A study to discover novel pharmaceutical cocrystals of pelubiprofen with a machine learning approach compared. Issue 21 (17th May 2022)

• Record Type: Journal Article
• Authors: Kim, Paul; Lee, In-Seo; Kim, Ji-Yoon; Mswahili, Medard E.; Jeong, Young-Seob; Yoon, Woo-Jin; Yun, Ho-Seop; Lee, Min-Jeong; Choi, Guang J.
• Is Part Of: CrystEngComm. Volume 24:Issue 21(2022)
• Page Start: 3938
• Page End: 3952
• Publication Date: 2022-05-17
• Subjects: Crystals -- Periodicals; Crystal growth -- Periodicals; Crystallography -- Periodicals; Cristaux -- Périodiques; Cristaux -- Croissance -- Périodiques; Cristallographie -- Périodiques; 548
• DOI: 10.1039/d2ce00153e
• Languages: English
• ISSNs: 1466-8033

Abstract

Abstract : Pharmaceutical cocrystals of pelubiprofen (PF) were discovered for the first time. 16 candidates to form cocrystals with PF were selected via the ANN model and the p K a rule. Abstract : Pelubiprofen (PF), a biopharmaceutical classification system (BCS) class II non-steroidal anti-inflammatory drug, has been on the market only in its crystalline form. To discover the first cocrystal form(s) of the drug, artificial neural network (ANN) modeling and the p K a rule were adopted to predict the most probable coformers that could form cocrystals with PF. Among candidate molecules examined theoretically and experimentally, isonicotinamide (INA) and nicotinamide (NCA) formed PF-based cocrystals through evaporative crystallization. The structures of the PF–INA and PF–NCA cocrystals were verified through multiple characterization techniques, including single-crystal X-ray diffraction. These two cocrystals demonstrated remarkably better water solubility and dissolution behaviors than did pure PF in both acidic and neutral solutions. Even with deficiency in the prediction capability, the combination of machine learning-based and knowledge-based coformer screening and the subsequent synthetic experiments would be a potential approach for discovering novel pharmaceutical cocrystals in the future.