Molecular modelling-driven formulation design for targeted pulmonary drug delivery.

Area of Science:

• Pharmacology and Pharmaceutical Sciences
• Computational Chemistry
• Biophysics

Background:

• Pulmonary drug delivery systems require rational design for effective lung targeting and therapeutic outcomes.
• Traditional trial-and-error methods are time-consuming and costly, hindering clinical translation.
• In silico techniques offer a promising alternative for early-stage screening and formulation optimization.

Purpose of the Study:

• To review the application of molecular modelling in rational design of pulmonary drug delivery systems.
• To highlight how in silico methods aid in critical formulation decisions for targeted delivery and controlled release.
• To explore the integration of molecular modelling with pharmacokinetic analysis for predicting in vivo behavior.

Main Methods:

• Molecular docking to predict drug-excipient and drug-polymer interactions.
• Quantum mechanics/molecular mechanics (QM/MM) and molecular dynamics (MD) simulations for stability and interaction analysis.
• Pharmacokinetic modelling integrated with molecular modelling for predicting lung deposition and systemic exposure.

Main Results:

• Molecular modelling accurately predicts drug-excipient and drug-polymer interactions, optimizing particle engineering and formulation stability.
• Simulations guide the design of nanocarriers (liposomes, nanoparticles, micelles) for deep lung delivery.
• Integration with pharmacokinetic modelling allows prediction of regional lung deposition and systemic exposure at a molecular level.

Conclusions:

• Molecular modelling is a vital component in next-generation formulation development for targeted pulmonary drug delivery.
• In silico approaches reduce trial-and-error, improving the likelihood of clinical translation.
• Future opportunities lie in AI- and machine-learning-augmented molecular modelling for personalized pulmonary drug delivery.