Of mice and men: bridging the translational disconnect in CNS drug discovery

Validation of a functional human AD model with four AD therapeutics utilizing patterned ipsc-derived cortical neurons integrated with microelectrode arrays

Preclinical methods are needed for screening potential Alzheimer's disease (AD) therapeutics that recapitulate phenotypes found in the Mild Cognitive Impairment (MCI) stage or even before this stage of the disease. This would require a phenotypic system that reproduces cognitive deficits without significant neuronal cell death to mimic the clinical manifestations of AD during these stages. Long-term potentiation (LTP), which is a correlate of learning and memory, was induced in mature human iPSC-derived cortical neurons cultured on microelectrode arrays utilizing circuit patterns connecting two adjacent electrodes. We demonstrated an LTP system that modeled the MCI and pre-MCI stages of Alzheimer's and validated this functional system utilizing four AD therapeutics, which was also verified utilizing patch-clamp electrophysiology. LTP was induced by tetanic electrical stimulation, and LTP maintenance was significantly reduced in the presence of Amyloid-Beta 42 (Aβ42) oligomers compared to the controls, however, co-treatment with AD therapeutics (Donepezil, Memantine, Rolipram and Saracatinib) corrected Aβ42-induced LTP impairment. The results illustrate the utility of the system as a validated platform to model MCI AD pathology, and potentially for the pre-MCI phase before significant neuronal death. This system also has the potential to become an ideal platform for high-content therapeutic screening for other neurodegenerative diseases.

Construct, Face, and Predictive Validity of Parkinson's Disease Rodent Models

Parkinson's disease (PD) is the second most common neurodegenerative disease globally. Current drugs only alleviate symptoms without halting disease progression, making rodent models essential for researching new therapies and understanding the disease better. However, selecting the right model is challenging due to the numerous models and protocols available. Key factors in model selection include construct, face, and predictive validity. Construct validity ensures the model replicates pathological changes seen in human PD, focusing on dopaminergic neurodegeneration and a-synuclein aggregation. Face validity ensures the model's symptoms mirror those in humans, primarily reproducing motor and non-motor symptoms. Predictive validity assesses if treatment responses in animals will reflect those in humans, typically involving classical pharmacotherapies and surgical procedures. This review highlights the primary characteristics of PD and how these characteristics are validated experimentally according to the three criteria. Additionally, it serves as a valuable tool for researchers in selecting the most appropriate animal model based on established validation criteria.

Validation of a functional human AD model with four AD therapeutics utilizing patterned iPSC-derived cortical neurons integrated with microelectrode arrays

Preclinical methods are needed for screening potential Alzheimer's disease (AD) therapeutics that recapitulate phenotypes found in the Mild Cognitive Impairment (MCI) stage or even before this stage of the disease. This would require a phenotypic system that reproduces cognitive deficits without significant neuronal cell death to mimic the clinical manifestations of AD during these stages. A potential functional parameter to be monitored is long-term potentiation (LTP), which is a correlate of learning and memory, that would be one of the first functions effected by AD onset. Mature human iPSC-derived cortical neurons and primary astrocytes were co-cultured on microelectrode arrays (MEA) where surface chemistry was utilized to create circuit patterns connecting two adjacent electrodes to model LTP function. LTP maintenance was significantly reduced in the presence of Amyloid-Beta 42 (Aβ42) oligomers compared to the controls, however, co-treatment with AD therapeutics (Donepezil, Memantine, Rolipram and Saracatinib) corrected Aβ42 induced LTP impairment. The results presented here illustrate the significance of the system as a validated platform that can be utilized to model and study MCI AD pathology, and potentially for the pre-MCI phase before the occurrence of significant cell death. It also has the potential to become an ideal platform for high content therapeutic screening for other neurodegenerative diseases.

2-Photon imaging of fluorescent proteins in living swine

A common point of failure in translation of preclinical neurological research to successful clinical trials comes in the giant leap from rodent models to humans. Non-human primates are phylogenetically close to humans, but cost and ethical considerations prohibit their widespread usage in preclinical trials. Swine have large, gyrencencephalic brains, which are biofidelic to human brains. Their classification as livestock makes them a readily accessible model organism. However, their size has precluded experiments involving intravital imaging with cellular resolution. Here, we present a suite of techniques and tools for in vivo imaging of porcine brains with subcellular resolution. Specifically, we describe surgical techniques for implanting a synthetic, flexible, transparent dural window for chronic optical access to the neocortex. We detail optimized parameters and methods for injecting adeno-associated virus vectors through the cranial imaging window to express fluorescent proteins. We introduce a large-animal 2-photon microscope that was constructed with off-the shelf components, has a gantry design capable of accommodating animals > 80 kg, and is equipped with a high-speed digitizer for digital fluorescence lifetime imaging. Finally, we delineate strategies developed to mitigate the substantial motion artifact that complicates high resolution imaging in large animals, including heartbeat-triggered high-speed image stack acquisition. The effectiveness of this approach is demonstrated in sample images acquired from pigs transduced with the chloride-sensitive fluorescent protein SuperClomeleon.

Hypolipidemic and Anti-Atherogenic Effects of Sesamol and Possible Mechanisms of Action: A Comprehensive Review

Sesamol is a phenolic lignan isolated from Sesamum indicum seeds and sesame oil. Numerous studies have reported that sesamol exhibits lipid-lowering and anti-atherogenic properties. The lipid-lowering effects of sesamol are evidenced by its effects on serum lipid levels, which have been attributed to its potential for significantly influencing molecular processes involved in fatty acid synthesis and oxidation as well as cholesterol metabolism. In this review, we present a comprehensive summary of the reported hypolipidemic effects of sesamol, observed in several in vivo and in vitro studies. The effects of sesamol on serum lipid profiles are thoroughly addressed and evaluated. Studies highlighting the ability of sesamol to inhibit fatty acid synthesis, stimulate fatty acid oxidation, enhance cholesterol metabolism, and modulate macrophage cholesterol efflux are outlined. Additionally, the possible molecular pathways underlying the cholesterol-lowering effects of sesamol are presented. Findings reveal that the anti-hyperlipidemic effects of sesamol are achieved, at least in part, by targeting liver X receptor α (LXRα), sterol regulatory element binding protein-1 (SREBP-1), and fatty acid synthase (FAS) expression, as well as peroxisome proliferator-activated receptor α (PPARα) and AMP activated protein kinase (AMPK) signaling pathways. A detailed understanding of the molecular mechanisms underlying the anti-hyperlipidemic potential of sesamol is necessary to assess the possibility of utilizing sesamol as an alternative natural therapeutic agent with potent hypolipidemic and anti-atherogenic properties. Research into the optimal sesamol dosage that may bring about such favorable hypolipidemic effects should be further investigated, most importantly in humans, to ensure maximal therapeutic benefit.

