Cell-based multiscale computational modeling of small molecule absorption and retention in the lungs

Abstract

Purpose: For optimizing the local, pulmonary targeting of inhaled medications, it is important to analyze the relationship between the physicochemical properties of small molecules and their absorption, retention and distribution in the various cell types of the airways and alveoli.

Methods: A computational, multiscale, cell-based model was constructed to facilitate analysis of pulmonary drug transport and distribution. The relationship between the physicochemical properties and pharmacokinetic profile of monobasic molecules was explored. Experimental absorption data of compounds with diverse structures were used to validate this model. Simulations were performed to evaluate the effect of active transport and organelle sequestration on the absorption kinetics of compounds.

Results: Relating the physicochemical properties to the pharmacokinetic profiles of small molecules reveals how the absorption half-life and distribution of compounds are expected to vary in different cell types and anatomical regions of the lung. Based on logP, pK(a) and molecular radius, the absorption rate constants (K(a)) calculated with the model were consistent with experimental measurements of pulmonary drug absorption.

Conclusions: The cell-based mechanistic model developed herein is an important step towards the rational design of local, lung-targeted medications, facilitating the design and interpretation of experiments aimed at optimizing drug transport properties in lung.

Figure 1

A. Diagram representing the general route of drug transport from the airways to the blood, across the histological architecture of lung tissue. 1: Surface lining (liquid) layer (Drug donor compartment); 2: Macrophage (in alveolar region only) 3: Airway epithelial cells 4: Extracellular fluid (interstitium) 5: Smooth muscle (in airways only) 6. Immune cells 7: Endothelium cells 8: Systemic circulation (Drug receiver compartment). B. Diagram representing the path of a monobasic compound across adjacent compartments separated by a phospholipid bilayer, as captured by the model. The neutral form of the molecule is indicated as [M], and the protonated, cationic form of the molecule is indicated as [MH⁺].

Figure 2

The relationship between the physicochemical properties and the absorption or tissue retention in the airways. For simulations, the initial dose was set to 50 nmol, and the logP (corresponding to logP_n) and pK_a were varied independently. X axis represents the logP, Y axis represents pK_a. Contour line indicates: A. The absT₅₀ (unit: minutes) of molecules in airway; B. The maximal amount of compounds (%) retained by airway tissue during the absorption process. C. The time (minutes) when the maximal percentage of the total amount of compounds was reached in airway tissue. D. The maximal amount of compounds (%) retained by smooth muscle during the absorption process.

Figure 3

The relationship between the physicochemical properties and the absorption or tissue retention in the alveolar region. For simulations, the initial dose was set to 50 nmol, and logP (corresponding to logP_n) and pK_a were varied independently. X axis represents the logP. Y axis represents pK_a. Contour line indicates: A. The absT₅₀ (unit: minutes) of molecules in alveolar region; B. The maximal amount of compounds (%) retained by alveolar tissue during the absorption process. C. The time (minutes) when the maximal percentage of amount of compounds was reached in alveolar tissue. D. The amount of compound (%) retained by alveolar epithelium during the absorption process.

Figure 4

The relationship between the physicochemical properties and the absorption in the whole lung with a dosage deposition of 70% in airways and 30% in alveolar region. For simulations, the initial dose was set to 50 nmol, and logP (corresponding to logP_n) and pK_a were varied independently. X axis represents the logP. Y axis represents the pK_a. Contour line indicates the absT₅₀ (minutes) of molecules in whole lung.

Figure 5

Observed K_a and predicted K_a are related, for small drug-like molecules of intermediate size range (Petitjean radius 5 to 8). Regression equation: logK_a (predicted) = 0.198 logK_a (observed) – 0.199; R²=0.86.

Figure 6

The correlation between the observed and predicted logK_a, obtained by partial least square (PLS) regression using the predicted logK_a and Petitjean radius as variables. The regression relationship was described by the following equation: logK_a (observed) = 1.48 – 0.23 radius + 2.01 logK_a (predicted); R²=0.87.

Figure 7

The effect of an apical airway efflux transporter with K_m = 423 μM. For simulations, V_max,area was varied from 1×10⁻¹⁵ to 1×10⁻⁶ (mol/sec/cm²). R is the percentage of total dose (50 nmol) deposited in the airways.

Figure 8

The simulated effect of organelle sequestration on lung pharmacokinetics of small molecules of varying physicochemical properties. For the simulations, the initial dose was 50 nmol. logP (corresponding to logP_n) and pK_a were varied independently. X axis represents the logP. Y axis represents pK_a. Contour line indicates: A. the difference in the absT₅₀ (minutes) between simulations carried out with and without organelles for the lung, with 70% of the dose in airways and 30% in alveolar region. B. the difference in the absT₅₀ (minutes) between simulations carried out with and without organelles for the airway. C. the difference in the absT₅₀ (minutes) between simulations carried out with and without organelles for the alveolar region.