ArticleNPJ systems biology and applications2023
A quantitative systems pharmacology model of the pathophysiology and treatment of COVID-19 predicts optimal timing of pharmacological interventions.
Article in NPJ systems biology and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Innovative Randomized Phase I Study and Dosing Regimen Selection to Accelerate and Inform Pivotal COVID-19 Trial of Nirmatrelvir.Clinical pharmacology and therapeutics · 2022Trial
- Quantitative Systems Pharmacology (QSP): Bridging Biology and Mechanism with Clinical Drug Development Decisions.Journal of clinical pharmacology · 2026Review
- Utilizing Virtual Clinical Trials to Inform Target Coverage That Drives RSV Antiviral Efficacy.CPT: pharmacometrics & systems pharmacology · 2026Article
- Empowering Clinical Development With Disease Progression Modeling: Recommendations From the Clinical Trials Transformation Initiative.Clinical and translational science · 2026Article
- A Step-by-Step Workflow for Performing In Silico Clinical Trials With Nonlinear Mixed Effects Models.CPT: pharmacometrics & systems pharmacology · 2025Article
- Impact of Model-Informed Drug Development on Drug Development Cycle Times and Clinical Trial Cost.Clinical pharmacology and therapeutics · 2025Article
- Viral Dynamic Models During COVID-19: Are We Ready for the Next Pandemic?CPT: pharmacometrics & systems pharmacology · 2025Review
- Simulation of clinical trials of oral treprostinil in pulmonary arterial hypertension using a virtual population.NPJ systems biology and applications · 2025Article
- GEODE: anFrontiers in pharmacology · 2025Article
- The Role of Cross-Institutional and Interdisciplinary Collaboration in Defining and Executing a Quantitative Systems Pharmacology Strategy.Handbook of experimental pharmacology · 2025Review
- Future Directions for Quantitative Systems Pharmacology.Handbook of experimental pharmacology · 2025Review
- A perfectly imperfect engine: Utilizing the digital twin paradigm in pulmonary hypertension.Pulmonary circulation · 2024Review
- A Comprehensive Review of the Clinical Pharmacokinetics, Pharmacodynamics, and Drug Interactions of Nirmatrelvir/Ritonavir.Clinical pharmacokinetics · 2024Review
- Anti-inflammatory effects of dexamethasone in COVID-19 patients: Translational population PK/PD modeling and simulation.Clinical and translational science · 2023Article
- Mechanistic Models of COVID-19: Insights into Disease Progression, Vaccines, and Therapeutics.International journal of antimicrobial agents · 2022Review
- Sources of inter-individual variability leading to significant changes in anti-PD-1 and anti-PD-L1 efficacy identified in mouse tumor models using a QSP framework.Frontiers in pharmacology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A quantitative systems pharmacology (QSP) model of the pathogenesis and treatment of SARS-CoV-2 infection can streamline and accelerate the development of novel medicines to treat COVID-19. Simulation of clinical trials allows in silico exploration of the uncertainties of clinical trial design and can rapidly inform their protocols. We previously published a preliminary model of the immune response to SARS-CoV-2 infection. To further our understanding of COVID-19 and treatment, we significantly updated the model by matching a curated dataset spanning viral load and immune responses in plasma and lung. We identified a population of parameter sets to generate heterogeneity in pathophysiology and treatment and tested this model against published reports from interventional SARS-CoV-2 targeting mAb and antiviral trials. Upon generation and selection of a virtual population, we match both the placebo and treated responses in viral load in these trials. We extended the model to predict the rate of hospitalization or death within a population. Via comparison of the in silico predictions with clinical data, we hypothesize that the immune response to virus is log-linear over a wide range of viral load. To validate this approach, we show the model matches a published subgroup analysis, sorted by baseline viral load, of patients treated with neutralizing Abs. By simulating intervention at different time points post infection, the model predicts efficacy is not sensitive to interventions within five days of symptom onset, but efficacy is dramatically reduced if more than five days pass post symptom onset prior to treatment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.