The main anti-TB drugs, mechanisms of actions and resistance-conferring polymorphisms.
Abstract
Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), is a leading cause of death in humans worldwide. The emergence of antibiotic-resistant strains of Mtb is a threat to tuberculosis control. A general belief is that drug resistance is acquired by Mtb during antibiotic treatment by accumulation of spontaneous mutations. Also, it is known that the drug resistance mutations (DRM) have an associated fitness cost, reducing the transmissibility and virulence of resistant strains. In this work we show that many canonical DRM are clade specific; i.e. they occur only in specific genetic lineages of Mtb and depend on a specific genetic context necessary for the reduction of the fitness cost and sustainability of the drug resistance phenotype. Dependence of the drug resistance on occurrence of genetic variants of multiple genes and specific activities of the encoded proteins allows combating the drug resistance by impairing the global genetic context. A new drug, FS-1, reverses antibiotic resistance by compromising this genetic context and aggravating the fitness cost of DRM.
Keywords
- antibiotic resistance
- drug resistance mutation
- genomic polymorphism
- drug resistance reversion
1. Introduction
Tuberculosis (TB), the infectious disease caused by
The first line antibiotics rifampicin (RIF) and isoniazid (INH), were developed against Mtb in the 1950s and 1960s, and are still the most effective treatments for TB. An estimated 20% of all Mtb isolates are resistant to at least one of the major antibiotics [2]. Multidrug-resistant tuberculosis (MDR-TB) is defined as TB that does not respond to at least rifampicin (RIF) and isoniazid (INH), while extensively drug-resistant TB (XDR-TB) is defined as TB resistant to INH and RIF in addition to resistance to any of the fluoroquinolones (FLQ) and to at least one of the three second-line injectable drugs: amikacin (AMK), capreomycin (CAP) or kanamycin (KAN). Antibiotic resistance arises when bacteria acquire mutations in drug target genes in an infected patient receiving antibiotics, usually as a result of mismanagement of treatment. Primary resistance arises when resistant strains are transmitted from one patient to another.
Efforts to control drug-resistant TB have relied on two beliefs: that most drug resistance is acquired
Results from improved molecular diagnostic methods have challenged these two beliefs. First, an increase in the prevalence of MDR- and XDR-TB appeared to be driving the spread of TB in some areas. For example, primary transmission of MDR- and XDR-TB is the main driving force of drug-resistant TB spread in sub-Saharan Africa [2]. Second, drug-resistance mutations have variable effects on fitness and transmissibility. Mutations associated with resistance to INH, RIF, and streptomycin (SM) have even been associated with low or no fitness costs [4]. Secondary mutations that compensate for drug resistance mutations appear rapidly after the emergence of drug resistance, in the same gene or in genes involved in linked metabolic pathways, and act to restore virulence and may even increase transmissibility [2].
The WHO recommends the Xpert MTB/RIF assay for the diagnosis of rifampicin resistance, and molecular line probe assays for the detection of resistance to first and second line drugs. Many countries with a high TB burden now implement the Xpert MTB/RIF assay, which can be used as a marker for MDR-TB, as INH resistance generally precedes RIF resistance [5]. Microbiological culture is still the reference standard for diagnosis of TB and of drug-resistance. TB remains very difficult to manage in resource-poor areas. Whole-genome sequencing (WGS) and detection of variants holds great promise for characterizing all of the resistance markers (as opposed to a limited range of mutations) as well as genotyping the strain of Mtb, but relies on a more complete understanding of the relationship between genotype, specific drug resistance mutations, activity states of multiple genes and encoded proteins, and the drug-resistance phenotype [6]. A new promising drug, FS-1, consisting of a nano-molecular complex of iodine atoms ligated to a dextrin-polypeptide network, was reported to cause antibiotic resistance reversion in MDR-TB by compromising the genetic context of the drug resistance phenotype and by aggravating the fitness cost of the drug resistance mutations [7].
