Translational PBPK and PKPD modeling for pulmonary drug delivery

Project I: Translational PBPK Modeling to Predict Human Pulmonary Kinetics after Lung Delivery
A general PBPK framework was developed to predict pulmonary pharmacokinetics (PK) of inhaled drugs, integrating drug deposition, dissolution, and tissue permeation. Rat data for salbutamol and fluticasone propionate were used to estimate lung partitioning and permeability parameters, which were then translated to human physiology. The model successfully predicted human plasma and epithelial lining fluid (ELF) concentrations, supporting interspecies translation for lung-delivered therapeutics.

Project II: Sampling Design for Lung Distribution Studies Using a Population PBPK Model
This project developed a minimal PBPK model to assess intrapulmonary PK of salbutamol using bronchoalveolar lavage, biopsies, and bronchosorption. Simulations highlighted how drug permeability and sampling technique influence the ELF/plasma ratios. Optimal sampling strategies were proposed based on drug characteristics, guiding efficient future study designs in pulmonary drug development.

Project III: Dose Optimization for Lung-Delivered Aminoglycosides with PBPK and PD Modeling
PBPK models for gentamicin and amikacin were developed in rats and translated to humans, simulating drug concentrations in the ELF and interstitial space. These models were integrated with published PKPD models to evaluate bacterial killing in ventilator-associated pneumonia (VAP). Preliminary findings suggest that dosing and lung deposition critically impact efficacy. Future steps include full PD simulations to guide clinical trial design.

Project IV: PKPD Modeling of Gallium-Based Nanoparticles for Pulmonary Infections
This planned project will explore gallium-based nanoparticles as inhalable therapies for respiratory tract infections. It will involve characterizing lung and systemic distribution in mice, linking exposure to bacterial clearance. In vitro release and time-kill studies, in vivo murine infection models, and PBPK-PD integration will form a framework to support clinical translation.

Project V: Predicting Time-Kill Dynamics from Optical Density Data Using Semi-Mechanistic PD Modeling
OD600 data were modelled to simulate bacterial growth and drug effects of gallium nanoparticles. A semi-mechanistic PKPD model was developed to bridge OD and CFU-based measurements. The model accurately predicted time-kill data, offering a high-throughput screening alternative for early-stage antibacterial evaluation. Further validation with broader datasets is ongoing.