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Bloomingdale P, Karelina T, Ramakrishnan V, Bakshi S, Véronneau‐Veilleux F, Moye M, Sekiguchi K, Meno‐Tetang G, Mohan A, Maithreye R, Thomas VA, Gibbons F, Cabal A, Bouteiller J, Geerts H. Hallmarks of neurodegenerative disease: A systems pharmacology perspective. CPT Pharmacometrics Syst Pharmacol 2022; 11:1399-1429. [PMID: 35894182 PMCID: PMC9662204 DOI: 10.1002/psp4.12852] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 07/17/2022] [Accepted: 07/19/2022] [Indexed: 11/09/2022] Open
Abstract
Age-related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease-modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.
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Affiliation(s)
- Peter Bloomingdale
- Quantitative Pharmacology and PharmacometricsMerck & Co., Inc.BostonMassachusettsUSA
| | | | | | - Suruchi Bakshi
- Certara QSPOssThe Netherlands,Certara QSPPrincetonNew JerseyUSA
| | | | - Matthew Moye
- Quantitative Pharmacology and PharmacometricsMerck & Co., Inc.BostonMassachusettsUSA
| | - Kazutaka Sekiguchi
- Shionogi & Co., Ltd.OsakaJapan,SUNY Downstate Medical CenterNew YorkNew YorkUSA
| | | | | | | | | | - Frank Gibbons
- Clinical Pharmacology and PharmacometricsBiogenCambridgeMassachusettsUSA
| | | | - Jean‐Marie Bouteiller
- Center for Neural EngineeringDepartment of Biomedical Engineering at the Viterbi School of EngineeringLos AngelesCaliforniaUSA,Institute for Technology and Medical Systems Innovation, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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Rose R, Mitchell E, Van Der Graaf P, Takaichi D, Hosogi J, Geerts H. A quantitative systems pharmacology model for simulating OFF-Time in augmentation trials for Parkinson’s disease: application to preladenant. J Pharmacokinet Pharmacodyn 2022; 49:593-606. [DOI: 10.1007/s10928-022-09825-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 09/13/2022] [Indexed: 11/29/2022]
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Geerts H, Spiros A. Simulating the Effects of Common Comedications and Genotypes on Alzheimer's Cognitive Trajectory Using a Quantitative Systems Pharmacology Approach. J Alzheimers Dis 2020; 78:413-424. [PMID: 33016912 DOI: 10.3233/jad-200688] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND Many Alzheimer's disease patients in clinical practice are on polypharmacy for treatment of comorbidities. OBJECTIVE While pharmacokinetic interactions between drugs have been relatively well established with corresponding treatment guidelines, many medications and common genotype variants also affect central brain circuits involved in cognitive trajectory, leading to complex pharmacodynamic interactions and a large variability in clinical trials. METHODS We applied a mechanism-based and ADAS-Cog calibrated Quantitative Systems Pharmacology biophysical model of neuronal circuits relevant for cognition in Alzheimer's disease, to standard-of-care cholinergic therapy with COMTVal158Met, 5-HTTLPR rs25531, and APOE genotypes and with benzodiazepines, antidepressants, and antipsychotics, all together 9,585 combinations. RESULTS The model predicts a variability of up to 14 points on ADAS-Cog at baseline (COMTVV 5-HTTLPRss APOE 4/4 combination is worst) and a four-fold range for the rate of progression. The progression rate is inversely proportional to baseline ADAS-Cog. Antidepressants, benzodiazepines, first-generation more than second generation, and most antipsychotics with the exception of aripiprazole worsen the outcome when added to standard-of-care in mild cases. Low dose second-generation benzodiazepines revert the negative effects of risperidone and olanzapine, but only in mild stages. Non APOE4 carriers with a COMTMM and 5HTTLPRLL are predicted to have the best cognitive performance at baseline but deteriorate somewhat faster over time. However, this effect is significantly modulated by comedications. CONCLUSION Once these simulations are validated, the platform can in principle provide optimal treatment guidance in clinical practice at an individual patient level, identify negative pharmacodynamic interactions with novel targets and address protocol amendments in clinical trials.
