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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5921_Библиотеки_им_академика_М_И_Перельмана
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Empirical correlations are only feasible when in vivo data are available.
When they are not, more complex mechanistic deposition models are
employed to predict regional deposition to the level of individual
airway generation. Readers are referred to a recent review by Fro hlich
and Salar-Behzadi for details [12]. In brief, generational deposition
models use a simpliied representation of the human lungs and
mathematical expressions to predict deposition probabilities based on
various deposition mechanisms. Many determinants that inluence
deposition, including airway geometry, breathing patterns, inhalation
maneuvers, and particle properties, can be incorporated into these
models to predict their likely impact on overall or regional deposition
patterns. These models yield a list of drug mass deposition fractions
over each lung generation. Deposition models can be combined with
PK models (see the section below on PBPK models) to predict regional
pulmonary exposure and systemic exposure over time [83].
2.4.2 PKModels
Physiologically-BasedPKModels
PBPK models comprise a quantitative approach that incorporates
mechanistic information – such as human anatomy and physiology
(including intrinsic and extrinsic patient factors) – and a drug’s
physiochemical properties (e.g., solubility, pKa values, partition
coeficients, and dissolution rates) to predict and deine PK (e.g.,
dissolution, absorption, distribution, metabolism, and elimination)
[18, 44, 83, 87, 88]. Unlike the data-driven PK/PD approach, PBPK
models are based on a series of differential equations that are
parameterized with a set of predeined physiologically driven
parameters [18]. Readers are referred to Zhuang and Lu for a critical
overview of PBPK models [89]. Generic PBPK inhalation models have
been implemented into the commercial software programs
GastroPlus™ and PulmoSim™ [18, 85]. PBPK models have been widely
used to describe pulmonary/systemic PK for small molecules only [18,
85]. To date, there is very limited evidence on the utility of PBPK
models for inhaled biological macromolecules.
TheGastroPlus™AdditionalDosageRoutesModule
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The Pulmonary Compartmental Absorption and Transit (PCAT) model
included in GastroPlus™ divides the lung into four compartments: the
extra-thoracic (naso- and oro-pharynx and larynx), thoracic (trachea
and bronchi), bronchiolar (bronchioles and terminal bronchioles) and
alveolar-interstitial (respiratory bronchioles, alveolar ducts and sacs,
and interstitial connective tissue) (Fig. 3) [18, 85, 90]. The amount of
drug deposited in each compartment can be deined manually (via
experimental data) or calculated by a built-in particle deposition
model developed by the International Commission on Radiological
Protection (ICRP 66 model) [18, 85].
Fig.3 Schematic of the Pulmonary Compartmental Absorption and Transit (PCAT)
model. (Adapted from Borghardt et al. [18])
The particle kinetics depend on the regional deposition site.
Particles that deposit in the extra-thoracic compartment can be either
swallowed and absorbed through the GIT – circumstances handled via
an advanced compartmental absorption and transit absorption model
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– or absorbed directly into systemic circulation via a passive process
[18, 85]. The dissolution processes of particles that deposit in the other
three pulmonary compartments are handled via different methods,
such as the Noyes–Whitney function [18, 85]. Pulmonary absorption
from each compartment is assumed to be passive, and rates depend on
the pulmonary system’s anatomical and physiological parameters (e.g.,
mucociliary clearance) and drug-dependent input parameters. It is
important to note that because the physiological parameters needed
for PBPK modeling are dificult to obtain in humans, the values
reported in the literature vary widely, making PBPK models dificult to
develop. Before utilizing these models to predict or translate, it is thus
important to demonstrate in silico-in vivo correlation and validate
using clinical data on inhaled drugs [18, 85].
PulmoSim™
The PulmoSim™ PBPK model (Fig. 4) differentiates two routes of an
inhaled drug [18, 91]. One fraction of the drug is swallowed and
absorbed by the GIT, and the rest is deposited in the lungs, where
particles irst undergo dissolution. Both dissolved and undissolved
drug can be moved via mucociliary clearance to the gut compartment
but dissolved drug that escapes this clearance is absorbed by lung
tissue before being absorbed into the systemic circulation.
Furthermore, the unbound fraction of a drug in the lungs appears to be
a key determinant of the drug’s absorption from the lungs to the
plasma. The PulmoSim™ PBPK model has the same limitations as the
PCAT model described above.
