Prediction of Efficacy of Vabicaserin, a 5-HT2C Agonist, for the Treatment of Schizophrenia Using a Quantitative Systems Pharmacology Model.
Ontology highlight
ABSTRACT:
SUBMITTER: Liu J
PROVIDER: S-EPMC4011163 | biostudies-other | 2014
REPOSITORIES: biostudies-other
Similar Datasets
Project description:BackgroundPsoriasis is a chronic immune-mediated inflammatory systemic disease with skin manifestations characterized by erythematous, scaly, itchy and/or painful plaques resulting from hyperproliferation of keratinocytes. Certolizumab pegol [CZP], a PEGylated antigen binding fragment of a humanized monoclonal antibody against TNF-alpha, is approved for the treatment of moderate-to-severe plaque psoriasis. Patients with psoriasis present clinical and molecular variability, affecting response to treatment. Herein, we utilized an in silico approach to model the effects of CZP in a virtual population (vPop) with moderate-to-severe psoriasis. Our proof-of-concept study aims to assess the performance of our model in generating a vPop and defining CZP response variability based on patient profiles.MethodsWe built a quantitative systems pharmacology (QSP) model of a clinical trial-like vPop with moderate-to-severe psoriasis treated with two dosing schemes of CZP (200 mg and 400 mg, both every two weeks for 16 weeks, starting with a loading dose of CZP 400 mg at weeks 0, 2, and 4). We applied different modelling approaches: (i) an algorithm to generate vPop according to reference population values and comorbidity frequencies in real-world populations; (ii) physiologically based pharmacokinetic (PBPK) models of CZP dosing schemes in each virtual patient; and (iii) systems biology-based models of the mechanism of action (MoA) of the drug.ResultsThe combination of our different modelling approaches yielded a vPop distribution and a PBPK model that aligned with existing literature. Our systems biology and QSP models reproduced known biological and clinical activity, presenting outcomes correlating with clinical efficacy measures. We identified distinct clusters of virtual patients based on their psoriasis-related protein predicted activity when treated with CZP, which could help unravel differences in drug efficacy in diverse subpopulations. Moreover, our models revealed clusters of MoA solutions irrespective of the dosing regimen employed.ConclusionOur study provided patient specific QSP models that reproduced clinical and molecular efficacy features, supporting the use of computational methods as modelling strategy to explore drug response variability. This might shed light on the differences in drug efficacy in diverse subpopulations, especially useful in complex diseases such as psoriasis, through the generation of mechanistically based hypotheses.
| S-EPMC10552644 | biostudies-literature
Project description:Alzheimer disease (AD) is a devastating neurodegenerative disorder with high unmet medical need. Drug development is hampered by limited understanding of the disease and its driving factors. Quantitative Systems Pharmacology (QSP) modeling provides a comprehensive quantitative framework to evaluate the relevance of biological mechanisms in the context of disease and to predict the efficacy of novel treatments. Here, we report a QSP model for AD with a particular focus on investigating the relevance of dysregulation of cholesterol and sphingolipids. We show that our model captures the modulation of several biomarkers in subjects with AD, as well as the response to pharmacological interventions. We evaluate the impact of targeting the sphingosine-1-phosphate 5 receptor (S1PR5) as a potential novel treatment option for AD, and model predictions increase our confidence in this novel disease pathway. Future applications for the QSP model are in validation of further targets and identification of potential treatment response biomarkers.
| S-EPMC6263662 | biostudies-literature
Project description:Chimeric antigen receptor (CAR)-T cell therapy has revolutionized treatment of relapsed/refractory non-Hodgkin lymphoma (NHL). However, since 36-60% of patients relapse, early response prediction is crucial. We present a novel population quantitative systems pharmacology model, integrating literature knowledge on physiology, immunology, and adoptive cell therapy together with 133 CAR-T cell phenotype, 1943 cytokine, and 48 metabolic tumor measurements. The model well described post-infusion concentrations of four CAR-T cell phenotypes and CD19+ metabolic tumor volume over 3 months after CAR-T cell infusion. Leveraging the model, we identified a low expansion subpopulation with significantly lower CAR-T cell expansion capacities amongst 19 NHL patients. Together with two patient-/therapy-related factors (autologous stem cell transplantation, CD4+/CD8+ T cells), the low expansion subpopulation explained 2/3 of the interindividual variability in the CAR-T cell expansion capacities. Moreover, the low expansion subpopulation had poor prognosis as only 1/4 of the low expansion subpopulation compared to 2/3 of the reference population were still alive after 24 months. We translated the expansion capacities into a clinical composite score (CCS) of 'Maximum naïve CAR-T cell concentrations/Baseline tumor burden' ratio and propose a CCSTN-value > 0.00136 (cells·µL-1·mL-1 as predictor for survival. Once validated in a larger cohort, the model will foster refining survival prediction and solutions to enhance NHL CAR-T cell therapy response.
