E Cambau1, M Viveiros2, D Machado2, L Raskine3, C Ritter4, E Tortoli5, V Matthys6, S Hoffner7, E Richter8, M L Perez Del Molino9, D M Cirillo5, D van Soolingen10, E C Böttger4. 1. AP-HP, Hôpital Lariboisière, Service de Bactériologie, Centre National de Référence des Mycobactéries et de la Résistance des Mycobactéries aux Antituberculeux; IAME UMR1137, INSERM, Université Paris Diderot, 75010 Paris, France emmanuelle.cambau@lrb.aphp.fr. 2. Grupo de Micobactérias, Unidade de Microbiologia Médica, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa (IHMT/UNL), Rua da Junqueira 100, 1349-008 Lisboa, Portugal. 3. AP-HP, Hôpital Lariboisière, Service de Bactériologie, Centre National de Référence des Mycobactéries et de la Résistance des Mycobactéries aux Antituberculeux; IAME UMR1137, INSERM, Université Paris Diderot, 75010 Paris, France. 4. Institut für Medizinische Mikrobiologie, Nationales Zentrum für Mykobakterien, Universität Zürich, Zürich, Switzerland. 5. IRCCS San Raffaele Scientific Institute, Emerging Bacterial Pathogens Unit Supranational Reference Laboratory, via Olgettina 60, 20132 Milan, Italy. 6. National Reference Centre of Tuberculosis and Mycobacteria, Communicable and Infectious Diseases, Scientific Institute of Public Health (WIV-ISP), Brussels, Belgium. 7. Department of Microbiology, Public Health Agency of Sweden and Department of Microbiology, Cell and Tumor Biology, Karolinska Institute, Stockholm, Sweden. 8. National Reference Center for Mycobacteria, Forschungszentrum Borstel, Borstel, Germany. 9. Servicio de Microbiología, CH Universitario de Santiago, Centro de Referencia de Micobacterias de Galicia, Choupana S/N, 15705 Santiago de Compostela, Spain. 10. Tuberculosis Reference Laboratory, National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands Department of Pulmonary Diseases/Department of Clinical Microbiology, Radboud University Medical Centre, PO Box 9101, Nijmegen, The Netherlands.
Abstract
OBJECTIVES: Treatment outcome of MDR-TB is critically dependent on the proper use of second-line drugs as per the result of in vitro drug susceptibility testing (DST). We aimed to establish a standardized DST procedure based on quantitative determination of drug resistance and compared the results with those of genotypes associated with drug resistance. METHODS: The protocol, based on MGIT 960 and the TB eXiST software, was evaluated in nine European reference laboratories. Resistance detection at a screening drug concentration was followed by determination of resistance levels and estimation of the resistance proportion. Mutations in 14 gene regions were investigated using established techniques. RESULTS: A total of 139 Mycobacterium tuberculosis isolates from patients with MDR-TB and resistance beyond MDR-TB were tested for 13 antituberculous drugs: isoniazid, rifampicin, rifabutin, ethambutol, pyrazinamide, streptomycin, para-aminosalicylic acid, ethionamide, amikacin, capreomycin, ofloxacin, moxifloxacin and linezolid. Concordance between phenotypic and genotypic resistance was >80%, except for ethambutol. Time to results was short (median 10 days). High-level resistance, which precludes the therapeutic use of an antituberculous drug, was observed in 49% of the isolates. The finding of a low or intermediate resistance level in 16% and 35% of the isolates, respectively, may help in designing an efficient personalized regimen for the treatment of MDR-TB patients. CONCLUSIONS: The automated DST procedure permits accurate and rapid quantitative resistance profiling of first- and second-line antituberculous drugs. Prospective validation is warranted to determine the impact on patient care.
OBJECTIVES: Treatment outcome of MDR-TB is critically dependent on the proper use of second-line drugs as per the result of in vitro drug susceptibility testing (DST). We aimed to establish a standardized DST procedure based on quantitative determination of drug resistance and compared the results with those of genotypes associated with drug resistance. METHODS: The protocol, based on MGIT 960 and the TB eXiST software, was evaluated in nine European reference laboratories. Resistance detection at a screening drug concentration was followed by determination of resistance levels and estimation of the resistance proportion. Mutations in 14 gene regions were investigated using established techniques. RESULTS: A total of 139 Mycobacterium tuberculosis isolates from patients with MDR-TB and resistance beyond MDR-TB were tested for 13 antituberculous drugs: isoniazid, rifampicin, rifabutin, ethambutol, pyrazinamide, streptomycin, para-aminosalicylic acid, ethionamide, amikacin, capreomycin, ofloxacin, moxifloxacin and linezolid. Concordance between phenotypic and genotypic resistance was >80%, except for ethambutol. Time to results was short (median 10 days). High-level resistance, which precludes the therapeutic use of an antituberculous drug, was observed in 49% of the isolates. The finding of a low or intermediate resistance level in 16% and 35% of the isolates, respectively, may help in designing an efficient personalized regimen for the treatment of MDR-TB patients. CONCLUSIONS: The automated DST procedure permits accurate and rapid quantitative resistance profiling of first- and second-line antituberculous drugs. Prospective validation is warranted to determine the impact on patient care.
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