Published April 6, 2026 | Version v1
Journal article Open

MACHINE LEARNING APPROACHES FOR PREDICTING DRUG RELEASE FROM NOVEL DRUG DELIVERY SYSTEMS

Authors/Creators

  • 1. Assistant Professor, Department of Pharmaceutics, ARKA JAIN University, Mohanpur, Sareikela, Jamshedpur, 831001.
  • 2. Lecturer, Department of Pharmacy, Sanaka Educational Trust's Group of Institutions, Malandighi, Kanasa, Durgapur, 713212.
  • 3. Associate Professor, Department of Pharmaceutical Analysis, Goenka College of Pharmacy, Laxmangarg- Sikar, Rajasthan.
  • 4. Professor, Department of Pharmacognosy, Goenka College of Pharmacy, Laxmangarh- Sikar, Rajasthan.
  • 5. Professor, Department of Pharmacy, Goenka College of Pharmacy, Laxmangarh, Sikar, Rajasthan.
  • 6. Ph.D.Scholar, Department of Forensic Science, Guru Ghasidas Vishwavidyalaya Bilaspur, Chhattisgarh.
  • 7. Ph.D. Scholar, Department of Chemistry, National Institute of Technology, Raipur, Chhattisgarh.
  • 8. Ph.D. Scholar, Department of Biomedical Engineering, National Institute of Technology, Raipur, Chhattisgarh.

Description

This paper aims to present how machine learning methods can be used to predict drug release from novel drug delivery systems, which is one of the most crucial problems in pharmaceutical sciences. The goal therefore is to explain how different machine learning methods can enhance the predictive capabilities for drug release kinetics compared to traditional mechanistic modelling approaches. To achieve this goal, the basic concepts underlying drug release and presentation of data-based approaches are given in detail to provide background knowledge to the interdisciplinary modelling approach. The main outcomes of the paper are that machine learning algorithms make the prediction process easier which also facilitates more tailored formulation design due to the identification of various relations in complex datasets. To conclude, the successful application of machine learning in predictive modelling is presented to have the potential to create disruption in traditional modelling in pharmaceutical science that will lead to more efficient design and optimisation of innovative drug delivery systems.

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