101 (2016) 31e40

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Tuberculosis

journal homepage: http://intl.elsevierhealth.com/journals/tube

REVIEW Recent developments in genomics, bioinformatics and drug discovery to combat emerging drug-resistant tuberculosis

* Soumya Swaminathan a, , Jagadish Chandrabose Sundaramurthi b, Alangudi Natarajan Palaniappan c, Sujatha Narayanan d a National Institute for Research in Tuberculosis (ICMR), Chetpet, , 600031, b Division of Biomedical Informatics, Department of Clinical Research, National Institute for Research in Tuberculosis (ICMR), Chetpet, Chennai, 600031, India c Department of Clinical Research, National Institute for Research in Tuberculosis (ICMR), Chetpet, Chennai, 600031, India d Department of Immunology, National Institute for Research in Tuberculosis (ICMR), Chetpet, Chennai, 600031, India article info summary

Article history: Emergence of drug-resistant tuberculosis (DR-TB) is a big challenge in TB control. The delay in diagnosis Received 24 February 2016 of DR-TB leads to its increased transmission, and therefore prevalence. Recent developments in genomics Received in revised form have enabled (WGS) of Mycobacterium tuberculosis (M. tuberculosis) from 3- 21 May 2016 day-old liquid culture and directly from uncultured sputa, while new bioinformatics tools facilitate to Accepted 8 August 2016 determine DR mutations rapidly from the resulting sequences. The present drug discovery and devel- opment pipeline is filled with candidate drugs which have shown efficacy against DR-TB. Furthermore, Keywords: some of the FDA-approved drugs are being evaluated for repurposing, and this approach appears Drug-resistant tuberculosis fi Whole genome sequencing promising as several drugs are reported to enhance ef cacy of the standard TB drugs, reduce drug Bioinformatics tolerance, or modulate the host immune response to control the growth of intracellular M. tuberculosis. Drug discovery Recent developments in genomics and bioinformatics along with new drug discovery collectively have Personalized medicine the potential to result in synergistic impact leading to the development of a rapid protocol to determine the drug resistance profile of the infecting strain so as to provide personalized medicine. Hence, in this review, we discuss recent developments in WGS, bioinformatics and drug discovery to perceive how they would transform the management of tuberculosis in a timely manner. © 2016 Elsevier Ltd. All rights reserved.

Contents

1. Introduction ...... 32 2. Drug resistance in tuberculosis ...... 32 2.1. Mechanism of resistance to first-line drugs ...... 32 2.2. Mechanism of resistance to second-line drugs ...... 33 2.2.1. Second-line injectable drugs (SLID) ...... 33 2.2.2. Fluroquinolones (FLQ) ...... 33 3. Exploring M. tuberculosis usingWGS...... 33 4. Heading towards WGS of M. tuberculosis directly from uncultured sputa ...... 34 5. Determination of drug resistance profile directly from WGS data using bioinformatics tools ...... 34 6. Dynamic drug discovery pipeline for tuberculosis ...... 35 7. Conclusion ...... 36 Acknowledgments ...... 37 Funding...... 37 Competing interests ...... 37

* Corresponding author. Present address: Indian Council of Medical Research, P.O. Box No. 4911, Ansari Nagar, , 110029, India. E-mail address: [email protected] (S. Swaminathan). http://dx.doi.org/10.1016/j.tube.2016.08.002 1472-9792/© 2016 Elsevier Ltd. All rights reserved. 32 S. Swaminathan et al. / Tuberculosis 101 (2016) 31e40

