Methods and systems for drug screening and computational modeling based on biologically realistic neurons

Abstract

A method for screening a test composition for potential efficacy in treatment of a disorder includes a first computer model representative of a volume of disease-afflicted neural tissue comprising biologically realistic neurons exposed to the test composition; and providing an initial excitation to the first computer model. Following a selected computation interval, a first outcome is determined. The first outcome indicates a response of the first computer model to the initial excitation and indicates whether the test composition has the potential to be effective in treating the disorder.

1 . A method comprising:
 creating a first computer model representative of a volume of disease-afflicted neural tissue exposed to a test composition, the model of neural tissue including data representing a plurality of interconnected neurons, wherein for each neuron, the model quantitatively simulates spatially compartmentalized conductance of a plurality of ion channels and spatially compartmentalized activity of a plurality of receptors;   providing an initial excitation to the first computer model; and   determining a first outcome indicative of a response of the first computer model to the initial excitation, the first outcome being representative of the efficacy of the test composition for treating the disorder.   
     
     
         2 . The method of  claim 1 , wherein creating a first computer model comprises simulating the presence of at least one of an ion channel antagonist, an ion channel agonist, a receptor antagonist, and a receptor agonist. 
     
     
         3 . The method of  claim 1 , wherein creating a first computer model comprises modeling neurons having an anatomically realistic dendritic arborization. 
     
     
         4 . The method of  claim 1 , wherein creating a first computer model further comprises modeling neurons having ion channel and receptor distributions that vary along the dendro-somatic axes thereof, the variation being based on experimental data. 
     
     
         5 . The method of  claim 1 , further comprising:
 creating a second computer representative of the volume of disease-afflicted neural tissue, wherein the volume is isolated from the test composition, the neural tissue comprising a plurality of interconnected neurons, wherein for each neuron, the model quantitatively simulates spatially compartmentalized conductance of a plurality of ion channels and spatially compartmentalized activity of a plurality of receptors;   providing the initial excitation to the second computer model;   determining a second outcome indicative of a response of the second computer model to the initial excitation; and   classifying the test composition as a candidate composition for treating the disorder on the basis of a difference between the first and second outcomes.   
     
     
         6 . The method of  claim 5 , further comprising:
 creating a third computer model representative of the volume of neural tissue, wherein the neural tissue is free of the disease and comprises a plurality of interconnected neurons, wherein for each neuron, the model quantitatively simulates spatially compartmentalized conductance of a plurality of ion channels and spatially compartmentalized activity of a plurality of receptors;   providing the initial excitation to the third computer model; and   determining a third outcome indicative of a response of the third computer model to the initial excitation; and   classifying the test composition as a candidate composition for treating the disorder on the basis of a similarity between the first and third outcomes.   
     
     
         7 . The method of  claim 6 , further comprising:
 determining a first epoch frequency associated with the first outcome;   determining a test epoch frequency associated with one of the second and third outcomes; and   classifying the test composition as a candidate composition for treatment of the disorder of the basis of a relationship between the first epoch frequency and the test epoch frequency.   
     
     
         8 . The method of  claim 1 , wherein creating a first computer model comprises:
 defining a model of a volume of neural tissue, the model having a population profile of biologically realistic neurons;   altering the model to simulate lesioning caused by the disorder; and   altering the model to simulate an effect of the test composition on the neural tissue.   
     
     
         9 . The method of  claim 1 , further comprising selecting the volume to be a volume of a hippocampus. 
     
     
         10 - 16 . (canceled) 
     
     
         17 . A system for screening a test composition, the system comprising:
 a processor;   a storage medium coupled to the processor, the storage medium encoding software that, when executed, causes the processor to:
 generate a computational network model having biologically realistic neurons and manifesting at least one of a neuropsychiatric or a neurological disorder; 
 incorporate pharmacological data of the test composition indicative of an effect on the biologically realistic neurons into the computational network model; and 
 predict, based on an outcome of incorporating the pharmacological data, the efficacy of the test composition for treatment of the disorder. 
   
     
     
         18 . The system of  claim 17 , wherein the model is configured to quantitatively simulate spatially compartmentalized conductance of a plurality of ion channels and spatially compartmentalized activity of a plurality of receptors in the biologically realistic neurons. 
     
     
         19 . The system of  claim 17 , wherein the neurons have anatomically realistic dendritic arborizations. 
     
     
         20 . The system of  claim 17 , wherein the model comprises an interface to receive experimental data indicative of ion channel and receptor distributions variation along the dendro-somatic axis of actual neurons. 
     
     
         21 . The system of  claim 17 , wherein the software, when executed, causes the processor to generate a computational network model further comprising changes in second messenger concentrations. 
     
     
         22 . The system of  claim 17 , wherein the software further causes the processor to:
 generate a computational network model manifesting a plurality of normal characteristics of human behavior; and   introduce physiological lesions to the normal computational network model consistent with suspected neuropathology of the neuropsychiatric or neurological disorder.   
     
     
         23 . The system of  claim 17 , wherein the software further causes the processor to add functional characteristics to generate a model of the medical disorder by degrading neurons of the computational network model in a manner analogous to the degradation of neurons in humans afflicted with the disorder. 
     
     
         24 . The system of  claim 17 , wherein the software causes the processor to apply the pharmacological data of the test composition by causing the processor to model effects of the test composition on ion channels. 
     
     
         25 . (canceled) 
     
     
         26 . A method for determining a potential efficacy of a test composition for treating a neural disorder, the method comprising:
 creating a first computer model representative of a disease-afflicted neuron exposed to the test composition, the model being configured to quantitatively simulate spatially compartmentalized conductance of a plurality of ion channels, spatially compartmentalized activity of a plurality of receptors, and an effect of the test composition on conductance of the plurality of ion channels;   providing an initial excitation to the first computer model;   determining a first outcome indicative of a response of the first computer model to the initial excitation, the first outcome being representative of the efficacy of the composition to treat the neural disorder.   
     
     
         27 . The method of  claim 26 , further comprising:
 creating a second computer model representative of the disease-afflicted neuron, wherein the neuron is isolated from the test composition;   providing the initial excitation to the second computer model; and   determining a second outcome indicative of a response of the second computer model to the initial excitation, wherein a difference between the first and second outcomes indicates that the test composition is a candidate drug for treating the neural disorder.   
     
     
         28 . The method of  claim 26 , further comprising:
 creating a third computer model representative of a neuron free of the disease;   providing the initial excitation to the second computer model; and   determining a third outcome indicative of a response of the third computer model to the initial excitation, wherein a similarity between the first and third outcomes indicates that the test composition is a candidate drug for treating the neural disorder.