Improving access to new diagnostics through harmonised regulation: priorities for action.

A new generation of diagnostic tests is being developed for use at the point of care that could save lives and reduce the spread of infectious diseases through early detection and treatment. It is important that patients in developing countries have access to these products at affordable prices and without delay. Regulation of medical products is intended to ensure safety and quality whilst balancing the need for timely access to beneficial new products. Current regulatory oversight of diagnostic tests in developing countries is highly variable and weak regulation allows poor-quality tests to enter the market. However, inefficient or overzealous regulation results in unnecessary delays, increases costs and acts as a barrier to innovation and market entry. Setting international standards and streamlining the regulatory process could reduce these barriers. Four priority activities have been identified where convergence of standards and protocols or joint review of data would be advantageous: (1) adoption of a common registration file for pre-market approval; (2) convergence of quality standards for manufacturing site inspections; (3) use of common evaluation protocols, as well as joint review of data, to reduce unnecessary duplication of lengthy and costly clinical performance studies; and (4) use of networks of laboratories for post-market surveillance in order to monitor ongoing quality of diagnostic devices. The adoption and implementation of such measures in developing countries could accelerate access to new diagnostic tests that are safe and affordable

Machine learning predicts accurately mycobacterium tuberculosis drug resistance from whole genome sequencing data

Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making. Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradientboosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without “co-occurrent resistance” markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard. Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for thirdline drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other largescale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation. Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation

The analysis of para-cresol production and tolerance in Clostridium difficile 027 and 012 strains

<p>Abstract</p> <p>Background</p> <p><it>Clostridium difficile </it>is the major cause of antibiotic associated diarrhoea and in recent years its increased prevalence has been linked to the emergence of hypervirulent clones such as the PCR-ribotype 027. Characteristically, <it>C. difficile </it>infection (CDI) occurs after treatment with broad-spectrum antibiotics, which disrupt the normal gut microflora and allow <it>C. difficile </it>to flourish. One of the relatively unique features of <it>C. difficile </it>is its ability to ferment tyrosine to <it>para</it>-cresol via the intermediate <it>para</it>-hydroxyphenylacetate (<it>p-</it>HPA). <it>P</it>-cresol is a phenolic compound with bacteriostatic properties which <it>C. difficile </it>can tolerate and may provide the organism with a competitive advantage over other gut microflora, enabling it to proliferate and cause CDI. It has been proposed that the <it>hpdBCA </it>operon, rarely found in other gut microflora, encodes the enzymes responsible for the conversion of <it>p-</it>HPA to <it>p</it>-cresol.</p> <p>Results</p> <p>We show that the PCR-ribotype 027 strain R20291 quantitatively produced more <it>p</it>-cresol <it>in-vitro </it>and was significantly more tolerant to <it>p</it>-cresol than the sequenced strain 630 (PCR-ribotype 012). Tyrosine conversion to <it>p</it>-HPA was only observed under certain conditions. We constructed gene inactivation mutants in the <it>hpdBCA </it>operon in strains R20291 and 630Δ<it>erm </it>which curtails their ability to produce <it>p</it>-cresol, confirming the role of these genes in <it>p-</it>cresol production. The mutants were equally able to tolerate <it>p</it>-cresol compared to the respective parent strains, suggesting that tolerance to <it>p</it>-cresol is not linked to its production.</p> <p>Conclusions</p> <p><it>C. difficile </it>converts tyrosine to <it>p</it>-cresol, utilising the <it>hpdBCA </it>operon in <it>C. difficile </it>strains 630 and R20291. The hypervirulent strain R20291 exhibits increased production of and tolerance to <it>p-</it>cresol, which may be a contributory factor to the virulence of this strain and other hypervirulent PCR-ribotype 027 strains.</p

Development of sample clean up methods for the analysis of Mycobacterium tuberculosis methyl mycocerosate biomarkers in sputum extracts by gas chromatography–mass spectrometry

A proof of principle gas chromatography–mass spectrometry method is presented, in combination with clean up assays, aiming to improve the analysis of methyl mycocerosate tuberculosis biomarkers from sputum. Methyl mycocerosates are generated from the transesterification of phthiocerol dimycocerosates (PDIMs), extracted in petroleum ether from sputum of tuberculosis suspect patients. When a high matrix background is present in the sputum extracts, the identification of the chromatographic peaks corresponding to the methyl derivatives of PDIMs analytes may be hindered by the closely eluting methyl ether of cholesterol, usually an abundant matrix constituent frequently present in sputum samples. The purification procedures involving solid phase extraction (SPE) based methods with both commercial Isolute-Florisil cartridges, and purpose designed molecularly imprinted polymeric materials (MIPs), resulted in cleaner chromatograms, while the mycocerosates are still present. The clean-up performed on solutions of PDIMs and cholesterol standards in petroleum ether show that, depending on the solvent mix and on the type of SPE used, the recovery of PDIMs is between 64 and 70%, whilst most of the cholesterol is removed from the system. When applied to petroleum ether extracts from representative sputum samples, the clean-up procedures resulted in recoveries of 36–68% for PDIMs, allowing some superior detection of the target analytes

Machine learning predicts accurately mycobacterium tuberculosis drug resistance from whole genome sequencing data

Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making. Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradient-boosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without “co-occurrent resistance” markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard. Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for third-line drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other large-scale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation. Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation

Regulation of medical diagnostics and medical devices in the East African community partner states.

