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

NU-1223, a simplified analog of alstonine, with 5-HT2cR agonist-like activity, rescues memory deficit and positive and negative symptoms in subchronic phencyclidine mouse model of schizophrenia

The serotonin (5-HT)2 C receptor(R) is a widely distributed G-protein-coupled receptor, expressed abundantly in the central nervous system. Alstonine is a natural product that has significant properties of atypical antipsychotic drugs (AAPDs), in part attributed to 5-HT2 CR agonism. Based on alstonine, we developed NU-1223, a simplified β carboline analog of alstonine, which shows efficacies comparable to alstonine and to other 5-HT2 CR agonists, Ro-60-0175 and lorcaserin. The 5-HT2 CR antagonism of some APDs, including olanzapine, contributes to weight gain, a major side effect which limits its tolerability, while the 5-HT2 CR agonists and/or modulators, may minimize weight gain. We used the well-established rodent subchronic phencyclidine (PCP) model to test the efficacy of NU-1223 on episodic memory, using novel object recognition (NOR) task, positive (locomotor activity), and negative symptoms (social interaction) of schizophrenia (SCH). We found that NU-1223 produced both transient and prolonged rescue of the subchronic PCP-induced deficits in NOR and SI. Further, NU-1223, but not Ro-60-0175, blocked PCP and amphetamine (AMPH)-induced increase in LMA in subchronic PCP mice. These transient efficacies in LMA were blocked by the 5-HT2 CR antagonist, SB242084. Sub-chronic NU-1223 treatment rescued NOR and SI deficits in subchronic PCP mice for at least 39 days after 3 days injection. Chronic treatment with NU-1223, ip, twice a day for 21 days, did not increase average body weight vs olanzapine. These findings clearly indicate NU-1223 as a class of small molecules with a possible 5-HT2 CR-agonist-like mechanism of action, attributing to its efficacy. Additional in-depth receptor mechanistic studies are warranted, as this small molecule, both transiently and chronically rescued PCP-induced deficits. Furthermore, NU-1223 did not induce weight gain post long-term administrations vs AAPDs such as olanzapine, making NU-1223 a putative therapeutic compound for SCH.

Quantitative system pharmacology as a legitimate approach to examine extrapolation strategies used to support pediatric drug development

Extrapolation strategies from adult data for designing pediatric drug development programs are explored using the quantitative systems pharmacology (QSP) modeling approach, a mechanistic drug and disease modeling framework that can predict clinical response and guide pediatric drug development in general. This innovative model‐informed drug discovery and development approach can leverage adult‐pediatric pharmacology and disease similarity metrics to validate extrapolation assumptions. We describe the QSP model strategy and framework for extrapolation to design pediatric drug development programs by leveraging adult data across a wide range of therapeutic areas and illustrating stage‐gate decisions informed by such an approach.

Toward Predicting Impact of Common Genetic Variants on Schizophrenia Clinical Responses With Antipsychotics: A Quantitative System Pharmacology Study

