Targeted next-generation sequencing

Targeted NGS technology couples amplification of selected genes with NGS technology to detect resistance to many drugs with a single test. Also, since targeted NGS can interrogate entire genes to identify specific mutations associated with resistance, tests based on this technology may be more accurate than existing WRDs. In addition, new tests based on NGS can detect resistance to new and repurposed drugs that are not currently included in any other molecular assays. Hence, tests based on targeted NGS offer great potential to provide comprehensive resistance detection matched to modern treatment regimens.

Recommendations
  1. In people with bacteriologically confirmed pulmonary TB disease, targeted next-generation sequencing technologies may be used on respiratory samples to diagnose resistance to rifampicin, isoniazid, fluoroquinolones, pyrazinamide and ethambutol rather than culture-based phenotypic drug susceptibility testing.
    (Conditional recommendation, certainty of evidence moderate [isoniazid and pyrazinamide], low [rifampicin, fluoroquinolones and ethambutol])
Remarks
  • Priority should be assigned to those at higher risk of resistance to first-line treatment medications, including individuals who:
    • continue to be smear or culture positive after 2 or more months of treatment, or experience treatment failure;
    • have previously had TB treatment,
    • are in contact with a person known to have resistance to TB drugs; or
    • reside in settings or belong to subgroups where there is a high probability of resistance to either rifampicin, isoniazid or fluoroquinolone (used in new shorter regimens), or where there is a high prevalence of M. tuberculosis strains harbouring mutations not detected by other rapid molecular tests.
  • This recommendation is conditional because of the lack of data on health benefits, the variable certainty of evidence on diagnostic accuracy, and the fact that accuracy is suboptimal for certain drugs. In addition, because this is a new technology that has not yet been widely implemented, there is still limited and variable evidence on costs, cost–effectiveness and feasibility of implementation.
  1. In people with bacteriologically confirmed rifampicin-resistant pulmonary TB disease, targeted NGS technologies may be used on respiratory samples to diagnose resistance to isoniazid, fluoroquinolones, bedaquiline, linezolid, clofazimine, pyrazinamide, ethambutol, amikacin and streptomycin rather than culture-based phenotypic drug susceptibility testing.
    (Conditional recommendation, certainty of evidence high [isoniazid, fluoroquinolones and pyrazinamide], moderate [ethambutol], low [bedaquiline, linezolid, clofazimine and streptomycin], very low [amikacin])  
Remarks
  • Priority should be given to those at a higher risk of resistance to medications used for the treatment of RR-TB, including individuals who:
    • continue to be smear or culture positive after 2 months or more of treatment or have experienced treatment failure;
    • have previously had TB treatment, including with the new and repurposed drugs;
    • are in contact with a person known to have resistance to TB drugs, including the new and repurposed drugs; or
    • have pre-XDR-TB with resistance to fluoroquinolones.
  • As above, this recommendation is conditional because of the lack of data on health benefits, the variable certainty of evidence on diagnostic accuracy, the fact that accuracy is suboptimal for certain drugs, and limited and variable evidence on costs, cost–effectiveness and feasibility of implementation.

The products and drugs for which eligible data met the class-based performance criteria are listed below:

Deeplex® Myc-TB (Genoscreen, France): rifampicin, isoniazid, pyrazinamide, ethambutol, fluoroquinolones, bedaquiline, linezolid, clofazimine, amikacin and streptomycin

AmPORE-TB® (Oxford Nanopore Diagnostics, United Kingdom): rifampicin, isoniazid, fluoroquinolones, linezolid, amikacin and streptomycin

TBseq® (Hangzhou ShengTing Medical Technology Co., China): ethambutol

Where a product has not yet met the requirements for a specific drug (i.e., the drug is not listed), further improvements to the product are needed, and a review of the evidence is necessary before clinical use.

Test description

Three products met the inclusion criteria for detection of drug resistance to at least one of the anti-TB drugs under evaluation.

  • The Deeplex® Myc-TB test (Genoscreen, France) is a targeted NGS-based kit for the simultaneous identification of mycobacterial species, genotyping and prediction of drug resistance of MTBC strains, directly applicable on sputum samples (50). The assay relies on deep sequencing of a 24-plex amplicon mix, and it targets 18 MTBC gene regions associated with resistance to anti-TB drugs. Mycobacterial species identification is performed by targeting the hsp65 gene; the spoligotyping target (CRISPR/Direct Repeat locus) and phylogenetic single nucleotide polymorphisms (SNPs) in targets associated with drug resistance are used for MTBC strain genotyping. The assay is performed using the Nextera XT and DNA Flex library preparation kits on the iSeq 100, MiniSeq, MiSeq and NextSeq sequencing platforms (Illumina). The solution includes an automated analysis pipeline of the sequencing data in a secure online application with integrated databases for results interpretation.

