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      Cross-interaction of tau PET tracers with monoamine oxidase B: evidence from in silico modelling and in vivo imaging

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          Abstract

          Purpose

          Several tracers have been designed for tracking the abnormal accumulation of tau pathology in vivo. Recently, concerns have been raised about the sources of off-target binding for these tracers; inconclusive data propose binding for some tracers to monoamine oxidase B (MAO-B).

          Methods

          Molecular docking and dynamics simulations were used to estimate the affinity and free energy for the binding of several tau tracers (FDDNP, THK523, THK5105, THK5317, THK5351, T807 [aka AV-1451, flortaucipir], T808, PBB3, RO-948, MK-6240, JNJ-311 and PI-2620) to MAO-B. These values were then compared with those for safinamide (MAO-B inhibitor). PET imaging was used with the tau tracer [ 18F]THK5317 and the MAO-B tracer [ 11C]DED in five patients with Alzheimer’s disease to investigate the MAO-B binding component of this first generation tau tracer in vivo.

          Results

          The computational modelling studies identified a binding site for all the tau tracers on MAO-B; this was the same site as that for safinamide. The binding affinity and free energy of binding for the tau tracers to MAO-B was substantial and in a similar range to those for safinamide. The most recently developed tau tracers MK-6240, JNJ-311 and PI-2620 appeared, in silico, to have the lowest relative affinity for MAO-B. The in vivo investigations found that the regional distribution of binding for [ 18F]THK5317 was different from that for [ 11C]DED, although areas of suspected off-target [ 18F]THK5317 binding were detected. The binding relationship between [ 18F]THK5317 and [ 11C]DED depended on the availability of the MAO-B enzyme.

          Conclusions

          The developed tau tracers show in silico and in vivo evidence of cross-interaction with MAO-B; the MAO-B component of the tracer binding was dependent on the regional concentration of the enzyme.

          Electronic supplementary material

          The online version of this article (10.1007/s00259-019-04305-8) contains supplementary material, which is available to authorized users.

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          Most cited references19

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          MMPBSA.py: An Efficient Program for End-State Free Energy Calculations.

          MM-PBSA is a post-processing end-state method to calculate free energies of molecules in solution. MMPBSA.py is a program written in Python for streamlining end-state free energy calculations using ensembles derived from molecular dynamics (MD) or Monte Carlo (MC) simulations. Several implicit solvation models are available with MMPBSA.py, including the Poisson-Boltzmann Model, the Generalized Born Model, and the Reference Interaction Site Model. Vibrational frequencies may be calculated using normal mode or quasi-harmonic analysis to approximate the solute entropy. Specific interactions can also be dissected using free energy decomposition or alanine scanning. A parallel implementation significantly speeds up the calculation by dividing frames evenly across available processors. MMPBSA.py is an efficient, user-friendly program with the flexibility to accommodate the needs of users performing end-state free energy calculations. The source code can be downloaded at http://ambermd.org/ with AmberTools, released under the GNU General Public License.
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            Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA.

            In the drug discovery process, accurate methods of computing the affinity of small molecules with a biological target are strongly needed. This is particularly true for molecular docking and virtual screening methods, which use approximated scoring functions and struggle in estimating binding energies in correlation with experimental values. Among the various methods, MM-PBSA and MM-GBSA are emerging as useful and effective approaches. Although these methods are typically applied to large collections of equilibrated structures of protein-ligand complexes sampled during molecular dynamics in water, the possibility to reliably estimate ligand affinity using a single energy-minimized structure and implicit solvation models has not been explored in sufficient detail. Herein, we thoroughly investigate this hypothesis by comparing different methods for the generation of protein-ligand complexes and diverse methods for free energy prediction for their ability to correlate with experimental values. The methods were tested on a series of structurally diverse inhibitors of Plasmodium falciparum DHFR with known binding mode and measured affinities. The results showed that correlations between MM-PBSA or MM-GBSA binding free energies with experimental affinities were in most cases excellent. Importantly, we found that correlations obtained with the use of a single protein-ligand minimized structure and with implicit solvation models were similar to those obtained after averaging over multiple MD snapshots with explicit water molecules, with consequent save of computing time without loss of accuracy. When applied to a virtual screening experiment, such an approach proved to discriminate between true binders and decoy molecules and yielded significantly better enrichment curves. (c) 2009 Wiley Periodicals, Inc.
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              Evidence for astrocytosis in prodromal Alzheimer disease provided by 11C-deuterium-L-deprenyl: a multitracer PET paradigm combining 11C-Pittsburgh compound B and 18F-FDG.

