ReviewCellular and molecular life sciences : CMLS2020
Multi-scale modeling of drug binding kinetics to predict drug efficacy.
Review in Cellular and molecular life sciences : CMLS, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Homomultimeric FAP Inhibitor-Based Radioligands for Cancer Theranostics: Design Principles, Structure-Function Relationships, and Preclinical Performance.Molecules (Basel, Switzerland) · 2026Review
- Shielded by the dead: how killed bacteria shape the dynamics and evolution of innate immunity.bioRxiv : the preprint server for biology · 2026Article
- Advanced hydrogel therapeutics for intervertebral disc degeneration: Engineering structural-functional properties in natural and synthetic biomaterials.Bioengineering & translational medicine · 2025Review
- Application of Physiologically-Based Pharmacokinetic (PBPK) Model in Drug Development and in Dietary Phytochemicals.Current pharmacology reports · 2025Review
- The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution.Microbiology (Reading, England) · 2023Article
- Assessing the relative importance of bacterial resistance, persistence and hyper-mutation for antibiotic treatment failure.Proceedings. Biological sciences · 2022Article
- vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding.BMC bioinformatics · 2022Article
- Current experimental methods to investigate the impact of specialized pro-resolving lipid mediators on Sjögren's syndrome.Frontiers in immunology · 2022Review
- Perspectives for systems biology in the management of tuberculosis.European respiratory review : an official journal of the European Respiratory Society · 2021Review
- Article
- Slow-binding reversible inhibitor of acetylcholinesterase with long-lasting action for prophylaxis of organophosphate poisoning.Scientific reports · 2020Article
- Systems biology: current status and challenges.Cellular and molecular life sciences : CMLS · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optimizing drug therapies for any disease requires a solid understanding of pharmacokinetics (the drug concentration at a given time point in different body compartments) and pharmacodynamics (the effect a drug has at a given concentration). Mathematical models are frequently used to infer drug concentrations over time based on infrequent sampling and/or in inaccessible body compartments. Models are also used to translate drug action from in vitro to in vivo conditions or from animal models to human patients. Recently, mathematical models that incorporate drug-target binding and subsequent downstream responses have been shown to advance our understanding and increase predictive power of drug efficacy predictions. We here discuss current approaches of modeling drug binding kinetics that aim at improving model-based drug development in the future. This in turn might aid in reducing the large number of failed clinical trials.
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Identifiers
What OpenQuestion holds
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.