A study to discover novel pharmaceutical cocrystals of pelubiprofen with a machine learning approach compared

Kim, Paul; Lee, In-Seo; Kim, Ji-Yoon; Mswahili, Medard Edmund; Jeong, Young-Seob; Yoon, Woojin; Yun, Hoseop; Lee, Min-Jeong; Choi, Guang J.

doi:10.1039/d2ce00153e

Cited by 12 publications

(9 citation statements)

References 69 publications

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“…The raw dataset used in this study was gathered, revised, and collected from previous studies, experimental reports, and cocrystal chemical experiments 1,49,50 . The raw dataset involved the chemical reaction between API with their respective multiple coformers and samples of it as depicted in the figure 1.…”

Section: Data Gathering and Collectionmentioning

confidence: 99%

“…In the area of pharmaceutical cocrystal engineering research, Mswahili et al 1 built several machine learning-based models for cocrystal prediction where the artificial neural network (ANN) somewhat outperformed others. Moreover, Min-Jeong Lee et al 49 and Paul Kim et al 50 adopted the aforementioned work as their main chemical experimental guide which led to promising and potential results on various cocrystals prediction. Recently, Fu Xiao et al 12 and Yuanyuan et al 51 built a GCN model for cocrystal prediction of Bexarotene and bioavailability improvement, and CCGNet was synthesized for quick discovery of coformer respectively.…”

Section: Introductionmentioning

confidence: 99%

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Multi-type Features Graph Neural Networks for Cocrystals Prediction using API-Coformers Interactions

Mswahili

Lee

et al. 2023

Preprint

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Active pharmaceutical ingredients (APIs) have gained direct pharmaceutical interest, along with their in vitro properties, and thus utilized as auxiliary solid dosage forms upon FDA guidance and approval on pharmaceutical cocrystals as a potential and attractive route for drug substance development. In this study, we implemented graph neural networks to predict the formationof cocrystals using our first created API-co-formers interactions graph dataset. We further compared our work with previous studies that implemented descriptor-based models (e.g., random forest, support vector machine, extreme gradient boosting,and artificial neural networks). All built graph-based models show compelling performance accuracies (i.e., 91.36, 94.60, and 95.95% for GCN, GraphSAGE, and R-GCN respectively). Furthermore, R-GCN prevailed among the built graph-based models due to its capability to learn the topological structure of the graph from the additionally provided information (i.e., non-ionic andnon-covalent interactions or link information) between APIs and coformers.

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Section: Data Gathering and Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-type Features Graph Neural Networks for Cocrystals Prediction using API-Coformers Interactions

Mswahili

Lee

et al. 2023

Preprint

Self Cite

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“…Furthermore, liquid-phase models have been adapted to estimate the mixing enthalpies between coformers and approaches that are based on the Hansen solubility approach . Several models based on molecular descriptors combined with machine learning have also been proposed. − However, none of these methods considers the crystal environment explicitly and its effects on the stability of a proposed cocrystal. Crystal structure prediction (CSP) methods, whose applicability and reliability have increased significantly over the last decades, address exactly the latter.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Supramolecular Interactions and Thermal Stability of Dapsone:Bipyridine Cocrystals by Combining Computational Chemistry with Experimentation

Racher

Petrick

Braun

2023

Crystal Growth & Design

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The application of computational screening methodologies based on H-bond propensity scores, molecular complementarity, molecular electrostatic potentials, and crystal structure prediction has guided the discovery of novel cocrystals of dapsone and bipyridine (DDS:BIPY). The experimental screen, which included mechanochemical and slurry experiments as well as the contact preparation, resulted in four cocrystals, including the previously known DDS:4,4′-BIPY (2:1, CC44-B) cocrystal. To understand the factors governing the formation of the DDS:2,2′-BIPY polymorphs (1:1, CC22-A and CC22-B) and the two DDS:4,4′-BIPY cocrystal stoichiometries (1:1 and 2:1), different experimental conditions (such as the influence of solvent, grinding/stirring time, etc.) were tested and compared with the virtual screening results. The computationally generated (1:1) crystal energy landscapes had the experimental cocrystals as the lowest energy structures, although distinct cocrystal packings were observed for the similar coformers. H-bonding scores and molecular electrostatic potential maps correctly indicated cocrystallization of DDS and the BIPY isomers, with a higher likelihood for 4,4′-BIPY. The molecular conformation influenced the molecular complementarity results, predicting no cocrystallization for 2,2′-BIPY with DDS. The crystal structures of CC22-A and CC44-A were solved from powder X-ray diffraction data. All four cocrystals were fully characterized by a range of analytical techniques, including powder X-ray diffraction, infrared spectroscopy, hot-stage microscopy, thermogravimetric analysis, and differential scanning calorimetry. The two DDS:2,2′-BIPY polymorphs are enantiotropically related, with form B being the stable polymorph at room temperature (RT) and form A being the higher temperature form. Form B is metastable but kinetically stable at RT. The two DDS:4,4′-BIPY cocrystals are stable at room conditions; however, at higher temperatures, CC44-A transforms to CC44-B. The cocrystal formation enthalpy order, derived from the lattice energies, was calculated as follows: CC44-B > CC44-A > CC22-A.

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“…36−39 In accordance with this, previous literature has reported three forms of pelubiprofen (PBF) NSAID, which included two cocrystals formed by combining PBF with isonicotinamide and nicotinamide coformers and a salt formed by combining PBF with a tromethamine coformer. 40,41 PBF, with the chemical name (E)-2-(4-((2-oxocyclohexylidene)methyl)phenyl)propanoic acid, was a recently developed NSAID for treating rheumatoid arthritis, osteoarthritis, and chronic back pain relief. 40 It falls under BCS class-II due to its low water solubility (0.0147 mg/mL) and high permeability (log P 3.38).…”

Section: Introductionmentioning

confidence: 99%

“…40 It falls under BCS class-II due to its low water solubility (0.0147 mg/mL) and high permeability (log P 3.38). 41 In this study, various PBF-GRAS coformer salts were synthesized based on supramolecular noncovalent interactions to enhance the solubility and dissolution properties of PBF. 42−44 Here, we have chosen amine coformers with the anticipation of the formation of salts through ammoniumcarboxylate (−NH + ••• − OOC−) interactions.…”

Section: Introductionmentioning

confidence: 99%

Solubility Enhancement of the Nonsteroidal Anti-inflammatory Drug of Pelubiprofen Salts

Allu,

An,

Park

et al. 2023

Crystal Growth & Design

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A study to discover novel pharmaceutical cocrystals of pelubiprofen with a machine learning approach compared

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 pKa rule.

Cited by 12 publications

References 69 publications

Multi-type Features Graph Neural Networks for Cocrystals Prediction using API-Coformers Interactions

Multi-type Features Graph Neural Networks for Cocrystals Prediction using API-Coformers Interactions

Exploring the Supramolecular Interactions and Thermal Stability of Dapsone:Bipyridine Cocrystals by Combining Computational Chemistry with Experimentation

Solubility Enhancement of the Nonsteroidal Anti-inflammatory Drug of Pelubiprofen Salts

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