Background Understanding intrapulmonary pharmacokinetics (PK) following inhalation remains a significant challenge in drug development and repurposing. Current lung sampling methods include bronchoalveolar lavage (BAL), biopsies, and the more recent bronchosorption technique, which enhances regional specificity while reducing potential quantification errors. This study aimed to develop a pulmonary population physiologically based pharmacokinetic (PBPK) model for inhaled salbutamol by integrating data from all three sampling techniques to improve PK predictions and to compare different sampling strategies to optimize future study designs. Methods A population-based minimal PBPK model was developed using data from a previously published study (NCT03524066) investigating salbutamol's pulmonary and plasma PK in 13 healthy volunteers after inhalation. Simulations assessed the impact of permeability on pulmonary PK profiles and BAL-derived epithelial lining fluid (ELF)-to-plasma ratios using salbutamol as a reference compound. Stochastic simulation-estimation (SSE) methods were employed to assess the feasibility of different sampling strategies for estimating key parameters of the PBPK model. First, we evaluated using one or two sampling techniques within a single bronchoscopy session. Second, we compared uniform and staggered bronchosorption-based sampling strategies for drugs from different permeability categories. Results The minimal PBPK model described pulmonary PK of salbutamol across the lung and estimated the unbound tissue–plasma partition coefficient for the lung (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">${K}_{\text{p},\text{u},\text{l}\text{u}\text{n}\text{g}}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>K</mi> <mrow> <mtext>p</mtext> <mo>,</mo> <mtext>u</mtext> <mo>,</mo> <mtext>lung</mtext> </mrow> </msub> </math></EquationSource> </InlineEquation>) and the effective permeability (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">${P}_{\text{e}\text{f}\text{f}}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>P</mi> <mtext>eff</mtext> </msub> </math></EquationSource> </InlineEquation>) of salbutamol as 11.0 and 0.543 m/h, respectively. Inter-individual variabilities (IIV) were found on plasma clearance and lung deposition fraction. No significant IIV was detected on <InlineEquation ID="IEq3"> <EquationSource Format="TEX">${K}_{\text{p},\text{u},\text{l}\text{u}\text{n}\text{g}}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>K</mi> <mrow> <mtext>p</mtext> <mo>,</mo> <mtext>u</mtext> <mo>,</mo> <mtext>lung</mtext> </mrow> </msub> </math></EquationSource> </InlineEquation> or <InlineEquation ID="IEq4"> <EquationSource Format="TEX">${P}_{\text{e}\text{f}\text{f}}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>P</mi> <mtext>eff</mtext> </msub> </math></EquationSource> </InlineEquation>. Simulations indicated that low-permeability drugs exhibited higher concentrations in the ELF, while high-permeability drugs accumulated more in lung tissues, after inhalation. Results from SSE showed that bronchosorption plus biopsy were the most informative two-technique combination and bronchosorption alone was the best single-technique option. Additionally, the optimal sampling strategy for both uniform and staggered sampling depended on drug permeability, with early time points favoured for high-permeability drugs and later or broader windows needed for low-permeability drugs. Conclusion A pulmonary population PBPK model for inhaled salbutamol was developed by integrating detailed intrapulmonary data from bronchoalveolar lavage, biopsy, and bronchosorption. The study revealed that parameter estimates of <InlineEquation ID="IEq5"> <EquationSource Format="TEX">${K}_{\text{p},\text{u},\text{l}\text{u}\text{n}\text{g}}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>K</mi> <mrow> <mtext>p</mtext> <mo>,</mo> <mtext>u</mtext> <mo>,</mo> <mtext>lung</mtext> </mrow> </msub> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq6"> <EquationSource Format="TEX">${P}_{\text{e}\text{f}\text{f}}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>P</mi> <mtext>eff</mtext> </msub> </math></EquationSource> </InlineEquation> were sensitive to the sampling technique. Staggered sampling strategies mitigated the risk of biased estimates, though the ideal sampling windows varied by drug’s permeability. These findings support model-informed, permeability-driven study design in inhaled drug development.

Informing Sampling Design for Lung Distribution Studies Using a Pulmonary Population Minimal PBPK Model

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2026年6月29日

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Haini Wen,
Muhammad Waqas Sadiq,
Markus Fridén,
Jens M. Hohlfeld,
Lena E. Friberg,
Elin M. Svensson

摘要

Background

Understanding intrapulmonary pharmacokinetics (PK) following inhalation remains a significant challenge in drug development and repurposing. Current lung sampling methods include bronchoalveolar lavage (BAL), biopsies, and the more recent bronchosorption technique, which enhances regional specificity while reducing potential quantification errors. This study aimed to develop a pulmonary population physiologically based pharmacokinetic (PBPK) model for inhaled salbutamol by integrating data from all three sampling techniques to improve PK predictions and to compare different sampling strategies to optimize future study designs.

Methods

A population-based minimal PBPK model was developed using data from a previously published study (NCT03524066) investigating salbutamol's pulmonary and plasma PK in 13 healthy volunteers after inhalation. Simulations assessed the impact of permeability on pulmonary PK profiles and BAL-derived epithelial lining fluid (ELF)-to-plasma ratios using salbutamol as a reference compound. Stochastic simulation-estimation (SSE) methods were employed to assess the feasibility of different sampling strategies for estimating key parameters of the PBPK model. First, we evaluated using one or two sampling techniques within a single bronchoscopy session. Second, we compared uniform and staggered bronchosorption-based sampling strategies for drugs from different permeability categories.

Results

The minimal PBPK model described pulmonary PK of salbutamol across the lung and estimated the unbound tissue–plasma partition coefficient for the lung ( ${K}_{\text{p},\text{u},\text{l}\text{u}\text{n}\text{g}}$ $K_{p, u, lung}$ ) and the effective permeability ( ${P}_{\text{e}\text{f}\text{f}}$ $P_{eff}$ ) of salbutamol as 11.0 and 0.543 m/h, respectively. Inter-individual variabilities (IIV) were found on plasma clearance and lung deposition fraction. No significant IIV was detected on ${K}_{\text{p},\text{u},\text{l}\text{u}\text{n}\text{g}}$ $K_{p, u, lung}$ or ${P}_{\text{e}\text{f}\text{f}}$ $P_{eff}$ . Simulations indicated that low-permeability drugs exhibited higher concentrations in the ELF, while high-permeability drugs accumulated more in lung tissues, after inhalation. Results from SSE showed that bronchosorption plus biopsy were the most informative two-technique combination and bronchosorption alone was the best single-technique option. Additionally, the optimal sampling strategy for both uniform and staggered sampling depended on drug permeability, with early time points favoured for high-permeability drugs and later or broader windows needed for low-permeability drugs.

Conclusion

A pulmonary population PBPK model for inhaled salbutamol was developed by integrating detailed intrapulmonary data from bronchoalveolar lavage, biopsy, and bronchosorption. The study revealed that parameter estimates of ${K}_{\text{p},\text{u},\text{l}\text{u}\text{n}\text{g}}$ $K_{p, u, lung}$ and ${P}_{\text{e}\text{f}\text{f}}$ $P_{eff}$ were sensitive to the sampling technique. Staggered sampling strategies mitigated the risk of biased estimates, though the ideal sampling windows varied by drug’s permeability. These findings support model-informed, permeability-driven study design in inhaled drug development.