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1. Automatic Tuberculosis and COVID-19 cough classification using deep learning

Author

Pahar, Madhurananda, Klopper, Marisa, Reeve, Byron, Warren, Rob, Theron, Grant, Diacon, Andreas, and Niesler, Thomas

Subjects
Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Quantitative Biology - Quantitative Methods
Abstract

We present a deep learning based automatic cough classifier which can discriminate tuberculosis (TB) coughs from COVID-19 coughs and healthy coughs. Both TB and COVID-19 are respiratory diseases, contagious, have cough as a predominant symptom and claim thousands of lives each year. The cough audio recordings were collected at both indoor and outdoor settings and also uploaded using smartphones from subjects around the globe, thus containing various levels of noise. This cough data include 1.68 hours of TB coughs, 18.54 minutes of COVID-19 coughs and 1.69 hours of healthy coughs from 47 TB patients, 229 COVID-19 patients and 1498 healthy patients and were used to train and evaluate a CNN, LSTM and Resnet50. These three deep architectures were also pre-trained on 2.14 hours of sneeze, 2.91 hours of speech and 2.79 hours of noise for improved performance. The class-imbalance in our dataset was addressed by using SMOTE data balancing technique and using performance metrics such as F1-score and AUC. Our study shows that the highest F1-scores of 0.9259 and 0.8631 have been achieved from a pre-trained Resnet50 for two-class (TB vs COVID-19) and three-class (TB vs COVID-19 vs healthy) cough classification tasks, respectively. The application of deep transfer learning has improved the classifiers' performance and makes them more robust as they generalise better over the cross-validation folds. Their performances exceed the TB triage test requirements set by the world health organisation (WHO). The features producing the best performance contain higher order of MFCCs suggesting that the differences between TB and COVID-19 coughs are not perceivable by the human ear. This type of cough audio classification is non-contact, cost-effective and can easily be deployed on a smartphone, thus it can be an excellent tool for both TB and COVID-19 screening., Comment: This paper has been published in 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)

Published
2022
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2. Wake-Cough: cough spotting and cougher identification for personalised long-term cough monitoring

Author

Pahar, Madhurananda, Klopper, Marisa, Reeve, Byron, Warren, Rob, Theron, Grant, Diacon, Andreas, and Niesler, Thomas

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

We present `wake-cough', an application of wake-word spotting to coughs using a Resnet50 and the identification of coughers using i-vectors, for the purpose of a long-term, personalised cough monitoring system. Coughs, recorded in a quiet (73$\pm$5 dB) and noisy (34$\pm$17 dB) environment, were used to extract i-vectors, x-vectors and d-vectors, used as features to the classifiers. The system achieves 90.02\% accuracy when using an MLP to discriminate between 51 coughers using 2-sec long cough segments in the noisy environment. When discriminating between 5 and 14 coughers using longer (100 sec) segments in the quiet environment, this accuracy improves to 99.78% and 98.39% respectively. Unlike speech, i-vectors outperform x-vectors and d-vectors in identifying coughers. These coughs were added as an extra class to the Google Speech Commands dataset and features were extracted by preserving the end-to-end time-domain information in a trigger phrase. The highest accuracy of 88.58% is achieved in spotting coughs among 35 other trigger phrases using a Resnet50. Thus, wake-cough represents a personalised, non-intrusive cough monitoring system, which is power-efficient as on-device wake-word detection can keep a smartphone-based monitoring device mostly dormant. This makes wake-cough extremely attractive in multi-bed ward environments to monitor patients' long-term recovery from lung ailments such as tuberculosis (TB) and COVID-19.