| S-EPMC8199881 | biostudies-literature
Project description:Immune checkpoint blockade therapy has clearly shown clinical activity in patients with triple-negative breast cancer, but less than half of the patients benefit from the treatments. While a number of ongoing clinical trials are investigating different combinations of checkpoint inhibitors and chemotherapeutic agents, predictive biomarkers that identify patients most likely to benefit remains one of the major challenges. Here we present a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that incorporates detailed mechanisms of immune-cancer cell interactions to make efficacy predictions and identify predictive biomarkers for treatments using atezolizumab and nab-paclitaxel. A QSP model was developed based on published data of triple-negative breast cancer. With the model, we generated a virtual patient cohort to conduct in silico virtual clinical trials and make retrospective analyses of the pivotal IMpassion130 trial that led to the accelerated approval of atezolizumab and nab-paclitaxel for patients with programmed death-ligand 1 (PD-L1) positive triple-negative breast cancer. Available data from clinical trials were used for model calibration and validation. With the calibrated virtual patient cohort based on clinical data from the placebo comparator arm of the IMpassion130 trial, we made efficacy predictions and identified potential predictive biomarkers for the experimental arm of the trial using the proposed QSP model. The model predictions are consistent with clinically reported efficacy endpoints and correlated immune biomarkers. We further performed a series of virtual clinical trials to compare different doses and schedules of the two drugs for simulated therapeutic optimization. This study provides a QSP platform, which can be used to generate virtual patient cohorts and conduct virtual clinical trials. Our findings demonstrate its potential for making efficacy predictions for immunotherapies and chemotherapies, identifying predictive biomarkers, and guiding future clinical trial designs.
| S-EPMC7883871 | biostudies-literature
Project description:PROBODY therapeutics (Pb-Tx) are protease-activatable prodrugs of monoclonal antibodies (mAbs) designed to target tumors where protease activity is elevated while avoiding normal tissue. They are composed of a parental mAb, a mask that inhibits antibody binding to target, and a protease-cleavable substrate between the mask and the mAb. We report a quantitative systems pharmacology model for the rational design and clinical translation of Pb-Tx. The model adequately described monkey pharmacokinetic data following the administration of six anti-CD166 Pb-Tx of varying mask strength and substrate cleavability and captured the trend of decreasing Pb-Tx systemic clearance with increasing mask strength. Projections to humans suggested both higher levels of Pb-Tx in tumor relative to parental mAb and an optimal mask strength for maximizing tumor receptor-mediated uptake. Simulations further suggested the majority of circulating species in humans would be intact/masked Pb-Tx, with no significant flux of cleaved/activated species from tumor to the systemic compartment.
| S-EPMC6765697 | biostudies-literature
Project description:Hepatitis B liver infection is caused by hepatitis B virus (HBV) and represents a major global disease problem when it becomes chronic, as is the case for 80-90% of vertical or early life infections. However, in the vast majority (>95%) of adult exposures, the infected individuals are capable of mounting an effective immune response leading to infection resolution. A good understanding of HBV dynamics and the interaction between the virus and immune system during acute infection represents an essential step to characterize and understand the key biological processes involved in disease resolution, which may help to identify potential interventions to prevent chronic hepatitis B. In this work, a quantitative systems pharmacology model for acute hepatitis B characterizing viral dynamics and the main components of the innate, adaptive, and tolerant immune response has been successfully developed. To do so, information from multiple sources and across different organization levels has been integrated in a common mechanistic framework. The final model adequately describes the chronology and plausibility of an HBV-triggered immune response, as well as clinical data from acute patients reported in the literature. Given the holistic nature of the framework, the model can be used to illustrate the relevance of the different immune pathways and biological processes to ultimate response, observing the negligible contribution of the innate response and the key contribution of the cellular response on viral clearance. More specifically, moderate reductions of the proliferation of activated cytotoxic CD8+ lymphocytes or increased immunoregulatory effects can drive the system towards chronicity.
