Open AccessPrediction and Preparation of Coamorphous Phases of a Bislactam
Author(s) -
Luke I. Chambers,
Osama M. Musa,
Jonathan W. Steed
Publication year - 2022
Publication title -
molecular pharmaceutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.13
H-Index - 127
eISSN - 1543-8392
pISSN - 1543-8384
DOI - 10.1021/acs.molpharmaceut.2c00357
Subject(s) - active ingredient , partial least squares regression , training set , biological system , stability (learning theory) , chemistry , mathematics , computer science , artificial intelligence , machine learning , medicine , pharmacology , biology
The effectiveness of a partial least squares-discriminant analysis coamorphous prediction model was tested using coamorphous screening data for a promising coamorphous former, the dimer of N -vinyl(caprolactam) (bisVCap) with a range of active pharmaceutical ingredients. The prediction model predicted 71% of the systems correctly. An experimental coamorphous screen was performed with this coformer with 13 different active pharmaceutical ingredients, and the results were compared to the predictions from the model. A total of 85% of the systems were correctly predicted. Stability assessments of three coamorphous systems showed that the prediction model score did not strongly correlate with the stability of the coamorphous material. The model performed well with small-molecule coformers, such as bisVCap, despite the difference in structure and properties compared to the amino-acid-based model training set.