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Fig.4 Schematic of the PulmoSim™. l = lung; g = gut; s = solution; t = tissue;
v = vasculature; k
diss
= dissolution rate constant; kmu = mucociliary rate constant;
k
gut
= elimination rate constant from the gut; kel = elimination rate constant from
the central compartment; FU fraction unbound; CLab = absorptive clearance across
tissue; V = physiological distribution volumes; QG = low rate to the gut vasculature;
QL = low rate to the pulmonary vasculature; QP = intercompartmental clearance;
light purple boxes = pulmonary compartments; light purple boxes = gut
compartments; dark purple boxes = vasculature compartments in the speciic
organs; green boxes = systemic disposition compartments. (Adapted from Borghardt
et al. [18])
Data-DrivenPK/PDApproaches
Unlike the mechanistic PBPK modeling approach, data-driven
approaches rely largely on available preclinical or clinical PK data,
selecting model structures based on pharmacostatistical methods and
biological plausibility [92]. Data-driven models are often less complex
than PBPK models, and their parameters often carry less biologically
meaningful information. One major advantage of data-driven models is
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that they can identify important predictors to explain PK
characteristics.
The literature includes many different types of pulmonary
absorption models [18], which can be generally categorized into two
main absorption model types, the choice of which is largely dependent
on the PK data. When no PK data from intravenous (IV) or other
administration routes are available for model development, Model I
(Fig. 5) is preferred. When IV data are available, complexity increases,
making Models II, IIIa, and IIIb preferable.
Fig.5 Structural models for pulmonary absorption. (Adapted from Himstedt et al.
[87]). CMT = compartment; F
Pul
= pulmonary bioavailability or designated lung
dose; F
slow/med/fast
= fraction of the lung dose slowly/intermediately/fastly
absorbed; k
slow/med/fast
= slow/intermediate/fast absorption rate constants;
k
trans
= transit rate constant; k
nal
= non-absorptive loss rate constant
Another major advantage of data-driven empirical PK/PD modeling
is that it can be used to establish a quantitative link between exposure
(i.e., PK) and the eficacy of an inhaled drug [93]. Recently, a
mechanism-based PK/PD model (MBM) for inhaled colistin was
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developed based on PK/PD data of infected neutropenic mice. It was
subsequently combined with a previously published PK model of
nebulized colistin methanesulfonate to predict the treatment
outcomes of different clinically prescribed dosage regimens in
patients. The MBM had previously successfully estimated the PK/PD
targets observed for inhaled colistin in neutropenic mice. In line with
previous clinical recommendations, deterministic simulation
employing human PK data predicted that an inhalation dosage regimen
of 60 mg colistin base activity every 12 hours is more likely to achieve
a ≥2-log10 CFU/lung bacterial reduction in critically ill patients with
lung infections caused by multidrug-resistant Pseudomonasaeruginosa
[93]. This recently developed MBM is a valuable and clinically useful
tool for optimizing inhalational dosage regimens.
3 Summary
The development of inhaled biological macromolecules has expanded
signiicantly over the last decade, making pulmonary delivery a
promising alternative to systemic delivery of biological
macromolecules. However, pulmonary deposition and ADME processes
are highly complex, and their interplay determines the pulmonary and
systemic PK/PD of inhaled biological macromolecules. Although great
progress has been made in understanding pulmonary deposition and
ADME processes, a signiicant knowledge gap remains, making
modeling of pulmonary kinetic processes challenging (Table 2). In
addition, the literature varies widely regarding the parameters needed
for PBPK modeling. Well-designed in vitro experiments, clinical
studies, and more sophisticated modeling approaches are urgently
needed to elucidate the highly complex pulmonary deposition and
ADME processes for the clinical development of inhaled medications.
Table2 Challenges of PBPK modeling
No. Challengesf orPBPKmodeling
1 Shortage of individuals and independent reviewers with M&S experience and
expertise.
2 The gap between available models and physiochemical and physiological
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No. Challengesf orPBPKmodeling
processes.
3 Limitations in experiments to estimate the desired parameters.
4 Limited available in vivo study data, and dificulty in sampling in studies. This
results in a lack of conidence in model extrapolation.
