Background: Although antipsychotic polypharmacy remains common in clinical practice, the pharmacodynamic interactions between antipsychotics remain poorly understood. Computer-modeling approaches have the potential to predict these interactions and provide guidance for polypharmacy.
Methods: We applied Quantitative Systems Pharmacology (QSP), a neurophysiology-based computer model of humanized neuronal circuits, to build a classifier to predict the risk for parkinsonism symptoms in patients with schizophrenia prescribed two concomitant antipsychotics, solely based on names and doses of the two drugs. This was achieved retrospectively, using anonymised data from South London and Maudsley NHS Foundation Trust (SLAM) electronic health records. The performance of the QSP model was compared to the performance of simple parameters such as: combination of affinity constants (1/Ksum); 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 31st December 2014. The Area under the Receiver Operating Characteristic (AUROC) curve for the QSP model was 0.66 (p=0.01), while AUROCs for D2R, 1/Ksum and chlorpromazine equivalent dose were 0.52 (p= 0.350), 0.53 (p= 0.347) and 0.52 (p=0.330) respectively.
Discussion: Our results indicate that QSP has the potential to predict the risk of parkinsonism associated with antipsychotic polypharmacy with minimal information, and thus might have potential decision-support applicability in clinical settings. In addition, the model might help estimate pharmacodynamic interactions in clinical trials and thus improve trial design.
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 ADAS-Cog readouts from historical clinical trials and simulated the differential impact of beta-amyloid (Ab40 and Ab42) oligomers on glutamate and nicotinic neurotransmission.
Results: Preclinical data suggest a beneficial effect of shorter Abeta forms within a limited dose-range. Such a beneficial effect of Ab40 on glutamate neurotransmission in human patients is absolutely necessary to reproduce (1) clinical data on ADAS-Cog in Minimal Cognitive Impairment (MCI) patients with and without amyloid load, (2) the effect of APOE genotype effect on the slope of the cognitive trajectory over time in placebo AD patients, and (3) higher sensitivity to cholinergic manipulation with scopolamine associated with higher beta-amyloid in MCI subjects. We further derive a relationship between units of Abeta load in our model and SUVR from amyloid imaging.
When introducing the documented clinical pharmacodynamic effects on Abeta levels for various amyloid-related clinical interventions in patients with low Abeta baseline, the platform predicts an overall significant worsening for passive vaccination solanezumab, beta-secretase inhibitor (BACE-I) verubecestat and gamma-secretase inhibitor (GSI), and semagacestat. In contrast, all three interventions improved cognition in subjects with moderate to high baseline-Ab levels with verubecestat anticipated to have the greatest effect (around 1.5 points), soleneuzumab (0.8 ADAS-Cog points) and semagacestat in between. This could explain the success of many amyloid interventions in transgene animals with an artificially high level of Ab.
Conclusion: If these predictions are confirmed in post-hoc analyses of failed clinical amyloid-modulating trials, it questions the rationale behind testing these interventions in early and prodromal subjects with low or zero amyloid load.
Long-acting injectable (LAI) formulations are increasingly used for improving patient compliance and long-term outcome with antipsychotic treatment. 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 (QSP), 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 1 day after the last oral medication dose or 4 weeks after the last LAI injection provides optimal benefit–risk compared to a delayed PP1M start after 1 week with either a 1 or 2 week overlap with oral paliperidone. Although a similar or better therapeutic effect can be achieved within 2 weeks for oral medications and LAI haloperidol (HALD) and 8 weeks for LAI aripiprazole (AERIS), we identified a potential transient under-treatment 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 EPS liability in most cases, but increased D2R inhibition (except for haloperidol) might potentially increase prolactin synthesis. Overall, these results suggest time-windows that the treating clinician must be most vigilant for potential efficacy and safety signals when switching to PP1M.
Neurodegenerative Diseases such as Alzheimer Disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale Omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bio-informatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths towards new therapeutic interventions.
This paper reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but course-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and is currently being used to generate a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized model.
We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches will be required for further progress in understanding and treating AD
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
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.
