Evidence map›Paper›PMID 35529449›Full record

ArticleFrontiers in pharmacology2022

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches.

Sathya Babu, Santhosh Kumar Nagarajan, Sruthy Sathish, Vir Singh Negi, Honglae Sohn, Thirumurthy Madhavan

Open access · goldAbstract read
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Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.1field-weighted citation impact, top 7% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 30 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 2 countries.

Sathya BabuComputational Biology Lab, Department of Genetic Engineering, School of Bioengineering, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, India.
Santhosh Kumar NagarajanComputational Biology Lab, Department of Genetic Engineering, School of Bioengineering, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, India.
Sruthy SathishComputational Biology Lab, Department of Genetic Engineering, School of Bioengineering, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, India.
Vir Singh NegiDepartment of Clinical Immunology, Jawaharlal Institute of Post-Graduate Medical Education and Research, Pondicherry, India.
Honglae SohnDepartment of Chemistry and Department of Carbon Materials, Chosun University, Gwangju, South Korea.
Thirumurthy MadhavanComputational Biology Lab, Department of Genetic Engineering, School of Bioengineering, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, India.
SRM Institute of Science and Technology · INChosun University · KRJawaharlal Institute of Post Graduate Medical Education and Research · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

JAK1 plays a significant role in the intracellular signaling by interacting with cytokine receptors in different types of cells and is linked to the pathogenesis of various cancers and in the pathology of the immune system. In this study, ligand-based pharmacophore modeling combined with virtual screening and molecular docking methods was incorporated to identify the potent and selective lead compounds for JAK1. Initially, the ligand-based pharmacophore models were generated using a set of 52 JAK1 inhibitors named C-2 methyl/hydroxyethyl imidazopyrrolopyridines derivatives. Twenty-seven pharmacophore models with five and six pharmacophore features were generated and validated using potency and selectivity validation methods. During potency validation, the Guner-Henry score was calculated to check the accuracy of the generated models, whereas in selectivity validation, the pharmacophore models that are capable of identifying selective JAK1 inhibitors were evaluated. Based on the validation results, the best pharmacophore models ADHRRR, DDHRRR, DDRRR, DPRRR, DHRRR, ADRRR, DDHRR, and ADPRR were selected and taken for virtual screening against the Maybridge, Asinex, Chemdiv, Enamine, Lifechemicals, and Zinc database to identify the new molecules with novel scaffold that can bind to JAK1. A total of 4,265 hits were identified from screening and checked for acceptable drug-like properties. A total of 2,856 hits were selected after ADME predictions and taken for Glide molecular docking to assess the accurate binding modes of the lead candidates. Ninety molecules were shortlisted based on binding energy and H-bond interactions with the important residues of JAK1. The docking results were authenticated by calculating binding free energy for protein-ligand complexes using the MM-GBSA calculation and induced fit docking methods. Subsequently, the cross-docking approach was carried out to recognize the selective JAK1 lead compounds. Finally, top five lead compounds that were potent and selective against JAK1 were selected and validated using molecular dynamics simulation. Besides, the density functional theory study was also carried out for the selected leads. Through various computational studies, we observed good potency and selectivity of these lead compounds when compared with the drug ruxolitinib. Compounds such as T5923555 and T5923531 were found to be the best and can be further validated using

Indexed as

density function theoryJAK1molecular dockingmolecular dynamics simulationpharmacophore modelingvirtual screening

Identifiers

PMID35529449
PMCPMC9068899
OpenAlexW4224300869

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LicenceCC BY
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Registered trials

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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.