Human Brain Neuropharmacology: A Platform for Translational Neuroscience
Authors: Mike Dragunow
Journal: Trends in Pharmacological Sciences (2020)

Disease-modifying therapies are desperately needed to treat hundreds of millions of people around the world afflicted by brain disorders, such as epilepsy, stroke and Alzheimer's disease.Unfortunately, despite a huge worldwide effort over the past 30 years, there has been a failure to develop such medications for most disorders.This failure can be attributed to many factors, including issues with preclinical testing (e.g., methodological problems, lack of modelling disease comorbidities, and species differences), clinical trial design, and fundamental problems associated with modelling phenotypically complex and variable human brain disorders in animals.To improve lab to clinic translation, we need to better understand the relationship between drug dose and drug concentration at the target in the human brain, standardise and improve the quality of preclinical testing, and improve clinical trials, for example, with the wider incorporation of patient stratification, given the genetic and phenotypic variability of most brain disorders.An additional approach to improve clinical translation is the use of a human brain-based drug discovery platform to help validate both brain drug targets as well as to directly test drugs for efficacy (and measure drug potency and potential off-targets effects) using patient-derived human brain cells. Central nervous system (CNS) drug development has been plagued by a failure to translate effective therapies from the lab to the clinic. There are many potential reasons for this, including poor understanding of brain pharmacokinetic (PK) and pharmacodynamic (PD) factors, preclinical study flaws, clinical trial design issues, the complexity and variability of human brain diseases, as well as species differences. To address some of these problems, we have developed a platform for CNS drug discovery comprising: drug screening of primary adult human brain cells; human brain tissue microarray analysis of drug targets; and high-content phenotypic screening methods. In this opinion, I summarise the theoretical basis and the practical development and use of this platform in CNS drug discovery. Central nervous system (CNS) drug development has been plagued by a failure to translate effective therapies from the lab to the clinic. There are many potential reasons for this, including poor understanding of brain pharmacokinetic (PK) and pharmacodynamic (PD) factors, preclinical study flaws, clinical trial design issues, the complexity and variability of human brain diseases, as well as species differences. To address some of these problems, we have developed a platform for CNS drug discovery comprising: drug screening of primary adult human brain cells; human brain tissue microarray analysis of drug targets; and high-content phenotypic screening methods. In this opinion, I summarise the theoretical basis and the practical development and use of this platform in CNS drug discovery. One of the most frustrating aspects of being a neuropharmacologist studying brain disorders is that we have made great advances in understanding brain disease causation, but no drug based upon these advances has yet progressed successfully to the clinic to modify disease progression. Recent Phase III translational failures illustrate this point: trials of anti-Alzheimer's disease (AD; see Glossary) medications targeting inflammation, amyloid processing, tau aggregation [1.Egan M.F. et al.Randomized trial of verubecestat for prodromal Alzheimer's disease.N. Engl. J. Med. 2019; 380: 1408-1420Crossref PubMed Scopus (259) Google Scholar, 2.Meyer P.F. et al.INTREPAD: a randomized trial of naproxen to slow progress of presymptomatic Alzheimer disease.Neurology. 2020; 94: 593-594PubMed Google Scholar, 3.Gauthier S. et al.Efficacy and safety of tau-aggregation inhibitor therapy in patients with mild or moderate Alzheimer's disease: a randomised, controlled, double-blind, parallel-arm, phase 3 trial.Lancet. 2016; 388: 2873-2884Abstract Full Text Full Text PDF PubMed Scopus (219) Google Scholar]; trials in moderate and severe traumatic brain injury [4.Skolnick B.E. et al.A clinical trial of progesterone for severe traumatic brain injury.N. Engl. J. 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Even within the biomedical field, CNS drugs are more likely to fail at Phase III than are non-CNS drugs [8.Kesselheim A.S. et al.Two decades of new drug development for central nervous system disorders.Nat. Rev. Drug Discov. 2015; 14: 815-816Crossref PubMed Scopus (77) Google Scholar]. Reasons why translation may fail are many and varied [6.Mitsumoto H. et al.Clinical trials in amyotrophic lateral sclerosis: why so many negative trials and how can trials be improved?.Lancet Neurol. 