info:eu-repo/semantics/article
Bayesian optimization of crystallization processes to guarantee end-use product properties
Fecha
2020-04-01Registro en:
Luna, Martín Francisco; Martínez, Ernesto Carlos; Bayesian optimization of crystallization processes to guarantee end-use product properties; Universidad Nacional del Sur; Latin American Applied Research; 50; 2; 1-4-2020; 109-114
0327-0793
1851-8796
CONICET Digital
CONICET
Autor
Luna, Martín Francisco
Martínez, Ernesto Carlos
Resumen
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 optimization algorithm is proposed. On this basis, a short sequence of experimental runs for pinpointing operating conditions that maximize the probability of successfully complying with end-use product properties is defined. Bayesian optimization can take advantage of the full information provided by the sequence of experiments made using a probabilistic model of the probability of success based on a one-class classification method. The proposed algorithm's performance is tested in silico using the crystallization and formulation of an API product where success is about fulfilling a dissolution profile as required by the FDA. Results obtained demonstrate that the sequence of generated experiments allows pinpointing operating conditions for reproducible quality.