Published
2021

3. COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features

Author

Pahar, Madhurananda, Klopper, Marisa, Warren, Robin, and Niesler, Thomas

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

We present an experimental investigation into the effectiveness of transfer learning and bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath and speech. This type of screening is non-contact, does not require specialist medical expertise or laboratory facilities and can be deployed on inexpensive consumer hardware. We use datasets that contain recordings of coughing, sneezing, speech and other noises, but do not contain COVID-19 labels, to pre-train three deep neural networks: a CNN, an LSTM and a Resnet50. These pre-trained networks are subsequently either fine-tuned using smaller datasets of coughing with COVID-19 labels in the process of transfer learning, or are used as bottleneck feature extractors. Results show that a Resnet50 classifier trained by this transfer learning process delivers optimal or near-optimal performance across all datasets achieving areas under the receiver operating characteristic (ROC AUC) of 0.98, 0.94 and 0.92 respectively for all three sound classes (coughs, breaths and speech). This indicates that coughs carry the strongest COVID-19 signature, followed by breath and speech. Our results also show that applying transfer learning and extracting bottleneck features using the larger datasets without COVID-19 labels led not only to improve performance, but also to minimise the standard deviation of the classifier AUCs among the outer folds of the leave-$p$-out cross-validation, indicating better generalisation. We conclude that deep transfer learning and bottleneck feature extraction can improve COVID-19 cough, breath and speech audio classification, yielding automatic classifiers with higher accuracy.

Published
2021
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4. Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment

Author

Pahar, Madhurananda, Klopper, Marisa, Reeve, Byron, Theron, Grant, Warren, Rob, and Niesler, Thomas

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Objective: The automatic discrimination between the coughing sounds produced by patients with tuberculosis (TB) and those produced by patients with other lung ailments. Approach: We present experiments based on a dataset of 1358 forced cough recordings obtained in a developing-world clinic from 16 patients with confirmed active pulmonary TB and 35 patients suffering from respiratory conditions suggestive of TB but confirmed to be TB negative. Using nested cross-validation, we have trained and evaluated five machine learning classifiers: logistic regression (LR), support vector machines (SVM), k-nearest neighbour (KNN), multilayer perceptrons (MLP) and convolutional neural networks (CNN). Main Results: Although classification is possible in all cases, the best performance is achieved using LR. In combination with feature selection by sequential forward selection (SFS), our best LR system achieves an area under the ROC curve (AUC) of 0.94 using 23 features selected from a set of 78 high-resolution mel-frequency cepstral coefficients (MFCCs). This system achieves a sensitivity of 93\% at a specificity of 95\% and thus exceeds the 90\% sensitivity at 70\% specificity specification considered by the World Health Organisation (WHO) as a minimal requirement for a community-based TB triage test. Significance: The automatic classification of cough audio sounds, when applied to symptomatic patients requiring investigation for TB, can meet the WHO triage specifications for the identification of patients who should undergo expensive molecular downstream testing. This makes it a promising and viable means of low cost, easily deployable frontline screening for TB, which can benefit especially developing countries with a heavy TB burden., Comment: This paper has been accepted in Physiological Measurement (2021)

Published
2021
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5. COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings

Author

Pahar, Madhurananda, Klopper, Marisa, Warren, Robin, and Niesler, Thomas

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone. This type of screening is non-contact, easy to apply, and can reduce the workload in testing centres as well as limit transmission by recommending early self-isolation to those who have a cough suggestive of COVID-19. The datasets used in this study include subjects from all six continents and contain both forced and natural coughs, indicating that the approach is widely applicable. The publicly available Coswara dataset contains 92 COVID-19 positive and 1079 healthy subjects, while the second smaller dataset was collected mostly in South Africa and contains 18 COVID-19 positive and 26 COVID-19 negative subjects who have undergone a SARS-CoV laboratory test. Both datasets indicate that COVID-19 positive coughs are 15\%-20\% shorter than non-COVID coughs. Dataset skew was addressed by applying the synthetic minority oversampling technique (SMOTE). A leave-$p$-out cross-validation scheme was used to train and evaluate seven machine learning classifiers: LR, KNN, SVM, MLP, CNN, LSTM and Resnet50. Our results show that although all classifiers were able to identify COVID-19 coughs, the best performance was exhibited by the Resnet50 classifier, which was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98. An LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs, with an AUC of 0.94 after selecting the best 13 features from a sequential forward selection (SFS). Since this type of cough audio classification is cost-effective and easy to deploy, it is potentially a useful and viable means of non-contact COVID-19 screening., Comment: This paper has been accepted in "Computers in Medicine and Biology" and currently under production

Published
2020
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6. A landscape of genomic alterations at the root of a near-untreatable tuberculosis epidemic.

