Improving Drug Bioavailability with Cyclodextrin Complexes: An integrative meta-modeling approach

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This study integrates molecular modeling and meta-analysis to evaluate the pharmacokinetic performance and molecular stability of various cyclodextrin-drug systems. A meta-analysis of 11 studies was performed to assess effect sizes for maximum concentration (Cmax) and area under curve (AUC), revealing substantial heterogeneity and overall improvement in drug exposure. Molecular docking, Molecular Dynamics (MD) simulations and free energy calculations were used to investigate the stability of the selected host-guest systems. Compared with the atorvastatin (ATV)/HP-β-cyclodextrin system, the Compound K/β-cyclodextrin complex exhibited the highest binding affinity with greater pharmacokinetic effect sizes. SBE-β-CD was identified as the most effective cyclodextrin for enhancing bioavailability, whereas γ-CD was the least effective. To further explore these findings, four ligands, ATV, β-caryophyllene, koumine and Compound K, were selected on the basis of their solubility (logS) values. A stronger affinity for SBE-β-CD over γ-CD was observed for all tested compounds. This trend was confirmed through replicate MD simulations of Compound K and koumine, which revealed greater stability with SBE-β-CD. These effects are attributed to the extended hydrophobic cavity of SBE-β-CD and reduced electrostatic repulsion, emphasizing the role of molecular shape and hydrophobicity in complex stability. This integrated analysis highlights the potential of SBE-β-CD to improve drug bioavailability through stable inclusion complex formation. These findings provide a mechanistic basis for cyclodextrin selection in formulation development. However, further clinical studies are needed to validate these computational predictions.

Chardi Shahiya , Samson Olusegun Afolabi , Ekaterina V. Skorb , and Sergey Shityakov (2026) Enhancing Drug Solubility and Bioavailability Through Cyclodextrin Inclusion Complexes: An Integrative Molecular Meta-Modeling Approach. Journal of Computational Biophysics and Chemistry 25:11, 2111-2131

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