Phosphodiesterase 10 inhibitors (PDE10-I), are conceptually attractive drugs with a potential great therapeutic window as their enriched striatal localization may likely stimulate D1R and reduce D2R downstream effects. However, so far selective PDE10-I with efficacy in animal models have not shown benefit in clinical trials and unexpectedly revealed a substantial dyskinesia motor side-effect. Areas covered: This paper reviews the underlying biological rationale of PDE10 as a target in schizophrenia, Parkinson's and Huntington's disease based on peer-reviewed published articles, the status of the different PDE10-I in clinical development for various CNS indications and explores possible reasons for the clinical trial failures and translational disconnect. Expert commentary: Possible explanations include non-optimal dose and titration schedule, but more importantly the differential non-linear pharmacodynamic interactions with individual comedications, the species difference in underlying neurobiology and the differences with the rich pharmacology of successful antipsychotics. The authors also present optogenetics, DREADD (Designer Receptor Exclusively Activated by Designer Drug) technology, organoids based on iPSC (induced Pluripotent Stem Cells) and advanced computer modeling and simulation as possible new technologies to further elucidate the complex nature of the emergent properties of key neuronal circuits that drive human behavior
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.
Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These “Big-Data” databases can potentially advance CNS research and drug development. However, although necessary they are not sufficient and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes.
While these these empirically-derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development.
The current treatment of Parkinson’s disease with dopamine-centric approaches such as L-DOPA and dopamine agonists, although very succesfull, 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% onwards. 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.
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, especially 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.
The concept of targeted therapies remains a holy grail for pharmaceutical drug industry for identifying responder populations or new drug targets. Here we provide Quantitative Systems Pharmacology (QSP) as an alternative to the more traditional approach of retrospective responder pharmacogenomics analysis (PGX) 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 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 D4-AMPAR coupling in cortical interneurons was found to drive the beneficial effect of iloperidone, likely corresponding to the rs2513265 upstream of the GRIA4 gene identified in a traditional PGX analysis. The 5-HT3 mediated effect on interneuron GABA conductance was identified as the process that moderately drove the differentiation of iloperidone vs. ziprasidone.
This paper suggests that reverse-engineered QSP is a powerful alternative tool to characterize the underlying neurobiology of a responder population and possibly identifying new targets.
Despite tremendous investments in basic research in CNS neurobiology the search for successful therapeutic treatments for diseases such as schizophrenia, Alzheimer’s disease and Parkinson’s disease has not resulted in major breakthroughs. Clinical development in CNS diseases has one of the lowest success rates in the pharmaceutical industry and many promising R&D projects failed in expensive Phase III trials. The last Alzheimer drug was approved ten years ago and for schizophrenia no real therapeutic breakthrough has been found in the last 60 years. Among the different explanations are the observations that animal CNS models are not very predictive, including the different affinity of candidate drugs for human versus rodent targets, differences in exposure and metabolites, incomplete pathology and the presence of comedications. The selection of validated targets is hampered by insufficient knowledge of the human neuropathology and the absence of robust surrogate markers. In contrast most successful CNS drugs do have a rich pharmacology and have often been found serendipitously.
This position paper focuses on the fundamental limitation of the single-target versus multi-target pharmacology strategy in CNS R&D. Rational target driven drug discovery originated in the early 90’s and was a consequence of the genetic revolution and the argument of one gene-one protein –one disease but sofar has not resulted in therapeutic breakthroughs in CNS disorders. We will provide arguments that highly selective single-target drugs are not sufficiently powerful to restore complex neuronal circuit homeostasis and that the genotypic variability and the polypharmacy of currently allowed comedications in patient trials is a fundamental barrier for these very selective R&D strategies.
Dialing in a symptomatic treatment effect through modulation of neurotransmitters in a disease modification project is an alternative strategy that can substantially de-risk CNS R&D. This would in principle allow producing symptomatic clinical benefit on functional clinical scales for approval after which much longer-term trials can be performed documenting the effect of the drug on disease progression using specific biomarkers.
For more complex neuronal circuits, we propose a humanized in silico Quantitative Systems Pharmacology platform as an alternative. A hypothetical example of a workflow for a rationally designed multi-target Drug Discovery will be discussed that combines better predictive validity and higher throughput than traditional animal models.
