{"id":6015,"date":"2022-02-22T16:03:06","date_gmt":"2022-02-22T21:03:06","guid":{"rendered":"https:\/\/blogs.cdc.gov\/genomics\/?p=6015"},"modified":"2024-04-09T18:24:51","modified_gmt":"2024-04-09T22:24:51","slug":"using-pharmacogenetics","status":"publish","type":"post","link":"https:\/\/blogs.cdc.gov\/genomics\/2022\/02\/22\/using-pharmacogenetics\/","title":{"rendered":"Using Pharmacogenetics to Enhance Tuberculosis (TB) Treatment"},"content":{"rendered":"

\"a<\/a>Through a funding opportunity from CDC\u2019s Office of Genomics and Precision Public Health<\/a> in collaboration with the CDC Office of Advanced Molecular Detection<\/a>, CDC\u2019s Division of Tuberculosis Elimination will conduct a 2-year project to assess relationships between pharmacogenetics (PG), TB drug exposure, relevant treatment outcomes, and safety.<\/p>\n

Researchers will use information collected in an international\u00a0phase 3 clinical trial<\/a> led by CDC\u2019s Tuberculosis Trials Consortium<\/a> (TBTC), in collaboration with the National Institutes of Health\u2019s (NIH) AIDS Clinical Trial Group (ACTG). A total of 2,516 participants at 34 clinical sites in 13 countries participated in the trial, which demonstrated that a four-month daily treatment regimen with high-dose rifapentine and moxifloxacin is as effective as (noninferior to) the standard daily six-month regimen in curing drug-susceptible TB disease.<\/p>\n

Samples for PG analyses collected from a large sub-set of 1,818 trial participants will be genotyped followed by genetic association analyses. This project aims to evaluate the contribution of pharmacogenetics to patient efficacy and safety outcomes by incorporating PG data into population pharmacokinetics (PK) models for six major TB drugs and risk algorithms for unfavorable treatment outcomes and to develop and evaluate PG-based dosing algorithms for novel high-dose rifapentine-based regimens. Accounting for PG can reduce unexplained variability in PK, eliminating the need to measure individual drug levels to predict outcomes, improving the ability of current algorithms to identify patients at high risk of unfavorable outcomes and safety events. PG-based dosing algorithms may ultimately be developed to optimize drug exposure and treatment outcomes.<\/p>\n

TB remains one of the world\u2019s top infectious disease killers.<\/h2>\n

Although preventable and treatable, tuberculosis (TB) disease<\/a> is one of the world\u2019s top infectious disease killers, claiming 1.4 million lives each year. One-fourth of the world\u2019s population \u2013 nearly 2 billion people \u2013 are infected with the TB bacteria and approximately 10 million become ill with the disease each year. Although the United States has reported record low\u00a0cases<\/a>, too many people still suffer from TB disease in this country. Up to 13 million people in the United States are estimated to have\u00a0latent TB infection<\/a> and, without treatment, are at risk for developing TB disease in the future. CDC\u2019s work in the United States supports a dual approach to find and treat active TB disease and test for and treat latent TB infection to prevent progression to disease.<\/p>\n

Defining interactions among individual drug PK\/pharmacodynamic, clinical outcomes and safety events will identify optimal approaches for safe and effective TB treatment.<\/h2>\n

While rifapentine-containing regimens for the treatment of both TB disease<\/a> and latent TB infection<\/a> are effective, there are frequent drug exposure-dependent side effects. TB treatment drugs such as rifapentine and isoniazid have large pharmacokinetic variability among patients, which is believed to be modulated by host genetics. Currently, researchers and healthcare providers cannot determine more precise dosing for various patient groups and cannot precisely predict increased risk of drug exposure-related side effects and\/or TB relapse.<\/p>\n

Therapeutic drug monitoring to overcome this limitation is not always feasible, especially in low resource settings. In other therapeutic areas, host genetics have been estimated to explain ~20% to 95% of interindividual variability in drug exposure and response. Genes relevant to first-line TB drugs have been associated with PK, treatment response and toxicity. However, sufficient evidence is lacking to provide definitive recommendations on the role of pharmacogenetics in TB treatment.<\/p>\n

More precise dosing will improve prevention and treatment of TB disease.<\/h2>\n

This project provides an unprecedented opportunity to develop a tailored approach, by leveraging existing information collected in the international\u00a0phase 3 clinical trial<\/a>, to identify specific dosage recommendations of TB drugs for populations, which can improve TB treatment across all patient groups. Researchers will utilize precision public health techniques to assess treatment outcomes for TB treatment regimens, which may lead to improvements in the prevention and treatment of TB disease.<\/p>\n","protected":false},"excerpt":{"rendered":"

Through a funding opportunity from CDC\u2019s Office of Genomics and Precision Public Health in collaboration with the CDC Office of Advanced Molecular Detection, CDC\u2019s Division of Tuberculosis Elimination will conduct a 2-year project to assess relationships between pharmacogenetics (PG), TB drug exposure, relevant treatment outcomes, and safety. Researchers will use information collected in an international\u00a0phase<\/p>\n","protected":false},"author":122,"featured_media":6017,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5236,5735],"tags":[150],"_links":{"self":[{"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/posts\/6015"}],"collection":[{"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/users\/122"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/comments?post=6015"}],"version-history":[{"count":5,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/posts\/6015\/revisions"}],"predecessor-version":[{"id":6021,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/posts\/6015\/revisions\/6021"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/media\/6017"}],"wp:attachment":[{"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/media?parent=6015"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/categories?post=6015"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.cdc.gov\/genomics\/wp-json\/wp\/v2\/tags?post=6015"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}