2. Genetic mechanisms of drug resistance in Mtb
The major antibiotics for the treatment of TB have four different mechanisms of action: (i) inhibition of RNA synthesis; (ii) inhibition of protein synthesis; (iii) inhibition of cell wall biosynthesis; and (iv) by interfering with the synthesis of cell membranes [8].
Since the early 1990s, numerous studies have described the genetic mechanisms of drug resistance in Mtb, and there is a quantity of data on the polymorphisms found in isolates resistant to specific antibiotics. Mtb is highly clonal, and as such there is little or no horizontal gene transfer, implying that antibiotic resistance is due to point mutations or deletions. Drug-resistance mutations occur in genes coding for the antibiotic target itself (e.g.,
Researchers have not fully elucidated the mechanisms by which drug resistance emerges and is preserved in Mtb populations [11]. Early mathematical models of MDR-TB suggested that DR mutations would impose fitness costs that would tend to select against the mutation in the population and thus limit the spread of TB [12]. However, current research has shown that DR mutations have a variable effect on fitness and transmissibility. INH, RIF and SM resistance have even been associated with low or no fitness costs [2, 4].
Table 1 summarizes the literature data [7, 13, 14, 15] on the roles of the major antibiotics used to treat TB and known genes involved in drug-resistance, as well as the mechanisms thought to be responsible for drug-resistance. Drug resistant phenotype in Mtb is associated exclusively with mutations at specific positions in bacterial genomes. No events of a horizontal acquisition of drug resistance genetic determinants were reported for Mtb. Mutations in protein coding genes either alter drug target molecules or reduce activity of enzymes converting pro-drug molecules into active antibiotics, e.g.,
Antibiotic name | Mechanism of action | Some polymorphisms in Mtb causing resistance | Mechanism of drug resistance | |
---|---|---|---|---|
First line drugs | Rifampicin, RIF | Inhibits bacterial RNA polymerase by binding it. When RIF binds to the RpoB target, hydroxyl radicals are formed and this has a cytotoxic effect. | Most mutations occur in cluster I of | Drug target is altered. In resistant bacteria, hydroxyl radicals are not formed when RIF binds to RpoB, so cells do not die. |
Ethambutol, EMB | Affects several cellular pathways, mostly arabinogalactan biosynthesis through inhibition of cell wall arabinan polymerization; RNA metabolism, transfer of mycolic acid into cell wall, phospholipid synthesis, spermidine synthesis | Point mutations in the | Alteration of the drug target | |
Isoniazid, INH | INH is a pro-drug, activated by the catalase-peroxidase enzyme KatG and then binds to InhA. Disrupts multiple pathways, mainly interferes with synthesis of mycolic acid. | Mutations to Mutations to Mutations in Mutations in | ||
Pyrazinamide, PZA | Activated by enzyme pyrazinamidase (PZase). Mechanism poorly understood. Disruption of the proton motive force required for essential membrane transport functions by POA at acidic pH. | Mutations in the | Pro-drug cannot be converted to its active form | |
Aminoglycosides: streptomycin, SM | Binds to the small 16S rRNA of the 30S subunit of bacterial ribosome, interfering with the binding of tRNA to the 30S subunit | Mutation of the ribosome target binding sites: 50% in the 20% mutations to the Also mutations in | Alteration of the drug target | |
Second line drugs | Aminoglycosides: kanamycin KAN, amikacin AMK | Binds to the small 16S rRNA of the 30S subunit of bacterial ribosome, interfering with the binding of tRNA to the 30S subunit | Mutation of the ribosome target binding sites genes | Alteration of drug target |
Capreomycin, CAP | Inhibits protein synthesis through modification of ribosomal structures at the 16S rRNA | Mutations in the mutations in the gene | Alteration of drug target | |
Ethionamide, ETH | ETH requires activation by monooxygenase EthA, inhibits mycolic acid synthesis by binding the ACP reductase InhA | 70% due to mutations in | Similar to INH: | |
Fluoroquinolones (FLQ), e.g., ofloxacin (OFX), moxifloxacin (MOX) | Trapping gyrase on DNA as ternary complexes, thereby blocking the movement of replication forks and transcription complexes | Usually multiple mutations in conserved quinolone resistance-determining region (QRDR) of [Mutations at position 80 of | FLQ traps the DNA-gyrase complex in which the DNA is broken. Resistant GyrA prevents chromosome breakage. | |
