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WEBINAR - Associating digital tools and QbD to speed-up process development

  • by Ypso-Facto
  • March 26, 2021
WEBINAR - Associating digital tools and QbD to speed-up process development

Regulatory bodies, including the FDA and EMA, are strongly promoting the implementation of QbD: a science and risk-based approach to drug development and manufacturing to increase the success rate, process robustness and eventually product quality.

In the meantime, different practitioners are a bit puzzled with this concept, and wonder if it is a must do step to meet regulatory expectations, a constraint to perform lengthy DoEs or a mean to develop efficiently and quickly reliable processes.

We will show that QbD is essentially a rational approach based on qualified experimental information ideally backed by sound theoretical concepts. In essence this is the methodology taught for decades in good engineering schools… something like identifying the PAR of CMAs and CPPs to meet the CQAs, to speak the dialect!

Using predictive numerical tools may be motivated by a desire to better understand the process and/or by saving time and minimizing the experimental burden required for process development.

We distinguish between two main approaches for modeling:

  1. Statistical approach: a “black-box”[1] model is built from experimental data to predict effects of critical factors and predict optimal responses.
  2. Mechanistic approach: the determination of a limited number of parameters associated with a model based on first principles allows to predict many experiments, possibly outside the investigated region.

How to choose one approach (or both, or none!)? Definitely, the actual need (scaling-up, optimizing, etc.), the knowledge (a basic stoichiometry, separation principles, etc.) and the data available (only production results, a few well thought experiments, etc.) should determine the answer.

By using a few simple illustrations, we will highlight the merits and limitations of these modeling approaches, and their impact on experimental burden savings.

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[1] An empirical model whose mathematical expression does not depend on the underlying physico-chemical phenomena


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