CNS disorders are lagging behind other indications in implementing genotype-dependent treatment algorithms for personalized medicine. This report uses a biophysically realistic computer model of an associative and dorsal motor cortico-striatal-thalamo-cortical loop and a working memory cortical model to investigate the pharmacodynamic effects of COMTVal158Met rs4680, 5-HTTLPR rs 25531 s/L and D2DRTaq1A1 genotypes on the clinical response of 7 antipsychotics. The effect of the genotypes on dopamine and serotonin dynamics and the level of target exposure for the drugs was calibrated from PET displacement studies. The simulations suggest strong gene-gene pharmacodynamic interactions unique to each antipsychotic. For PANSS Total, the D2DRTaq1 allele has the biggest impact, followed by the 5-HTTLPR rs25531. The A2A2 genotype improved efficacy for all drugs, with a more complex outcome for the 5-HTTLPR rs25531 genotype. Maximal range in PANSS Total for all 27 individual combinations is 3 (aripiprazole) to 5 points (clozapine). The 5-HTTLPR L/L with aripiprazole and risperidone and the D2DRTaq1A2A2 allele with haloperidol, clozapine and quetiapine reduce the motor side-effects with opposite effects for the s/s genotype. The COMT genotype has a limited effect on antipsychotic effect and EPS. For cognition, the COMT MM 5-HTTLPR L/L genotype combination has the best performance for all antipsychotics, except clozapine. Maximal difference is 25% of the total dynamic range in a 2-back working memory task. Aripiprazole is the medication that is best suited for the largest number of genotype combinations (10) followed by Clozapine and risperidone (6), haloperidol and olanzapine (3) and quetiapine and paliperidone for one genotype. In principle, the platform could identify the best antipsychotic treatment balancing efficacy and side-effects for a specific individual genotype. Once the predictions of this platform are validated in a clinical setting the platform has potential to support rational personalized treatment guidance in clinical practice.

Exploring the relation between BOLD fMRI and cognitive performance using a computer-based quantitative systems pharmacology model: Applications to the COMTVAL158MET genotype and ketamine

BOLD fMRI is increasingly used mostly in an observational way to probe the effect of genotypes or therapeutic intervention in normal and diseased subjects. We use a mechanism-based quantitative systems pharmacology computer model of a human cortical microcircuit, previously calibrated for the 2-back working memory paradigm, adding established biophysical principles, of glucose metabolism, oxygen consumption, neurovascular effects and the paramagnetic impact on blood oxygen levels to calculate a readout for the voxel-based BOLD fMRI signal. The objective was to study the effect of the Catechol-O-methyl Transferase Val158Met (COMT) genotype on performance and BOLD fMRI. While the simulation suggests that on average virtual COMTVV genotype subjects perform worse, subjects with lower GABA, lower 5-HT₃ and higher 5-HT_1A activation can improve cognitive performance to the level of COMTMM subjects but at the expense of higher BOLD fMRI signal. In a schizophrenia condition, increased NMDA, GABA tone and noise levels, and lower D₁R activity can improve cognitive outcome with greater BOLD fMRI signal in COMT Val-carriers. We further generate hypotheses about why ketamine in healthy controls increases the BOLD fMRI signal but reduces cognitive performance. These simulations suggest a strong non-linear relationship between BOLD fMRI signal and cognitive performance. When validated, this mechanistic approach can be useful for moving beyond the descriptive nature of BOLD fMRI imaging and supporting the proper interpretation of imaging biomarkers in CNS disorders.

Serotonin/dopamine interaction: Electrophysiological and neurochemical evidence

The interaction between serotonin (5-HT) and dopamine (DA) in the central nervous system (CNS) plays an important role in the adaptive properties of living animals to their environment. These are two modulatory, divergent systems shaping and regulating in a widespread manner the activity of neurobiological networks and their interaction. The concept of one interaction linking these two systems is rather elusive when looking at the mechanisms triggered by these two systems across the CNS. The great variety of their interacting mechanisms is in part due to the diversity of their neuronal origin, the density of their fibers in a given CNS region, the distinct expression of their numerous receptors in the CNS, the heterogeneity of their intracellular signaling pathway that depend on the cellular type expressing their receptors, and the state of activity of neurobiological networks, conditioning the outcome of their mutual influences. Thus, originally conceptualized as inhibition of 5-HT on DA neuron activity and DA neurotransmission, this interaction is nowadays considered as a multifaceted, mutual influence of these two systems in the regulation of CNS functions. These new ways of understanding this interaction are of utmost importance to envision the consequences of their dysfunctions underlined in several CNS diseases. It is also essential to conceive the mechanism of action of psychotropic drugs directly acting on their function including antipsychotic, antidepressant, antiparkinsonian, and drug of abuse together with the development of therapeutic strategies of Alzheimer's diseases, epilepsy, obsessional compulsive disorders. The 5-HT/DA interaction has a long history from the serendipitous discovery of antidepressants and antipsychotics to the future, rationalized treatments of CNS disorders.