Para-aminosalicylic acid, PAS | PAS is a prod-drug and thymidylate synthase A is required for conversion to active form PAS inhibits folic acid biosynthesis and uptake of iron | Mutations in the Also: mutations in | Pro-drug cannot be converted to active drug | |
Cycloserine, CS | Interrupts peptidoglycan synthesis (for cell wall) by inhibiting the enzymes d-alanine racemase (AlrA) and d-alanine:d-alanine ligase (Ddl) | To be determined | Unknown |
3. Drug-resistance against the background of Mtb genetic clades and current diagnostic approaches
The disease TB first appeared roughly 70,000 years ago [17]. Studies show that Mtb arose as an obligate human pathogen and that different strains co-evolved with humans, migrated out of Africa, and that the populations expanded with their human hosts [18]. The migrations of modern humans out of Africa and the increased population density during the Neolithic period could be at the origin of its expansion. This theory is consistent with the bacterium’s phylogeny and phylogeography [19].
Genetic analyses of global strains have revealed that distinct lineages of Mtb have emerged in different regions of the world. The considerable genetic diversity between these lineages is linked to ancient human migrations out of Africa and to more recent movements and population growth [20]. Hershberg
During diagnostic procedures, it is helpful to find the lineage of the infecting Mtb strain(s), because some lineages might have acquired specific virulence and/or resistance features before expanding [21]. Clades differ by growth rate and in patterns of host-pathogen interaction in terms of cytokine induction and rate of uptake by macrophages [22]. Lineage 2 (Beijing clade) also is associated with hyper-virulence and with an extended drug resistance pattern [23].
Here we discuss research papers investigating the feasibility of replacing phenotypic drug testing of Mtb with molecular diagnostic techniques. All of them rely on understanding the genetic mechanisms underlying the development and persistence of drug-resistance in Mtb strains, including the context of lineages with varying evolutionary histories.
Köser
Rodwell
Genome-wide association studies (GWAS) exploit the rapid turnover and high throughput of NGS, identifying variants in natural populations linked to phenotypic traits by statistical association. Bacterial GWAS have not been frequently used because their population structures reduce the power of association or produce false positives [25]. The clonal nature of bacterial reproduction—especially prevalent in Mtb—means that spurious variations can be strongly associated with particular phenotypes [26]. However, Earle
Coll
Feuerriegel
One of the few studies using gene pairs associated with drug resistance was by Cui
Mortimer
A variety of bioinformatic approaches have been useful for resolving the evolution of the various lineages of
4. Non-random associations between polymorphic sites in genomes of M. tuberculosis
Data for this research was sourced from the GMTV database [17], which consists of SNPs and indels for a large number of Mtb strains for which whole genome sequencing was performed. Also, this database integrates clinical, epidemiological and microbiological data for all the recorded Mtb isolates. Analysis of this study compared distribution patterns of 58,025 amino acid substitutions in 1089 Mtb strains from the GMTV database. The polymorphisms were determined relative to the H37Rv reference strain [38]. Frequencies of all polymorphisms were calculated for the entire set of 1089 Mtb genomes and for Mtb lineages as they were identified in the GMTV database. Analysis of the data showed that many DR polymorphisms were strongly associated with specific Mtb lineages. A mosaic plot of the data is shown in Figure 1. Genomes of the Beijing, Haarlem and Lineage 4.3 clades contained numerous DR mutations, while only a few of them were observed in the Lineage 4.1, Ural and X-type. Bacteria of the latter clades appeared to be mostly drug-susceptible. Statistically significant prevalence of DR mutations in bacteria of specific Mtb clades was confirmed by Fisher’s exact test with Bonferroni adjustment. Of these, 25 DR-polymorphism/lineage pairs showed an odds ratio above 1.
