Introduction to Bayesian Methodologies in CMC

Duration: 60 minutes


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Why watch


Chemistry, manufacturing, and control (CMC) is a crucial stage in drug development. In CMC, activities such as defining product characteristics and modeling manufacturing processes are carried out to ensure the safety, consistency, and efficiency of drug products. In this webinar, we will demonstrate the importance of applying Bayesian statistics to CMC via several case studies.

Case Studies

  •  Case study 1: Incorporation of uncertainties from multiple sources in manufacturing to provide better control in process consistency and drug safety
  •  Case study 2: Risk-based approach through probability statements which evaluate equivalency of analytical methods, and assessment of a drug product staying sufficiently within specification throughout its shelf-life
  •  Case study 3: Using stability data to determine the release windows within which the probability of a drug product meeting specification at the end of its shelf-life is high

A full day of inspirational speakers

Proud to bring inspirational speakers from across the globe. 












Laurent Natalis

Senior Manager Statistics, PharmaLex

Laurent Natalis is a non-clinical statistician consultant working as Senior Manager Statistics at PharmaLex (Belgium). After a PhD in Evolutionary Ecology at the Earth and Life Institute (UCL, Belgium), Laurent joined the pharmaceutical world in 2010 in a biotech focused on the development of cell therapy. In 2013, he joined PharmaLex and contributed to the implementation of Bayesian statistics in various non-clinical fields, including stability studies, specification settings, DoEs, Design Space definition, Internal Release Limits computation, gage R&R studies, and application of QbD paradigm to the pharmaceutical industry. Laurent has a strong expertise in the application of Bayesian methodologies to mimic and optimize various CMC processes.


Angel (Yuelin) Lu

Manager, PharmaLex

Angel (Yuelin) Lu has a Ph.D. in statistics from Baylor University (2019 US). Her thesis involved modeling vaccine efficacy for selection bias samples and prediction of survival probability in prime-boost vaccination regime, both using Bayesian methods. She joined PharmaLex in 2017 and has worked on various non-clinical projects focusing on design of experiments, manufacturing, and bioassay development. In particular, she has significant experience using Bayesian methodologies in non-clinical applications, as well as with linear mixed models. Her clients include several of the largest pharmaceutical companies, and her work has taken her to client sites in the United States, Europe and China.


Olympia Tumolva

Statistican, PharmaLex

Olympia Tumolva holds a bachelor’s degree in Statistics and master’s degrees in Statistics with specialization in econometrics (University of the Philippines Los Banos) and biostatistics (Hasselt University in Belgium).
She has been working at PharmaLex as a statistician for 3 years. She has been primarily involved in non-clinical and pre-clinical projects.