Ethical approval ...... 37 References ...... 37

1. Introduction knowledge gap between phenotypic and genotypic determination of drug resistance, and we also highlight the implications of these Emergence of drug resistance in Mycobacterium tuberculosis)is mutations in the evolution of molecular-based rapid assays. one of the major obstacles for successful control of tuberculosis (TB). Globally, about 3.3% of new cases and 20% of previously 2.1. Mechanism of resistance to first-line drugs treated cases are reported to have MDR-TB (multidrug-resistant tuberculosis: resistant to rifampicin and isoniazid); about 9.7% of INH is a prodrug activated by a bacterial enzyme catalase- the MDR-TB patients develop XDR-TB (extensively drug-resistant peroxidase (katG) and the activated drug inhibits the enoyl reduc- tuberculosis: resistant to rifampicin, isoniazid, one of the quino- tase (inhA) which subsequently block mycolic acid pathway of lones and one of the aminoglycosides) [1]. Another form of TB, M. tuberculosis. Therefore, mutations in either katG or inhA cause namely, TDR-TB (totally drug-resistant tuberculosis: resistant to all resistance to INH. Based on a systematic review [14], it was found of the first-line and second-line drugs), has been reported from that mutations in katG315 explain 64.2% of phenotypic resistance Italy, Iran, India and South Africa [2]. Among 480 000 estimated while mutation inhA-15 explain 19.2% of INH resistance. Frequency MDR-TB cases in 2014, only 123 000 were detected and reported of these mutations vary between different geographical regions; for [1]. The burden of drug-resistant tuberculosis (DR-TB) is reported to example, the frequency of katG315 mutation is 55.5% in western be very high among HIV positive individuals than HIV negative TB pacific region while 73.5% in Africa and 78.4% in South East Asia patients [3,4] a fact which we have also observed in South Indian [14]. Furthermore, several more mutations in katG and inhA and population [5]. Drugs used to treat DR-TB are more toxic, more mutations in several other genes are also associated with INH expensive, and require a longer duration [6]. Besides, the cure rate resistance [15]. Therefore, determining INH resistance based on of DR-TB is reported to be only 48%, versus 90% in drug-susceptible conventional sequencing is a complex process. By performing a new TB cases [6]. Time taken to determine the drug resistance meta-analysis, Bai et al. (2016) reported a pooled sensitivity and pattern is a critical factor that can lead to inordinate delay in specificity of 91% and 99% respectively for MTBDRplus assay in rapid initiating appropriate treatment. detection of INH resistance which include mutations from katG and The present situation clearly indicates that there is an urgent inhA [16]. Having known at least 22 different genes to be associated need to detect drug resistance rapidly and initiate personalized with resistance to INH, and the mutations listed in TBDReaMDB treatment for every patient, so as to prevent the transmission of database [15], the gap between the phenotypic and genotypic DR-TB and effectively control TB globally. Therefore, in this review determination of INH resistance is likely to continue as long as we discuss molecular mechanism of drug resistance, recent de- mutations from fewer genes alone are used as diagnostic markers. velopments in the field of genomics and bioinformatics which However, inclusion of all the mutations from multiple genes in a could together potentially lead to develop a protocol to diagnose rapid molecular diagnostic assay for each of the TB drugs may also drug resistance profile in TB in a couple of days. We also highlight be practically not feasible. the improvements in drug discovery with special focus on those RIF is an important TB drug that inhibits an enzyme DNA- candidate drugs useful against DR-TB and infer how these de- directed RNA polymerase, encoded by a gene rpoB. Therefore, velopments together will facilitate personalized treatment for TB. mutations in the rpoB are involved in the emergence of RIF resistant M. tuberculosis. More than 95% of the rifampicin resistant mutations 2. Drug resistance in tuberculosis occur in a 81-bp region, known as rifampicin resistance- determining region (RRDR) that spans codons 507 to 533 Rifampicin (RIF), isoniazid (INH), ethambutol (EMB), pyr- [11,17,18]. Within RRDR, mutations in codon 531, 526 and 516 are azinamide (PZA) are used as the first line drugs for tuberculosis. reported to account for 86% RIF resistance [17]. Using a meta- Fluoroquinolones (levofloxacin, moxifloxacin and ofloxacin) and analysis, it was observed that the pooled sensitivity and speci- aminoglycosides (kanamycin, amikacin, capreomycin) form the ficity of MTBDRplus, which includes mutations from rpoB to be 96% ® major second line-drugs for tuberculosis. Majority of the drug and 98% respectively [16]. Similarly, for Xpert MTB/RIF, the pooled resistance in M. tuberculosis is caused by mutations in genes which sensitivity and specificity were 89% and 99% respectively [19]. are either drug targets or activators of pro-drugs. Conventional However, the sensitivity of Xpert® MTB/RIF was reported to methods to determine resistance to anti-tuberculosis drugs involve comedown (67%) when applied on smear-negative pulmonary isolation, culture and drug susceptibility testing (DST) which take tuberculosis [19,20]. On the other hand, the achievement of high- about 2 months. Even with the Mycobacteria Growth Indicator quality sequencing from a smear-negative sputum sample Tube (MGIT) system, the process takes about 1e2 weeks [7e9]. recently [21], indicates that there is a ray of hope for improvement Shortening the delay between specimen collection and appropriate in