Co-occurrence of alleles of different polymorphic sites was identified by calculating the linkage disequilibrium (LD) and χ2-statistics. In total, 288,840 pairs of polymorphisms showing statistically reliable associations (χ2 above 6.63 corresponds to a p-value ≤ 0.01) were identified between 823 polymorphic sites including 10 DR mutations [10].Functional associations between DR mutations (denoted as mutations from an initial
In the case of estimation of the risk of DR mutation from
Risks of secondary mutations
In Eqs. (2)–(5), values
The reasoning behind the further analysis is displayed in Figure 2, where two contingency tables of co-distribution of an arginine to leucine replacement at position 463 in the protein KatG rendering INH resistance [40] and two other secondary mutations are shown. Both pairs of mutations are characterized by strong linkage disequilibrium above 0.9. First, the co-distribution of the DR mutation KatG R463L and a polymorphism D69Y in a drug efflux protein Stp (Rv2333c) is considered (Figure 2-1). The replacement of the aspartate residue by tyrosine at position 69 of the protein Stp is rather common in the Mtb population and it has not been associated with any DR phenotype. However, this study showed that 91–99% of the DR mutation KatG R463L depends on the presence of the Stp D69Y substitution. In contrast, the likelihood of a D → Y replacement in the protein Stp does not depend significantly on the state of the KatG R463L polymorphism. The estimated attributable risk is in the range of 21–27%. The confidence ranges of attributable risks in Figure 2 are denoted as
Let us consider another co-distribution of the same DR-related polymorphism KatG R463L and a leucine to serine substitution at position 896 in PPE35 protein (Rv1918c), which is shown in Figure 2-2. These two mutations are strongly associated with each other, but this dependence is highly symmetric: in more than 90% of cases both mutations co-occur in the same genomes. It may indicate a genetic drift event when the DR phenotype is characteristic for a sub-lineage of isolates sharing common ancestry and the neutral mutation in the hypermutable PPE35 protein is a genetic marker of the sublineage.
For further analysis, only those secondary polymorphisms which influenced the DR mutations significantly, but were independent, were selected; i.e. cases were selected when confidence ranges of attributable risks
A selection of secondary mutations predetermining acquisition of nine of the most widely distributed DR mutations rendering resistance to FLQ, INH, EMB, SM and para-aminosalisylic acid (PAS) in multidrug resistant Mtb are shown in Table 2. Values
Secondary mutations | Drug resistance mutations | Annotation | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
GyrA S95 T (FLQ) | KatG S315 T,N (INH) | KatG R463L (INH) | AccD6 D229G (INH) | ThyA T202A (PAS) | EmbC V981 L (EMB) | RpsL K43R (SM) | GidB E92D (SM) | GidB L16R (SM) | ||
Rv0193c K417*,E | 86.4 to 94.6 | 87.5 to 97.7 | 77.4 to 93.1 | 71.9 to 90.9 | 80.7 to 99.6 | 78.1 to 98.5 | 79.6 to 99.5 | 81.1 to 96.0 | Hypothetical protein | |
Rv1186c P207A,T | 84.0 to 93.9 | 82.8 to 96.4 | 79.8 to 95.7 | 76.5 to 95.0 | 64.6 to 94.7 | 68.7 to 96.6 | 71.8 to 98.1 | 74.4 to 93.9 | Hypothetical protein | |
Rv1321 S144R | 81.8 to 91.1 | 84.7 to 96.0 | 80.6 to 94.5 | 72.5 to 90.6 | 67.1 to 92.9 | 76.7 to 97.4 | 75.6 to 97.3 | 76.0 to 92.7 | Hypothetical protein | |
Rv2017 A262E | 76.5 to 86.5 | 83.9 to 95.1 | 73.5 to 89.4 | 70.6 to 88.6 | 72.9 to 95.0 | 75.7 to 96.4 | 77.8 to 97.5 | 76.6 to 92.4 | Transcriptional regulator | |
