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Structure-Based Prediction of Molecular Interactions for Stabilizing Volatile Drugs.

Yuchen Zhao1, Danmei Bai1, Boyang Yang1

  • 1Department of Pharmaceutics, School of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) accelerates volatile drug formulation by predicting excipient interactions. This study identified soybean phospholipid (PC) as an optimal excipient for d-borneol, enhancing nano-formulation stability.

Keywords:
high-throughput screening of excipientsliposomemolecular interaction predictionstructure prediction modelvolatile drug stabilization

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry
  • Materials Science

Background:

  • Volatile drugs face clinical limitations due to high evaporation rates.
  • Traditional excipient screening is inefficient and resource-intensive.
  • An AI-driven approach is needed to optimize drug-excipient interactions.

Purpose of the Study:

  • To develop an AI strategy for efficient screening of drug-excipient interactions.
  • To identify suitable excipients for stabilizing volatile drugs like d-borneol.
  • To create stable nano-formulations for volatile pharmaceuticals.

Main Methods:

  • Utilized Protenix structure prediction for high-throughput simulation of d-borneol and 472 excipients.
  • Selected top 50 candidates based on simulation, followed by molecular docking and stability experiments.
  • Validated AI predictions against experimental data.

Main Results:

  • AI predictions showed high consistency with experimental validation.
  • Soybean phospholipid (PC) emerged as the optimal excipient for d-borneol.
  • PC-based lyophilized liposomal formulations significantly reduced d-borneol volatilization and improved stability.

Conclusions:

  • This study pioneers the use of structure prediction models for volatile drug excipient screening.
  • Successfully developed a stable nano-formulation for d-borneol using AI-identified excipients.
  • Offers a novel paradigm for volatile pharmaceutical nano-formulation development.