unfig

  • The AmPORE-TB® test (Oxford Nanopore Diagnostics, United Kingdom) – previously referred to as Nano-TB) – is a targeted NGS-based kit for the simultaneous identification of mycobacterial species and the detection of MTBC genetic variants associated with antimicrobial resistance in DNA extracted from sputum samples.20 The assay relies on sequencing of a 27-plex amplicon mix: 24 drug-resistance targets, a genotyping target, a non-tuberculous mycobacteria (NTM) identification target (hsp65) and an internal control. The 24 drug-resistance targets are MTBC gene regions that are associated with resistance to anti-TB drugs. Mycobacterial species identification is performed by targeting the hsp65 gene; the spoligotyping target (CRISPR/Direct Repeat locus) is used for MTBC strain genotyping. The assay is performed using the OND AmPORE-TB kit (OND-TBDR001-XX) and Flow Cells (OND-FLO-MIN001-XX) on the GridION Diagnostic Sequencing System (OND). The sequencing control software on the device can automatically start and report the results for the analysis workflows installed. The AmPORE-TB includes analysis software pre-installed on a device that processes readouts produced by the sequencing control software and creates an easy-to-interpret report, all performed locally on the device.

unfig1

  • The TBseq® test (Hangzhou ShengTing Medical Technology Co., China) is a kit based on targeted NGS that is used for the simultaneous identification of mycobacterial species and the prediction of drug resistance of MTBC strains; it is directly applicable to clinical specimens such as sputum and bronchoalveolar lavage fluid (51). The assay relies on deep sequencing of a multiplex amplification mix and it targets 21 MTBC genes associated with resistance to anti-TB drugs. Mycobacterial species identification is performed by targeting the 16S and hsp65 gene regions. The assay is performed using the Universal Gene Sequencing Kit (ShengTing) to generate libraries that are sequenced on either a MinION or a GridION platform (Oxford Nanopore Technologies). The solution includes automated analysis software (Nano TNGS V1.0) for sequencing data processing and a secure online application (TBseq® Web App) with integrated databases for interpretation of results.

unfig2

Justification and evidence

Diagnostic accuracy and health benefits

Two health questions were designed using the PICO approach, to form the basis for the evidence search, retrieval and analysis.

  1. Should targeted NGS as the initial test be used to diagnose drug resistance in individuals with bacteriologically confirmed pulmonary TB disease?
    This question applies to:
    • rifampicin, using a composite reference standard of phenotypic DST and whole genome sequencing (WGS), and Xpert MTB/RIF® or Xpert Ultra®;
    • isoniazid, using phenotypic DST as the reference standard;
    • levofloxacin, using phenotypic DST as the reference standard;
    • moxifloxacin, using phenotypic DST as the reference standard;
    • pyrazinamide, using a composite reference standard of phenotypic DST and WGS; and
    • ethambutol, using a composite reference standard of phenotypic DST and WGS.
       
  2. Should targeted NGS be used to diagnose drug resistance in individuals with bacteriologically confirmed rifampicin-resistant pulmonary TB disease?
    This question applies to:
    • isoniazid, using phenotypic DST as the reference standard;
    • levofloxacin, using phenotypic DST as the reference standard;
    • moxifloxacin, using phenotypic DST as the reference standard;
    • pyrazinamide, using a composite reference standard of phenotypic DST and WGS;
    • bedaquiline, using phenotypic DST as the reference standard;
    • linezolid, using phenotypic DST as the reference standard;
    • clofazimine, using phenotypic DST as the reference standard;
    • amikacin, using phenotypic DST as the reference standard;
    • ethambutol, using a composite reference standard of phenotypic DST and WGS; and
    • streptomycin, using phenotypic DST as the reference standard. 

A broad search was conducted to find, appraise and synthesize evidence about health benefits and the diagnostic test accuracy of targeted NGS compared with phenotypic drug sensitivity testing for patients with bacteriologically confirmed TB or with bacteriologically confirmed rifampicin-resistant pulmonary TB disease. A comprehensive search of three databases (Medline, Ovid Embase and Scopus) for relevant citations was performed. No date restriction was applied and the search was initially performed on 7 September 2022 and repeated on 17 January 2023. In addition, WHO made a public call for data and contacted well-known experts in the field to ask whether they had, or knew of, unpublished data that could contribute.

No data were found for the impact of targeted NGS on patient-level health effects. For the analysis of diagnostic accuracy, because few data were available in the literature, all data identified from the literature were included after correspondence with the authors. Hence, no manual data extraction from publications was required. A post-hoc decision was made to perform only an individual patient data (IPD) meta-analysis; thus, any study that could not provide IPD was excluded. Two report authors made independent assessments of methodological quality using QUADAS-2. Disagreements were resolved by discussion and uncertainties or disagreements were reviewed by an independent third party.

Subanalyses were performed to assess the diagnostic test accuracy in PLHIV and for semiquantitative results (derived from cycle thresholds) from Xpert MTB/RIF® or Xpert Ultra®, where “very low” or “low” concentrations of M. tuberculosis were compared with “medium” or “high” concentrations. The very low or low semiquantitative categories represent paucibacillary disease states, such as those frequently observed in paediatric TB.