2-Photon imaging of fluorescent proteins in living swine

A common point of failure in translation of preclinical neurological research to successful clinical trials comes in the giant leap from rodent models to humans. Non-human primates are phylogenetically close to humans, but cost and ethical considerations prohibit their widespread usage in preclinical trials. Swine have large, gyrencencephalic brains, which are biofidelic to human brains. Their classification as livestock makes them a readily accessible model organism. However, their size has precluded experiments involving intravital imaging with cellular resolution. Here, we present a suite of techniques and tools for in vivo imaging of porcine brains with subcellular resolution. Specifically, we describe surgical techniques for implanting a synthetic, flexible, transparent dural window for chronic optical access to the neocortex. We detail optimized parameters and methods for injecting adeno-associated virus vectors through the cranial imaging window to express fluorescent proteins. We introduce a large-animal 2-photon microscope that was constructed with off-the shelf components, has a gantry design capable of accommodating animals > 80 kg, and is equipped with a high-speed digitizer for digital fluorescence lifetime imaging. Finally, we delineate strategies developed to mitigate the substantial motion artifact that complicates high resolution imaging in large animals, including heartbeat-triggered high-speed image stack acquisition. The effectiveness of this approach is demonstrated in sample images acquired from pigs transduced with the chloride-sensitive fluorescent protein SuperClomeleon.

Factor XII deficiency: a clinical and molecular genetic study

Factor XII deficiency is a rare inherited disorder caused by clotting factor XII (FXII, F12) deficiency. It is often asymptomatic but can have both thrombotic and haemorrhagic symptoms. The aim of this study was to describe the spectrum of F12 gene mutations in a Russian population and learn more about the relationship between F12 variants and clinical phenotypes. We obtained and analysed genetic and clinical data from 33 apparently unrelated patients with FXII plasma levels below 60% and genetic data from 26 healthy controls with no history of FXII deficiency. Forty mutant alleles and six different deleterious substitutions were identified. Of these substitutions, three were major in the Russian population (c.-62C > T, c.-57G > C and c.1532-1G > A, total frequency 92.5%) and the three others (p.615 del C, c.1180_1181delCA, and CD218 TAT- > CAT p.Tyr218His) were rare and novel in the world population. Eight patients with mild FXII deficiency were found to be homozygous for a hypomorphic variant of functional polymorphism C46T and have no other deleterious substitutions in the F12 gene. Contrary to data in the literature, our study showed that mild haemorrhagic manifestations are common among patients with FXII deficiency.

Distinct cell type-specific protein signatures in GRN and MAPT genetic subtypes of frontotemporal dementia

Frontotemporal dementia is characterized by progressive atrophy of frontal and/or temporal cortices at an early age of onset. The disorder shows considerable clinical, pathological, and genetic heterogeneity. Here we investigated the proteomic signatures of frontal and temporal cortex from brains with frontotemporal dementia due to GRN and MAPT mutations to identify the key cell types and molecular pathways in their pathophysiology. We compared patients with mutations in the GRN gene (n = 9) or with mutations in the MAPT gene (n = 13) with non-demented controls (n = 11). Using quantitative proteomic analysis on laser-dissected tissues we identified brain region-specific protein signatures for both genetic subtypes. Using published single cell RNA expression data resources we deduced the involvement of major brain cell types in driving these different protein signatures. Subsequent gene ontology analysis identified distinct genetic subtype- and cell type-specific biological processes. For the GRN subtype, we observed a distinct role for immune processes related to endothelial cells and for mitochondrial dysregulation in neurons. For the MAPT subtype, we observed distinct involvement of dysregulated RNA processing, oligodendrocyte dysfunction, and axonal impairments. Comparison with an in-house protein signature of Alzheimer’s disease brains indicated that the observed alterations in RNA processing and oligodendrocyte function are distinct for the frontotemporal dementia MAPT subtype. Taken together, our results indicate the involvement of different brain cell types and biological mechanisms in genetic subtypes of frontotemporal dementia. Furthermore, we demonstrate that comparison of proteomic profiles of different disease entities can separate general neurodegenerative processes from disease-specific pathways, which may aid the development of disease subtype-specific treatment strategies.

Conventional laboratory housing increases morbidity and mortality in research rodents: results of a meta-analysis

BACKGROUND

Over 120 million mice and rats are used annually in research, conventionally housed in shoebox-sized cages that restrict natural behaviours (e.g. nesting and burrowing). This can reduce physical fitness, impair thermoregulation and reduce welfare (e.g. inducing abnormal stereotypic behaviours). In humans, chronic stress has biological costs, increasing disease risks and potentially shortening life. Using a pre-registered protocol ( https://atrium.lib.uoguelph.ca/xmlui/handle/10214/17955 ), this meta-analysis therefore tested the hypothesis that, compared to rodents in 'enriched' housing that better meets their needs, conventional housing increases stress-related morbidity and all-cause mortality.

RESULTS

Comprehensive searches (via Ovid, CABI, Web of Science, Proquest and SCOPUS on May 24 2020) yielded 10,094 publications. Screening for inclusion criteria (published in English, using mice or rats and providing 'enrichments' in long-term housing) yielded 214 studies (within 165 articles, using 6495 animals: 59.1% mice; 68.2% male; 31.8% isolation-housed), and data on all-cause mortality plus five experimentally induced stress-sensitive diseases: anxiety, cancer, cardiovascular disease, depression and stroke. The Systematic Review Center for Laboratory animal Experimentation (SYRCLE) tool assessed individual studies' risks of bias. Random-effects meta-analyses supported the hypothesis: conventional housing significantly exacerbated disease severity with medium to large effect sizes: cancer (SMD = 0.71, 95% CI = 0.54-0.88); cardiovascular disease (SMD = 0.72, 95% CI = 0.35-1.09); stroke (SMD = 0.87, 95% CI = 0.59-1.15); signs of anxiety (SMD = 0.91, 95% CI = 0.56-1.25); signs of depression (SMD = 1.24, 95% CI = 0.98-1.49). It also increased mortality rates (hazard ratio = 1.48, 95% CI = 1.25-1.74; relative median survival = 0.91, 95% CI = 0.89-0.94). Meta-regressions indicated that such housing effects were ubiquitous across species and sexes, but could not identify the most impactful improvements to conventional housing. Data variability (assessed via coefficient of variation) was also not increased by 'enriched' housing.

CONCLUSIONS

Conventional housing appears sufficiently distressing to compromise rodent health, raising ethical concerns. Results also add to previous work to show that research rodents are typically CRAMPED (cold, rotund, abnormal, male-biased, poorly surviving, enclosed and distressed), raising questions about the validity and generalisability of the data they generate. This research was funded by NSERC, Canada.

