ArticleComparative medicine2016
Biodistribution Analyses of a Near-Infrared, Fluorescently Labeled, Bispecific Monoclonal Antibody Using Optical Imaging.
Article in Comparative medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 9 citations in OpenAlex.
- More Than a Glow: How Cyanine Dyes Shape Tracer Pharmacokinetics.Angewandte Chemie (International ed. in English) · 2026Review
- Mixing Matrix-corrected Whole-body Pharmacokinetic Modeling Using Longitudinal Micro-computed Tomography and Fluorescence-mediated Tomography.Molecular imaging and biology · 2021Article
- Understanding the in vivo Fate of Advanced Materials by Imaging.Advanced functional materials · 2020Article
- Image-guided mathematical modeling for pharmacological evaluation of nanomaterials and monoclonal antibodies.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology · 2020Review
- Multimodal Nanocarrier Probes Reveal Superior Biodistribution Quantification by Isotopic Analysis over Fluorescence.ACS nano · 2020Article
- Targeted drug delivery via caveolae-associated protein PV1 improves lung fibrosis.Communications biology · 2019Article
- Article
- Article
- Development of a Plasma Spot Assay for Detection of Fluorophore-labeled Antibodies in Mouse Imaging Studies.In vivo (Athens, Greece)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, biodistribution analyses of pharmaceutical compounds in preclinical animal models have become an integral part of drug development. Here we report on the use of optical imaging biodistribution analyses in a mouse xenograft model to identify tissues that nonspecifically retained a bispecific antibody under development. Although our bispecific antibody bound both the epidermal growth factor receptor and insulin growth factor 1 receptor are expressed on H358, nonsmall-cell lung carcinoma cells, the fluorescence from labeled bispecific antibody was less intense than expected in xenografted tumors. Imaging analyses of live mice and major organs revealed that the majority of the Alexa Fluor 750 labeled bispecific antibody was sequestered in the liver within 2 h of injection. However, results varied depending on which near-infrared fluorophore was used, and fluorescence from the livers of mice injected with bispecific antibody labeled with Alexa Fluor 680 was less pronounced than those labeled with Alexa Fluor 750. The tissue distribution of control antibodies remained unaffected by label and suggests that the retention of fluorophores in the liver may differ. Given these precautions, these results support the incorporation of optical imaging biodistribution analyses in biotherapeutic development strategies.
Indexed as
Identifiers
27053562PMC4825957W2499605321What 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.