ArticlemAbs2014
Maximizing the potency of an anti-TLR4 monoclonal antibody by exploiting proximity to Fcγ receptors.
Article in mAbs, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Friend or foe for obesity: How hepatokines remodel adipose tissues and translational perspective.Genes & diseases · 2023Review
- Computational-Driven Epitope Verification and Affinity Maturation of TLR4-Targeting Antibodies.International journal of molecular sciences · 2021Article
- Monoclonal Antibody to CD14, TLR4, or CD11b: Impact of Epitope and Isotype Specificity on ROS Generation by Human Granulocytes and Monocytes.Oxidative medicine and cellular longevity · 2020Review
- Antibody Structure and Function: The Basis for Engineering Therapeutics.Antibodies (Basel, Switzerland) · 2019Review
- Effect of TLR4/MyD88 signaling pathway on sepsis-associated acute respiratory distress syndrome in rats, via regulation of macrophage activation and inflammatory response.Experimental and therapeutic medicine · 2018Article
- IgG Fc engineering to modulate antibody effector functions.Protein & cell · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to treat Toll like receptor 4 (TLR4)-mediated diseases, we generated a potent antagonistic antibody directed against human TLR4, Hu 15C1. This antibody's potency can be modulated by engaging not only TLR4 but also Fcγ receptors (FcγR), a mechanism that is driven by avidity and not cell signaling. Here, using various formats of the antibody, we further dissect the relative contributions of the Fv and Fc portions of Hu 15C1, discovering that the relationship to potency of the different antibody arms is not linear. First, as could be anticipated, we observed that Hu 15C1 co-engages up to 3 receptors on the same plasma membrane, i.e., 2 TLR4 molecules (via its variable regions) and either FcγRI or FcγRIIA (via the Fc). The Kd of these interactions are in the nM range (3 nM of the Fv for TLR4 and 47 nM of the Fc for FcγRI). However, unexpectedly, neutralization experiments revealed that, due to the low level of cell surface TLR4 expression, the avidity afforded by engagement through 2 Fv arms was significantly limited. In contrast, the antibody's neutralization capacity increases by 3 logs when able to exploit Fc-FcγR interactions. Taken together, these results demonstrate an unforeseen level of contribution by FcγRs to an antibody's effectiveness when targeting a cell surface protein of relatively low abundance. These findings highlight an exploitable mechanism by which FcγR-bearing cells may be more powerfully targeted, envisioned to be broadly applicable to other reagents aimed at neutralizing cell surface targets on cells co-expressing FcγRs.
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What OpenQuestion holds
Registered trials
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.