A computer-based quantitative systems pharmacology model of negative symptoms in schizophrenia: exploring glycine modulation of excitation-inhibition balance

IUPHAR review: Computational Psychiatry 2.0. A new tool for supporting combination therapy of psychopharmacology with neuromodulation in schizophrenia

Recent clinical trial successes in schizophrenia with non-dopaminergic agents have rejuvenated the field after a long period of unsuccesfull attempts. At the same time, non-invasive neurostimulation has been increasingly applied in other mental health disorders while a few studies have been performed in schizophrenia. The time has arrived to consider combining psychotherapy with neuromodulation. However, a systematic approach to optimize trial designs is needed. "Computational Psychiatry" has been defined as computational neuroscience modeling using biophysically and anatomically realistic representations of key brain areas based on neuroimaging data and biological knowledge. In this position paper, we will expand this concept to include modeling drug exposure and pharmacology in combination with non-invasive neuromodulation. This computational approach can be used to optimize the impact of psychotherapy and active neuromodulation. This computational platform generates a new in silico biomarker, the "information bandwidth", that might be related to clinical outcomes in schizophrenia. This is based on the assumption that the information processing capacity of the human brain can be represented by a measure of the entropy that quantifies the level of uncertainty associated with the brain processes. Previously we have shown that this readout in a computational neuroscience model of the closed cortical-striatal-thalamocortical loop is highly correlated with clinical changes in positive symptoms after antipsychotic treatment. In this paper we will present a strategy on how this expanded Computational Psychiatry approach can support optimization of clinical trial design combining neuromodulation with psychopharmacology, as well as the understanding and mitigating of the placebo response.

Quantitative Systems Pharmacology Development and Application in Neuroscience

Successful clinical development of therapeutics in neurology and psychiatry is challenging due to the complexity of the brain, the lack of validated surrogate markers and the nature of clinical assessments. On the other hand, tremendous advances have been made in unraveling the neurophysiology of the human brain thanks to technical developments in noninvasive biomarkers in both healthy and pathological conditions.Quantitative systems pharmacology (QSP) aims to integrate this increasing knowledge into a mechanistic model of key biological processes that drive clinical phenotypes with the objective to support research and development of successful therapies. This chapter describes both modeling of molecular pathways resulting in measurable biomarker changes, similar to modeling in other indications, as well as extrapolating in a mechanistic way these biomarker outcomes to predict changes in relevant functional clinical scales.Simulating the effect of therapeutic interventions on clinical scales uses the modeling methodology of computational neurosciences, which is based on the premise that human behavior is driven by firing activity of specific neuronal networks. While driven by pathology, the clinical behavior can also be influenced by various medications and common genotype variants. To address this occurrence, computational neuropharmacology QSP models can be developed and, in principle, applied as virtual twins, which are in silico clones of real patients.Overall, central nervous system (CNS) QSP is an important additional tool for supporting research and development from the preclinical stage to post-marketing studies and clinical practice. Overall, CNS QSP is an important additional tool for supporting research and development from the preclinical stage to post-marketing studies and clinical practice.

Ketosis regulates K+ ion channels, strengthening brain-wide signaling disrupted by age

Aging is associated with impaired signaling between brain regions when measured using resting-state fMRI. This age-related destabilization and desynchronization of brain networks reverses itself when the brain switches from metabolizing glucose to ketones. Here, we probe the mechanistic basis for these effects. First, we confirmed their robustness across measurement modalities using two datasets acquired from resting-state EEG (Lifespan: standard diet, 20-80 years, N = 201; Metabolic: individually weight-dosed and calorically-matched glucose and ketone ester challenge,μ a g e = 26.9 ± 11.2 years , N = 36). Then, using a multiscale conductance-based neural mass model, we identified the unique set of mechanistic parameters consistent with our clinical data. Together, our results implicate potassium (K+) gradient dysregulation as a mechanism for age-related neural desynchronization and its reversal with ketosis, the latter finding of which is consistent with direct measurement of ion channels. As such, the approach facilitates the connection between macroscopic brain activity and cellular-level mechanisms.

