(478e) Optimization of Process Risk, Cost, and Environmental Sustainability during Late Stage Drug Substance Control Strategy Development | AIChE

(478e) Optimization of Process Risk, Cost, and Environmental Sustainability during Late Stage Drug Substance Control Strategy Development


Sipple, P. - Presenter, Bristol Myers Squibb
Hickey, M., Bristol Myers Squibb
Fenster, M., Bristol-Myers Squibb Company
Kopp, N. D., Bristol-Myers Squibb Co.
Tabora, J., Bristol-Myers Squibb Company
Zang, J., Bristol Myers Squibb
Fraunhoffer, K., Bristol Myers Squibb
During late stage pharmaceutical process development, objectives such as cost, environmental

sustainability, and risk to product quality, are important to consider. Optimization at this stage balances

these objectives against the complexity of evaluating and incorporating process adjustments. Here, we

present a series of process improvements made during late stage development of a small-molecule drug

substance. These improvements were supported by empirical and mechanistic process understanding

incorporating measures of risk, cost, and sustainability.

To direct the investment of limited development resources, a quantitative analysis was done to

calculate key process metrics. A key insight from this analysis was that the process to produce one

specific intermediate was estimated to account for three quarters of the drug substance’s

manufacturing cost. This analysis directed investment into improving the process yield across the eight

chemical transformations, involving a wide variety of unit operations to make this intermediate.

Specifically, significant cost reduction was achieved through multivariate optimization of an oxidation

reaction, identification and mitigation of mass loss during extraction and distillation, and multivariate

optimization of yield vs. impurity purge during crystallization.

Another improvement later in the synthesis involved the reduction of significant process risk

present in a three-reaction telescope through the development of an additional isolation after the first

reaction in this sequence. The reduced process risk was quantitated as a failure rate, using multivariate

Bayesian modeling to ensure process robustness despite potential parameter variations. Finally, changes

were made to the penultimate process step that improved sustainability and reduced cost without

increasing process risk. These changes were enabled through generation of mechanistic models that

enhanced kinetic and thermodynamic understanding of the reaction and the subsequent water uptake

of the product. Developing fundamental engineering understanding and enhancing it with quantitative

metrics both informs where to focus development resources and provides a fully comprehensive data

package summarizing knowledge, residual risks, and documenting improvements in a format that is easy

to communicate to stakeholders and enables quality by design.