Experimental Serotonergic Agents for the Treatment of Schizophrenia

Schizophrenia remains one of the most chronic and highly disabling mental disorder. To date, the pathomechanism of schizophrenia is not fully understood and current treatments are characterized by some limitations. First- and second-generation antipsychotics have shown clinical efficacy in treating positive symptoms, while are poorly effective on both negative symptoms and cognitive deficits. Moreover, they can involve many metabolic and neurological side effects, leading to low therapeutic compliance. Many evidence suggested that serotonin may play a complex role in the neurobiology of schizophrenia. Therefore, new drugs targeting 5-HT receptors (5-HTRs) have become an important area of research in schizophrenia in the hope that treatment efficacy may be improved without inducing side effects observed with currently available antipsychotics. Research using the main database sources was conducted to obtain an overview of preclinical and clinical pharmacological 5-HTR-targeted therapies in patients with schizophrenia. We identified 17 experimental serotonergic agents, under study for their potential use in schizophrenia treatment. Particularly, AVN-211, LuAF-35700 and Brilaroxazine are currently under clinical development. Moreover, some compounds showed some pro-cognitive and antipsychotic-like properties in animal models, while other agents showed contradictory effects in improving symptoms and were removed from the development program. Although some serotonergic drugs seem promising for improving the treatment of schizophrenia, further studies regarding the pathophysiological mechanisms of schizophrenia and novel compounds as well as high-quality trials are necessary in order to improve schizophrenia outcomes.

Learning from amyloid trials in Alzheimer's disease. A virtual patient analysis using a quantitative systems pharmacology approach

BACKGROUND

Many trials of amyloid-modulating agents fail to improve cognitive outcome in Alzheimer's disease despite substantial reduction of amyloid β levels.

METHODS

We applied a mechanism-based Quantitative Systems Pharmacology model exploring the pharmacodynamic interactions of apolipoprotein E (APOE), Catechol -O -methyl Transferase (COMTVal158Met), and 5-HT transporter (5-HTTLPR) rs25531 genotypes and aducanumab.

RESULTS

The model predicts large clinical variability. Anticipated placebo differences on Alzheimer's Disease Assessment Scale (ADAS)-COG in the aducanumab ENGAGE and EMERGE ranged from 0.77 worsening to 1.56 points improvement, depending on the genotype-comedication combination. 5-HTTLPR L/L subjects are found to be the most resilient. Virtual patient simulations suggest improvements over placebo between 4% and 20% at the 10 mg/kg dose, depending on the imbalance of the 5-HTTLPR genotype and exposure. In the Phase II PRIME trial, maximal anticipated placebo difference at 10 mg/kg ranges from 0.3 worsening to 5.3 points improvement.

DISCUSSION

These virtual patient simulations, once validated against clinical data, could lead to better informed future clinical trial designs.

Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices

Systems pharmacology approaches have the capability of quantitatively linking the key biological molecules relevant to a drug candidate's mechanism of action (drug-induced signaling pathways) to the clinical biomarkers associated with the proposed target disease, thereby quantitatively facilitating its development and life cycle management. In this review, the model attributes of published quantitative systems pharmacology (QSP) modeling for lowering cholesterol, treating salt-sensitive hypertension, and treating rare diseases as well as describing bone homeostasis and related pharmacological effects are critically reviewed with respect to model quality, calibration, validation, and performance. We further reviewed the common practices in optimizing QSP modeling and prediction. Notably, leveraging genetics and genomic studies for model calibration and validation is common. Statistical and quantitative assessment of QSP prediction and handling of model uncertainty are, however, mostly lacking as are the quantitative and statistical criteria for assessing QSP predictions and the covariance matrix of coefficients between the parameters in a validated virtual population. To accelerate advances and application of QSP with consistent quality, a list of key questions is proposed to be addressed when assessing the quality of a QSP model in hopes of stimulating the scientific community to set common expectations. The common expectations as to what constitutes the best QSP modeling practices, which the scientific community supports, will advance QSP modeling in the realm of informed drug development. In the long run, good practices will extend the life cycles of QSP models beyond the life cycles of individual drugs.