Computational Approaches for Supporting Combination Therapy in the Post-Aducanumab Era in Alzheimer’s Disease

With the approval of aducanumab on the “Accelerated Approval Pathway” and the recognition of amyloid load as a surrogate marker, new successful therapeutic approaches will be driven by combination therapy as was the case in oncology after the launch of immune checkpoint inhibitors. However, the sheer number of therapeutic combinations substantially complicates the search for optimal combinations. Data-driven approaches based on large databases or electronic health records can identify optimal combinations and often using artificial intelligence or machine learning to crunch through many possible combinations but are limited to the pharmacology of existing marketed drugs and are highly dependent upon the quality of the training sets. Knowledge-driven in silico modeling approaches use multi-scale biophysically realistic models of neuroanatomy, physiology, and pathology and can be personalized with individual patient comedications, disease state, and genotypes to create ‘virtual twin patients’. Such models simulate effects on action potential dynamics of anatomically informed neuronal circuits driving functional clinical readouts. Informed by data-driven approaches this knowledge-driven modeling could systematically and quantitatively simulate all possible target combinations for a maximal synergistic effect on a clinically relevant functional outcome. This approach seamlessly integrates pharmacokinetic modeling of different therapeutic modalities. A crucial requirement to constrain the parameters is the access to preferably anonymized individual patient data from completed clinical trials with various selective compounds. We believe that the combination of data- and knowledge driven modeling could be a game changer to find a cure for this devastating disease that affects the most complex organ of the universe.

Innovative treatment targeting gangliosides aimed at blocking the formation of neurotoxic α-synuclein oligomers in Parkinson's disease

Parkinson's disease (PD) is a major neurodegenerative disorder which exhibits many of the characteristics of a pandemic. Current therapeutic strategies are centered on the dopaminergic system, with limited efficacy, so that a treatment that has a direct impact on the underlying disease pathogenesis is urgently needed. Although α-synuclein is a privileged target for such therapies, this protein has been in the past wrongly considered as exclusively intracellular, so that the impact of paracrine neurotoxicity mechanisms in PD have been largely ignored. In this article we review the data showing that lipid rafts act as plasma membrane machineries for the formation of α-synuclein pore-like oligomers which trigger an increase of intracellular Ca²⁺. This Ca²⁺ influx is responsible for a self-sustained cascade of neurotoxic events, including mitochondrial oxidative stress, tau phosphorylation, Ca²⁺ release from the endoplasmic reticulum, Lewy body formation, and extracellular release of α-synuclein in exosomes. The first step of this cascade is the binding of α-synuclein to lipid raft gangliosides, suggesting that PD should be considered as both a proteinopathy and a ganglioside membrane disorder lipidopathy. Accordingly, blocking α-synuclein-ganglioside interactions should annihilate the whole neurotoxic cascade and stop disease progression. A pipeline of anti-oligomer molecules is under development, among which an in-silico designed synthetic peptide AmyP53 which is the first drug targeting gangliosides and thus able to prevent the formation of α-synuclein oligomers and all downstream neurotoxicity. These new therapeutic avenues challenge the current symptomatic approaches by finally targeting the root cause of PD through a long-awaited paradigm shift.

Creating stem cell-derived neuromuscular junctions in vitro

Recent development of novel therapies has improved mobility and quality of life for people suffering from inheritable neuromuscular disorders. Despite this progress, the majority of neuromuscular disorders are still incurable, in part due to a lack of predictive models of neuromuscular junction (NMJ) breakdown. Improvement of predictive models of a human NMJ would be transformative in terms of expanding our understanding of the mechanisms that underpin development, maintenance, and disease, and as a testbed with which to evaluate novel therapeutics. Induced pluripotent stem cells (iPSCs) are emerging as a clinically relevant and non‐invasive cell source to create human NMJs to study synaptic development and maturation, as well as disease modeling and drug discovery. This review will highlight the recent advances and remaining challenges to generating an NMJ capable of eliciting contraction of stem cell‐derived skeletal muscle in vitro. We explore the advantages and shortcomings of traditional NMJ culturing platforms, as well as the pioneering technologies and novel, biomimetic culturing systems currently in use to guide development and maturation of the neuromuscular synapse and extracellular microenvironment. Then, we will explore how this NMJ‐in‐a‐dish can be used to study normal assembly and function of the efferent portion of the neuromuscular arc, and how neuromuscular disease‐causing mutations disrupt structure, signaling, and function.

Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review

Drug-induced liver injury (DILI) causes one in three market withdrawals due to adverse drug reactions, causing preventable human suffering and massive financial loss. We applied evidence-based methods to investigate the role of preclinical studies in predicting human DILI using two anti-diabetic drugs from the same class, but with different toxicological profiles: troglitazone (withdrawn from US market due to DILI) and rosiglitazone (remains on US market). Evidence Stream 1: A systematic literature review of in vivo studies on rosiglitazone or troglitazone was conducted (PROSPERO registration CRD42018112353). Evidence Stream 2: in vitro data on troglitazone and rosiglitazone were retrieved from the US EPA ToxCast database. Evidence Stream 3: troglitazone- and rosiglitazone-related DILI cases were retrieved from WHO Vigibase. All three evidence stream analyses were conducted according to evidence-based methodologies and performed according to pre-registered protocols. Evidence Stream 1: 9288 references were identified, with 42 studies included in analysis. No reported biomarker for either drug indicated a strong hazard signal in either preclinical animal or human studies. All included studies had substantial limitations, resulting in "low" or "very low" certainty in findings. Evidence Stream 2: Troglitazone was active in twice as many in vitro assays (129) as rosiglitazone (60), indicating a strong signal for more off-target effects. Evidence Stream 3: We observed a fivefold difference in both all adverse events and liver-related adverse events reported, and an eightfold difference in fatalities for troglitazone, compared to rosiglitazone. In summary, published animal and human trials failed to predict troglitazone's potential to cause severe liver injury in a wider patient population, while in vitro data showed marked differences in the two drugs' off-target activities, offering a new paradigm for reducing drug attrition in late development and in the market. This investigation concludes that death and disability due to adverse drug reactions may be prevented if mechanistic information is deployed at early stages of drug development by pharmaceutical companies and is considered by regulators as a part of regulatory submissions.

Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies

The development and application of quantitative systems pharmacology models in neuroscience have been modest relative to other fields, such as oncology and immunology, which may reflect the complexity of the brain. Technological and methodological advancements have enhanced the quantitative understanding of brain physiology and pathophysiology and the effects of pharmacological interventions. To maximize the knowledge gained from these novel data types, pharmacometrics modelers may need to expand their toolbox to include additional mathematical and statistical frameworks. A session was held at the 10th annual American Conference on Pharmacometrics (ACoP10) to highlight several recent advancements in quantitative and systems neuroscience. In this mini‐review, we provide a brief overview of technological and methodological advancements in the neuroscience therapeutic area that were discussed during the session and how these can be leveraged with quantitative systems pharmacology modeling to enhance our understanding of neurological diseases. Microphysiological systems using human induced pluripotent stem cells (IPSCs), digital biomarkers, and large‐scale imaging offer more clinically relevant experimental datasets, enhanced granularity, and a plethora of data to potentially improve the preclinical‐to‐clinical translation of therapeutics. Network neuroscience methodologies combined with quantitative systems models of neurodegenerative disease could help bridge the gap between cellular and molecular alterations and clinical end points through the integration of information on neural connectomics. Additional topics, such as the neuroimmune system, microbiome, single‐cell transcriptomic technologies, and digital device biomarkers, are discussed in brief.

Are Animal Models Needed to Discover, Develop and Test Pharmaceutical Drugs for Humans in the 21st Century?

Despite many decades of research, much of which has focused on studies in animals, we humans continue to suffer from multiple diseases for which there are no cures or treatments [...].

Links between Nutrition, Infectious Diseases, and Microbiota: Emerging Technologies and Opportunities for Human-Focused Research

The interaction between nutrition and human infectious diseases has always been recognized. With the emergence of molecular tools and post-genomics, high-resolution sequencing technologies, the gut microbiota has been emerging as a key moderator in the complex interplay between nutrients, human body, and infections. Much of the host-microbial and nutrition research is currently based on animals or simplistic in vitro models. Although traditional in vivo and in vitro models have helped to develop mechanistic hypotheses and assess the causality of the host-microbiota interactions, they often fail to faithfully recapitulate the complexity of the human nutrient-microbiome axis in gastrointestinal homeostasis and infections. Over the last decade, remarkable progress in tissue engineering, stem cell biology, microfluidics, sequencing technologies, and computing power has taken place, which has produced a new generation of human-focused, relevant, and predictive tools. These tools, which include patient-derived organoids, organs-on-a-chip, computational analyses, and models, together with multi-omics readouts, represent novel and exciting equipment to advance the research into microbiota, infectious diseases, and nutrition from a human-biology-based perspective. After considering some limitations of the conventional in vivo and in vitro approaches, in this review, we present the main novel available and emerging tools that are suitable for designing human-oriented research.

All-trans retinoic acid induces reprogramming of canine dedifferentiated cells into neuron-like cells

The specification of cell identity depends on the exposure of cells to sequences of bioactive ligands. All-trans retinoic acid (ATRA) affects neuronal development in the early stage, and it is involved in neuronal lineage reprogramming. We previously established a fibroblast-like dedifferentiated fat cells (DFATs) derived from highly homogeneous mature adipocytes, which are more suitable for the study of cellular reprogramming. Canine cognitive dysfunction is similar to human cognitive dysfunction, suggesting that dogs could be a pathological and pharmacological model for human neuronal diseases. However, the effect of ATRA on neuronal reprogramming in dogs has remained unclear. Therefore, in this study, we investigated the effect of ATRA on the neuronal reprogramming of canine DFATs. ATRA induced the expression of neuronal marker mRNA/protein. The neuron-like cells showed Ca²⁺ influx with depolarization (50 mM KCl; 84.75 ± 4.05%) and Na⁺ channel activation (50 μM veratridine; 96.02 ± 2.02%). Optical imaging of presynaptic terminal activity and detection of neurotransmitter release showed that the neuron-like cells exhibited the GABAergic neuronal property. Genome-wide RNA-sequencing analysis shows that the transcriptome profile of canine DFATs is effectively reprogrammed towards that of cortical interneuron lineage. Collectively, ATRA can produce functional GABAergic cortical interneuron-like cells from canine DFATs, exhibiting neuronal function with > 80% efficiency. We further demonstrated the contribution of JNK3 to ATRA-induced neuronal reprogramming in canine DFATs. In conclusion, the neuron-like cells from canine DFATs could be a powerful tool for translational research in cell transplantation therapy, in vitro disease modeling, and drug screening for neuronal diseases.

Are researchers moving away from animal models as a result of poor clinical translation in the field of stroke? An analysis of opinion papers

Objectives

Despite decades of research using animals to develop pharmaceutical treatments for patients who have had a stroke, few therapeutic options exist. The vast majority of interventions successful in preclinical animal studies have turned out to have no efficacy in humans or to be harmful to humans. In view of this, we explore whether there is evidence of a move away from animal models in this field.

Methods

We used an innovative methodology, the analysis of opinion papers. Although we took a systematic approach to literature searching and data extraction, this is not a systematic review because the study involves the synthesis of opinions, not research evidence. Data were extracted from retrieved papers in chronological order and analysed qualitatively and descriptively.

Results

Eighty eligible papers, published between 1979 and 2018, were identified. Most authors were from academic departments of neurology, neuroscience or stroke research. Authors agreed that translational stroke research was in crisis. They held diverse views about the causes of this crisis, most of which did not fundamentally challenge the use of animal models. Some, however, attributed the translational crisis to animal–human species differences and one to a lack of human in vitro models. Most of the proposed solutions involved fine-tuning animal models, but authors disagreed about whether such modifications would improve translation. A minority suggested using human in vitro methods alongside animal models. One proposed focusing only on human in vitro methods.

Conclusion

Despite recognising that animal models have been unsuccessful in the field of stroke, most researchers exhibited a strong resistance to relinquishing them. Nevertheless, there is an emerging challenge to the use of animal models, in the form of human-focused in vitro approaches. For the sake of stroke patients there is an urgent need to revitalise translational stroke research and explore the evidence for these new approaches.

Challenges in Alzheimer's Disease Drug Discovery and Development: The Role of Modeling, Simulation, and Open Data

Alzheimer's disease (AD) is the leading cause of dementia worldwide. With 35 million people over 60 years of age with dementia, there is an urgent need to develop new treatments for AD. To streamline this process, it is imperative to apply insights and learnings from past failures to future drug development programs. In the present work, we focus on how modeling and simulation tools can leverage open data to address drug development challenges in AD.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Neuronal Circuit-Based Computer Modeling as a Phenotypic Strategy for CNS R&D

