User:Péter Érdi/Dynamic neuropharmacology

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    The article starts by talking about ligand-receptor communication and then neural behavior but all examples of these diseases are system level of many neurons together. i find the definition of dynamical diseases confusing and perhaps wrong. a rewrite is required to discuss and define behavior in the system level

    Dynamical neuropharamcology is an emerging field to test and design drugs by dynamical models to offer new therapeutic strategies against neurological and psychiatric disorders.


    From structure-based analysis to dynamic models

    In neuropharmacology, diverse computational methods, including the innovative approaches of cheminformatics and bioinformatics, are used to aid drug design. The most important methods focus on the availability of the experimentally determined 3D structure of the target macromolecule and its binding molecules, and provide detailed analysis of the receptor-ligand interactions. If the spatial structure of the target is known, methods of receptor-based drug design are used to determine the candidate binding compounds. By contrast, when the structure of a ligand is known, indirect methods are used (i.e. ligand-based drug design) to find potential binding compounds with a similar molecular structure. (Ekins and Wang, 2006).

    While the standard structure based design of drugs for psychiatric disorders is based on drug-receptor interactions, the systems physiology perspective (Buzsáki, 2006) exploits the effects of drugs on the spatiotemporal brain activities. The theoretical framework for understanding the normal and pathological spatiotemporal activities is dynamical system theory.

    Dynamical system theory and computational neuroscience combined with traditional molecular and electrophysiological methods would open new avenues in drug discovery that may lead to genuinely new neurological and psychiatric therapies (Aradi and Érdi, 2006). More specifically, these model-based highly valuable techniques are able to integrate multiple disciplines at different spatiotemporal scales (Érdi et al., 2006).

    Neurobiological and psychiatric disorders as dynamical diseases

    Neurological and psychiatric disorders can be interpreted as dynamical diseases. The concept emerged about somewhat from chaos theory, (Belair et al., 1995) and seems to be fruitful to explain a variety of disorders. The term "dynamical disease" means that these disorders are characterized by sudden changes in qualitative dynamic behavior.

    Impairment in the proper temporal organization might underlie the various behavioral and cognitive deficits associated with psychiatric disorders. Network level temporal correlations are often expressed in the form of oscillations. Network oscillations are robust phenotypes that are well-preserved throughout mammalian evolution, and are specifically and differentially affected by a large spectrum of psychotropic drugs, and so these patterns can be used in early screening. In a single cell level, temporal correlations between individual neurons give us important insight in the functioning of micro-circuits. Change in relative timing between different neuron populations indicates alteration in their operation as well.

    • Epilepsy is a paradigmatic example of dynamical diseases (Milton and Jung, 2003).
      • Epilepsy itself is characterized by the occurrence of seizures (i.e. ictal activities). During epileptic seizures oscillatory activities emerge, which usually propagate through several distinct brain regions. The epileptic neural activities are generally displayed in the local field potential measured by local EEG. The epileptic activity occurs in a population of neurons when the membrane potentials of the neurons are "abnormally" synchronized.
      • The emergence of seizures was explained by computational models to interpret transitions between the two states of a bistable system, namely between normal and epileptic activity. However, the prediction and control of epileptic seizures became a hot, and controversial topic. It has become clear that dynamical models can predict seizure development and the administration of drugs could be designed accordingly providing novel therapeutic procedures for epileptic patients. Although, statistical analysis helps to predict the emergence of seizures, we still need to be cautious regarding its potential clinical applications.
    • There are many other diseases where the dynamical characteristics seem to be relevant and the concept of dynamical disease can be applied. Parkinson's diseases is also considered as dynamical disease. Later a method was suggested for detecting preclinical tremor in Parkinson's disease . It has been shown by using nonlinear dynamical theories and calculations that patients with Parkinson's disease had an EEG series with higher complexity than normal persons during the performance of complicated motor tasks. The explanatory hypothesis for this increased complexity states that additional superfluous cortical networks are recruited due to impaired inhibition.
    • Depression is also thought to be dynamical disease and now it seems to be clear that there is a correspondence between clinical and electro-physiological dimensions, i.e. clinical remission and brain dynamics reorganization.
    • Dynamical analysis of scalp EEG data is used to address the question whether schizophrenia originates from the reduction of functional connectivity among brain regions known as disconnection syndrome. It needs more analysis to see how impairment of global (interregional) and local (intraregional) connections contribute to the emergence of schizophrenia. A functional computational model suggests that schizophrenia might be the results of massive pruning of local connections in association cortex.
    • Nonlinear dynamical methods gave new insights to study the neurodynamics in Alzheimer's disease. The analysis suggested that there is a reduced complexity and level of synchronization in the EEG patterns presumably due to impaired connectivity among different cortical regions.
    • Dynamically evolving processes also can be captured in studies of migraine, i.e. migraine aura lasts less than an hour, and precedes headache, and it is characterized by visual and other distortions. Hallucinatory patterns have been modeled by reaction-diffusion models leading to waves as has been observed in other excitable media.

    Models of neuromodulatory effects: molecular screening versus drug design

    Dynamical models used for neuro-pharmacological purposes should integrate molecular interactions and large scale neural network dynamics. These models should be able to incorporate several different levels: details describing the kinetics of drug-receptor interactions ; changes in single cells activity and network dynamics in response to the modulator. They should give a faithful description of the system on each of these levels (see Fig. 1).

    Figure 1: The basic idea of dynamic neuropharmacology illustrated by the example of anxiety. The behavioral changes are associated with high theta power in the hippocampus. A detailed computational model of the septo-hippocampal system helps in understanding the effects of subunit-selective modulators of GABA-A synapses.