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van den Brink WJ, Hartman R, van den Berg D, Flik G, Gonzalez‐Amoros B, Koopman N, Elassais‐Schaap J, van der Graaf PH, Hankemeier T, de Lange EC. Blood-Based Biomarkers of Quinpirole Pharmacology: Cluster-Based PK/PD and Metabolomics to Unravel the Underlying Dynamics in Rat Plasma and Brain. CPT Pharmacometrics Syst Pharmacol 2019; 8:107-117. [PMID: 30680960 PMCID: PMC6389346 DOI: 10.1002/psp4.12370] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
A key challenge in the development of central nervous system drugs is the availability of drug target specific blood-based biomarkers. As a new approach, we applied cluster-based pharmacokinetic/pharmacodynamic (PK/PD) analysis in brain extracellular fluid (brainECF ) and plasma simultaneously after 0, 0.17, and 0.86 mg/kg of the dopamine D2/3 agonist quinpirole (QP) in rats. We measured 76 biogenic amines in plasma and brainECF after single and 8-day administration, to be analyzed by cluster-based PK/PD analysis. Multiple concentration-effect relations were observed with potencies ranging from 0.001-383 nM. Many biomarker responses seem to distribute over the blood-brain barrier (BBB). Effects were observed for dopamine and glutamate signaling in brainECF , and branched-chain amino acid metabolism and immune signaling in plasma. Altogether, we showed for the first time how cluster-based PK/PD could describe a systems-response across plasma and brain, thereby identifying potential blood-based biomarkers. This concept is envisioned to provide an important connection between drug discovery and early drug development.
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Affiliation(s)
- Willem J. van den Brink
- Division of Systems Biomedicine and PharmacologyLeiden Academic Center for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Robin Hartman
- Division of Systems Biomedicine and PharmacologyLeiden Academic Center for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Dirk‐Jan van den Berg
- Division of Systems Biomedicine and PharmacologyLeiden Academic Center for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | | | - Belén Gonzalez‐Amoros
- Division of Systems Biomedicine and PharmacologyLeiden Academic Center for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Nanda Koopman
- Division of Systems Biomedicine and PharmacologyLeiden Academic Center for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Jeroen Elassais‐Schaap
- Division of Systems Biomedicine and PharmacologyLeiden Academic Center for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Piet Hein van der Graaf
- Division of Systems Biomedicine and PharmacologyLeiden Academic Center for Drug ResearchLeiden UniversityLeidenThe Netherlands
- Certara QSPCanterbury Innovation HouseCanterburyUK
| | - Thomas Hankemeier
- Division of Systems Biomedicine and PharmacologyLeiden Academic Center for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Elizabeth C.M. de Lange
- Division of Systems Biomedicine and PharmacologyLeiden Academic Center for Drug ResearchLeiden UniversityLeidenThe Netherlands
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Geerts H, Spiros A, Roberts P, Alphs L. A quantitative systems pharmacology study on optimal scenarios for switching to paliperidone palmitate once-monthly. Schizophr Res 2018; 197:261-268. [PMID: 29395607 DOI: 10.1016/j.schres.2017.11.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 09/19/2017] [Accepted: 11/12/2017] [Indexed: 12/14/2022]
Abstract
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.
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Affiliation(s)
- Hugo Geerts
- In Silico Biosciences, 686 Westwind Dr, Berwyn, PA 19312, United States; Perelman School of Medicine, 3401 Spruce Street, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Athan Spiros
- In Silico Biosciences, 686 Westwind Dr, Berwyn, PA 19312, United States
| | - Patrick Roberts
- In Silico Biosciences, 686 Westwind Dr, Berwyn, PA 19312, United States; Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, United States
| | - Larry Alphs
- Janssen Scientific Affairs, LLC., 125 Trenton-Harbourton Rd-A32404, Titusville, NJ 08560, United States.
