AI-Assisted Pharmaceutical Formulation Design: Comparative Development and Experimental Evaluation of Sustained-Release Lornoxicam Tablets.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	AI-Assisted Pharmaceutical Formulation Design: Comparative Development and Experimental Evaluation of Sustained-Release Lornoxicam Tablets.
Συγγραφείς:	Abdulkarim, Muthanna, Bawazir, Waleed, Issa, Arwa Alhaj, Tarboush, Laian, Abbara, Amal, Mahrous, Gamal, Alghaith, Adel, Almanasra, Sally, Suwais, Khaled
Πηγή:	Pharmaceuticals (14248247); Jul2026, Vol. 19 Issue 7, p1070, 24p
Θεματικοί όροι:	ChatGPT, Drug tablets, Pharmaceutical technology, Language models, Anti-inflammatory agents, Chemical dissolution kinetics
Περίληψη:	Background/Objectives: The integration of artificial intelligence (AI) into pharmaceutical development has the potential to accelerate early-stage formulation design. In this study, large language models (ChatGPT (GPT-4o, OpenAI) and DeepSeek (DeepSeek-R1, DeepSeek AI) were evaluated as supportive tools for the design of sustained-release lornoxicam matrix tablets. Using constrained formulation prompts and a predefined excipient space, each model generated candidate formulations intended for direct compression, with the objective of producing sustained-release systems capable of mimicking the dissolution behaviour of a commercial reference product (LOROX OD 16 mg). Methods: The proposed formulations were prepared experimentally and evaluated for physicochemical properties, including weight variation, hardness, friability, and drug content, as well as in vitro dissolution performance over 24 h. Dissolution profiles were compared with the reference product using similarity (f2) and difference (f1) factors, and release behaviour was further characterized using kinetic models. Results: All formulations demonstrated sustained-release behaviour without evidence of dose dumping. One ChatGPT-generated formulation (F3C) met the regulatory criteria for dissolution similarity to the reference product (f1 = 9.66, f2 = 71.31), while the remaining formulations showed variable release behaviour with f2 values ranging from 28.61 to 49.70. However, F3C exceeded the pharmacopeial friability limit marginally (1.108%), while DeepSeek formulations F5D and F6D exceeded pharmacopeial assay acceptance limits. Kinetic modelling indicated a range of transport mechanisms from anomalous diffusion to super Case II transport depending on polymer composition. Conclusions: Although both AI systems successfully generated experimentally viable formulations, prediction accuracy analysis showed high trend-level correlations between AI-predicted and experimental dissolution profiles. However, the magnitude of quantitative error was substantial, with RMSE values exceeding 17% and MAPE values ranging from approximately 38% to 60%. These findings indicate that the models captured general release trends but did not provide reliable quantitative dissolution predictions. [ABSTRACT FROM AUTHOR]
	Copyright of Pharmaceuticals (14248247) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων:	Biomedical Index

Full Text Finder

καταχωρήστε σχόλιο πρώτοι!