precise prediction of drug resistance using whole genome regimen selection is critical for reducing the rate of transmission of sequencing (WGS) based study. DR-TB, preventing additional resistance, and improving treatment Though the definition of the MDR-TB includes only two of the outcome. Though molecular methods like GeneXpert (MTB/RIF) first-line drugs (INH and RIF), additional resistance to PZA, EMB and and MTBDRplus Line Probe Assay help to diagnose drug resistance streptomycin (SM) make the treatment options lesser and complex. early, they are limited to a few genes [10e13]. Generally, multiple PZA is a prodrug, activated by pyrazinamidase (pncA)of mutations in one or several genes are involved in resistance to TB M. tuberculosis into its active form pyrazinoic acid which subse- drugs. This makes the molecular-based prediction of resistance to quently kills non-growing persistent M. tuberculosis. Therefore all of the TB drugs difficult. In the following section we discuss mutations in pncA are involved in resistance to PZA. However, 641 various mutations that are associated with drug resistance, the different mutations in 171 codons are reported all along pncA and S. Swaminathan et al. / Tuberculosis 101 (2016) 31e40 33 its promoter region [22]. Three hot spots (clusters) 3e17; 61e85; molecular assays, and also due to other unknown mechanisms and 132e142 are reported to be present in pncA; however, the operating in the bacteria, a holistic approach is urgently needed for sensitivities of these 3 hotspots to explain PZA resistance are only rapid diagnosis of DR-TB. 10%, 13% and 1% respectively. Furthermore, more than 13% PZA- resistant M. tuberculosis do not have any mutations in pncA or its 3. Exploring M. tuberculosis using WGS promoter [22], thus making the early and accurate diagnosis of PZA-resistance more complicated. This indicates that there might Recent developments in the next-generation sequencing tech- be other genes involved in the resistance to PZA. A comprehensive nologies have enabled to perform WGS of M. tuberculosis in a large whole genome analysis of PZA-resistant bacteria versus PZA sen- numbers from all over the world. Large-scale WGS data generated sitive bacteria would throw more light on to the holistic mechanism around the world enable us help to explore the genomics of the of PZA resistance. Similarly, codon 306 of embB is reported to be the M. tuberculosis and understand several underlying mechanisms. For most frequently mutated region (50%e68%) causing resistance to example, presence of co-occurrence of drug resistance and fitness- EMB [18,23]. Genotype MTBDRsl assay which includes mutations compensatory mutations was determined using WGS-based study from embB has sensitivity and specificity of 68.6% and 87.1% of 1000 M. tuberculosis isolates from Russia [32] and 116 isolates of respectively for ethambutol resistance [24]. Resistance to SM is multi-country samples [33]; novel mutations were identified from caused by mutations in rpsL (about 50%) and rrs (about 20%) [18,25]. genes which closely interact with genes involved in drug efflux Since all of the known mutations accounting for resistance to first- pumps, DNA repair, replication or recombination, or cell wall line drugs do not explain phenotypic resistance completely, there biogenesis or remodeling or genes which are associated with drug could be other mechanisms of resistance operating in the bacteria, resistance [33]. Several more such studies were carried out to un- which are yet be to be determined. derstand association of certain unreported genes, intergenic regions and nonsynonymous SNPs with drug resistance in M. tuberculosis 2.2. Mechanism of resistance to second-line drugs [34e38]. WGS is also being widely employed to understand several other mechanisms of tuberculosis viz., global spread of M. tuberculosis 2.2.1. Second-line injectable drugs (SLID) [39], evolution of drug resistant mutations and XDR-TB [40],intra- Kanamycin, amikacin and capreomycin are a group of amino- patient microevolution [41e45], recent transmission [46], outbreaks glycosides drugs also known as second-line injectable drugs (SLID) [47e50], relapse and re-infection by M. tuberculosis [51,52],latency used in the treatment of MDR-TB. The mechanism of SLID is that [53,54]. Applications of WGS, especially to study the emergence and they bind to the 16S rRNA in the 30S ribosomal subunit and inhibit control of antimicrobial resistance [55,56], surveillance and control of protein synthesis of M. tuberculosis. Hence, mutations in 16S rRNA TB [57] and personalizing therapy for multi-drug resistant TB [58,59] (rrs) impart resistance to SLID. Especially a single mutation in rrs, have been discussed in detail in these articles which readers may find A1401G is reported to explain majority of SLID resistance, viz., useful. The global interest in utilizing the strength of WGS in amikacin: 78%, capreomycin: 76%, and kanamycin: 56% [26]. The exploring pathogenic, immunologic and drug-resistance associated sensitivity and specificity of GenoType MTBDRsl assay for SLID was mechanisms of TB strongly indicate that WGS-based studies are likely reported to be 86.4% and 90.1% respectively [27]. Further, additional to impact clinical applications of TB significantly in the near future. mutations (C-14T, G-10A or G-37 T) in eis are also involved in Among several utilities of WGS, the most important application resistance to kanamycin in the absence of mutations in rrs [28,29]. is that it detects drug resistance earlier than any other currently Since, GenoType MTBDRsl has included the mutations from eis available methods along with new information about drug resis- promoter region 10 to 14 (in addition to rrs), the sensitivity and tance to improve the