Speaker
Description
Design of experiments for process scale-up can be described as a double-edged sword for the phar-
maceutical industry: intensification of experiments expands the knowledge of the process (uncer-
tainty reduction) but increases resource expenditure. On the other hand, moving forward without
enough process understanding is the first stone in a path of deviations, lack of quality, and even
safety concerns.
In the past years, Bayesian sampling methodologies have surfaced to incorporate uncertainty and
lead to better guided risk/optimal decision making in terms of process conditions, and reduction
of required experiments. Utilizing Bayesian sampling for design space offers several significant
advantages: First, it allows for the incorporation of prior knowledge, leading to more informed
and efficient experimental designs [1]. Secondly, by continuously updating beliefs with new data,
Bayesian sampling enables a dynamic and adaptive approach, enhancing the accuracy and reli-
ability of results. This method also provides a rigorous framework for quantifying uncertainty,
ensuring robust decision-making even in complex scenarios [2]. Additionally, Bayesian sampling
can effectively identify the probability space with reduced experimental work, leading to an earlier
definition of a Normalized Operating Range (NOR) within a scale-up approach to a pharmaceutical
process.
In this work, a batch gas generating process with 10 different reactions occurring (reagents, prod-
ucts and by-products) is evaluated with the proposed Bayesian Design Space [1], with different pro-
cess parameters defined (time, temperature, reagent“A”initial concentration and reagent/solvent
“B”initial concentration) and consumption CQA’s required for the same process. The results showed
that a reduced amount of experiment (less than 6) were required to achieve an acceptable NOR for
the process, and the outcome allowed for a safe transfer to a higher volume unit (manufacturing)
with all safety and quality requirements achieved.
[1]–Kusumo, K. et al.,“Bayesian Approach to Probabilistic Design Space Characterization: A
Nested Sampling Strategy”, I&EC research, 2019
[2]–Kennedy, P. et al.,“Nested Sampling Strategy for Bayesian Design Space Characterization”
,
Comp. Aided Chem. Eng., 2020
| Type of presentation | Contributed Talk |
|---|