Abstract
Pharmacokinetics is a key component of pharmacology and is an essential aspect during drug discovery and development phases that evaluates the safety and efficacy profiles. Mathematical models mainly physiologically based pharmacokinetic (PBPK) models have been increasingly used that can help in drug screening and identification; dose optimisation prior to preclinical and clinical trials using in vitro data, thus saving time and resources. PBPK models describe the pharmacokinetic processes – absorption, distribution, metabolism and elimination (ADME) using various mathematical correlations including in vitro – in vivo extrapolations in humans. Nanoparticles are been increasingly used for drug delivery due to their advantages over conventional formulations such as enhanced absorption, longer half-life, good safety and efficacy, targeted delivery etc. However, studies using nanoparticles in humans can be associated with various obstacles including ethical and logistical, hindering the drug development process. PBPK models overcome the earlier mentioned problems and can evaluate various biological and molecular processes that define drug pharmacokinetics using in vitro data. This chapter summarises the approach of PBPK models, its challenges and possibilities to assess the key ADME mechanisms involved during various mucosal routes of administration using several allometric, anthropometric and rate equations to inform drug pharmacokinetics in humans.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ratain MJ, P.W.J., Principles of pharmacokinetics, in Holland-Frei cancer medicine, P.R. Kufe DW, Weichselbaum RR, et al., Editor. 2003, Hamilton (ON): BC Decker.
Strimbu K, Tavel JA. What are biomarkers? Curr Opin HIV AIDS. 2010;5(6):463–6.
Rizk M, et al. Importance of drug pharmacokinetics at the site of action. Clin Transl Sci. 2017;10(3):133–42.
Tamimi NAM, Ellis P. Drug development: from concept to marketing! Nephron Clin Pract. 2009;113(3):c125–31.
Ruane PJ, et al. Antiviral activity, safety, and pharmacokinetics/pharmacodynamics of Tenofovir Alafenamide as 10-day monotherapy in HIV-1-positive adults. J Acquir Immune Defic Syndr. 2013;63(4):449–55.
Tsamandouras N, Rostami-Hodjegan A, Aarons L. Combining the ‘bottom up’ and ‘top down’ approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data. Br J Clin Pharmacol. 2015;79(1):48–55.
Peters SA. Variability, uncertainty, and sensitivity analysis. In: Physiologically-based pharmacokinetic (PBPK) modeling and simulations: Wiley; 2012. p. 161–81.
Teorell, T., Kinetics of distribution of substances administered to the body I the extravascular modes of administration. Archives Internationales De Pharmacodynamie Et De Therapie. 1937;57: p. 205–225.
Rowland M, Peck C, Tucker G. Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol. 2011;51(1):45–73.
European Medicines Agency Guideline on the qualification and reporting of physiologically based pharmacokinetic (PBPK) modelling and SIMULATION 2016., http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2016/07/WC500211315.pdf.
United States Food and Drug Administration. Guidance for Industry: physiologically Based Pharmacokinetic Analyses—Format and Content. 2016 last update [cited Access 2016.; Available from: https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM531207.pdf.
Wagner C, et al. Application of physiologically based pharmacokinetic (PBPK) modeling to support dose selection: report of an FDA public workshop on PBPK. CPT Pharmacometrics Syst Pharmacol. 2015;4(4):226–30.
Dolgin E. Long-acting HIV drugs advanced to overcome adherence challenge. Nat Med. 2014;20:323–4.
Sager JE, et al. Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification. Drug Metab Dispos. 2015;43(11):1823–37.
Bosgra S, et al. An improved model to predict physiologically based model parameters and their inter-individual variability from anthropometry. Crit Rev Toxicol. 2012;42(9):751–67.
Salamat, M., et al., Anthropometric predictive equations for estimating body composition. Adv Biomed Res.2015;4(1): p. 34–34.
Alexis F, et al. Factors affecting the clearance and biodistribution of polymeric nanoparticles. Mol Pharm. 2008;5(4):505–15.
