Perspectives for personalized therapy for patients with multidrug-resistant tuberculosis

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1 Review Symposium doi: /joim Perspectives for personalized therapy for patients with multidrug-resistant tuberculosis C. Lange 1,2,3,4, W. A. Alghamdi 5, M. H. Al-Shaer 5, S. Brighenti 6, A. H. Diacon 7,8, A. R. DiNardo 9,H.P.Grobbel 1,2,3,M.I.Gr oschel 10,11, F. von Groote-Bidlingmaier 7, M. Hauptmann 2,12, J. Heyckendorf 1,2,3,N.K ohler 1,2,3, T. A. Kohl 11,M.Merker 11,S.Niemann 2,11,C.A.Peloquin 5,M.Reimann 1,2,3, U. E. Schaible 2,12,13,14,D.Schaub 1,2,3, V. Schleusener 11, T. Thye 15 & T. Sch on 16,17 From the 1 Clinical Infectious Diseases, Research Center Borstel; 2 Tuberculosis Unit, German Center for Infection Research (DZIF), Borstel; 3 International Health/Infectious Diseases, University of L ubeck, L ubeck, Germany; 4 Department of Medicine, Karolinska Institute, Stockholm, Sweden; 5 Department of Pharmacotherapy and Translational Research, Infectious Disease Pharmacokinetics Laboratory, College of Pharmacy, University of Florida, Gainesville, FL, USA; 6 Department of Medicine, Center for Infectious Medicine (CIM), Karolinska Institutet, Karolinska University Hospital Huddinge, Stockholm, Sweden; 7 Task Applied Science, Bellville; 8 Division of Physiology, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa; 9 Section of Global and Immigrant Health, Baylor College of Medicine, Houston, TX, USA; 10 Department of Pumonary Diseases & Tuberculosis, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; 11 Molecular and Experimental Mycobacteriology, National Reference Center for Mycobacteria; 12 Cellular Microbiology, Research Center Borstel, Borstel; 13 Biochemical Microbiology & Immunochemistry, University of L ubeck, L ubeck; 14 LRA INFECTIONS 21, Borstel; 15 Department of Infectious Disease Epidemiology, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany; 16 Department of Clinical and Experimental Medicine; and 17 Department of Clinical Microbiology and Infectious Diseases, Kalmar County Hospital, Link oping University, Link oping, Sweden Content List Read more articles from the symposium: The 10th International Conference on the Pathogenesis of Mycobacterial Infections Abstract. Lange C, Alghamdi WA, Al-Shaer MH, Brighenti S, Diacon AH, DiNardo AR, Grobbel HP, Gr oschel MI, von Groote-Bidlingmaier F, Hauptmann M, Heyckendorf J, K ohler N, Kohl TA, Merker M, Niemann S, Peloquin CA, Reimann M, Schaible UE, Schaub D, Schleusener V, Thye T, Sch on T (Research Center Borstel, Borstel; German Center for Infection Research (DZIF), Borstel; University of L ubeck, L ubeck, Germany; Karolinska Institute, Stockholm, Sweden; University of Florida, Gainesville, FL, USA; Karolinska University Hospital Huddinge, Stockholm, Sweden; Task Applied Science, Bellville; Stellenbosch University, Tygerberg, South Africa; Baylor College of Medicine, Houston, TX, USA; University of Groningen, Groningen, The Netherlands; Research Center Borstel, Borstel; Research Center Borstel, Borstel; University of L ubeck, L ubeck; LRA INFECTIONS 21, Borstel; Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany; and Link oping University, Link oping, Sweden). Perspectives for personalized therapy for patients with multidrug-resistant tuberculosis (Review Symposium). J Intern Med 2018; 284: According to the World Health Organization (WHO), tuberculosis is the leading cause of death attributed to a single microbial pathogen worldwide. In addition to the large number of patients affected by tuberculosis, the emergence of Mycobacterium tuberculosis drug-resistance is complicating tuberculosis control in many high-burden countries. During the past 5 years, the global number of patients identified with multidrug-resistant tuberculosis (MDR-TB), defined as bacillary resistance at least against rifampicin and isoniazid, the two most active drugs in a treatment regimen, has increased by more than 20% annually. Today we experience a historical peak in the number of patients affected by MDR-TB. The management of MDR-TB is characterized by delayed diagnosis, uncertainty of the extent of bacillary drug-resistance, imprecise standardized drug regimens and dosages, very long duration of therapy and high frequency of adverse events which all translate into a poor prognosis for many of the affected patients. Major scientific and technological advances in recent years provide new perspectives through treatment regimens tailormade to individual needs. Where available, such personalized treatment has major implications on the treatment outcomes of patients with MDR-TB. The challenge now is to bring these adances to those patients that need them most. Keywords: individualized medicine, MDR-TB, personalized medicine, XDR-TB. All authors contributed equally. ª 2018 The Association for the Publication of the Journal of Internal Medicine 163

2 Introduction Tuberculosis (TB) is the leading cause of morbidity attributed to a single microorganism worldwide. Despite enormous international efforts, the number of patients who developed TB has globally increased steadily from 8.6 million to 10.4 million from [1]. The emergence of antimicrobial resistant strains of Mycobacterium tuberculosis (M. tuberculosis), the causative organism of TB, is especially alarming [2]. In the past 5 years, the numbers of patients identified with multidrugresistant (MDR)-TB, defined as bacillary resistance against at least isoniazid (INH) and rifampicin (RIF), increased from to per year [1]. Part of this massive increase is due to improved diagnostics and better detection of MDR-TB. However, the estimated number of prevalent MDR-TB cases also increased in the same period from to [1]. The clinical presentation of a patient with MDR-TB does not differ from a patient with drug susceptible TB. Symptoms include cough >2 weeks, fever, night-sweats and weight loss. Patients with pulmonary disease typically present with abnormalities on chest X-ray and thoracic computed tomography (TCT). Automated M. tuberculosis nucleic amplification by GeneXpert MTB/RIF (Cepheid, Sunnyvale, California, USA) performed on sputum or other bronchopulmonary specimens is now the WHO recommended rule-in test for pulmonary MDR-TB [3]. The test can identify mutations in the bacillary ribosomal polymerase gene (rpob) conferring phenotypical resistance against rifampicin within 2-h with a pooled sensitivity of 94% and pooled specificity of 98%, respectively [4]. The next generation of the assay, the GeneXpert MTB/RIF Ultra has even higher sensitivity than the GeneXpert MTB/RIF. This will benefit patients with paucibacillary disease which is often associated with HIV-infection [5], although the test-specificity is lower [6]. As the majority of M. tuberculosis strains with rifampicin resistance are also resistant against isoniazid, the GeneXpert MTB/RIF (Ultra) is an accepted surrogate test for the detection of MDR-TB. However, relying only on the result of a GeneXpert MTB/RIF (Ultra) frequently leads to erroneous treatment regimens [7], because many cases with MDR-TB are also resistant to other agents. Line-probe assays (LPAs) (e.g. Hain Lifescience, Nehren, Germany; AID Diagnostika, Straßberg, Germany) provide a different method for the amplification of M. tuberculosis nucleic acid from culture of from bronchopulmonary specimens. LPAs take approximately 48 h until results become available and are more labor-intensive than the GeneXpert assays. The benefit of LPAs is that they allow identifying resistance conferring mutations for RIF, INH, second line injectable drugs (SLID) and fluoroquinolones (FQ) [8]. Recently a novel GeneXpert assay has been introduced (which is not yet marketed) that also allows a rapid prediction of additional resistance to INH, FQ and SLID [9] and could potentially replace or complement LPAs to guide physicians in the initial choice of drugs for a treatment regimen. Treatment regimens based on the results of molecular drug-resistance testing may need to be adjusted when results of phenotypic drug resistant testing become available. For a long time, management of patients with MDR-TB followed a standardized approach with fixed drug-combination regimens and dosages. Thus, it is no surprise that insufficient treatments have contributed significantly to the selection of resistant strains of M. tuberculosis. In the absence of a biomarker that informs physicians about the optimal duration of combination anti-tb drug regimens, the WHO still recommends that the standard duration of MDR-TB treatment should be 20 months [10]. Although the WHO has recently proposed a shorter course MDR-TB regimen of 9 12 months duration [10], less than 10% of affected patients from Europe are eligible for this regimen as many of those patients originate from areas where resistance to other drugs than INH and RIF are common [11, 12]. Treatment costs for MDR-TB are enormous. According to a recent survey from the Tuberculosis Network European Trialsgroup (TBNET), the cost for one adequate MDR-TB treatment course is approximately US$. In XDR-TB, defined as MDR- TB plus additional bacillary resistance to a FQ and a SLID, costs of the medication of one treatment course equal approximately US $ in Europe. In contrast, the cost of drugs for a treatment course of drug-susceptible TB are around 350 US $ in total [13]. MDR-TB still carries a poor prognosis for almost half of the affected patients. According to the latest report by the WHO, only 54% of patients achieve a successful treatment outcome [1]. The proportion of those who are actually cured is unknown. Recently important scientific advances have been achieved that may influence the management of 164 ª 2018 The Association for the Publication of the Journal of Internal Medicine

3 patients with MDR-TB. Genotypic prediction of distinct levels of phenotypic drug susceptibility testing and selection of tailor made therapies, therapeutic drug monitoring, genetic prediction of treatment responses and adverse events, host directed therapies, microbiome-based interventions and biomarker-guided treatment durations may lead to substantial improvements in the treatment outcomes for patients with MDR-TB. Stratified phenotypic drug susceptibility Phenotypic drug susceptibility testing (pdst) for M. tuberculosis is typically performed at a single concentration ( critical concentration ), used in clinical routine as a breakpoint (BP) to separate susceptible from resistant isolates. This concentration corresponds to the epidemiological cut-off value (ECOFF) which is the highest minimum inhibitory concentration (MIC) that still shows phenotypical drug-susceptibility [14, 15]. The ECOFF represents the most conservative BP by assuming that any MIC increase above that level results in reduced treatment efficacy with standard dosing. Certain drugs such as RIF, rifabutin, INH and FQ, pharmacokinetic and pharmacodynamic (PK/PD) and/or clinical outcome data support that isolates with MICs slightly higher than the ECOFF may be accessible for treatment with increased dosing and/or therapeutic drug monitoring (TDM) [15 22]. Thus, quantifying the level of resistance using MICs, has a role in personalized treatment for MDR-TB in combination with plasma drug concentrations and evaluation of drug resistance mutations. Resistance mechanisms that confer substantial MIC increases above the ECOFF, too high to be compensated for by increased dosing, are best described as high-level resistance (HLR) [23] (e.g. the rrs A1401G mutation which confers a > 100- fold MIC increase to kanamycin [24] and katg S315T, a > 30-fold MIC increase to INH) [17, 25]. By contrast, other resistance mechanisms such as eis g-10a [26] for kanamycin, gyra A90V for FQ [27] and inha c-15t for INH [17, 20] lead to modest MIC increases (e.g. by 4- to 8-fold). Such isolates may be classified into two categories: intermediate (I) and low-level resistance (LLR). The I-classification would require PK/PD and/or clinical outcome data to support that patients could be treated with increased dosing [28] such as for INH. LLR is applies where such evidence is lacking and the drug should then be avoided unless no other options are available as increased dosing may lead to severe side effects (Table 1). The SIR-classification is used for most pathogens and facilitates clinical interpretation, genotype-phenotype correlation and is also beneficial for TDM as single MIC determinations are less accurate to base dose changes on than SIR defined cut-offs [29]. A problematic aspect of MIC testing is that numerous pdst methods are used for M. tuberculosis [15] which are not directly comparable. It is essential for the development of PK/PD-targets and accurate TDM that a stable reference pdst method is defined, where M. tuberculosis H37Rv is included routinely and performs within predefined MIC control ranges [30]. This is of particular importance for bedaquiline and delamanid