High-Throughput Screening of Drug−Brain Tissue Binding and in Silico Prediction for Assessment of Central Nervous System Drug Delivery

Wan, Hong; Rehngren, Mikael; Giordanetto, Fabrizio; Bergström, Fredrik; Tunek, Anders

doi:10.1021/jm070375w

Cited by 133 publications

(118 citation statements)

References 34 publications

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“…For each radioligand, f p and f ND were predicted using the datasets shown in Figure 2. Even though there was inconsistent use of lipophilicity for estimation of f p and f ND between our study (which used Moriguchi logP) and the studies of Wan et al (23) and Mauer et al (24) (which used clogP), we observed positive correlations between predicted and clinical k 2 in the healthy and AD groups (R 2 5 0.81 and 0.85, respectively) (Fig. 3B).…”

Section: Predicted Permeability and Kcontrasting

confidence: 57%

“…f P and f ND were estimated from in vitro binding experiments using mouse brain and plasma and the in silico lipophilicity of 24 CNS drugs (e.g., risperidone, haloperidol, and fluvoxamine) reported by Wan et al (23) and Maurer et al (24). The datasets were obtained from references, and then the regression relationships for clogP versus f ND and f ND versus f P were determined.…”

Section: Prediction Of Clinical Suvr In Amyloid Pet Imagingmentioning

confidence: 99%

“…This result supports the possibility that use of the Moriguchi logP without in vitro experiments for f p and f ND estimation can be applied to other amyloid radioligands. There have been several studies with reported data sets for in silico logP, in vitro f p , and f ND for CNS drugs or radioligands (17,23,24,29). The data sets selected for use may change the predicted f P and f ND .…”

Section: Predicted Permeability and Kmentioning

confidence: 99%

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Prediction of the Clinical SUV Ratio in Amyloid PET Imaging Using a Biomathematic Modeling Approach Toward the Efficient Development of a Radioligand

et al. 2017

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Our study aimed to develop a method to mathematically predict the kinetic parameters K 1 (influx rate constant), k 2 (efflux rate constant), and BP ND (nondisplaceable binding potential) of amyloid PET tracers and obtain SUV ratios (SUVRs) from predicted timeactivity curves of target and reference regions. Methods: We investigated 10 clinically applied amyloid PET radioligands: 11 CPittsburgh compound B, 11 C-BF-227, 11 C-AZD2184, 11 C-SB-13, 18 F-FACT, 18 F-florbetapir, 18 F-florbetaben, 18 F-flutemetamol, 18 F-FDDNP, and 18 F-AZD4694. For each tracer, time-activity curves of both target and reference regions were generated using a simplified 1-tissue-compartment model, with an arterial plasma input function and the predicted kinetic parameters. K 1 , k 2 , and BP ND were derived from the lipophilicity (logP), apparent volume, free fraction in plasma, free fraction in tissue, dissociation constant, and density of amyloid b using biomathematic modeling. Density was fixed at 3 nM to represent healthy control conditions and 50 nM to represent severe Alzheimer disease (AD). Predicted SUVRs for the healthy and AD groups were then obtained by dividing the integrated time-activity curve of the target region by that of the reference region. To validate the presented method, the predicted K 1 , k 2 , BP ND , and SUVR for the healthy and AD groups were compared with the respective clinically observed values. Results: The correlation between predicted and clinical kinetic parameters had an R 2 value of 0.73 for K 1 in the healthy group, 0.71 for K 1 in the AD group, 0.81 for k 2 in the healthy group, 0.85 for k 2 in the AD group, and 0.63 for BP ND in the AD group. The regression relationship between the predicted SUVR (y) and the clinical SUVR (x) for the healthy and the AD groups was y 5 2.73x -2.11 (R 2 5 0.72). Conclusion:The proposed method showed a good correlation between predicted and clinical SUVR for the 10 clinically applied amyloid tracers.

