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Rational formulation design through retrospective machine learning methodology: Case study ibuprofen.

Dylan Garamani1, Erik Sjögren1, Albert Mihranyan1

  • 1Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-751 24 Uppsala, Sweden.

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Summary
This summary is machine-generated.

Machine learning identified key patterns in immediate release ibuprofen formulations. Specific ibuprofen variants significantly improve drug release and reduce pharmacokinetic variability.

Keywords:
Agentic drug developmentArtificial intelligenceDevelopability classification systemFormulationNon-steroidal anti-inflammatory drugs

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Area of Science:

  • Pharmaceutical Sciences
  • Pharmacokinetics
  • Computational Chemistry

Background:

  • Rational drug formulation is crucial for predictable therapeutic outcomes.
  • Understanding excipient and active ingredient influences on pharmacokinetics is essential for immediate release oral dosage forms.

Purpose of the Study:

  • To investigate rational formulation design using machine learning (ML) for immediate release ibuprofen oral dosage products.
  • To identify patterns influencing the pharmacokinetic profile of ibuprofen formulations.

Main Methods:

  • Extracted and standardized registry data using pandas in Python.
  • Analyzed patterns of dissolution-modifying excipients and ibuprofen variants.
  • Investigated the influence of these patterns on clinical pharmacokinetic profiles.

Main Results:

  • Film-coated tablets with ibuprofen acid and sodium lauryl sulfate are common.
  • Ibuprofen variants (sodium dihydrate, lysine, arginine) show faster release, reduced Tmax, increased Cmax, and lower bioavailability variance.
  • Identified key formulation patterns influencing ibuprofen pharmacokinetics.

Conclusions:

  • Machine learning aids in understanding rational formulation strategies for ibuprofen.
  • Specific ibuprofen variants offer improved pharmacokinetic profiles.
  • Findings support regulatory decisions for predictable bioavailability and reproducible clinical responses.