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Wong H, Bohnert T, Damian-Iordache V, Gibson C, Hsu CP, Krishnatry AS, Liederer BM, Lin J, Lu Q, Mettetal JT, Mudra DR, Nijsen MJ, Schroeder P, Schuck E, Suryawanshi S, Trapa P, Tsai A, Wang H, Wu F. Translational pharmacokinetic-pharmacodynamic analysis in the pharmaceutical industry: an IQ Consortium PK-PD Discussion Group perspective. Drug Discov Today 2017; 22:1447-1459. [DOI: 10.1016/j.drudis.2017.04.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Revised: 04/03/2017] [Accepted: 04/25/2017] [Indexed: 02/06/2023]
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Geerts H, Spiros A, Roberts P, Carr R. Towards the virtual human patient. Quantitative Systems Pharmacology in Alzheimer's disease. Eur J Pharmacol 2017; 817:38-45. [PMID: 28583429 DOI: 10.1016/j.ejphar.2017.05.062] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2016] [Revised: 05/05/2017] [Accepted: 05/31/2017] [Indexed: 12/26/2022]
Abstract
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.
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Affiliation(s)
- Hugo Geerts
- In Silico Biosciences, Lexington, MA, USA; Perelman School of Medicine, Univ. of Pennsylvania, Philadelphia, PA, USA.
| | | | - Patrick Roberts
- Department of Biomedical Engineering, Oregon Health & Science University, Portland OR, USA
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Spiros A, Roberts P, Geerts H. Semi-mechanistic computer simulation of psychotic symptoms in schizophrenia with a model of a humanized cortico-striatal-thalamocortical loop. Eur Neuropsychopharmacol 2017; 27:107-119. [PMID: 28062203 DOI: 10.1016/j.euroneuro.2016.12.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Revised: 11/20/2016] [Accepted: 12/24/2016] [Indexed: 12/13/2022]
Abstract
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.
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Affiliation(s)
- Athan Spiros
- In Silico Biosciences, Berwyn, PA, United States
| | - Patrick Roberts
- In Silico Biosciences, Berwyn, PA, United States; Washington State University, Vancouver, WA, United States
| | - Hugo Geerts
- In Silico Biosciences, Berwyn, PA, United States; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
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9
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Roberts P, Spiros A, Geerts H. A Humanized Clinically Calibrated Quantitative Systems Pharmacology Model for Hypokinetic Motor Symptoms in Parkinson's Disease. Front Pharmacol 2016; 7:6. [PMID: 26869923 PMCID: PMC4735425 DOI: 10.3389/fphar.2016.00006] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 01/11/2016] [Indexed: 01/15/2023] Open
Abstract
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.
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Affiliation(s)
- Patrick Roberts
- In Silico BiosciencesBerwyn, PA, USA; Washington State UniversityVancouver, WA, USA
| | | | - Hugo Geerts
- In Silico BiosciencesBerwyn, PA, USA; Perelman School of Medicine, University of PennsylvaniaPhiladelphia, PA, USA
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Siekmeier PJ. Computational modeling of psychiatric illnesses via well-defined neurophysiological and neurocognitive biomarkers. Neurosci Biobehav Rev 2015; 57:365-80. [DOI: 10.1016/j.neubiorev.2015.09.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Revised: 09/23/2015] [Accepted: 09/27/2015] [Indexed: 12/22/2022]
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Geerts H, Roberts P, Spiros A. Assessing the synergy between cholinomimetics and memantine as augmentation therapy in cognitive impairment in schizophrenia. A virtual human patient trial using quantitative systems pharmacology. Front Pharmacol 2015; 6:198. [PMID: 26441655 PMCID: PMC4585031 DOI: 10.3389/fphar.2015.00198] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Accepted: 08/31/2015] [Indexed: 11/30/2022] Open
Abstract
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.
