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9 - Preclinical Longitudinal In Vivo Biomarker Platform for Alzheimer’s Disease Drug Discovery

from Section 2 - Non-clinical Assessment of Alzheimer’s Disease Candidate Drugs

Published online by Cambridge University Press:  03 March 2022

By
Min Su Kang ,
Eduardo R. Zimmer ,
Julie Ottoy ,
Monica Shin ,
Marcel Seungsu Woo ,
Arturo Aliaga ,
Gassan Massarweh ,
A. Claudio Cuello ,
Serge Gauthier  and
Pedro Rosa-Neto
Edited by
Jeffrey Cummings ,
Jefferson Kinney  and
Howard Fillit
Jeffrey Cummings
Affiliation:
University of Nevada, Las Vegas
Jefferson Kinney
Affiliation:
University of Nevada, Las Vegas
Howard Fillit
Affiliation:
Alzheimer’s Drug Discovery Foundation
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Summary

The incorporation of target engagement, efficacy, and imaging abnormalities biomarkers on preclinical (animal) drug development brings the promise of accelerating drug development. In this chapter, we will highlight innovative methodological considerations that will bring greater predictive power relative to the traditional approaches in the preclinical stage of drug discovery. First, we discuss various animal models used in Alzheimer’s disease research and important aspects to consider when choosing the appropriate model to test a novel therapeutic intervention. Second, compared to the traditional histological methods, utilizing in vivo biomarkers in preclinical assessment allows quantifying disease pathophysiology with complex longitudinal designs. We discuss the feasibility and implications of longitudinal study designs and how the same in vivo biomarkers used in human clinical trials can be implemented to evaluate the preclinical development stages. Lastly, we discuss why the incorporation of methods from human clinical trials can advance the preclinical phases of drug discovery.

Keywords

Alzheimer’s diseasedrug discoverypreclinical longitudinal studiesanimal modelsin vivo biomarkersPETMRIFluidsCSFplasma
Type
Chapter
Information
Alzheimer's Disease Drug Development
Research and Development Ecosystem
 , pp. 106 - 122
Publisher: Cambridge University Press
Print publication year: 2022

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References

Polanco, JC, Li, C, Bodea, LG, et al. Amyloid-beta and tau complexity: towards improved biomarkers and targeted therapies. Nat Rev Neurol 2018; 14: 22–39.CrossRefGoogle ScholarPubMed
Gotz, J, Bodea, LG, Goedert, M. Rodent models for Alzheimer disease. Nat Rev Neurosci 2018; 19: 583–98.CrossRefGoogle ScholarPubMed
Grieb, P. Intracerebroventricular streptozotocin injections as a model of Alzheimer’s disease: in search of a relevant mechanism. Mol Neurobiol 2016; 53: 1741–52.Google Scholar
Kamat, PK, Rai, S, Nath, C. Okadaic acid induced neurotoxicity: an emerging tool to study Alzheimer’s disease pathology. Neurotoxicology 2013; 37: 163–72.CrossRefGoogle ScholarPubMed
Kim, HY, Lee, DK, Chung, BR, Kim, HV, Kim, Y. Intracerebroventricular injection of amyloid-beta peptides in normal mice to acutely induce Alzheimer-like cognitive deficits. J Vis Exp 2016; 109: 53308.Google Scholar
de Calignon, A, Polydoro, M, Suarez-Calvet, M, et al. Propagation of tau pathology in a model of early Alzheimer’s disease. Neuron 2012; 73: 685–97.Google Scholar
Braak, H, Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 1991; 82: 239–59.CrossRefGoogle ScholarPubMed
Hardy, J, Selkoe, DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 2002; 297: 353–6.CrossRefGoogle ScholarPubMed