Although many antipsychotics can reasonably control positive symptoms in schizophrenic patients’return to society, it 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 modeling approach that combines the preclinical basic neurophysiology of a biophysically realistic neuronal network of the ventromedial cortical-ventral striatal neurophysiology interaction with specific negative symptoms properties derived from human imaging studies and calibrated with retrospective clinical studies. 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 to better understand the human neurophysiology of negative symptoms, especially with targets that show non-monotonic dose-responses.
A quantitative systems pharmacology model that combines in vitro/preclinical neurophysiology data, human imaging data and patient disease information was used to blindly predict clinical efficacy of vabicaserin, a 5-HT2C full agonist, in monotherapy and subsequently to assess adjunctive therapy in schizophrenia. The model predicted a concentration-dependent improvement of PANSS in schizophrenia monotherapy with vabicaserin. At the exposures of 100 mg BID and 200 mg BID, the predicted improvements on PANSS for a virtual patient trial were 5.12 [1.88 to 9.06] and 6.37 [1.87 to 11.4] (mean [range]), respectively, which are comparable to the observed Phase IIa results. Adjunctive with antipsychotics, the model predicted minimal additional improvements of PANSS at the current clinical exposure limit of vabicaserin, suggesting limited clinical benefit of vabicaserin as an adjunctive therapy. In conclusion, the quantitative systems pharmacology model predicted the effect size range of the clinical study results. Thus, utilization of such models may be a novel approach for quantitative clinical efficacy predictions in neuroscience disease areas.
Background: Possible solutions for the low success rate in CNS Drug discovery and development in CNS diseases include drug repurposing. OBJECTIVES. As a possible alternative to prohibitively expensive systematic testing in animal models, we propose to use a humanized quantitative systems pharmacology (QSP) platform as an example of a well-validated phenotypic assay in Parkinson’s disease (PD) tremor for filtering out possible interesting molecules that then can be tested in preclinical animal models. This will significantly reduce discovery time and costs, while at the same time providing a better predictability to the human clinical outcome. The method will be applied to the Prestwick library, a library of FDA approved and off-patent medications.
Methods: The platform contains 30 CNS physiologically implemented targets and simulates biophysically realistic neuronal network interactions between supplemental motor cortex and motor striatum based on preclinical neurophysiology and human electrophysiology data. Importantly, the platform is further calibrated using retrospective clinical data on Parkinsonian side-effects with antipsychotics.
Results: We use this QSP platform to screen pharmacological profiles of serotonergic drugs in the Prestwick library. We identified five interesting multi-pharmacology agents, including trazodone that in a previously reported study improved clinical PD scales as augmentation strategy.
Conclusion: The Quantitative Systems Pharmacology platform is a powerful modeling and simulation tool with a relevant human clinical scale as output, where multi-target drugs effect can be simulated and promising candidates for further study in pharmacological profiling or animal models can be identified.
Background: 5-HT4 receptors in cortex and hippocampus area are considered a possible target for modulation of cognitive functions in Alzheimer’s disease (AD). A systems pharmacology approach was adopted to evaluate the potential of the 5-HT4 modulation in providing beneficial effects on cognition in AD.
Methods: A serotonergic synaptic cleft model was developed by integrating serotonin firing, release, synaptic half-life, drug/tracer properties (affinity and agonism) as inputs and 5-HT4 activity as output. The serotonergic model was calibrated using both in vivo data on free 5-HT levels in preclinical models and human imaging data. The model was further expanded to other neurotransmitter systems and incorporated into a computer-based cortical network model which implemented the physiology of 12 different membrane CNS targets. A biophysically realistic, multi-compartment model of 80 pyramidal cells and 40 interneurons was further calibrated using data reported for working memory tasks in healthy humans and schizophrenia patients. Model output was the duration of the network firing activity in response to an external stimulus. Alzheimer’s disease (AD) pathology, in particular synapse and neuronal cell loss in addition to cholinergic deficits, was calibrated to align with the natural clinical disease progression. The model was used to provide insights into the effect of 5-HT4 activation on working memory and to prospectively simulate the response of PF-04995274, a 5-HT4 partial agonist, in a scopolamine-reversal trial in healthy human subjects.
