Trial reportThe AAPS journal2019
Evaluating a Multiscale Mechanistic Model of the Immune System to Predict Human Immunogenicity for a Biotherapeutic in Phase 1.
Trial report in The AAPS journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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
11 citing papers in PubMed, 16 citations in OpenAlex.
- Review: application and opportunities for machine learning and artificial intelligence in preclinical immunogenicity risk assessment.Frontiers in immunology · 2026Review
- Mechanism-Based Modeling Approaches to Quantify the Effect of Immunogenicity on the Pharmacokinetics of Therapeutic Proteins in Drug Development.CPT: pharmacometrics & systems pharmacology · 2025Review
- A Semi-Mechanistic Mathematical Model of Immune Tolerance Induction to Support Preclinical Studies of Human Monoclonal Antibodies in Rats.Pharmaceutics · 2025Article
- IQ Survey Results on Current Industry Practices: Part 2-Quantitative Evaluations of Immunogenicity Assessment.Clinical pharmacology and therapeutics · 2025Review
- An overview of immunotoxicity in drug discovery and development.Toxicology letters · 2025Review
- Assessing the performance of QSP models: biology as the driver for validation.Journal of pharmacokinetics and pharmacodynamics · 2024Review
- Immunoinformatic Risk Assessment of Host Cell Proteins During Process Development for Biologic Therapeutics.The AAPS journal · 2023Review
- Characterization and root cause analysis of immunogenicity to pasotuxizumab (AMG 212), a prostate-specific membrane antigen-targeting bispecific T-cell engager therapy.Frontiers in immunology · 2023Article
- Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune-Oncology Therapies: A Tutorial for Clinical Pharmacologists.Clinical pharmacology and therapeutics · 2020Review
- Immunogenicity in Clinical Practice and Drug Development: When is it Significant?Clinical and translational science · 2020Article
- 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
8 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A mechanistic model of the immune response was evaluated for its ability to predict anti-drug antibody (ADA) and their impact on pharmacokinetics (PK) and pharmacodynamics (PD) for a biotherapeutic in a phase 1 clinical trial. Observed ADA incidence ranged from 33 to 67% after single doses and 27-50% after multiple doses. The model captured the single dose incidence well; however, there was overprediction after multiple dosing. The model was updated to include a T-regulatory (Treg) cell mediated tolerance, which reduced the overprediction (relative decrease in predicted incidence rate of 21.5-59.3% across multidose panels) without compromising the single dose predictions (relative decrease in predicted incidence rate of 0.6-13%). The Treg-adjusted model predicted no ADA impact on PK or PD, consistent with the observed data. A prospective phase 2 trial was simulated, including co-medication effects in the form of corticosteroid-induced immunosuppression. Predicted ADA incidences were 0-10%, depending on co-medication dosage. This work demonstrates the utility in applying an integrated, iterative modeling approach to predict ADA during different stages of clinical development.
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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.