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Affiliation(s)
- Hugo Geerts
- In Silico Biosciences Berwyn, PA, USA ; Perelman School of Medicine, University of Pennsylvania Philadelphia, PA, USA
| | - Patrick Roberts
- Department of Veterinary and Comparative Anatomy, Pharmacology and Physiology, Washington State University Pullman, WA, USA
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Multitarget drug discovery projects in CNS diseases: quantitative systems pharmacology as a possible path forward. Future Med Chem 2015; 6:1757-69. [PMID: 25574530 DOI: 10.4155/fmc.14.97] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
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.
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Geerts H, Roberts P, Spiros A, Potkin S. Understanding responder neurobiology in schizophrenia using a quantitative systems pharmacology model: application to iloperidone. J Psychopharmacol 2015; 29:372-82. [PMID: 25691503 DOI: 10.1177/0269881114568042] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
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.
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Affiliation(s)
- Hugo Geerts
- In Silico Biosciences, Berwyn, PA, USA Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Patrick Roberts
- In Silico Biosciences, Berwyn, PA, USA Oregon Health and Science University, Portland, OR, USA
| | | | - Steven Potkin
- Department of Psychiatry, University of California, Irvine, CA, USA
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Spiros A, Roberts P, Geerts H. A computer-based quantitative systems pharmacology model of negative symptoms in schizophrenia: exploring glycine modulation of excitation-inhibition balance. Front Pharmacol 2014; 5:229. [PMID: 25374541 PMCID: PMC4204440 DOI: 10.3389/fphar.2014.00229] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2014] [Accepted: 09/23/2014] [Indexed: 02/04/2023] Open
Abstract
Although many antipsychotics can reasonably control positive symptoms in schizophrenia, patients' return to society 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 mechanistic modeling approach. This Quantitative Systems Pharmacology approach combines preclinical basic neurophysiology of a biophysically realistic neuronal ventromedial cortical-ventral striatal network identified from human imaging studies that are associated with negative symptoms. 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. In general it can be helpful to better understand the human neurophysiology of negative symptoms, especially with targets that show non-monotonic dose-responses.
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Affiliation(s)
- Athan Spiros
- Computational Neuropharmacology, In Silico Biosciences, Inc. Berwyn, PA, USA
| | - Patrick Roberts
- Computational Neuropharmacology, In Silico Biosciences, Inc. Berwyn, PA, USA ; Department of Biomedical Engineering, Oregon Health and Science University Portland, OR, USA
| | - Hugo Geerts
- Computational Neuropharmacology, In Silico Biosciences, Inc. Berwyn, PA, USA ; Department of Laboratory Pathology, Perelman School of Medicine, University of Pennsylvania Philadelphia, PA, USA
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Liu J, Ogden A, Comery TA, Spiros A, Roberts P, Geerts H. Prediction of Efficacy of Vabicaserin, a 5-HT2C Agonist, for the Treatment of Schizophrenia Using a Quantitative Systems Pharmacology Model. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2014; 3:e111. [PMID: 24759548 PMCID: PMC4011163 DOI: 10.1038/psp.2014.7] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2013] [Accepted: 02/06/2014] [Indexed: 01/12/2023]
Abstract
A quantitative systems pharmacology model that combines in vitro/preclinical neurophysiology data, human imaging data, and patient disease information was used to blindly predict steady-state 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 positive and negative syndrome scales (PANSS) in schizophrenia monotherapy with vabicaserin. At the exposures of 100 and 200 mg b.i.d., the predicted improvements on PANSS in virtual patient trials were 5.12 (2.20, 8.56) and 6.37 (2.27, 10.40) (mean (95% confidence interval)), respectively, which are comparable to the observed phase IIa results. At the current clinical exposure limit of vabicaserin, the model predicted an ~9-point PANSS improvement in monotherapy, and <4-point PANSS improvement adjunctive with various antipsychotics, suggesting limited clinical benefit of vabicaserin in schizophrenia treatment. In conclusion, the updated quantitative systems pharmacology model of PANSS informed the clinical development decision of vabicaserin in schizophrenia.
