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A Nanopore Sequencing-based Pharmacogenomic Panel to Personalize Tuberculosis Drug Dosing
Renu Verma1,2,3, Kesia Esther da Silva1, Neesha Rockwood4,5,6
1Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California.
American Journal of Respiratory and Critical Care Medicine
|April 22, 2024
Summary
A new Nanopore sequencing panel accurately identifies genetic variations impacting tuberculosis drug metabolism. This technology enables personalized dosing for isoniazid and rifampin, improving treatment outcomes and reducing adverse reactions.
Area of Science:
- Pharmacogenomics
- Molecular Diagnostics
- Infectious Disease Research
Background:
- Standardized antitubercular drug dosing results in variable plasma levels, leading to adverse reactions, delayed response, and relapse.
- Genetic mutations influencing drug metabolism contribute significantly to pharmacokinetic variability, yet predictive pharmacogenomic assays are lacking.
Purpose of the Study:
- To develop and validate a Nanopore sequencing panel for identifying genetic polymorphisms that affect antitubercular drug metabolism.
- To enable personalized dosing strategies for tuberculosis (TB) treatment and prevention.
Main Methods:
- Developed a Nanopore sequencing panel targeting 15 single nucleotide polymorphisms (SNPs) in five key drug metabolism genes.
- Validated the panel using DNA from the 1,000 Genomes Project and sequenced samples from 100 active TB patients in South Africa.
- Evaluated genotype-pharmacokinetic relationships for isoniazid (INH) and rifampin (RIF).
Main Results:
- The panel demonstrated 100% concordance with Illumina sequencing for variant identification.
- Identified 33% slow, 47% intermediate, and 20% rapid isoniazid acetylators, with significantly different INH clearance rates.
- Observed a 17.3% lower rifampin clearance in individuals with specific homozygous AADAC gene substitutions.
Conclusions:
- Targeted sequencing using a low-cost, portable Nanopore platform can effectively detect pharmacogenetic variants influencing TB drug metabolism.
- This approach facilitates personalized dosing for TB treatment, potentially improving efficacy and safety.
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