Evidence map›Paper›PMID 32618119›Full record

ArticleCPT: pharmacometrics & systems pharmacology2020

QSP-IO: A Quantitative Systems Pharmacology Toolbox for Mechanistic Multiscale Modeling for Immuno-Oncology Applications.

Richard J Sové, Mohammad Jafarnejad, Chen Zhao, Hanwen Wang, Huilin Ma, Aleksander S Popel

Abstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 1 pooled it
–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

41 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Learning Patient Similarity from Genomics for Precision Oncology.medRxiv : the preprint server for health sciences · 2025
    Article
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  15. An industry perspective on current QSP trends in drug development.Journal of pharmacokinetics and pharmacodynamics · 2024
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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

6 authors.

Richard J SovéDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0003-1491-616X
Mohammad JafarnejadDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Chen ZhaoDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Hanwen WangDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0001-5480-431X
Huilin MaDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Aleksander S PopelDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Funding

Predictive experiment-based multiscale models of the tumor immune microenvironment and immunotherapy in breast cancerR01CA138264 · NCI · JOHNS HOPKINS UNIVERSITY · PI POPEL, ALEKSANDER S. · 2009 to 2023
$8.1M
Integrating bioinformatics into multiscale models for hepatocellular carcinomaU01CA212007 · NCI · JOHNS HOPKINS UNIVERSITY · PI EWALD, ANDREW JOSEF, FERTIG, ELANA · 2018 to 2022
$3.2M
NCI NIH HHS R01 CA138264NCI NIH HHS U01 CA212007NIH HHS R01CA138264NIH HHS U01CA212007
6 · The paper itself

Abstract

Immunotherapy has shown great potential in the treatment of cancer; however, only a fraction of patients respond to treatment, and many experience autoimmune-related side effects. The pharmaceutical industry has relied on mathematical models to study the behavior of candidate drugs and more recently, complex, whole-body, quantitative systems pharmacology (QSP) models have become increasingly popular for discovery and development. QSP modeling has the potential to discover novel predictive biomarkers as well as test the efficacy of treatment plans and combination therapies through virtual clinical trials. In this work, we present a QSP modeling platform for immuno-oncology (IO) that incorporates detailed mechanisms for important immune interactions. This modular platform allows for the construction of QSP models of IO with varying degrees of complexity based on the research questions. Finally, we demonstrate the use of the platform through two example applications of immune checkpoint therapy.

Indexed as

Allergy and ImmunologyBiomarkers, TumorComputer SimulationDrug DevelopmentDrug DiscoveryDrug IndustryEvaluation Studies as TopicHumansImmune Checkpoint ProteinsImmunotherapyMedical OncologyModels, BiologicalModels, ImmunologicalModels, TheoreticalNeoplasmsPharmacologyBiomarkers, TumorImmune Checkpoint Proteins

Identifiers

PMID32618119
PMCPMC7499194

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.