Wong AD et al (2013) The blood-brain barrier: an engineering perspective. Front Neuroeng 6:7

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kesselheim AS, Hwang TJ, Franklin JM (2015) Two decades of new drug development for central nervous system disorders. Nat Rev Drug Discov 14(12):815–816

Article  CAS  PubMed  Google Scholar 

Kaitin KI, DiMasi JA (2011) Pharmaceutical innovation in the 21st century: new drug approvals in the first decade, 2000–2009. Clin Pharmacol Ther 89(2):183–188

Article  CAS  PubMed  Google Scholar 

Schinkel AH (1999) P-Glycoprotein, a gatekeeper in the blood-brain barrier. Adv Drug Deliv Rev 36(2–3):179–194

Article  CAS  PubMed  Google Scholar 

Clay AT, Sharom FJ (2014) Multidrug resistance protein: P-glycoprotein. In: Guofeng YM, Morris M (eds) Drug transporters. Wiley, Hoboken

Google Scholar 

de Lange EC, Hammarlund-Udenaes M (2015) Translational aspects of blood-brain barrier transport and central nervous system effects of drugs: from discovery to patients. Clin Pharmacol Ther 97(4):380–394

Article  PubMed  Google Scholar 

de Lange EC et al (2005) Toward the prediction of CNS drug-effect profiles in physiological and pathological conditions using microdialysis and mechanism-based pharmacokinetic-pharmacodynamic modeling. AAPS J 7(3):E532–E543

Article  PubMed  PubMed Central  Google Scholar 

Bagchi S et al (2019) In-vitro blood-brain barrier models for drug screening and permeation studies: an overview. Drug Des Dev Ther 13:3591–3605

Article  CAS  Google Scholar 

Mahar Doan KM et al (2002) Passive permeability and P-glycoprotein-mediated efflux differentiate central nervous system (CNS) and non-CNS marketed drugs. J Pharmacol Exp Ther 303(3):1029–1037

Article  PubMed  Google Scholar 

Schwab D et al (2003) Comparison of in vitro P-glycoprotein screening assays: recommendations for their use in drug discovery. J Med Chem 46(9):1716–1725

Article  CAS  PubMed  Google Scholar 

Storelli F et al (2022) The next frontier in ADME science: predicting transporter-based drug disposition, tissue concentrations and drug-drug interactions in humans. Pharmacol Ther 238:108271

Article  CAS  PubMed  Google Scholar 

Loryan I et al (2022) Unbound brain-to-plasma partition coefficient, K(p, uu, brain)-a game changing parameter for CNS drug discovery and development. Pharm Res 39(7):1321–1341

Article  CAS  PubMed  PubMed Central  Google Scholar 

Summerfield SG et al (2006) Improving the in vitro prediction of in vivo central nervous system penetration: integrating permeability, P-glycoprotein efflux, and free fractions in blood and brain. J Pharmacol Exp Ther 316(3):1282–1290

Article  CAS  PubMed  Google Scholar 

Langthaler K et al (2024) Application of a new MDCKII-MDR1 cell model to measure the extent of drug distribution in vitro at equilibrium for prediction of in vivo unbound brain-to-plasma drug distribution. Fluids Barriers CNS 21(1):11

Article  CAS  PubMed  PubMed Central  Google Scholar 

Uchida Y et al (2011) Blood-brain barrier (BBB) pharmacoproteomics: reconstruction of in vivo brain distribution of 11 P-glycoprotein substrates based on the BBB transporter protein concentration, in vitro intrinsic transport activity, and unbound fraction in plasma and brain in mice. J Pharmacol Exp Ther 339(2):579–588

Article  CAS  PubMed  Google Scholar 

Nicolaï J et al (2020) Impact of in vitro passive permeability in a P-gp-transfected LLC-PK1 model on the prediction of the rat and human unbound brain-to-plasma concentration ratio. Pharm Res 37(9):175