as isolates with resistance mechanisms may have marginally increased MICs compared to ECOFFs in combination with technical challenges for pdst [31 36]. Until genotype phenotype correlations are resolved and clinical BPs according to the SIRsystem are defined in a reference method, MICs are useful for optimizing treatment in M/XDR TB. As for the wild-type MIC distribution, each single drug resistance mutation has its own MIC range and level of resistance [27]. The WHO recommends testing moxifloxacin at two MIC levels. Based on the current literature, isolates with intermediate MICs (0.5 1 mgl 1 for MGIT and 1 2 mgl 1 for 7H10) may be accessible for treatment at higher moxifloxacin doses (800 mg daily to adults) according to limited clinical outcome data [16]. This intermediate range corresponds to specific mutations (e.g. gyra A90V and D94A), whereas other mutations, such as gyra D94G, confer higher MICs and consequently fall into the category of HLR [16, 37 39]. Such stratified pdst is already in use for INH where inha c-15t promotor mutations corresponding to intermediate MICs at mgl 1 have been associated to favourable clinical outcomes and an increased dosing is suggested (15 20 mg kg 1 ) whereas katg S315T mutations are associated to higher MICs (>2 mgl 1 ) which cannot be overcome by increased dosing [17, 18, 25]. Resistance is not a binary phenomenon. Instead, each resistance mutation has a distinct MIC distribution and confers either modest or substantial MIC increases. In practice, however, our understanding of the levels of resistance conferred by individual mutations is still limited [7, 15, 20, 40], ª 2018 The Association for the Publication of the Journal of Internal Medicine 165

4 which is complicated by the fact that distributions may overlap and are not necessarily easily distinguishable using pdst [7, 27]. As a result, individualized treatment regimens should be based on a combination of both genotypic and phenotypic DST results. A tentative proposal for such a combined algorithm is presented in Table 1. A potential strategy for integrating MICs in personalized medicine for TB is to optimize all drug concentrations measured at week 2 in relation to MICs which may be available after a minimum of 3 4 weeks. Drug resistance mutations are essential to design personalized treatment regimens before MICresults are available. Such strategies are of importance for enhanced treatment efficacy in all TB patients but in particular when treatment options Table 1 Tentative interpretative guide for personalized treatment using MIC determinations (mg L 1 ) and commonly detected high-confidence mutations ECOFF; S (range) Intermediate (I) a resistance (LLR) a Low level MIC-range and corresponding mutation(s) High level resistance (HLR) Clinical interpretation Standard dose Dose increase and TDM Avoid Do not use FQs (gyra) Moxifloxacin ; and/or A90V, NA 2 and/or D94G/H/N/Y MGIT/7H9 b S91P, D94A Moxifloxacin - 7H10 0.5; and/or A90V, S91P, D94A Levofloxacin - 1; NA 2 4 and/or MGIT/7H9 b A90V, S91P, D94A Levofloxacin 7H10 1; NA 2 4 and/or A90V, S91P, D94A SLIDs (rrs, eis, tlya) Amikacin - MGIT 1; NA c-14t (eis), C1402T (rrs) Amikacin - 7H10 2; NA c-14t (eis), C1402T (rrs) Kanamycin - MGIT 2.5; NA g-10a (eis), C1402T (rrs) Kanamycin - 7H10 4; NA g-10a (eis), C1402T (rrs) NA 4 and/or D94G/H/N/Y 8 and/or D94G/H/N/Y 8 and/or D94G/H/N/Y 2; and/or A1401G 4; and/or A1401G 5; and/or A1401G, c-14t (eis) 8 and/or A1401G, c-14t (eis) Capreomycin - MGIT 2.5; NA NA 5 and/or tlya, A1401G, C1402T Capreomycin 7H10 4; NA NA 8 and/or tlya, A1401G, C1402T INH (inha, katg) Isoniazid - MGIT 0.1; and/or c-15t (inha) NA 2 and/or S315T (katg) Isoniazid - 7H10 0.2; and/or c-15t (inha) NA 2 and/or S315T (katg) 166 ª 2018 The Association for the Publication of the Journal of Internal Medicine

5 Table 1 (Continued ) ECOFF; S (range) Intermediate (I) a resistance (LLR) a Low level MIC-range and corresponding mutation(s) High level resistance (HLR) Clinical interpretation Standard dose Dose increase and TDM Avoid Do not use Rifamycins (rpob) Rifampicin - MGIT 1; and/or H526L, NA 4; S531L; H526D/Y D516Y, L533P Rifampicin - 7H10 1; and/or H526L, D516Y, L533P Rifabutin - 7H : and/or D516V, D516Y NA NA 4; S531L; H526D/Y 0.5; S531L; H526D/Y NA, not applicable. Where available, MIC ranges corresponding to selected commonly detected high-confidence resistance mutations [55] for fluoroquinolones (FQs), second line injectable drugs (SLIDs), rifamycins and isoniazid (INH) are listed. The suggested clinical interpretation and genotype-phenotype correlations from the limited data at hand [15 18, 20 22, ] are based on the SIR-system with the following definitions: S = Isolate with a high likelihood for therapeutic success at standard dosing. I = Clinical outcome and PK/PD data exist to suggest that increased dosing may be used, preferably together with TDM. The drug should not be counted as an ordinary drug for M/XDR therapy. LLR = High likelihood for therapeutic failure at standard dosing and no clinical outcome data to suggest efficacy at higher doses. Potential option with increased dosing in specialized centres with TDM and careful monitoring for side effects. HLR = Not accessible for treatment at standard or increased dosing without considerable risk of toxicity a Because of methodological variability of pdst and MIC-determination and until a reference method for pdst has been defined, genotype should be used in combination with pdst for mutations classified as I or LLR as there are small MIC differences and somethimes overlap close to the ECOFF between wild-type and low level resistant populations. b Broth microdilution (BMD) Middlebrook 7H9 methods are presently not endorsed by the WHO for DST. is scarce and increased dosing for I/LLR isolates is considered. In those cases, close collaboration between clinicians, microbiologists and pharmacologists is of particular importance. Molecular drug-susceptibility Ideally, the MIC as the measure of the level of phenotypic drug resistance can be predicted by molecular detection of resistance confining mutations in the bacillary genome. Rational design of individualized TB treatment based on precise knowledge of the genetic repertoire of the infecting M. tuberculosis strains is key to ensure effective therapy and to prevent spread of resistant strains as well as further expansion of drug resistance. Drug resistance in M. tuberculosis strains results from spontaneous mutations in drug