Data were included from both published and unpublished prospective, observational clinical studies of targeted NGS platform diagnostic accuracy. All studies where targeted NGS had been performed directly from processed clinical samples were included, whereas those performed exclusively on cultured isolates were excluded. All studies were required to have comparator phenotypic DST data as a reference; in the cases of rifampicin, ethambutol and pyrazinamide, studies were required to also have WGS, to allow a composite reference to be generated. Rifampicin resistance results and semiquantitative results from Xpert MTB/RIF® or Xpert Ultra® were requested from all studies.

Given that this was a review of the diagnostic accuracy of a class of diagnostic platforms, all the data from each platform alone were analysed to assess which to include in an analysis to inform a class recommendation. Where the performance of any one platform appeared to be an outlier for sensitivity or specificity, that platform was excluded from subsequent metaanalyses. A platform was considered to be an outlier for a particular drug if the point estimate for sensitivity was more than 10 percentage points worse than the best performing platform, or where the point estimate for specificity was more than 5 percentage points worse.

An IPD meta-analysis was performed instead of a classical meta-analysis, because the studies identified in the literature were generally too small to contribute to a classical meta-analysis, particularly for the new and repurposed drugs. In addition, this type of approach allowed for relevant co-variables to be included in the model; it could also control for repeated testing on the same samples using different platforms, which was the case for much of the available data.

For each dependent variable, a multivariable model included a number of co-variables as fixed effects. These included rifampicin resistance as determined by Xpert MTB/RIF® or Xpert Ultra® for all drugs other than rifampicin; semiquantitative cycle threshold (CT) value from Xpert MTB/RIF® or Xpert Ultra®; and a co-variable to indicate which samples featured in duplicate, meaning that some samples were sequenced on two different platforms and thus were represented twice in the analysis. For models looking specifically at diagnostic test accuracy in PLHIV, the HIV test result was included as a co-variable. Finally, the study site was included as a random effect. The models were run in Stata (version 17) using the melogit command, and the outputs were transformed using the margins command. Models were run for all PICO questions for sensitivity and specificity.

The certainty of the evidence of the pooled studies was assessed systematically for each of the PICO questions using the GRADE approach, which produces an overall quality assessment (or certainty) of evidence and has a framework for translating evidence into recommendations. The GRADEpro Guideline Development Tool software (20) was used to generate summary of findings tables for the sensitivity and specificity of each drug. The numbers of samples classified as true, false positive or negative were then calculated across a range of three prevalences of drug resistance, chosen to be representative of different global settings. The quality of evidence was rated as high (not downgraded), moderate (downgraded one level), low (downgraded two levels) or very low (downgraded more than two levels), based on five factors: risk of bias, indirectness, inconsistency, imprecision and other considerations. The quality (certainty) of evidence was downgraded by one level when a serious issue was identified and by two levels when a very serious issue was identified in any of the factors used to judge the quality of evidence.

The data sources for the IPD data analysis are shown in Fig. 2.3.9. The analysis included data from published studies, a large multicountry trial conducted by FIND, and several other studies across multiple countries. Most of the studies only evaluated the Deeplex assay, while the FIND trial evaluated both the Deeplex and the AmPORE-TB. Only one study evaluated TBseq. For each drug, one or two platforms were dropped from the analysis based on the overall number of resistant or susceptible samples available for that platform and drug, or because the accuracy of the platform did not meet the diagnostic test accuracy criteria for inclusion when compared with the best performing platform.

Fig. 2.3.9. Studies included in the IPD meta-analysis for targeted NGS

#fig2.3.9

Data synthesis was structured around the two preset PICO questions, as outlined below.

PICO 1: Should targeted NGS as the initial test be used to diagnose drug resistance in patients with bacteriologically confirmed pulmonary TB disease?

The available evidence included in the final pooled analysis varied by drug, from 12 studies with 1440 participants for the sensitivity of isoniazid to three studies with 269 participants for the specificity of pyrazinamide (Table 2.3.5). The pooled estimates were determined using a multivariable, mixed-effects model. All drugs were downgraded by one level for indirectness for sensitivity and specificity, because all studies were enriched for rifampicin resistance, leading to applicability concerns. In addition, for rifampicin, levofloxacin and pyrazinamide, specificity was downgraded a further level for imprecision; however, for ethambutol, it was downgraded for risk of bias because different samples were used for the index and reference tests. The overall certainty of the evidence for test accuracy ranged from moderate to very low.

The test performance was determined to be accurate for all drugs included in the assessment, with a pooled sensitivity of at least 95% for isoniazid, moxifloxacin and ethambutol, more than 93% for rifampicin and levofloxacin, and 88% for pyrazinamide. The pooled specificity was at least 96% for all drugs.

The reference standard was culture-based phenotypic DST for isoniazid, levofloxacin and moxifloxacin, and a combination of phenotypic DST and WGS for rifampicin, pyrazinamide and ethambutol. The percentage of tests with indeterminate results ranged from 9% (levofloxacin and moxifloxacin) to 18% (pyrazinamide), with higher indeterminate rates in samples with lower bacterial load (semiquantitative category low or very low).