Results: The model output suggested a beneficial effect of 5-HT4 agonism on working memory. The model also projected no effect or an exacerbation of scopolamine impairment for low intrinsic activity 5-HT4 agonists, which was supported by the subsequent human trial outcome. The clinical prediction of the disease model strongly suggests that 5-HT4 agonists with high intrinsic activity may have a beneficial effect on cognition in AD patients.
Successful disease modifying drug development for Alzheimer's disease (AD) has hit a roadblock with the recent failures of amyloid-based therapies, highlighting the translational disconnect between preclinical animal models and clinical outcome. Although disease modifying therapies are the Holy Grail to pursue, symptomatic therapies addressing cognitive and neuropsychiatric aspects of the disease are also extremely important for the quality of life of patients and caregivers. Despite the fact that neuropsychiatric problems in Alzheimer patients are the major driver for costs associated with institutionalization, no good preclinical animal models with predictive validity have been documented. We propose a combination of quantitative systems pharmacology (QSP), phenotypic screening and preclinical animal models as a novel strategy for addressing the bottleneck in both cognitive and neuropsychiatric drug discovery and development for AD.
Preclinical animal models such as transgene rats documenting changes in neurotransmitters with tau and amyloid pathology will provide key information that together with human imaging, pathology and clinical data will inform the virtual patient model. In this way QSP modeling can partially overcome the translational disconnect and reduce the attrition of drug programs in the clinical setting.
This approach is different from target driven drug discovery as it aims to restore emergent properties of the networks and therefore likely will identify multitarget drugs. We review examples on how this hybrid humanized QSP approach has been helpful in predicting clinical outcomes in schizophrenia treatment and cognitive impairment in AD and expand on how this strategy could be applied to neuropsychiatric symptoms in dementia. We believe such an innovative approach when used carefully could change the Research and Development paradigm for symptomatic treatment in AD.
While current antipsychotics reasonably well control positive symptoms in schizophrenia, cognitive impairment remains largely unaddressed. The Matrics initiative lays out a regulatory path forward and a number of targets have been tested in the clinic, sofar without much success.
To address this translational disconnect, we have developed a mechanism based humanized computer model of a relevant key cortical brain network with schizophrenia pathology involved with the maintenance aspect of working memory. The model is calibrated using published clinical experiments on N-Back working memory tests. We further simulate the opposite effect of GABA modulators lorazepam and flumazenil and of a published augmentation trial of clozapine with risperidone, illustrating the introduction of new targets and the capacity of predicting the effects of polypharmacy.
This humanized approach allows for early prospective and quantitative assessment of cognitive outcome in a CNS R&D project, thereby hopefully increasing the success rate of clinical trials.
Quantitative systems pharmacology (QSP) is a recent addition to the modeling and simulation toolbox for drug discovery and development and is based upon mathematical modeling of biophysical realistic biological processes in the disease area of interest. The combination of preclinical neurophysiology information with clinical data on pathology, imaging and clinical scales makes it a real translational tool. We will discuss the specific characteristics of QSP and where it differs from PK/PD modeling, such as the ability to provide support in target validation, clinical candidate selection and multi-target MedChem projects. In clinical development the approach can provide additional and unique evaluation of the effect of comedications, genotypes and disease states (patient populations) even before the initiation of actual trials.
A powerful property is the ability to perform failure analysis. By giving examples from the CNS R&D field in schizophrenia and Alzheimer's disease, we will illustrate how this approach can make a difference for CNS R&D projects.
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.
A substantial number of therapeutic drugs for Alzheimer's disease (AD) have failed in late-stage trials, highlighting the translational disconnect with pathology-based animal models. To bridge the gap between preclinical animal models and clinical outcomes, we implemented a conductance-based computational model of cortical circuitry to simulate working memory as a measure for cognitive function. The model was initially calibrated using preclinical data on receptor pharmacology of catecholamine and cholinergic neurotransmitters. The pathology of AD was subsequently implemented as synaptic and neuronal loss and a decrease in cholinergic tone. The model was further calibrated with clinical Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog) results on acetylcholinesterase inhibitors and 5-HT6 antagonists to improve the model's prediction of clinical outcomes.
