ReviewDrug discovery today2017
Learning from the past for TB drug discovery in the future.
Review in Drug discovery today, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 29 citations in OpenAlex.
- Diversity of secondary metabolites from marine Streptomyces with potential anti-tubercular activity: a review.Archives of microbiology · 2025Review
- NOS2/miR-493-5p Signaling Regulates in the LPS-Induced Inflammatory Response in the RAW264.7 Cells.Biochemical genetics · 2023Article
- Advances in computational frameworks in the fight against TB: The way forward.Frontiers in pharmacology · 2023Review
- Tricyclic SpiroLactams Kill Mycobacteria In Vitro and In Vivo by Inhibiting Type II NADH Dehydrogenases.Journal of medicinal chemistry · 2022Article
- Machine Learning Models forMolecular pharmaceutics · 2022Article
- Synthesis and Antimycobacterial Activity of 3-Phenyl-1Molecules (Basel, Switzerland) · 2021Article
- Physicochemical properties andRSC medicinal chemistry · 2020Review
- Molecule Property Analyses of Active Compounds forJournal of medicinal chemistry · 2020Review
- Biosynthesis of Galactan inAntibiotics (Basel, Switzerland) · 2020Review
- Why Wait? The Case for Treating Tuberculosis with Inhaled Drugs.Pharmaceutical research · 2019Review
- Deuteration of BTZ043 Extends the Lifetime of Meisenheimer Intermediates to the Antituberculosis Nitroso Oxidation State.ACS medicinal chemistry letters · 2019Article
- A rapid method for estimation of the efficacy of potential antimicrobials in humans and animals by agar diffusion assay.Chemical biology & drug design · 2019Article
- Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.Molecular pharmaceutics · 2018Article
- Article
- Dual Mechanism of Action of 5-Nitro-1,10-Phenanthroline against Mycobacterium tuberculosis.Antimicrobial agents and chemotherapy · 2017Article
- Emerging Approaches to Tuberculosis Drug Development: At Home in the Metabolome.Trends in pharmacological sciences · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Tuberculosis drug discovery has shifted in recent years from a primarily target-based approach to one that uses phenotypic high-throughput screens. As examples of this, through our EU-funded FP7 collaborations, New Medicines for Tuberculosis was target-based and our more-recent More Medicines for Tuberculosis project predominantly used phenotypic screening. From these projects we have examples of success (DprE1) and failure (PimA) going from drug to target and from target to drug, respectively. It is clear that we still have much to learn about the drug targets and the complex effects of the drugs on Mycobacterium tuberculosis. We propose a more integrated approach that learns from earlier drug discovery efforts that could help to move drug discovery forward.
Indexed as
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.