With the success rate of drugs for CNS indications at an all-time low, new approaches are needed to turn the tide of failed clinical trials. This paper reviews the history of CNS drug Discovery over the last 60 years and proposes a new paradigm based on the lessons learned. The initial wave of successful therapeutics discovered using careful clinical observations was followed by an emphasis on a phenotypic target-agnostic approach, often leading to successful drugs with a rich pharmacology. The subsequent introduction of molecular biology and the focus on a target-driven strategy has largely dominated drug discovery efforts over the last 30 years, but has not increased the probability of success, because these highly selective molecules are unlikely to address the complex pathological phenotypes of most CNS disorders. In many cases, reliance on preclinical animal models has lacked robust translational power. We argue that Quantitative Systems Pharmacology (QSP), a mechanism-based computer model of biological processes informed by preclinical knowledge and enhanced by neuroimaging and clinical data could be a new powerful knowledge generator engine and paradigm for rational polypharmacy. Progress in the academic discipline of computational neurosciences, allows one to model the effect of pathology and therapeutic interventions on neuronal circuit firing activity that can relate to clinical phenotypes, driven by complex properties of specific brain region activation states. The model is validated by optimizing the correlation between relevant emergent properties of these neuronal circuits and historical clinical and imaging datasets. A rationally designed polypharmacy target profile will be discovered using reverse engineering and sensitivity analysis. Small molecules will be identified using a combination of Artificial Intelligence methods and computational modeling, tested subsequently in heterologous cellular systems with human targets. Animal models will be used to establish target engagement and for ADME-Tox, with the QSP approach complemented by in vivo preclinical models that can be further refined to increase predictive validity. The QSP platform can also mitigate the variability in clinical trials with the concept of virtual patients. Because the QSP platform integrates knowledge from a wide variety of sources in an actionable simulation, it offers the possibility of substantially improving the success rate of CNS R&D programs while, at the same time, reducing both cost and the number of animals.

Sequencing Disparity in the Genomic Era

Advances in sequencing technology have resulted in the expectation that genomic studies will become more representative of organismal diversity. To test this expectation, we explored species representation of nonhuman eukaryotes in the Sequence Read Archive. Though species richness has been increasing steadily, species evenness is decreasing over time. Moreover, the top 1% most studied organisms increasingly represent a larger proportion of total experiments, demonstrating growing bias in favor of a small minority of species. To better understand molecular processes and patterns, genomic studies should reverse current trends by adopting more comparative approaches.

Is it possible to overcome issues of external validity in preclinical animal research? Why most animal models are bound to fail

Background

The pharmaceutical industry is in the midst of a productivity crisis and rates of translation from bench to bedside are dismal. Patients are being let down by the current system of drug discovery; of the several 1000 diseases that affect humans, only a minority have any approved treatments and many of these cause adverse reactions in humans. A predominant reason for the poor rate of translation from bench to bedside is generally held to be the failure of preclinical animal models to predict clinical efficacy and safety. Attempts to explain this failure have focused on problems of internal validity in preclinical animal studies (e.g. poor study design, lack of measures to control bias). However there has been less discussion of another key factor that influences translation, namely the external validity of preclinical animal models.

Review of problems of external validity

External validity is the extent to which research findings derived in one setting, population or species can be reliably applied to other settings, populations and species. This paper argues that the reliable translation of findings from animals to humans will only occur if preclinical animal studies are both internally and externally valid. We review several key aspects that impact external validity in preclinical animal research, including unrepresentative animal samples, the inability of animal models to mimic the complexity of human conditions, the poor applicability of animal models to clinical settings and animal–human species differences. We suggest that while some problems of external validity can be overcome by improving animal models, the problem of species differences can never be overcome and will always undermine external validity and the reliable translation of preclinical findings to humans.

Conclusion

We conclude that preclinical animal models can never be fully valid due to the uncertainties introduced by species differences. We suggest that even if the next several decades were spent improving the internal and external validity of animal models, the clinical relevance of those models would, in the end, only improve to some extent. This is because species differences would continue to make extrapolation from animals to humans unreliable. We suggest that to improve clinical translation and ultimately benefit patients, research should focus instead on human-relevant research methods and technologies.

Parkinson's disease research: adopting a more human perspective to accelerate advances

Parkinson's disease (PD) affects 1% of the population over 60 years old and, with global increases in the aging population, presents huge economic and societal burdens. The etiology of PD remains unknown; most cases are idiopathic, presumed to result from genetic and environmental risk factors. Despite 200 years since the first description of PD, the mechanisms behind initiation and progression of the characteristic neurodegenerative processes are not known. Here, we review progress and limitations of the multiple PD animal models available and identify advances that could be implemented to better understand pathological processes, improve disease outcome, and reduce dependence on animal models. Lessons learned from reducing animal use in PD research could serve as guideposts for wider biomedical research.

Use of quantitative clinical pharmacology to improve early clinical development success in neurodegenerative diseases

INTRODUCTION

The success rate of pharmaceutical Research & Development (R&D) is much lower compared to other industries such as micro-electronics or aeronautics with the probability of a successful clinical development to approval in central nervous system (CNS) disorders hovering in the single digits (7%). Areas covered: Inspired by adjacent engineering-based industries, we argue that quantitative modeling in CNS R&D might improve success rates. We will focus on quantitative techniques in early clinical development, such as PharmacoKinetic-PharmacoDynamic modeling, clinical trial simulation, model-based meta-analysis and the mechanism-based physiology-based pharmacokinetic modeling, and quantitative systems pharmacology. Expert commentary: Mechanism-based computer modeling rely less on existing clinical datasets, therefore can better generalize than Big Data analytics, including prospectively and quantitatively predicting the clinical outcome of new drugs. More specifically, exhaustive post-hoc analysis of failed trials using individual virtual human trial simulation could illuminate underlying causes such as lack of sufficient functional target engagement, negative pharmacodynamic interactions with comedications and genotypes, and mismatched patient population. These insights are beyond the capacity of artificial intelligence (AI) methods as they are many more possible combinations than subjects. Unlike 'black box' approaches in AI, mechanism-based platforms are transparent and based on biologically sound assumptions that can be interrogated.

Impact of amyloid-beta changes on cognitive outcomes in Alzheimer's disease: analysis of clinical trials using a quantitative systems pharmacology model

BACKGROUND

Despite a tremendous amount of information on the role of amyloid in Alzheimer's disease (AD), almost all clinical trials testing this hypothesis have failed to generate clinically relevant cognitive effects.

METHODS

We present an advanced mechanism-based and biophysically realistic quantitative systems pharmacology computer model of an Alzheimer-type neuronal cortical network that has been calibrated with Alzheimer Disease Assessment Scale, cognitive subscale (ADAS-Cog) readouts from historical clinical trials and simulated the differential impact of amyloid-beta (Aβ40 and Aβ42) oligomers on glutamate and nicotinic neurotransmission.

RESULTS

Preclinical data suggest a beneficial effect of shorter Aβ forms within a limited dose range. Such a beneficial effect of Aβ40 on glutamate neurotransmission in human patients is absolutely necessary to reproduce clinical data on the ADAS-Cog in minimal cognitive impairment (MCI) patients with and without amyloid load, the effect of APOE genotype effect on the slope of the cognitive trajectory over time in placebo AD patients and higher sensitivity to cholinergic manipulation with scopolamine associated with higher Aβ in MCI subjects. We further derive a relationship between units of Aβ load in our model and the standard uptake value ratio from amyloid imaging. When introducing the documented clinical pharmacodynamic effects on Aβ levels for various amyloid-related clinical interventions in patients with low Aβ baseline, the platform predicts an overall significant worsening for passive vaccination with solanezumab, beta-secretase inhibitor verubecestat and gamma-secretase inhibitor semagacestat. In contrast, all three interventions improved cognition in subjects with moderate to high baseline Aβ levels, with verubecestat anticipated to have the greatest effect (around ADAS-Cog value 1.5 points), solanezumab the lowest (0.8 ADAS-Cog value points) and semagacestat in between. This could explain the success of many amyloid interventions in transgene animals with an artificial high level of Aβ, but not in AD patients with a large variability of amyloid loads.

