Evidence map›Paper›PMID 38862767›Full record

ArticleAAPS PharmSciTech2024

Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface.

Yuting Wang, Yanling Jiang, Yu Zhou, Huai He, Jincao Tang, Anqing Luo, Zeng Liu, Chi Ma, Qin Xiao, Tianbing Guan and 1 more

Abstract read
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In one paragraph

Article in AAPS PharmSciTech, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yuting WangChongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China.
Yanling JiangChongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China.
Yu ZhouChongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China.
Huai HeChongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China.
Jincao TangChongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China.
Anqing LuoChongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China.
Zeng LiuChongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China.
Chi MaChongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China.
Qin XiaoChongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China.
Tianbing GuanChongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China.
Chuanyun DaiChongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China. cydai@126.com.ORCID http://orcid.org/0000-0001-8890-3017

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nifedipine (NIF) is a dihydropyridine calcium channel blocker primarily used to treat conditions such as hypertension and angina. However, its low solubility and low bioavailability limit its effectiveness in clinical practice. Here, we developed a cocrystal prediction model based on Graph Neural Networks (CocrystalGNN) for the screening of cocrystals with NIF. And scoring 50 coformers using CocrystalGNN. To validate the reliability of the model, we used another prediction method, Molecular Electrostatic Potential Surface (MEPS), to verify the prediction results. Subsequently, we performed a second validation using experiments. The results indicate that our model achieved high performance. Ultimately, cocrystals of NIF were successfully obtained and all cocrystals exhibited better solubility and dissolution characteristics compared to the parent drug. This study lays a solid foundation for combining virtual prediction with experimental screening to discover novel water-insoluble drug cocrystals.

Indexed as

Calcium Channel BlockersCrystallizationNeural Networks, ComputerNifedipineSolubilityStatic ElectricityCalcium Channel BlockersNifedipinecocrystal predictionelectrostatic potential surfacegraph neural networksnifedipinevirtual screening

Identifiers

PMID38862767

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.