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Section: Predicted Permeability and Kcontrasting

confidence: 57%

Section: Prediction Of Clinical Suvr In Amyloid Pet Imagingmentioning

confidence: 99%

Section: Predicted Permeability and Kmentioning

confidence: 99%

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Prediction of the Clinical SUV Ratio in Amyloid PET Imaging Using a Biomathematic Modeling Approach Toward the Efficient Development of a Radioligand

et al. 2017

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“…This is also a fundamental assumption when the slice method is used to calculate C u,brainISF from measured total amounts in brain. Tissue partitioning is determined mainly by nonspecific membrane partitioning (Wan et al, 2007) and should not be saturable even at high concentrations. Given the importance of this assumption, an investigation of the linearity of V u,brain was merited.…”

Section: Discussionmentioning

confidence: 99%

“…D and V cell represent the dilution factor and the cell volume (0.8 ml ⅐ g brain Ϫ1 ), respectively. Equilibrium dialysis of brain homogenate was performed as described previously (Wan et al, 2007).…”

Section: Methodsmentioning

confidence: 99%

Development of a High-Throughput Brain Slice Method for Studying Drug Distribution in the Central Nervous System

Fridén

Ducrozet²,

Middleton³

et al. 2009

Drug Metab Dispos

117

133

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ABSTRACT:New, more efficient methods of estimating unbound drug concentrations in the central nervous system (CNS) combine the amount of drug in whole brain tissue samples measured by conventional methods with in vitro estimates of the unbound brain volume of distribution (V u,brain ). Although the brain slice method is the most reliable in vitro method for measuring V u,brain , it has not previously been adapted for the needs of drug discovery research. The aim of this study was to increase the throughput and optimize the experimental conditions of this method. Equilibrium of drug between the buffer and the brain slice within the 4 to 5 h of incubation is a fundamental requirement. However, it is difficult to meet this requirement for many of the extensively binding, lipophilic compounds in drug discovery programs. In this study, the dimensions of the incubation vessel and mode of stirring influenced the equilibration time, as did the amount of brain tissue per unit of buffer volume. The use of casette experiments for investigating V u,brain in a linear drug concentration range increased the throughput of the method. The V u,brain for the model compounds ranged from 4 to 3000 ml ⅐ g brain ؊1 , and the sources of variability are discussed.The optimized setup of the brain slice method allows precise, robust estimation of V u,brain for drugs with diverse properties, including highly lipophilic compounds. This is a critical step forward for the implementation of relevant measurements of CNS exposure in the drug discovery setting.Current drug discovery programs strategically focus on achieving a mechanistic and quantitative understanding of the time course of pharmacological effects and how they are related to drug exposure and the drug-target interaction. This approach identifies key issues for lead optimization as well as facilitating predictions of human exposure to the drug. A critical step in the establishment of these pharmacokinetic-pharmacodynamic relationships is the estimation of exposure to the drug in experimental animals. The unbound drug concentration in plasma is the most relevant and convenient measure of systemic exposure. For centrally acting drugs, however, the bloodbrain barrier can regulate drug exposure in the CNS, and the unbound plasma concentration may no longer reflect the concentration at the target site. Our definition of CNS exposure is the unbound drug concentration in the brain interstitial fluid, C u,brainISF , which surrounds most central drug targets. If a drug target is intracellular, however, exposure would be best defined as the unbound drug concentration in the intracellular fluid. This concentration is regulated not only by events at the blood-brain barrier but also by drug transporters at the brain cell plasma membrane (Dallas et al., 2006). Calculation of C u,brainISF , a rational and increasingly common approach in drug discovery for assessing CNS exposure, is approached by measuring the amount of drug in brain tissue (A brain ) and estimating the unbound brain volume of dis...

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Integrated Approaches to Blood–Brain Barrier

Rong

Feng

2012

Encyclopedia of Drug Metabolism and Interactions

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In vivo, in vitro and in silico methodologies are discussed to address the challenges of CNS drug discovery and development. The most important parameter that governs in vivo pharmacology is the free drug concentration at the site of action in the brain. Four different approaches can be used to estimate free drug concentration in human brain. Lessons learned on prediction successes are highlighted using thirty‐two Pfizer clinical drug candidates with human CSF and/or receptor occupancy data.

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High-Throughput Screening of Drug−Brain Tissue Binding and in Silico Prediction for Assessment of Central Nervous System Drug Delivery

Cited by 133 publications

References 34 publications

Prediction of the Clinical SUV Ratio in Amyloid PET Imaging Using a Biomathematic Modeling Approach Toward the Efficient Development of a Radioligand

Prediction of the Clinical SUV Ratio in Amyloid PET Imaging Using a Biomathematic Modeling Approach Toward the Efficient Development of a Radioligand

Development of a High-Throughput Brain Slice Method for Studying Drug Distribution in the Central Nervous System

Integrated Approaches to Blood–Brain Barrier

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