diagnostic accuracy; this application has great specificity of GenoType MTBDRsl for determining kanamycin clinical value in short-term as well as long-term scenario of global resistance alone was observed to be better than SLID as a whole TB control programme. For example, recently, a total of 120 mu- (95.4% and 91.4% respectively) [27]. These results clearly indicate tations were identified as resistance determining in M. tuberculosis the need for identification of other mutations/genes involved in the by employing WGS in 2099 isolates retrospectively; these 120 resistance of SLID, thus they can be used as molecular markers for mutations were reported to have sensitivity and specificity of 89.2% the rapid diagnosis of SLID resistance. and 98.4% respectively in explaining the drug resistance with respect to phenotypic results [60]. Further, using WGS based study, 2.2.2. Fluroquinolones (FLQ) Cohen et al. (2015) described that the emergence of drug resistant FLQ (levofloxacin, moxifloxacin and ofloxacin) are used to treat mutations in M. tuberculosis in KwaZulu-Natal would have accu- MDR-TB, thus its role in the control of TB is very critical. FLQ inhibit mulated over four decades to evolve into MDR and XDR, even DNA gyrase which is encoded by gyrA and gyrB. Codons 74 to 113 in before the explosion of HIV [40]; these findings indicate the need gyrA and codons 500 to 538 in gyrB are known as the quinolone for prudent usage of newer antitubercular antibiotics in order to resistance-determining region (QRDR) [30], and different prevent the emergence of resistance in M. tuberculosis for these numbering systems are used to denote the QRDR [31]. Among mu- antibiotics. Furthermore, recently, following a fatal case of XDR-TB, tations in gyrA, 81% are reported to occur in QRDR while 19% occur Outhred et al. (2015) performed WGS retrospectively on the earliest away from it; in gyrB, only 44% mutations occur within the QRDR. In available M. tuberculosis isolate and observed that the in silico gyrA, mutations in codon 94 explain 37% of FLQ resistance, while susceptibility profile generated from sequence to be comparable or codons 90, 91 and 94 together explain 54% of the FLQ-resistance [18]. superior to the DST results; the research team also observed that The overall sensitivity and specificity of GenoType MTBDRsl assay for the regimen provided to the patient was suboptimal due to delayed FQ were 93.0% and 98.3% respectively when compared to phenotypic DST results; and in retrospect, the patient had best possible chance DST [27].Inadditiontobothfirst-line and second-line drugs, the of favorable outcome if the WGS data were available at the right mechanism of resistance to several other drugs including newer time [61]. This study serves as a strong evidence and support for drugs, bedaquiline and delamanid have also to be accounted in order early employment of WGS in clinical settings to improve individual to have a complete resistance profile for rapid clinical usage. patient care that will have significant impact to monitor and control The very fact that there are gaps between phenotypic and mo- the transmission dynamics of the DR-TB. Furthermore, routine or lecular based rapid assay for the determination of DR-TB due to frequent usage of WGS study in clinical settings would help us to limitations in the number of genes and mutations included in the establish a pooled database and find out the precise differentiator 34 S. Swaminathan et al. / Tuberculosis 101 (2016) 31e40 between drug resistant and drug sensitive M. tuberculosis in a long meaningful information from the resulting sequences, multiple term scenario. Such a close and scrupulous genomic monitoring of complex computational steps have to be followed carefully with the M. tuberculosis would radically improve the diagnosis of the DR-TB. help of multitudes of tools and databases [67]. The procedure involved in the analysis of WGS data could be divided into 3 major 4. Heading towards WGS of M. tuberculosis directly from steps, namely, quality assessment of raw data, read alignment to a uncultured sputa reference genome and variant calling; however, annotation of variants and data visualization are also listed as separate steps by Immense improvements in WGS technology are being increas- other researchers [67]. An armamentarium of bioinformatics tools ingly integrated into clinical practice to improve patient care. Utili- is available to perform each of these steps. For example, quality zation of these technical advances to their fullest potential in the assessment is performed to remove, trim or correct reads based on fight against M. tuberculosis is challenged by the fact that the path- pre-defined criteria as the generated sequence is prone to have ogen has to be first cultured for several weeks in order to obtain sequence artifacts such as base calling errors, INDELs, poor quality sufficient amounts of DNA for sequencing. However, this challenge is reads and adaptor contamination [67]. FastQC is one of the being gradually captivated through introduction of methods that frequently used bioinformatics programs for quality assessment as could circumvent the need for conventional culture-based DNA it is compatible with all major sequencing platforms; however, procurement. Recently, WGS of XDR M. tuberculosis strains were more than 10 different tools are available for quality assessment of accomplished using DNA extracted from 3-day-old positive cultures the WGS data [67]. The central challenge in the analysis of WGS grown in MGIT culture system [62]. The obtained WGS data revealed data is mapping of millions of short-reads with that of a reference mixed infections and drug resistant mutations. Votintseva et al. genome sequence [68], which is performed after