Chillistone S, Hardman JG. Factors affecting drug absorption and distribution. Anaesth Intensive Care Med. 2017;18(7):335–9.
Huang W, Lee SL, Yu LX. Mechanistic approaches to predicting Oral drug absorption. AAPS J. 2009;11(2):217–24.
Yu LX, Amidon GL. A compartmental absorption and transit model for estimating oral drug absorption. Int J Pharm. 1999;186(2):119–25.
Bergström CAS, et al. Absorption classification of oral drugs based on molecular surface properties. J Med Chem. 2003;46(4):558–70.
Bakshi RP, et al. Long-acting injectable atovaquone nanomedicines for malaria prophylaxis. Nat Commun. 2018;9(1):315.
Agoram B, Woltosz WS, Bolger MB. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv Drug Deliv Rev. 2001;50:S41–67.
Ensign LM, et al. Mucus Penetrating Nanoparticles: Biophysical Tool and Method of Drug and Gene Delivery. Adv Mater (Deerfield Beach, Fla). 2012;24(28):3887–94.
Lundquist P, Artursson P. Oral absorption of peptides and nanoparticles across the human intestine: opportunities, limitations and studies in human tissues. Adv Drug Deliv Rev. 2016;106:256–76.
Gertz M, et al. Prediction of human intestinal first-pass metabolism of 25 CYP3A substrates from in vitro clearance and permeability data. Drug Metab Dispos. 2010;38(7):1147–58.
Winiwarter S, et al. Correlation of human Jejunal permeability (in vivo) of drugs with experimentally and theoretically derived parameters. A multivariate data analysis approach. J Med Chem. 1998;41(25):4939–49.
Sun D, et al. Comparison of human duodenum and Caco-2 gene expression profiles for 12,000 gene sequences tags and correlation with permeability of 26 drugs. Pharm Res. 2002;19(10):1400–16.
Xia B, et al. Development of a novel oral cavity compartmental absorption and transit model for sublingual administration: illustration with zolpidem. AAPS J. 2015;17(3):631–42.
Löndahl J, et al. Measurement techniques for respiratory tract deposition of airborne nanoparticles: a critical review. J Aerosol Med Pulm Drug Deliv. 2014;27(4):229–54.
Jaworski J, Redlarski G. A compartment model of alveolar–capillary oxygen diffusion with ventilation–perfusion gradient and dynamics of air transport through the respiratory tract. Comput Biol Med. 2014;51:159–70.
Yu J. A subcellular compartmental modeling approach to pulmonary drug development. In: Medicinal Chemistry: The University of Michigan; 2011.
Bolger, M.B., et al. Fluorometholone Ocular Suspension PBPK simulations using the OCAT™ model in GastroPlus™. In GTCBio Ocular Disease Conference. 2012. San Francisco, CA.
Kay K, et al. Physiologically-based pharmacokinetic model of vaginally administered dapivirine ring and film formulations. Br J Clin Pharmacol. 2018;84(9):1950–69.
Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J Pharm Sci. 2002;91(1):129–56.
Li M, et al. Physiologically based pharmacokinetic (PBPK) modeling of pharmaceutical nanoparticles. AAPS J. 2017;19(1):26–42.
Vilanova O, et al. Understanding the kinetics of protein–nanoparticle corona formation. ACS Nano. 2016;10(12):10842–50.
Almeida JPM, et al. In vivo biodistribution of nanoparticles. Nanomedicine. 2011;6(5):815–35.
McDonald TO, et al. Antiretroviral solid drug nanoparticles with enhanced oral bioavailability: production, characterization, and in vitro-in vivo correlation. Adv Healthc Mater. 2014;3(3):400–11.
Reszka R, et al. Body distribution of free, liposomal and nanoparticle-associated mitoxantrone in B16-melanoma-bearing mice. J Pharmacol Exp Ther. 1997;280(1):232–7.
Evans MV, et al. A physiologically based pharmacokinetic model for intravenous and ingested Dimethylarsinic acid in mice. Toxicol Sci. 2008;104(2):250–60.
