| Modeling of brain diseases in a clinically relevant way | Prevailing disease concepts (e.g., Aβ cascade hypothesis of Alzheimer’s disease [6, 7]) impede focus of additional disease pathomechanisms, which remain understudied but might represent more promising therapeutic targets Uniform and monogenic disease models do not reflect multifactorial and polygenetic nature of human diseases (e.g., transient intraluminal middle cerebral artery occlusion for ischemic stroke, transgenic Alzheimer’s models) Homogeneity of laboratory animals, which are typically inbred, young, male and otherwise healthy. Homogeneity contrasts human genetic diversity, age and risk factors, and comorbidities of human patients Animal genetics does not predispose to human disease processes Animal models do not mimic life habits, nutritional, hygiene and social environments of humans Lack of standardization of animal models between research laboratories
| Elucidate additional pathomechanisms and their utility as treatment targets (e.g., role of microvasculature [31–33] and inflammation [34–36] in Alzheimer’s disease) Use more complex disease models (e.g., thromboembolic model of ischemic stroke, Alzheimer’s models that involve vascular pathology) Use of outbred animals, aged animals, animals of both sexes, animals with risk factors and comorbidities [45, 64], animals from diverse genetic reference panels (e.g., Collaborative Cross) [42, 43] Use of human organoids, iPSC-derived neurons, patient-derived grafts or humanized animals as research objects Model environmental factors in animals (e.g., enriched environments), consider so-called exposomes [40, 41] in data analysis Standardized procedures, joint training in workshops [44]
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| Selecting meaningful clinical readouts in animals | Observer-based symptom-oriented clinical (neurological/psychiatric) scales/tests in animals do not mimic patient-centered disability endpoints in humans; daily-life relevance mostly unclear Animal studies frequently based on small or biased cohorts that are insufficiently powered Lack of standardization of animal behavioral testing procedures between laboratories
| Disparity cannot easily be resolved [48], tests in animals should as closely as possible evaluate daily-life relevant contents Adequately powered cohorts, stringent randomization and blinding Standardized procedures, joint training in workshops
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| Selecting meaningful clinical endpoints in humans | Clinical scores do not appropriately measure disease stage (e.g., UPDRS) or are liable to bias by chance (e.g., EDSS) or memory errors (e.g., CDR), which limits data reliability/validity Use of grossly granulated scales unable to reveal fine improvements (e.g., mRS) Randomized controlled trials often postulating optimistic effect sizes of treatments
| Refine scales or develop patient-centered tools, for which reliability/validity is thoroughly tested Develop more finely granulated scales in interaction with drug authorities (i.e., FDA, EMA) More realistic effect size assessments, which require larger studies
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| Characterizing structural and functional tissue responses | Classical histochemical and molecular biological tools face limitations in spatial resolution and temporal sensitivity Classical histochemical and molecular biological tools unable to capture cellular heterogeneity Brain tissue assessments neglect systemic disease processes Brain tissue assessments frequently neglect undesirable side effects
| In vivo imaging (e.g., multiphoton microscopy [14, 15], PET [16]) enables time-resolved assessments, superresolution microscopy [12] and cryo-electron microscopy [13] exceeds spatial resolution limits Single cell multiomics allows deep tissue phenotyping, linking cell phenotypes and functional states [21, 22]; need of conceptual data integration framework Characterize systemic immune involvement and remote organ interactions (e.g., brain-heart axis, brain-gut axis) Refined concepts to detect safety risks and side effects
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| Bridging experimental and clinical studies by biomarkers | | Reinforce biomarker development, search for non-invasive biomarkers, replace brain biomarkers by CSF or blood biomarkers where adequate, select promising treatments for clinical translation based on suitable biomarker existence
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| Challenges associated with research findings from single laboratories or single clinical departments | Single center research findings carry risk of lack of replication in other places due to lab-specific model or center-specific patient characteristics Single center studies restricted regarding animal or patient numbers recruited, data sets of moderate size providing gross efficacy assessments only Highly heterogeneous brain diseases or patient populations underrepresented in clinical trials, particularly in single center studies Funding of multicenter studies recently saw significant cuts in some countries due to political developments, which put at risk collaborative research activities
| Collaborative multicenter consortia able to validate concepts across models or populations Multicenter studies allow identifying hidden patterns in large data sets, enabling refined efficacy assessments Multicenter, including community-based, studies can ensure that diverse populations are included, improving research generalizability. Adaptive trial designs allow treatment tailoring Continuation of multicenter and collaborative research funding
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| Development of personalized treatment concepts | Heterogeneity of diseases, which precludes “one-size-fits-all” approaches, poses therapeutic challenges Immediate effects observed in single patients not always translate into sustained clinical improvements Widespread translation of treatments into clinical practice often limited [92], need for specialized equipment and trained personnel posing challenges [93]
| Advanced neuroimaging (e.g., fMRI) and neurophysiological assessments (e.g., high-density EEG) can help identifying therapeutic targets amenable for personalized therapy [83, 84] Chronicity of disorder, age and sex differences, compensatory mechanisms, interaction with other treatments, and treatment timing need to be taken into account; combination with other therapeutic modalities (e.g., pharmacotherapy, psychotherapy, cognitive training) may allow sustained responses Evidence-based guidelines and standardized protocols, rigorous cost-effectiveness analyses, user-friendly training programs
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| Disease-overarching biological principles or therapeutic activities as opportunity for treatment development | Highly subdivided therapeutic landscape impedes larger scale progress in translational neuroscience Targeting the brain insufficient in diseases with strong systemic pathophysiology or diseases exhibiting strong remote organ (e.g., brain-heart) interactions
| Choose shared disease mechanisms that are common to a wide range of brain diseases as therapeutic target (e.g., proinflammatory responses associated with neurodegenerative processes) Treat systemic disease process that underlies the brain pathology (e.g., cardiac dysfunction, macroangiopathy or microangiopathy in stroke)
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