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Biochemical Pharmacology (v.81, #12)
Editorial: Translational Medicine special issue
by Mark Day; Gerard B. Fox; Gerard J. Marek (pp. 1353-1355).
Translating cognition from animals to humans by J.F. Keeler; T.W. Robbins (pp. 1356-1366).
Many clinical disorders, whether neurological (e.g. Alzheimer's disease) or neuropsychiatric (e.g. schizophrenia and depression), exhibit cognitive symptoms that require pharmacological treatment. Cognition is multi-faceted and includes processes of perception, attention, working memory, long-term memory, executive function, language and social cognition. This article reviews how it is feasible to model many aspects of human cognition with the use of appropriate animal models and associated techniques, including the use of computer controlled tests (e.g. touch-screens), for optimising translation of experimental research to the clinic. When investigating clinical disorders, test batteries should aim to profile cognitive function in order to determine which aspects are impaired and which are preserved. In this review we have paid particular attention to the validation of translational methods; this may be done through the application of common theoretical principles, by comparing the effects of psychological manipulations and, wherever feasible, with the demonstration of homologous neural circuitry or equivalent pharmacological actions in the animal and human paradigms. Of particular importance is the use of ‘back-translation’ to ensure that the animal model has validity, for example, in predicting the effects of therapeutic drugs already found in human studies. It is made clear that the choice of appropriate behavioral tests is an important element of animal models of neuropsychiatric or neurological disorder; however, of course it is also important to select appropriate manipulations, whether genetic, neurodevelopmental, neurotoxic, or pharmacological, for simulating the neural substrates relevant to the disorders that lead to predictable behavioral and cognitive impairments, for optimising the testing of candidate compounds.
Keywords: Cognition; Working memory; Long-term memory; Learning; Attention; Executive function; Model; Prefrontal cortex; Hippocampus; Cerebral cortex
Schizophrenia risk genes: Implications for future drug development and discovery by Garret O’Connell; Stephen M. Lawrie; Andrew M. McIntosh; Jeremy Hall (pp. 1367-1373).
Present-day development of improved treatments for schizophrenia is hindered by uncertain models of disease, inter-individual response variability in clinical trials and a paucity of sensitive measures of treatment effects. Findings from genetic research emphasize the potential for schizophrenia risk genes to help develop focused treatments, discover new drug targets and provide markers of clinical subtypes. Advances in genetic technologies also provide novel modes of drug discovery in schizophrenia such as transcriptomics, epigenetics and transgenic animal models. In this review, we discuss proven and proposed ways risk genes can be used to enhance the development and discovery of treatments for schizophrenia and highlight key studies in these approaches.
Keywords: Risk genes; Schizophrenia; Drugs; Pharmacogenomics; Transgenic animal models
Paradigm shift in translational neuroimaging of CNS disorders by Ünal Sakoğlu; Jaymin Upadhyay; Chih-Liang Chin; Prasant Chandran; Scott J. Baker; Todd B. Cole; Gerard B. Fox; Mark Day; Feng Luo (pp. 1374-1387).
Structural MRI, task-evoked functional MRI and resting-state functional MRI in preclinical and clinical settings provide the capability of translational neuroimaging.During the last two decades, functional neuroimaging technology, especially functional magnetic resonance imaging (fMRI), has improved tremendously, with new attention towards resting-state functional connectivity of the brain. This development has allowed scientists to study changes in brain structure and function, and probe these two properties under conditions of evoked stimulation, disease and drug administration. In the domain of functional imaging, the identification and characterization of central nervous system (CNS) functional networks have emerged as potential biomarkers for CNS disorders in humans. Recent attempts to translate clinical neuroimaging methodology to preclinical studies have also been carried out, which offer new opportunities in translational neuroscience research. In this paper, we review recent developments in structural and functional MRI and their use to probe functional connectivity in various CNS disorders such as schizophrenia, mood disorders, Alzheimer's disease (AD) and pain.
Keywords: Brain; Neuroimaging; Functional magnetic resonance imaging; fMRI; Drug development; Functional connectivity; Resting-state networks; Default mode network; Schizophrenia; Mood disorders; Depression; Psychiatric illnesses; Alzheimer's disease; Pain
Translational research in addiction: Toward a framework for the development of novel therapeutics by Neil E. Paterson (pp. 1388-1407).
The development of novel substance use disorder (SUD) therapeutics is insufficient to meet the medical needs of a growing SUD patient population. The identification of translatable SUD models and tests is a crucial step in establishing a framework for SUD therapeutic development programs. The present review begins by identifying the clinical features of SUDs and highlights the narrow regulatory end-point required for approval of a novel SUD therapeutic. A conceptual overview of dependence is provided, followed by identification of potential intervention targets in the addiction cycle. The main components of the addiction cycle provide the framework for a discussion of preclinical models and their clinical analogs, all of which are focused on isolated behavioral end-points thought to be relevant to the persistence of compulsive drug use. Thus, the greatest obstacle to successful development is the gap between the multiplicity of preclinical and early clinical end-points and the regulatory end-point of sustained abstinence. This review proposes two pathways to bridging this gap: further development and validation of the preclinical extended access self-administration model; inclusion of secondary end-points comprising all of the measures highlighted in the present discussion in Phase 3 trials. Further, completion of the postdictive validation of analogous preclinical and clinical assays is of high priority. Ultimately, demonstration of the relevance and validity of a variety of end-points to the ultimate goal of abstinence will allow researchers to identify truly relevant therapeutic mechanisms and intervention targets, and establish a framework for SUD therapeutic development that allows optimal decision-making and resource allocation.
Keywords: Substance use disorder; Dependence; Translational; Extended access self-administration; Drug development
Aligning strategies for using EEG as a surrogate biomarker: A review of preclinical and clinical research by Steven C. Leiser; John Dunlop; Mark R. Bowlby; David M. Devilbiss (pp. 1408-1421).