Table 2.3.5. The accuracy and certainty of evidence of targeted NGS for the detection of resistance to anti-TB drugs among bacteriologically confirmed pulmonary TB

#tab2.3.5

There were no data on the impact of targeted NGS on patient outcomes such as time to treatment or treatment outcome.

PICO 2: Should targeted NGS be used to diagnose drug resistance in patients with bacteriologically confirmed rifampicin-resistant pulmonary TB disease?

The available evidence varied by drug, from 12 studies with 1440 participants for sensitivity of isoniazid to three studies with 31 participants for sensitivity of bedaquiline (Table 2.3.6). The pooled estimates were determined using a multivariable, mixed-effects model.

The overall certainty was high for some of the drugs. Levofloxacin was downgraded one level for inconsistency. Bedaquiline and linezolid were downgraded by two levels for imprecision in sensitivity because the number of resistant samples was below the threshold set and the confidence intervals were wide. Clofazimine was also downgraded by two levels, one for inconsistency (because two studies were outliers) and another level for imprecision (because the confidence intervals were wide). Amikacin was downgraded by one level for sensitivity and specificity because critical concentrations outside those recommended by WHO were used for a large proportion of samples. Amikacin sensitivity was further downgraded by two more levels, one for inconsistency and the other for imprecision. Ethambutol was downgraded by one level for risk of bias because different samples were used for the index and reference tests. Streptomycin specificity was downgraded by two levels, one for inconsistency and the other for imprecision. The overall certainty of the evidence for test accuracy ranged from high to very low.

The test performance among people with RR-TB was determined to be accurate for isoniazid, levofloxacin, moxifloxacin, ethambutol and streptomycin (pooled sensitivity ≥95%) and acceptable for pyrazinamide (90%), bedaquiline (68%), linezolid (69%), clofazimine (70%) and amikacin (87%). The pooled specificity was 95% or greater for all drugs except streptomycin (75%). The reference standard was culture-based phenotypic DST for all drugs except for ethambutol and pyrazinamide, where a combination of phenotypic DST and WGS was used. The percentage of tests with indeterminate results ranged from 9% (levofloxacin and moxifloxacin) to 21% (ethambutol); indeterminate rates were higher in samples with a lower bacterial load (semiquantitative category low or very low).

Table 2.3.6. The accuracy and certainty of evidence of targeted NGS for the detection of resistance to anti-TB drugs among bacteriologically confirmed rifampicin-resistant pulmonary TB

#tab2.3.6

There were no data on the impact of targeted NGS on patient outcomes such as time to treatment or treatment outcome.

Three web annexes give additional information, as follows:

  • details of studies included in the current analysis (Web Annex 4.18: Review of the diagnostic accuracy of targeted NGS technologies for detection of drug resistance among people diagnosed with TB);
  • a summary of the results and details of the evidence quality assessment (Web Annex 2.10: GRADE profiles of targeted next-generation sequencing for detection of TB drug resistance); and
  • a summary of the GDG panel judgements (Web Annex 3.10: Evidence to decision tables: targeted next-generation sequencing for detection of TB drug resistance).

Cost–effectiveness analysis

The cost and cost–effectiveness data for targeted NGS were assessed through a systematic review of the published literature and a generalized model-based cost–effectiveness analysis commissioned by WHO.

The systematic review on the cost and cost–effectiveness of using either targeted NGS or WGS to diagnose DR-TB searched three databases: PubMed, Embase and Scopus. The search was run on 30 October 2022 and had no time restriction. All costing data were inflated to 2021 US dollars. Findings were synthesized descriptively, given the considerable degree of heterogeneity in study methodology and outcomes. Among the studies included in the systematic review, three were on targeted NGS only, three were on targeted NGS and WGS, and four were on WGS only. For targeted NGS based on a single study (n=1), the cost per sample was between US$ 69.64 for Illumina MiSeq on 24 samples, and US$ 73.47 for Nanopore MinION on 12 samples; however, this costing was limited to only some components and did not include human resource costs or overhead costs. For WGS (n=5), cost per sample ranged from US$ 63.00 on Nanopore MinION to US$ 277.00 on Illumina MiSeq; given that studies used an inconsistent number of component costs, comparisons were challenging. Based on the review, the most significant cost component was the sequencing step, and the largest component costs were reagents and consumables, including those necessary for sequencing, sample processing and targeted NGS steps library preparation. Study authors identified four major cost drivers: use of different sequencers, depth and breadth of coverage, inefficiencies in initial sample runs, and economies of scale via batching or cross-batching.

The cost data from the systematic review were limited; therefore, an empirical unit costing was performed, in consultation with manufacturers and FIND. At the time of this work, only pricing for Deeplex Myc-TB was available and it was used for estimation of cost for the class. Unit costs included consumables, equipment, staffing and overheads (where available); also, costs assumed targeted NGS testing for all drugs. Based on the empirical analysis, the cost of targeted NGS was estimated to be:

  • US$ 134 to US$ 257 in South Africa;
  • US$ 120 to US$ 198 in Georgia; and
  • US$ 121 to US$ 175 in India.

These costs are dependent on patient volume, batching and negotiated cost per targeted NGS kit.

