Method and apparatus for computer modeling of the interaction between and among cortical and subcortical areas in the human brain for the purpose of predicting the effect of drugs in psychiatric and cognitive diseases

• US 8,332,158 B2
• Filed: 03/06/2012
• Issued: 12/11/2012
• Est. Priority Date: 11/10/2005
• Status: Active Grant

• Current Assignee: In-Silico Sciences Incorporated
• Original Assignee: In-Silico Sciences Incorporated
• Inventors: Geerts, Hugo, Spiros, Athan
• Primary Examiner(s): Riggs, II, Larry D
• Application Number:
  US13/412,626
• Publication Number:
  US 20120232862A1
• Time in Patent Office: 280 Days
• Field of Search: None
• US Class Current: 702/19
• CPC Class Codes: G09B 23/28 for medicine
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A computer model of a diseased human brain includes inputs representing a drug and outputs representing the clinical effect of that drug on psychiatric and cognitive diseases. Diseases that can be modeled include psychiatric disorders, such as schizophrenia, bipolar disorder, major depression, ADHD, autism, obsessive-compulsive disorder, substance abuse and cognitive deficits therein and neurological disorders such as Alzheimer'"'"'s disease, Mild Cognitive impairment, Parkinson'"'"'s disease, stroke, vascular dementia, Huntington'"'"'s disease, epilepsy and Down syndrome. The computer model preferably uses the biological state of interactions between and among cortico and subcortical areas of the human brain, to define the biological processes related to the biological state of the generic synapse model, the striatum, Locus Coeruleus, Dorsal raphe, hippocampus, amygdala and cortex, as well as certain mathematical relationships related to interactions among biological variables associated with the biological processes and to correlations between the biological variables and clinical effects on a clinical scale.

Claims

1. A method for predicting clinical effects of a proposed pharmacological therapy for treating a known psychiatric disease associated with neuronal circuits within and among cortical and sub-cortical brain regions, comprising the steps:

simulating a plurality of interconnected neuronal circuits within and among different regions of a human brain afflicted with said psychiatric disease, each of the simulated circuits being separately modeled with the use of actual data about the psychiatric disease, wherein

said different regions include the cortex, and at least one of the striatum, hippocampus, amygdala, ventral tegmentum area, locus coeruleus, dorsal raphe and substantia nigra, and

the simulated circuits include

simulated interactions within and among said different brain regions through neuronal projections, and

simulated synaptic transmissions incorporating action potentials and their effect on receptors of at least one of the following types;

dopaminergic, serotonergic, noradrenergic, cholinergic, glutamatergic and GABA-ergic;

simulating the functional effects of each of a plurality of known pharmacological therapies on the receptors in said simulated circuits to thereby produce corresponding simulated biological effects on the interconnected neuronal circuits;

deriving a regression equation quantitatively correlating said simulated biological effects of said known therapies with the known clinical effects of said therapies on a clinical scale related to said known psychiatric disease;

adjusting at least some parameters associated with said simulated circuits in order to develop modified simulated circuits resulting in a regression equation output having increased confidence measures of correlation between said simulated biological effects and said known clinical effects;

simulating the functional effects of a proposed pharmacological therapy on the receptors in said modified simulated circuits to thereby produce corresponding simulated biological effects on the interconnected neuronal circuits and inputting said simulated biological effects of said proposed pharmacological therapy into said regression equation; and

outputting from the regression equation a predicted quantitative clinical effect of the proposed pharmacological therapy on said clinical scale, together with a corresponding confidence measure for said predicted quantitative clinical effect,

wherein said known psychiatric disease is schizophrenia, and

the steps are performed in a programmed computer.

2. The method of claim 1, further comprising the step of using the simulated biological effects of said known therapies from the modified simulated circuits and the known clinical effects of said known therapies to derive a revised regression equation.

3. The method of claim 1, wherein the proposed pharmacological therapy is selected from a plurality of cerebra-active therapeutic agents.

4. The method of claim 1, wherein the proposed pharmacological therapy includes a combination of different pharmacological agents each having a respective proposed dose.

5. The method of claim 1, wherein the proposed pharmacological therapy includes a proposed dose of least one pharmacological agent having unknown clinical effects on said clinical scale.

6. The method of claim 1, wherein the proposed pharmacological therapy includes a proposed dose of at least one known pharmacological agent.

7. The method of claim 1, wherein at least some of the actual data used to model the simulated neuronal circuits includes known circadian profiles of physiological parameters to thereby vary at least some of the simulated biological effects over the course of a 24 hour day.

8. The method of claim 1, wherein at least some of the actual data used to model the parameters of the simulated neuronal circuits is specific to one or more genotypes, whereby the predicted clinical effects will be applicable to those specific genotypes.

9. The method of claim 1, wherein at least some of the actual data used to model functional effects of pharmacological therapies on the receptors in the simulated neuronal circuits reflects drug plasma levels.

10. The method of claim 9, wherein at least some of the actual data used to model functional effects of pharmacological therapies on the receptors in the simulated neuronal circuits reflects distribution of receptor densities.

11. The method of claim 10, further comprising the steps:

defining a sample of virtual patients each having a respective plasma and receptor density profile; and

for each virtual patient in the sample, predicting a respective quantitative clinical effect of the proposed pharmacological therapy.

12. The method of claim 1, wherein the predicted quantitative clinical effect is measured on more than one clinical scale.

13. The method of claim 12, wherein the predicted quantitative clinical effect includes clinical efficacy.

14. The method of claim 12, wherein the predicted quantitative clinical effect includes side-effect liability.

15. The method of claim 12, wherein the predicted quantitative clinical effect includes both clinical efficacy and side-effect liability.

16. The method of claim 1, further comprising the steps:

separately predicting a respective quantitative clinical effect on the same clinical scale for each of several proposed pharmacological therapies; and

using those predicted clinical effects to rank the proposed pharmacological therapies.

17. The method of claim 1, further comprising the steps:

varying receptor activation states in simulated neuronal synapses;

selecting receptor activation states which result in a predicted clinical effect above a predetermined threshold;

deriving a desired pharmacological target profile from the selected receptor activation states; and

outputting a predicted clinical effect for a pharmacological therapy having the desired pharmacological target profile.