ArticleNature biotechnology2016
Mapping the effects of drugs on the immune system.
Article in Nature biotechnology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses 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
43 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Opportunities and challenges of proximity labeling for microbe-host cell interactions in tumor microenvironment.Frontiers in cellular and infection microbiology · 2025Pooled it
- Critical period plasticity-related transcriptional aberrations in schizophrenia and bipolar disorder.Schizophrenia research · 2019Pooled it
- Autologous Dendritic Cells in Combination With Chemotherapy Restore Responsiveness of T Cells in Breast Cancer Patients: A Single-Arm Phase I/II Trial.Frontiers in immunology · 2021Trial
- Antiviral drug discovery and development: challenges and future directions.Signal transduction and targeted therapy · 2026Review
- The impact of concomitant medications on treatment outcomes in patients with cancer receiving immune checkpoint inhibitors.Nature reviews. Cancer · 2026Review
- Natural and synthetic chemicals acting as enhancers of bacterial pathogenic success within the human host.Frontiers in cellular and infection microbiology · 2026Review
- Factors Affecting SARS-CoV-2 IgG Production after Vaccination and/or Disease: A Large-Scale Seroprevalence Study.Vaccines · 2023Article
- COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming.Briefings in bioinformatics · 2023Article
- Sensor Array-Enabled Identification of Drugs for Repolarization of Macrophages to Anti-Inflammatory Phenotypes.Analytical chemistry · 2023Article
- A T Cell-Engaging Tumor Organoid Platform for Pancreatic Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- A pilot metabolomic study of drug interaction with the immune response to seasonal influenza vaccination.NPJ vaccines · 2023Article
- Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics.Open biology · 2022Review
- Immunity to SARS-CoV-2 induced by infection or vaccination.Journal of internal medicine · 2022Review
- Pharmaceutical therapeutics for articular regeneration and restoration: state-of-the-art technology for screening small molecular drugs.Cellular and molecular life sciences : CMLS · 2021Review
- Multi-Compartment Lymph-Node-on-a-Chip Enables Measurement of Immune Cell Motility in Response to Drugs.Bioengineering (Basel, Switzerland) · 2021Article
- Playing With Fire: Proinflammatory Virulence Mechanisms of Group AFrontiers in cellular and infection microbiology · 2021Review
- Unraveling Gene Fusions for Drug Repositioning in High-Risk Neuroblastoma.Frontiers in pharmacology · 2021Article
- Opportunities and Challenges in Democratizing Immunology Datasets.Frontiers in immunology · 2021Review
- Progress and applications of mass cytometry in sketching immune landscapes.Clinical and translational medicine · 2020Review
- Multi-Compartment 3D-Cultured Organ-on-a-Chip: Towards a Biomimetic Lymph Node for Drug Development.Pharmaceutics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Understanding how drugs affect the immune system has consequences for treating disease and minimizing unwanted side effects. Here we present an integrative computational approach for predicting interactions between drugs and immune cells in a system-wide manner. The approach matches gene sets between transcriptional signatures to determine their similarity. We apply the method to model the interactions between 1,309 drugs and 221 immune cell types and predict 69,995 interactions. The resulting immune-cell pharmacology map is used to predict how five drugs influence four immune cell types in humans and mice. To validate the predictions, we analyzed patient records and examined cell population changes from in vivo experiments. Our method offers a tool for screening thousands of interactions to identify relationships between drugs and the immune system.
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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.