A Nanopore Sequencing-based Pharmacogenomic Panel to Personalize Tuberculosis Drug Dosing

Renu Verma; Kesia Esther da Silva; Neesha Rockwood; Roeland E Wasmann; Nombuso Yende; Taeksun Song; Eugene Kim; Paolo Denti; Robert J Wilkinson; Jason R Andrews

doi:10.1164/rccm.202309-1583OC

A Nanopore Sequencing-based Pharmacogenomic Panel to Personalize Tuberculosis Drug Dosing

Am J Respir Crit Care Med. 2024 Mar 5. doi: 10.1164/rccm.202309-1583OC. Online ahead of print.

Authors

Renu Verma^{1

2

3}, Kesia Esther da Silva⁴, Neesha Rockwood^{5

6

7}, Roeland E Wasmann⁸, Nombuso Yende⁹, Taeksun Song⁹, Eugene Kim¹⁰, Paolo Denti¹¹, Robert J Wilkinson^{12

6

13}, Jason R Andrews¹⁰

Affiliations

¹ Stanford University School of Medicine, 10624, Infectious Diseases and Geographic Medicine, Stanford, California, United States.
² Institute of Bioinformatics, 29126, International Technology Park, Bangalore, Karnataka, India.
³ Manipal Academy of Higher Education, 76793, MAHE, Manipal, Karnataka, India; vrenu@stanford.edu.
⁴ Stanford University School of Medicine, 10624, 1. Division of Infectious Diseases and Geographic Medicine, Stanford, California, United States.
⁵ University of Cape Town Faculty of Health Sciences, 63726, Institute of Infectious Disease and Molecular Medicine and Dept. Medicine, Observatory, Western Cape, South Africa.
⁶ Imperial College London, 4615, Department of Infectious Diseases, London, United Kingdom of Great Britain and Northern Ireland.
⁷ University of Colombo Faculty of Medicine, 63735, Department of Medical Microbiology and Immunology, Colombo, Western, Sri Lanka.
⁸ University of Cape Town Department of Medicine, 71984, Division of Clinical Pharmacology, Department of Medicine, Observatory, Western Cape, South Africa.
⁹ University of Cape Town Faculty of Health Sciences, 63726, Department of Pathology and Institute of Infectious Disease and Molecular Medicine, Observatory, Western Cape, South Africa.
¹⁰ Stanford University School of Medicine, 10624, Division of Infectious Diseases and Geographic Medicine, Stanford, California, United States.
¹¹ University of Cape Town Faculty of Health Sciences, 63726, Division of Clinical Pharmacology, Department of Medicine, Observatory, Western Cape, South Africa.
¹² University of Cape Town Faculty of Health Sciences, 63726, Center for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine and Dept. Medicine, Observatory, Western Cape, South Africa.
¹³ Francis Crick Institute Mill Hill Laboratory, 443502, Mill Hill, London, United Kingdom of Great Britain and Northern Ireland.

PMID: 38647526
DOI: 10.1164/rccm.202309-1583OC

Abstract

Rationale: Standardized dosing of anti-tubercular (TB) drugs leads to variable plasma drug levels, which are associated with adverse drug reactions, delayed treatment response, and relapse. Mutations in genes affecting drug metabolism explain considerable interindividual pharmacokinetic variability; however, pharmacogenomic (PGx) assays that predict metabolism of anti-TB drugs have been lacking.

Objectives: To develop a Nanopore sequencing panel and validate its performance in active TB patients to personalize treatment dosing.

Measurements and main results: We developed a Nanopore sequencing panel targeting 15 single nucleotide polymorphisms (SNP) in 5 genes affecting the metabolism of anti-tuberculous drugs. For validation, we sequenced DNA samples (n=48) from the 1000 genomes project and compared variant calling accuracy with Illumina genome sequencing. We then sequenced DNA samples from patients with active TB (n=100) from South Africa on a MinION Mk1C and evaluated the relationship between genotypes and pharmacokinetic parameters for INH and RIF.

Results: The PGx panel achieved 100% concordance with Illumina sequencing in variant identification for the samples from the 1000 Genomes Project. In the clinical cohort, coverage was >100x for 1498/1500 (99.8%) amplicons across the 100 samples. One third (33%) of participants were identified as slow, 47% were intermediate and 20% were rapid isoniazid acetylators. Isoniazid clearance was 2.2 times higher among intermediate acetylators and 3.8 times higher among rapid acetylators compared with slow acetylators (p<0.0001).. Rifampin clearance was 17.3% (2.50-29.9) lower in individuals with homozygous AADAC rs1803155 G>A substitutions (p=0.0015).

Conclusion: Targeted sequencing can enable detection of polymorphisms influencing TB drug metabolism on a low-cost, portable instrument to personalize dosing for TB treatment or prevention. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

Keywords: isoniazid; Nanopore; NAT2; pharmacogenomics; targeted sequencing; tuberculosis.