Author

Klopper, Marisa, Heupink, Tim, Hill-Cawthorne, Grant, Streicher, Elizabeth, Dippenaar, Anzaan, de Vos, Margaretha, Abdallah, Abdallah, Limberis, Jason, Merker, Matthias, Burns, Scott, Niemann, Stefan, Dheda, Keertan, Posey, James, Pain, Arnab, and Warren, Robin

Subjects
Atypical Beijing, Bedaquiline, Beyond-XDR-TB, Drug-resistant, Missed resistance, Tuberculosis, Weakened regimen, Whole genome sequencing, Epidemics, Female, Genomics, Humans, Male, Mutation, Tuberculosis, Multidrug-Resistant
Abstract

BACKGROUND: Atypical Beijing genotype Mycobacterium tuberculosis strains are widespread in South Africa and have acquired resistance to up to 13 drugs on multiple occasions. It is puzzling that these strains have retained fitness and transmissibility despite the potential fitness cost associated with drug resistance mutations. METHODS: We conducted Illumina sequencing of 211 Beijing genotype M. tuberculosis isolates to facilitate the detection of genomic features that may promote acquisition of drug resistance and restore fitness in highly resistant atypical Beijing forms. Phylogenetic and comparative genomic analysis was done to determine changes that are unique to the resistant strains that also transmit well. Minimum inhibitory concentration (MIC) determination for streptomycin and bedaquiline was done for a limited number of isolates to demonstrate a difference in MIC between isolates with and without certain variants. RESULTS: Phylogenetic analysis confirmed that two clades of atypical Beijing strains have independently developed resistance to virtually all the potent drugs included in standard (pre-bedaquiline) drug-resistant TB treatment regimens. We show that undetected drug resistance in a progenitor strain was likely instrumental in this resistance acquisition. In this cohort, ethionamide (ethA A381P) resistance would be missed in first-line drug-susceptible isolates, and streptomycin (gidB L79S) resistance may be missed due to an MIC close to the critical concentration. Subsequent inadequate treatment historically led to amplification of resistance and facilitated spread of the strains. Bedaquiline resistance was found in a small number of isolates, despite lack of exposure to the drug. The highly resistant clades also carry inhA promoter mutations, which arose after ethA and katG mutations. In these isolates, inhA promoter mutations do not alter drug resistance, suggesting a possible alternative role. CONCLUSION: The presence of the ethA mutation in otherwise susceptible isolates from ethionamide-naïve patients demonstrates that known exposure is not an adequate indicator of drug susceptibility. Similarly, it is demonstrated that bedaquiline resistance can occur without exposure to the drug. Inappropriate treatment regimens, due to missed resistance, leads to amplification of resistance, and transmission. We put these results into the context of current WHO treatment regimens, underscoring the risks of treatment without knowledge of the full drug resistance profile.

Published
2020

7. The Hidden Epidemic of Isoniazid-Resistant Tuberculosis in South Africa.

Author

Klopper, Marisa, van der Merwe, Charnay J., van der Heijden, Yuri F., Folkerts, Megan, Loubser, Johannes, Streicher, Elizabeth M., Mekler, Kate, Hayes, Cindy, Engelthaler, David M., Metcalfe, John Z., and Warren, Robin M.

