystem and industrial collaboration, China is positioned as a global strategic accelerator, unlocking the potential of RWD and precision medicine to address unmet medical needs for global therapeutic innovation.
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
Link to materials: Slides
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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