ArticleCurrent opinion in toxicology2017
Quantitative systems toxicology.
Article in Current opinion in toxicology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Quantitative systems toxicology: modelling to mechanistically understand and predict drug safety.Nature reviews. Drug discovery · 2026Review
- Insights and perspectives into the etiology of TKI-induced cardiotoxicity.ASPET discovery · 2026Article
- Application of Mechanistic Mathematical Modeling to Toxicology: Quantitative Systems Toxicology (QST).Handbook of experimental pharmacology · 2025Review
- Quantitative systems toxicology modeling in pharmaceutical research and development: An industry-wide survey and selected case study examples.CPT: pharmacometrics & systems pharmacology · 2024Review
- Machine Learning-BasedChemical research in toxicology · 2024Article
- Towards in vitro models for reducing or replacing the use of animals in drug testing.Nature biomedical engineering · 2024Article
- The estimation and translation uncertainties in applying NOAEL to clinical dose escalation.Clinical and translational science · 2024Article
- Developing a pragmatic consensus procedure supporting the ICH S1B(R1) weight of evidence carcinogenicity assessment.Frontiers in toxicology · 2024Article
- A dynamic model of the intestinal epithelium integrates multiple sources of preclinical data and enables clinical translation of drug-induced toxicity.CPT: pharmacometrics & systems pharmacology · 2023Article
- The Combination of a Human Biomimetic Liver Microphysiology System with BIOLOGXsym, a Quantitative Systems Toxicology (QST) Modeling Platform for Macromolecules, Provides Mechanistic Understanding of Tocilizumab- and GGF2-Induced Liver Injury.International journal of molecular sciences · 2023Article
- Development of an adverse drug event network to predict drug toxicity.Current research in toxicology · 2020Article
- Assessing Effects of BHV-0223 40 mg Zydis Sublingual Formulation and Riluzole 50 mg Oral Tablet on Liver Function Test Parameters Utilizing DILIsym.Toxicological sciences : an official journal of the Society of Toxicology · 2020Article
- The DILI-sim Initiative: Insights into Hepatotoxicity Mechanisms and Biomarker Interpretation.Clinical and translational science · 2019Review
- Analyzing the Mechanisms Behind Macrolide Antibiotic-Induced Liver Injury Using Quantitative Systems Toxicology Modeling.Pharmaceutical research · 2019Article
- Machine Learning Models for the Prediction of Chemotherapy-Induced Peripheral Neuropathy.Pharmaceutical research · 2019Article
- Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure-Activity Relationship (QSAR) Analysis.Frontiers in bioengineering and biotechnology · 2019Article
- Can Bile Salt Export Pump Inhibition Testing in Drug Discovery and Development Reduce Liver Injury Risk? An International Transporter Consortium Perspective.Clinical pharmacology and therapeutics · 2018Review
- Boolean network modeling in systems pharmacology.Journal of pharmacokinetics and pharmacodynamics · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S. Environmental Protection Agency recognized the need to develop innovative toxicity testing strategies and asked the National Research Council to develop a long-range vision and strategy for toxicity testing in the 21st century. The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and
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.