MUMBAI, India, Aug. 12 -- Intellectual Property India has published a patent application (202611071370 A) filed by Akash Johri; Renu Tiwari; Deepika Chauhan; Saloni Manglik; Divya; Harish Sharma; Akansha Tygayi; Mohd. Azam; Anurag; Aryan; Harsh Mishra; Satyam Yadav; and Anuj Kumar on June 09, 2026, for Nanoformulation Of Phytoconstituents With Machine Learning Assistance For A Targeted Drug Delivery System.
Inventors include Akash Johri; Renu Tiwari; Deepika Chauhan; Saloni Manglik; Divya; Harish Sharma; Akansha Tygayi; Mohd. Azam; Anurag; Aryan; Harsh Mishra; Satyam Yadav; and Anuj Kumar.
The application for the patent was published on August 07, 2026, under issue no. 32/2026.
Abstract: ABSTRACT TITLE: NANOFORMULATION OF PHYTOCONSTITUENTS WITH MACHINE LEARNING ASSISTANCE FOR A TARGETED DRUG DELIVERY SYSTEM The present invention discloses a machine learning assisted nanoformulation system (100) for targeted drug delivery of phytoconstituents. The system (100) integrates a phytoconstituent extraction and characterization module (110), a machine learning optimization engine (120) implementing predictive models and multi-objective optimization algorithms, a nanoformulation synthesis unit (130), a targeted delivery subsystem (140) with ligand conjugation and stimuli-responsive release mechanisms, a characterization and evaluation module (150), and a feedback and iterative refinement module (160). The machine learning optimization engine (120) predicts optimal nanoformulation parameters based on phytoconstituent physicochemical profiles, replacing conventional trial-and-error approaches. The closed- loop feedback mechanism enables continuous model improvement, progressively enhancing encapsulation efficiency, bioavailability, and therapeutic efficacy of phytoconstituent-loaded nanoformulations for site-specific drug delivery. [FIG 1]
Disclaimer: Curated by HT Syndication.