resistance associated genes encoded on the chromosome [20, 41]. These variants include SNPs, small insertions or deletions (InDels), and genomic rearrangements (insertions, deletions, inversions), which modify either the drug target itself, silence drug activating enzymes in the case of pro-drugs, or circumvent drug action by increasing the gene product targeted by the drug [42]. Bacterial cells harboring resistance-conferring mutations can be selected by ineffective treatment regimens caused by e. g. mono therapy with just one effective drug, patients noncompliance [43] or inadequate drug supply [44 47]. The development of multiple drug resistance results from several periods of sequential monotherapy during which resistance to other drugs is acquired; a phenomenon referred to as amplification of drug resistance [48]. Resistance to an antibiotic develops at different rates that depend on the drugs used, ranging from approximately 1 in 10 8 bacilli for RIF, to about 1 in 10 6 bacilli for INH, streptomycin and ethambutol [49]. Recent studies indicated that this mutation rate might also vary in strains from different phylogenetic lineages of the M. tuberculosis complex [50, 51]. ª 2018 The Association for the Publication of the Journal of Internal Medicine 167

6 Table 2 High confidence mutations involved in drug resistance of Mycobacterium tuberculosis strains Drug group Drug name Gene Mutations First-line Rifampicin rpob D516A, D516F, D516G, D516G+L533P, D516ins, D516N, D516V, D626E, Del N518, F505V+D516Y, F514dupl, H526C, H526D, H526F, H526G, H526L, H526R, H526Y, M515I+D516Y, Q513-F514ins, Q513H+L533P, Q513K, Q513L, Q513P, S512T, S522Q, S531F, S531L, S531Q, S531W, S531Y Isoniazid inha-maba c-15t+i194t [7], c-15t+s49a [7], katg S315I, S315N, S315T, pooled frameshifts and premature stop codons Second-line Moxifloxacin gyra A90V, D94A, D94G, D94N, D94Y, G88C, S91P (group A; Ofloxacin/ gyra A90V, D94A, D94G, D94H, D94N, D94Y, G88A, G88C, S91P fluoroquinolones) Levofloxacin gyrb A504V, E459K Second-line Amikacin rrs a1401 g, g1484t (group B; Kanamycin eis c-14t, g-10a, g-37t, c-12t [7] second line rrs a1401 g, c1402t, g1484t injectable drugs) Capreomycin rrs a1401 g, c1402t, g1484t tlya N236K, pooled frameshifts and premature stop codons Streptomycin rpsl K43R, K43T, K88Q, K88R, T40I rrs a514c, a514t, c462t, c513t, c517t Second-line Ethionamide/ inha c-15t+i194t, c-15t+s49a, c-15t [7] (group C) Prothionamide etha Pooled frameshifts and premature stop codons [7] Cycloserine alr [208] t-8c, M319T, Y364D, Y364C, R373L, R373G ald [209] Pooled frameshifts and premature stop codons Linezolid rplc [210] T460C rrl [211] g2299t, g2814t Second-line (group D1) Pyrazinamide pnca a-11 g, A134V, A3E, A46V, C138Y, C14R, C72R, D12A, D12N, D49G, D49N, D63G, D8E, D8G, D8N, F94L, F94S, G108R, G132A, G132D, G132S, G162D, G17D, G24D, G97C, G97D, G97S, H137P, H51Q, H51R, H57D, H57P, H57R, H57Y, H71D, H71Q, H71Y, H82R, I6T, indel - R148ins (inframe), K96N, K96R, L116P, L116R, L120P, L151S, L159P, L172P, L19P, L4S, L85P, L85R, M175T, M175V, P54S, P62L, P62Q, Q10P, Q141P, R123P, S104R, S59P, S66P, S67P, t-12c, T135N, T135P, T142A, T142K, T142M, T160P, T168P, T76P, t-7c, V125F, V125G, V128G, V139G, V139L, V155G, V180F, V180G, V7G, W68C,W68R, Y103H, Y34D, pooled frameshifts and premature stop codons Ethambutol EmbB [212] M306I, M306V, D354A, G406D, G406C, G406S, Q497R [212] embc-emba [213] c-8t, c-12t, c-16t (often in linkage with embb mutations) [213] Second-line (group D2) Second-line (group D3) Delamanid fbia D49Y [214], L250Stop [33] ddn [33] W88Stop Bedaquiline Rv0678 [214] M1A PAS folc [215] E153A, E153G, S150G, F152S, I43T, I43A, E40G ribd [216] g-12a High confidence resistance conferring mutations are based on published likelihood ratios, and supplemented with other well-investigated mutations with sufficient evidence to be associated with resistant phenotypes. Table modified from Miotto et al. [55]. Drugs are classified according to recent WHO guidelines [10]. 168 ª 2018 The Association for the Publication of the Journal of Internal Medicine

7 The basic concept underlying the use of any molecular assay for resistance prediction is the interpretation of a genomic variant towards a change of phenotypic drug resistance. This is expressed by an increase in the MIC to a level higher than the ECOFF for a particular drug. These genotype phenotype relationships have been investigated in numerous studies over the last years mainly by correlating the occurrence of variants with phenotypic drug resistance testing data using various methods and cut-offs and/or using allelic exchange experiments incorporating resistant alleles in susceptible laboratory strains, e.g. M. tuberculosis H37Rv [41, 52 54]. Whilst more than 400 variants in over 60 genomic regions have been described to be involved in resistance, a solid account on the relationship between the genetic and the classical drug susceptibility data is available merely for a subset of them [55]. A recent systematic review investigated raw genotype and phenotype correlation data from 43 countries in an attempt to provide a standardized analytical account of all mutations useful for genotypic drug resistance prediction [56] (Table 2). Using likelihood ratio thresholds to assess genotype phenotype data, 286 mutations in 20 target regions were graded into high, moderate or minimal confidence for being associated with resistance. Of note, resistance prediction was highly sensitive and specific for RIF (90.3% and 96.3%) and INH (78.2% and 94.4%). Applying this approach to mutations conferring resistance to second line drugs yielded less robust results. However, once larger datasets for these drugs become available, the performance is likely to increase in the future [49]. Molecular tools offer rapid resistance prediction compared to phenotypic DST. Whilst amplificationbased tools e.g. the GeneXpert or LPAs, allow prompt resistance prediction for one or few drugs, Next Generation Sequencing (NGS) based whole genome sequencing (WGS) offers the most comprehensive approach to determine resistance [52, 57, 58]. By surveying all genes that potentially confer resistance to first-line, second-line and new drugs, WGS goes beyond classical resistance-prediction assays [59] and facilitates the timely design of tailor-made, individualized treatment regimens for patients with TB. WGS from sputum-samples remains technically challenging [60]. Yet, once this technique is routinely operable, the