Peters S. Evaluation of a generic physiologically based pharmacokinetic model for Lineshape analysis. Clin Pharmacokinet. 2008;47(4):261–75.
Thompson MD, Beard DA. Development of appropriate equations for physiologically based pharmacokinetic modeling of permeability-limited and flow-limited transport. J Pharmacokinet Pharmacodyn. 2011;38(4):405–21.
Rekić D, et al. In silico prediction of efavirenz and rifampicin drug–drug interaction considering weight and CYP2B6 phenotype. Br J Clin Pharmacol. 2011;71(4):536–43.
Riley RJ, McGinnity DF, Austin RP. A unified model for predicting human hepatic, metabolic clearance from in vitro intrinsic clearance data in hepatocytes and microsomes. Drug Metab Dispos. 2005;33(9):1304–11.
Yildirimer L, et al. Toxicology and clinical potential of nanoparticles. Nano Today. 2011;6(6):585–607.
Ravindran S, et al. Pharmacokinetics, metabolism, distribution and permeability of nanomedicine. Curr Drug Metab. 2018;19(4):327–34.
Kadam RS, Bourne DWA, Kompella UB. Nano-advantage in enhanced drug delivery with biodegradable nanoparticles: contribution of reduced clearance. Drug Metab Dispos. 2012;40(7):1380–8.
Miyake M, et al. Evaluation of intestinal metabolism and absorption using the ussing chamber system equipped with intestinal tissue from rats and dogs. Eur J Pharm Biopharm. 2018;122:49–53.
Shebley M, et al. Physiologically based pharmacokinetic model qualification and reporting procedures for regulatory submissions: a consortium perspective. Clin Pharmacol Ther. 2018;104(1):88–110.
Li M, et al. Physiologically based pharmacokinetic (PBPK) modeling of pharmaceutical nanoparticles. AAPS J. 2017;19(1):26–42.
Yuan D, et al. Physiologically based pharmacokinetic modeling of nanoparticles. J Pharm Sci. 2019;108(1):58–72.
Bachler G, von Goetz N, Hungerbuhler K. A physiologically based pharmacokinetic model for ionic silver and silver nanoparticles. Int J Nanomedicine. 2013;8:3365–82.
Bachler G, von Goetz N, Hungerbuhler K. Using physiologically based pharmacokinetic (PBPK) modeling for dietary risk assessment of titanium dioxide (TiO2) nanoparticles. Nanotoxicology. 2015;9(3):373–80.
Kumar S, Singh SK. In silico-in vitro-in vivo studies of experimentally designed carvedilol loaded silk fibroin-casein nanoparticles using physiological based pharmacokinetic model. Int J Biol Macromol. 2017;96:403–20.
Jung F, et al. A comparison of two biorelevant in vitro drug release methods for nanotherapeutics based on advanced physiologically-based pharmacokinetic modelling. Eur J Pharm Biopharm. 2018;127:462–70.
Moss DM, et al. Applications of physiologically based pharmacokinetic modeling for the optimization of anti-infective therapies. Expert Opin Drug Metab Toxicol. 2015;11(8):1203–17.
Kiser JJ, et al. Isoniazid pharmacokinetics, pharmacodynamics and dosing in south African infants. Ther Drug Monit. 2012;34(4):446–51.
Moss DM, Siccardi M. Optimizing nanomedicine pharmacokinetics using physiologically based pharmacokinetics modelling. Br J Pharmacol. 2014;171(17):3963–79.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 American Association of Pharmaceutical Scientists
About this chapter
Cite this chapter
Rajoli, R.K.R. (2020). Pharmacokinetic Modelling to Study the Biodistribution of Nanoparticles. In: Muttil, P., Kunda, N. (eds) Mucosal Delivery of Drugs and Biologics in Nanoparticles. AAPS Advances in the Pharmaceutical Sciences Series, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-030-35910-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-35910-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35909-6
Online ISBN: 978-3-030-35910-2
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)