    Potential drugs, and more generally, neuromodulators exert diverse effects on the dynamic behavior of single neurons, synaptic transmission, neural networks and neural centers. Neuromodulators may alter the intrinsic properties of neurons, such as passive membrane properties, voltage activated ion channels directly or via second messenger pathways, and/or they affect synaptic transmission (Marder and Thirumalai, 2002). Such kinds of modifications occurring at microscopic level imply effects at macrsocopic level such as EEG, behavior etc.

    Kinetic models of synaptic events range in level from detailed chemical description of transmitter receptor to simplified representations (Destexhe et al., 1998). Since the modulation of synaptic transmission is a key event in neuropharamcology, models of synaptic transmission has a fundamental role in any studies of the interaction among transmitter, receptor and modulator (drug).

    The development of combined pharmacological, electrophysiological and computational techniques would make it possible to investigate the modulatory effects of putative drugs on synaptic currents, and consequently on local field potentials and even on behavioral states.

    Rhythmic brain activities are generated by local circuits composed of different types of neurons. These small circuits are able to show distinct spatiotemporal patterns under the influence of neuromodulators associated with different modes of operations (e.g., sleep states). The models used for neuro-pharmacological purposes should be able to generate all these patterns, reproducing the firing patterns of the different cell types.

    Neural activities reflect both normal and pathological states, and they may be subject to modulatory effects. Pharmacological modification of fast oscillations in the alpha and beta band (15-80 Hz) was reviewed by (Whittington et al., 2000). In particular, excitatory and inhibitory transmission, and potassium conductances are specific targets of pharmacological manipulation in these studies. It was shown that disruption of fast oscillations has a role in anesthesia, and cognitive or motor disorders. Although, it is well known that the allosteric modulators may influence (i.e. enhance or suppress) the electrical rhythmicity (and consequently they act as mood regulators) by changing the synaptic transmission, mechanisms of the effects are far from being clear.

    As another example, anxiolytic drugs diminish septo-hippocampal theta (4-12 Hz) activity contributing to their either therapeutic or unwanted side effects (Fig. 1). It has been proposed that the anxiolytic action of benzodiazepines is predominantly mediated by alpha-2/3 subunits containing GABA-A receptors, whereas alpha-1 subunit-containing GABA-A receptors mediate their sedative effects. Hajós et al. (2004) constructed a detailed computational model of the septo-hippocampal circuitry, including distinctively located GABA-A receptors containing different subunits. Their computational findings indicated different roles of distinctively located GABA-A receptors in theta generation. In a subsequent study (Ujfalussy et al., 2007) they modeled the effect of subunit selective GABA-A modulators on medial-septal theta-pacemaker neurons. They showed that zolpidem (alpha-1 selective) prevents theta by inhibiting the pacemaker neurons, while L-838,417 (alpha-2) leaves the pacemaker network intact detaining theta activity in the hippocampus itself.

    A new computational method integrating conductance-based compartmental modeling technique and a detailed kinetic description of pharmacological modulation of transmitter - receptor interactions to test the electrophysiological and behavioral effects of potential drugs might be a conceptually new perspective of computational molecular screening. An inverse method is also suggested to design procedures that pharmacologically modulate a given neural system in order to realize a prescribed electrical temporal pattern.


    Major drawback comes from the natural complexity of this method: dynamic neuropharmacology build on a large number of assumptions connecting the molecular to the behavioral level. Deductions might have limited validity without having tools to test these assumptions.

    There are many questions to be clarified on each level: Dynamical neural correlates of psychiatric disorders have been identified only several specific cases. Computational models generally are limited to describe particular experiments only, neglecting important features. The challenge is to construct models sufficiently good to capture the basic properties of the neural tissue integrating different levels of neural organization from the molecular to the system one. Dynamic neuropharmacology could be a complex, efficient and relatively cheap method for drug screening.


    • Aradi I and Érdi P (2006): Computational neuropharmacology: dynamical approaches in drug discovery. Trends in Pharmacological Sciences, 27(5), 240-243
    • Buzsáki G (2006): Rhythms of the Brain Oxford University Press
    • Destexhe A, Mainen ZF and Sejnowski TJ (1998): Kinetic Models of Synaptic Transmission. in Methods in Neuronal Modeling (2nd edn) (Koch, C., Segev, I., eds), pp. 1-30, MIT Press, Cambridge, MA
    • Ekins S and Wang B (2006): Computer Applications in Pharmaceutical Research and Development (Wiley Series in Drug Discovery and Development)
    • Érdi P, Kiss T, Tóth J, Ujfalussy B and Zalányi L (2006): From systems biology to dynamical neuropharmacology: Proposal for a new methodology. IEE Proceedings in Systems Biology 153(4), 299-308
    • Hajós M, WE Hoffmann, Orbán G, Kiss T and Érdi P (2004): Modulation of septo-hippocampal theta activity by GABAA receptors: Experimental and computational approach. Neuroscience 126(3), 599-610
    • Marder, E. and Thirumalai, V. (2002): Cellular, synaptic and network effects of neuromodulation. Neural Networks 15, 479-493
    • Ujfalussy B, Kiss T, Orbán G, WE Hoffmann, Érdi P and Hajós M. (2007): Pharmacological and Computational Analysis of alpha-subunit Preferential GABA-A Positive Allosteric Modulators on the Rat Septo-Hippocampal Activity. Neuropharmacology 52(3), 733-743
    • Whittington MA, Faulkner HJ, Doheny HC and Traub RD. (2000): Neuronal fast oscillations as a target site for psychoactive drugs. Pharmacol Ther. 86(2), 171-90

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