2014; 13: 1127-1138Abstract Full Text Full Text PDF PubMed Scopus (189) Google Scholar,9.Pound P. Ritskes-Hoitinga M. Is it possible to overcome issues of external validity in preclinical animal research? Why most animal models are bound to fail.J. Transl. 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Interestingly, although the main animal model for MS, experimental autoimmune encephalomyelitis (EAE), has not generally proven predictive in MS drug development [12.Ransohoff R.M. Animal models of multiple sclerosis: the good, the bad and the bottom line.Nat. Neurosci. 2012; 15: 1074-1077Crossref PubMed Scopus (213) Google Scholar], research using this model was pivotal in identifying α4β1 integrin as a key target for the penetration of lymphocytes into the brain in EAE [13.Yednock T.A. et al.Prevention of experimental autoimmune encephalomyelitis by antibodies against alpha 4 beta 1 integrin.Nature. 1992; 356: 63-66Crossref PubMed Scopus (1486) Google Scholar,14.Steinman L. The discovery of natalizumab, a potent therapeutic for multiple sclerosis.J. Cell Biol. 2012; 199: 413-416Crossref PubMed Scopus (51) Google Scholar]. This work eventually led to the development of natalizumab for relapsing-remitting MS [14.Steinman L. 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Unfortunately, these successful examples are very much in the minority.Table 1Potential Factors Underlying the Failure to Translate Preclinical Studies to the Clinic for Disease Modification in Brain DisordersFactorsHuman brain and clinical trialsAnimal models and studiesRefsBrain disease• Complex multifactorial mechanisms• Varied genotypes• Varied phenotypes• Context-specific• Comorbidities• Often age-related• Simple: usually only one mechanism tested• Identical genotypes• Identical phenotypes• No disease context• No comorbidities• Age not always appropriate[6.Mitsumoto H. et al.Clinical trials in amyotrophic lateral sclerosis: why so many negative trials and how can trials be improved?.Lancet Neurol. 2014; 13: 1127-1138Abstract Full Text Full Text PDF PubMed Scopus (189) Google Scholar,9.Pound P. Ritskes-Hoitinga M. Is it possible to overcome issues of external validity in preclinical animal research? Why most animal models are bound to fail.J. Transl. 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Drug Discov. 2008; 7: 659-666Crossref PubMed Scopus (93) Google Scholar]Species differences• No microglial iNOS• Multiple human astrocyte types• Human neuronal dendritic complexity• Elevated histone acetylation in AD• Reduced HDAC2 levels in AD• HDAC inhibitors promote cortical thinning in AD and epilepsy• AD microglial transcriptome• Valproic acid does not induce caspase 3-mediated apoptosis of human microglia• NXY-059 not protective of human neurons• Propentofylline weakly anti-inflammatory in human microglia• Microglial iNOS• Few rodent astrocyte types• Not observed in rodents• Reduced histone acetylation in AD models• Elevated HDAC2 levels in AD models• HDAC inhibitors enhance cognition and reverse AD model deficits• AD mouse transcriptome has patchy overlap with humans• Valproic acid induces caspase 3-mediated apoptosis of rodent microglia• NXY-059 neuroprotective in rodent stroke models• Propentofylline strongly anti-inflammatory in rodent microglia[6.Mitsumoto H. et al.Clinical trials in amyotrophic lateral sclerosis: why so many negative trials and how can trials be improved?.Lancet Neurol. 2014; 13: 1127-1138Abstract Full Text Full Text PDF PubMed Scopus (189) Google Scholar,9.Pound P. 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Disord. 2016; 201: 131-136Crossref PubMed Scopus (9) Google Scholar,54.Dragunow M. The adult human brain in preclinical drug development.Nat. Rev. Drug Discov. 2008; 7: 659-666Crossref PubMed Scopus (93) Google Scholar,59.Dragunow M. et al.Valproic acid induces caspase 3-mediated apoptosis in microglial cells.Neuroscience. 2006; 140: 1149-1156Crossref PubMed Scopus (54) Google Scholar,62.Gibbons H.M. et al.Valproic acid induces microglial dysfunction, not apoptosis, in human glial cultures.Neurobiol. Dis. 2011; 41: 96-103Crossref PubMed Scopus (39) Google Scholar]Context differences• iPSC-derived brain cells lack context of brain diseases• Immortalised cell lines have abnormal genotypes and phenotypes• Animal husbandry variations may impact translation (e.g., environmental enrichment, microbiome, etc.)[23.Allen M. et al.Origin of the U87MG glioma cell line: good news and bad news.Sci. Transl. Med. 2016; 8: 354re3Crossref PubMed Scopus (231) Google Scholar,24.Graham E.S. et al.M1 muscarinic receptor activation mediates cell death in M1-HEK293 cells.PLoS ONE. 2013; 8e72011Crossref PubMed Scopus (13) Google Scholar]Trial/study differences• High quality especially at Phase III• Stringent statistical methods (e.g., power calculations) and• Standardised study designs and reporting• Positive and negative trial results reported• Multiple outcome measures analysed and blinded• Treatments often tested in late-stage disease• Trial duration limitations• Mixed study quality• Mixed statistical methods• Mixed study designs and reporting• Publication bias toward positive study reporting• Tendency for use of fewer outcome measures and not always blinded• Treatments often tested in early-stage disease[6.Mitsumoto H. et al.Clinical trials in amyotrophic lateral sclerosis: why so many negative trials and how can trials be improved?.Lancet Neurol. 