Integrated experimental, computational and machine learning approaches for the development of Apremilast-Aceclofenac...

Area of Science:

• Solid-state pharmaceutics
• Molecular pharmaceutics
• Drug delivery systems

Background:

• Drug coamorphization mechanisms are challenging to understand.
• Developing novel drug delivery systems is crucial for enhancing therapeutic efficacy.
• Apremilast (APR) and Aceclofenac (ACF) are key drugs for inflammatory conditions.

Purpose of the Study:

• To design and characterize drug-drug coamorphous systems (CAMs) of apremilast and aceclofenac.
• To elucidate the molecular interactions and miscibility within the CAMs.
• To evaluate the enhanced physicochemical, thermodynamic, and therapeutic properties of the CAMs.

Main Methods:

• Preparation of CAMs via melt-quenching in various molar ratios.
• Characterization using PXRD, DSC, FT-IR, and NMR spectroscopy.
• Computational analysis including DFT and MDS; development of AI/ML models for T_g prediction.

Main Results:

• Successfully prepared three monophasic CAMs (AA11, AA12, AA21) with confirmed coamorphization.
• Identified significant intermolecular interactions (H-bonding, π-π stacking, halogen bonding).
• Achieved substantial improvements in solubility (up to 3.6-fold for APR, 3.2-fold for ACF) and dissolution rates, with excellent cytocompatibility and anti-inflammatory effects.

Conclusions:

• The developed integrative framework provides a predictive, mechanistic basis for designing coamorphous systems.
• The study offers a broadly applicable strategy for dual-drug delivery, particularly for inflammatory disorders like psoriasis.
• Coamorphous systems demonstrate enhanced stability and reduced degradation rates compared to individual drugs.