ABSTRACT
In chronic diseases such as Alzheimer's disease (AD), the arsenal of biomarkers available to determine the effectiveness of symptomatic treatment is very limited. Interpretation of the results provided in literature is cumbersome and it becomes difficult to predict their standardization to a larger patient population. Indeed, cognitive assessment alone does not appear to have sufficient predictive value of drug efficacy in early clinical development of AD treatment. In recent years, research has contributed to the emergence of new tools to assess brain activity relying on innovative technologies of imaging and electrophysiology. However, the relevance of the use of these newer markers in treatment response assessment is waiting for validation. This review shows how the early clinical assessment of symptomatic drugs could benefit from the inclusion of suitable pharmacodynamic markers. This review also emphasizes the importance of re-evaluating a step-by-step strategy in drug development.
Subject(s)
Alzheimer Disease/drug therapy , Alzheimer Disease/metabolism , Alzheimer Disease/physiopathology , Animals , Biomarkers, Pharmacological , Humans , Treatment OutcomeABSTRACT
In this paper, we present a neural network model of the interactions between cortex and the basal ganglia during prehensile movements. Computational neuroscience methods are used to explore the hypothesis that the altered kinematic patterns observed in Parkinson's disease patients performing prehensile movements is mainly due to an altered neuronal activity located in the networks of cholinergic (ACh) interneurons of the striatum. These striatal cells, under a strong influence of the dopaminergic system, significantly contribute to the neural processing within the striatum and in the cortico-basal ganglia loops. In order to test this hypothesis, a large-scale model of neural interactions in the basal ganglia has been integrated with previous models accounting for the cortical organization of goal directed reaching and grasping movements in normal and perturbed conditions. We carry out a discussion of the model hypothesis validation by providing a control engineering analysis and by comparing results of real experiments with our simulation results in conditions resembling these original experiments.
Subject(s)
Basal Ganglia/physiology , Models, Neurological , Movement/physiology , Neural Networks, Computer , Parkinson Disease/physiopathology , Humans , Models, TheoreticalABSTRACT
Parkinson's disease is a neurodegenerative disorder associated with aging characterized by a motor extrapiramidal alteration secondary to the progressive death of dopaminergic neurons of the substantia nigra pars compacta. The cause of this neuronal loss remains unknown but post mortem studies on brains of parkinsonian patients showed high index of inflammatory mechanism markers. This point has gone to open new lines of research in order to ascertain what role have these inflammatory process in neuronal degeneration and has opened new therapeutic possibilities to stop or at least to brake the neurodegenerative process.