Efficacy and safety of add-on sodium benzoate, a D-amino acid oxidase inhibitor, in treatment of schizophrenia: A systematic review and meta-analysis

BACKGROUND

The role of sodium benzoate, an NMDA receptor enhancer, in schizophrenia has been evaluated in a few clinical trials, but results are contradictory and inconclusive. The present meta-analysis has evaluated the efficacy and safety of add-on sodium benzoate for the treatment of schizophrenia.

METHODS

After performing a literature search on MEDLINE/PubMed, Scopus, Cochrane databases and International Clinical Trial Registry Platform, reviewers assessed eligibility and extracted data from four relevant articles. PRISMA guidelines were followed in the selection, analysis, and reporting of findings. The random-effect model was used to estimate effect size. Quality assessment was done using the risk of bias assessment tool, and sensitivity analysis was done in case of high heterogeneity.

RESULTS

Add-on sodium benzoate can improve positive symptoms of schizophrenia significantly (MD: -1.87; 95%CI: -3.25 to -0.48; p = 0.008) but had no significant favourable effect on negative symptoms (p = 0.84), general psychopathology (p = 0.49), and total PANSS score (p = 0.19) over the control. There was no significant improvement in GAF (p = 0.43), CGI (p = 0.58), cognitive function (p = 0.46) and quality of life (p = 0.73). Extrapyramidal symptoms were significantly higher (MD: 0.39; 95% CI:0.19-0.60; p = 0.0002) in the sodium benzoate group in comparison to the control group; however, there was no significant difference in respect to other adverse events.

CONCLUSION

Sodium benzoate can improve the positive symptoms of schizophrenia without any beneficial effect on other symptomatology, cognition, quality of life and functioning. Further studies are needed to evaluate long-term efficacy, safety and use in specific subgroups of patients.

Glutamate in schizophrenia: Neurodevelopmental perspectives and drug development

Research into the neurobiological processes that may lead to the onset of schizophrenia places growing emphasis on the glutamatergic system and brain development. Preclinical studies have shown that neurodevelopmental, genetic, and environmental factors contribute to glutamatergic dysfunction and schizophrenia-related phenotypes. Clinical research has suggested that altered brain glutamate levels may be present before the onset of psychosis and relate to outcome in those at clinical high risk. After psychosis onset, glutamate dysfunction may also relate to the degree of antipsychotic response and clinical outcome. These findings support ongoing efforts to develop pharmacological interventions that target the glutamate system and could suggest that glutamatergic compounds may be more effective in specific patient subgroups or illness stages. In this review, we consider the updated glutamate hypothesis of schizophrenia, from a neurodevelopmental perspective, by reviewing recent preclinical and clinical evidence, and discuss the potential implications for novel therapeutics.

Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

Neuronal Circuit-Based Computer Modeling as a Phenotypic Strategy for CNS R&D

With the success rate of drugs for CNS indications at an all-time low, new approaches are needed to turn the tide of failed clinical trials. This paper reviews the history of CNS drug Discovery over the last 60 years and proposes a new paradigm based on the lessons learned. The initial wave of successful therapeutics discovered using careful clinical observations was followed by an emphasis on a phenotypic target-agnostic approach, often leading to successful drugs with a rich pharmacology. The subsequent introduction of molecular biology and the focus on a target-driven strategy has largely dominated drug discovery efforts over the last 30 years, but has not increased the probability of success, because these highly selective molecules are unlikely to address the complex pathological phenotypes of most CNS disorders. In many cases, reliance on preclinical animal models has lacked robust translational power. We argue that Quantitative Systems Pharmacology (QSP), a mechanism-based computer model of biological processes informed by preclinical knowledge and enhanced by neuroimaging and clinical data could be a new powerful knowledge generator engine and paradigm for rational polypharmacy. Progress in the academic discipline of computational neurosciences, allows one to model the effect of pathology and therapeutic interventions on neuronal circuit firing activity that can relate to clinical phenotypes, driven by complex properties of specific brain region activation states. The model is validated by optimizing the correlation between relevant emergent properties of these neuronal circuits and historical clinical and imaging datasets. A rationally designed polypharmacy target profile will be discovered using reverse engineering and sensitivity analysis. Small molecules will be identified using a combination of Artificial Intelligence methods and computational modeling, tested subsequently in heterologous cellular systems with human targets. Animal models will be used to establish target engagement and for ADME-Tox, with the QSP approach complemented by in vivo preclinical models that can be further refined to increase predictive validity. The QSP platform can also mitigate the variability in clinical trials with the concept of virtual patients. Because the QSP platform integrates knowledge from a wide variety of sources in an actionable simulation, it offers the possibility of substantially improving the success rate of CNS R&D programs while, at the same time, reducing both cost and the number of animals.

