Evidence map›Paper›PMID 37696344›Full record

ArticleInternational journal of pharmaceutics2023

Automated measurement of distance-walked as a "sixth vital sign" for detecting infusion reactions during preclinical testing.

Liam S Chase, Michael H Zaleski, Lianne J Morrell, Jacob S Brenner

Open access · greenAbstract read
In one paragraph

Article in International journal of pharmaceutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 33% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

4 authors at 1 institution in 1 country.

Liam S ChaseDepartment of Pharmacology, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: liam.chase@pennmedicine.upenn.edu.
Michael H ZaleskiDepartment of Pharmacology, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Lianne J MorrellDepartment of Pharmacology, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Jacob S BrennerDepartment of Pharmacology, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: Jacob.Brenner@pennmedicine.upenn.edu.
University of Pennsylvania · US

Funding

Controlling complement to unleash nanomedicine for acute critical illnessesR01HL160694 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI BRENNER, JACOB, MUZYKANTOV, VLADIMIR R · 2022 to 2025
$2.7M
Nanomedicine for ARDS: A new paradigm to target drugs to multiple cell types within alveolar capillariesR01HL153510 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI BRENNER, JACOB · 2020 to 2024
$2.0M
RBC-mediated mopping of cytokines for the treatment of pneumoniaR21AI166778 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI BRENNER, JACOB · 2021 to 2022
$447k
NHLBI NIH HHS R01 HL153510NHLBI NIH HHS R01 HL160694NIAID NIH HHS R21 AI166778
6 · The paper itself

Abstract

Infusion reactions are a major risk for advanced therapeutics (e.g., engineered proteins nanoparticles, etc.), which can trigger the complement cascade, anaphylaxis, and other life-threatening immune responses. However, during the early phases of development, it is uncommon to assess for infusion reactions, given the labor involved in measuring multiple physiological parameters in rodents. Therefore, we sought to develop an automated quantification of rodent locomotion to serve as a sensitive screening tool for infusion reactions, with minimal added labor-time for each experiment. Here we present the detailed methods for building a motion tracking cage for mice, requiring ∼$100 of materials, ∼2 h to build and set up completely, and employing freely available software (DeepLabCut). The distance-walked after injection was first shown to have the predicted effects for stimulants (caffeine), sedatives (ketamine), and toxins (lipopolysaccharide). Additionally, the distance-walked more sensitively detected the effects of these compounds than did pulse oximetry-based measurements of the classical vital signs of heart rate, respiratory rate, and blood oxygen saturation. Finally, we examined a nanomedicine formulation that has been in preclinical development, liposomes targeted to the cell adhesion molecule ICAM. While this formulation has been studied across dozens of publications, it has not previously been noted to produce an infusion reaction. However, the automated motion tracking cage showed that ICAM-liposomes markedly reduce the distance-walked, which we confirmed by measuring the other vital signs. Importantly, the motion tracking cage added < 5 min of labor time per 5-mouse condition, while pulse oximetry with a neck cuff (by far the most stable oximetry signal in mice) required ∼ 100 min of labor time. Thus, automated measurement of distance-walked can indeed serve as a "sixth vital sign" for detecting infusion reactions during preclinical testing. Additionally, the device to measure distance-walked is easy and cheap to build and requires negligible labor time for each experiment, enabling distance-walked to be recorded in nearly every infusion experiment.

Indexed as

LiposomesOximetryAnimalsHeart RateMiceVital SignsWalkingLiposomesAcute critical illnessArtificial intelligenceDeepLabCutInfusion reactionPhysiological measurementTargeted drug delivery

Identifiers

PMID37696344
PMCPMC10718583
OpenAlexW4386579591

What OpenQuestion holds

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LicenceTDM
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.