Open AccessBAYESIAN OPTIMIZATION OF CRYSTALLIZATION PROCESSES TO GUARANTEE END-USE PRODUCT PROPERTIES
Author(s) -
Martín Francisco Luna,
Ernesto Martínez
Publication year - 2020
Publication title -
latin american applied research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.123
H-Index - 23
eISSN - 1851-8796
pISSN - 0327-0793
DOI - 10.52292/j.laar.2020.388
Subject(s) - sequence (biology) , probabilistic logic , bayesian probability , computer science , process (computing) , product (mathematics) , bayesian optimization , crystallization , basis (linear algebra) , mathematical optimization , algorithm , mathematics , machine learning , engineering , artificial intelligence , chemical engineering , biology , operating system , genetics , geometry
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.