Predicting parkinsonism side-effects of antipsychotic polypharmacy prescribed in secondary mental healthcare

BACKGROUND

Computer-modelling approaches have the potential to predict the interactions between different antipsychotics and provide guidance for polypharmacy.

AIMS

To evaluate the accuracy of the quantitative systems pharmacology platform to predict parkinsonism side-effects in patients prescribed antipsychotic polypharmacy.

METHODS

Using anonymized data from South London and Maudsley NHS Foundation Trust electronic health records we applied quantitative systems pharmacology, a neurophysiology-based computer model of humanized neuronal circuits, to predict the risk for parkinsonism symptoms in patients with schizophrenia prescribed two concomitant antipsychotics. The performance of the quantitative systems pharmacology model was compared with the performance of simple parameters such as: combination of affinity constants (1/K_sum); sum of D₂R occupancies (D₂R) and chlorpromazine equivalent dose.

RESULTS

We identified 832 patients with schizophrenia who were receiving two antipsychotics for six or more months, between 1 January 2007 and 31 December 2014. The area under the receiver operating characteristic (AUROC) curve for the quantitative systems pharmacology model was 0.66 ( p = 0.01), while AUROCs for D₂R, 1/K_sum and chlorpromazine equivalent dose were 0.52 ( p = 0.350), 0.53 ( p = 0.347) and 0.52 ( p = 0.330) respectively.

CONCLUSION

Our results indicate that quantitative systems pharmacology has the potential to predict the risk of parkinsonism associated with antipsychotic polypharmacy from minimal source information, and thus might have potential decision-support applicability in clinical settings.

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.

A quantitative systems pharmacology study on optimal scenarios for switching to paliperidone palmitate once-monthly

Long-acting injectable (LAI) antipsychotic formulations are increasingly used for improving patient compliance and long-term outcomes. Transitioning to LAIs raises questions regarding how optimum efficacy can be rapidly achieved while minimizing potential efficacy and safety concerns related to overlapping plasma levels of prior treatments and the new LAI. Ideally, randomized clinical trials would provide guidance regarding transition algorithms, but the number of studies and sample size required to address relevant questions makes this approach unachievable. We have used quantitative systems pharmacology, a clinically calibrated, mechanism-based computer model for schizophrenia to identify optimal switching scenarios to injectable paliperidone palmitate once-monthly (PP1M) from oral antipsychotics. We show that starting PP1M 1day after the last oral medication dose or 4weeks after the last LAI injection provides optimal benefit-risk compared to a delayed PP1M start after 1week with either a 1- or 2-week overlap with oral paliperidone. Although a similar or better therapeutic effect can be achieved within 2weeks for oral medications and LAI haloperidol decanoate and 8weeks for LAI aripiprazole, we identified a potential transient undertreatment liability in all cases except for risperidone. Switching from oral olanzapine may lead to a small reduction of antipsychotic efficacy in some patients. Switching to PP1M decreases extrapyramidal symptom liability in most cases, but increased dopamine D₂ receptor inhibition (except for haloperidol) might potentially increase prolactin synthesis. Overall, these results suggest time-windows for which the treating clinician must be most vigilant for potential efficacy and safety signals when switching to PP1M.

Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection

Schizophrenia is a debilitating psychiatric condition often associated with poor quality of life and decreased life expectancy. Lack of progress in improving treatment outcomes has been attributed to limited knowledge of the underlying biology, although large-scale genomic studies have begun to provide insights. We report a new genome-wide association study of schizophrenia (11,260 cases and 24,542 controls), and through meta-analysis with existing data we identify 50 novel associated loci and 145 loci in total. Through integrating genomic fine-mapping with brain expression and chromosome conformation data, we identify candidate causal genes within 33 loci. We also show for the first time that the common variant association signal is highly enriched among genes that are under strong selective pressures. These findings provide new insights into the biology and genetic architecture of schizophrenia, highlight the importance of mutation-intolerant genes and suggest a mechanism by which common risk variants persist in the population.

Impact of amyloid-beta changes on cognitive outcomes in Alzheimer's disease: analysis of clinical trials using a quantitative systems pharmacology model

BACKGROUND

Despite a tremendous amount of information on the role of amyloid in Alzheimer's disease (AD), almost all clinical trials testing this hypothesis have failed to generate clinically relevant cognitive effects.

METHODS

We present an advanced mechanism-based and biophysically realistic quantitative systems pharmacology computer model of an Alzheimer-type neuronal cortical network that has been calibrated with Alzheimer Disease Assessment Scale, cognitive subscale (ADAS-Cog) readouts from historical clinical trials and simulated the differential impact of amyloid-beta (Aβ40 and Aβ42) oligomers on glutamate and nicotinic neurotransmission.

RESULTS

Preclinical data suggest a beneficial effect of shorter Aβ forms within a limited dose range. Such a beneficial effect of Aβ40 on glutamate neurotransmission in human patients is absolutely necessary to reproduce clinical data on the ADAS-Cog in minimal cognitive impairment (MCI) patients with and without amyloid load, the effect of APOE genotype effect on the slope of the cognitive trajectory over time in placebo AD patients and higher sensitivity to cholinergic manipulation with scopolamine associated with higher Aβ in MCI subjects. We further derive a relationship between units of Aβ load in our model and the standard uptake value ratio from amyloid imaging. When introducing the documented clinical pharmacodynamic effects on Aβ levels for various amyloid-related clinical interventions in patients with low Aβ baseline, the platform predicts an overall significant worsening for passive vaccination with solanezumab, beta-secretase inhibitor verubecestat and gamma-secretase inhibitor semagacestat. In contrast, all three interventions improved cognition in subjects with moderate to high baseline Aβ levels, with verubecestat anticipated to have the greatest effect (around ADAS-Cog value 1.5 points), solanezumab the lowest (0.8 ADAS-Cog value points) and semagacestat in between. This could explain the success of many amyloid interventions in transgene animals with an artificial high level of Aβ, but not in AD patients with a large variability of amyloid loads.

CONCLUSIONS

If these predictions are confirmed in post-hoc analyses of failed clinical amyloid-modulating trials, one should question the rationale behind testing these interventions in early and prodromal subjects with low or zero amyloid load.

5-HT2C Agonists Modulate Schizophrenia-Like Behaviors in Mice

All FDA-approved antipsychotic drugs (APDs) target primarily dopamine D₂ or serotonin (5-HT_2A) receptors, or both; however, these medications are not universally effective, they may produce undesirable side effects, and provide only partial amelioration of negative and cognitive symptoms. The heterogeneity of pharmacological responses in schizophrenic patients suggests that additional drug targets may be effective in improving aspects of this syndrome. Recent evidence suggests that 5-HT_2C receptors may be a promising target for schizophrenia since their activation reduces mesolimbic nigrostriatal dopamine release (which conveys antipsychotic action), they are expressed almost exclusively in CNS, and have weight-loss-promoting capabilities. A difficulty in developing 5-HT_2C agonists is that most ligands also possess 5-HT_2B and/or 5-HT_2A activities. We have developed selective 5-HT_2C ligands and herein describe their preclinical effectiveness for treating schizophrenia-like behaviors. JJ-3-45, JJ-3-42, and JJ-5-34 reduced amphetamine-stimulated hyperlocomotion, restored amphetamine-disrupted prepulse inhibition, improved social behavior, and novel object recognition memory in NMDA receptor hypofunctioning NR1-knockdown mice, and were essentially devoid of catalepsy. However, they decreased motivation in a breakpoint assay and did not promote reversal learning in MK-801-treated mice. Somewhat similar effects were observed with lorcaserin, a 5-HT_2C agonist with potent 5-HT_2B and 5-HT_2A agonist activities, which is approved for treating obesity. Microdialysis studies revealed that both JJ-3-42 and lorcaserin reduced dopamine efflux in the infralimbic cortex, while only JJ-3-42 decreased it in striatum. Collectively, these results provide additional evidence that 5-HT_2C receptors are suitable drug targets with fewer side effects, greater therapeutic selectivity, and enhanced efficacy for treating schizophrenia and related disorders than current APDs.