greatest clinical impact of WGS-based regimens will result from the time saved to start effective therapy. A recent investigation supports that WGS-based treatment regimens are feasible and yield highly accordant regimens compared to those derived from phenotypic drug susceptibility testing [7]. The application of WGS-based resistance prediction is challenged by inconclusive genotype phenotype correlations for some loci and the complex data analysis and interpretation. First software solutions with automated workflows to detect resistance-conferring mutations from WGS data are already available. Nevertheless, performance remains highly variable and each tool is only as accurate as its integrated knowledge base [61, 62]. A comprehensive international database of confidence-graded mutations for predicting drug resistance is urgently needed to support tools for rapid and comprehensive characterization of resistance variants in clinical M. tuberculosis strains. A major obstacle for the clinical implementation of genotypic-to-phenotypic DST in TB is the ability to perform WGS-based genotypic DST on native sputum specimen. Recently it has been demonstrated that this bottleneck can be overcome [63]. With the prospect of automated WGS-based genotypic DST performed on native specimens and computing of the results by standardized treatment alghorithms, within the forthcoming 5 years physicians may have personalized, tailor-made treatment suggestions for their patients at hand shortly after the diagnosis of drug resistant TB is made on a sputum specimen. Biomarker guided treatment decisions A biomarker is defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention [64]. Biomarkers may serve as surrogate for a specific end-point and ideally can be measured at an earlier point of time than the onset of the end-point. In the field of TB, biomarkers are regularly dealt with using specific PCRs that substitute for culture as the reference standard (i.e. GeneXpert) for the diagnosis of active disease [65]. However, there are other purposes where biomarkers may play an important role for the future management of patients with TB (Table 3) [66]. For example and in contrast to standardized patient management approaches, biomarkers have the potential to precisely describe therapy responses, which may eventually enable clinicians to ª 2018 The Association for the Publication of the Journal of Internal Medicine 169

8 Table 3 Examples for biomarker candidates with corresponding material, detection methods, specific molecular targets and associated clinical end-points Clinical Detection Marker samples methods Mycobacterial cultures Sputum Liquid culture Mycobacterial DNA Sputum, plasma and urine PCR Targets (examples) Associated end-points Viable mycobacteria Outcome [69] Specific MTB DNA, IS6110 gene Smear and culture positivity, treatment failure References [73, 74] Mycobacterial RNA Sputum PCR Specific MTB RNA Relapse [91, 217] Lipoarabinomannan Urine Lateral flow Lipoarabinomannan Active TB [71] Host RNA signatures Whole blood RNAseq, RNA Arrays, PCR ETV7, GBP5, BATF2, FCGR1B Active disease, outcome [83, 84, 89] Host metabolomics Cytokines and proteins Serum/ plasma Serum/ plasma Massspectronomy, ELISA ELISA Gut microbiota markers Outcome [218] IP-10, NCAM, CRP, transthyretin PET-CT NA PET-CT Metabolically active lesions Conventional X-ray NA X-ray Extent of pulmonary disease NA, not applicable. Active disease, time to culture conversion Relapse [91] 2 month sputum smear status [78, 79] [219] individualize therapy durations of patients with TB by defining the optimal time-point of therapy end [66]. The only example where this approach has been successfully implemented in the field of infectious diseases is by sequential measurements of the serum procalcitonin (PCT) in community acquired pneumonia. PCT guided discontinuation of therapy has been shown to shorten exposure to antibiotics without an increase in mortality [67]. There are some examples for promising markers possibly serving as therapy response tools in MDR- TB in the future [68]. Sequential cultures to identify the time-point of culture conversion are already widely used in the management of MDR-TB [69] (Fig. 1). Here, early culture conversion correlates with cure, whereas late conversion time-points after therapy initiation are predictive for therapy failure. The time to positivity of sequential cultures may also be applied to evaluate therapy s effect to bacterial loads in sputum samples [70]. However, culture conversion usually takes place during the first months of therapy leaving the longest time of drug-therapy without being able to exactly assess drug therapy s effect with these methods. Alternatives to sputum cultures, which indirectly prove the pathogen s presence, may inform clinicians for the need for further therapy. The lipoarabinomannan (LAM) in urine samples has been shown to improve diagnostic algorithms significantly, especially when dealing with immunosuppressed HIV patients [71]. However, since this marker yields positive results before treatment initiation where higher overall bacterial loads are to be found it may not be suitable to serve during late time-points during therapy when most bacteria are already killed. Specific mycobacterial PCRs from sputum and urine were analyzed to serve as markers for therapy response [72 74]. Although mycobacterial DNA from these compartments were identified even late after therapy initiation, the pathogen s viability cannot be determined, which questions these 170 ª 2018 The Association for the Publication of the Journal of Internal Medicine

