ArticlePloS one2012
Blinded prospective evaluation of computer-based mechanistic schizophrenia disease model for predicting drug response.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making.Clinical pharmacology and therapeutics · 2025Review
- Quantitative Systems Pharmacology Development and Application in Neuroscience.Handbook of experimental pharmacology · 2025Review
- Toward Predicting Impact of Common Genetic Variants on Schizophrenia Clinical Responses With Antipsychotics: A Quantitative System Pharmacology Study.Frontiers in neuroscience · 2021Article
- Learning from amyloid trials in Alzheimer's disease. A virtual patient analysis using a quantitative systems pharmacology approach.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2020Article
- The nature of bradykinesia in schizophrenia treated with antipsychotics.Psychiatry research · 2019Article
- Predicting parkinsonism side-effects of antipsychotic polypharmacy prescribed in secondary mental healthcare.Journal of psychopharmacology (Oxford, England) · 2018Article
- Impact of amyloid-beta changes on cognitive outcomes in Alzheimer's disease: analysis of clinical trials using a quantitative systems pharmacology model.Alzheimer's research & therapy · 2018Article
- A Humanized Clinically Calibrated Quantitative Systems Pharmacology Model for Hypokinetic Motor Symptoms in Parkinson's Disease.Frontiers in pharmacology · 2016Article
- Assessing the synergy between cholinomimetics and memantine as augmentation therapy in cognitive impairment in schizophrenia. A virtual human patient trial using quantitative systems pharmacology.Frontiers in pharmacology · 2015Article
- Prediction of Efficacy of Vabicaserin, a 5-HT2C Agonist, for the Treatment of Schizophrenia Using a Quantitative Systems Pharmacology Model.CPT: pharmacometrics & systems pharmacology · 2014Article
- Basal ganglia volume in unmedicated patients with schizophrenia is associated with treatment response to antipsychotic medication.Psychiatry research · 2014Article
- A computer-based quantitative systems pharmacology model of negative symptoms in schizophrenia: exploring glycine modulation of excitation-inhibition balance.Frontiers in pharmacology · 2014Article
- Quantitative systems pharmacology as an extension of PK/PD modeling in CNS research and development.Journal of pharmacokinetics and pharmacodynamics · 2013Article
- A quantitative system pharmacology computer model for cognitive deficits in schizophrenia.CPT: pharmacometrics & systems pharmacology · 2013Article
- A strategy for developing new treatment paradigms for neuropsychiatric and neurocognitive symptoms in Alzheimer's disease.Frontiers in pharmacology · 2013Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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.
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Registered trials
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