Pharmacogenomic landscape of COVID-19 therapies from Indian population genomes
Numerous drugs are being widely prescribed for COVID-19 treatment without any direct evidence for the drug safety/efficacy in patients across diverse ethnic populations. We analyzed whole genomes of 1029 Indian individuals (IndiGen) to understand the extent of drug–gene (pharmacogenetic), drug–drug ...
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| Veröffentlicht in: | Pharmacogenomics 2021-07, Vol.22 (10), p.603-618 |
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| creator | Sahana, S Sivadas, Ambily Mangla, Mohit Jain, Abhinav Bhoyar, Rahul C Pandhare, Kavita Mishra, Anushree Sharma, Disha Imran, Mohamed Senthivel, Vigneshwar Divakar, Mohit Kumar Rophina, Mercy Jolly, Bani Batra, Arushi Sharma, Sumit Siwach, Sanjay Jadhao, Arun G Palande, Nikhil V Jha, Ganga Nath Ashrafi, Nishat Mishra, Prashant Kumar Vidhya, AK Jain, Suman Dash, Debasis Kumar, Nachimuthu Senthil Vanlallawma, Andrew Sarma, Ranjan Jyoti Chhakchhuak, Lalchhandama Kalyanaraman, Shantaraman Mahadevan, Radha Kandasamy, Sunitha Devi, Pabitha Rajagopal, Raskin Erusan Ramya, J Ezhil Devi, P Nirmala Bajaj, Anjali Gupta, Vishu Mathew, Samatha Goswami, Sangam Prakash, Savinitha Joshi, Kandarp Kumla, Meya Sreedevi, S Gajjar, Devarshi Soraisham, Ronibala Yadav, Rohit Devi, Yumnam Silla Gupta, Aayush Mukerji, Mitali Ramalingam, Sivaprakash Binukumar, BK Sivasubbu, Sridhar Scaria, Vinod |
| description | Numerous drugs are being widely prescribed for COVID-19 treatment without any direct evidence for the drug safety/efficacy in patients across diverse ethnic populations.
We analyzed whole genomes of 1029 Indian individuals (IndiGen) to understand the extent of drug–gene (pharmacogenetic), drug–drug and drug–drug–gene interactions associated with COVID-19 therapy in the Indian population.
We identified 30 clinically significant pharmacogenetic variants and 73 predicted deleterious pharmacogenetic variants. COVID-19-associated pharmacogenes were substantially overlapped with those of metabolic disorder therapeutics.
,
and
are the most shared pharmacogenes. Fifteen COVID-19 therapeutics were predicted as likely drug–drug interaction candidates when used with four
inhibitor drugs.
Our findings provide actionable insights for future validation studies and improved clinical decisions for COVID-19 therapy in Indians. |
| doi_str_mv | 10.2217/pgs-2021-0028 |
| format | Article |
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We analyzed whole genomes of 1029 Indian individuals (IndiGen) to understand the extent of drug–gene (pharmacogenetic), drug–drug and drug–drug–gene interactions associated with COVID-19 therapy in the Indian population.
We identified 30 clinically significant pharmacogenetic variants and 73 predicted deleterious pharmacogenetic variants. COVID-19-associated pharmacogenes were substantially overlapped with those of metabolic disorder therapeutics.
,
and
are the most shared pharmacogenes. Fifteen COVID-19 therapeutics were predicted as likely drug–drug interaction candidates when used with four
inhibitor drugs.
