Evidence map›Paper›PMID 32767003›Full record

ReviewThe AAPS journal2020

Capturing the Magic Bullet: Pharmacokinetic Principles and Modeling of Antibody-Drug Conjugates.

Peiying Zuo

Abstract readReview
PubMed Publisher
In one paragraph

Review in The AAPS journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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  5. Article
  6. Review
  7. Review
  8. Review
  9. Review
  10. Article
  11. Article
  12. Article
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  14. Article
  15. Clinical Pharmacology of Antibody-Drug Conjugates.Antibodies (Basel, Switzerland) · 2021
    Review
  16. Review
  17. 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

1 author.

Peiying ZuoPharmacometrics US, Clinical Pharmacology & Exploratory Development, Astellas Pharma, Inc., USA, 1 Astellas Way, Northbrook, Illinois, 60062, USA. peiying.zuo@astellas.com.ORCID 0000-0003-4621-8526

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past two decades, antibody-drug conjugates (ADCs) have emerged as a promising class of drugs for cancer therapy and have expanded to nononcology fields such as inflammatory diseases, atherosclerosis, and bacteremia. Eight ADCs are currently approved by FDA for clinical applications, with more novel ADCs under clinical development. Compared with traditional chemotherapy, ADCs combine the target specificity of antibodies with chemotherapeutic capabilities of cytotoxic drugs. The benefits include reduced systemic toxicity and enhanced therapeutic index for patients. However, the heterogeneous structures of ADCs and their dynamic changes following administration create challenges in their development. The understanding of ADC pharmacokinetics (PK) is crucial for the optimization of clinical dosing regimens when translating from animal to human. In addition, it contributes to the optimization of dose selection and clinical monitoring with regard to safety and efficacy. This manuscript reviews the PK characteristics of ADCs and summarizes the diverse approaches for PK modeling that can be used to evaluate an ADC at the preclinical and clinical stages to support their successful development. Despite the numerous available options, fit-for-purpose modeling approaches for the PK and PD of ADCs should be critically planned and well-thought-out to adequately support the development of an ADC.

Indexed as

Models, TheoreticalAntineoplastic Agents, ImmunologicalHumansImmunoconjugatesAntineoplastic Agents, ImmunologicalImmunoconjugatesADCantibody-drug conjugatesmodelingpharmacokinetics

Identifiers

What Socratic 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 Socratic graph.