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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry
  • Materials Science

Background:

  • Traditional pharmaceutical formulation development is resource-intensive, relying heavily on lab and animal studies.
  • Developing effective drug dosage forms requires precise control over critical quality attributes.
  • Digitalization offers a potential solution to accelerate and refine formulation processes.

Purpose of the Study:

  • To introduce a novel generative artificial intelligence (AI) method for creating digital drug product models from exemplar images.
  • To validate the AI method's capability in predicting critical quality attributes and optimizing formulations.
  • To demonstrate the application of AI in reducing experimental workload in pharmaceutical development.

Main Methods:

  • Utilized a generative AI image generator guided by critical quality attributes (e.g., particle size, drug loading).
  • Created realistic digital variations of drug products for in-silico analysis and optimization.
  • Validated the method through case studies involving oral tablets and long-acting implants.

Main Results:

  • The AI method accurately predicted a percolation threshold of 4.2% microcrystalline cellulose in oral tablets.
  • Generated implant formulations with optimized drug loading and particle size distributions.
  • Synthesized digital structures showed comparable particle size distributions and release properties to real samples.

Conclusions:

  • Generative AI provides a powerful tool for digital drug product development, enabling accurate predictions and optimization.
  • This AI-driven approach can significantly reduce the time and cost associated with traditional formulation studies.
  • The validated method holds promise for accelerating the discovery and optimization of novel pharmaceutical dosage forms.