9 Fig. 1 Kaplan Meier curves of sputum culture conversion rates of in-patients with non-m/xdr-tb (blue) and M/XDR-TB (red) at the Medical Clinic of the Research Center Borstel Germany, Sputum was sampled and evaluated on a weekly basis. results relevance for the clinic. In analogy to the time to positivity, cycle thresholds from PCRs are indicative for bacterial loads still not differentiating between bacteria being alive or killed already [73]. These approaches are restricted to pulmonary TB only. Therapy responses in extrapulmonary TB are even more complicated to assess. The evaluation of host responses to the pathogen may be more appropriate way to indirectly prove the presence of M. tuberculosis by demonstrating the immune system s effort to fight the bacterium [66]. As a simple approach, clinical scores can give general information about disease severity and their improvement due to effective therapy, but may not be ideal to define therapy durations [75] (Heyckendorf et al. IJTLD 2018 in press). Tests based on immunological markers, which can be translated to point of care assays would be highly desirable to also reach patients being treated in even low-resource settings. As an example for markers that could be applied to blood samples in a simple lateral flow assay, the combination of certain inflammatory proteins in blood and pleural effusions were shown to be predictive for active TB [76]. A seven-marker protein signature from measured in TB patient s serum was identified as promising screening method for the diagnosis of active TB irrespectively of the HIV status [77] and combinations of protein markers may also be appropriate to describe therapy response since their level decrease after therapy initiation [78, 79]. As a further example for targets that can be simply measured, changes of immunoglobulins (A, G and M) against mycobacterial antigens in response to anti-tuberculosis therapy seem to also have the potential to depict successful therapy [80]. B cell responses to active TB may have been underestimated in the past since recent gene expression studies show the expression of FasL and IL5RA and the activation of B cells during anti- TB therapy may be used as method to monitor successful therapy [81, 82]. Gene-expression analysis may be interesting in the aspect of therapy monitoring since transcriptional signatures were shown to be predictive for active TB before the onset of the disease [83]. Interferongamma driven gene-expression responses were identified in patients with active TB and important ª 2018 The Association for the Publication of the Journal of Internal Medicine 171

10 changes of these signatures were observed after the introduction of anti-tuberculosis therapy making transcriptomics an important tool to screen for biomarkers that might identify the optimal timepoint of therapy end [84 88]. Although transcriptomics can most probably not be translated to routine clinical practice, the identified signatures of only few markers can be measured in simpler PCR-based platforms. For example, a 5-gene signature of genes from mitochondrial signal cascades that could predict patient outcome early during therapy was described [89]. Moreover, evidence for patients having excessive cytolytic responses to the pathogen may have an increased risk to experience relapse also pointing out the individuals genetic traits contributing to their chance to reach cure [90]. In the recent past, PET-CT scans have been introduced as promising method to assess the metabolic state of TB lesions in the lung as marker for on-going infection [91]. Patients with continuing signs of inflammation had an increased risk of experiencing relapse after therapy. However, this imaging method is reserved to high resource settings and the overall importance of metabolically active lesions at the end of therapy remains unclear. Trials assessing the above mentioned biomarker candidates to individualize therapy duration in MDR-TB are still missing. Such trials would be essential to compare patient outcomes undergoing standardized treatment vs. biomarker defined durations. Therapeutic drug monitoring The standard WHO MDR-TB treatment is a regimen with at least five effective TB medicines during the intensive phase, including pyrazinamide and four core second-line drugs, lasting 20 months [10]. Challenges include the long duration of treatment and drug-related toxicities [92, 93]. High plasma drug concentrations might increase the risk of some adverse effects [94], whilst low concentrations are associated with therapeutic failure and acquired drug resistance [95]. The therapeutic window for plasma concentrations may be relatively narrow, and plasma drug concentrations vary widely from patient to patient. Most TB drugs are given orally, and bioavailability can be affected by gut function, transporters and hepatic enzymes. Variable hepatic and renal function can alter the rate of drug elimination. In addition, HIV-infection and nutritional status, concomitant food intake, co-administered drugs and chronic diseases, such as diabetes can influence drug exposures [96, 97]. TDM addresses all these sources of variability by directly measuring drug delivery in the individual patient (Fig. 2). TDM should be an essential part of the treatment plan of TB patients in order to optimize drugs 0 exposures from the outset of treatment, rather than a reaction to failing treatment. Only a limited number of centers perform TDM routinely in TB patients. Blood is collected, typically at two time-points after an observed dose under clinical routine conditions (described below). The blood is centrifuged, and the plasma is promptly frozen to prevent drug degradation. High-performance liquid chromatography (HPLC) or gas chromatography (GC), most often with triple quadrupole mass spectrometry (MS/MS) detection, is used to measure the concentrations. Assays are designed either for single or groups of drugs [96]. Multidrug assays can measure several TB drugs simultaneously, reducing turn-around times. TDM has technical hurdles the collection, processing and transport of frozen samples, and the need for expensive equipment and skilled chemists. Dried blood spots (DBS) aim to replace frozen plasma for selected, stable drugs [98 100]. Blood is obtained by finger prick, and a drop is placed onto special filter paper. DBS do not require freezing, are easy to transport, and are potentially less contagious than frozen plasma. The trough concentrations of most TB drugs approach zero, so 2- and 6-h postdose samples provide information regarding the rate (i.e. peak concentration [C max ]) and extent of drug absorption [96]. For intravenous drugs, samples can be obtained 60-min post infusion, followed by a second sample at least 4 h later. Recently approved TB drugs bedaquiline and delamanid have long plasma half-lives [101, 102]. Sampling might include a peak and a trough concentration, but this requires further investigation. The efficacy of most anti-tb drugs is linked to the free drug area under the concentration-time curve (AUC) indexed to the minimal inhibitory concentration (MIC); i.e. the fauc/mic [103]. Preclinical models that combine drug pharmacokinetics (PK) with measures of bacterial load or time to culture conversion 172 ª 2018 The Association for the Publication of the Journal of Internal Medicine

