ArticleCPT: pharmacometrics & systems pharmacology2016
Assessing the Impact of Tissue Target Concentration Data on Uncertainty in In Vivo Target Coverage Predictions.
Article in CPT: pharmacometrics & systems pharmacology, 2016. 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
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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.
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Who cites it
7 citing papers in PubMed.
- A Tutorial on the Development of a Physiologically Inspired PKRO Model for Monoclonal Antibodies.CPT: pharmacometrics & systems pharmacology · 2026Article
- Minimally sufficient experimental design using identifiability analysis.NPJ systems biology and applications · 2024Article
- Asia-Inclusive Clinical Research and Development Enabled by Translational Science and Quantitative Clinical Pharmacology: Toward a Culture That Challenges the Status Quo.Clinical pharmacology and therapeutics · 2023Article
- Article
- A Mechanistic Site-Of-Action Model: A Tool for Informing Right Target, Right Compound, And Right Dose for Therapeutic Antagonistic Antibody Programs.Frontiers in bioinformatics · 2021Article
- Article
- Optimal Affinity of a Monoclonal Antibody: Guiding Principles Using Mechanistic Modeling.The AAPS journal · 2017Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding pharmacological target coverage is fundamental in drug discovery and development as it helps establish a sequence of research activities, from laboratory objectives to clinical doses. To this end, we evaluated the impact of tissue target concentration data on the level of confidence in tissue coverage predictions using a site of action (SoA) model for antibodies. By fitting the model to increasing amounts of synthetic tissue data and comparing the uncertainty in SoA coverage predictions, we confirmed that, in general, uncertainty decreases with longitudinal tissue data. Furthermore, a global sensitivity analysis showed that coverage is sensitive to experimentally identifiable parameters, such as baseline target concentration in plasma and target turnover half-life and fixing them reduces uncertainty in coverage predictions. Overall, our computational analysis indicates that measurement of baseline tissue target concentration reduces the uncertainty in coverage predictions and identifies target-related parameters that greatly impact the confidence in coverage predictions.
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Registered trials
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