Evidence map›Paper›PMID 33205423›Full record

ReviewJournal of clinical pharmacology2020

Impact of Physiologically Based Pharmacokinetics, Population Pharmacokinetics and Pharmacokinetics/Pharmacodynamics in the Development of Antibody-Drug Conjugates.

Chunze Li, Shang-Chiung Chen, Yuan Chen, Sandhya Girish, Matts Kaagedal, Dan Lu, Tong Lu, Divya Samineni, Jin Y Jin

Erratum issuedAbstract readReview
In one paragraph

Review in Journal of clinical pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Review
  7. Model-informed drug discovery and development approaches to inform clinical trial design and regulatory decisions: A primer for the MENA region.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2024
    Review
  8. Review
  9. Review
  10. Review
  11. Article
  12. Correction.Journal of clinical pharmacology · 2021
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Chunze LiGenentech Inc., South San Francisco, California, USA.
Shang-Chiung ChenRetrophin, San Diego, California, USA.
Yuan ChenGenentech Inc., South San Francisco, California, USA.
Sandhya GirishGenentech Inc., South San Francisco, California, USA.
Matts KaagedalGenentech Inc., South San Francisco, California, USA.
Dan LuGenentech Inc., South San Francisco, California, USA.
Tong LuGenentech Inc., South San Francisco, California, USA.
Divya SamineniGenentech Inc., South San Francisco, California, USA.
Jin Y JinGenentech Inc., South San Francisco, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antibody-drug conjugates are important molecular entities in the treatment of cancer, with 8 antibody-drug conjugates approved by the US Food and Drug Administration since 2000 and many more in early- and late-stage clinical development. These conjugates combine the target specificity of monoclonal antibodies with the potent anticancer activity of small-molecule therapeutics. The complex structure of antibody-drug conjugates poses unique challenges to pharmacokinetic (PK) and pharmacodynamic (PD) characterization because it requires a quantitative understanding of the PK and PD properties of multiple different molecular species (eg, conjugate, total antibody, and unconjugated payload) in different tissues. Quantitative clinical pharmacology using mathematical modeling and simulation provides an excellent approach to overcome these challenges, as it can simultaneously integrate the disposition, PK, and PD of antibody-drug conjugates and their components in a quantitative manner. In this review, we highlight diverse quantitative clinical pharmacology approaches, ranging from system models (eg, physiologically based pharmacokinetic [PBPK] modeling) to mechanistic and empirical models (eg, population PK/PD modeling for single or multiple analytes, exposure-response modeling, platform modeling by pooling data across multiple antibody-drug conjugates). The impact of these PBPK and PK/PD models to provide insights into clinical dosing justification and inform drug development decisions is also highlighted.

Indexed as

Models, BiologicalComputer SimulationDose-Response Relationship, DrugDrug DevelopmentDrug InteractionsHumansImmunoconjugatesImmunologic FactorsImmunoconjugatesImmunologic Factorsantibody-drug conjugatesdrug discovery and developmentexposure-responsePBPKPK/PDpopulation PK

Identifiers

PMID33205423
PMCPMC7756373

What OpenQuestion holds

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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.