Leon, WC, Canneva, F, Partridge, V, et al. A novel transgenic rat model with a full Alzheimer’s-like amyloid pathology displays pre-plaque intracellular amyloid-beta-associated cognitive impairment. J Alzheimers Dis 2010; 20: 113–26.CrossRefGoogle ScholarPubMed
Cohen, RM, Rezai-Zadeh, K, Weitz, TM, et al. A transgenic Alzheimer rat with plaques, tau pathology, behavioral impairment, oligomeric Abeta, and frank neuronal loss. J Neurosci 2013; 33: 6245–56.Google Scholar
Malcolm, JC, Breuillaud, L, Do Carmo, S, et al. Neuropathological changes and cognitive deficits in rats transgenic for human mutant tau recapitulate human tauopathy. Neurobiol Dis 2019; 127: 323–38.CrossRefGoogle ScholarPubMed
Do Carmo, S, Cuello, AC. Modeling Alzheimer’s disease in transgenic rats. Mol Neurodegener 2013; 8: 37.Google Scholar
Zimmer, ER, Parent, MJ, Cuello, AC, Gauthier, S, Rosa-Neto, P. MicroPET imaging and transgenic models: a blueprint for Alzheimer’s disease clinical research. Trends Neurosci 2014; 37: 629–41.Google Scholar
Kuang, E, Wan, Q, Li, X, et al. ER stress triggers apoptosis induced by Nogo-B/ASY overexpression. Exp Cell Res 2006; 312: 1983–8.CrossRefGoogle ScholarPubMed
Saito, T, Matsuba, Y, Mihira, N, et al. Single APP knock-in mouse models of Alzheimer’s disease. Nat Neurosci 2014; 17: 661–3.Google Scholar
Bard, F, Cannon, C, Barbour, R, et al. Peripherally administered antibodies against amyloid beta-peptide enter the central nervous system and reduce pathology in a mouse model of Alzheimer disease. Nat Med 2000; 6: 916–19.CrossRefGoogle Scholar
DeMattos, RB, Bales, KR, Cummins, DJ, et al. Peripheral anti-A beta antibody alters CNS and plasma A beta clearance and decreases brain Abeta burden in a mouse model of Alzheimer’s disease. Proc Natl Acad Sci USA 2001; 98: 8850–5.Google Scholar
Bohrmann, B, Baumann, K, Benz, J, et al. Gantenerumab: a novel human anti-A beta antibody demonstrates sustained cerebral amyloid-beta binding and elicits cell-mediated removal of human amyloid-beta. J Alzheimers Dis 2012; 28: 49–69.Google Scholar
Lord, A, Gumucio, A, Englund, H, et al. An amyloid-beta protofibril-selective antibody prevents amyloid formation in a mouse model of Alzheimer’s disease. Neurobiol Dis 2009; 36: 425–34.CrossRefGoogle Scholar
Sevigny, J, Chiao, P, Bussiere, T, et al. The antibody aducanumab reduces Abeta plaques in Alzheimer’s disease. Nature 2016; 537: 50–6.CrossRefGoogle ScholarPubMed
Cummings, JL, Morstorf, T, Zhong, K. Alzheimer’s disease drug-development pipeline: few candidates, frequent failures. Alzheimers Res Ther 2014; 6: 37.Google Scholar
Jack, CR Jr., Bennett, DA, Blennow, K, et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement 2018; 14: 535–62.Google Scholar
Alzheimer’s Association. 2019 Alzheimer’s disease facts and figures. Alzheimers Dement 2019; 15: 321–87.Google Scholar
Amadoru, S, Dore, V, McLean, CA, et al. Comparison of amyloid PET measured in centiloid units with neuropathological findings in Alzheimer’s disease. Alzheimers Res Ther 2020; 12: 22.CrossRefGoogle ScholarPubMed
Maeda, J, Ji, B, Irie, T, et al. Longitudinal, quantitative assessment of amyloid, neuroinflammation, and anti-amyloid treatment in a living mouse model of Alzheimer’s disease enabled by positron emission tomography. J Neurosci 2007; 27: 10957–68.CrossRefGoogle Scholar
Maeda, J, Zhang, MR, Okauchi, T, et al. In vivo positron emission tomographic imaging of glial responses to amyloid-beta and tau pathologies in mouse models of Alzheimer’s disease and related disorders. J Neurosci 2011; 31: 4720–30.Google Scholar
Snellman, A, Lopez-Picon, FR, Rokka, J, et al. Longitudinal amyloid imaging in mouse brain with 11C-PIB: comparison of APP23, Tg2576, and APPswe-PS1dE9 mouse models of Alzheimer disease. J Nucl Med 2013; 54: 1434–41.Google Scholar