Our findings provide actionable insights for future validation studies and improved clinical decisions for COVID-19 therapy in Indians.</description><identifier>ISSN: 1462-2416</identifier><identifier>EISSN: 1744-8042</identifier><identifier>DOI: 10.2217/pgs-2021-0028</identifier><identifier>PMID: 34142560</identifier><language>eng</language><publisher>London, UK: Future Medicine Ltd</publisher><subject>COVID-19 therapies ; drug–drug interactions ; drug–drug–gene interactions ; Indian population ; pharmacogenomics</subject><ispartof>Pharmacogenomics, 2021-07, Vol.22 (10), p.603-618</ispartof><rights>2021 Future Medicine Ltd</rights><rights>2021 Future Medicine Ltd 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c414t-581fdbf405b6b9d5cf48dc34471283bc20aa79022595b215a44758bc02af7fc33</citedby><cites>FETCH-LOGICAL-c414t-581fdbf405b6b9d5cf48dc34471283bc20aa79022595b215a44758bc02af7fc33</cites><orcidid>0000-0003-1267-3803</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216321/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216321/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids></links><search><creatorcontrib>Sahana, S</creatorcontrib><creatorcontrib>Sivadas, Ambily</creatorcontrib><creatorcontrib>Mangla, Mohit</creatorcontrib><creatorcontrib>Jain, Abhinav</creatorcontrib><creatorcontrib>Bhoyar, Rahul C</creatorcontrib><creatorcontrib>Pandhare, Kavita</creatorcontrib><creatorcontrib>Mishra, Anushree</creatorcontrib><creatorcontrib>Sharma, Disha</creatorcontrib><creatorcontrib>Imran, Mohamed</creatorcontrib><creatorcontrib>Senthivel, Vigneshwar</creatorcontrib><creatorcontrib>Divakar, Mohit Kumar</creatorcontrib><creatorcontrib>Rophina, Mercy</creatorcontrib><creatorcontrib>Jolly, Bani</creatorcontrib><creatorcontrib>Batra, Arushi</creatorcontrib><creatorcontrib>Sharma, Sumit</creatorcontrib><creatorcontrib>Siwach, Sanjay</creatorcontrib><creatorcontrib>Jadhao, Arun G</creatorcontrib><creatorcontrib>Palande, Nikhil V</creatorcontrib><creatorcontrib>Jha, Ganga Nath</creatorcontrib><creatorcontrib>Ashrafi, Nishat</creatorcontrib><creatorcontrib>Mishra, Prashant Kumar</creatorcontrib><creatorcontrib>Vidhya, AK</creatorcontrib><creatorcontrib>Jain, Suman</creatorcontrib><creatorcontrib>Dash, Debasis</creatorcontrib><creatorcontrib>Kumar, Nachimuthu Senthil</creatorcontrib><creatorcontrib>Vanlallawma, Andrew</creatorcontrib><creatorcontrib>Sarma, Ranjan Jyoti</creatorcontrib><creatorcontrib>Chhakchhuak, Lalchhandama</creatorcontrib><creatorcontrib>Kalyanaraman, Shantaraman</creatorcontrib><creatorcontrib>Mahadevan, Radha</creatorcontrib><creatorcontrib>Kandasamy, Sunitha</creatorcontrib><creatorcontrib>Devi, Pabitha</creatorcontrib><creatorcontrib>Rajagopal, Raskin Erusan</creatorcontrib><creatorcontrib>Ramya, J Ezhil</creatorcontrib><creatorcontrib>Devi, P Nirmala</creatorcontrib><creatorcontrib>Bajaj, Anjali</creatorcontrib><creatorcontrib>Gupta, Vishu</creatorcontrib><creatorcontrib>Mathew, Samatha</creatorcontrib><creatorcontrib>Goswami, Sangam</creatorcontrib><creatorcontrib>Prakash, Savinitha</creatorcontrib><creatorcontrib>Joshi, Kandarp</creatorcontrib><creatorcontrib>Kumla, Meya</creatorcontrib><creatorcontrib>Sreedevi, S</creatorcontrib><creatorcontrib>Gajjar, Devarshi</creatorcontrib><creatorcontrib>Soraisham, Ronibala</creatorcontrib><creatorcontrib>Yadav, Rohit</creatorcontrib><creatorcontrib>Devi, Yumnam Silla</creatorcontrib><creatorcontrib>Gupta, Aayush</creatorcontrib><creatorcontrib>Mukerji, Mitali</creatorcontrib><creatorcontrib>Ramalingam, Sivaprakash</creatorcontrib><creatorcontrib>Binukumar, BK</creatorcontrib><creatorcontrib>Sivasubbu, Sridhar</creatorcontrib><creatorcontrib>Scaria, Vinod</creatorcontrib><title>Pharmacogenomic landscape of COVID-19 therapies from Indian population genomes</title><title>Pharmacogenomics</title><description>Numerous drugs are being widely prescribed for COVID-19 treatment without any direct evidence for the drug safety/efficacy in patients across diverse ethnic populations.
We analyzed whole genomes of 1029 Indian individuals (IndiGen) to understand the extent of drug–gene (pharmacogenetic), drug–drug and drug–drug–gene interactions associated with COVID-19 therapy in the Indian population.
We identified 30 clinically significant pharmacogenetic variants and 73 predicted deleterious pharmacogenetic variants. COVID-19-associated pharmacogenes were substantially overlapped with those of metabolic disorder therapeutics.
,
and
are the most shared pharmacogenes. Fifteen COVID-19 therapeutics were predicted as likely drug–drug interaction candidates when used with four
inhibitor drugs.
