A mechanistic framework for a priori pharmacokinetic predictions of orally inhaled drugs

Abstract

The fate of orally inhaled drugs is determined by pulmonary pharmacokinetic processes such as particle deposition, pulmonary drug dissolution, and mucociliary clearance. Even though each single process has been systematically investigated, a quantitative understanding on the interaction of processes remains limited and therefore identifying optimal drug and formulation characteristics for orally inhaled drugs is still challenging. To investigate this complex interplay, the pulmonary processes can be integrated into mathematical models. However, existing modeling attempts considerably simplify these processes or are not systematically evaluated against (clinical) data. In this work, we developed a mathematical framework based on physiologically-structured population equations to integrate all relevant pulmonary processes mechanistically. A tailored numerical resolution strategy was chosen and the mechanistic model was evaluated systematically against data from different clinical studies. Without adapting the mechanistic model or estimating kinetic parameters based on individual study data, the developed model was able to predict simultaneously (i) lung retention profiles of inhaled insoluble particles, (ii) particle size-dependent pharmacokinetics of inhaled monodisperse particles, (iii) pharmacokinetic differences between inhaled fluticasone propionate and budesonide, as well as (iv) pharmacokinetic differences between healthy volunteers and asthmatic patients. Finally, to identify the most impactful optimization criteria for orally inhaled drugs, the developed mechanistic model was applied to investigate the impact of input parameters on both the pulmonary and systemic exposure. Interestingly, the solubility of the inhaled drug did not have any relevant impact on the local and systemic pharmacokinetics. Instead, the pulmonary dissolution rate, the particle size, the tissue affinity, and the systemic clearance were the most impactful potential optimization parameters. In the future, the developed prediction framework should be considered a powerful tool for identifying optimal drug and formulation characteristics.

Predicting systemic and pulmonary tissue barrier concentration of orally inhaled drug products

“…Whenever possible, the base range of these parameters were within the known bounds of experimental values. For example, the SLL thickness in the terminal alveolar sacs has been reported with values of 0.01-0.08 μm by Olsson et al, [44] 0.1-0.2 μm by Wauthoz and Amighi, [45] 0.07 μm by Patton and Byron [10] 3.7 [50,51] Fu,plasma Fraction unbound (%) 0.125 0.1-0.12 [52] 0.12 [53] 16.1 [19] 0.02 1.16 [19] 0.013-0.020 [52] 0.1 [53,54] B2P Blood-toplasma (ratio) 0.9 0.8-0.9 [55] 0.6-0.9 [52] 0.6 0.7 [56] 0.95 [57] 0.6-0.8 [52] 1.83 [19] Bq Oral bioavailability 0.1 0.11 [14,49] 0.01 0.01 [49] 0 [57] <0.01 [58] Systemic clearance Clearance (mL/min) 1591.65 1000-1400[59] 900-1800 [60] 1416 [14] 1400 [49,54] a 1400 [52] 847.28 1216 [14] 1150 [49,53] 1100-1500 [61] a 840 [52] a 1190 [54] Lung mucus and SLL Diffusion coeff (µ 2 /sec) 400.639 230-510 [62] 325.02 600 [43] 22.7 [57] Terminal alveolar sacs region Solubility coeff (µg/mL) 18.365 16(aq) [63] 28(aq) [64] 1004 (PB) [65] 0.524 <0.15(aq) [52] 0.14(aq) ...…”

“…The previously published values and our final optimized values of the parameters are shown in Table 1. 3.7 [50,51] Fu,plasma Fraction unbound (%) 0.125 0.1-0.12 [52] 0.12 [53] 16.1 [19] 0.02 1.16 [19] 0.013-0.020 [52] 0.1 [53,54] B2P Blood-toplasma (ratio) 0.9 0.8-0.9 [55] 0.6-0.9 [52] 0.6 0.7 [56] 0.95 [57] 0.6-0.8 [52] 1.83 [19] Bq Oral bioavailability 0.1 0.11 [14,49] 0.01 0.01 [49] 0 1416 [14] 1400 [49,54] a 1400 [52] 847.28 1216 [14] 1150 [49,53] 1100-1500 [61] a 840 [52] a 1190 [54] Lung mucus and SLL Diffusion coeff (µ 2 /sec) 400.639 230-510 [62] 325.02 600 [43] 22.7 [57] Terminal alveolar sacs region Solubility coeff (µg/mL) 18.365 16(aq) [63] 28(aq) [64] 1004 (PB) [65] 0.524 <0.15(aq) [52] 0.14(aq) [49] Tracheobronchial and Alveolar region 23.237 470 (SDS) [66] 49 (in silico) [67] 21(aq) [52] 30 (surfactant) [19] 0.011 45 [43] 2 (SLF) [68] 13.1...…”

“…To address the aforementioned challenges, we present here a multiscale computational framework (Fig 1) that involves: i) our recently published full-scale 24 generation (Gen) 3D lung model with distinct barrier regions spanning trachea (Gen 0) to tracheobronchial (Gen 1-15) to alveolar (Gen [16][17][18][19][20][21][22][23] and to the terminal alveolar sacs (Gen 24); [26] ii) our previously published and modified computational fluid dynamics (CFD) module, called quasi-3D (Q3D), to calculate inhalation flow profile and PSD-based drug deposition, [27,28] iii) a first-principles-based and lung region-specific dissolution and absorption module, iv) a tracheobronchial-region specific MCC module, and v) a gut absorption module, all connected to whole-body PBPK. Our simulation outcomes were validated on two distinct ICSs: budesonide (conditions specific to the Novolizer® device, which under normal inspiratory flow rates shows similar deposition of budesonide in the lungs of healthy volunteers as the Turbuhaler®) [29]) and fluticasone propionate (conditions specific to the Diskus® device).…”

“…), and evaluated against data from different clinical studies. [19] Commercially, the two main available PBPK software packages to model inhaled drug pharmacokinetics are Gastroplus™ (Simulations Plus Inc., Lancaster, CA, USA) which has mechanistic modules for regional deposition, dissolution, and permeation of inhaled drugs, [20] and SimCyp Simulator™ (Certara, Sheffield, United Kingdom) that has pulmonary delivery modules by reducing dissolution and epithelial permeation into a single first order process through a single pulmonary compartment. [For further information on available modeling approaches and their role in the development of OIDPs and devices, please refer to the reviews by Borghardt et al, [21] Backman et al, [22] and Walenga et al [13]] Nonetheless, although these previous modeling efforts and available tools have proven to be useful, assessment of lung-selectivity has so far proven to be elusive and questions remain.…”

“…[23][24][25] Second, in the physiological lung models, the heterogeneous nature of the lung with distinct differences between the tracheobronchial (also called conduction or central regions), alveolar regions (also called respiratory or peripheral regions), and alveolus (i.e., terminal alveolar sacs) should be made. In few previous modeling studies, [18,19] the first two regions have been included in the modeling, but so far to the authors' knowledge, no one has reported the separation of terminal alveolar sacs as a separate region. This is important because alveolar sacs are anatomically and physiologically distinct from the alveolar region due to the presence of a very thin air-blood barrier and surfactant layer.…”