MUMBAI, India, Jan. 9 -- Intellectual Property India has published a patent application (202521118213 A) filed by Dr. Dipti Gohil; Dr. A. K. Seth; Dr. Ashim Kumar Sen; Dr. Rajesh Maheshwari; Dr. Dhanya B. Sen; Dr. Ghanshyam Parmar; Dr. Rahul Trivedi; Dr. Dillip Kumar Dash; Dr. Shivkant Patel; and Ms. Krupa Joshi, Vadodara, Gujarat, on Nov. 27, 2025, for 'ai-guided prediction model for designing optimized transdermal formulations.'
Inventor(s) include Dr. Dipti Gohil; Dr. A. K. Seth; Dr. Ashim Kumar Sen; Dr. Rajesh Maheshwari; Dr. Dhanya B. Sen; Dr. Ghanshyam Parmar; Dr. Rahul Trivedi; Dr. Dillip Kumar Dash; Dr. Shivkant Patel; and Ms. Krupa Joshi.
The application for the patent was published on Jan. 9, under issue no. 02/2026.
According to the abstract released by the Intellectual Property India: "The present invention provides an AI-guided prediction and optimization system for designing advanced transdermal drug delivery formulations. Transdermal systems traditionally require extensive trial-and-error experimentation due to the complex interplay among drug physicochemical properties, polymer matrices, permeation enhancers, solvents, and patch design parameters. These iterative processes significantly increase development time, cost, and variability in permeation outcomes. The invention overcomes these limitations by integrating machine learning models, engineered formulation descriptors, and computational simulations into a unified platform that predicts formulation performance before laboratory preparation. The system accepts input parameters such as drug solubility, logP, molecular weight, polymer ratios, enhancer levels, plasticizer concentration, and patch thickness. Using a trained machine learning engine, it generates predictions for key indicators including flux, permeability coefficient, lag time, and cumulative drug release. A dedicated optimization module further recommends the most suitable formulation composition based on user-defined objectives. Additionally, a permeation simulation unit provides graphical visualization of diffusion behaviour over time. A feedback learning mechanism enables continuous improvement of predictive accuracy as new experimental data are added. By offering reliable predictions, optimized formulations, and reduced experimental workload, the invention establishes a data-driven and efficient approach to developing transdermal drug delivery systems."
Disclaimer: Curated by HT Syndication.