              Astrocytes colocalize with fibrillar amyloid-β (Aβ) plaques in postmortem Alzheimer disease (AD) brain tissue. It is therefore of great interest to develop a PET tracer for visualizing astrocytes in vivo, enabling the study of the regional distribution of both astrocytes and fibrillar Aβ. A multitracer PET investigation was conducted for patients with mild cognitive impairment (MCI), patients with mild AD, and healthy controls using (11)C-deuterium-L-deprenyl ((11)C-DED) to measure monoamine oxidase B located in astrocytes. Along with (11)C-DED PET, (11)C-Pittsburgh compound B ((11)C-PIB; fibrillar Aβ deposition), (18)F-FDG (glucose metabolism), T1 MRI, cerebrospinal fluid, and neuropsychologic data were acquired from the patients. (11)C-DED PET was performed in MCI patients (n = 8; mean age ± SD, 62.6 ± 7.5 y; mean Mini Mental State Examination, 27.5 ± 2.1), AD patients (n = 7; mean age, 65.1 ± 6.3 y; mean Mini Mental State Examination, 24.4 ± 5.7), and healthy age-matched controls (n = 14; mean age, 64.7 ± 3.6 y). A modified reference Patlak model, with cerebellar gray matter as a reference, was chosen for kinetic analysis of the (11)C-DED data. (11)C-DED data from 20 to 60 min were analyzed using a digital brain atlas. Mean regional (18)F-FDG uptake and (11)C-PIB retention were calculated for each patient, with cerebellar gray matter as a reference. ANOVA analysis of the regional (11)C-DED binding data revealed a significant group effect in the bilateral frontal and bilateral parietal cortices related to increased binding in the MCI patients. All patients, except 3 with MCI, showed high (11)C-PIB retention. Increased (11)C-DED binding in most cortical and subcortical regions was observed in MCI (11)C-PIB+ patients relative to controls, MCI (11)C-PIB (negative) patients, and AD patients. No regional correlations were found between the 3 PET tracers. Increased (11)C-DED binding throughout the brain of the MCI (11)C-PIB+ patients potentially suggests that astrocytosis is an early phenomenon in AD development.
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                Author and article information

                Contributors
                +46-8-52483532 , Agneta.K.Nordberg@ki.se
                Journal
                Eur J Nucl Med Mol Imaging
                Eur. J. Nucl. Med. Mol. Imaging
                European Journal of Nuclear Medicine and Molecular Imaging
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1619-7070
                1619-7089
                27 March 2019
                27 March 2019
                2019
                : 46
                : 6
                : 1369-1382
                Affiliations
                [1 ]ISNI 0000 0004 0512 3288, GRID grid.411313.5, Department of Theoretical Chemistry and Biology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, , AlbaNova University Center, ; S-106 91 Stockholm, Sweden
                [2 ]ISNI 0000 0004 1937 0626, GRID grid.4714.6, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, , Karolinska Institutet, ; Stockholm, Sweden
                [3 ]ISNI 0000 0000 9241 5705, GRID grid.24381.3c, Theme Neurology, , Karolinska University Hospital, ; Stockholm, Sweden
                [4 ]ISNI 0000 0004 1936 9457, GRID grid.8993.b, Department of Physics and Astronomy, , Uppsala University, ; Box 516, SE-751 20 Uppsala, Sweden
                [5 ]ISNI 0000 0000 9241 5705, GRID grid.24381.3c, Theme Aging, , Karolinska University Hospital, ; Stockholm, Sweden
                Article
                4305
                10.1007/s00259-019-04305-8
                6486902
                30919054
                a30d9e1c-7fc6-4c24-a2fd-63dd69741336
                © The Author(s) 2019

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 12 July 2018
                : 4 March 2019
                Funding
                Funded by: Swedish Foundation for Strategic Research
                Funded by: KTH/SLL
                Funded by: Swedish Infrastructure Committee
                Award ID: SNIC2018-3-3
                Award Recipient :
                Funded by: Swedish Research Council
                Award ID: 05817, 02695, 06086
                Award Recipient :
                Funded by: Regional Agreement on Medical Training and Clinical Research (ALF) for Stockholm County Council
                Funded by: Stiftelsen för Gamla Tjänarinnor (SE)
                Funded by: Sigurd and Elsa Goljes Memorial
                Funded by: FundRef http://dx.doi.org/10.13039/100010822, Axel Linders Stiftelse;
                Funded by: Gun and Bertil Stohne Foundation
                Funded by: KI Funds
                Funded by: Hjärnfonden (SE)
                Funded by: Swedish Alzheimer’s Foundation
                Funded by: Dementia Foundation
                Funded by: EU FW7 large-scale integrating project INMiND
                Categories
                Original Article
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2019

                Radiology & Imaging
                tau pet imaging,off-target binding,monoamine oxidase b,alzheimer’s disease,molecular docking,binding free energy calculations

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