Recognizing the lack of economic evidence on this topic, a hypothetical cost–effectiveness modelling study was undertaken to assess the cost–effectiveness (Objective 1) and affordability (Objective 2) of these tests for the diagnosis of DR-TB in various high TB burden settings.

Objective 1: To assess the potential cost–effectiveness of introducing the targeted NGS technology for the diagnosis of DR-TB in Georgia, India and South Africa. 

This assessment included modelling the cost–effectiveness of targeted NGS in three separate scenarios with distinct comparison options:

  • Cost–effectiveness of targeted NGS for DST among individuals with RR-TB after a rapid molecular test for rifampicin resistance as a replacement for phenotypic DST (PICO 2).
  • Cost–effectiveness of targeted NGS for DST among individuals with RR-TB after a rapid molecular test for rifampicin resistance as a replacement for current in-country DST practice (PICO 2).
  • Cost–effectiveness of targeted NGS as the initial test for TB drug resistance in patients with bacteriologically confirmed TB compared with rapid molecular testing for drug resistance and phenotypic DST in a high DR-TB burden setting (PICO 1).

In the first scenario, targeted NGS was compared with universal phenotypic DST; in the second scenario, targeted NGS was compared with current in-country phenotypic DST practice among individuals with detected rifampicin resistance (PICO 2). This was done across three countries: Georgia, India and South Africa. Current DST practice in Georgia and South Africa includes Xpert XDR® followed by phenotypic DST; in India it includes LPAs and phenotypic DST done in parallel. A final scenario included targeted NGS compared to rapid molecular testing for drug resistance and phenotypic DST as initial tests for TB drug resistance among all TB patients (PICO 1) but was modelled for only one setting, Georgia – a high DR-TB burden setting. Epidemiological data were sourced from published literature; targeted NGS diagnostic accuracy data were sourced from the systematic review and IDP analysis conducted for this guideline. Economic data were sourced from published literature and a systematic and scoping review done in parallel by our team and supplemented with empirical data collection.

A decision analysis modelling approach was used to estimate the incremental cost–effectiveness of using targeted NGS for the diagnosis of DR-TB compared with various existing DST scenarios. This was done from the perspective of the health care system and accounts only for the health care system costs required to diagnose and treat TB. The estimation did not account for societal costs, or any direct or indirect costs incurred by patients. In addition, costs for sample transportation were not included in this analysis. The primary outcome was the incremental cost–effectiveness ratio (ICER), which was calculated as the incremental cost in US dollars per disability-adjusted life year (DALY) averted.

Main findings for PICO 1: Using targeted NGS as an initial test 

Using targeted NGS as an initial test for DST in the high DR-TB burden setting of Georgia led to more health gains (DALYs=0.49) compared with Xpert MTB/RIF or Xpert Ultra, followed by phenotypic DST (DALY=0.51). The ICER per DALY averted was US$ 9261 (95% uncertainty range [UR]: US$ 5258–32 040/DALY averted), which was considered cost effective at a willingness-to-pay (WTP) threshold of three times the country GDP per capita (US$ 15 609), with 80% of simulated iterations falling below the WTP threshold.

Main findings for PICO 2: Using targeted NGS among those with RR-TB

Using targeted NGS as a replacement for universal phenotypic DST among RR-TB patients, targeted NGS was dominated by phenotypic DST, with targeted NGS having higher costs and leading to fewer health gains. This finding was driven by the high diagnostic accuracy of phenotypic DST (which was assumed to be universal in this scenario), and an assumption of no difference in loss to follow-up between targeted NGS and phenotypic DST. When in-country DST practice was used as the comparator (instead of universal phenotypic DST), targeted NGS led to more health gains than in-country DST across all three countries. Targeted NGS was cost effective in South Africa (ICER: US$ 15 619/DALY averted, 95% UR: cost saving –US$ 114 782, at a WTP threshold of US$ 21 165), but was not cost effective in Georgia (ICER: US$ 18,375/ DALY averted, UR: cost saving –US$ 158 972/DALY averted, at a WTP threshold of US$ 15 065). In India, where LPA, liquid culture and DST are being used as part of in-country DST, targeted NGS dominated the country’s current DST practice, with lower costs and more health gains (95% UR: cost saving –US$ 60 083).

Main findings: scenario analyses

Several key scenario analyses were investigated. In the base case approach, loss to follow-up was assumed to be equivalent between phenotypic DST and targeted NGS; in a scenario where there was no loss to follow-up in targeted NGS compared with 10% in phenotypic DST, targeted NGS was cost effective in South Africa (ICER: US$ 13 004/DALY averted, WTP: US$ 21 165) and Georgia (ICER: US$ 13 640/DALY averted, WTP: US$ 15 069) and targeted NGS still dominated in-country DST practice in India. In scenarios where sequencing platforms are used for multiple different diseases to reduce the unit test cost of targeted NGS, the cost–effectiveness of targeted NGS improves in all three countries. A batching scenario was investigated, with an assumed 20% fewer samples per targeted NGS run, and led to an increased unit test cost for targeted NGS; in this scenario, the targeted NGS approach retained cost–effectiveness only in South Africa. When a 50% price reduction in targeted NGS test kit cost was assumed, targeted NGS cost–effectiveness further improved in all countries.