2014; 13: 1127-1138Abstract Full Text Full Text PDF PubMed Scopus (189) Google Scholar,7.Schmidt-Pogoda A. et al.why most acute stroke studies are positive in animals but not in patients: a systematic comparison of preclinical, early phase, and phase 3 clinical trials of neuroprotective agents.Ann. Neurol. 2020; 87: 40-51Crossref PubMed Scopus (38) Google Scholar,10.Watzlawick R. et al.Outcome heterogeneity and bias in acute experimental spinal cord injury: a meta-analysis.Neurology. 2019; 93: e40-e51Crossref PubMed Scopus (14) Google Scholar,16.Veening-Griffioen D.H. et al.Are some animal models more equal than others? A case study on the translational value of animal models of efficacy for Alzheimer's disease.Eur. J. Pharmacol. 2019; 859172524Crossref PubMed Scopus (20) Google Scholar]PK/PD issues• Drug concentration in brain at site of action not well defined• Drug concentration at site of action in brain well defined[11.de Lange E.C.M. et al.Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics.Expert Opin. Drug Discov. 2017; 12: 1207-1218Crossref PubMed Scopus (32) Google Scholar] Open table in a new tab Although >100 'treatments' have been discovered for reducing neuronal death in stroke preclinical studies, none of these worked in the clinic [7.Schmidt-Pogoda A. et al.why most acute stroke studies are positive in animals but not in patients: a systematic comparison of preclinical, early phase, and phase 3 clinical trials of neuroprotective agents.Ann. Neurol. 2020; 87: 40-51Crossref PubMed Scopus (38) Google Scholar]. Why is this? Schmidt-Pogoda et al. [7.Schmidt-Pogoda A. et al.why most acute stroke studies are positive in animals but not in patients: a systematic comparison of preclinical, early phase, and phase 3 clinical trials of neuroprotective agents.Ann. Neurol. 2020; 87: 40-51Crossref PubMed Scopus (38) Google Scholar] conducted a systematic analysis of both Phase III clinical trials and preclinical studies of stroke. Using a set of assumptions and associated criteria, they concluded that several factors were involved: publication bias, where successful preclinical studies are more likely to be published; clear reduced efficacy in clinical trials compared with preclinical studies; low-quality preclinical and early clinical trials contributing to treatment effect overestimation; time to treatment differences with preclinical trials (and some early clinical trials) being most effective when treatments were given within 3 h of experimental stroke, compared with delays of >6 h for most failed clinical trials; as well as statistical issues when power calculations are applied to preclinical studies. The AD field is also notable for hundreds of failed clinical trials of disease-modifying medications that showed great promise in preclinical AD models. Recently, Veening-Griffioen et al. [16.Veening-Griffioen D.H. et al.Are some animal models more equal than others? A case study on the translational value of animal models of efficacy for Alzheimer's disease.Eur. J. Pharmacol. 2019; 859172524Crossref PubMed Scopus (20) Google Scholar] systematically evaluated the usefulness of animal models of AD. They defined several features of these models that may be responsible for this lack of translation, including: most models testing a single AD hypothesis (compared with AD, which is multifactorial); models using healthy and young animals (in contrast to AD, which occurs in older patients, often with other comorbidities); and the use of single cognitive outcome measures in animal models (compared with the complexity of cognition in AD, which changes with comorbidities and AD stage and is a complex process involving attention, perception, speech, etc.). Compared with clinical trials, they also noted the lower reporting quality of many animal trials. To overcome some of these issues, the authors suggested the sharing of all raw data from studies; the use of standardised study design execution and reporting; and the creation of a translational profile for models and their systematic validation. In the ALS field, repeated failures have been ascribed to preclinical testing issues (e.g., drug targets that are poorly characterised, treatments given at presymptomatic stages); pharmacological problems (interactions with riluzole, which is the current standard of care for ALS, low dosages); and trial design (disease variability not factored into trials, trial duration too short, unrealistic treatment effect notions perhaps missing small positive changes, and a lack of biomarkers to assess actual target engagement) [6.Mitsumoto H. et al.Clinical trials in amyotrophic lateral sclerosis: why so many negative trials and how can trials be improved?.Lancet Neurol. 