Article  PubMed  Google Scholar 

Hammarlund-Udenaes M et al (2008) On the rate and extent of drug delivery to the brain. Pharm Res 25(8):1737–1750

Article  CAS  PubMed  Google Scholar 

Murata Y et al (2022) In vitro to in vivo extrapolation linked to physiologically based pharmacokinetic models for assessing the brain drug disposition. AAPS J 24(1):28

Article  PubMed  Google Scholar 

Verscheijden LFM et al (2021) Differences in P-glycoprotein activity in human and rodent blood-brain barrier assessed by mechanistic modelling. Arch Toxicol 95(9):3015–3029

Article  CAS  PubMed  PubMed Central  Google Scholar 

Verscheijden LFM et al (2021) Physiologically based pharmacokinetic/pharmacodynamic model for the prediction of morphine brain disposition and analgesia in adults and children. PLoS Comput Biol 17(3):e1008786

Article  CAS  PubMed  PubMed Central  Google Scholar 

Li J et al (2017) Quantitative and mechanistic understanding of AZD1775 penetration across human blood-brain barrier in glioblastoma patients using an IVIVE-PBPK modeling approach. Clin Cancer Res 23(24):7454–7466

Article  CAS  PubMed  PubMed Central  Google Scholar 

Badhan RK, Chenel M, Penny JI (2014) Development of a physiologically-based pharmacokinetic model of the rat central nervous system. Pharmaceutics 6(1):97–136

Article  PubMed  PubMed Central  Google Scholar 

Ball K et al (2012) Development of a physiologically based pharmacokinetic model for the rat central nervous system and determination of an in vitro-in vivo scaling methodology for the blood-brain barrier permeability of two transporter substrates, morphine and oxycodone. J Pharm Sci 101(11):4277–4292

Article  CAS  PubMed  Google Scholar 

Gaohua L et al (2016) Development of a permeability-limited model of the human brain and cerebrospinal fluid (CSF) to integrate known physiological and biological knowledge: estimating time varying CSF drug concentrations and their variability using in vitro data. Drug Metab Pharmacokinet 31(3):224–233

Article  CAS  PubMed  Google Scholar 

Li J et al (2021) Physiologically based pharmacokinetic modeling of central nervous system pharmacokinetics of CDK4/6 inhibitors to guide selection of drug and dosing regimen for brain cancer treatment. Clin Pharmacol Ther 109(2):494–506

Article  CAS  PubMed  Google Scholar 

Fenneteau F et al (2009) Assessing drug distribution in tissues expressing P-glycoprotein through physiologically based pharmacokinetic modeling: model structure and parameters determination. Theor Biol Med Model 6:2

Article  PubMed  PubMed Central  Google Scholar 

Dijkers M (2019) Reduce, reuse, recycle: good stewardship of research data. Spinal Cord 57(3):165–166

Article  PubMed  Google Scholar 

Volpe DA (2008) Variability in Caco-2 and MDCK cell-based intestinal permeability assays. J Pharm Sci 97(2):712–725

Article  CAS  PubMed  Google Scholar 

Harwood MD et al (2023) Interlaboratory variability in the Madin-Darby canine kidney cell proteome. Mol Pharm 20(7):3505–3518

Article  CAS  PubMed  PubMed Central  Google Scholar 

Punt A et al (2023) Impact of in vitro experimental variation in kinetic parameters on physiologically based kinetic (PBK) model simulations. Altex 40(2):237–247

PubMed  Google Scholar 

Lee JB et al (2017) Quantitative analysis of lab-to-lab variability in Caco-2 permeability assays. Eur J Pharm Biopharm 114:38–42

Article  CAS  PubMed  Google Scholar 

Saleh MAA et al (2021) Lumbar cerebrospinal fluid-to-brain extracellular fluid surrogacy is context-specific: insights from LeiCNS-PK3.0 simulations. J Pharmacokinet Pharmacodyn 48(5):725–741

Article  CAS