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.

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

Many disease-modifying clinical development programs in Alzheimer's disease (AD) have failed to date, and development of new and advanced preclinical models that generate actionable knowledge is desperately needed. This review reports on computer-based modeling and simulation approach as a powerful tool in AD research. Statistical data-analysis techniques can identify associations between certain data and phenotypes, such as diagnosis or disease progression. Other approaches integrate domain expertise in a formalized mathematical way to understand how specific components of pathology integrate into complex brain networks. Private-public partnerships focused on data sharing, causal inference and pathway-based analysis, crowdsourcing, and mechanism-based quantitative systems modeling represent successful real-world modeling examples with substantial impact on CNS diseases. Similar to other disease indications, successful real-world examples of advanced simulation can generate actionable support of drug discovery and development in AD, illustrating the value that can be generated for different stakeholders.

Atypical antipsychotics and inverse agonism at 5-HT2 receptors

It is now well accepted that receptors can regulate cellular signaling pathways in the absence of a stimulating ligand, and inverse agonists can reduce this ligand-independent or "constitutive" receptor activity. Both the serotonin 5-HT2A and 5-HT2C receptors have demonstrated constitutive receptor activity in vitro and in vivo. Each has been identified as a target for treatment of schizophrenia. Further, most, if not all, atypical antipsychotic drugs have inverse agonist properties at both 5-HT2A and 5-HT2C receptors. This paper describes our current knowledge of inverse agonism of atypical antipsychotics at 5-HT2A/2C receptor subtypes in vitro and in vivo. Exploiting inverse agonist properties of APDs may provide new avenues for drug development.

A Humanized Clinically Calibrated Quantitative Systems Pharmacology Model for Hypokinetic Motor Symptoms in Parkinson's Disease

The current treatment of Parkinson’s disease with dopamine-centric approaches such as L-DOPA and dopamine agonists, although very successful, is in need of alternative treatment strategies, both in terms of disease modification and symptom management. Various non-dopaminergic treatment approaches did not result in a clear clinical benefit, despite showing a clear effect in preclinical animal models. In addition, polypharmacy is common, sometimes leading to unintended effects on non-motor cognitive and psychiatric symptoms. To explore novel targets for symptomatic treatment and possible synergistic pharmacodynamic effects between different drugs, we developed a computer-based Quantitative Systems Pharmacology (QSP) platform of the closed cortico-striatal-thalamic-cortical basal ganglia loop of the dorsal motor circuit. This mechanism-based simulation platform is based on the known neuro-anatomy and neurophysiology of the basal ganglia and explicitly incorporates domain expertise in a formalized way. The calculated beta/gamma power ratio of the local field potential in the subthalamic nucleus correlates well (R² = 0.71) with clinically observed extra-pyramidal symptoms triggered by antipsychotics during schizophrenia treatment (43 drug-dose combinations). When incorporating Parkinsonian (PD) pathology and reported compensatory changes, the computer model suggests a major increase in b/g ratio (corresponding to bradykinesia and rigidity) from a dopamine depletion of 70% onward. The correlation between the outcome of the QSP model and the reported changes in UPDRS III Motor Part for 22 placebo-normalized drug-dose combinations is R² = 0.84. The model also correctly recapitulates the lack of clinical benefit for perampanel, MK-0567 and flupirtine and offers a hypothesis for the translational disconnect. Finally, using human PET imaging studies with placebo response, the computer model predicts well the placebo response for chronic treatment, but not for acute treatment in PD.