quality assess- (2015) have developed a modified Nextera XT protocol to extract and ment. Dedicated tools including Bowtie [69], Bowtie2 [70],BWA purify mycobacterial DNA within hours to 3 days from 1 ml of early [71,72],MAQ[73], and several more tools are available for short positive MGIT culture (median culture age of 4 days); they suc- read alignment [67,74,75]. Variant calling is a process to identify all cessfully sequenced 98% of the clinical samples and mapped them to nucleotide differences (SNPs and indels) in the selected genome the reference M. tuberculosis H37Rv genome with more than 90% of with respect to the reference genome [76]. An array of tools are sequence coverage [63]. Another development in sequencing is available for determining the variants including germline muta- metagenomics, which enable sequencing of DNA directly from un- tions, somatic mutations, copy number variations and structural cultured samples without target-specificamplification or enrich- variations [67] including Pilon, an exclusive tool for microbial ge- ment [64]. Recently, by employing metagenomics-based WGS nomes [77].Pilon has been employed successfully in variant calling studies, Doughty et al. (2014) detected sequences of M. tuberculosis to study the evolution of extensively drug-resistant M. tuberculosis complex from 8 smear- and culture-positive sputum samples [65]. from KwaZulu-Natal [40]. Selection of tools from the bioinformatics Metagenomics was also successfully applied to determine mixed armamentarium for each step and employing them separately re- infection by M tuberculosis genotypes from eighteenth-century hu- quires bioinformatics expertise, and the whole exercise consume man remains [64,66]. Though the clinical utility of metagenomics- significant amount of time [78]. based WGS studies for tuberculosis appears to be distant due to its Newer bioinformatics tools have been developed exclusively for poor coverage with reference genome (M. tuberculosis H37Rv), these the analysis of WGS data of M. tuberculosis which enable us to studies potentially serve as proof of principle that metagenomics- circumvent both selection of tools and the necessity for performing based WGS studies can be used to detect and characterize every individual steps separately. For example, a new bioinfor- M. tuberculosis sequences without culturing, and by using relatively matics tool, ‘Mykrobe predictor’ can determine resistance profile simple DNA extraction, WGS and bioinformatics protocols. for RIF, INH, EMB, SM, moxifloxacin, ofloxacin, amikacin, capreo- Another milestone in reducing the time from sample receipt to mycin, and kanamycin directly from raw NGS sequence data in drug resistance testing was reached when Brown et al. (2015) 3 min [79]; it has been reported to predict drug resistance with sequenced the whole genome of M. tuberculosis directly from un- 82.6% sensitivity and 98.5% specificity for M. tuberculosis. Coll et al. cultured sputa using commercially synthesized biotinylated RNA (2015) developed another method called TB-Profiler, based on a baits designed specifically for M. tuberculosis DNA [21]. These RNA library of 1325 already reported mutations known to be associated baits hybridize with M. tuberculosis DNA fragments extracted from with anti-tuberculous drug resistance [80]. This tool can predict uncultured sputum sample, leaving aside the contaminating hu- drug resistant mutations specific to 11 different anti-tuberculosis man DNA. The ‘M. tuberculosis DNA-RNA bait hybrid’ is then drugs directly from the WGS data within 10 min. The sensitivity captured by streptavidin-coated magnetic beads and the unbound and specificity of TB-Profiler in predicting MDR-TB was reported to human genomic fragments are washed off. These enriched be 91.2% and 98.4%, while the sensitivity in predicting XDR-TB was M. tuberculosis DNA fragments are loaded for sequencing. Though relatively lower (75.9%) and the specificity remained the same the depth of the coverage achieved by Brown et al. (2015) was only (98.4%). Though TB-Profiler and Xpert MTB/RIF are comparable 20, the read alignment with the reference genome was >90% and methods for detecting RIF resistance, TB-Profiler is certainly a the concordance between predicted genotypic resistance and better method for detecting INH resistance as compared to phenotypic resistance profiles was 100% among 20 of the 24 strains MTBDRplus. PhyResSE is another online tool that gives 97.83%e sequenced. The whole process could be accomplished within a time 100% concordance with the Sanger sequencing method in detecting limit of 96 h even from low grade smear positive sputa [21]. Despite drug resistant mutations in RIF, INH, SM, EMB, PZA targets [81]. the fact that this method is reported to be expensive, it could be a Steiner et al. (2014) developed another bioinformatics tool, KvarQ promising step towards sequencing the whole genome of that can scan directly bacterial genome sequences for drug resis- M. tuberculosis directly from the uncultured sputa. tance mutations [82]. In another WGS-based study, Walker et al. (2015) have come out with a list 120 mutations in 14 candidate 5. Determination of drug resistance profile directly from WGS genes as resistance determining; however, the sensitivity and data using bioinformatics tools specificity of detecting drug resistance based on these 120 muta- tions in a validation-set of M. tuberculosis strains were found to be Computational analysis and precise interpretation of large scale 89.2% and 98.4% respectively [60]. In all of the approaches dis- data is the bottle neck in all WGS based studies. In order to extract cussed here, there remains a gap in concordance between in silico S. Swaminathan et al. / Tuberculosis 101 (2016) 31e40 35