CONCLUSIONS

If these predictions are confirmed in post-hoc analyses of failed clinical amyloid-modulating trials, one should question the rationale behind testing these interventions in early and prodromal subjects with low or zero amyloid load.

Evaluation of Metabolic and Synaptic Dysfunction Hypotheses of Alzheimer's Disease (AD): A Meta-Analysis of CSF Markers

BACKGROUND

Alzheimer's disease (AD) is currently incurable and a majority of investigational drugs have failed clinical trials. One explanation for this failure may be the invalidity of hypotheses focusing on amyloid to explain AD pathogenesis. Recently, hypotheses which are centered on synaptic and metabolic dysfunction are increasingly implicated in AD.

OBJECTIVE

Evaluate AD hypotheses by comparing neurotransmitter and metabolite marker concentrations in normal versus AD CSF.

METHODS

Meta-analysis allows for statistical comparison of pooled, existing cerebrospinal fluid (CSF) marker data extracted from multiple publications, to obtain a more reliable estimate of concentrations. This method also provides a unique opportunity to rapidly validate AD hypotheses using the resulting CSF concentration data. Hubmed, Pubmed and Google Scholar were comprehensively searched for published English articles, without date restrictions, for the keywords "AD", "CSF", and "human" plus markers selected for synaptic and metabolic pathways. Synaptic markers were acetylcholine, gamma-aminobutyric acid (GABA), glutamine, and glycine. Metabolic markers were glutathione, glucose, lactate, pyruvate, and 8 other amino acids. Only studies that measured markers in AD and controls (Ctl), provided means, standard errors/deviation, and subject numbers were included. Data were extracted by six authors and reviewed by two others for accuracy. Data were pooled using ratio of means (RoM of AD/Ctl) and random effects meta-analysis using Cochrane Collaboration's Review Manager software.

RESULTS

Of the 435 identified publications, after exclusion and removal of duplicates, 35 articles were included comprising a total of 605 AD patients and 585 controls. The following markers of synaptic and metabolic pathways were significantly changed in AD/controls: acetylcholine (RoM 0.36, 95% CI 0.24-0.53, p<0.00001), GABA (0.74, 0.58-0.94, p<0.01), pyruvate (0.48, 0.24-0.94, p=0.03), glutathione (1.11, 1.01- 1.21, p=0.03), alanine (1.10, 0.98-1.23, p=0.09), and lower levels of significance for lactate (1.2, 1.00-1.47, p=0.05). Of note, CSF glucose and glutamate levels in AD were not significantly different than that of the controls.

CONCLUSION

This study provides proof of concept for the use of meta-analysis validation of AD hypotheses, specifically via robust evidence for the cholinergic hypothesis of AD. Our data disagree with the other synaptic hypotheses of glutamate excitotoxicity and GABAergic resistance to neurodegeneration, given observed unchanged glutamate levels and decreased GABA levels. With regards to metabolic hypotheses, the data supported upregulation of anaerobic glycolysis, pentose phosphate pathway (glutathione), and anaplerosis of the tricarboxylic acid cycle using glutamate. Future applications of meta-analysis indicate the possibility of further in silico evaluation and generation of novel hypotheses in the AD field.

From Reproducibility to Translation in Neurodegenerative Disease

Despite tremendous investment and preclinical success in neurodegenerative disease, effective disease-altering treatments for patients have remained elusive. One highly cited reason for this discrepancy is flawed animal study design and reporting. If this can be broadly remedied, reproducibility of preclinical studies will improve. However, without concurrent efforts to improve generalizability, these improvements may not translate effectively from animal experiments to more complex human neurodegenerative diseases. Mechanistic and phenotypic variability of neurodegenerative disease is such that most models are only able to interrogate individual aspects of complex phenomena. One approach is to consider animals as models of individual targets rather than as models of individual diseases and to migrate the concept of predictive validity from the individual model to the body of experiments that demonstrate translatability of a target. Both exploratory and therapeutic preclinical studies are dependent upon study design methods that promote rigor and reproducibility. However, the body of evidence that is needed to demonstrate efficacy in therapeutic studies is substantially broader than that needed for exploratory studies. In addition to requiring rigor within individual experiments, convincing evidence for therapeutic potential must assess the relationships between model choice, intended goal of the intervention, pharmacologic criteria, and integration of biomarker data with outcome measures that are clinically relevant to humans. It is conceivable that proof-of-concept studies will migrate to cell-based systems and that animal systems will be increasingly reserved for more distal translational purposes. If this occurs, it is likely to prompt reexamination of what the term "translational" truly means.

Knowledge-driven computational modeling in Alzheimer's disease research: Current state and future trends

Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to be required for further progress in understanding and treating AD.

APOE ε4/ε4 diminishes neurotrophic function of human iPSC-derived astrocytes

The ε4 allele of the APOE gene encoding apolipoprotein E (apoE) is a strong genetic risk factor for aging-related cognitive decline as well as late-onset Alzheimer's disease (AD) compared to the common ε3 allele. In the central nervous system, apoE is produced primarily by astrocytes and functions in transporting lipids including cholesterol to support neuronal homeostasis and synaptic integrity. Although mouse models and corresponding primary cells have provided valuable tools for studying apoE isoform-dependent functions, recent studies have shown that human astrocytes have a distinct gene expression profile compare with rodent astrocytes. Human induced pluripotent stem cells (iPSCs) derived from individuals carrying specific gene variants or mutations provide an alternative cellular model more relevant to humans upon differentiation into specific cell types. Thus, we reprogramed human skin fibroblasts from cognitively normal individuals carrying APOE ε3/ε3 or ε4/ε4 genotype to iPSC clones and further differentiated them into neural progenitor cells and then astrocytes. We found that human iPSC-derived astrocytes secreted abundant apoE with apoE4 lipoprotein particles less lipidated compared to apoE3 particles. More importantly, human iPSC-derived astrocytes were capable of promoting neuronal survival and synaptogenesis when co-cultured with iPSC-derived neurons with APOE ε4/ε4 astrocytes less effective in supporting these neurotrophic functions than those with APOE ε3/ε3 genotype. Taken together, our findings demonstrate APOE genotype-dependent effects using human iPSC-derived astrocytes and provide novel evidence that the human iPSC-based model system is a strong tool to explore how apoE isoforms contribute to neurodegenerative diseases.