5 Lack of information on intra- and inter-subject variability. Dificult to
incorporate physiological differences between normal and special populations
into the PBPK models.
6 Knowledge gap in the physiochemical properties of the formulation, PK/PD
behaviors, and their representation by in silico models.
7 Models coded in one M&S platform cannot be easily translated to a different
M&S platform.
Con lictofInterest
The authors declare that they have no known inancial or interpersonal
conlicts that would have appeared to have an impact on the research
presented in this study. Dr Yu-Wei Lin is currently an employee of
Certara, USA. Ms. Audrey Huili Lim is currently employed by the
Institute for Clinical Research, National Institutes of Health, Shah Alam,
Malaysia.
References
1. Movia D, Prina-Mello A. Preclinical development of orally inhaled drugs (OIDs)
—are animal models predictive or shall we move towards in vitro non-animal
models? Animals. 2020;10:1259.
[PubMed][PubMedCentral]
2.
WHO. Chronic obstructive pulmonary disease (COPD). Available online: https://
www. who. int/ news-room/ fact-sheets/ detail/ chronic-obstructive-pulmonarydisease-. Accessed on 23 Mar 2023.
3.
WHO. Asthma. Available online: https:// www. who. int/ news-room/ q-a-detail/
asthma. Accessed on 23 Mar 2023.
4.
WHO. The top 10 causes of death 2020. Available from: https:// www. who. int/
news-room/ fact-sheets/ detail/ the-top-10-causes-of-death.
https://t.me/medicina_free
5.
GBI Research. Global respiratory drugs market to 2023—a changing therapeutic
landscape as key patents expire and biologics, targeted therapies and CFTR
modulators for asthma and cystic ibrosis treatment emerge as market growth
drivers. Available online: http:// gbiresearch. com/ report-store/ market-reports/
therapy-analysis/ global-respiratory-drugs-market-to2023-a-changingtherapeutic-landscape-as-key-patents-expire-and-biologics-targeted-therapi.
Accessed on 26 Mar 2022.
6.
Liang W, Pan HW, Vllasaliu D, Lam JKW. Pulmonary delivery of biological drugs.
Pharmaceutics. 2020;12(11):1025.
[PubMed][PubMedCentral]
7.
Jozala AF, Geraldes DC, Tundisi LL, Feitosa VA, Breyer CA, Cardoso SL, et al.
Biopharmaceuticals from microorganisms: from production to puriication. Braz
J Microbiol. 2016;47(Suppl 1):51–63.
[PubMed][PubMedCentral]
8.
Pham JV, Yilma MA, Feliz A, Majid MT, Maffetone N, Walker JR, et al. A review of
the microbial production of bioactive natural products and biologics. Front
Microbiol. 2019;10:1404.
[PubMed][PubMedCentral]
9.
Adami G, Saag KG, Chapurlat RD, Guanabens N, Haugeberg G, Lems WF, et al.
Balancing beneits and risks in the era of biologics. Ther Adv Musculoskelet Dis.
2019;11:1759720x19883973.
[PubMed][PubMedCentral]
10.
Mishra B, Singh J. Novel drug delivery systems and signiicance in respiratory
diseases. In: Targeting chronic inlammatory lung diseases using advanced drug
delivery systems. London: Academic Press; 2020. p. 57–95.
11.
Labiris NR, Dolovich MB. Pulmonary drug delivery. Part I: physiological factors
affecting therapeutic effectiveness of aerosolized medications. Br J Clin
Pharmacol. 2003;56(6):588–99.
[PubMed][PubMedCentral]
12.
Frohlich E, Salar-Behzadi S. Oral inhalation for delivery of proteins and peptides
to the lungs. Eur J Pharm Biopharm. 2021;163:198–211.
[PubMed]
13.
Erdo F, Bors LA, Farkas D, Bajza A , Gizurarson S. Evaluation of intranasal delivery
route of drug administration for brain targeting. Brain Res Bull. 2018;143:155–
70.
https://t.me/medicina_free
[PubMed]
14.
Katzung BG, Masters SB, Trevor AJ. Basic & clinical pharmacology. New
York/London: McGraw-Hill; 2004.
15.