Subjects
WHOLE genome sequencing, ISONIAZID, TUBERCULOSIS, GENOTYPES, PHENOTYPES
Abstract

Rationale: Isoniazid-resistant tuberculosis (Hr-TB) is often overlooked in diagnostic algorithms because of reliance on first-line molecular assays testing only for rifampicin resistance. Objectives: To determine the prevalence, outcomes, and molecular mechanisms associated with rifampin-susceptible, isoniazid-resistant TB (Hr-TB) in the Eastern Cape, South Africa. Methods: Between April 2016 and October 2017, sputum samples were collected from patients with rifampin-susceptible TB at baseline and at Weeks 7 and 23 of drug-susceptible TB treatment. We performed isoniazid phenotypic and genotypic drug susceptibility testing, including FluoroTypeMTBDR, Sanger sequencing, targeted next-generation sequencing, and whole-genome sequencing. Results: We analyzed baseline isolates from 766 patients with rifampin-susceptible TB. Of 89 patients (11.7%) who were found to have Hr-TB, 39 (44%) had canonical katG or inhA promoter mutations; 35 (39%) had noncanonical katG mutations (including 5 with underlying large deletions); 4 (5%) had mutations in other candidate genes associated with isoniazid resistance. For 11 (12.4%), no cause of resistance was found. Conclusions: Among patients with rifampin-susceptible TB who were diagnosed using first-line molecular TB assays, there is a high prevalence of Hr-TB. Phenotypic drug susceptibility testing remains the gold standard. To improve the performance of genetic-based phenotyping tests, all isoniazid resistance–associated regions should be included, and such tests should have the ability to identify underlying mutations. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Bedaquiline for treatment of non-tuberculous mycobacteria (NTM): a systematic review and meta-analysis.

Author

Omar, Shatha, Whitfield, Michael G, Nolan, Margaret B, Ngom, Justice T, Ismail, Nabila, Warren, Rob M, and Klopper, Marisa

Subjects
MYCOBACTERIA, LUNG infections, ANIMAL models in research, ANTIBACTERIAL agents, MYCOBACTERIUM
Abstract

Background Non-tuberculous mycobacteria (NTM) infections are increasing in incidence and associated mortality. NTM are naturally resistant to a variety of antibiotics, complicating treatment. We conducted a literature assessment on the efficacy of bedaquiline in treating NTM species in vitro and in vivo (animal models and humans); meta-analyses were performed where possible. Method Four databases were searched using specific terms. Publications were included according to predefined criteria. Bedaquiline's impact on NTM in vitro , MICs and epidemiological cut-off (ECOFF) values were evaluated. A meta-analysis of bedaquiline efficacy against NTM infections in animal models was performed. Culture conversion, cure and/or relapse-free cure were used to evaluate the efficacy of bedaquiline in treating NTM infection in humans. Results Fifty studies met the inclusion criteria: 33 assessed bedaquiline's impact on NTM in vitro , 9 in animal models and 8 in humans. Three studies assessed bedaquiline's efficacy both in vitro and in vivo. Due to data paucity, an ECOFF value of 0.5 mg/mL was estimated for Mycobacterium abscessus only. Meta-analysis of animal studies showed a 1.86× reduction in bacterial load in bedaquiline-treated versus no treatment within 30 days. In humans, bedaquiline-including regimens were effective in treating NTM extrapulmonary infection but not pulmonary infection. Conclusions Bedaquiline demonstrated strong antibacterial activity against various NTM species and is a promising drug to treat NTM infections. However, data on the genomic mutations associated with bedaquiline resistance were scarce, preventing statistical analyses for most mutations and NTM species. Further studies are urgently needed to better inform treatment strategies. [ABSTRACT FROM AUTHOR]

Published
2024
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10. MDR M. tuberculosis outbreak clone in Eswatini missed by Xpert has elevated bedaquiline resistance dated to the pre-treatment era

Author

Beckert, Patrick, Sanchez-Padilla, Elisabeth, Merker, Matthias, Dreyer, Viola, Kohl, Thomas A., Utpatel, Christian, Köser, Claudio U., Barilar, Ivan, Ismail, Nazir, Omar, Shaheed Vally, Klopper, Marisa, Warren, Robin M., Hoffmann, Harald, Maphalala, Gugu, Ardizzoni, Elisa, de Jong, Bouke C., Kerschberger, Bernhard, Schramm, Birgit, Andres, Sönke, Kranzer, Katharina, Maurer, Florian P., Bonnet, Maryline, and Niemann, Stefan

Published
2020
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15. Bridging Healthcare Barriers with mHealth: A Systematic Review of Tuberculosis Applications and their Limitations.

