ArticleCPT: pharmacometrics & systems pharmacology2023
Learn-confirm in model-informed drug development: Assessing an immunogenicity quantitative systems pharmacology platform.
Article in CPT: pharmacometrics & systems pharmacology, 2023. 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, 7 citations in OpenAlex.
- Global Trends in Integrating Machine Learning (ML) with Model-Informed Drug Development (MIDD): A Bibliometric and Systematic Review (2015-2025).Pharmaceutics · 2026Review
- Use of Modeling and Simulation to Inform the Development of Monoclonal Antibodies to Treat Moderate-to-Severe Asthma: A Retrospective Review of EMA Centralized Procedure From 2014 to 2024.CPT: pharmacometrics & systems pharmacology · 2026Article
- Immunogenicity Publication Bias and Its Consequences for Predictive Models: A Call for Transparent Reporting.CPT: pharmacometrics & systems pharmacology · 2026Article
- Review: application and opportunities for machine learning and artificial intelligence in preclinical immunogenicity risk assessment.Frontiers in immunology · 2026Review
- A Model-Based Approach to Evaluate Anti-Drug Antibody Impact on Drug Exposure With Biologics: A Case Example With the CD3 T-Cell Bispecific Cibisatamab.CPT: pharmacometrics & systems pharmacology · 2025Article
- Towards new approach methodologies for biological therapeutics: a novel model-informed metric to assess immunogenicity risk.Frontiers in immunology · 2025Article
- The Role of Cross-Institutional and Interdisciplinary Collaboration in Defining and Executing a Quantitative Systems Pharmacology Strategy.Handbook of experimental pharmacology · 2025Review
- QSP Designer: Quantitative systems pharmacology modeling with modular biological process map notation and multiple language code generation.CPT: pharmacometrics & systems pharmacology · 2023Article
- Learn-confirm in model-informed drug development: Assessing an immunogenicity quantitative systems pharmacology platform.CPT: pharmacometrics & systems pharmacology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immunogenicity against therapeutic proteins frequently causes attrition owing to its potential impact on pharmacokinetics, pharmacodynamics, efficacy, and safety. Predicting immunogenicity is complex because of its multifactorial drivers, including compound properties, subject characteristics, and treatment parameters. To integrate these, the Immunogenicity Simulator was developed using published, predominantly late-stage trial data from 15 therapeutic proteins. This single-blinded evaluation with subject-level data from 10 further monoclonals assesses the Immunogenicity Simulator's credibility for application during the drug development process.
Indexed as
Identifiers
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.