New therapeutic opportunities for 5-HT2C receptor ligands in neuropsychiatric disorders

The 5-HT2C receptor (R) displays a widespread distribution in the CNS and is involved in the action of 5-HT in all brain areas. Knowledge of its functional role in the CNS pathophysiology has been impaired for many years due to the lack of drugs capable of discriminating among 5-HT2R subtypes, and to a lesser extent to the 5-HT1B, 5-HT5, 5-HT6 and 5-HT7Rs. The situation has changed since the mid-90s due to the increased availability of new and selective synthesized compounds, the creation of 5-HT2C knock out mice, and the progress made in molecular biology. Many pharmacological classes of drugs including antipsychotics, antidepressants and anxiolytics display affinities toward 5-HT2CRs and new 5-HT2C ligands have been developed for various neuropsychiatric disorders. The 5-HT2CR is presumed to mediate tonic/constitutive and phasic controls on the activity of different central neurobiological networks. Preclinical data illustrate this complexity to a point that pharmaceutical companies developed either agonists or antagonists for the same disease. In order to better comprehend this complexity, this review will briefly describe the molecular pharmacology of 5-HT2CRs, as well as their cellular impacts in general, before addressing its central distribution in the mammalian brain. Thereafter, we review the preclinical efficacy of 5-HT2C ligands in numerous behavioral tests modeling human diseases, highlighting the multiple and competing actions of the 5-HT2CRs in neurobiological networks and monoaminergic systems. Notably, we will focus this evidence in the context of the physiopathology of psychiatric and neurological disorders including Parkinson's disease, levodopa-induced dyskinesia, and epilepsy.

Assessing the synergy between cholinomimetics and memantine as augmentation therapy in cognitive impairment in schizophrenia. A virtual human patient trial using quantitative systems pharmacology

While many drug discovery research programs aim to develop highly selective clinical candidates, their clinical success is limited because of the complex non-linear interactions of human brain neuronal circuits. Therefore, a rational approach for identifying appropriate synergistic multipharmacology and validating optimal target combinations is desperately needed. A mechanism-based Quantitative Systems Pharmacology (QSP) computer-based modeling platform that combines biophysically realistic preclinical neurophysiology and neuropharmacology with clinical information is a possible solution. This paper reports the application of such a model for Cognitive Impairment In Schizophrenia (CIAS), where the cholinomimetics galantamine and donepezil are combined with memantine and with different antipsychotics and smoking in a virtual human patient experiment. The results suggest that cholinomimetics added to antipsychotics have a modest effect on cognition in CIAS in non-smoking patients with haloperidol and risperidone and to a lesser extent with olanzapine and aripiprazole. Smoking reduces the effect of cholinomimetics with aripiprazole and olanzapine, but enhances the effect in haloperidol and risperidone. Adding memantine to antipsychotics improves cognition except with quetiapine, an effect enhanced with smoking. Combining cholinomimetics, antipsychotics and memantine in general shows an additive effect, except for a negative interaction with aripiprazole and quetiapine and a synergistic effect with olanzapine and haloperidol in non-smokers and haloperidol in smokers. The complex interaction of cholinomimetics with memantine, antipsychotics and smoking can be quantitatively studied using mechanism-based advanced computer modeling. QSP modeling of virtual human patients can possibly generate useful insights on the non-linear interactions of multipharmacology drugs and support complex CNS R&D projects in cognition in search of synergistic polypharmacy.