11 (a) (b) (c) (d) Patient 1 2 h MIC Best sampling scheme Dosing information Genetic biomarkers (e) AUC prediction Individualized treatment Concentration in target range? Fig. 2 Potential algorithm for therapeutic drug monitoring (TDM). A patient with TB (a) provides blood samples to estimate PK parameters at steady state (typically >1 week from treatment initiation). The patient s drug concentration is analyzed from dried blood spots (b) in a multidrug assay (c). AUC and Cmax (d) are plotted against the MIC. The MIC is determined using broth microdilution or BACTEC 960 MGIT. Before DST or MICs are available, antimicrobial drug resistance is estimated from rapid methods based on the detection of resistance mutations. A computerized PK/PD model (e) is used to predict the best sampling scheme based on factors, such as patient s characteristics, and it allows the evaluation of the patient s drug concentration and the corresponding treatment individualization. The PK/PD model, disease severity and biomarkers are used to individualize the treatment length and predict outcome. AUC: area under the curve; Cmax: peak (maximum) concentration; DST: drug susceptibility testing; TB: tuberculosis; MIC: minimal inhibitory concentration; PK/PD: pharmacokinetic/pharmacodynamic; TDM: therapeutic drug monitoring. can determine synergistic and antagonistic drug effects, useful for regimen selection. Pharmacokinetic/pharmacodynamic (PK/PD) models are useful in determining PK targets for achieving maximum efficacy. In the clinic, measuring the AUCs and MICs is of value. A full AUC may require 7 samples, which is not practical in the clinic. Therefore, limited sampling strategies have been proposed to estimate AUC [ ]. These use regression equations, or population PK modeling with Bayesian estimation [56]. Clinical software includes BestDose (lapk.org) and TDMx (TDMx.eu). Models combine drug information (e.g. doses, times and plasma concentrations) and patient information (e.g. age, sex, body mass index, liver and kidney function) to determine the optimal dose. TB-specific population PK models have been published for many TB drugs [ ]. Prospective PK/PD studies are needed to better assess outcomes, allowing for updated reference ranges. Current ranges are best described as typical ranges with limited data directly relating them to outcomes. Recent clinical studies have directly linked drug exposures with clinical outcomes [111, 112]. Adding host genetic information could further improve dose optimization by predicting patients with increased risk for adverse effects, drug drug interactions, or unusually high or low C max or AUC values due to genetic variations. Controlling drug exposure relative to MIC is the most direct path to optimizing therapy for the individual patient. Unfortunately, TDM including MICs are not routinely accessible during TB treatment in most centers and MIC determination using the Bactec 960 system (MGIT) is laborious. New, less workand space-intensive methods are currently under validation. A Middlebrook 7H9 medium-based ª 2018 The Association for the Publication of the Journal of Internal Medicine 173

12 microdilution assay in a 96-well format that covers most of the second-line drugs is, for example, being evaluated by the international Comprehensive Resistance Prediction for Tuberculosis Consortium (CRyPTIC). The technological innovations described can increase the availability and use of TDM. Given the wide variability in PK, MICs and host factors across the population, TDM could provide treatment outcome advantages for patients with MDR-TB. Genetic testing for variability in treatment responses and adverse events Understanding the mechanisms which determine TB treatment response is important to refine therapy, and to prevent side effects when treating patients with drug resistant tuberculosis. Amongst the factors with impact on treatment response, the host genetic architecture appears to play a substantial role. The most recognized example in this regard is variation in the human N-acetyltransferase 2 (NAT2) gene linked to differing INH concentration [113, 114]. However, no valid data exists on the impact of host genetics on drug levels and drug responses of second line antibiotics applied in MDR- TB. There is a profound lack of human genetic studies analysing variability in treatment response of bedaquiline, delamanid, linezolid and aminoglycosides. Associations of human genetic variants were identified, though, with the occurrence or aggravation of severe side effects due to TB therapy. Patients carrying risk variants in genes associated with the hereditary form of the long QT syndrome (LQTS) are at particular risk for developing severe cardiac arrhythmia when treating with TB drugs known to prolong the QT interval, such as bedaquiline, delamanid, clofazimine, clarithromycin and moxifloxacin [115, 116]. The majority (>75%) of hereditary LQTS cases carry rare and common mutations in the genes KCNQ1, KCNH2 and SCN5A, and >600 variants (ClinVar; nih.gov/clinvar/) with pathogenic potential in these genes have been identified [116, 117]. In addition, aminoglycoside-induced deafness appears to be influenced by the host genetic variant m.1555a/g of the mitochondrial 12S ribosomal RNA MT-RNR1 [118]. Although the highly penetrant variant occurs only with a frequency of 0.2% in European children and adults, a single dose of aminoglycosides might induce lifelong deafness in patients carrying this mutation, and screening for this variant in populations, where aminoglycosides are routinely applied might thus be reasonable [118, 119]. However, whether screening for human risk variants is generally practicable and useful in patients with MDR- TB is unclear, as only a low number of genes and variants with impact on treatment outcome have been identified, and the expense for testing has to be balanced against the expected benefit. In general, it has to be stated that the data basis for a host genetic influence on treatment variability of MDR-TB is poor due to a lack of adequate studies. With the perspective to implement host genetics in personalized medicine of MDR-TB, and to establish individual genetic risk profiles of MDR-TB patients, more and larger pharmacogenetic investigations are needed to identify relevant genetic variants with impact on the treatment outcome. Host directed therapies In the preantibiotic era approximately 20% of patients with active TB survived