Snellman, A, Rokka, J, Lopez-Picon, FR, et al. Applicability of [(11)C]PiB micro-PET imaging for in vivo follow-up of anti-amyloid treatment effects in APP23 mouse model. Neurobiol Aging 2017; 57: 84–94.Google Scholar
Snellman, A, Rokka, J, Lopez-Picon, FR, et al. In vivo PET imaging of beta-amyloid deposition in mouse models of Alzheimer’s disease with a high specific activity PET imaging agent [(18)F]flutemetamol. EJNMMI Res 2014; 4: 37.Google Scholar
Toyama, H, Ye, D, Ichise, M, et al. PET imaging of brain with the beta-amyloid probe, [11C]6-OH-BTA-1, in a transgenic mouse model of Alzheimer’s disease. Eur J Nucl Med Mol Imaging 2005; 32: 593–600.Google Scholar
Kuntner, C, Kesner, AL, Bauer, M, et al. Limitations of small animal PET imaging with [18F]FDDNP and FDG for quantitative studies in a transgenic mouse model of Alzheimer’s disease. Mol Imaging Biol 2009; 11: 236–40.CrossRefGoogle Scholar
Poisnel, G, Dhilly, M, Moustie, O, et al. PET imaging with [18 F]AV-45 in an APP/PS1-21 murine model of amyloid plaque deposition. Neurobiol Aging 2012; 33: 2561–71.Google Scholar
Brendel, M, Jaworska, A, Griessinger, E, et al. Cross-sectional comparison of small animal [18F]-florbetaben amyloid-PET between transgenic AD mouse models. PLoS One 2015; 10: e0116678.Google Scholar
Brendel, M, Jaworska, A, Overhoff, F, et al. Efficacy of chronic BACE1 inhibition in PS2APP mice depends on the regional Abeta deposition rate and plaque burden at treatment initiation. Theranostics 2018; 8: 4957–68.Google Scholar
Parent, MJ, Zimmer, ER, Shin, M, et al. Multimodal imaging in rat model recapitulates Alzheimer’s disease biomarkers abnormalities. J Neurosci 2017; 37: 12263–71.CrossRefGoogle ScholarPubMed
Zimmer, ER, Leuzy, A, Gauthier, S, Rosa-Neto, P. Developments in tau PET imaging. Can J Neurol Sci 2014; 41: 547–53.CrossRefGoogle ScholarPubMed
Leuzy, A, Chiotis, K, Lemoine, L, et al. Tau PET imaging in neurodegenerative tauopathies: still a challenge. Mol Psychiatry 2019; 24: 1112–34.Google Scholar
Fodero-Tavoletti, MT, Okamura, N, Furumoto, S, et al. 18F-THK523: a novel in vivo tau imaging ligand for Alzheimer’s disease. Brain 2011; 134: 1089–100.Google Scholar
Maruyama, M, Shimada, H, Suhara, T, et al. Imaging of tau pathology in a tauopathy mouse model and in Alzheimer patients compared to normal controls. Neuron 2013; 79: 1094–108.Google Scholar
Ishikawa, A, Tokunaga, M, Maeda, J, et al. In vivo visualization of tau accumulation, microglial activation, and brain atrophy in a mouse model of tauopathy rTg4510. J Alzheimers Dis 2018; 61: 1037–52.Google Scholar
Ni, R, Ji, B, Ono, M, et al. Comparative in vitro and in vivo quantifications of pathologic tau deposits and their association with neurodegeneration in tauopathy mouse models. J Nucl Med 2018; 59: 960–6.Google Scholar
Brendel, M, Yousefi, BH, Blume, T, et al. Comparison of (18)F-T807 and (18)F-THK5117 PET in a mouse model of tau pathology. Front Aging Neurosci 2018; 10: 174.Google Scholar
Hattori, N, Huang, SC, Wu, HM, et al. Acute changes in regional cerebral (18)F-FDG kinetics in patients with traumatic brain injury. J Nucl Med 2004; 45: 775–83.Google ScholarPubMed
Zimmer, ER, Parent, MJ, Souza, DG, et al. [(18)F]FDG PET signal is driven by astroglial glutamate transport. Nat Neurosci 2017; 20: 393–5.CrossRefGoogle ScholarPubMed
Stoessl, AJ. Glucose utilization: still in the synapse. Nat Neurosci 2017; 20: 382–4.Google Scholar
Silverman, DH. Brain 18F-FDG PET in the diagnosis of neurodegenerative dementias: comparison with perfusion SPECT and with clinical evaluations lacking nuclear imaging. J Nucl Med 2004; 45: 594–607.Google Scholar
