Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics

Figures & data

Figure 1. The combinatory mapping approach. (Redrawn from [23] with permission of Springer).

Figure 2. The structure of the generic CNS PBPK model Structure: Black: Plasma PK model, Red: CNS PBPK model. Parameters: Black: estimated plasma PK parameters; Blue: system-specific parameters; Green: drug-specific parameters; Purple: combination of system-specific and drug-specific specific parameters. (Redrawn from [39] with permission of Wiley & Sons).

Figure 3. BPPK model prediction (red lines for median and 95% percentiles) and observed data (black dots) obtained in the five color-filled compartments.

Figure 4. Cartoon of transversal cross section of human brain with the dopaminergic receptor areas indicated. Receptor density is region specific and receptor subtype specific.

Figure 5. Compared plasma pharmacokinetics of high affinity compounds (top row) and their low affinity analogues (bottom row). The circles represent observed plasma concentrations in rats, the lines represent model predictions. (Reprinted from [45] with permission of ASPET).

Table 1. Biomarker classification [71] and approaches to assess quantitative information.

Biomarker	Description	Approaches	References
Type 0	Genotype or phenotype	Genotype and phenotype are determinants of the drug response, that influences target site exposure or response due to variation in the expression of e.g. enzymes or receptors. High quality gene expression data have significantly impacted the direction of investigation by allowing for a better molecular understanding of BBB development, function, and dysfunction. Genome-wide microarray expression data sets for the BBB have become available. Also changes in disease conditions have been investigated. Information on protein expression levels of transporters and receptors can be obtained using quantitative targeted absolute proteomics, whereas information on enzyme conversion rates can be identified using quantitative targeted metabolomics.	[72–77]
Type 1	Drug concentrations in general and at the target site in particular.	Quantitative biomarkers that represent the target site distribution of drugs and metabolites for compounds that act in the CNS are difficult to obtain in man, but are readily available in vivo in animals.	[3,6,31,32,34,75]
Type 2	Degree of target occupancy	In theory, effects may occur at different degrees of target occupancy and may be species dependent. The relationship between target occupancy and effect is therefore important for the understanding of inter- and intraindividual variability. Information on target occupancy is available by bioassays in vitro, tracer displacement in postmortem tissue in vivo and noninvasive PET/SPECT imaging.	[42,44,47,48,78–80]
Type 3	Quantification of the target site activation	By means of in vitro bioassays, information can be obtained on receptor activation in animal and man. Techniques like quantitative electroencephalograms (EEG) and functional-magnetic resonance imaging (fMRI) can obtain specific receptor activation data in preclinical and clinical in vivo setting.	[51,58,81–85]
Type 4	Physiological measures	Physiological measures should be measured in the integral biological system, as they are often controlled by homeostatic feedback mechanisms. Such measures can, for example, be on pituitary hormones that play a very important role in communication between CNS and the periphery. Also for physiological measurements quantitative EEG, PET scanning, and functional MRI techniques are very useful.	[18,51,62,82,84–89]
Type 5	Disease processes	Characterization of disease processes which are particularly useful in clinical settings.	[37,85–87]
Type 6	Clinical endpoints	Clinical endpoints such as occurrence of a disease, symptom, sign, or laboratory abnormality that links to target outcomes.	[88]

Loryan I, Sinha V, Mackie C, et al. Mechanistic understanding of brain drug disposition to optimize the selection of potential neurotherapeutics in drug discovery. Pharm Res. 2014;23:1–17.

 Web of Science ®Google Scholar

Yamamoto Y, Välitalo P, Huntjens D, et al. Predicting drug concentration-time profiles in multiple relevant CNS compartments using a comprehensive physiologically-based pharmacokinetic model. CPT Pharmacometrics Syst Pharmacol. 2017 Sep 11. doi: 10.1002/psp4.12250. [Epub ahead of print].

 PubMedGoogle Scholar

Yamazaki S, Shen Z, Jiang Y, et al. Application of target-mediated drug disposition model to small molecule heat shock protein 90 inhibitors. Drug Metab Dispos. 2013;41(6):1285–1294.

 PubMed Web of Science ®Google Scholar

Danhof M, Alvan G, Dahl SG, et al. Mechanism-based pharmacokinetic–pharmacodynamic modeling—a new classification of biomarkers. Pharm Res. 2005;22(9):1432–1437.

 PubMed Web of Science ®Google Scholar

Uchida Y, Tachikawa M, Obuchi W, et al. A study protocol for quantitative targeted absolute proteomics (QTAP) by LC-MS/MS: application for inter-strain differences in protein expression levels of transporters, receptors, claudin-5, and marker proteins at the blood–brain barrier in ddY, FVB, an. Fluids Barriers CNS. 2013;10(1):21.

 PubMedGoogle Scholar

Bettler B, Fakler B. Ionotropic AMPA-type glutamate and metabotropic GABAB receptors: determining cellular physiology by proteomes. Curr Opin Neurobiol. 2017;45:16–23.

 PubMed Web of Science ®Google Scholar

Sawada Y, Kawai R, McManaway M, et al. Kinetic analysis of transport and opioid receptor binding of [3H](-)-cyclofoxy in rat brain in vivo: implications for human studies. J Cereb Blood Flow Metab. 1991;11(2):183–203.

 PubMed Web of Science ®Google Scholar

Wang Y, Welty DF. The simultaneous estimation of the influx and efflux blood-brain barrier permeability of gabapentin using a microdialysis-pharmacokinetic approach. Pharm Res. 1996;13(3):398–403.

