BAYESIAN OPTIMIZATION OF CRYSTALLIZATION PROCESSES TO GUARANTEE END-USE PRODUCT PROPERTIES

LUNA, MARTIN FRANCISCO; MARTINEZ, ERNESTO CARLOS

LATIN AMERICAN APPLIED RESEARCH

LATIN AMERICAN APPLIED RESEARCH

Año: 2020 vol. 50 p. 109 - 114

0327-0793

For pharmaceutical solid products, the issue of reproducibly obtaining their desired end-use properties depending on crystal size and form is the main problem to be addressed and solved in process development. Lacking a reliable first-principles model of a crystallization process, a Bayesian optimi-zation algorithm is proposed. On this basis, a short sequence of experimental runs for pinpointing oper-ating conditions that maximize the probability of suc-cessfully complying with end-use product properties is defined. Bayesian optimization can take advantage of the full information provided by the sequence of ex-periments made using a probabilistic model of the probability of success based on a one-class classifica-tion method. The proposed algorithm?s performance is tested in silico using the crystallization and formu-lation of an API product where success is about ful-filling a dissolution profile as required by the FDA. Results obtained demonstrate that the sequence of generated experiments allows pinpointing operating conditions for reproducible quality