As an independent validation, we reproduced clinical data for apolipoprotein E (APOE) genotypes showing that the ApoE4 genotype reduces the network performance much more in mild cognitive impairment conditions than at later stages of AD pathology. We then demonstrated the differential effect of memantine, an N-Methyl-D-aspartic acid (NMDA) subunit selective weak inhibitor, in early and late AD pathology, and show that inhibition of the NMDA receptor NR2C/NR2D subunits located on inhibitory interneurons compensates for the greater excitatory decline observed with pathology.
This quantitative systems pharmacology approach is shown to be complementary to traditional animal models, with the potential to assess potential off-target effects, the consequences of pharmacologically active human metabolites, the effect of comedications, and the impact of a small number of well described genotypes.
We discuss whether a new paradigm, quantitative systems pharmacology (QSP), based on computational neuroscience modeling combined with proper drug target engagement and pharmacology, human pathology and imaging studies, calibration and validation using clinical studies in human subjects might improve the success rate of Central Nervous Systems R&D projects. We would suggest that an improved understanding of neuronal circuit interactions using a humanized computer-based integration of physiology and pharmacology knowledge can substantially de-risk new CNS projects.
Geerts CPT 2012
Despite tremendous advances in understanding the neurobiology of schizophrenia, no real therapeutic breakthrough has been made since the serendipitous discovery of chlorpromazine. We describe a computer-based mechanistic disease simulation model of biophysically realistic neurons that captures most of the subcortical neuromodulatory processes important in the pathophysiology of schizophrenia and calibrate this with a large retrospective clinical database for PANSS Total and EPS liability.
Additionally we make this platform more actionable for practical problems in CNS pharmaceutical R&D by introducing a synaptic receptor competition model that accurately captures clinical target exposures. The model accounts for threefold more variance than the simple D2R occupancy rule and is also superior in a number of independent clinical data-sets. As such the model is a first step in a better understanding of the human neurobiology of schizophrenia.
Using the human pharmacology as input the model can estimate a dose-response as a function of target engagement for clinical efficacy on PANSS Total and EPS side-effect liability for a new investigative compound. This Quantitative Systems Pharmacology platform by simulating the effect of comedications and genotypes in a clinical setting is a useful addition to the psychiatry pharmaceutical R&D world.
Spiros DDR 2012
Although many preclinical programs in CNS Research and Development intend to develop highly selective and potent molecules against the primary target; they are often not devoid of activity against other off-target receptors. The simple rule of taking the ratios of affinities for the candidate drug at the different receptors is flawed as the affinity of the endogenous ligand for that off-target receptor or the drug exposure is not taken into account.
We have developed a mathematical receptor competition model that takes into account the competition between active drug moiety and the endogenous neurotransmitter to better assess the off-target effects on postsynaptic receptor activation under the correct target exposure conditions. As an example we investigated the possible functional effects of weak off-target effects for dopamine D1R in a computer simulation of a dopaminergic cortical synapse that is calibrated using published fast-cyclic rodent voltammetry and human imaging data in subjects with different Catechol-O-Methyl -Transferase (COMT) genotypes. We identify the conditions under which off-target effects at the D1R can lead to clinically detectable consequences on cognitive tests, like the N-back working memory test and show that certain concentrations of dimebolin, a recently tested Alzheimer drug can affect D1R activation resulting in clinically detectable cognitive decrease. This approach can be extended to other receptor systems and can improve the selection of clinical candidate compounds by possibly dialing out harmful off-target effects or dialing in beneficial off-target effects in a quantitative and controlled way
Introduction: Nicotinic receptors (nAChR) a class of ligand-gated ion-channels are attractive targets in a variety of CNS diseases. The low-affinity a7 nAChR modulate the levels of various neurotransmitters, their receptor density is affected in schizophrenia and a SNP in the promoter region has been associated with higher risk for schizophrenia.
Area Covered: This article reviews the scientific rationale for a7 nAchR stimulation and presents a selection of a7 positive modulators which are in development for cognitive deficits, both in Alzheimer's disease and in Cognitive Impairment associated with Schizophrenia (CIAS). The available clinical information is reviewed and the translational difficulties are discussed.