BACKGROUND: Medical devices and in vitro diagnostic tests (IVD) are vital components of health delivery systems but access to these important tools is often limited in Africa. The regulation of health commodities by National Regulatory Authorities is intended to ensure their safety and quality whilst ensuring timely access to beneficial new products. Streamlining and harmonizing regulatory processes may reduce delays and unnecessary expense and improve access to new products. Whereas pharmaceutical products are widely regulated less attention has been placed on the regulation of other health products. A study was undertaken to assess regulation of medical diagnostics and medical devices across Partner States of the East African Community (EAC). METHODS: Data was collected during October 2012 through desk based review of documents and field research, including face to face interviews with the assistance of a structured questionnaire with closed and open ended questions. Key areas addressed were (i) existence and role of National Regulatory Authorities; (ii) policy and legal framework for regulation; (iii) premarket control; (iv) marketing controls; (v) post-marketing control and vigilance; (vi) country capacity for regulation; (vii) country capacity for evaluation studies for IVD and (viii) priorities and capacity building for harmonization in EAC Partner States. RESULTS: Control of medical devices and IVDs in EAC Partner States is largely confined to national disease programmes such as tuberculosis, HIV and malaria. National Regulatory Authorities for pharmaceutical products do not have the capacity to regulate medical devices and in some countries laboratory based organisations are mandated to ensure quality of products used. Some activities to evaluate IVDs are performed in research laboratories but post market surveillance is rare. Training in key areas is considered essential to strengthening regulatory capacity for IVDs and other medical devices. CONCLUSIONS: Regulation of medical devices and in vitro diagnostics has been neglected in EAC Partner States. Regulation is weak across the region, and although the majority of States have a legal mandate to regulate medical devices there is limited capacity to do so. Streamlining regulation in the EAC is seen as a positive aspiration with diagnostic tests considered a priority area for harmonisation

Detection of Mycobacterium tuberculosis in Sputum by Gas Chromatography-Mass Spectrometry of Methyl Mycocerosates Released by Thermochemolysis

Tuberculosis requires rapid diagnosis to prevent further transmission and allow prompt administration of treatment. Current methods for diagnosing pulmonary tuberculosis lack sensitivity are expensive or are extremely slow. The identification of lipids using gas chromatography- electron impact mass spectrometry (GC-EI/MS) could provide an alternative solution. We have studied mycocerosic acid components of the phthiocerol dimycocerosate (PDIM) family of lipids using thermochemolysis GC-EI/MS. To facilitate use of the technology in a routine diagnostic laboratory a simple extraction procedure was employed where PDIMs were extracted from sputum using petroleum ether, a solvent of low polarity. We also investigated a method using methanolic tetramethylammonium hydroxide, which facilitates direct transesterification of acidic components to methyl esters in the inlet of the GC-MS system. This eliminates conventional chemical manipulations allowing rapid and convenient analysis of samples. When applied to an initial set of 40 sputum samples, interpretable results were obtained for 35 samples with a sensitivity relative to culture of 94% (95%CI: 69.2,100) and a specificity of 100% (95%CI: 78.1,100). However, blinded testing of a larger set of 395 sputum samples found the assay to have a sensitivity of 61.3% (95%CI: 54.9,67.3) and a specificity of 70.6% (95%CI: 62.3,77.8) when compared to culture. Using the results obtained we developed an improved set of classification criteria, which when applied in a blinded re-analysis increased the sensitivity and specificity of the assay to 64.9% (95%CI: 58.6,70.8) and 76.2% (95%CI: 68.2,82.8) respectively. Highly variable levels of background signal were observed from individual sputum samples that inhibited interpretation of the data. The diagnostic potential of using thermochemolytic GC-EI/MS of PDIM biomarkers for diagnosis of tuberculosis in sputum has been established; however, further refinements in sample processing are required to enhance the sensitivity and robustness of the test

A novel pathway producing dimethylsulphide in bacteria is widespread in soil environments

The volatile compound dimethylsulphide (DMS) is important in climate regulation, the sulphur cycle and signalling to higher organisms. Microbial catabolism of the marine osmolyte dimethylsulphoniopropionate (DMSP) is thought to be the major biological process generating DMS. Here we report the discovery and characterisation of the first gene for DMSP-independent DMS production in any bacterium. This gene, mddA, encodes a methyltransferase that methylates methanethiol (MeSH) and generates DMS. MddA functions in many taxonomically diverse bacteria including sediment-dwelling pseudomonads, nitrogen-fixing bradyrhizobia and cyanobacteria, and mycobacteria, including the pathogen Mycobacterium tuberculosis. The mddA gene is present in metagenomes from varied environments, being particularly abundant in soil environments, where it is predicted to occur in up to 76% of bacteria. This novel pathway may significantly contribute to global DMS emissions, especially in terrestrial environments, and could represent a shift from the notion that DMSP is the only significant precursor of DMS