Bohnen, NI, Djang, DS, Herholz, K, Anzai, Y, Minoshima, S. Effectiveness and safety of 18F-FDG PET in the evaluation of dementia: a review of the recent literature. J Nucl Med 2012; 53: 59–71.CrossRefGoogle ScholarPubMed
Mosconi, L, Tsui, WH, Herholz, K, et al. Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. J Nucl Med 2008; 49: 390–8.Google Scholar
Chételat, G, Arbizu, J, Barthel, H, et al. Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer’s disease and other dementias. Lancet Neurol 2020; 19: 951–62.Google Scholar
Luo, F, Rustay, NR, Ebert, U, et al. Characterization of 7- and 19-month-old Tg2576 mice using multimodal in vivo imaging: limitations as a translatable model of Alzheimer’s disease. Neurobiol Aging 2012; 33: 933–44.Google Scholar
Martin-Moreno, AM, Brera, B, Spuch, C, et al. Prolonged oral cannabinoid administration prevents neuroinflammation, lowers beta-amyloid levels and improves cognitive performance in Tg APP 2576 mice. J Neuroinflamm 2012; 9: 8.CrossRefGoogle ScholarPubMed
Heneka, MT, Ramanathan, M, Jacobs, AH, et al. Locus ceruleus degeneration promotes Alzheimer pathogenesis in amyloid precursor protein 23 transgenic mice. J Neurosci 2006; 26: 1343–54.Google Scholar
Poisnel, G, Herard, AS, El Tannir El Tayara, N, et al. Increased regional cerebral glucose uptake in an APP/PS1 model of Alzheimer’s disease.Neurobiol Aging 2012; 33: 1995–2005.Google Scholar
Lu, XY, Huang, S, Chen, QB, et al. Metformin ameliorates Abeta pathology by insulin-degrading enzyme in a transgenic mouse model of Alzheimer’s disease. Oxid Med Cell Longev 2020; 2020: 2315106.CrossRefGoogle Scholar
Sung, KK, Jang, DP, Lee, S, et al. Neural responses in rat brain during acute immobilization stress: a [F-18]FDG micro PET imaging study. Neuroimage 2009; 44: 1074–80.CrossRefGoogle Scholar
Toyama, H, Ichise, M, Liow, J-S, et al. Evaluation of anesthesia effects on [18F]FDG uptake in mouse brain and heart using small animal PET. Nucl Med Biol 2004; 31: 251–6.Google Scholar
Ottoy, J, Verhaeghe, J, Niemantsverdriet, E, et al. Validation of the semiquantitative static SUVR method for (18)F-AV45 PET by pharmacokinetic modeling with an arterial input function. J Nucl Med 2017; 58: 1483–9.Google Scholar
Price, JC, Klunk, WE, Lopresti, BJ, et al. Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J Cereb Blood Flow Metab 2005; 25: 1528–47.CrossRefGoogle ScholarPubMed
Buckner, RL, Snyder, AZ, Shannon, BJ, et al. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 2005; 25: 7709–17.Google Scholar
Lu, H, Zou, Q, Gu, H, et al. Rat brains also have a default mode network. Proc Natl Acad Sci USA 2012; 109: 3979–84.Google Scholar
Jack, CR Jr., Wiste, HJ, Vemuri, P, et al. Brain beta-amyloid measures and magnetic resonance imaging atrophy both predict time-to-progression from mild cognitive impairment to Alzheimer’s disease. Brain 2010; 133: 3336–48.Google Scholar
Terry, RD, Masliah, E, Salmon, DP, et al. Physical basis of cognitive alterations in Alzheimer’s disease: synapse loss is the major correlate of cognitive impairment. Ann Neurol 1991; 30: 572–80.Google Scholar
Jagust, W. Imaging the evolution and pathophysiology of Alzheimer disease. Nat Rev Neurosci 2018; 19: 687–700.CrossRefGoogle ScholarPubMed
Ottoy, J, Niemantsverdriet, E, Verhaeghe, J, et al. Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and (18)F-FDG-PET imaging. Neuroimage Clin 2019; 22: 101771.Google Scholar
Heggland, I, Storkaas, IS, Soligard, HT, Kobro-Flatmoen, A, Witter, MP. Stereological estimation of neuron number and plaque load in the hippocampal region of a transgenic rat model of Alzheimer’s disease. Eur J Neurosci 2015; 41: 1245–62.Google Scholar