Orme M, Sjoqvist F, Birkett D, Brøsen K, Cascorbi I, Gustafsson LL, et al. Clinical
pharmacology in research, teaching and health care. Basic Clin Pharmacol
Toxicol. 2010;107(1):531–59.
[PubMed]
16.
Rodrıguez-Gasco n A, Solinıs MA , Isla A. The role of PK/PD analysis in the
development and evaluation of antimicrobials. Pharmaceutics. 2021;13(6):833.
[PubMed][PubMedCentral]
17.
Tuntland T, Ethell B, Kosaka T, Blasco F, Zang RX, Jain M, et al. Implementation of
pharmacokinetic and pharmacodynamic strategies in early research phases of
drug discovery and development at Novartis Institute of Biomedical Research.
Front Pharmacol. 2014;5:174.
[PubMed][PubMedCentral]
18.
Borghardt JM, Weber B, Staab A, Kloft C. Pharmacometric models for
characterizing the pharmacokinetics of orally inhaled drugs. AAPS J.
2015;17(4):853–70.
[PubMed][PubMedCentral]
19.
Powers KA, Dhamoon AS. Physiology, pulmonary ventilation and perfusion. In:
StatPearls [Internet]. Treasure Island: StatPearls Publishing; 2022.
20.
Haddad M, Sharma S. Physiology, lung. In: StatPearls [Internet]. Treasure Island:
StatPearls Publishing; 2022.
21.
Liu X, Jin L, Upham JW, Roberts MS. The development of models for the
evaluation of pulmonary drug disposition. Expert Opin Drug Metab Toxicol.
2013;9(4):487–505.
[PubMed]
22.
Williams PL. Gray’s anatomy: the anatomical basis of medicine and surgery. 38th
ed. London: Churchill Livingstone; 1996. 2092 p.
23.
Van de Moortele T, Wendt CH, Coletti F. Morphological and functional properties
of the conducting human airways investigated by in vivo computed tomography
and in vitro MRI. J Appl Physiol (1985). 2018;124(2):400–13.
[PubMed]
https://t.me/medicina_free
24.
Patton JS. Mechanisms of macromolecule absorption by the lungs. Adv Drug
Deliv Rev. 1996;19(1):3–36.
25.
Patton JS, Byron PR. Inhaling medicines: delivering drugs to the body through
the lungs. Nat Rev Drug Discov. 2007;6(1):67–74.
[PubMed]
26.
Bur M, Lehr C-M. Pulmonary cell culture models to study the safety and eficacy
of innovative aerosol medicines. Expert Opin Drug Deliv. 2008;5(6):641–52.
[PubMed]
27.
Weibel ER. Morphological basis of alveolar-capillary gas exchange. Physiol Rev.
1973;53(2):419–95.
[PubMed]
28.
Hsia CC, Hyde DM, Weibel ER. Lung structure and the intrinsic challenges of gas
exchange. Compr Physiol. 2016;6(2):827–95.
[PubMed][PubMedCentral]
29.
Mercer RR, Russell ML, Roggli VL, Crapo JD. Cell number and distribution in
human and rat airways. Am J Respir Cell Mol Biol. 1994;10(6):613–24.
[PubMed]
30.
Mathias NR, Yamashita F, Lee VHL. Respiratory epithelial cell culture models for
evaluation of ion and drug transport. Adv Drug Deliv Rev. 1996;22(1):215–49.
31.
Gartner LP. Respiratory system textbook of histology. 2nd ed. Elsevier Inc.; 2021.
p. 355–78.
32.
Kia’i N, Bajaj T. Histology, respiratory epithelium. In: StatPearls [Internet].
Treasure Island: StatPearls Publishing; 2022.
33.
Ward HE, Nicholas TE. Alveolar type I and type II cells. Aust NZ J Med.
1984;14(5 Suppl 3):731–4.
34.
Ruaro B, Salton F, Braga L, Wade B, Confalonieri P, Volpe MC, et al. The history
and mystery of alveolar epithelial type II cells: focus on their physiologic and
pathologic role in lung. Int J Mol Sci. 2021;22(5):2566.
[PubMed][PubMedCentral]
35.
King RJ. Pulmonary surfactant. J Appl Physiol Respir Environ Exerc Physiol.
1982;53(1):1–8.
[PubMed]
https://t.me/medicina_free
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