| S-EPMC8449028 | biostudies-literature
Project description:Although the positive symptoms of schizophrenia are reasonably well-controlled by current antipsychotics, 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, so far 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 (WM). The model is calibrated using published clinical experiments on N-back WM tests. We further simulate the opposite effect of ?-aminobutyric acid (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 central nervous system (CNS) research and development project, thereby hopefully increasing the success rate of clinical trials.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e36; doi:10.1038/psp.2013.12; advance online publication 3 April 2013.
| S-EPMC3636495 | biostudies-literature
Project description:Chimeric antigen receptor T-cell (CART) therapy is a new and promising cancer therapy. However, severe toxicity due to cytokine release syndrome (CRS) in CART-treated patients highlighted the possible danger of this new therapy. Disease burden and CART doses are the potential factors associated with CRS but the detail relationships between these factors and the severity of the CRS remain largely unknown. In this study, the quantitative systems pharmacology (QSP) approach is used to quantify the complex relationships among CART doses, disease burden, and pro inflammatory cytokines in human subjects and to gain relevant insights into the determinant of clinical toxicity/efficacy in development of CART therapy. The expansion of CART and elimination of B cells are more highly correlated with disease burden than the administered CART doses. To our best knowledge, this is the first QSP model that can describe the observed clinical data from CART-treated patients with cancer. This QSP model is a valuable tool for deepening our understanding of how the mechanism of action connects to the clinical outcomes and, therefore, may serve as an important model-based platform to guide the development and personalized dosing of the CART therapy.
| S-EPMC6662387 | biostudies-literature
Project description:A mechanistic, multistate, mathematical model of inflammatory bowel disease (IBD) was developed by including key biological mechanisms in blood and gut, including cell differentiation, cytokine production, and clinical biomarkers. The model structure is consistent between healthy volunteers and IBD disease phenotype, with 24 parameters changed between diseases. Modular nature of the model allows for easy incorporation of new mechanisms or modification of existing interactions. Model simulations for steady-state levels of proteins and cells in the blood and gut using a population approach are consistent with published data. By simulating the response of two clinical biomarkers, C-reactive protein and fecal calprotectin, to parameter perturbations, the model explores hypotheses for possible treatment mechanisms. With additional experimental validation and addition of drug treatments, the model provides a platform to test hypothesis on treatment effects in IBD.
| S-EPMC7877855 | biostudies-literature
Project description:Several PI3K inhibitors are in clinical development for the treatment of various forms of cancers, including pan-PI3K inhibitors targeting all four PI3K isoforms (α, β, γ, and δ), and isoform-selective inhibitors. Diarrhea and immune-mediated colitis are among the adverse events observed with PI3K inhibition which limits the maximal tolerated dose. A quantitative systems pharmacology model was developed to investigate PI3K-inhibitor-induced colitis. The effects of individual PI3K isoforms on relevant cellular pathways were incorporated into a mechanistic representation of mucosal inflammation. A virtual clinical population captures the observed clinical variability in the onset timing and rates of diarrhea and colitis for seven clinically tested PI3K inhibitors. Model-based analysis suggests that colitis development is governed by both the inhibition of PI3Kδ, which drives T cell differentiation and proliferation, and PI3Kα, which regulates epithelial barrier integrity. Specifically, when PI3Kα is inhibited below a given threshold, epithelial barrier dysfunction precipitates an exaggerated T effector response due to PI3Kδ-inhibition, leading to risk of diarrhea and colitis. This synergy explains why the lowest diarrhea and colitis rates are seen with the weakest PI3Kδ inhibition (alpelisib), and higher rates are seen with strong PI3Kδ inhibition if PI3Kα is even mildly inhibited (e.g., idelalisib), whereas strong PI3Kδ inhibition in the absence of PI3Kα inhibition does not result in high colitis rates (umbralisib). Thus, the model-based analysis suggests that PI3Kα and δ inhibition play unique but synergistic roles in driving colitis. Finally, we explore if and how dose-regimen might influence colitis rates for molecules that inhibit both PI3Kα and PI3Kδ.
| S-EPMC9124351 | biostudies-literature