ArticleJournal of pharmacokinetics and pharmacodynamics2014
Predicting the effects of 8C2, a monoclonal anti-topotecan antibody, on plasma and tissue disposition of topotecan.
Article in Journal of pharmacokinetics and pharmacodynamics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Camptothein-Based Anti-Cancer Therapies and Strategies to Improve Their Therapeutic Index.Cancers · 2025Review
- Use of Payload Binding Selectivity Enhancers to Improve Therapeutic Index of Maytansinoid-Antibody-Drug Conjugates.Molecular cancer therapeutics · 2023Article
- Payload-Binding Fab Fragments Increase the Therapeutic Index of MMAE Antibody-Drug Conjugates.Molecular cancer therapeutics · 2023Article
- Recent Advances in Translational Pharmacokinetics and Pharmacodynamics Prediction of Therapeutic Antibodies Using Modeling and Simulation.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Bivalent engagement of endothelial surface antigens is critical to prolonged surface targeting and protein delivery in vivo.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2020Article
- Development of a Translational Physiologically Based Pharmacokinetic Model for Antibody-Drug Conjugates: a Case Study with T-DM1.The AAPS journal · 2017Article
- Application of Pharmacokinetic-Pharmacodynamic Modeling and Simulation for Antibody-Drug Conjugate Development.Pharmaceutical research · 2015Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
We are investigating an inverse targeting strategy to reduce the dose limiting systemic toxicities resultant from intraperitoneal administration of topotecan, a model chemotherapeutic drug. This approach utilizes systemic co-administration of anti-topotecan antibodies to alter the plasma and tissue disposition kinetics of topotecan. To better predict the effects of 8C2, a high affinity anti-topotecan monoclonal antibody, on the pharmacokinetics of topotecan, two mathematical models have been developed and evaluated. Model 1 is a hybrid physiologically based pharmacokinetic (PBPK) model that was created by merging a PBPK model for topotecan with a simple two compartment model of 8C2 pharmacokinetics. Model 2 is a comprehensive PBPK model developed by merging a PBPK model for IgG with a PBPK model for topotecan. To help validate the simulation results from both the models, a tissue distribution experiment was conducted, in which topotecan and 8C2 were co-administered in mice. Experimental and simulated data were compared by calculating the median percent prediction error (%PE) for all tissues. For both models, the median %PE values for all the tissues were less than 100 %, indicating that the predicted values were, on average, less than twofold the observed plasma and tissue topotecan concentrations values. In general model 2 was found to be more predictive of the data set than model 1, as the overall median %PE value for model 2 (%PE = 63) was less than model 1 (%PE = 73).
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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.