Electroencephalography (EEG) and related methodologies offer the promise of predicting the likelihood that novel therapies and compounds will exhibit clinical efficacy early in preclinical development. These analyses, including quantitative EEG (e.g. brain mapping) and evoked/event-related potentials (EP/ERP), can provide a physiological endpoint that may be used to facilitate drug discovery, optimize lead or candidate compound selection, as well as afford patient stratification and Go/No-Go decisions in clinical trials. Currently, the degree to which these different methodologies hold promise for translatability between preclinical models and the clinic have not been well summarized. To address this need, we review well-established and emerging EEG analytic approaches that are currently being integrated into drug discovery programs throughout preclinical development and clinical research. Furthermore, we present the use of EEG in the drug development process in the context of a number of major central nervous system disorders including Alzheimer's disease, schizophrenia, depression, attention deficit hyperactivity disorder, and pain. Lastly, we discuss the requirements necessary to consider EEG technologies as a biomarker. Many of these analyses show considerable translatability between species and are used to predict clinical efficacy from preclinical data. Nonetheless, the next challenge faced is the selection and validation of EEG endpoints that provide a set of robust and translatable biomarkers bridging preclinical and clinical programs.
Keywords: Abbreviations; 5HT; 5-hydroxytryptamine: Serotonin; Aβ (Abeta); Amyloid Beta peptide; APP; Amyloid Precursor Protein; AD; Alzheimer's disease; AEP; auditory evoked potential; ASA; acetylsalicylic acid; ADAS-cog; Alzheimer's disease assessment scale (cognitive part); ADHD; attention deficit hyperactivity disorder; B1; bradykinin-1 receptor; Bf-S; Befindlichkeits-Skala; CGI-S; clinical global impression-severity; CNS; central nervous system; ECoG; electrocorticograph; EEG; electroencephalography; EP; evoked potential; ERP; event-related potential; HV; healthy volunteers; LCMV; linearly constrained minimum variance; LDAEP; loudness dependent auditory evoked potential; LORETA; low resolution brain electromagnetic tomography; LSEP; laser-evoked somatosensory evoked potentials; MADRS; Montgomery-Åsberg depression rating scale; MAOI; monoamine oxidase inhibitors; MCI; mild cognitive impairment; MDD; major (clinical) depressive disorder; MMSE; mini mental state examination; MPH; methylphenidate; MUSIC; multiple signal classification; NK1; neurokinin-1; NMDA; N-methyl-D-aspartic acid glutamate receptor subtype; NREM; non-rapid eye movement; NSAIDs; non-steroidal anti-inflammatory drug; PANSS; positive and negative syndrome scale; PS1; presinillin-1; PSAPP; presinillin/amyloid precursor protein; qEEG; quantitative EEG; REM; rapid eye movement; SCL-90; symptom checklist-90; SEP; sensory evoked potential; SSEP; somatosensory evoked potential; tEEG; translational EEG; VAS; visual analogue scale; VEP; visual evoked potentialEEG; Preclinical; Clinical; Biomarker; Translational
Developing predictive CSF biomarkers—A challenge critical to success in Alzheimer's disease and neuropsychiatric translational medicine by Dorothy G. Flood; Gerard J. Marek; Michael Williams (pp. 1422-1434).
Cartoon of diagnostic data points.The need to develop effective treatments for Alzheimer's disease has been confounded by repeated clinical failures where promising new chemical entities that have been extensively characterized in preclinical models of Alzheimer's disease have failed to show efficacy in the human disease state. This has been attributed to: the selection of drug targets that have yet to be shown as causal to the disease as distinct from being the result of the disease process, a lack of congruence in the animal models of Alzheimer's disease, wild-type and transgenic, to the human disease, and the enrollment of patients in proof of concept clinical trials who are at too advanced a stage of the disease to respond to any therapeutic. The development of validated biomarkers that can be used for disease diagnosis and progression is anticipated to improve patient enrollment in clinical trials, to develop new animal models and to identify new disease targets for drug discovery. The present review assesses the status of current efforts in developing CSF biomarkers for Alzheimer's disease and briefly discusses the status of CSF biomarker efforts in schizophrenia, depression, Parkinson's disease and multiple sclerosis.
Keywords: Alzheimer's disease; CSF biomarkers; Schizophrenia; Depression; Parkinson's disease; Drug discovery
Validation of experimental medicine methods in psychiatry: The P1vital approach and experience by Gerard R. Dawson; Kevin J. Craig; Colin T. Dourish (pp. 1435-1441).
In the pharmaceutical industry deciding whether to progress a compound to the next stage of development or choosing between compounds in a development portfolio is laden with risk. This is particularly true of compounds developed to treat CNS disorders. The use of pre-clinical models in CNS drug development is well established but these models often lack predictive validity and many compounds fail when they reach the target patient group. Bridging the gap between pre-clinical CNS models and patient studies, P1vital's objective is to develop human volunteer models that will enable rapid, accurate and reliable decision making about which compounds to progress into patient trials. The research strategy of P1vital and its academic research network is to focus on science that progresses the development of clinical efficacy models. As part of this strategy P1vital established a CNS Experimental Medicine Consortium with members from both academic research and the pharmaceutical industry. This consortium is unique in that experimental medicine models initially developed through academic research are selected for further validation in a process that is managed by the Pharma members of the P1vital CNS Experimental Medicine Consortium steering (PEM) committee. The P1vital consortium is very much a work in progress. However, since its inception in 2007 the consortium has successfully delivered results from five clinical studies in four therapeutic areas namely, anxiety, cognitive disorders, schizophrenia and depression.
Keywords: CNS; Drug development; Cognitive disorders