GalU Q235R | 76.6 to 86.6 | 81.8 to 93.8 | 74.9 to 90.3 | 69.4 to 87.8 | 70.2 to 93.6 | 72.9 to 95.0 | 78.0 to 97.6 | 75.3 to 91.6 | UTP-glucose-1-phosphate uridylyltransferase | |
Rv3204 T34A | 74.7 to 84.8 | 82.5 to 94.1 | 72.2 to 88.3 | 69.1 to 87.3 | 68.5 to 92.4 | 79.6 to 97.8 | 75.7 to 96.4 | 74.8 to 91.0 | DNA-methyltransferase | |
CorA K139*,E | 76.2 to 86.1 | 85.4 to 95.9 | 70.7 to 87.3 | 67.3 to 86.1 | 68.0 to 92.2 | 73.5 to 95.1 | 75.4 to 96.3 | 75.9 to 91.8 | Magnesium and cobalt transporter | |
VapC47 S46 L | 74.1 to 84.4 | 84.5 to 95.3 | 71.0 to 87.5 | 71.8 to 89.1 | 71.2 to 93.8 | 73.8 to 95.2 | 75.7 to 96.4 | 69.3 to 87.4 | VapC47 toxin | |
EccC3 P214R | 77.6 to 87.4 | 79.7 to 92.5 | 71.2 to 87.8 | 65.1 to 84.8 | 70.2 to 93.6 | 72.9 to 95.0 | 74.8 to 96.2 | 66.8 to 85.9 | Type VII secretion protein | |
Rv2542 T211A | 71.8 to 82.4 | 81.3 to 93.4 | 66.4 to 84.1 | 64.9 to 84.4 | 71.1 to 93.8 | 73.8 to 95.2 | 75.6 to 96.4 | 70.6 to 88.3 | Hypothetical protein | |
PstA1 M5T | 87.2 to 95.3 | 74.6 to 90.6 | 82.9 to 96.4 | 84.1 to 97.6 | 79.8 to 99.5 | 78.8 to 99.5 | 82.3 to 96.8 | Phosphate-transport integral membrane ABC transporter | ||
TsnR L232P | 88.6 to 96.2 | 72.4 to 89.0 | 79.9 to 94.7 | 76.6 to 93.8 | 80.1 to 99.5 | 80.6 to 95.9 | 23S rRNA methyl-transferase | |||
AroG D265E | 81.7 to 92.8 | 81.5 to 93.4 | 77.3 to 91.6 | 73.3 to 93.5 | 68.5 to 90.8 | 79.5 to 96.9 | 83.4 to 95.3 | Phenylalanine-repressible DAHP synthetase | ||
ProX L85P | 61.4 to 75.4 | 81.2 to 92.1 | 80.0 to 92.1 | 76.2 to 94.2 | 84.9 to 95.1 | Osmoprotectant | ||||
59.2 to 74.3 | 82.1 to 93.2 | 78.2 to 91.3 | 83.3 to 98.1 | 82.5 to 94.0 | Sugar ABC transporter | |||||
Stp D69Y | 53.8 to 71.1 | 90.9 to 98.6 | 90.7 to 99.0 | 83.1 to 98.9 | 88.2 to 97.8 | Drug efflux protein | ||||
AceAa G179D | 53.2 to 70.8 | 90.8 to 98.6 | 87.9 to 97.8 | 82.9 to 98.8 | 89.4 to 98.4 | Isocitratelyase | ||||
GalTb T174A | 54.0 to 71.1 | 84.6 to 95.3 | 87.1 to 97.3 | 85.3 to 99.7 | 89.7 to 98.5 | Galactose-1-phosphate uridylyl-transferase | ||||
Rv0324 T168A | 54.8 to 72.3 | 87.2 to 96.9 | 90.5 to 98.9 | 82.6 to 98.8 | 89.2 to 98.4 | Transcriptional regulator | ||||
EspK C729S | 54.0 to 70.5 | 83.6 to 94.4 | 85.5 to 96.2 | 79.5 to 96.9 | 83.4 to 94.9 | ESX-1 secretion-Associated protein |
5. The concept of the drug resistance reversion and implementation thereof
The concept of drug resistance reversion was applied in recent studies [7, 41]. Drug resistance mutations are often incompatible with one another, as shown by negative linkage disequilibrium values. This suggests that the cumulative fitness cost of mutations is often too high for the resulting strain to be viable. FS-1 is a new drug which seems to exploit this tendency. Active units of FS-1 are aggregated micelles containing complexes of tri-iodide molecules coordinated by metal ions and integrated into a dextrin-polypeptide moiety. The basic formula of the micelle is:
where
Studies of XDR-TB infection in animal models showed the reversion of Mtb pathogens to a more drug sensitive phenotype after treatment with FS-1 despite the remaining DR related mutations in their genomes [7]. Drug resistance reversion was also confirmed on an
Clinical trials of FS-1 has been undertaken in Kazakhstan and registered in the Clinical Trial database (www.ClinicalTrials.gov) under an accession number NCT02607449. It was found that FS-1 had a high absorption rate after per-oral administration, which was not affected by food intake. Peak plasma concentration of FS-1 was observed within 1–2 h after administration. Gastric juice activated the infusion of FS-1 in stomach. Pharmacokinetic study of FS-1 showed a long residence time of the drug in the blood stream and an elevated accumulation in the liver. The drug was excreted from the test organism mainly by the kidneys.