Table 1 Present drug discovery pipeline of TB that includes novel candidate drugs and those are being repurposed for TB.

S. No. Drugs in the pipeline Current status of research Short description

1 Bedaquiline Phase-III (provisionally Bedaquiline has been provisionally approved by FDA for the treatment of DR-TB [106]. It is also approved) being evaluated in phase III for the treatment of XDR-TB in combination with pretomanid and linezolid [95,107]. 2 Delamanid Phase-III (provisionally Delamanid has been approved provisionally in several countries for use in combination regimen for approved) the treatment of DR-TB among adults [108] and demonstrated to be useful in the treatment of XDR- TB [109]. 3 Pretomanid Phase III Pretomanid in combination with moxifloxacin, and pyrazinamide was demonstrated to have bactericidal activity in drug-susceptible and MDR -tuberculosis in a phase 2b trial; no phenotypic resistance was reported to any of the drugs in the regimen [110]. 4 AZD-5847 Phase II AZD5847 is an oxazolidinone demonstrated to be active against both extracellular and intracellular M. tuberculosis in vitro [111]. 5 Sutezolid Phase II Sutezolid is effective against latent M. tuberculosis bacilli in mice, and reduced the number of CFU (PNU-100480) significantly in combination with rifampin [112]. 6 SQ109 Phase II SQ109 is reported to be active against both drug-susceptible and drug-resistant M. tuberculosis, and improve RIF activity synergistically [113]. 7 Verapamil Demonstrated in animal Verapamil is primarily used in the treatment of hypertension and currently being evaluated for models, and currently repurposing for TB. Verapamil reduce drug tolerance [100,102]; and in combination with TB in Phase III chemotherapy verapamil accelerates bacterial clearance in mice [101]. 8 Metformin Demonstrated in animal Metformin is primarily used for the treatment of diabetes and currently being evaluated for models; and observed to repurposing for TB. Metformin ameliorated lung pathology and inflammatory response, enhanced be useful in a retrospective immune response and the efficacy of INH in mice; in retrospective human cohorts metformin was human cohort. observed to improve the control of M. tuberculosis infection and decreased disease severity [98]. 9 CPZEN-45 Preclinical stage CPZEN-45 was isolated from an actinomycete strain demonstrated to have activity against drug- sensitive and drug-resistant M. tuberculosis strains; CPZEN-45 inhibits WecA (Rv1302), that is essential for the formation of the cell wall of M. tuberculosis [114]. 10 DC-159a Preclinical stage DC-159a is a novel fluoroquinolone candidate drug demonstrated to be active in vitro against quinolone-resistant multidrug-resistant M. tuberculosis and clinically important nontuberculous mycobacteria [90]. 11 Q203 Phase I Q203 is an imidazopyridine amide (IPA) class candidate drug, which showed activity against MDR and XDR M. tuberculosis clinical isolates in vivo in mouse model [91]. 12 SQ609 Preclinical stage SQ609 is a dipiperidine compound reported to inhibit more than 90% of intracellular bacterial growth in M. tuberculosis infected macrophages in vitro, and had a prolonged therapeutic effect in mice models [115]. 13 SQ641 Preclinical stage SQ641 is an analogue of capuramycin, reported to be active against intracellular M. tuberculosis in murine models [116]. 14 TBI-166 Preclinical stage TBI-166 is a riminophenazine analog known to have enhanced in vitro activity compared to clofazimine against drug-sensitive and drug-resistant M. tuberculosis [93]. 15 PBTZ-169 Preclinical stage PBTZ-169 is efficient in zebrafish and mouse models of tuberculosis; active against MDR- and XDR- clinical isolates of M. tuberculosis; in combination with bedaquiline plus pyrazinamide, PBTZ-169 was reported to be significantly better than RHZ (RIF, INH, PZA) regimen in in vivo mice model [92]. 16 Statins Preclinical (Repurposing) Statins (simvastatin and atorvastatin) increase the in vivo activity of the first-line tuberculosis regimen in animal model [103,117] and (simvastatin and rosuvastatin) reduce the bacterial burden in human macrophages and in mice in vivo [118]. 17 Doxycycline Preclinical (Repurposing) Doxycycline reduces TB-dependent matrix metalloproteinases (MMP-1 and -9) secretion which cause lung tissue damage, and reduces M. tuberculosis growth in the guinea pig model of tuberculosis [119]. Further, doxycycline along with amikacin synergistically inhibits drug resistant M. tuberculosis under in vitro condition [120]. 18 Disulfiram Preclinical (Repurposing) Disulfiram inhibits MDR/XDR- M. tuberculosis, exhibits bactericidal activity within macrophages (THP-1 cell line), and kills M. tuberculosis in mice [121].