Macdonald, IR, DeBay, DR, Reid, GA, et al. Early detection of cerebral glucose uptake changes in the 5×FAD mouse. Curr Alzheimer Res 2014; 11: 450–60.Google Scholar
Yoshiyama, Y, Higuchi, M, Zhang, B, et al. Synapse loss and microglial activation precede tangles in a P301S tauopathy mouse model. Neuron 2007; 53: 337–51.Google Scholar
Chiquita, S, Ribeiro, M, Castelhano, J, et al. A longitudinal multimodal in vivo molecular imaging study of the 3×Tg-AD mouse model shows progressive early hippocampal and taurine loss. Hum Mol Genet 2019; 28: 2174–88.Google Scholar
Kang, MS, Aliaga, AA, Shin, M, et al. Amyloid-beta modulates the association between neurofilament light chain and brain atrophy in Alzheimer’s disease. Mol Psychiatry 2020;https://doi.org/10.1038/s41380-020-0818-1.Google Scholar
Vincent, JL, Patel, GH, Fox, MD, et al. Intrinsic functional architecture in the anaesthetized monkey brain. Nature 2007; 447: 83–6.Google Scholar
Rilling, JK, Barks, SK, Parr, LA, et al. A comparison of resting-state brain activity in humans and chimpanzees. Proc Natl Acad Sci USA 2007; 104: 17146–51.CrossRefGoogle ScholarPubMed
Greicius, MD, Srivastava, G, Reiss, AL, Menon, V. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci USA 2004; 101: 4637–42.Google Scholar
Buckner, RL, Sepulcre, J, Talukdar, T, et al. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci 2009; 29: 1860–73.Google Scholar
Sheline, YI, Raichle, ME. Resting state functional connectivity in preclinical Alzheimer’s disease. Biol Psychiatry 2013; 74: 340–7.Google Scholar
Wang, L, Zang, Y, He, Y, et al. Changes in hippocampal connectivity in the early stages of Alzheimer’s disease: evidence from resting state fMRI. Neuroimage 2006; 31: 496–504.Google Scholar
Huijbers, W, Mormino, EC, Schultz, AP, et al. Amyloid-beta deposition in mild cognitive impairment is associated with increased hippocampal activity, atrophy and clinical progression. Brain 2015; 138: 1023–35.CrossRefGoogle ScholarPubMed
Salloway, S, Sperling, R, Gilman, S, et al. A Phase 2 multiple ascending dose trial of bapineuzumab in mild to moderate Alzheimer disease. Neurology 2009; 73: 2061–70.CrossRefGoogle ScholarPubMed
Sperling, RA, Jack, CR Jr., Black, SE, et al. Amyloid-related imaging abnormalities in amyloid-modifying therapeutic trials: recommendations from the Alzheimer’s Association Research Roundtable Workgroup. Alzheimers Dement 2011; 7: 367–85.Google Scholar
Luo, F, Rustay, NR, Seifert, T, et al. Magnetic resonance imaging detection and time course of cerebral microhemorrhages during passive immunotherapy in living amyloid precursor protein transgenic mice. J Pharmacol Exp Ther 2010; 335: 580–8.CrossRefGoogle ScholarPubMed
Constantinides, C, Murphy, K. Molecular and integrative physiological effects of isoflurane anesthesia: the paradigm of cardiovascular studies in rodents using magnetic resonance imaging. Front Cardiovasc Med 2016; 3: 23.Google Scholar
Stenroos, P, Paasonen, J, Salo, RA, et al. Awake rat brain functional magnetic resonance imaging using standard radio frequency coils and a 3D printed restraint kit. Front Neurosci 2018; 12: 548.Google Scholar
Paasonen, J, Stenroos, P, Salo, RA, Kiviniemi, V, Grohn, O. Functional connectivity under six anesthesia protocols and the awake condition in rat brain. Neuroimage 2018; 172: 9–20.Google Scholar
Wu, TL, Mishra, A, Wang, F, et al. Effects of isoflurane anesthesia on resting-state fMRI signals and functional connectivity within primary somatosensory cortex of monkeys. Brain Behav 2016; 6: e00591.Google Scholar