2014; 13: 1127-1138Abstract Full Text Full Text PDF PubMed Scopus (189) Google Scholar]. Potential methods to improve clinical trials include drug candidates being trialled alone as well as with riluzole, stratification of trial participants based on ALS subtypes, early electrophysiological assessments, longer study durations, and testing of drug combinations targeting multiple disease processes [6.Mitsumoto H. et al.Clinical trials in amyotrophic lateral sclerosis: why so many negative trials and how can trials be improved?.Lancet Neurol. 2014; 13: 1127-1138Abstract Full Text Full Text PDF PubMed Scopus (189) Google Scholar]. In spinal cord research, Watzlawick et al. [10.Watzlawick R. et al.Outcome heterogeneity and bias in acute experimental spinal cord injury: a meta-analysis.Neurology. 2019; 93: e40-e51Crossref PubMed Scopus (14) Google Scholar] recently undertook a meta-analysis of preclinical studies and concluded that many studies did not report the use of blind assessments of recovery; studies that used one outcome measure tended to show greater effects compared with those that used multiple measures; and publication bias was also a significant factor. To overcome these issues and improve the translation of preclinical spinal cord injury studies to the clinic, these researchers suggested guidelines including the reporting of negative results; sharing data between labs; more stringent experimental methods and reporting (blind assessments, sample size calculations, and criteria for inclusion and exclusion); and the use of standard testing and trial methods for reducing design variation [10.Watzlawick R. et al.Outcome heterogeneity and bias in acute experimental spinal cord injury: a meta-analysis.Neurology. 2019; 93: e40-e51Crossref PubMed Scopus (14) Google Scholar]. In addition to the issues raised earlier, another key issue that likely impacts translation from the lab to the clinic is the relatively poor understanding of the relationship between drug dosage and actual concentration of new chemical entities (NCEs) at the target site for CNS-acting drugs. These PK and PD principles are critical to developing drugs for CNS disorders [11.de Lange E.C.M. et al.Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics.Expert Opin. Drug Discov. 2017; 12: 1207-1218Crossref PubMed Scopus (32) Google Scholar]. de Lange et al. [11.de Lange E.C.M. et al.Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics.Expert Opin. Drug Discov. 2017; 12: 1207-1218Crossref PubMed Scopus (32) Google Scholar] made a strong case for the importance of understanding the kinetic properties of NCEs for CNS disorders so that the correct dosages, dosing intervals, and ultimately improved efficacy can be achieved. They pointed out that, for CNS drugs, the PK in the plasma will be different to the PK in the brain, adding a greater level of complexity to CNS drug testing. Recent developments showing lymphatic and glymphatic systems in the brain increase the complexity of the PK in the brain even further. Given that drug concentration at the target over time is the key factor in controlling efficacy (assuming target engagement is achieved) and that understanding this relationship is generally not possible directly in humans, we are faced with attempting to develop models that can predict this relationship. For CNS-acting drugs this is further complicated by the anatomical and cellular heterogeneity of the brain. de Lange et al. proposed the use of in silico models to help predict the transport of NCEs across the blood–brain barrier (BBB) and, ultimately, the distribution of the drug across the brain over time. Unfortunately, these models are still in their infancy. Non-invasive methods [positron emission tomography (PET), single photon emission computed tomography (SPECT), etc.] to assess drug concentration in multiple brain regions of the human brain are likely to be pivotal in this endeavour to understand the PK properties of NCEs. For enhancing CNS drug PD target validation, they highlighted pharmacometabolomics and phenotypic screening [17.Cho Y.S. Kwon H.J. Identification and validation of bioactive small molecule target through phenotypic screening.Bioorg. Med. Chem. 2012; 20: 1922-1928Crossref PubMed Scopus (27) Google Scholar] as potentially useful approaches and also the importance of understanding the kinetics of target binding to receptors, for example, not only in one brain region, but also in multiple regions because target concentrations will likely vary between brain regions. Using multiple biomarkers of disease as end-points in studying drug action was also highlighted. A lack of consideration of such issues is likely to be an important driver of the poor translation for CNS disorders, although it does not alone account for the differential failure of disease-modifying versus symptomatic therapies. Other factors that may impact the translation of preclinical to clinical studies include variations in conditions of lab animal husbandry. Factors such as housing (with varying levels of environmental stimulation [18.Young D. et al.Environmental enrichment inhibits spontaneous apoptosis, prevents seizures and is neuroprotective.Nat. Med. 1999; 5: 448-453Crossref PubMed Scopus (633) Google Scholar]) and varied gut microbiomes [19.Franklin C.L. Ericsson A.C. Microbiota and reproducibility of rodent models.Lab. Anim. (NY). 