The preclinical trial of FS-1 included pharmacological studies (primary and secondary pharmacokinetics); general toxicity determination; tests for mutagenesis, inhibition of reproductive performance, immune toxicity, mucous membrane irritation and several other general physicochemical studies of the compound. FS-1 caused no irritation of the stomach mucosa when applied in concentrations of up to 5.0 mg/kg. No ulcerogenic, allergenic, immune toxic, mutagenic or carcinogenic side effects were observed after repeated administration of FS-1. Also, no cytotoxicity or embryonic toxicity was observed. Toxicological studies attributed FS-1 to low toxicity compounds with a reduced accumulation in an organism (drug accumulation coefficient was 1.85). The maximum endurable dose of FS-1 identified in rats was 496 mg/kg, and in mice, 993 mg/kg. The average lethal dose (LD50) in rats was found to be 992 mg/kg for both male and female individuals. Therapeutic doses of FS-1 in clinical trials on humans for the treatment of patients with lung XDR-TB infection ranged from 1.0 to 5.0 mg/kg. During the clinical trials, FS-1 was administrated for up to 6 months in combination with the antibiotics commonly prescribed for XDR-TB treatment. Currently, in the third stage of the clinical trials, FS-1 is administrated at a concentration of 2.5 mg/ml for 6 months. Clinical studies complied with the regulations and recommendations of the Ministry of Health of Kazakhstan and were approved by the respective committees of the Ministry.
The first phase of clinical trials was undertaken in 2009–2010 at the Central Clinical Hospital of the Executive Officer of the President of Kazakhstan. During this phase, the drug tolerance and safety of a unitary and repeated per-oral intake of the drug by healthy volunteers were determined. Hematological parameters, including measuring the concentrations of important microelements, i.e., potassium, sodium, magnesium and calcium; functions of liver and kidney, electro-physiological parameters of myocardium, metabolism of proteins, hydrocarbons and lipids, were monitored. Biochemical parameters of the blood plasma of volunteers remained normal during the study. It was found that the administration of FS-1 activated cellular immunity and synthesis of γ-interferon.
The second phase of clinical trials was conducted in 2010–2012 at the Municipal anti-tuberculosis clinic in Almaty, at the National Centre of Tuberculosis in Almaty and at the Regional anti-tuberculosis clinic of the Karaganda region in Kazakhstan. In total, 220 volunteer patients with active XDR-TB lung tuberculosis were involved in this phase of trials. The volunteers ranged from 18 to 65 years old, with a body mass within 10% of the average body weight of male and female adults, with no contraindications to the common MDR-TB antibiotic therapy. Informed consent principles, which imply voluntariness of participation and understanding of the matter of the trial, were complied with. Contraindications to participation in the trial were: pregnancy; oncological diseases; HIV; 3-fold higher than normal ALT/AST or increased creatinine in blood; dermatomycosis; mental disorders; hypothyroidism; any allergies, especially an allergy to iodine-containing preparations; and any other cardiovascular, kidney or liver decompensated concomitant diseases.