Introducing Therioepistemology: the study of how knowledge is gained from animal research

This focus issue of Lab Animal coincides with a tipping point in biomedical research. For the first time, the scale of the reproducibility and translatability crisis is widely understood beyond the small cadre of researchers who have been studying it and the pharmaceutical and biotech companies who have been living it. Here we argue that an emerging literature, including the papers in this focus issue, has begun to congeal around a set of recurring themes, which themselves represent a paradigm shift. This paradigm shift can be characterized at the micro level as a shift from asking "what have we controlled for in this model?" to asking "what have we chosen to ignore in this model, and at what cost?" At the macro level, it is a shift from viewing animals as tools (the furry test tube), to viewing them as patients in an equivalent human medical study. We feel that we are witnessing the birth of a new discipline, which we term Therioepistemology, or the study of how knowledge is gained from animal research. In this paper, we outline six questions that serve as a heuristic for critically evaluating animal-based biomedical research from a therioepistemological perspective. These six questions sketch out the broad reaches of this new discipline, though they may change or be added to as this field evolves. Ultimately, by formalizing therioepistemology as a discipline, we can begin to discuss best practices that will improve the reproducibility and translatability of animal-based research, with concomitant benefits in terms of human health and animal well-being.

Towards the virtual human patient. Quantitative Systems Pharmacology in Alzheimer's disease

Development of successful therapeutic interventions in Central Nervous Systems (CNS) disorders is a daunting challenge with a low success rate. Probable reasons include the lack of translation from preclinical animal models, the individual variability of many pathological processes converging upon the same clinical phenotype, the pharmacodynamical interaction of various comedications and last but not least the complexity of the human brain. This paper argues for a re-engineering of the pharmaceutical CNS Research & Development strategy using ideas focused on advanced computer modeling and simulation from adjacent engineering-based industries. We provide examples that such a Quantitative Systems Pharmacology approach based on computer simulation of biological processes and that combines the best of preclinical research with actual clinical outcomes can enhance translation to the clinical situation. We will expand upon (1) the need to go from Big Data to Smart Data and develop predictive and quantitative algorithms that are actionable for the pharma industry, (2) using this platform as a "knowledge machine" that captures community-wide expertise in an active hypothesis-testing approach, (3) learning from failed clinical trials and (4) the need to go beyond simple linear hypotheses and embrace complex non-linear hypotheses. We will propose a strategy for applying these concepts to the substantial individual variability of AD patient subgroups and the treatment of neuropsychiatric problems in AD. Quantitative Systems Pharmacology is a new 'humanized' tool for supporting drug discovery and development in general and CNS disorders in particular.

The Mouse Lemur, a Genetic Model Organism for Primate Biology, Behavior, and Health

Systematic genetic studies of a handful of diverse organisms over the past 50 years have transformed our understanding of biology. However, many aspects of primate biology, behavior, and disease are absent or poorly modeled in any of the current genetic model organisms including mice. We surveyed the animal kingdom to find other animals with advantages similar to mice that might better exemplify primate biology, and identified mouse lemurs (Microcebus spp.) as the outstanding candidate. Mouse lemurs are prosimian primates, roughly half the genetic distance between mice and humans. They are the smallest, fastest developing, and among the most prolific and abundant primates in the world, distributed throughout the island of Madagascar, many in separate breeding populations due to habitat destruction. Their physiology, behavior, and phylogeny have been studied for decades in laboratory colonies in Europe and in field studies in Malagasy rainforests, and a high quality reference genome sequence has recently been completed. To initiate a classical genetic approach, we developed a deep phenotyping protocol and have screened hundreds of laboratory and wild mouse lemurs for interesting phenotypes and begun mapping the underlying mutations, in collaboration with leading mouse lemur biologists. We also seek to establish a mouse lemur gene "knockout" library by sequencing the genomes of thousands of mouse lemurs to identify null alleles in most genes from the large pool of natural genetic variants. As part of this effort, we have begun a citizen science project in which students across Madagascar explore the remarkable biology around their schools, including longitudinal studies of the local mouse lemurs. We hope this work spawns a new model organism and cultivates a deep genetic understanding of primate biology and health. We also hope it establishes a new and ethical method of genetics that bridges biological, behavioral, medical, and conservation disciplines, while providing an example of how hands-on science education can help transform developing countries.

Current nonclinical testing paradigms in support of safe clinical trials: An IQ Consortium DruSafe perspective

The transition from nonclinical to First-in-Human (FIH) testing is one of the most challenging steps in drug development. In response to serious outcomes in a recent Phase 1 trial (sponsored by Bial), IQ Consortium/DruSafe member companies reviewed their nonclinical approach to progress small molecules safely to FIH trials. As a common practice, safety evaluation begins with target selection and continues through iterative in silico and in vitro screening to identify molecules with increased probability of acceptable in vivo safety profiles. High attrition routinely occurs during this phase. In vivo exploratory and pivotal FIH-enabling toxicity studies are then conducted to identify molecules with a favorable benefit-risk profile for humans. The recent serious incident has reemphasized the importance of nonclinical testing plans that are customized to the target, the molecule, and the intended clinical plan. Despite the challenges and inherent risks of transitioning from nonclinical to clinical testing, Phase 1 studies have a remarkably good safety record. Given the rapid scientific evolution of safety evaluation, testing paradigms and regulatory guidance must evolve with emerging science. The authors posit that the practices described herein, together with science-based risk assessment and management, support safe FIH trials while advancing development of important new medicines.

Established patterns of animal study design undermine translation of disease-modifying therapies for Parkinson's disease

Translation of disease-modifying therapies in neurodegenerative disease has been disappointing. Parkinson's disease (PD) was used to compare patterns of preclinical study design for symptomatic and potentially disease-modifying interventions. We examined the relationship of model, intervention type and timing, outcomes and outcome measures in 543 animal and human studies (1973-2015) across a contemporary cohort of animal and human interventional studies (n = 445), animal studies for approved interventions (n = 28), animal and human studies for those that failed to translate (n = 70). Detailed study design data were collected for 216 studies in non-human primate (NHP) and rodent toxin-induced models. Species-specific patterns of study design prevailed regardless of whether interventions were symptomatic or potentially disease-modifying. In humans and NHPs, interventions were typically given to both sexes well after the PD phenotype was established, and clinical outcome measures were collected at single (symptomatic) or multiple (disease-modifying) time-points. In rodents, interventions often preceded induction of the model, acute toxic protocols were common, usually given to young males, clinical outcome measures were used less commonly, and outcomes were less commonly assessed at multiple time points. These patterns were more prevalent in mice than rats. In contrast, study design factors such as randomization and blinding did not differ appreciably across symptomatic and disease-modifying intervention categories. The translational gap for potentially disease-modifying interventions in PD in part results from study designs, particularly in mice, that fail to model the progressive nature and relatively late intervention characteristic of PD, or that anchor mechanistic and neuropathologic data to longitudinal clinical outcomes. Even if measures to improve reproducibility are broadly adopted, perpetuation of these norms will continue to impede effective translation.

