Design and Optimization of Indomethacin Nanocrystals using Machine Learning and Molecular Dynamics Simulations

Area of Science:

• Pharmaceutical Sciences
• Materials Science
• Computational Chemistry

Background:

• Developing effective drug delivery systems for poorly water-soluble drugs like indomethacin (IND) is crucial for improving therapeutic efficacy.
• Traditional methods for nanocrystal formulation can be time-consuming and challenging to scale.
• Integrating advanced technologies is needed for efficient and scalable drug delivery system development.

Purpose of the Study:

• To develop a stable nanocrystalline drug delivery system for indomethacin (IND) using machine learning and Hummer Acoustic Resonance (HAR) technology.
• To enhance the solubility and dissolution rate of IND.
• To investigate the scalability and solid-state properties of the developed nanocrystal system.

Main Methods:

• High-throughput screening with HAR technology to identify optimal stabilizers (P188-PVA).
• Molecular dynamics simulations to understand drug-stabilizer interactions.
• Box-Behnken design (BBD) and artificial neural networks (ANN) for systematic optimization of formulation and HAR process parameters.
• Scale-up studies using HAR technology (5- and 50-fold).
• Evaluation of drying techniques (freeze-drying, spray-drying, fluidized-bed drying).
• Characterization using PXRD, DSC, and in vitro dissolution studies.

Main Results:

• Identified P188-PVA as an optimal stabilizer for IND nanocrystal suspensions.
• Optimized IND nanocrystal formulation and HAR process conditions using integrated BBD-ANN modeling.
• Successfully scaled up the IND nanocrystal formulation by 5- and 50-fold using HAR technology.
• Confirmed the crystalline nature of IND nanocrystals and their significantly improved in vitro dissolution compared to raw IND.
• Demonstrated the potential applicability of the methodology for other poorly water-soluble drugs.

Conclusions:

• A stable and scalable nanocrystalline drug delivery system for indomethacin was rapidly developed by integrating machine learning with HAR technology.
• The developed system significantly enhances IND solubility and dissolution, showing promise for improving the bioavailability of poorly soluble drugs.
• The proposed methodology offers a versatile approach for the efficient development and scale-up of nanocrystal formulations for various challenging drug compounds.