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Affiliation(s)
- J Liu
- Clinical Pharmacology, Pfizer, Groton, Connecticut, USA
| | - A Ogden
- Clinical Pharmacology, Pfizer, Groton, Connecticut, USA
| | - T A Comery
- Neuroscience Research Unit, Pfizer, Cambridge, Massachusetts, USA
| | - A Spiros
- In Silico Biosciences, Lexington, Massachusetts, USA
| | - P Roberts
- 1] In Silico Biosciences, Lexington, Massachusetts, USA [2] Oregon Health and Science University, Portland, Oregon, USA
| | - H Geerts
- 1] In Silico Biosciences, Lexington, Massachusetts, USA [2] Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Geerts H, Spiros A, Roberts P, Carr R. Quantitative systems pharmacology as an extension of PK/PD modeling in CNS research and development. J Pharmacokinet Pharmacodyn 2013; 40:257-65. [PMID: 23338980 DOI: 10.1007/s10928-013-9297-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2012] [Accepted: 01/10/2013] [Indexed: 10/27/2022]
Abstract
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.
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Geerts H, Spiros A, Roberts P, Twyman R, Alphs L, Grace AA. Blinded prospective evaluation of computer-based mechanistic schizophrenia disease model for predicting drug response. PLoS One 2012; 7:e49732. [PMID: 23251349 PMCID: PMC3522663 DOI: 10.1371/journal.pone.0049732] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2012] [Accepted: 10/16/2012] [Indexed: 11/21/2022] Open
Abstract
The tremendous advances in understanding the neurobiological circuits involved in schizophrenia have not translated into more effective treatments. An alternative strategy is to use a recently published ‘Quantitative Systems Pharmacology’ computer-based mechanistic disease model of cortical/subcortical and striatal circuits based upon preclinical physiology, human pathology and pharmacology. The physiology of 27 relevant dopamine, serotonin, acetylcholine, norepinephrine, gamma-aminobutyric acid (GABA) and glutamate-mediated targets is calibrated using retrospective clinical data on 24 different antipsychotics. The model was challenged to predict quantitatively the clinical outcome in a blinded fashion of two experimental antipsychotic drugs; JNJ37822681, a highly selective low-affinity dopamine D2 antagonist and ocaperidone, a very high affinity dopamine D2 antagonist, using only pharmacology and human positron emission tomography (PET) imaging data. The model correctly predicted the lower performance of JNJ37822681 on the positive and negative syndrome scale (PANSS) total score and the higher extra-pyramidal symptom (EPS) liability compared to olanzapine and the relative performance of ocaperidone against olanzapine, but did not predict the absolute PANSS total score outcome and EPS liability for ocaperidone, possibly due to placebo responses and EPS assessment methods. Because of its virtual nature, this modeling approach can support central nervous system research and development by accounting for unique human drug properties, such as human metabolites, exposure, genotypes and off-target effects and can be a helpful tool for drug discovery and development.
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Affiliation(s)
- Hugo Geerts
- In Silico Biosciences, Berwyn, Pennsylvania, United States of America.
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Geerts H, Spiros A, Roberts P, Carr R. Has the time come for predictive computer modeling in CNS drug discovery and development? CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2012; 1:e16. [PMID: 23835798 PMCID: PMC3600733 DOI: 10.1038/psp.2012.17] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
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, imaging studies, and calibration and validation using clinical studies in human subjects might improve the success rate of central nervous systems research and development (CNS R&D) projects. We 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.
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Affiliation(s)
- H Geerts
- 1] Department of Biomedical Engineering, In Silico Biosciences, Berwyn, Pennsylvania, USA [2] Department of Biomedical Engineering, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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