Expert Opinion: In contrast to preclinical models, clinical proof-of-concept studies sofar have not shown clear unequivocal cognitive benefit, although there are signs of clinical efficacy on specific cognitive scales and on negative symptoms. Possible problems associated with the clinical development include the impact of dosage and dosing schedule on the balance between activation and desensitization of the ion-channel, the selection of comedication, robust human target engagement data and the choice of clinical readout scales. A better understanding of the human biology of a7 nAChR is essential for improving the successful clinical development of this promising target.
Despite tremendous advances in the basic understanding of CNS diseases, successful clinical drug development in psychiatry and neurology has been limited, putting huge pressure on remaining CNS R&D programs in pharmaceutical companies. In this paper we propose that re-engineering parts of the pharmaceutical Research and Development process by integrating complex modeling and simulation approaches – similar to the aerospace and micro-electronics industry – has the potential to increase the clinical predictability of animal models and to reduce the attrition rate in clinical drug development.
This paper will present top-down Mechanistic Disease Modeling approaches in relation to bottom-up Systems Biology with specific emphasis on CNS drug R&D. Both combine basic research data with human clinical outcome, but in contrast to System Biology that generically models intracellular pathways and protein-protein networks, Mechanistic Disease Modeling models the emergent properties of neuronal cell firing activity in large interacting neuronal networks. Such an outcome is much closer to physiological and behavioral processes that drive actual clinical scales.
We will illustrate some practical applications in the area of Alzheimer’s disease and schizophrenia for CNS Research and Development, such as guiding multitarget drug discovery, evaluating both the harmful and beneficial off-target human effects of candidate drugs; exploring the effect of comedications and functional genotypes on the candidate drug efficacy and sensitivity analysis for responder identification.
Species differences in physiology and unique active human metabolites contribute to the limited predictive value of preclinical rodent models for many CNS drugs. In order to explore possible drivers for this translational disconnect, we developed a computer model of a dopaminergic synapse that simulates the competition between three agents and their binding to pre- and postsynaptic receptors, based on the affinities for their targets and their actual concentrations. The model includes presynaptic autoreceptor effects on neurotransmitter release and modulation by presynaptic firing frequency and is calibrated with actual experimental data on free dopamine levels in the striatum of the rodent and the primate. Using this model, we simulated the postsynaptic dopamine D2 receptor activation levels of bifeprunox and aripiprazole, two relatively similar dopamine D2 receptor agonists.
The results indicate a substantial difference in dose-response for the two compounds when applying primate calibration parameters as opposed to rodent calibration parameters. In addition, when introducing the major human and rodent metabolites of aripiprazole with their specific pharmacological activities, the model predicts that while bifeprunox would result in a higher postsynaptic D2 receptor antagonism in the rodent, in contrast aripiprazole would result in a higher D2 receptor antagonism in the primate model. Furthermore only the highest dose of aripiprazole, but not bifeprunox reaches similar postsynaptic functional D2 receptor antagonism as 4 mg haloperidol in the primate model. The model further identifies a limited optimal window of functionality for dopamine D2 receptor partial agonists.
These results suggest that computer modeling of key CNS processes, using well validated calibration paradigms, can increase the predictive value in the clinical setting of preclinical animal model outcomes.
The tremendous advances in transgene animal technology, especially in the area of Alzheimer’s disease, have not resulted in a significantly better success rate for drugs entering clinical development. Despite substantial increases in R&D budgets, the number of approved NME in general does not increase, leading to the so-called innovation gap. While animal models have been very useful in documenting the possible pathological mechanism in many CNS diseases, they are not very predictive in the area of drug development.
This paper reports on a number of less-appreciated fundamental differences between animal models and human patients in the context of drug discovery, such as different affinities of the same drug for human vs. rodent target subtypes and the absence of many functional genotypes in animal models with special emphasis on Alzheimer’s disease and schizophrenia. We also offer a number of possible solutions to bridge the translational disconnect and improve the predictability of preclinical models, such as more emphasis on good quality translational studies, more information sharing and embracing multi-target strategies.
Re-engineering the process for drug discovery and development, similar to other more successful industries is another possible, but disrupting solution to the growing innovation gap. This includes the development of hybrid computational models, based upon documented preclinical physiology and pharmacology, but populated and validated with clinical data from the actual patients.