Title: Machine Learning Enabled Monitoring of Clinical Trials in Real Time via Probabilistic Programming
Speaker: Jianchang Lin, Takeda
Jianchang Lin is Senior Director, Statistics in Takeda Pharmaceuticals with extensive drug development experience across various therapeutic areas in oncology, cell therapy, immunology and rare disease including several successful global approvals and access in US, EU, Japan and China. He has published over 70 papers in statistical and clinical peer-reviewed journals/book chapters and served as editors for two books published in Springer with research interests in innovative trial designs, quantitative decision making, RWD/RWE and advanced data science. He is also an Associate Editor of Journal of Biopharmaceutical Statistics and currently serving as program chair elect for ASA Biopharmaceutical section, Industry Co-chair for 2024 Regulatory-Industry Statistics Workshop, etc.
Abstract: Monitoring of clinical trials by sponsors is a critical quality control measure to ensure the scientific integrity of trials and safety of subjects. With increasing complexity of data collection (increased volume, variety, and velocity), and the use of contract research organizations (CROs)/vendors, sponsor oversight of trial site performance and trial clinical data has become challenging, time-consuming, and extremely expensive. Across different clinical development phases (excluding estimated site overhead costs and costs for sponsors to monitor the study), trial site monitoring is among the top three cost drivers of clinical trial expenditures (9–14% of total cost). In this presentation, we will introduce a machine learning based SMRT platform from recent MIT-Takeda AI program collaboration, which can help to enhance operational efficiency in clinical trial oversight and monitoring. Specifically, SMRT platform achieves these results via probabilistic programming, an emerging AI paradigm that offers an alternate scaling route that can be more data-efficient, compute-efficient, and robust than deep learning. SMRT automatically learns structured, multivariate, generative models for clinical trial data, by inferring and updating the source code of probabilistic programs, and detects anomalies by calculating conditional probabilities of new data in real time. Through advanced predictive analytic features and automation, the platform allows to improve patient safety and site performance by detecting potential issues.
Title: Model Informed Drug Development motivations and examples
Speaker: Rich Anziano, Pharmetheus
Richard Anziano has been a statistician within the pharmaceutical industry for over 30 years. His expertise includes statistics, model informed drug development, and trial design in therapeutic areas such as neuroscience, pain, and infectious diseases. He was the Global Head of Statistics and Chief Statistician at Pfizer Inc, USA, where he led quantitative assessment of late-stage probabilities of technical and regulatory success through modeling and simulation for all late-stage assets compared to industry or therapeutic area benchmarks. Prior to this role, he had been a member of the statistics leadership for the prior 12 years, led groups of various sizes accountable for the quality and integrity of analyses for those groups, and served as lead statistician for compounds under development. He has
M.Sc. in Statistics (1993) from University of Connecticut, USA.
Abstract: TBD
Speaker: Satrajit Roychoudhury, Pfizer
Dr. Satrajit Roychoudhury is an Executive Director and Head of the Statistical Research and Innovation group in Pfizer Inc. He has 17 years of extensive experience in working with different phases of clinical trials for drug and vaccine. His research interest includes survival analysis, use of model-based approaches and Bayesian methods in clinical trials. He served as the industry co-chair for ASA Biopharmaceutical Section Regulatory-Industry Workshop in 2018 and co-chair for DIA/FDA Biostatistics Industry and Regulator Forum in 2023. Satrajit is an elected Fellow of the American Statistical Association and recipient of Royal Statistical Society(RSS)/Statisticians in the Pharmaceutical Industry (PSI) Statistical Excellence in the Pharmaceutical Industry Award in 2023 and Young Statistical Scientist Award from the International Indian Statistical Association in 2019. He authored Statistical Approaches in Oncology Clinical Development and co-authored several book chapters on statistical methods in drug development.
Title: Life in Pharma through the Eyes of an Early Career Professional
Speaker: Arinjita Bhattacharyya, Merck
Dr. Arinjita Bhattacharyya is an Associate Principal Scientist at the Biostatistics and Research Decision Sciences department at Merck. She has been with Merck for the past three years. She graduated with a PhD in Biostatistics from the University of Louisville, Kentucky, in 2020. Dr. Bhattacharyya has extensive knowledge of clinical trials with multiple first authored publications with several awards. She has served as a journal editor and reviewer for numerous noteworthy journals such as Contemporary Clinical Trials, Statistics in Medicine and have served in several committees such as the American Statistical Association, ENAR. She has gained hands on experience on drug development process and is involved with research projects within and outside Merck. She has delivered several invited talks and organized sessions at several conferences. Her research interests lie in Bayesian shrinkage priors, clinical trials, real world data.
Abstract: The accelerated development of therapeutics and vaccines to combat the deadly Covid-19 pandemic has made the world aware of the significant contribution of data scientists and statisticians in designing the clinical trials, conducting the analysis, and reporting the findings. This has led to the invention and dissemination of drugs and vaccines for public health benefits. The daily life of a statistician in the pharmaceutical industry involves designing complex clinical experiments, developing new methodologies and tools, implementing new techniques, collaborating with clinical scientists for reviewing, and interpreting the analysis results. Through this mentoring session, you will be able to get a close snapshot of the life of an early career statistician in pharma and gain knowledge about the roles and responsibilities, day-to-day projects, and effective skills that help students and talents to procure internship and employment opportunities. It will provide a hands-on opportunity to learn from the personal experiences. This talk aims to help quantitative talents to explore the world of pharma and navigate through the unknowns from the lens of an early career professional.