Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs

Kanji, Rakesh; Sharma, Abhinav; Bagler, Ganesh

doi:10.1039/c5mb00312a

Cited by 10 publications

(8 citation statements)

References 19 publications

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“…A single drug can bind to multiple proteins, similarly, a single protein (molecular target) can interact with several drugs. These kinds of interactions between molecular targets and drugs can result in therapeutic effects along with clinically relevant adverse effects ( Kanji et al, 2015 ). The analysis of the drug-target interactions is an important step in the discovery of additional applications of the drugs already approved in the market, also called-drug repurposing, and in drug safety through the explanation of undesirable adverse effects caused by drug administration.…”

Section: Resultsmentioning

confidence: 99%

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

Panda

Mishra²,

Sharma³

et al. 2022

Front. Pharmacol.

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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.

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Section: Resultsmentioning

confidence: 99%

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

Panda

Mishra²,

Sharma³

et al. 2022

Front. Pharmacol.

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show abstract

“…Earlier studies have considered various drug features (2D & 3D properties, drug substructures) as well as features such as drug targets, molecular pathways and GO terms for prediction of side effects [ 5 , 8 , 16 , 19 ]. Among other features, 2D & 3D properties of drug are known to yield good prediction performance of side effects [ 22 ].…”

Section: Resultsmentioning

confidence: 99%

A hierarchical anatomical classification schema for prediction of phenotypic side effects

et al. 2018

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Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects. We highlight the intrinsic issue with SIDER data and methodological flaws in relying on performance measures such as AUC while attempting to predict side effects.We argue for the use of metrics that are robust to class imbalance for evaluation of classifiers. Importantly, we present a ‘hierarchical anatomical classification schema’ which aggregates side effects into organs, sub-systems, and systems. With the help of a weighted performance measure, using 5-fold cross-validation we show that this strategy facilitates biologically meaningful side effects prediction at different levels of anatomical hierarchy. By implementing various machine learning classifiers we show that Random Forest model yields best classification accuracy at each level of coarse-graining. The manually curated, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects.

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Section: Ligand Similarity/diversity and Toxicity Analysismentioning

confidence: 99%

Comprehensive assessment of Indian variations in the druggable kinome landscape highlights distinct insights at the sequence, structure and pharmacogenomic stratum

Panda

Mishra

Sharma

et al. 2021

Preprint

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The population diversity in India contains a treasure of clinically relevant rare mutations which may have evolved differently in different subpopulations. While there are many sub-groups present in the nation, the publicly available database like the 1000 Genome data (1KG) contains limited samples for indian ethnicity. Such databases are critical for the pharmaceutical and drug development industry where the 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 SNVs and amino acid exchange frequencies whereas 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 were investigated. In-silico screening of the known drugs to these Indian variation-containing proteins reveal 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.

show abstract

Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs

Cited by 10 publications

References 19 publications

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

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

A hierarchical anatomical classification schema for prediction of phenotypic side effects

Comprehensive assessment of Indian variations in the druggable kinome landscape highlights distinct insights at the sequence, structure and pharmacogenomic stratum

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