Objective 2: To assess the financial impact of introducing targeted NGS as a replacement for existing DST for diagnosis of DR-TB among TB patients across three countries: Georgia, India and South Africa.

A budget impact assessment was undertaken to estimate the financial consequences of adopting targeted NGS for DST for all patients diagnosed with TB, and replacing in-country DST practice in Georgia (PICO 1). The analysis suggested that implementing targeted NGS for all patients diagnosed with TB would be more expensive than testing all patients with Xpert MTB/RIF or Xpert Ultra, followed by phenotypic DST (see Fig. 2.3.10).

Fig. 2.3.10. Budget impact assessment results comparing current standard practice for DST with implementation of targeted NGS for all patients diagnosed with TB in Georgia

#fig2.3.10

A budget impact assessment was undertaken to estimate the financial consequences of adopting targeted NGS for DST after a rapid molecular test for rifampicin resistance, and replacing in-country DST practice in Georgia, India and South Africa (PICO 2). In-country DST practice included Xpert XDR combined with phenotypic DST in Georgia and South Africa, and Xpert XDR combined with LPA in Georgia over a 1-year and 5-year period. It was assumed that the eligible RR-TB patient populations requiring DST were 58 837, 8200 and 187 in South Africa, India and Georgia, respectively, and that the TB reduction rate over the 5 years was stable (2). To estimate the impact on the country-specific budget, the economic costs generated by the model were multiplied by the number of patients.

Results from a 1-year budget impact assessment for PICO 2 are presented in Fig. 2.3.11. In India, it was estimated that implementing targeted NGS would cost about US$ 57 130 727 – slightly lower than the current practice of LPA combined with phenotypic DST, which has a cost of US$ 57 719 097. In South Africa, it was estimated that implementing targeted NGS would result in a rise in budget to about US$ 27 888 200, slightly more than LPA combined with phenotypic DST, which has a cost of US$ 26 428 600. Finally in Georgia, where there are fewer bacteriologically confirmed patients, it was estimated that implementing targeted NGS would cost about US$ 592 221, slightly more than LPA combined with phenotypic DST, which has a cost of US$ 568 480.

Fig. 2.3.11. Budget impact assessment results comparing current standard practice for DST to implementing targeted NGS for patients with RR-TB in India, South Africa and Georgia

#fig2.3.11

User perspective

A rapid review was commissioned to identify and synthesize qualitative evidence on the use of targeted NGS for the detection of TB drug resistance; in particular, the aim was to examine the implementation considerations related to acceptability, feasibility, and values, preferences and equity. The review searched Medline with no year or language limits. The search was run on 19 August 2022, and then rerun on 10 October 2022 to include WGS-related studies for the detection of TB drug resistance. The review did not identify any eligible studies for analysis and synthesis. Based on the systematic search, three records were identified; in addition, based on the open, hand and expert searches, 27 records were found. On full-text review of the 30 records, none were found to be eligible for inclusion. Given that no direct evidence was found, note was made of a Cochrane qualitative evidence synthesis published in 2022 that examined recipient and provider perspectives on rapid molecular tests for TB and drug resistance (52); that study provides relevant (though indirect) evidence on the subject. The authors noted that people with TB valued reaching diagnostic closure with an accurate diagnosis, avoiding diagnostic delays and keeping diagnostic-associated costs low, whereas health care providers valued aspects of accuracy and the resulting confidence in low-complexity NAAT results, rapid turnaround times and low costs to people seeking a diagnosis.

To address the direct evidence gap, WHO commissioned an additional qualitative cross-sectional study comprising semi-structured interviews, primarily with laboratory staff and management personnel directly involved with implementing targeted NGS in the three FIND trial sites, as well as with three global experts involved in TB care and diagnostics. In total, there were 17 respondents, and the work was conducted during September to October 2022. The objective was to explore the perceptions and experiences of those implementing targeted NGS technology, with respect to acceptability, feasibility, and values, preferences and equity. The main findings are summarized below.

Acceptability

A consistently positive sentiment was expressed for the acceptability and potential utility of targeted NGS technology. Targeted NGS was seen as a “major advancement” in molecular MDR-TB diagnostics.

  • The main reasons for the high level of acceptability were the comprehensiveness (resistance diagnosis for more drugs and for the newest and repurposed drugs), the convenience of using a sputum sample (as compared with culture samples), and the rapidity (quick results compared with phenotypic testing times; 3–5 days as compared with 4–6 weeks).
  • There was also the sense that there is a good window of opportunity to benefit from the utility of targeted NGS technology; that is, the technology is arriving at the right time, given that resistance to newer TB drugs is likely to increase as the use of these drugs becomes routine.

Feasibility 

Although there was high praise for the capability and potential utility of targeted NGS technology, several challenges were identified when testing samples using the targeted NGS platforms, which may limit the feasibility of targeted NGS for routine uptake at the present time. The overall sentiment was that the targeted NGS technology needs to be further developed before it can be considered fully ready for operational use.