Semi-mechanistic computer simulation of psychotic symptoms in schizophrenia with a model of a humanized cortico-striatal-thalamocortical loop

Despite new insights into the pathophysiology of schizophrenia and clinical trials with highly selective drugs, no new therapeutic breakthroughs have been identified. We present a semi-mechanistic Quantitative Systems Pharmacology (QSP) computer model of a biophysically realistic cortical-striatal-thalamo-cortical loop. The model incorporates the direct, indirect and hyperdirect pathway of the basal ganglia and CNS drug targets that modulate neuronal firing, based on preclinical data about their localization and coupling to voltage-gated ion channels. Schizophrenia pathology is introduced using quantitative human imaging data on striatal hyperdopaminergic activity and cortical dysfunction. We identified an entropy measure of neuronal firing in the thalamus, related to the bandwidth of information processing that correlates well with reported historical clinical changes on PANSS Total with antipsychotics after introduction of their pharmacology (42 drug-dose combinations, r²=0.62). This entropy measure is further validated by predicting the clinical outcome of 28 other novel stand-alone interventions, 14 of them with non-dopamine D₂R pharmacology, in addition to 8 augmentation trials (correlation between actual and predicted clinical scores r²=0.61). The platform predicts that most combinations of antipsychotics have a lower efficacy over what can be achieved by either one; negative pharmacodynamical interactions are prominent for aripiprazole added to risperidone, haloperidol, quetiapine and paliperidone. The model also recapitulates the increased probability for psychotic breakdown in a supersensitive environment and the effect of ketamine in healthy volunteers. This QSP platform, combined with similar readouts for motor symptoms, negative symptoms and cognitive impairment has the potential to improve our understanding of drug effects in schizophrenia patients.

Expectations for the methodology and translation of animal research: a survey of the general public, medical students and animal researchers in North America

To determine what are considered acceptable standards for animal research (AR) methodology and translation rate to humans, a validated survey was sent to: a) a sample of the general public, via Sampling Survey International (SSI; Canada), Amazon Mechanical Turk (AMT; USA), a Canadian city festival (CF) and a Canadian children's hospital (CH); b) a sample of medical students (two first-year classes); and c) a sample of scientists (corresponding authors and academic paediatricians). There were 1379 responses from the general public sample (SSI, n = 557; AMT, n = 590; CF, n = 195; CH, n = 102), 205/330 (62%) medical student responses, and 23/323 (7%, too few to report) scientist responses. Asked about methodological quality, most of the general public and medical student respondents expect that: AR is of high quality (e.g. anaesthesia and analgesia are monitored, even overnight, and 'humane' euthanasia, optimal statistical design, comprehensive literature review, randomisation and blinding, are performed), and costs and difficulty are not acceptable justifications for lower quality (e.g. costs of expert consultation, or more laboratory staff). Asked about their expectations of translation to humans (of toxicity, carcinogenicity, teratogenicity and treatment findings), most expect translation more than 60% of the time. If translation occurred less than 20% of the time, a minority disagreed that this would "significantly reduce your support for AR". Medical students were more supportive of AR, even if translation occurred less than 20% of the time. Expectations for AR are much higher than empirical data show to have been achieved.

The promises of quantitative systems pharmacology modelling for drug development

Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

Big data to smart data in Alzheimer's disease: The brain health modeling initiative to foster actionable knowledge

Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These "big-data" databases can potentially advance CNS research and drug development. However, although necessary, they are not sufficient, and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes. Although these empirically derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development.

The Human Model: Changing Focus on Autism Research

The lack of live human brain cells for research has slowed progress toward understanding the mechanisms underlying autism spectrum disorders. A human model using reprogrammed patient somatic cells offers an attractive alternative, as it captures a patient's genome in relevant cell types. Despite the current limitations, the disease-in-a-dish approach allows for progressive time course analyses of target cells, offering a unique opportunity to investigate the cellular and molecular alterations before symptomatic onset. Understanding the current drawbacks of this model is essential for the correct data interpretation and extrapolation of conclusions applicable to the human brain. Innovative strategies for collecting biological material and clinical information from large patient cohorts are important for increasing the statistical power that will allow for the extraction of information from the noise resulting from the variability introduced by reprogramming and differentiation methods. Working with large patient cohorts is also important for understanding how brain cells derived from diverse human genetic backgrounds respond to specific drugs, creating the possibility of personalized medicine for autism spectrum disorders.

Intracerebroventricular Streptozotocin Injections as a Model of Alzheimer's Disease: in Search of a Relevant Mechanism

Streptozotocin (STZ), a glucosamine-nitrosourea compound derived from soil bacteria and originally developed as an anticancer agent, in 1963 has been found to induce diabetes in experimental animals. Since then, systemic application of STZ became the most frequently studied experimental model of insulin-dependent (type 1) diabetes. The compound is selectively toxic toward insulin-producing pancreatic beta cells, which is explained as the result of its cellular uptake by the low-affinity glucose transporter 2 (GLUT2) protein located in their cell membranes. STZ cytotoxicity is mainly due to DNA alkylation which results in cellular necrosis. Besides pancreatic beta cells, STZ applied systemically damages also other organs expressing GLUT2, such as kidney and liver, whereas brain is not affected directly because blood-brain barrier lacks this transporter protein. However, single or double intracerebroventricular (icv) STZ injection(s) chronically decrease cerebral glucose uptake and produce multiple other effects that resemble molecular, pathological, and behavioral features of Alzheimer's disease (AD). Taking into consideration that glucose hypometabolism is an early and persistent sign of AD and that Alzheimer's brains present features of impaired insulin signaling, icv STZ injections are exploited by some investigators as a non-transgenic model of this disease and used for preclinical testing of pharmacological therapies for AD. While it has been assumed that icv STZ produces cerebral glucose hypometabolism and other effects directly through desensitizing brain insulin receptors, the evidence for such mechanism is poor. On the other hand, early data on insulin immunoreactivity showed intense insulin expression in the rodent brain, and the possibility of local production of insulin in the mammalian brain has never been conclusively excluded. Also, there are GLUT2-expressing cells in the brain, in particular in the circumventricular organs and hypothalamus; some of these cells may be involved in glucose sensing. Thus, icv STZ may damage brain glucose insulin producing cells and/or brain glucose sensors. Mechanistic explanation of the mode of action of icv STZ, which is currently lacking, would provide a valuable contribution to the field of animal models of Alzheimer's disease.