Use of quantitative clinical pharmacology to improve early clinical development success in neurodegenerative diseases

INTRODUCTION

The success rate of pharmaceutical Research & Development (R&D) is much lower compared to other industries such as micro-electronics or aeronautics with the probability of a successful clinical development to approval in central nervous system (CNS) disorders hovering in the single digits (7%). Areas covered: Inspired by adjacent engineering-based industries, we argue that quantitative modeling in CNS R&D might improve success rates. We will focus on quantitative techniques in early clinical development, such as PharmacoKinetic-PharmacoDynamic modeling, clinical trial simulation, model-based meta-analysis and the mechanism-based physiology-based pharmacokinetic modeling, and quantitative systems pharmacology. Expert commentary: Mechanism-based computer modeling rely less on existing clinical datasets, therefore can better generalize than Big Data analytics, including prospectively and quantitatively predicting the clinical outcome of new drugs. More specifically, exhaustive post-hoc analysis of failed trials using individual virtual human trial simulation could illuminate underlying causes such as lack of sufficient functional target engagement, negative pharmacodynamic interactions with comedications and genotypes, and mismatched patient population. These insights are beyond the capacity of artificial intelligence (AI) methods as they are many more possible combinations than subjects. Unlike 'black box' approaches in AI, mechanism-based platforms are transparent and based on biologically sound assumptions that can be interrogated.

Towards the virtual human patient. Quantitative Systems Pharmacology in Alzheimer's disease

Development of successful therapeutic interventions in Central Nervous Systems (CNS) disorders is a daunting challenge with a low success rate. Probable reasons include the lack of translation from preclinical animal models, the individual variability of many pathological processes converging upon the same clinical phenotype, the pharmacodynamical interaction of various comedications and last but not least the complexity of the human brain. This paper argues for a re-engineering of the pharmaceutical CNS Research & Development strategy using ideas focused on advanced computer modeling and simulation from adjacent engineering-based industries. We provide examples that such a Quantitative Systems Pharmacology approach based on computer simulation of biological processes and that combines the best of preclinical research with actual clinical outcomes can enhance translation to the clinical situation. We will expand upon (1) the need to go from Big Data to Smart Data and develop predictive and quantitative algorithms that are actionable for the pharma industry, (2) using this platform as a "knowledge machine" that captures community-wide expertise in an active hypothesis-testing approach, (3) learning from failed clinical trials and (4) the need to go beyond simple linear hypotheses and embrace complex non-linear hypotheses. We will propose a strategy for applying these concepts to the substantial individual variability of AD patient subgroups and the treatment of neuropsychiatric problems in AD. Quantitative Systems Pharmacology is a new 'humanized' tool for supporting drug discovery and development in general and CNS disorders in particular.

Semi-mechanistic computer simulation of psychotic symptoms in schizophrenia with a model of a humanized cortico-striatal-thalamocortical loop

Despite new insights into the pathophysiology of schizophrenia and clinical trials with highly selective drugs, no new therapeutic breakthroughs have been identified. We present a semi-mechanistic Quantitative Systems Pharmacology (QSP) computer model of a biophysically realistic cortical-striatal-thalamo-cortical loop. The model incorporates the direct, indirect and hyperdirect pathway of the basal ganglia and CNS drug targets that modulate neuronal firing, based on preclinical data about their localization and coupling to voltage-gated ion channels. Schizophrenia pathology is introduced using quantitative human imaging data on striatal hyperdopaminergic activity and cortical dysfunction. We identified an entropy measure of neuronal firing in the thalamus, related to the bandwidth of information processing that correlates well with reported historical clinical changes on PANSS Total with antipsychotics after introduction of their pharmacology (42 drug-dose combinations, r²=0.62). This entropy measure is further validated by predicting the clinical outcome of 28 other novel stand-alone interventions, 14 of them with non-dopamine D₂R pharmacology, in addition to 8 augmentation trials (correlation between actual and predicted clinical scores r²=0.61). The platform predicts that most combinations of antipsychotics have a lower efficacy over what can be achieved by either one; negative pharmacodynamical interactions are prominent for aripiprazole added to risperidone, haloperidol, quetiapine and paliperidone. The model also recapitulates the increased probability for psychotic breakdown in a supersensitive environment and the effect of ketamine in healthy volunteers. This QSP platform, combined with similar readouts for motor symptoms, negative symptoms and cognitive impairment has the potential to improve our understanding of drug effects in schizophrenia patients.

