ArticlemAbs
Physiologically-based modeling to predict the clinical behavior of monoclonal antibodies directed against lymphocyte antigens.
Article in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled 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.
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
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review.European journal of clinical pharmacology · 2018Pooled it
- Review
- Challenging Traditional ADME Assumptions for Physiologically Based Pharmacokinetic Models for Intravenous Administration of Iron-Carbohydrate Nanomedicines: Potential Utility of Gold Nanoparticle Models as a Roadmap.Clinical pharmacokinetics · 2025Review
- Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics.Scientific reports · 2025Article
- Dosing optimization of rituximab for primary membranous nephropathy by population pharmacokinetic and pharmacodynamic study.Frontiers in pharmacology · 2024Article
- Mechanistically Weighted Metric to Predict In Vivo Antibody-Receptor Occupancy: An Analytical Approach.The Journal of pharmacology and experimental therapeutics · 2023Article
- Amgen v. Sanofi: Critical Impact on the Value of Innovative Science in Antibody Discovery.The AAPS journal · 2023Article
- Review of the Existing Translational Pharmacokinetics Modeling Approaches Specific to Monoclonal Antibodies (mAbs) to Support the First-In-Human (FIH) Dose Selection.International journal of molecular sciences · 2022Review
- Drug Exposure to Establish Pharmacokinetic-Response Relationships in Oncology.Clinical pharmacokinetics · 2020Review
- A translational platform PBPK model for antibody disposition in the brain.Journal of pharmacokinetics and pharmacodynamics · 2019Article
- Two-pore physiologically based pharmacokinetic model with de novo derived parameters for predicting plasma PK of different size protein therapeutics.Journal of pharmacokinetics and pharmacodynamics · 2019Article
- Physiologically-based modeling of monoclonal antibody pharmacokinetics in drug discovery and development.Drug metabolism and pharmacokinetics · 2019Review
- Pharmacokinetic Considerations for Antibody-Drug Conjugates against Cancer.Pharmaceutical research · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many clinically approved and investigational monoclonal antibody (mAb)-based therapeutics are directed against proteins located in the systemic circulation, including cytokines, growth factors, lymphocyte proteins, and shed antigens. Interaction between mAb and target may lead to non-linear pharmacokinetics (PK), characterized by rapid, target-mediated elimination. Several groups have reported that determinants of target-mediated elimination could include mAb-target binding, target expression, and target turnover. Recently, we scaled a physiologically-based pharmacokinetic model for mAb disposition to man and used it to predict the non-linear PK of mAbs directed against tumor epithelial proteins. In this work, we extended the previously described model to account for the influence of lymphocyte proteins on mAb PK in man. To account for the dynamic behavior of lymphocytes in the circulation, lymphocyte cycling between blood and lymphoid organs was described using first-order transfer rate constants. Use of lymphocyte cycling and reported target turnover rates in the model allowed the accurate prediction of the pharmacokinetics and pharmacodynamics (PD) of 4 mAbs (TRX1, MTRX1011a, rituximab, daclizumab) directed against 3 lymphocyte targets (CD4, CD20, CD25). The results described here suggest that the proposed model structure may be useful in the a priori prediction of the PK/PD properties of mAbs directed against antigens in the circulation.
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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.