prediction and phenotypic results. Validation of these tools technology would potentially result in discovering other mutations employing country-specific data, M. tuberculosis' genotypeespe- that are involved in drug resistance. Put together, these newer cific data, or ‘country-specific-cum-genotype-specific’ data would methods and growing data in databases have the potential to lead possibly improve concordance between the phenotypic results and to an improved version of a bioinformatics tool that can explain in silico prediction, which could in turn suit universal applications. drug resistance directly from WGS data. Such developments would Drug resistant mutations in M. tuberculosis have been made subsequently help us to fill the gap existing between phenotypic available in a couple of online database namely, TBDReaMDB [15] determination and genotypic prediction of drug resistance. Though and MUBII-TB-DB [83]. These databases list mutations compre- bioinformatics prediction need not replace phenotypic DST hensively curated from various literature. The developments in completely, it would play a major role in enabling physicians to WGS also have resulted in several databases having information recommend personalized regimen. about drug resistance; for example, tbvar is a database that includes more than 450 isolates from 37 different data sets covering SNPs 6. Dynamic drug discovery pipeline for tuberculosis with 632 novel variants [84]. PolyTB is another genome-wide variation database that contains variants collected from more Resistance has been reported for many of the available anti- than 1500 isolates from multiple countries [85]. Another database, tuberculosis drugs, and the possibility for emergence of resis- GMTV catalogues genome variations of 1084 genomes of tance to newer drugs in the future cannot be ruled out. Therefore, M. tuberculosis from Russia [86]. Parallel developments in bioin- for sustained success in TB control a reservoir of new drugs has to formatics tools in analyzing the WGS data and archiving results be created through constant efforts in drug discovery. Presently, the systematically in databases along with developments in WGS global drug pipeline appears encouraging as several drugs are 36 S. Swaminathan et al. / Tuberculosis 101 (2016) 31e40

Table 2 International consortia for developing novel solutions for tuberculosis.

