Not all partial dopamine D2 receptor agonists are the same in treating schizophrenia. Exploring the effects of bifeprunox and aripiprazole using a computer model of a primate striatal dopaminergic synapse

Species differences in physiology and unique active human metabolites contribute to the limited predictive value of preclinical rodent models for many central nervous system (CNS) drugs. In order to explore possible drivers for this translational disconnect, we developed a computer model of a dopaminergic synapse that simulates the competition among three agents and their binding to pre- and postsynaptic receptors, based on the affinities for their targets and their actual concentrations. The model includes presynaptic autoreceptor effects on neurotransmitter release and modulation by presynaptic firing frequency and is calibrated with actual experimental data on free dopamine levels in the striatum of the rodent and the primate. Using this model, we simulated the postsynaptic dopamine D2 receptor activation levels of bifeprunox and aripiprazole, two relatively similar dopamine D2 receptor agonists. The results indicate a substantial difference in dose–response for the two compounds when applying primate calibration parameters as opposed to rodent calibration parameters. In addition, when introducing the major human and rodent metabolites of aripiprazole with their specific pharmacological activities, the model predicts that while bifeprunox would result in a higher postsynaptic D2 receptor antagonism in the rodent, aripiprazole would result in a higher D2 receptor antagonism in the primate model. Furthermore, only the highest dose of aripiprazole, but not bifeprunox, reaches postsynaptic functional D2 receptor antagonism similar to 4 mg haloperidol in the primate model. The model further identifies a limited optimal window of functionality for dopamine D2 receptor partial agonists. These results suggest that computer modeling of key CNS processes, using well-validated calibration paradigms, can increase the predictive value in the clinical setting of preclinical animal model outcomes

Investigating models for cross-linker mediated actin filament dynamics

Actin, a major component of the cytoskeleton, is responsible for the shape and structural properties of many eucaryotic cells. Actin filaments are made of monomers which bind in a single strand. Cross-linkers bind two filaments together and influence the relative orientation of the filaments. Cross-linkers such as a-actinin favor parallel alignment of filaments, while others such as actin-binding protein favor orthogonal alignment. The filament length, concentration of cross-linkers and cross-linker association-dissociation rates all affect the types of network that form. The resulting network dictates the structural properties of the cell. In this thesis I study how filament length, concentration of cross-linkers and crosslinker association-dissociation rates influence actin network development. I use existing models and create new models to explore these interactions. Integro-partial differential equations and other techniques are used to model the system. Using experimentally determined biological parameters, I predict the geometry and distribution of the resulting network. Computer simulations verify the model predictions. I compare these predictions with experimental results. Increasing the cross-linker concentration first strengthens the isotropic network, but then, beyond a transition, forces the network to be inhomogeneous and more fluid-like. As the cross-linker concentration increases even further, more filaments bind to the network, resulting in a stronger, more solid actin solution. Decreasing the cross-linker dissociation rate constant has the same effects as increasing the cross-linker concentration. Finally, I find that changing the filament length greatly affects rates of diffusion, influencing instabilities. Increasing the filament length, favors alignment and clustering, as well as formation of bundles. Filament length influences the spacing between clusters. Increasing the length, forces clusters to be spaced further apart until they eventually disappear. I also find that there is an optimal length for bundle formation. When considering a distribution of filament lengths, I expect to see a wider dispersal of filaments near the bundle transition point.Science, Faculty ofMathematics, Department ofGraduat

Testing A Model for the Dynamics of Actin Structures with Biological Parameter Values

A simple mathematical model for the dynamics of network-bundle transitions in actin filaments has been proposed previously (Civelekoglu and Edelstein-Keshet, 1994) and some of its mathematical properties have been described (Mogilner and Edelstein-Keshet, 1995; Mogilner and Edelstein-Keshet, 1996). Other models in this class have since been considered and investigated mathematically (Geigant and Stoll, 1996; Geigant et al., 1997). In this paper, we have made the first steps in connecting parameters in the model with biologically measurable quantities such as published values of rate constants for filament-crosslinker association. We describe how this connection was made, and give some preliminary numerical simulation results for the behavior of the model under biologically realistic parameter regimes. A key result is that filament length influences the bundle-network transition. 2 INTRODUCTION Actin filaments are an essential part of the cytoskeleton, the cohesive meshwork of filamen..

Blinded prospective evaluation of computer-based mechanistic schizophrenia disease model for predicting drug response.

The tremendous advances in understanding the neurobiological circuits involved in schizophrenia have not translated into more effective treatments. An alternative strategy is to use a recently published 'Quantitative Systems Pharmacology' computer-based mechanistic disease model of cortical/subcortical and striatal circuits based upon preclinical physiology, human pathology and pharmacology. The physiology of 27 relevant dopamine, serotonin, acetylcholine, norepinephrine, gamma-aminobutyric acid (GABA) and glutamate-mediated targets is calibrated using retrospective clinical data on 24 different antipsychotics. The model was challenged to predict quantitatively the clinical outcome in a blinded fashion of two experimental antipsychotic drugs; JNJ37822681, a highly selective low-affinity dopamine D(2) antagonist and ocaperidone, a very high affinity dopamine D(2) antagonist, using only pharmacology and human positron emission tomography (PET) imaging data. The model correctly predicted the lower performance of JNJ37822681 on the positive and negative syndrome scale (PANSS) total score and the higher extra-pyramidal symptom (EPS) liability compared to olanzapine and the relative performance of ocaperidone against olanzapine, but did not predict the absolute PANSS total score outcome and EPS liability for ocaperidone, possibly due to placebo responses and EPS assessment methods. Because of its virtual nature, this modeling approach can support central nervous system research and development by accounting for unique human drug properties, such as human metabolites, exposure, genotypes and off-target effects and can be a helpful tool for drug discovery and development

Predicting parkinsonism side effects of antipsychotic polypharmacy prescribed in secondary mental healthcare

Supplemental material, jop-2018-3453-File002 for Predicting parkinsonism side-effects of antipsychotic polypharmacy prescribed in secondary mental healthcare by Giouliana Kadra, Athan Spiros, Hitesh Shetty, Ehtesham Iqbal, Richard D Hayes, Robert Stewart and Hugo Geerts in Journal of Psychopharmacology</p