 PubMed Web of Science ®Google Scholar

Westerhout J, Ploeger B, Smeets J, et al. Physiologically based pharmacokinetic modeling to investigate regional brain distribution kinetics in rats. AAPS J. 2012;14(3):543–553.

 PubMed Web of Science ®Google Scholar

Westerhout J, Van Den Berg DJ, Hartman R, et al. Prediction of methotrexate CNS distribution in different species - influence of disease conditions. Eur J Pharm Sci. 2014;57(1):11–24.

 PubMed Web of Science ®Google Scholar

Yamamoto Y, Välitalo PA, Van Den Berg D-J, et al. A generic multi-compartmental CNS distribution model structure for 9 drugs allows prediction of human brain target site concentrations. Pharm Res. 2017;34(2):333–351.

 PubMed Web of Science ®Google Scholar

Bergen AA, Kaing S, Ten Brink JB, et al. Gene expression and functional annotation of human choroid plexus epithelium failure in Alzheimer’s disease. BMC Genomics. 2015;16(1):956.

 PubMed Web of Science ®Google Scholar

Perry DC, Mullis KB, Oie S, et al. Opiate antagonist receptor binding in vivo: evidence for a new receptor binding model. Brain Res. 1980;199(1):49–61.

 PubMed Web of Science ®Google Scholar

de Witte WEA, Danhof M, van der Graaf PH, et al. In vivo target residence time and kinetic selectivity: the association rate constant as determinant. Trends Pharmacol Sci. 2016;37(10):831–842.

 PubMed Web of Science ®Google Scholar

de Witte WEA, Wong YC, Nederpelt I, et al. Mechanistic models enable the rational use of in vitro drug-target binding kinetics for better drug effects in patients. Expert Opin Drug Discov. 2016;11(1):45–63.

 PubMed Web of Science ®Google Scholar

Wong YC, Ilkova T, van Wijk R, et al. Development of a population pharmacokinetic model to predict brain distribution and dopamine D2 receptor occupancy of raclopride in non-anesthetized rat. 2017. Manuscript under revision

 Google Scholar

Borroto-Escuela DO, Carlsson J, Ambrogini P, et al. Understanding the role of GPCR heteroreceptor complexes in modulating the brain networks in health and disease. Front Cell Neurosci. 2017;11(February):1–20.

 PubMed Web of Science ®Google Scholar

Bagli M, Süverkrüp R, Quadflieg R, et al. Pharmacokinetic-pharmacodynamic modeling of tolerance to the prolactin- secreting effect of chlorprothixene after different modes of drug administration. J Pharmacol Exp Ther. 1999;291(2):547–554.

 PubMed Web of Science ®Google Scholar

Liu S, Cai W, Liu S, et al. Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain Inform. 2015;2(3):167–180.

 PubMedGoogle Scholar

Groenendaal D, Freijer J, Rosier A, et al. Pharmacokinetic/pharmacodynamic modelling of the EEG effects of opioids: the role of complex biophase distribution kinetics. Eur J Pharm Sci. 2008;34(2–3):149–163.

 PubMed Web of Science ®Google Scholar

Freeman ME, Kanyicska B, Lerant A, et al. Prolactin: structure, function, and regulation of secretion. Physiol Rev. 2000;80(4):1523–1631.

 PubMed Web of Science ®Google Scholar

Chan PLS, Nutt JG, Holford NHG. Modeling the short- and long-duration responses to exogenous levodopa and to endogenous levodopa production in Parkinson’s disease. J Pharmacokinet Pharmacodyn. 2004;31(3):243–268.

 PubMed Web of Science ®Google Scholar

van den Brink WJ, Wong YC, Gülave B, et al. Revealing the neuroendocrine response after remoxipride treatment using multi-biomarker discovery and quantifying it by PK/PD modeling. AAPS J. 2017;19(1):274–285.

 PubMed Web of Science ®Google Scholar

Stevens J, Ploeger BA, Hammarlund-Udenaes M, et al. Mechanism-based PK-PD model for the prolactin biological system response following an acute dopamine inhibition challenge: quantitative extrapolation to humans. J Pharmacokinet Pharmacodyn. 2012;39(5):463–477.

 PubMed Web of Science ®Google Scholar

Borsook D, Becerra L, Fava M. Use of functional imaging across clinical phases in CNS drug development. Transl Psychiatry. 2013;3(7):e282.

 PubMed Web of Science ®Google Scholar

Brawek B, Garaschuk O. Monitoring in vivo function of cortical microglia. Cell Calcium. 2017;64:109–117.

 PubMed Web of Science ®Google Scholar

Boxenbaum H. Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics. J Pharmacokinet Biopharm. 1982;10(2):201–227.

 PubMedGoogle Scholar

de Lange ECM. PBPK modeling approach for predictions of human CNS drug brain distribution. Li Di and Edward H. Kerns (eds). Blood-Brain Barrier in Drug Discovery. Wiley. 2015;296–323.

 Google Scholar

Müller UC, Deller T, Korte M. Not just amyloid: physiological functions of the amyloid precursor protein family. Nat Rev Neurosci. 2017;18(5):281–298.

 PubMed Web of Science ®Google Scholar

Holford N, Nutt JG. Disease progression, drug action and Parkinson’s disease: why time cannot be ignored. Eur J Clin Pharmacol. 2008;64(2):207–216.

 PubMed Web of Science ®Google Scholar