Evidence map›Paper›PMID 28540623›Full record

ArticleThe AAPS journal2017

QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models.

Yougan Cheng, Craig J Thalhauser, Shepard Smithline, Jyotsna Pagidala, Marko Miladinov, Heather E Vezina, Manish Gupta, Tarek A Leil, Brian J Schmidt

Abstract read
PubMed Publisher
In one paragraph

Article in The AAPS journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing 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

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

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. A Framework for Quantitative Systems Pharmacology Model Execution.Handbook of experimental pharmacology · 2025
    Review
  10. Assessing the performance of QSP models: biology as the driver for validation.Journal of pharmacokinetics and pharmacodynamics · 2024
    Review
  11. Towards a comprehensive assessment of QSP models: what would it take?Journal of pharmacokinetics and pharmacodynamics · 2024
    Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Evaluation framework for systems models.CPT: pharmacometrics & systems pharmacology · 2022
    Article
  17. Article
  18. Review
  19. Review
  20. Article
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

9 authors.

Yougan ChengBristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA.
Craig J ThalhauserBristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA.
Shepard SmithlineBristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA.
Jyotsna PagidalaBristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA.
Marko MiladinovBristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA.
Heather E VezinaBristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA.
Manish GuptaBristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA.
Tarek A LeilBristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA.
Brian J SchmidtBristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA. brian.schmidt@bms.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.

Indexed as

Models, TheoreticalWorkflowCalibrationComputer SimulationImmunoconjugatesImmunoconjugatesoptimizationordinary differential equationsquantitative systems pharmacologyvirtual patientvirtual population

Identifiers

PMID28540623

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.