Author

Zajac, Kimsey, Aslan, Aycan, Greve, Maike, Kolbe, Lutz, Theron, Grant, Niesler, Thomas, Cobelens, Frank, Klopper, Marisa, Ssengooba, Willy, and Joloba, Moses

Subjects
MOBILE health, CELL phones, TUBERCULOSIS treatment, MOBILE apps, MEDICAL technology
Abstract

Tuberculosis remains one of the most significant burdens for resource-constrained environments with limited healthcare supply, such as the Global South. However, factors such as the adoption of mobile phones create a favorable environment for mobile health technology, yielding a promising avenue for the development of the Global South through digital innovation. Previous studies, however, have not provided a comprehensive assessment of available tuberculosis applications and their features. To bridge this gap, we conducted a systematic review of existing tuberculosis applications to gain a deeper understanding of the current landscape. Our findings indicate that current tuberculosis apps have significant limitations, such as requiring an internet connection or being available in only one language. Our study contributes to extant research by presenting the first systematic analysis of tuberculosis applications and provides practical insights by highlighting areas of improvement that should be addressed in future app development. [ABSTRACT FROM AUTHOR]

Published
2023

19. Emergence and spread of extensively and totally drug-resistant tuberculosis, South Africa

Author

Klopper, Marisa, Warren, Robin Mark, Hayes, Cindy, van Pittius, Nicolaas Claudius Gey, Streicher, Elizabeth Maria, Muller, Borna, Sirgel, Frederick Adriaan, Chabula-Nxiweni, Mamisa, Hoosain, Ebrahim, Coetzee, Gerrit, van Helden, Paul David, Victor, Thomas Calldo, and Trollip, Andre Phillip

Subjects
Drug resistance in microorganisms -- Research, Tuberculosis -- Risk factors -- Research -- Drug therapy, Disease transmission -- Research, Health
Abstract

The emergence of drug-resistant tuberculosis (TB) is of major concern to TB control in South Africa. A countrywide survey in 2002 revealed that 1.8% of all new TB patients and [...]

Published
2013
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21. Additional file 2 of MDR M. tuberculosis outbreak clone in Eswatini missed by Xpert has elevated bedaquiline resistance dated to the pre-treatment era

Author

Beckert, Patrick, Sanchez-Padilla, Elisabeth, Merker, Matthias, Dreyer, Viola, Kohl, Thomas A., Utpatel, Christian, Köser, Claudio U., Barilar, Ivan, Ismail, Nazir, Shaheed Vally Omar, Klopper, Marisa, Warren, Robin M., Hoffmann, Harald, Gugu Maphalala, Ardizzoni, Elisa, Jong, Bouke C. De, Kerschberger, Bernhard, Schramm, Birgit, Andres, Sönke, Kranzer, Katharina, Maurer, Florian P., Bonnet, Maryline, and Niemann, Stefan

Abstract

Additional file 2. Supplemental methods and description of resistance mutations.

Published
2020
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22. Additional file 5 of A landscape of genomic alterations at the root of a near-untreatable tuberculosis epidemic

Author

Klopper, Marisa, Heupink, Tim Hermanus, Hill-Cawthorne, Grant, Streicher, Elizabeth Maria, Anzaan Dippenaar, Vos, Margaretha De, Abdallah, Abdallah Musa, Limberis, Jason, Merker, Matthias, Burns, Scott, Niemann, Stefan, Keertan Dheda, Posey, James, Arnab Pain, and Warren, Robin Mark

Subjects
sense organs
Abstract

Additional file 5. Tables indicating the nucleotide- and amino acid changes that occur in all members of each of the AA1SA clades A through C, as well as additional information regarding gene function and Provean predictions.

