ReviewBritish journal of pharmacology2012
A guide to picking the most selective kinase inhibitor tool compounds for pharmacological validation of drug targets.
Review in British journal of pharmacology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Article
- Solubility, pH-Solubility Profile, pH-Rate Profile, and Kinetic Stability of the Tyrosine Kinase Inhibitor, Alectinib.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Discovery and optimization of narrow spectrum inhibitors of Tousled like kinase 2 (TLK2) using quantitative structure activity relationships.European journal of medicinal chemistry · 2024Article
- Discovery and Optimization of Narrow Spectrum Inhibitors of Tousled Like Kinase 2 (TLK2) Using Quantitative Structure Activity Relationships.bioRxiv : the preprint server for biology · 2023Article
- Article
- Dietary Oncopharmacognosy as a Crosswalk between Precision Oncology and Precision Nutrition.Nutrients · 2023Review
- Utilization of Supervised Machine Learning to Understand Kinase Inhibitor Toxophore Profiles.International journal of molecular sciences · 2023Article
- Natural Products in Precision Oncology: Plant-Based Small Molecule Inhibitors of Protein Kinases for Cancer Chemoprevention.Nutrients · 2023Review
- An open source plant kinase chemogenomics set.Plant direct · 2022Article
- Solid Tumors and Kinase Inhibition: Management and Therapy Efficacy Evolution.International journal of molecular sciences · 2022Review
- Target-specific compound selectivity for multi-target drug discovery and repurposing.Frontiers in pharmacology · 2022Article
- Landscape of drug-resistance mutations in kinase regulatory hotspots.Briefings in bioinformatics · 2021Article
- JAK2 regulates Nav1.6 channel function via FGF14Biochimica et biophysica acta. Molecular cell research · 2020Article
- A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes.Nature communications · 2020Article
- P38α MAPK Signaling-A Robust Therapeutic Target for Rab5-Mediated Neurodegenerative Disease.International journal of molecular sciences · 2020Review
- An activator of G protein-coupled receptor and MEK1/2-ERK1/2 signaling inhibits HIV-1 replication by altering viral RNA processing.PLoS pathogens · 2020Article
- Clinically Precedented Protein Kinases: Rationale for Their Use in Neurodegenerative Disease.Frontiers in aging neuroscience · 2020Review
- Synergistic effects of a cremophor EL drug delivery system and its U0126 cargo in anDrug delivery · 2019Article
- Poly(ADP-ribose)-dependent ubiquitination and its clinical implications.Biochemical pharmacology · 2019Review
- Error Tolerance of Machine Learning Algorithms across Contemporary Biological Targets.Molecules (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To establish the druggability of a target, genetic validation needs to be supplemented with pharmacological validation. Pharmacological studies, especially in the kinase field, are hampered by the fact that many reference inhibitors are not fully selective for one target. Fortunately, the initial trickle of selective inhibitors released in the public domain has steadily swelled into a stream. However, rationally picking the most selective tool compound out of the increasing amounts of available inhibitors has become progressively difficult due to the lack of accurate quantitative descriptors of drug selectivity. A recently published approach, termed 'selectivity entropy', is an improved way of expressing selectivity as a single-value parameter and enables rank ordering of inhibitors. We provide a guide to select the best tool compounds for pharmacological validation experiments of candidate drug targets using selectivity entropy. In addition, we recommend which inhibitors to use for studying the biology of the 20 most investigated kinases that are clinically relevant: Abl (ABL1), AKT1, ALK, Aurora A/B, CDKs, MET, CSF1R (FMS), EGFR, FLT3, ERBB2 (HER2), IKBKB (IKK2), JAK2/3, JNK1/2/3 (MAPK8/9/10), MEK1/2, PLK1, PI3Ks, p38α (MAPK14), BRAF, SRC and VEGFR2 (KDR).
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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.