2017; 46: 114-122Crossref PubMed Scopus (134) Google Scholar] are likely to increase variability between preclinical studies and their translation to the clinic. Raising the quality of animal husbandry and other aspects of preclinical studies is now promoted through the use of reporting guidelines for various types of study, including the ARRIVE guidelines for preclinical animal studiesi. Other likely contributing factors, which are described next, are human brain disease complexity and variability, and species differences. Human brain disorders are complex and varied between individuals. Even genetic disorders, such as Huntington's disease (HD), are phenotypically very complex. The expanded polyglutamine (CAG) repeat length in the huntingtin gene that causes HD, although correlating with disease onset, is not a good correlate of symptoms or neuropathology. Rather, individuals with the same CAG repeat length can exhibit varied symptoms, which are underlined by varied brain pathology. For example, loss of cerebellar Purkinje cells occurs in patients with HD with clear motor symptoms, but not in those exhibiting mainly mood symptoms, even though they have similar CAG repeat-length mutations [20.Singh-Bains M.K. et al.Cerebellar degeneration correlates with motor symptoms in Huntington disease.Ann. Neurol. 2019; 85: 396-405Crossref PubMed Scopus (19) Google Scholar]. Modelling this in animal studies is almost impossible. Studies of variation in neurodegenerative diseases often expand our understanding of disease causation. By contrast, in preclinical models, variation (outliers) are often assumed to be spurious data points due to experimental rather than biological variation. Variation, apart from the intervention, is actively suppressed (animals are mostly genetically and epigenetically/environmentally identical), and this allows for treatment effects to be obvious and clear. Humans with brain disorders also often have significant comorbidities, such as metabolic syndrome, hypertension, gum disease, and so on, that also impact their disease as well as their recovery. Comorbidities are seldom modelled in preclinical studies, which may also contribute to the lack of translation. Recently, in the stroke area, attempts have been made to model comorbidities in preclinical models and these are to be encouraged [21.Howells D.W. et al.Bringing rigour to translational medicine.Nat. Rev. Neurol. 2014; 10: 37-43Crossref PubMed Scopus (81) Google Scholar]. Brain cells in human brain disease do not exist in isolation: they interact with multiple other types of brain cell and, therefore, exist within a diseased ecosystem in a diseased environment. This context is vital in understanding their roles in brain disorders. For neurodegenerative disorders, such as AD and Parkinson's disease (PD) and in stroke, age is a major risk factor. Yet, many in vitro studies using animal-derived brain cells will obtain the brain tissue from embryonic or young animals because these sources generate the greatest cell yields for experimentation. Where studies have compared embryonic with aged animal brain cells within the same species, there is clear evidence of major biological differences [22.Floden A.M. Combs C.K. Beta-amyloid stimulates murine postnatal and adult microglia cultures in a unique manner.J. Neurosci. 2006; 26: 4644-4648Crossref PubMed Scopus (97) Google Scholar]. If we add to this age differences and species differences, then the differences are likely to be even greater. Drug testing on immortalised cells is even less likely to reflect the context of the disease being studied, especially because the origins of some lines are disputed [23.Allen M. et al.Origin of the U87MG glioma cell line: good news and bad news.Sci. Transl. Med. 2016; 8: 354re3Crossref PubMed Scopus (231) Google Scholar]. HEK293 cells are often used for receptor studies, but the expression of receptors in these cells with aberrant genetic make-up can lead to nonphysiological actions. For example, M1 muscarinic receptors expressed in HEK293 cells display similar signalling cascades to native receptors (e.g., ERK phosphorylation), but show abnormal properties, such as inducing cell death, a phenotype not observed in vivo [24.Graham E.S. et al.M1 muscarinic receptor activation mediates cell death in M1-HEK293 cells.PLoS ONE. 2013;