Evidence map›Paper›PMID 32185565›Full record

ReviewThe AAPS journal2020

Clinical Immunogenicity Risk Assessment Strategy for a Low Risk Monoclonal Antibody.

Robert Kernstock, Gizette Sperinde, Deborah Finco, Roslyn Davis, Diana Montgomery

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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.3field-weighted citation impact, top 45% 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

6 citing papers in PubMed, 9 citations in OpenAlex.

  1. Trial
  2. Article
  3. Review
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  6. 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

5 authors at 3 institutions in 2 countries.

Robert KernstockImmunochemistry Department, PPD Laboratories, Richmond, VA, USA.
Gizette SperindeBioAnalytical Sciences, Genentech, Inc., South San Francisco, CA, USA.
Deborah FincoDeborah Finco Consulting, LLC, Groton, CT, USA.
Roslyn DavisCSL Innovation Pty. Ltd., Melbourne, Australia.
Diana MontgomeryPredictive and Clinical Immunogenicity, Merck & Co., Inc., West Point, PA, USA. diana.montgomery@merck.com.
CSL (Australia) · AUPharmaceutical Product Development (United States) · USUnited States Military Academy · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article provides a theoretical case-study risk assessment report for a low-risk monoclonal antibody (mAb) therapeutic. In terms of risk, there are considerations around risks to safety, but also risks regarding effects on pharmacokinetics (PK), pharmacodynamics (PD), and efficacy. Much of the discussion in this document is around the risk of immunogenicity incidence. A higher incidence of immunogenicity would necessitate a detailed review of the PK, efficacy and safety in anti-drug antibody (ADA) positive and ADA negative subjects, in order to evaluate potential effects. The publication is intended to provide a framework of some the current thought processes around assessing immunogenicity risk and for building strategies to mitigate those risks. For this example, we have created a hypothetical antibody, ABC-123, targeting a membrane protein on antigen presenting cells, for the treatment of rheumatoid arthritis (RA). This hypothetical antibody therapeutic is provided as an example for the purposes of risk assessment for a low risk molecule, although any application of similar approach would be case by case.

Indexed as

Immunogenetic PhenomenaAnimalsAntibodies, MonoclonalHumansRisk AssessmentAntibodies, Monoclonalpharmacodynamicspharmacokineticsrheumatoid arthritis

Identifiers

PMID32185565
OpenAlexW3012399016

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.