Evidence map›Paper›PMID 37549723›Full record

ArticleAnalytical biochemistry2023

Parameter estimation and identifiability analysis for a bivalent analyte model of monoclonal antibody-antigen binding.

Kyle Nguyen, Kan Li, Kevin Flores, Georgia D Tomaras, S Moses Dennison, Janice M McCarthy

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Analytical biochemistry, 2023. 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
–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

6 citing papers in PubMed.

  1. Article
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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

6 authors.

Kyle NguyenBiomathematics Graduate Program, North Carolina State University, Raleigh, 27607, NC, USA; Center for Research in Scientific Computation, North Carolina State University, Raleigh, 27607, NC, USA. Electronic address: kcnguye2@ncsu.edu.
Kan LiCenter for Human Systems Immunology, Duke University, Durham, 27701, NC, USA; Department of Surgery, Duke University, Durham, 27710, NC, USA.
Kevin FloresCenter for Research in Scientific Computation, North Carolina State University, Raleigh, 27607, NC, USA; Department of Mathematics, North Carolina State University, Raleigh, 27607, NC, USA.
Georgia D TomarasCenter for Human Systems Immunology, Duke University, Durham, 27701, NC, USA; Department of Surgery, Duke University, Durham, 27710, NC, USA; Department of Integrative Immunobiology, Duke University, Durham, 27710, NC, USA; Department of Molecular Genetics and Microbiology, Duke University, Durham, 27710, NC, USA; Duke Human Vaccine Institute, Duke University, Durham, 27710, NC, USA.
S Moses DennisonCenter for Human Systems Immunology, Duke University, Durham, 27701, NC, USA; Department of Surgery, Duke University, Durham, 27710, NC, USA.
Janice M McCarthyCenter for Human Systems Immunology, Duke University, Durham, 27701, NC, USA; Department of Biostatistics and Bioinformatics, Duke University, Durham, 27710, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surface plasmon resonance (SPR) is an extensively used technique to characterize antigen-antibody interactions. Affinity measurements by SPR typically involve testing the binding of antigen in solution to monoclonal antibodies (mAbs) immobilized on a chip and fitting the kinetics data using 1:1 Langmuir binding model to derive rate constants. However, when it is necessary to immobilize antigens instead of the mAbs, a bivalent analyte (1:2) binding model is required for kinetics analysis. This model is lacking in data analysis packages associated with high throughput SPR instruments and the packages containing this model do not explore multiple local minima and parameter identifiability issues that are common in non-linear optimization. Therefore, we developed a method to use a system of ordinary differential equations for analyzing 1:2 binding kinetics data. Salient features of this method include a grid search on parameter initialization and a profile likelihood approach to determine parameter identifiability. Using this method we found a non-identifiable parameter in data set collected under the standard experimental design. A simulation-guided improved experimental design led to reliable estimation of all rate constants. The method and approach developed here for analyzing 1:2 binding kinetics data will be valuable for expeditious therapeutic antibody discovery research.

Indexed as

Antigen-Antibody ReactionsAntigensAntibodies, MonoclonalKineticsLikelihood FunctionsSurface Plasmon ResonanceAntibodies, MonoclonalAntigensBinding kineticsBivalent analyteParameter identifiabilitySurface plasmon resonance

Identifiers

PMID37549723
PMCPMC10511885

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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.