ReviewJournal of pharmacokinetics and pharmacodynamics2018
Boolean network modeling in systems pharmacology.
Review in Journal of pharmacokinetics and pharmacodynamics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.
- Recent applications of quantitative systems pharmacology and machine learning models across diseases.Journal of pharmacokinetics and pharmacodynamics · 2022Pooled it
- MISSTE: a multiscale integrative spatial simulator for understanding the mechanisms underlying tissue ecosystems.bioRxiv : the preprint server for biology · 2026Article
- Unveiling toxicological adverse outcomes: toward construction and simulation of large-scale networks.Toxicological sciences : an official journal of the Society of Toxicology · 2026Review
- Experimentally calibrated multiscale model predicts schedule dependent drug combination effects.NPJ systems biology and applications · 2026Article
- Personalized Boolean network dynamic profiling and machine learning approaches to identify combinatorial treatments for prostate cancer.Frontiers in pharmacology · 2026Article
- Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.Advanced drug delivery reviews · 2025Review
- Mathematical Modeling of Neuroinflammation in Neurodegenerative Diseases.CPT: pharmacometrics & systems pharmacology · 2025Review
- Data-driven inference of Boolean networks from transcriptomes to predict cellular differentiation and reprogramming.NPJ systems biology and applications · 2025Article
- Reverse control of biological networks to restore phenotype landscapes.Science advances · 2025Article
- The dawn of a new era: can machine learning and large language models reshape QSP modeling?Journal of pharmacokinetics and pharmacodynamics · 2025Review
- The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification.Annual review of biomedical engineering · 2025Review
- The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases.Pharmaceutics · 2024Review
- A Dataset for Constructing the Network Pharmacology of Overactive Bladder and Its Application to Reveal the Potential Therapeutic Targets of Rhynchophylline.Pharmaceuticals (Basel, Switzerland) · 2024Article
- On the History of Ecosystem Dynamical Modeling: The Rise and Promises of Qualitative Models.Entropy (Basel, Switzerland) · 2023Article
- Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses.Frontiers in bioinformatics · 2023Article
- A system pharmacology Boolean network model for the TLR4-mediated inflammatory response in early sepsis.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- In silico Methods for Identification of Potential Therapeutic Targets.Interdisciplinary sciences, computational life sciences · 2022Review
- A multiscale cell-based model of tumor growth for chemotherapy assessment and tumor-targeted therapy through a 3D computational approach.Cell proliferation · 2022Article
- Editor's note on the themed issue: integration of machine learning and quantitative systems pharmacology.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- From data to QSP models: a pipeline for using Boolean networks for hypothesis inference and dynamic model building.Journal of pharmacokinetics and pharmacodynamics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.
Indexed as
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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.