Mechanistic Understanding of Brain Drug Disposition to Optimize the Selection of Potential Neurotherapeutics in Drug Discovery

Loryan, Irena; Sinha, Vikash; Mackie, Claire; Peer, Achiel Van; Drinkenburg, Wilhelmus; Vermeulen, An; Morrison, Denise; Monshouwer, Mario; Heald, Donald; Hammarlund‐Udenaes, Margareta

doi:10.1007/s11095-014-1319-1

Cited by 63 publications

(111 citation statements)

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“…The unbound ratio of brain-to-plasma concentration (K p , uu ) was calculated from the ratio of AUC 0-t for total brain and plasma concentrations (K p ), f u,plasma and f u,brain , using eq. 8 (Fridén et al, 2011;Loryan et al, 2014):…”

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confidence: 99%

“…where the f u,brain,corrected values are the f u,brain values corrected for lysosomal trapping using the pH partitioning model proposed by Fridén et al 2011). Other parameters such as the predicted unbound cytosolic-to-extracellular drug concentration ratio (K p,uu,cyto,pred ), the predicted lysosomic-to-cytosolic unbound drug concentration ratio (K p,uu,lyso,pred ), the predicted unbound drug intracellularto-extracellular partitioning coefficient (K p,uu,cell,pred ), and the predicted volume of distribution of unbound drug in the brain (V u,brain,pred ) were also estimated according to Fridén et al (2011) and Loryan et al (2014).…”

Section: Downloaded Frommentioning

confidence: 99%

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Elucidation of the Impact of P-glycoprotein and Breast Cancer Resistance Protein on the Brain Distribution of Catechol-O-Methyltransferase Inhibitors

et al. 2017

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P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) are clinically important efflux transporters that act cooperatively at the blood-brain barrier, limiting the entry of several drugs into the central nervous system (CNS) and affecting their pharmacokinetics, therapeutic efficacy, and safety. In the present study, the interactions of catechol--methyltransferase (COMT) inhibitors (BIA 9-1059, BIA 9-1079, entacapone, nebicapone, opicapone, and tolcapone) with P-gp and BCRP were investigated to determine the contribution of these transporters in their access to the brain. In vitro cellular accumulation and bidirectional transport assays were conducted in Madin-Darby canine kidney (MDCK) II, MDCK-MDR1, and MDCK-BCRP cells. In vivo pharmacokinetic studies were carried out for tolcapone and BIA 9-1079 in rats, with and without elacridar, a well-known P-gp and BCRP modulator. The results suggest that BIA 9-1079, nebicapone, and tolcapone inhibit BCRP in a concentration-dependent manner. Moreover, with net flux ratios higher than 2 and decreased over 50% in the presence of verapamil or Ko143, BIA 9-1079 was identified as a P-gp substrate while BIA 9-1059, entacapone, opicapone, and nebicapone were revealed to be BCRP substrates. In vivo, brain exposure was limited for tolcapone and BIA 9-1079, although tolcapone crossed the blood-brain barrier at a greater rate and to a greater extent than BIA 9-1079. The extent of brain distribution of both compounds was significantly increased in the presence of elacridar, attesting to the involvement of efflux transporters. These findings provide relevant information and improve the understanding of the mechanisms that govern the access of these COMT inhibitors to the CNS.

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confidence: 99%

Section: Downloaded Frommentioning

confidence: 99%

Elucidation of the Impact of P-glycoprotein and Breast Cancer Resistance Protein on the Brain Distribution of Catechol-O-Methyltransferase Inhibitors

et al. 2017

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“…These models differ in terms of temporal and spatial resolution, and in their consideration of drug protein binding (7–10). For example, the combinatorial mapping approach has been recently introduced using unbound drug concentration with the brain slice technique (10,11). This approach can predict unbound drug CNS exposure at steady state in multiple brain compartments, but does not allow temporal characterization of drug concentration changes.…”