Ferris, CF, Smerkers, B, Kulkarni, P, et al. Functional magnetic resonance imaging in awake animals. Rev Neurosci 2011; 22: 665–74.CrossRefGoogle ScholarPubMed
Dopfel, D, Zhang, N. Mapping stress networks using functional magnetic resonance imaging in awake animals. Neurobiol Stress 2018; 9: 251–63.Google Scholar
Low, LA, Bauer, LC, Pitcher, MH, Bushnell, MC. Restraint training for awake functional brain scanning of rodents can cause long-lasting changes in pain and stress responses. Pain 2016; 157: 1761–72.Google Scholar
Mizuma, H, Shukuri, M, Hayashi, T, Watanabe, Y, Onoe, H. Establishment of in vivo brain imaging method in conscious mice. J Nucl Med 2010; 51: 1068–75.CrossRefGoogle ScholarPubMed
Alstrup, AK, Smith, DF. Anaesthesia for positron emission tomography scanning of animal brains. Lab Anim 2013; 47: 12–18.Google Scholar
Momosaki, S, Hatano, K, Kawasumi, Y, et al. Rat-PET study without anesthesia: anesthetics modify the dopamine D1 receptor binding in rat brain. Synapse 2004; 54: 207–13.CrossRefGoogle ScholarPubMed
Kyme, AZ, Zhou, VW, Meikle, SR, Fulton, RR. Real-time 3D motion tracking for small animal brain PET. Phys Med Biol 2008; 53: 2651–66.Google Scholar
Schulz, D, Southekal, S, Junnarkar, SS, et al. Simultaneous assessment of rodent behavior and neurochemistry using a miniature positron emission tomograph. Nat Methods 2011; 8: 347–52.Google Scholar
Miranda, A, Kang, MS, Blinder, S, et al. PET imaging of freely moving interacting rats. Neuroimage 2019; 191: 560–7.Google Scholar
Miranda, A, Staelens, S, Stroobants, S, Verhaeghe, J. Fast and accurate rat head motion tracking with point sources for awake brain PET. IEEE Trans Med Imaging 2017; 36: 1573–82.CrossRefGoogle ScholarPubMed
Nakamura, A, Kaneko, N, Villemagne, VL, et al. High performance plasma amyloid-beta biomarkers for Alzheimer’s disease. Nature 2018; 554: 249–54.Google Scholar
Janelidze, S, Mattsson, N, Palmqvist, S, et al. Plasma p-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia. Nat Med 2020; 26: 379–86.Google Scholar
Niemantsverdriet, E, Ottoy, J, Somers, C, et al. The cerebrospinal fluid Abeta1-42/Abeta1-40 ratio improves concordance with amyloid-PET for diagnosing Alzheimer’s disease in a clinical setting. J Alzheimers Dis 2017; 60: 561–76.Google Scholar
Hansson, O, Zetterberg, H, Buchhave, P, et al. Prediction of Alzheimer’s disease using the CSF Abeta42/Abeta40 ratio in patients with mild cognitive impairment. Dement Geriatr Cogn Disord 2007; 23: 316–20.Google Scholar
Barten, DM, Cadelina, GW, Hoque, N, et al. Tau transgenic mice as models for cerebrospinal fluid tau biomarkers. J Alzheimers Dis 2011; 24: 127–41.CrossRefGoogle ScholarPubMed
Acker, CM, Forest, SK, Zinkowski, R, Davies, P, d’Abramo, C. Sensitive quantitative assays for tau and phospho-tau in transgenic mouse models. Neurobiol Aging 2013; 34: 338–50.Google Scholar
Preische, O, Schultz, SA, Apel, A, et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat Med 2019; 25: 277–83.CrossRefGoogle ScholarPubMed
Bacioglu, M, Maia, LF, Preische, O, et al. Neurofilament light chain in blood and CSF as marker of disease progression in mouse models and in neurodegenerative diseases. Neuron 2016; 91: 56–66.Google Scholar
Kang, MS, Shin, M, Ottoy, J, et al. Preclinical in vivo longitudinal assessment of KG207-M as a disease-modifying Alzheimer’s disease therapeutic. J Cereb Blood Flow Metab August 2021; doi: 10.1177/0271678X211035625.Google Scholar
Dirnagl, U, Przesdzing, I, Kurreck, C, Major, S. A laboratory critical incident and error reporting system for experimental biomedicine. PLoS Biol 2016; 14: e2000705.Google Scholar

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