Published
2020
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23. Additional file 3 of MDR M. tuberculosis outbreak clone in Eswatini missed by Xpert has elevated bedaquiline resistance dated to the pre-treatment era

Author

Beckert, Patrick, Sanchez-Padilla, Elisabeth, Merker, Matthias, Dreyer, Viola, Kohl, Thomas A., Utpatel, Christian, Köser, Claudio U., Barilar, Ivan, Ismail, Nazir, Shaheed Vally Omar, Klopper, Marisa, Warren, Robin M., Hoffmann, Harald, Gugu Maphalala, Ardizzoni, Elisa, Jong, Bouke C. De, Kerschberger, Bernhard, Schramm, Birgit, Andres, Sönke, Kranzer, Katharina, Maurer, Florian P., Bonnet, Maryline, and Niemann, Stefan

Abstract

Additional file 3: Fig. S1. Phylogenetic diversity and drug susceptibility of the 412 M. tuberculosis complex isolates from Eswatini. Fig. S2. Lineage distribution across Eswatini. Fig. S3. Isolates selected for further whole-genome sequencing analysis.

Published
2020
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24. Geospatial distribution of Mycobacterium tuberculosis genotypes in Africa

Author

Chihota, Violet N., primary, Niehaus, Antoinette, additional, Streicher, Elizabeth M., additional, Wang, Xia, additional, Sampson, Samantha L., additional, Mason, Peter, additional, Källenius, Gunilla, additional, Mfinanga, Sayoki G., additional, Pillay, Marnomorney, additional, Klopper, Marisa, additional, Kasongo, Webster, additional, Behr, Marcel A., additional, Gey van Pittius, Nicolaas C., additional, van Helden, Paul D., additional, Couvin, David, additional, Rastogi, Nalin, additional, and Warren, Robin M., additional

Published
2018
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27. The rationale for using rifabutin in the treatment of MDR and XDR tuberculosis outbreaks

Author

Sirgel, Frederick A, Warren, Robin M, Böttger, Erik C, Klopper, Marisa, Victor, Thomas C, van Helden, Paul D, Sirgel, Frederick A, Warren, Robin M, Böttger, Erik C, Klopper, Marisa, Victor, Thomas C, and van Helden, Paul D

Abstract

Genetically related Mycobacterium tuberculosis strains with alterations at codon 516 in the rpoB gene were observed amongst a substantial number of patients with drug resistant tuberculosis in the Eastern Cape Province (ECP) of South Africa. Mutations at codon 516 are usually associated with lower level rifampicin (RIF) resistance, while susceptibility to rifabutin (RFB) remains intact. This study was conducted to assess the rationale for using RFB as a substitution for RIF in the treatment of MDR and XDR tuberculosis outbreaks. Minimum inhibitory concentrations (MICs) of 34 drug resistant clinical isolates of M tuberculosis were determined by MGIT 960 and correlated with rpoB mutations. RFB MICs ranged from 0.125 to 0.25 µg/ml in the 34 test isolates thereby confirming phenotypic susceptibility as per critical concentration (CC) of 0.5 µg/ml. The corresponding RIF MICs ranged between 5 and 15 µg/ml, which is well above the CC of 1.0 µg/ml. Molecular-based drug susceptibility testing provides important pharmacogenetic insight by demonstrating a direct correlation between defined rpoB mutation and the level of RFB susceptibility. We suggest that isolates with marginally reduced susceptibility as compared to the epidemiological cut-off for wild-type strains (0.064 µg/ml), but lower than the current CC (≤0.5 µg/ml), are categorised as intermediate. Two breakpoints (0.064 µg/ml and 0.5 µg/ml) are recommended to distinguish between susceptible, intermediate and RFB resistant strains. This concept may assist clinicians and policy makers to make objective therapeutic decisions, especially in situations where therapeutic options are limited. The use of RFB in the ECP may improve therapeutic success and consequently minimise the risk of ongoing transmission of drug resistant M. tuberculosis strains.

