Evidence map›Paper›PMID 36870005›Full record

ReviewJournal of pharmacokinetics and pharmacodynamics2023

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development.

Ana Ruiz-Garcia, Paul Baverel, Dean Bottino, Michael Dolton, Yan Feng, Ignacio González-García, Jaeyeon Kim, Seth Robey, Indrajeet Singh, David Turner and 5 more

Open access · hybridAbstract readReview
In one paragraph

Review in Journal of pharmacokinetics and pharmacodynamics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
30.8field-weighted citation impact, top 1% of its field
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

35 citing papers in PubMed, 40 citations in OpenAlex.

  1. Trial
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  11. RNA therapeutic targeting of recalcitrant and rare cancers.Exploration of targeted anti-tumor therapy · 2026
    Review
  12. Article
  13. Review
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  15. Article
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  17. FDA-AACR Strategies for Optimizing Dosages for Oncology Drug Products: Selecting Optimized Dosages for Registrational Trials.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025
    Review
  18. Article
  19. Review
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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

15 authors at 11 institutions in 3 countries.

Ana Ruiz-GarciaModeling and Simulation, Metrum Research Group, Tariffville, CT, USA. Ana.Ruiz5@gilead.com.ORCID 0000-0002-5462-3110
Paul BaverelClinical Pharmacology, F. Hoffmann-La Roche AG, Basel, Switzerland.
Dean BottinoOncology Modeling and Simulation, Takeda, Newton, MA, USA.
Michael DoltonClinical Pharmacology, Genentech, South San Francisco, CA, USA.
Yan FengClinical Pharmacology, Genmab, Copenhagen, Denmark.
Ignacio González-GarcíaSimulations Plus, Cognigen, Alicante, Spain.
Jaeyeon KimEarly Development Analytics, Novartis Institutes for BioMedical Research, Cambridge, MA, USA.
Seth RobeyQuantitative Pharmacology and Pharmacometrics, Merck, Rahway, NJ, USA.
Indrajeet SinghClinical Pharmacology, Gilead Sciences, Foster City, CA, USA.
David TurnerClinical Pharmacology, Genentech, South San Francisco, CA, USA.
Shu-Pei WuClinical Pharmacology and Pharmacometrics, BioNTech SE, Boston, MA, USA.
Donghua YinClinical Pharmacology, Early Oncology, Pfizer Inc., New York, NY, USA.
Di ZhouClinical Pharmacology, Modeling & Simulation, Amgen Inc., Thousand Oaks, CA, USA.
Hao ZhuDivision of Pharmacometrics, Office of Clinical Pharmacology at FDA, Silver Spring, MD, USA.
Peter BonateClinical Pharmacology and Exploratory Development, Astellas, Northbrook, IL, USA.
Amgen (United States) · USAstellas Pharma (United States) · USBioNTech (United States) · USGenmab (Denmark) · DKGilead Sciences (United States) · USMetrum Research Group (United States) · USPfizer (United States) · USQuantitative BioSciences · USRoche (Switzerland) · CHTakeda (United States) · USUnited States Food and Drug Administration · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

Indexed as

Drug DevelopmentMedical OncologyComputer SimulationDrug IndustryC-QTDisease progressionExposure–responseLogistic regressionMarkovOncologySemi-mechanisticTime-to-eventTumor growth dynamics

Identifiers

PMID36870005
PMCPMC10169901
OpenAlexW4323050139

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.