Integrated computer-aided formulation design:A case study of andrographolide/cyclodextrin ternary formulation

Current formulation development strongly relies on trial-and-error experiments in the laboratory by pharmaceutical scientists,which is time-consuming,high cost and waste materials.This research aims to integrate various computational tools,including machine learning,molecular dynamic simulation and physiologically based absorption modeling(PBAM),to enhance andrographolide(AG)/cyclodextrins(CDs)formulation design.The light GBM prediction model we built before was utilized to predict AG/CDs inclusion's binding free energy.AG/γ-CD inclusion complexes showed the strongest binding affinity,which was experimentally validated by the phase solubility study.The molecular dynamic simulation was used to investigate the inclusion mechanism between AG andγ-CD,which was experimentally characterized by DSC,FTIR and NMR techniques.PBAM was applied to simulate the in vivo behavior of the formulations,which were validated by cell and animal experiments.Cell experiments revealed that the presence of D-α-Tocopherol polyethylene glycol succinate(TPGS)significantly increased the intracellular uptake of AG in MDCKMDR1 cells and the absorptive transport of AG in MDCK-MDR1 monolayers.The relative bioavailability of the AG-CD-TPGS ternary system in rats was increased to 2.6-fold and 1.59-fold compared with crude AG and commercial dropping pills,respectively.In conclusion,this is the first time to integrate various computational tools to develop a new AG-CD-TPGS ternary formulation with significant improvement of aqueous solubility,dissolution rate and bioavailability.The integrated computational tool is a novel and robust methodology to facilitate pharmaceutical formulation design.

Integrated in silico formulation design of selfemulsifying drug delivery systems

The drug formulation design of self-emulsifying drug delivery systems(SEDDS)often requires numerous experiments,which are time-and money-consuming.This research aimed to rationally design the SEDDS formulation by the integrated computational and experimental approaches.4495 SEDDS formulation datasets were collected to predict the pseudo-ternary phase diagram by the machine learning methods.Random forest(RF)showed the best prediction performance with 91.3% for accuracy,92.0% for sensitivity and 90.7% for specificity in 5-fold cross-validation.The pseudo-ternary phase diagrams of meloxicam SEDDS were experimentally developed to validate the RF prediction model and achieved an excellent prediction accuracy(89.51%).The central composite design(CCD)was used to screen the best ratio of oil-surfactant-cosurfactant.Finally,molecular dynamic(MD)simulation was used to investigate the molecular interaction between excipients and drugs,which revealed the diffusion behavior in water and the role of cosurfactants.In conclusion,this research combined machine learning,central composite design,molecular modeling and experimental approaches for rational SEDDS formulation design.The integrated computer methodology can decrease traditional drug formulation design works and bring new ideas for future drug formulation design.