Multitarget drug discovery projects in CNS diseases: quantitative systems pharmacology as a possible path forward

Clinical development in brain diseases has one of the lowest success rates in the pharmaceutical industry, and many promising rationally designed single-target R&D projects fail in expensive Phase III trials. By contrast, successful older CNS drugs do have a rich pharmacology. This article will provide arguments suggesting that highly selective single-target drugs are not sufficiently powerful to restore complex neuronal circuit homeostasis. A rationally designed multitarget project can be derisked by dialing in an additional symptomatic treatment effect on top of a disease modification target. Alternatively, we expand upon a hypothetical workflow example using a humanized computer-based quantitative systems pharmacology platform. The hope is that incorporating rationally multipharmacology drug discovery could potentially lead to more impactful polypharmacy drugs.

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

Modern neuropsychopharmacology commenced in the 1950s with the serendipitous discovery of first-generation antipsychotics and antidepressants which were therapeutically effective yet had marked adverse effects. Today, a broader palette of safer and better-tolerated agents is available for helping people that suffer from schizophrenia, depression and other psychiatric disorders, while complementary approaches like psychotherapy also have important roles to play in their treatment, both alone and in association with medication. Nonetheless, despite considerable efforts, current management is still only partially effective, and highly-prevalent psychiatric disorders of the brain continue to represent a huge personal and socio-economic burden. The lack of success in discovering more effective pharmacotherapy has contributed, together with many other factors, to a relative disengagement by pharmaceutical firms from neuropsychiatry. Nonetheless, interest remains high, and partnerships are proliferating with academic centres which are increasingly integrating drug discovery and translational research into their traditional activities. This is, then, a time of transition and an opportune moment to thoroughly survey the field. Accordingly, the present paper, first, chronicles the discovery and development of psychotropic agents, focusing in particular on their mechanisms of action and therapeutic utility, and how problems faced were eventually overcome. Second, it discusses the lessons learned from past successes and failures, and how they are being applied to promote future progress. Third, it comprehensively surveys emerging strategies that are (1), improving our understanding of the diagnosis and classification of psychiatric disorders; (2), deepening knowledge of their underlying risk factors and pathophysiological substrates; (3), refining cellular and animal models for discovery and validation of novel therapeutic agents; (4), improving the design and outcome of clinical trials; (5), moving towards reliable biomarkers of patient subpopulations and medication efficacy and (6), promoting collaborative approaches to innovation by uniting key partners from the regulators, industry and academia to patients. Notwithstanding the challenges ahead, the many changes and ideas articulated herein provide new hope and something of a framework for progress towards the improved prevention and relief of psychiatric and other CNS disorders, an urgent mission for our Century.

Understanding responder neurobiology in schizophrenia using a quantitative systems pharmacology model: application to iloperidone

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.

Optimization of 2-phenylcyclopropylmethylamines as selective serotonin 2C receptor agonists and their evaluation as potential antipsychotic agents

The discovery of a new series of compounds that are potent, selective 5-HT2C receptor agonists is described herein as we continue our efforts to optimize the 2-phenylcyclopropylmethylamine scaffold. Modifications focused on the alkoxyl substituent present on the aromatic ring led to the identification of improved ligands with better potency at the 5-HT2C receptor and excellent selectivity against the 5-HT2A and 5-HT2B receptors. ADMET studies coupled with a behavioral test using the amphetamine-induced hyperactivity model identified four compounds possessing drug-like profiles and having antipsychotic properties. Compound (+)-16b, which displayed an EC50 of 4.2 nM at 5-HT2C, no activity at 5-HT2B, and an 89-fold selectivity against 5-HT2A, is one of the most potent and selective 5-HT2C agonists reported to date. The likely binding mode of this series of compounds to the 5-HT2C receptor was also investigated in a modeling study, using optimized models incorporating the structures of β2-adrenergic receptor and 5-HT2B receptor.