Evidence map›Paper›PMID 35694844›Full record

ArticleClinical pharmacology and therapeutics2022

Calibration and Validation of a Mechanistic COVID-19 Model for Translational Quantitative Systems Pharmacology - A Proof-of-Concept Model Development for Remdesivir.

Mohammadreza Samieegohar, James L Weaver, Kristina E Howard, Anik Chaturbedi, John Mann, Xiaomei Han, Joel Zirkle, Ghazal Arabidarrehdor, Rodney Rouse, Jeffry Florian and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical pharmacology and therapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Mohammadreza SamieegoharDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
James L WeaverDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.ORCID 0000-0001-8601-4216
Kristina E HowardDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Anik ChaturbediDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
John MannDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Xiaomei HanDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Joel ZirkleDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Ghazal ArabidarrehdorDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Rodney RouseDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Jeffry FlorianDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
David G StraussDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.ORCID 0000-0003-3023-3297
Zhihua LiDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.ORCID 0000-0002-2772-0839

Funding

FDA HHS
6 · The paper itself

Abstract

With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an urgent need to accelerate the traditional drug development process. Many studies identified potential COVID-19 therapies based on promising nonclinical data. However, the poor translatability from nonclinical to clinical settings has led to failures of many of these drug candidates in the clinical phase. In this study, we propose a mechanism-based, quantitative framework to translate nonclinical findings to clinical outcome. Adopting a modularized approach, this framework includes an in silico disease model for COVID-19 (virus infection and human immune responses) and a pharmacological component for COVID-19 therapies. The disease model was able to reproduce important longitudinal clinical data for patients with mild and severe COVID-19, including viral titer, key immunological cytokines, antibody responses, and time courses of lymphopenia. Using remdesivir as a proof-of-concept example of model development for the pharmacological component, we developed a pharmacological model that describes the conversion of intravenously administered remdesivir as a prodrug to its active metabolite nucleoside triphosphate through intracellular metabolism and connected it to the COVID-19 disease model. After being calibrated with the placebo arm data, our model was independently and quantitatively able to predict the primary endpoint (time to recovery) of the remdesivir clinical study, Adaptive Covid-19 Clinical Trial (ACTT). Our work demonstrates the possibility of quantitatively predicting clinical outcome based on nonclinical data and mechanistic understanding of the disease and provides a modularized framework to aid in candidate drug selection and clinical trial design for COVID-19 therapeutics.

Indexed as

COVID-19 Drug TreatmentAdenosine MonophosphateAlanineAntiviral AgentsCalibrationHumansNetwork PharmacologySARS-CoV-2Adenosine MonophosphateAlanineAntiviral Agentsremdesivir

Identifiers

PMID35694844
PMCPMC9349538

What OpenQuestion holds

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Registered trials

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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.