Evidence map›Paper›PMID 35865963›Full record

ArticleFrontiers in pharmacology2022

Comprehensive Assessment of Indian Variations in the Druggable Kinome Landscape Highlights Distinct Insights at the Sequence, Structure and Pharmacogenomic Stratum.

Gayatri Panda, Neha Mishra, Disha Sharma, Rintu Kutum, Rahul C Bhoyar, Abhinav Jain, Mohamed Imran, Vigneshwar Senthilvel, Mohit Kumar Divakar, Anushree Mishra and 5 more

Abstract read
In one paragraph

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 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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

15 authors.

Gayatri PandaDepartment of Computational Biology, Indraprastha Institute of Information Technology, Okhla, India.
Neha MishraDepartment of Computational Biology, Indraprastha Institute of Information Technology, Okhla, India.
Disha SharmaAcademy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
Rintu KutumAcademy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
Rahul C BhoyarCSIR-Institute of Genomics and Integrative Biology, Delhi, India.
Abhinav JainAcademy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
Mohamed ImranAcademy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
Vigneshwar SenthilvelAcademy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
Mohit Kumar DivakarAcademy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
Anushree MishraCSIR-Institute of Genomics and Integrative Biology, Delhi, India.
Parth GargDepartment of Computational Biology, Indraprastha Institute of Information Technology, Okhla, India.
Priyanka BanerjeeInstitute for Physiology, Charité-University Medicine Berlin, Berlin, Germany.
Sridhar SivasubbuAcademy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
Vinod ScariaAcademy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
Arjun RayDepartment of Computational Biology, Indraprastha Institute of Information Technology, Okhla, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

India confines more than 17% of the world's population and has a diverse genetic makeup with several clinically relevant rare mutations belonging to many sub-group which are undervalued in global sequencing datasets like the 1000 Genome data (1KG) containing limited samples for Indian ethnicity. Such databases are critical for the pharmaceutical and drug development industry where diversity plays a crucial role in identifying genetic disposition towards adverse drug reactions. A qualitative and comparative sequence and structural study utilizing variant information present in the recently published, largest curated Indian genome database (IndiGen) and the 1000 Genome data was performed for variants belonging to the kinase coding genes, the second most targeted group of drug targets. The sequence-level analysis identified similarities and differences among different populations based on the nsSNVs and amino acid exchange frequencies whereas a comparative structural analysis of IndiGen variants was performed with pathogenic variants reported in UniProtKB Humsavar data. The influence of these variations on structural features of the protein, such as structural stability, solvent accessibility, hydrophobicity, and the hydrogen-bond network was investigated. In-silico screening of the known drugs to these Indian variation-containing proteins reveals critical differences imparted in the strength of binding due to the variations present in the Indian population. In conclusion, this study constitutes a comprehensive investigation into the understanding of common variations present in the second largest population in the world and investigating its implications in the sequence, structural and pharmacogenomic landscape. The preliminary investigation reported in this paper, supporting the screening and detection of ADRs specific to the Indian population could aid in the development of techniques for pre-clinical and post-market screening of drug-related adverse events in the Indian population.

Indexed as

adverse drug reactionsdockingIndian genetic variationsIndiGenome Consortiumpharmacogenomicssingle nucleotide variants

Identifiers

PMID35865963
PMCPMC9294532

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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.