The following feasibility challenges were identified:

  • Start-up and setting-up challenges: Multiple problems were identified with starting and setting up the technology. These problems related to the newness of the technology and the trial setting, importing technology and specialist supplies, lack of in-country technical assistance for problem-solving and need for more hands-on training practice. 
  • High technical complexity of the test: Targeted NGS technology was seen as a high complexity molecular test that was technically challenging. For example, preparing the sample for sequencing involves multiple steps that require attention to detail and precision, leaving little room for error. Preparation of the library is particularly complex for the Deeplex platform, although both the Deeplex and the Nanopore platforms are quite complex. In both platforms, it was thought that there were too few opportunities for early recognition and correction of errors, increasing the risk of failed runs.
  • Specialized laboratory infrastructure and human resource requirements: Because targeted NGS is a molecular-based testing platform, it requires highly specialized laboratory infrastructure (e.g. multiple rooms to prevent amplicon contamination and specialized cold storage facilities). Also, highly specialized molecular and medical scientists are needed to perform the tests. In LMIC settings, such specialized laboratory infrastructure and staff may only be available at centralized laboratories (i.e. not at regional laboratories).
  • Special requirements for operating the test: In addition to highly specialized laboratory infrastructure and staff, the testing technology also requires an uninterrupted supply of electricity, high internet connectivity, high computer capacity, clean water and temperature controls – requirements that may pose challenges in some LMIC settings.
  • Supply chain challenges: Major challenges were reported relating to the required supply chain for implementing targeted NGS. Procurement bottlenecks and delays coupled with shelf-life limitations of reagents jeopardize continuous access to specialist supplies.
  • Data management and storage requirements: There were concerns that data analysis and data storage requirements were not fully developed, including systems for backing up data, ownership of data and security of data. Another issue that needs to be considered is how targeted NGS and routine laboratory information systems can be interlinked.
  • Continuous updating of the WHO catalogue of mutations is required: There was agreement that the usefulness of the targeted NGS technology depends on the informational support provided by the WHO catalogue of mutations (53), which allows for meaningful interpretation of resistance data; thus, there is a need for the WHO catalogue to be continuously updated.
  • Feasibility concerns differed for the different targeted NGS platforms: The overall sentiment was that all targeted NGS platforms needed to be further developed before they are fully ready for operational use, some more than others. The high level of technical complexity of the sample preparation stages (mainly the library preparation stage) was considered a key challenge for the Deeplex platform, and the need for improved computer analysis and storage capacity was a challenge for the Oxford Nanopore platform, although both required a high level of precision and attention to detail. There is also a need to incorporate steps for early error recognition.

Values, preferences and equity

The overall sentiment is that MDR-TB diagnostic technology needs to balance accuracy, speed, affordability, equity and cost–effectiveness, and that targeted NGS technology would need to address these considerations before it can be implemented in LMIC settings. These considerations were consistent across the different stakeholder groups who participated in the study.

  1. Centralized versus decentralized placement may have equity implications for access: Given the high-level specialized laboratory infrastructure, specialized human resources and technical complexity needed for targeted NGS, the technology may be suitable for placement only at centralized, reference laboratories. This may have equity access considerations if it means less access for some regions of a country that lack reference laboratories. This may also have implications for costs (e.g. costs for transport of sputum), probability of sample loss and time to results.
  2. Affordability and cost–effectiveness are major concerns: There was a major concern about the financial costs of the targeted NGS technology and the affordability for LMIC. Participants were worried about the cost of the equipment and the costs of ongoing specialist supplies (especially reagents), as well as the cost of maintaining equipment. They noted that costing calculations should be comprehensive and should include the cost of special consumables, extra general laboratory consumables and additional infrastructure needs (e.g. extra space, temperature control and internet connectivity). There were concerns that cost–effectiveness calculations should be comprehensive and should include assessment of the impact of the use of targeted NGS testing on improving TB outcomes.
  3. The MDR/RR-TB case burden of a country could influence equitable access at centralized levels. In some settings with high caseloads, the targeted NGS technology capacity in central laboratories may not be sufficient for processing large caseloads in good time; also, in settings with low caseloads, waiting for sufficient samples to batch-test will cause delays.
Implementation considerations

Although the evidence that is available supports the use of targeted NGS to detect drug resistance after TB diagnosis, to guide clinical decision-making for DR-TB treatment, the following factors need to be considered when implementing these tests:

  • Regulatory approval from national regulatory authorities or other relevant bodies is required before implementation of these diagnostic tests.
  • In its current format, targeted NGS is a high complexity test that is most suitable for centralized laboratories equipped with specialized skills and infrastructure.
  • Targeted NGS tests do not replace existing rapid tests that are more accessible and easier to perform for detecting resistance to rifampicin, isoniazid and fluoroquinolones. However, if targeted NGS can be performed rapidly, it can be considered as an alternative initial option for prioritized populations. Those who will benefit most from these tests are individuals who require rapid and comprehensive DST but have limited access to phenotypic DST.
  • Priority should be given to samples with a high bacillary load as determined by initial bacteriological tests (e.g. semiquantitative high/medium or smear-positive grading). In situations where the bacillary load is low (e.g. semiquantitative low/very low/trace or smear-negative grading), the recommendations still hold, although rates of indeterminate results are likely to be higher; therefore, phenotypic DST is likely still required for samples with a low bacillary load.
  • Similarly, the recommendations apply to children, adolescents and PLHIV populations because these populations have a higher frequency of samples with low bacterial load.
  • The recommendation is based on data obtained from sputum and bronchoalveolar lavage specimens, and can be extrapolated to other lower respiratory tract samples (e.g. endotracheal aspirates). However, further research is needed to evaluate the use of these tests on alternative sample types for diagnosing pulmonary TB in children (e.g. nasopharyngeal and stool samples) and diagnosing extrapulmonary TB.
  • Since sensitivity for bedaquiline, linezolid and clofazimine resistance is suboptimal, consideration of the pretest probability is important in interpreting the targeted NGS results for these drugs. Further testing of samples with a susceptible result (using culture-based phenotypic DST) would be warranted, particularly when the risk of resistance is high. Since specificity is high, a result that indicates resistance may be used to guide the therapy, particularly among those at risk for resistance. In the case of pretomanid, the basis for resistance has not been fully elucidated; hence, culture-based DST is also required for this drug.
Research priorities

Several key research priorities emerged from the reviews of the available evidence on targeted NGS for detecting TB drug resistance. They fall into three main categories: clinical research, implementation research, and monitoring and evaluation.

Clinical research:

  • Conduct clinical trials to assess the impact of targeted NGS on patient-important outcomes²¹.
  • Evaluate the accuracy and impact on patient-important outcomes of targeted NGS among populations of individuals diagnosed with TB, across a range of prevalences of rifampicin or other drug resistance).
  • Assess the accuracy and impact on patient-important outcomes of targeted NGS for detecting resistance to new and repurposed drugs, including pretomanid, across varied geographical and epidemiological settings.
  • Assess the accuracy and impact on patient-important outcomes of targeted NGS for analysing extrapulmonary samples, including CSF for meningitis, non-sputum samples (e.g. nasopharyngeal aspirate, gastric aspirate or stool) for children, and alternative sample types (e.g. tongue swabs) in both adults and children.
  • Undertake additional qualitative and quantitative research to further understand the perspectives of end-users and clinicians regarding the acceptability and feasibility of using targeted NGS.

Implementation research: 

  • Develop and evaluate effective and efficient implementation models by integrating targeted NGS into laboratory networks and optimizing algorithms, with the aim of enhancing timely access to testing and treatment initiation, and improving patient outcomes.
  • Develop strategies to enhance the efficiency of targeted NGS testing, including sample processing and concentration techniques, determining optimal thresholds of bacterial load from initial tests before performing targeted NGS, and employing molecular transport medium for the ambient storage and transfer of samples to testing sites.
  • Regularly update the WHO catalogue of mutations (53), incorporating additional genetic targets and including new drugs (e.g. pretomanid) to enhance the sensitivity and specificity of targeted NGS.
  • Explore technological advancements to simplify the testing process, automate steps (especially library preparation), develop decentralized targeted NGS solutions and investigate potential synergies with existing initial tests (e.g. using leftover DNA or smear-positive slides).
  • Conduct comprehensive mapping of sequencing capacity within countries and perform diagnostic network optimization exercises. Placement of the technology should consider the demand for sequencing across multiple diseases, facilitating cross-disciplinary use of the machines and shared costs.
  • Compile and use lessons learned from applying targeted NGS technology in other diseases (e.g. COVID-19) to develop effective implementation strategies for TB.

Monitoring and evaluation: 

  • Standardize the nomenclature for reporting of results across different targeted NGS technologies, for integration into health information data systems.
  • Ensure separate recording of true failures and unclassified mutations, and monitor trends over time as an essential component of result reporting.
  • Regularly monitor performance data, including overall resistance rates, resistance rates by specific drugs or targets and turnaround times (both total and in-laboratory).
  • Incorporate quality monitoring measures, such as tracking indeterminate rates, sequencing coverage and depth, and participating in external quality assurance programmes.
  • Establish an external quality assurance programme for sequencing that covers all relevant targets of interest.
  • Integrate the sequencing data generated into existing surveillance systems to monitor the prevalence and trends in drug resistance effectively. Share the data to update the WHO mutation catalogue.
  • Collect cost data to address important questions, such as the costs associated with introducing and scaling up targeted NGS in different settings, the trade-offs between turnaround time and batching, and the optimal balance in various settings.
  • Assess the impact of multidisease testing on programme operations and costs, including disease-specific testing volumes, turnaround times, costing, resource sharing and resource requirements.
  • Evaluate the impact of time to treatment initiation or modification, treatment outcomes and overall cost–effectiveness of targeted NGS implementation.

20 Oxford Nanopore Diagnostics provided a draft protocol for the test.

21 Mortality, Cure, Lost to follow up; Time to diagnosis; Time to treatment 

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