True alignment of preclinical and clinical research to enhance success in CNS drug development: a review of the current evidence

Central nervous system pharmacological research and development has reached a critical turning point. Patients suffering from disorders afflicting the central nervous system are numerous and command significant attention from the pharmaceutical industry. However, given the numerous failures of promising drugs, many companies are no longer investing in or, indeed, are divesting from this therapeutic area. Central nervous system drug development must change in order to develop effective therapies to treat these patients. Preclinical research is a cornerstone of drug development; however, it is frequently criticised for its lack of predictive validity. Animal models and assays can be shown to be more predictive than reported and, on many occasions, the lack of thorough preclinical testing is potentially to blame for some of the clinical failures. Important factors such as translational aspects, nature of animal models, variances in acute versus chronic dosing, development of add-on therapies and understanding of the full dose-response relationship are too often neglected. Reducing the attrition rate in central nervous system drug development could be achieved by addressing these important questions before novel compounds enter the clinical phase. This review illustrates the relevance of employing these criteria to translational central nervous system research, better to ensure success in developing new drugs in this therapeutic area.

Neuropharmacology beyond reductionism - A likely prospect

Neuropharmacology had several major past successes, but the last few decades did not witness any leap forward in the drug treatment of brain disorders. Moreover, current drugs used in neurology and psychiatry alleviate the symptoms, while hardly curing any cause of disease, basically because the etiology of most neuro-psychic syndromes is but poorly known. This review argues that this largely derives from the unbalanced prevalence in neuroscience of the analytic reductionist approach, focused on the cellular and molecular level, while the understanding of integrated brain activities remains flimsier. The decline of drug discovery output in the last decades, quite obvious in neuropharmacology, coincided with the advent of the single target-focused search of potent ligands selective for a well-defined protein, deemed critical in a given pathology. However, all the widespread neuro-psychic troubles are multi-mechanistic and polygenic, their complex etiology making unsuited the single-target drug discovery. An evolving approach, based on systems biology considers that a disease expresses a disturbance of the network of interactions underlying organismic functions, rather than alteration of single molecular components. Accordingly, systems pharmacology seeks to restore a disturbed network via multi-targeted drugs. This review notices that neuropharmacology in fact relies on drugs which are multi-target, this feature having occurred just because those drugs were selected by phenotypic screening in vivo, or emerged from serendipitous clinical observations. The novel systems pharmacology aims, however, to devise ab initio multi-target drugs that will appropriately act on multiple molecular entities. Though this is a task much more complex than the single-target strategy, major informatics resources and computational tools for the systemic approach of drug discovery are already set forth and their rapid progress forecasts promising outcomes for neuropharmacology.

D-Cycloserine in Neuropsychiatric Diseases: A Systematic Review

D-Cycloserine, known from tuberculosis therapy, has been widely introduced to neuropsychiatric studies, since its central active mechanism as a partial NMDA-agonist has been found. In this review, we evaluate its therapeutic potential in neuropsychological disorders and discuss its pitfalls in terms of dosing and application frequency as well as its safety in low-dose therapy. Therefore, we identified 91 clinical trials by performing a Medline search. We demonstrate in part preliminary but increasing evidence that D-cycloserine may be effective in various psychiatric diseases, including schizophrenia, anxiety disorders, addiction, eating disorders, major depression, and autism as well as in neurological diseases, including dementia, Alzheimer's disease, and spinocerebellar degeneration. D-Cycloserine in low-dose therapy is safe, but there is still a need for new drugs with higher specificity to the different N-methyl-D-aspartate-receptor subunits.