S. No. Consortium/Network Focus Website

1 Stop TB Partnership To develop and implement new preventive, http://www.stoptb.org/ diagnostic and therapeutic tools and strategies to stop TB. 2 TB Alliance To discover and develop better and affordable http://www.tballiance.org/ drugs for tuberculosis. 3 CPTR (The Critical Path to TB Drug Regimens) To accelerate the development of new and http://www.cptrinitiative.org/ improved drug regimens for tuberculosis. 4 OSDD (Open Source for Drug Discovery) Drug discovery and development for http://www.osdd.net/ various diseases including tuberculosis. 5 PanACEA (Pan Africa Consortium for To shorten and simplify treatment of TB. http://panacea-tb.net/ the Evaluation of Antituberculosis Antibiotics) 6 MM4TB (More Medicines for Tuberculosis) To discover anti-infective agents that will combat TB. http://www.mm4tb.org/ 7 iM4TB (Innovative Medicines for Tuberculosis) To develop better, faster-acting and http://im4tb.org/ affordable medicines for TB. 8 PreDiCT-TB To find the most rapid and reliable ways of identifying http://www.predict-tb.eu/ the most potent combinations of new drugs and hasten their arrival in the clinic. 9 TBSGC (Tuberculosis Structural Large-scale structure determination of http://www.webtb.org/ Genomics Consortium) M. tuberculosis proteins for future drug discovery. evaluated in various phases of preclinical and clinical development reported to enhance efficacy of the stranded TB drugs, enhance the [87] and listed in Table 1. Bedaquiline and delamanid have already host immune response, control the tissue damage and control the been approved provisionally for treatment of DR-TB [88]. Most of growth of intracellular M. tuberculosis [98,99,104]. For example, the drugs in the pipeline target essential functions of metformin have been reported as a promising adjunctive therapy M. tuberculosis, namely, cell wall synthesis (SQ109, and PBTZ-169), for TB as it reduces the intracellular growth of M. tuberculosis [98]. DNA replication and transcription (DC-159a), RNA synthesis (Rifa- Verapamil enhance the efficacy of TB drugs, and reduce the drug pentine), protein synthesis (AZD-5847 and Sutezolid), oxidative tolerance by inhibiting efflux pump proteins of M. tuberculosis phosporylation (Q203), etc [89]. Most of the candidate drugs in the [99e102]. A clinical study has been planned in our institute, Na- pipeline have been reported to have better inhibitory activity than tional Institute for Research in Tuberculosis, Chennai (India) to currently used ones and active against drug resistant tuberculosis. determine adjunct effect of verapamil on RIF, INH, PZA and EMB in For example, DC-159a is a new fluoroquinolone in the preclinical shortening the treatment duration in TB patients, and to determine phase reported to be effective against quinolone-resistant multi- minimum effective dose of verapamil; this study also aims to drug-resistant M. tuberculosis isolates with 4- to 32-fold more po- elucidate additive effect of verapamil in TB patients without heart tency than the currently available quinolones [90]. Another conditions or high blood pressure [105]. Several other drugs are preclinical candidate Q203 has been reported to show activity being investigated for repurposing for TB and they are in different against MDR and XDR M. tuberculosis clinical isolates in in vivo stages of research pipeline [104] and provided in Table 1. The animal models [91]. In combination with bedaquiline, PBTZ-169 has increased interest and global efforts to bring in novel drugs and been reported to have synergistic bactericidal activity against repurpose FDA-approved drugs for tuberculosis enhance our belief M. tuberculosis [92]. Additionally, TBI-166 is reported to have that we will have sustainable options of drugs for personalized enhanced in vitro activity against drug-sensitive and drug-resistant treatment for DR-TB in the near future. clinical isolates than clofazimine [93]. Apart from individual drugs being evaluated for DR-TB, a novel regimen, Nix-TB is being studied in a phase 3 clinical trial with 7. Conclusion intent to cure XDR-TB. The Nix-TB regimen includes bedaquiline, pretomanid and linezolid and the trial is currently ongoing in South The early deployment of WGS on M. tuberculosis would help to Africa [94,95]. Since the Nix-TB regimen contains all drugs as determine drug resistance profile, and serve to choose the right injection-free and has potential to reduce the treatment period, it combination of drugs for effective treatment. This point is well could possibly transform the XDR-TB treatment if the clinical trial advocated by a report by Outhred et al. (2015) in which they turned out to be successful. All of these efforts and progress indi- described a fatal case of DR-TB who could have been offered an cate that the present drug discovery pipeline holds promise for the optimal curative regimen if WGS based drug resistant profile was battle against DR-TB. However, successful translation of these drugs available earlier in the clinical course [61]. The increased interest to for treatment will depend on the outcome of clinical trials which sequence large number of whole genomes of M. tuberculosis glob- need not always be favorable. Recognizing the threat posed by the ally would help to resolve the remaining disparities in predicting emergence of DR-TB to the human race, researchers, policy makers, drug resistance-conferring mutations precisely in concordance philanthropists and funding agencies from across the world have with DST. Towards this goal, we have planned to do a large-scale come forward to work in partnership towards the goal of acceler- WGS of M. tuberculosis isolates from prospective patients of ating drug discovery and development. This has resulted in the different parts of India in next 3 years. Such efforts would help to establishment of several consortia and networks (Table 2) which develop a “refined library of mutations” to explain drug resistance comprise of researchers from diverse scientific disciplines and in- with “improved accuracy”. Convergence of scientific efforts from stitutes across the globe. High-throughput screening to identify various parts of the world to discover new drugs or repurpose FDA- novel inhibitors [96,97] and efforts to repurpose drugs like met- approved drugs for tuberculosis appear promising for new de- formin [98] verapamil [99e102] and simvastatin [103] will also velopments. Thus, we believe, the global progress in the disciplines help in bringing additional candidate drugs into the pipeline. of genomics, bioinformatics and drug discovery would synergisti- Particularly, repurposing of several FDA-approved drugs have been cally result in developing a rapid protocol to determine drug S. 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