ArticleJournal of pharmacokinetics and pharmacodynamics2012
Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer.
Article in Journal of pharmacokinetics and pharmacodynamics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review.European journal of clinical pharmacology · 2018Pooled it
- A clinical population pharmacokinetic/pharmacodynamic model for BIIB059, a monoclonal antibody for the treatment of systemic and cutaneous lupus erythematosus.Journal of pharmacokinetics and pharmacodynamics · 2020Trial
- Albumin Levels Are Predictive of Cachexia-Induced Time-Dependent Clearance of Therapeutic Antibodies: A Physiologically Based Pharmacokinetic Model of Durvalumab.CPT: pharmacometrics & systems pharmacology · 2026Article
- Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.Advanced drug delivery reviews · 2025Review
- A minimal physiologically based pharmacokinetic model to study the combined effect of antibody size, charge, and binding affinity to FcRn/antigen on antibody pharmacokinetics.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Role of Gut Microbiota in Predisposition to Colon Cancer: A Narrative Review.Indian journal of microbiology · 2024Review
- 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
- Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict PET Image Quality of Three Generations EGFR TKI in Advanced-Stage NSCLC Patients.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Recent Advances in Translational Pharmacokinetics and Pharmacodynamics Prediction of Therapeutic Antibodies Using Modeling and Simulation.Pharmaceuticals (Basel, Switzerland) · 2022Review
- A Physiologically Based Pharmacokinetic Framework for Quantifying Antibody Distribution Gradients from Tumors to Tumor-Draining Lymph Nodes.Antibodies (Basel, Switzerland) · 2022Article
- Targeted Delivery of Endosomal Escape Peptides to Enhance Immunotoxin Potency and Anti-cancer Efficacy.The AAPS journal · 2022Article
- Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Monoclonal Antibody Tumor Disposition.International journal of molecular sciences · 2022Article
- Development and Evaluation of Competitive Inhibitors of Trastuzumab-HER2 Binding to Bypass the Binding-Site Barrier.Frontiers in pharmacology · 2022Article
- Variability in Human In Vitro Enzyme Kinetics.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Pharmacokinetics and pharmacodynamics of therapeutic antibodies in tumors and tumor-draining lymph nodes.Mathematical biosciences and engineering : MBE · 2020Article
- Image-guided mathematical modeling for pharmacological evaluation of nanomaterials and monoclonal antibodies.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology · 2020Review
- The role of mouse models in colorectal cancer research-The need and the importance of the orthotopic models.Animal models and experimental medicine · 2020Review
- Pharmacokinetic Characterization and Tissue Distribution of Fusion Protein Therapeutics by Orthogonal Bioanalytical Assays and Minimal PBPK Modeling.Molecules (Basel, Switzerland) · 2020Article
- Physiologically-based modeling of monoclonal antibody pharmacokinetics in drug discovery and development.Drug metabolism and pharmacokinetics · 2019Review
- MPBPK-TMDD models for mAbs: alternative models, comparison, and identifiability issues.Journal of pharmacokinetics and pharmacodynamics · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
This investigation evaluated the utility of a physiologically based pharmacokinetic (PBPK) model, which incorporates model parameters representing key determinants of monoclonal antibody (mAb) target-mediated disposition, to predict, a priori, mAb disposition in plasma and in tissues, including tumors that express target antigens. Monte Carlo simulation techniques were employed to predict the disposition of two mAbs, 8C2 (as a non-binding control mouse IgG1 mAb) and T84.66 (a high-affinity murine IgG1 anti-carcinoembryonic antigen mAb), in mice bearing no tumors, or bearing colorectal HT29 or LS174T xenografts. Model parameters were obtained or derived from the literature. (125)I-T84.66 and (125)I-8C2 were administered to groups of SCID mice, and plasma and tissue concentrations were determined via gamma counting. The PBPK model well-predicted the experimental data. Comparisons of the population predicted versus observed areas under the plasma concentration versus time curve (AUC) for T84.66 were 95.4 ± 67.8 versus 84.0 ± 3.0, 1,859 ± 682 versus 2,370 ± 154, and 5,930 ± 1,375 versus 5,960 ± 317 (nM × day) at 1, 10, and 25 mg/kg in LS174T xenograft-bearing SCID mice; and 215 ± 72 versus 233 ± 30, 3,070 ± 346 versus 3,120 ± 180, and 7,884 ± 714 versus 7,440 ± 626 in HT29 xenograft-bearing mice. Model predicted versus observed 8C2 plasma AUCs were 312.4 ± 30 versus 182 ± 7.6 and 7,619 ± 738 versus 7,840 ± 24.3 (nM × day) at 1 and 25 mg/kg. High correlations were observed between the predicted median plasma concentrations and observed median plasma concentrations (r (2) = 0.927, for all combinations of treatment, dose, and tumor model), highlighting the utility of the PBPK model for the a priori prediction of in vivo data.
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