A computer-based quantitative systems pharmacology model of negative symptoms in schizophrenia: exploring glycine modulation of excitation-inhibition balance.
Although many antipsychotics can reasonably control positive symptoms in schizophrenia, patients' return to society is often hindered by negative symptoms and cognitive deficits. As an alternative to animal rodent models that are often not very predictive for the clinical situation, we developed a new computer-based mechanistic modeling approach. This Quantitative Systems Pharmacology approach combines preclinical basic neurophysiology of a biophysically realistic neuronal ventromedial cortical-ventral striatal network identified from human imaging studies that are associated with negative symptoms. Calibration of a few biological coupling parameters using a retrospective clinical database of 34 drug-dose combinations resulted in correlation coefficients greater than 0.60, while a robust quantitative prediction of a number of independent trials was observed. We then simulated the effect of glycine modulation on the anticipated clinical outcomes. The quantitative biochemistry of glycine interaction with the different NMDA-NR2 subunits, neurodevelopmental trajectory of the NMDA-NR2B in the human schizophrenia pathology, their specific localization on excitatory vs. inhibitory interneurons and the electrogenic nature of the glycine transporter resulted in an inverse U-shape dose-response with an optimum in the low micromolar glycine concentration. Quantitative systems pharmacology based computer modeling of complex humanized brain circuits is a powerful alternative approach to explain the non-monotonic dose-response observed in past clinical trial outcomes with sarcosine, D-cycloserine, glycine, or D-serine or with glycine transporter inhibitors. In general it can be helpful to better understand the human neurophysiology of negative symptoms, especially with targets that show non-monotonic dose-responses.
Computational Neuropharmacology, In Silico Biosciences, Inc. Berwyn, PA, USA.
Athan
A
Spiros
Computational Neuropharmacology, In Silico Biosciences, Inc. Berwyn, PA, USA ; Department of Biomedical Engineering, Oregon Health and Science University Portland, OR, USA.
Patrick
P
Roberts
Computational Neuropharmacology, In Silico Biosciences, Inc. Berwyn, PA, USA ; Department of Laboratory Pathology, Perelman School of Medicine, University of Pennsylvania Philadelphia, PA, USA.
Hugo
H
Geerts
computer model; dose-response relationship; drug; glycine; negative symptoms; schizophrenia