Published
2013

28. Programmatically Selected Multidrug-Resistant Strains Drive the Emergence of Extensively Drug-Resistant Tuberculosis in South Africa

Author

Müller, Borna, primary, Chihota, Violet N., additional, Pillay, Manormoney, additional, Klopper, Marisa, additional, Streicher, Elizabeth M., additional, Coetzee, Gerrit, additional, Trollip, Andre, additional, Hayes, Cindy, additional, Bosman, Marlein E., additional, Gey van Pittius, Nicolaas C., additional, Victor, Thomas C., additional, Gagneux, Sebastien, additional, van Helden, Paul D., additional, and Warren, Robin M., additional

Published
2013
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31. Isoniazid Resistance and Dosage as Treatment for Patients with Tuberculosis.

Author

Nagel S, Streicher EM, Klopper M, Warren RM, and Van Helden PD

Subjects
Animals, Antitubercular Agents administration & dosage, Antitubercular Agents pharmacokinetics, Antitubercular Agents pharmacology, Humans, Isoniazid administration & dosage, Isoniazid pharmacokinetics, Isoniazid pharmacology, Mycobacterium tuberculosis drug effects, Mycobacterium tuberculosis growth & development, Treatment Outcome, Antitubercular Agents therapeutic use, Drug Resistance, Bacterial, Isoniazid therapeutic use, Tuberculosis, Pulmonary drug therapy
Abstract

Background: The first-line TB antibiotic isoniazid (INH) serves as a central component of combined first-line anti-tuberculosis drug therapy. However, resistance to INH has hindered the functioning of this drug. Resistance is caused by several known and unknown mutations in genes/regions in Mycobacterium tuberculosis (M. tuberculosis), followed by selection of these mutants in the presence of the drug. INH resistance can be categorised as either "high-level" (minimum inhibitory concentration (MIC) of > 1µg/mL to INH) or "low-level" (MIC between 0.1-1.0 µg/L) resistance and is dependent on the specific mutation acquired. The level of resistance is relevant, as INH resistance is often considered to be the first step in development of Multi-Drug Resistant (MDR) and extremely resistant (XDR) TB. Isoniazid is a pro-drug in which first pass metabolism happens via N-acetyltransferase and is fast, intermediate or slow, depending on the genetics of the host. Thus, low-level INH resistance, particularly in the presence of fast metabolism, could allow additional mutations, development of high-level resistance and progression to multi-drug resistance., Methods: A structured search of bibliographic databases for peer-reviewed research literature was performed. Set parameters and specific inclusion criteria were used to filter the literature, based on our specified review questions. The quality and relevance of included papers was deduced using standard tools. The relevant content of cited papers was described, and an inferential qualitative content analysis methodology was utilised to analyse the inferences and findings of included studies using a conceptual framework., Results: Seventy-eight papers were included in the review, of which a sub-set (36) of the papers describe how different genetic mutations result in low or high-level resistance to INH. These papers were also used to set up a diagram detailing how each mutation affects INH functionality in order to visualise the interactome of INH and M.tuberculosis A further twenty-eight out of the seventy-eight papers detail the methods for testing for INH resistance, current treatment regimens and factors that influence treatment outcome in order to better understand the role of INH within the current anti-tuberculosis treatment therapy and how its use can be optimised., Conclusion: The findings of this review suggest that low-level INH resistance, in the presence of fast-acetylation, is an underrated component of the global TB epidemic worldwide, and may be a significant problem in terms of treatment outcome and progression to antibiotic resistance. Thus, more research must be done to test whether personalised diagnostics and targeted high dose treatment with INH will reduce the incidence of isoniazid mono-resistant and multi-drug resistant (MDR) tuberculosis., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

Published
2017
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