title: Understanding responder neurobiology in schizophrenia using a quantitative systems pharmacology model: application to iloperidone.
abstract: The concept of targeted therapies remains a holy grail for the pharmaceutical drug industry for identifying responder populations or new drug targets. Here we provide quantitative systems pharmacology as an alternative to the more traditional approach of retrospective responder pharmacogenomics analysis and applied this to the case of iloperidone in schizophrenia. This approach implements the actual neurophysiological effect of genotypes in a computer-based biophysically realistic model of human neuronal circuits, is parameterized with human imaging and pathology, and is calibrated by clinical data. We keep the drug pharmacology constant, but allowed the biological model coupling values to fluctuate in a restricted range around their calibrated values, thereby simulating random genetic mutations and representing variability in patient response. Using hypothesis-free Design of Experiments methods the dopamine D4 R-AMPA (receptor-alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) receptor coupling in cortical neurons was found to drive the beneficial effect of iloperidone, likely corresponding to the rs2513265 upstream of the GRIA4 gene identified in a traditional pharmacogenomics analysis. The serotonin 5-HT3 receptor-mediated effect on interneuron gamma-aminobutyric acid conductance was identified as the process that moderately drove the differentiation of iloperidone versus ziprasidone. This paper suggests that reverse-engineered quantitative systems pharmacology is a powerful alternative tool to characterize the underlying neurobiology of a responder population and possibly identifying new targets.
authors.affiliation: In Silico Biosciences, Berwyn, PA, USA Perelman School of Medicine, University of Pennsylvania, PA, USA Hugo-Geerts@In-Silico-Biosciences.com.
authors.forename: Hugo
authors.initials: H
authors.lastname: Geerts
authors.affiliation: In Silico Biosciences, Berwyn, PA, USA Oregon Health and Science University, Portland, OR, USA.
authors.forename: Patrick
authors.initials: P
authors.lastname: Roberts
authors.affiliation: In Silico Biosciences, Berwyn, PA, USA.
authors.forename: Athan
authors.initials: A
authors.lastname: Spiros
authors.affiliation: Department of Psychiatry, University of California, Irvine, CA, USA.
authors.forename: Steven
authors.initials: S
authors.lastname: Potkin
mesh_terms: D014150:Antipsychotic Agents / Q000494:pharmacology*; D003198:Computer Simulation; D005838:Genotype; D006801:Humans; D007555:Isoxazoles / Q000494:pharmacology*; D008954:Models, Biological; D058990:Molecular Targeted Therapy; D009446:Neurobiology / Q000379:methods; D010597:Pharmacogenetics; D010879:Piperazines / Q000494:pharmacology; D010880:Piperidines / Q000494:pharmacology*; D012559:Schizophrenia / Q000188:drug therapy* / Q000503:physiopathology; D013844:Thiazoles / Q000494:pharmacology
chemical_list: D014150:Antipsychotic Agents; D007555:Isoxazoles; D010879:Piperazines; D010880:Piperidines; D013844:Thiazoles; C092292:ziprasidone; C081732:iloperidone
keywords: Quantitative systems pharmacology; responder population; targeted therapies