Section: Introductionmentioning

confidence: 99%

A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations

et al. 2016

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PurposePredicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human brain disposition.MethodsA mathematical model consisting of several physiological brain compartments in the rat was developed using rich concentration-time profiles from nine structurally diverse drugs in plasma, brain extracellular fluid, and two cerebrospinal fluid compartments. The effect of active drug transporters was also accounted for. Subsequently, the model was translated to predict human concentration-time profiles for acetaminophen and morphine, by scaling or replacing system- and drug-specific parameters in the model.ResultsA common model structure was identified that adequately described the rat pharmacokinetic profiles for each of the nine drugs across brain compartments, with good precision of structural model parameters (relative standard error <37.5%). The model predicted the human concentration-time profiles in different brain compartments well (symmetric mean absolute percentage error <90%).ConclusionsA multi-compartmental brain pharmacokinetic model was developed and its structure could adequately describe data across nine different drugs. The model could be successfully translated to predict human brain concentrations.Electronic supplementary materialThe online version of this article (doi:10.1007/s11095-016-2065-3) contains supplementary material, which is available to authorized users.

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“…The SA BCFSB was divided into SA BCSFB1 which is the SA around CSF LV , and SA BCSFB2 which is SA around CSF TFV , like those in the rat CNS PBPK model (Yamamoto et al, 2017b). The total volume of lysosomes (V LYSO ) was calculated using the volume ratio of the lysosomes to the brain intracellular fluid of brain parenchyma cells (1:80) which was reported in rats (Loryan et al, 2014). In human, the volume of brain intracellular fluid (Vbrain ICF ) is 960 mL (Thorne et al, 2004), therefore V LYSO was calculated to be 12 mL.…”

Section: System-specific Parametersmentioning

confidence: 99%

Prediction of human CNS pharmacokinetics using a physiologically-based pharmacokinetic modeling approach

Yamamoto

Välitalo

Wong

et al. 2018

European Journal of Pharmaceutical Sciences

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Knowledge of drug concentration-time profiles at the central nervous system (CNS) target-site is critically important for rational development of CNS targeted drugs. Our aim was to translate a recently published comprehensive CNS physiologically-based pharmacokinetic (PBPK) model from rat to human, and to predict drug concentration-time profiles in multiple CNS compartments on available human data of four drugs (acetaminophen, oxycodone, morphine and phenytoin). Values of the system-specific parameters in the rat CNS PBPK model were replaced by corresponding human values. The contribution of active transporters for the four selected drugs was scaled based on differences in expression of the pertinent transporters in both species. Model predictions were evaluated with available pharmacokinetic (PK) data in human brain extracellular fluid and/or cerebrospinal fluid, obtained under physiologically healthy CNS conditions (acetaminophen, oxycodone, and morphine) and under pathophysiological CNS conditions where CNS physiology could be affected (acetaminophen, morphine and phenytoin). The human CNS PBPK model could successfully predict their concentration-time profiles in multiple human CNS compartments in physiological CNS conditions within a 1.6-fold error. Furthermore, the model allowed investigation of the potential underlying mechanisms that can explain differences in CNS PK associated with pathophysiological changes. This analysis supports the relevance of the developed model to allow more effective selection of CNS drug candidates since it enables the prediction of CNS target-site concentrations in humans, which are essential for drug development and patient treatment.

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Mechanistic Understanding of Brain Drug Disposition to Optimize the Selection of Potential Neurotherapeutics in Drug Discovery

Abstract: With the rather easily-performed combinatory mapping approach, it was possible to provide quantitative information supporting the decision making in the drug discovery setting.

Cited by 63 publications

References 49 publications

Elucidation of the Impact of P-glycoprotein and Breast Cancer Resistance Protein on the Brain Distribution of Catechol-O-Methyltransferase Inhibitors

Elucidation of the Impact of P-glycoprotein and Breast Cancer Resistance Protein on the Brain Distribution of Catechol-O-Methyltransferase Inhibitors

A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations

Prediction of human CNS pharmacokinetics using a physiologically-based pharmacokinetic modeling approach

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