Blinded Prospective Evaluation of Computer-Based Mechanistic Schizophrenia Disease Model for Predicting Drug Response

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 D2 antagonist and ocaperidone, a very high affinity dopamine D2 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.

Learning from the past and looking to the future: Emerging perspectives for improving the treatment of psychiatric disorders

“…Now it is being applied in a translational framework to predict the beneficial and adverse effects of novel drugs in humans. "Quantitative Systems Pharmacology" exploits preclinical data from in vitro/in vivo studies ENP25 SV CHAPTER TEXT AND CITATION TO SUBMIT AND SEND 14 12 14 38 38 together with human neuroanatomical and imaging observations to model the influence of selective and multi-target drugs on neural networks, and hence to predict therapeutic efficacy and side-effects in early clinical trials (Geerts, 2011;Geerts et al, 2012;Huang et al, 2011;Schneider, 2010). One example is provided by the 5-HT2C agonist, vabicaserin.…”

“…First, perhaps the best-known issue in trials of depression and schizophrenia is the increasing difficulty of separating drug from placebo which, in the context of a modern clinical trial, resembles a form of "psychotherapy" with multifarious neurobiological effects (Alphs et al, 2012;Enck et al, 2013;Melander et al, 2008;Benedetti, 2014). Ironically, this advantage will not usually be available in the real-world of treatment (Enck et al, 2013).…”

“…Strategies for minimising the placebo response (which can be regionally variable in a multi-centric trial) include: greater patient homogeneity at recruitment; rigorous patient selection for disease/avoidance of borderline subjects; complementary clinician/patient readouts of efficacy; lead-in arms; and computer simulation of placebo dropouts. Since, they have been expertly discussed elsewhere, they do not need to be evoked in detail here (Alphs et al, 2012;De Bodinat et al, 2010;Enck et al, 2013;Gelenberg et al, 2008;Pilla Reddy et al, 2012). Nonetheless, it is worth briefly evoking one surprising and recently-identified factor detrimental to placebo-controlled trials, particularly in the US.…”

Big data to smart data in Alzheimer's disease: Real‐world examples of advanced modeling and simulation

“…The predictive power of QSP in schizophrenia has been demonstrated with the phase II predictions of JNJ37822681, a low-affinity selective D 2 R antagonist and ocaperidone, a high-affinity multi-target D 2 antagonist [28]. In this study, the modelers were kept blinded to the actual clinical outcome and the QSP model correctly predicted a high-motor side-effect liability that was not observed in preclinical animal models and that led to the demise of the clinical development project.…”

Prediction of Efficacy of Vabicaserin, a 5‐HT2C Agonist, for the Treatment of Schizophrenia Using a Quantitative Systems Pharmacology Model

“…To accomplish this, the previously developed quantitative systems pharmacology model of PANSS 7,8,9 was extended to incorporate 5-HT 2C receptor effects in three ways: (i) effect on dopamine firing frequency, (ii) effect on cholinergic striatal interneurons, and (iii) effect on γ-aminobutyric acid (GABA) interneurons. The model was then recalibrated to determine the best coupling parameters for these mechanisms.…”