{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T13:21:30Z","timestamp":1780752090793,"version":"3.54.1"},"reference-count":15,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T00:00:00Z","timestamp":1611619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Severe cutaneous adverse drug reactions (SCAR) such as the Stevens\u2013Johnson syndrome\/toxic epidermal necrolysis (SJS\/TEN) and drug rash with eosinophilia and systemic symptoms\/drug-induced hypersensitivity syndrome (DIHS) can be induced by a plethora of medications. The field of pharmacogenomics aims to prevent severe adverse drug reactions by using our knowledge of the inherited or acquired genetic risk of drug metabolizing enzymes, drug targets, or the human leukocyte antigen (HLA) genotype. Dermatologists are experts in the diagnosis and management of severe cutaneous adverse drug reactions (SCAR) in both the inpatient and outpatient setting. However, most dermatologists in the US have not focused on the prevention of SCAR. Therefore, this paper presents a case series and review of the literature highlighting salient examples of how dermatologists can apply pharmacogenomics in the diagnosis and especially in the prevention of SCAR induced by allopurinol and sulfamethoxazole\/trimethoprim, two commonly prescribed medications.<\/jats:p>","DOI":"10.3390\/jpm11020071","type":"journal-article","created":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T08:29:16Z","timestamp":1611649756000},"page":"71","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Pharmacogenomics of Allopurinol and Sulfamethoxazole\/Trimethoprim: Case Series and Review of the Literature"],"prefix":"10.3390","volume":"11","author":[{"given":"Ogechi","family":"Ikediobi","sequence":"first","affiliation":[{"name":"Department of Dermatology, University of California, San Diego, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremy A.","family":"Schneider","sequence":"additional","affiliation":[{"name":"Department of Dermatology, University of California, San Diego, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"969","DOI":"10.3389\/fphar.2020.00969","article-title":"Pharmacogenetic Testing for Prevention of Severe Cutaneous Adverse Drug Reactions","volume":"11","author":"Chang","year":"2020","journal-title":"Front. Pharmacol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1111\/1756-185X.13143","article-title":"Diagnostic utility of HLA-B*5801 screening in severe allopurinol hypersensitivity syndrome: An updated systematic review and meta-analysis","volume":"20","author":"Yu","year":"2017","journal-title":"Int. J. Rheum. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2705","DOI":"10.1111\/bph.12598","article-title":"Arylamine N-acetyltransferases: From drug metabolism and pharmacogenetics to drug discovery","volume":"171","author":"Sim","year":"2014","journal-title":"Br. J. Pharmacol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1097\/FPC.0b013e3283498ee9","article-title":"Human N-acetyltransferase 1 (NAT1) *10 and *11 alleles increase protein expression via distinct mechanisms and associate with sulfamethoxazole-induced hypersensitivity","volume":"21","author":"Wang","year":"2011","journal-title":"Pharm. Genom."},{"key":"ref_5","first-page":"1187","article-title":"Racial\/ethnic variation and risk factors for allopurinol-associated severe cutaneous adverse reactions: A cohort study","volume":"77","author":"Keller","year":"2018","journal-title":"Ann. Rheum. Dis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1016\/j.semarthrit.2016.10.009","article-title":"The cost-effectiveness of HLA-B*5801 screening to guide initial urate-lowering therapy for gout in the united states","volume":"46","author":"Jutkowitz","year":"2017","journal-title":"Semin. Arthritis Rheum."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1002\/art.41247","article-title":"2020 American College of Rheumatology Guideline for the Management of Gout","volume":"72","author":"FitzGerald","year":"2020","journal-title":"Arthritis Rheumatol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ikediobi, O. (2020). A Personalized Medicine Approach to Guide Allopurinol Use and Prevent Serious Adverse Events: Ethnicity Is Not a Proxy for Genetics. Clin. Pharmacol. Ther.","DOI":"10.1002\/cpt.2124"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1016\/j.bcp.2008.07.024","article-title":"Comparison of CYP1A2 and NAT2 phenotypes between black and white smokers","volume":"76","author":"Muscat","year":"2008","journal-title":"Biochem. Pharmacol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1177\/0091270010377630","article-title":"N-acetyltransferase-2 (NAT2) gene polymorphisms and enzyme activity in Serbs: Unprecedented high prevalence of rapid acetylators in a white population","volume":"51","author":"Djordjevic","year":"2011","journal-title":"J. Clin. Pharmacol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1097\/FPC.0000000000000373","article-title":"N-acetyltransferase 2 enzyme genotype\u2013phenotype discordances in both HIV-negative and HIV-positive Nigerians","volume":"29","author":"Kotila","year":"2019","journal-title":"Pharm. Genom."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sabbagh, A., Langaney, A., Darlu, P., G\u00e9rard, N., Krishnamoorthy, R., and Poloni, E.S. (2008). Worldwide distribution of NAT2 diversity: Implications for NAT2 evolutionary history. BMC Genet., 9.","DOI":"10.1186\/1471-2156-9-21"},{"key":"ref_13","unstructured":"Food and Drug Administration (2020, December 02). Table of Pharmacogenomic Biomarkers in Drug Labeling, Available online: http:\/\/www.fda.gov."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1038\/clpt.2012.96","article-title":"Pharmacogenomics Knowledge for Personalized Medicine","volume":"92","author":"McDonagh","year":"2012","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_15","unstructured":"U.S. Outpatient Drug Usage Statistics (2020, December 26). ClinCalc DrugStats Database Version 21.1. Prescription Data Source: Medical Expenditure Panel Survey (MEPS) 2008\u20132018. Available online: www.clincalc.com\/DrugStats\/."}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/11\/2\/71\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:15:32Z","timestamp":1760159732000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/11\/2\/71"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,26]]},"references-count":15,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["jpm11020071"],"URL":"https:\/\/doi.org\/10.3390\/jpm11020071","relation":{},"ISSN":["2075-4426"],"issn-type":[{"value":"2075-4426","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,26]]}}}