Our findings provide actionable insights for future validation studies and improved clinical decisions for COVID-19 therapy in Indians.</description><subject>COVID-19 therapies</subject><subject>drug–drug interactions</subject><subject>drug–drug–gene interactions</subject><subject>Indian population</subject><subject>pharmacogenomics</subject><issn>1462-2416</issn><issn>1744-8042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kEtLxDAQx4Morq4evfcLRJNp0sdFkPW1sLge1GtI02Q30iYlaQW_vVlXBA-eZob5P-CH0AUllwC0vBo2EQMBigmB6gCd0JIxXBEGh2lnBWBgtJih0xjfk4IWjByjWc4oA16QE_T0vJWhl8pvtPO9VVknXRuVHHTmTbZYvy1vMa2zcauDHKyOmQm-z5autdJlgx-mTo7Wu-zbruMZOjKyi_r8Z87R6_3dy-IRr9YPy8XNCqvUPGJeUdM2hhHeFE3dcmVY1aqcsZJClTcKiJRlTQB4zRugXKYPrxpFQJrSqDyfo-t97jA1vW6VdmOQnRiC7WX4FF5a8ffj7FZs_IeoEoIcaArA-wAVfIxBm18vJWIHViSwYgdW7MAmfb3Xm2mcgo7Kaqe02F-pxyrr9D_eL-ozfts</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Sahana, S</creator><creator>Sivadas, Ambily</creator><creator>Mangla, Mohit</creator><creator>Jain, Abhinav</creator><creator>Bhoyar, Rahul C</creator><creator>Pandhare, Kavita</creator><creator>Mishra, Anushree</creator><creator>Sharma, Disha</creator><creator>Imran, 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Disha ; Imran, Mohamed ; Senthivel, Vigneshwar ; Divakar, Mohit Kumar ; Rophina, Mercy ; Jolly, Bani ; Batra, Arushi ; Sharma, Sumit ; Siwach, Sanjay ; Jadhao, Arun G ; Palande, Nikhil V ; Jha, Ganga Nath ; Ashrafi, Nishat ; Mishra, Prashant Kumar ; Vidhya, AK ; Jain, Suman ; Dash, Debasis ; Kumar, Nachimuthu Senthil ; Vanlallawma, Andrew ; Sarma, Ranjan Jyoti ; Chhakchhuak, Lalchhandama ; Kalyanaraman, Shantaraman ; Mahadevan, Radha ; Kandasamy, Sunitha ; Devi, Pabitha ; Rajagopal, Raskin Erusan ; Ramya, J Ezhil ; Devi, P Nirmala ; Bajaj, Anjali ; Gupta, Vishu ; Mathew, Samatha ; Goswami, Sangam ; Prakash, Savinitha ; Joshi, Kandarp ; Kumla, Meya ; Sreedevi, S ; Gajjar, Devarshi ; Soraisham, Ronibala ; Yadav, Rohit ; Devi, Yumnam Silla ; Gupta, Aayush ; Mukerji, Mitali ; Ramalingam, Sivaprakash ; Binukumar, BK ; Sivasubbu, Sridhar ; Scaria, 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We analyzed whole genomes of 1029 Indian individuals (IndiGen) to understand the extent of drug–gene (pharmacogenetic), drug–drug and drug–drug–gene interactions associated with COVID-19 therapy in the Indian population.
We identified 30 clinically significant pharmacogenetic variants and 73 predicted deleterious pharmacogenetic variants. COVID-19-associated pharmacogenes were substantially overlapped with those of metabolic disorder therapeutics.
,
and
are the most shared pharmacogenes. Fifteen COVID-19 therapeutics were predicted as likely drug–drug interaction candidates when used with four
inhibitor drugs.
Our findings provide actionable insights for future validation studies and improved clinical decisions for COVID-19 therapy in Indians.</abstract><cop>London, UK</cop><pub>Future Medicine Ltd</pub><pmid>34142560</pmid><doi>10.2217/pgs-2021-0028</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0003-1267-3803</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1462-2416 |
| ispartof | Pharmacogenomics, 2021-07, Vol.22 (10), p.603-618 |
| issn | 1462-2416 1744-8042 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8216321 |
| source | PubMed Central |
| subjects | COVID-19 therapies drug–drug interactions drug–drug–gene interactions Indian population pharmacogenomics |
| title | Pharmacogenomic landscape of COVID-19 therapies from Indian population genomes |
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