for more than 10 years [120]. Why most patients die from untreated TB, whilst some patients are able to successfully fight TB with their immune system is still unknown. The importance of the host immune response to control M. tuberculosis infection is highlighted by the increased risk of TB progression in the rare primary immune deficiencies, Mendelian Susceptibility to Mycobacterial Diseases (MSMDs), HIVinfection and also upon treatment with TNF antagonists [121, 122]. Early initiation of antiretroviral therapy (ART) restores host immunity and thus decreases mortality in patients with TB, which support the notion that an enhanced cellular immune response could improve TB outcomes [123]. Whilst improving overall mortality, early initiation of ART also increases morbidity, via increased immune reconstitution inflammatory syndrome (IRIS), highlighting the double-edged sword of immune response. Similarly, although Th1 responses are imperative in TB control, high local and systemic IFN-gamma and IL-18 levels have been detected in patients with more severe form of TB [124], which suggests that a specific and balanced immune response is required to obtain cure. Host directed therapies (HDT) have shown promising results in some cancers with poor prognosis, including metastatic melanoma, non-small-cell lung cancer and renal cell carcinoma [125]. Basically, HDT can modulate TB immunity in three 174 ª 2018 The Association for the Publication of the Journal of Internal Medicine

13 different ways, (i) to increase the ability of the host immune system to effectively eliminate M. tuberculosis, (ii) to limit excess inflammation and collateral tissue damage associated with M. tuberculosis infection and (iii) to interfere with host molecules or signaling pathways that are required by M. tuberculosis for replication or persistence [126]. M. tuberculosis has developed strategies to manipulate host immunity at different levels including inhibition of phagolysosomal fusion and autophagy as well as reduced activation of dentritic cells and imperative Th1 responses [127]. The balance of the immune response to M. tuberculosis is critical: TNF deficiency results in intracellular M. tuberculosis growth whilst excessive TNF results in macrophage necrosis and extracellular M. tuberculosis growth [ ]. Excessive PD-1 signaling results in M. tuberculosis growth whilst PD-1 deficiency results in exuberant immunity with collateral host destruction [131, 132]. Therefore, effective HDT should have the ability to target multiple immune pathways and balance immunity in an attempt to treat and improve chronic TB infection, which may reduce the risk for drug-resistance and clinical complications. Attractive candidates for HDT are newly discovered small molecules or repurposed drugs i.e. registered drugs with a new target or new indication to promote synergistic and overlapping effects of HDT and bacterially directed therapies i.e. conventional anti-tb drugs. A brief overview of some HDT concepts with relevance for TB [133] include broadly acting corticosteroids i.e. glucocorticoids such as dexamethasone [134] and prednisone [135], which are inhibitors of systemic inflammation that reduce mortality along with anti-tuberculosis drugs especially in severe forms of TB [134]. Adjunctive prednisone treatment showed amelioration of symptoms for TB-IRIS [136] (Table 4). However, these drugs have the disadvantage that not only unwanted bystander inflammation is reduced but also specific anti-tb immunity. Similarly, nonsteroidal anti-inflammatory drugs (NSAIDs) may also improve TB outcomes by balancing host immunity, though primarily demonstrated so far by using murine TB models [137, 138] including modulation of the eicosanoid pathway using multiple clinically approved drugs to block the production of antiinflammatory lipotoxins [139]. NSAIDs have been shown to prevent excessive neutrophil migration and inflammation [129] that may contribute to inflammatory aggregates in the lung that prevent interaction of protective T cells and macrophages [140]. MPO derived ROS mediated necrotic cell death has also been identified in vitro as target to restore antimycobacterial efficacy of myeloid cells [141]. Other compounds can reduce TNF mediated pathologies including necrosis to preserve antimicrobial effector mechanisms in TB [142, 143]. Instead, adjunct cytokine therapy with recombinant IFN-gamma have shown reduced production of pro-inflammatory cytokines in the lung and enhanced sputum-conversion rates in patients with cavitary TB [144]. A popular HDT approach is to enhance macrophage effector functions, including induction of the antimicrobial peptide human cathelicidin, LL- 37 [145]. Nutritional supplementation with vitamin D 3 and phenylbutyrate could induce LL-37- dependent autophagy resulting in reduced intracellular M. tuberculosis growth [146] and also enhanced clinical recovery in patients with pulmonary TB [147]. Inhibitors of pathways controlling lipid metabolism [142] as well as tyrosine kinase activity [148] in the host cell have also shown efficacy to reduce M. tuberculosis growth in macrophages. Another HDT strategy concerns the microenvironment of the granuloma, where inhibition of tissue-degradation by matrix metalloproteinases (MMPs) could reduce early granuloma formation and bacterial spread [149, 150] and doxycyclin could serve as inhibitor and already clinically approved drug [151]. Interestingly, tetracyclines and its derivates, especially doxycycline, inhibit MMP activity independently of their antimicrobial properties, and therefore, these drugs have been frequently used as important MMP inhibitors in different disease conditions [152]. Other drugs may improve blood circulation, accessibility and drug-delivery to the TB granuloma that may enhance the efficacy of standard TB-drugs as well as novel small molecules [153]. It was recently shown that in vivo inhibition of indoleamine 2,3-dioxygenase (IDO), induced by M. tuberculosis inside macrophages in the lung, resulted in reduced bacterial growth, pathology, and clinical signs of TB disease, leading to increased host survival [154]. This increased protection was accompanied by increased lung T cell proliferation and also altered migration of effector T cells into the TB granulomas [154]. Immune checkpoint therapy involving blockade of inhibitory receptors such as PD-1 and CTLA-4, have been ª 2018 The Association for the Publication of the Journal of Internal Medicine 175