The therapeutic efficacy of the drug was evaluated by bacteriological examination of sputum samples of patients on Lowenstein-Jensen medium for the presence of Mtb isolates. Other tests performed during the trial were: microscopic examination of sputum smears; controlling the positive dynamics of recovery by regular X-ray examinations and by general clinical tests; positive body weight dynamics; and the efficacy of prevention of disease relapses. The efficacy and safety of the regimen of per-oral administration of FS-1 in concentrations of 2.0–5.0 mg/kg during the 6 months in combination with commonly prescribed antibiotics against XDR-TB were confirmed in the second phase of the trial. No serious side effects of the treatment were recorded. In particular, thyroid gland function was monitored for adverse effects. No statistically reliable alterations in the concentration of thyroid hormones in blood were observed, which indicated no deleterious effect of this iodine-containing drug on thyroid gland functions. The time of complete recovery from XDR-TB was reduced, with no disease relapses during the 12 months surveillance, resulting in a significant reduction of the average cost of XDR-TB treatment (Table 3).
XDR TB treatment expenses | Conventional antibiotic therapy | Combined therapy by antibiotics with FS-1 |
---|---|---|
Time of 100% sanation from | 12–24 months | 3–6 months |
Percentage of relapses in 12 month surveillance period | 46.1% | Not observed |
Daily therapy cost in clinics of Kazakhstan | $ 11.7 | $ 12.5 |
Full cost of the treatment course including the treatment of disease relapses | $ 4274 or up to $ 8548 in the case of TB relapses | $ 2256 (no TB relapses were recorded) |
Mtb isolates were collected on a regular basis during the second phase of the FS-1 clinical trials. It was found that the percentage of drug resistant isolates decreased continuously during the treatment course with FS-1 despite the administration of the antibiotics. It was hypothesized that the therapeutic activity of FS-1 may be associated with the reversion of antibiotic resistance [37]. This hypothesis was then confirmed in an
The third phase of clinical trials began in 2014 and is still in progress. The drug FS-1 has been approved as an antibacterial medicine for per-oral administration in a complex of commonly prescribed anti-tuberculosis drugs for the treatment of XDR-TB in Kazakhstan (approval certificate РК-ЛС-5№021305 from 08-04-2015).
6. Conclusion
The idea that the DR phenotype is determined by multiple genes was supported in a review by Trauner
Acknowledgments
This research was funded by grant 105996 provided by the National Research Foundation of South Africa and by grant 0115РК00389 of the program “Study on the reversion of antibiotic resistance in pathogenic microorganisms” provided by the Ministry for Investments and Development of Kazakhstan.
References
- 1.
WHO. Global Tuberculosis Report. 2017. DOI: WHO/HTM/TB/2017.23 - 2.
Dheda K, Gumbo T, Maartens G, Dooley KE, McNerney R, Murray M, Furin J, Nardell EA, London L, Lessem E, Theron G, van Helden P, Niemann S, Merker M, Dowdy D, Van Rie A, Siu GKH, Pasipanodya JG, Rodrigues C, Clark TG, Sirgel FA, Esmail A, Lin H-H, Atre SR, Schaaf HS, Chang KC, Lange C, Nahid P, Udwadia ZF, Horsburgh CR Jr, Churchyard GJ, Menzies D, Hesseling AC, Nuermberger E, McIlleron H, Fennelly KP, Goemaere E, Jaramillo E, Low M, Jara CM, Padayatchi N, Warren RM. The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis. The Lancet Respiratory Medicine. 2017; 5 :291-360. DOI: 10.1016/S2213-2600(17)30079-6 - 3.
Rodwell TC, Valafar F, Douglas J, Qian L, Garfein RS, Chawla A, Torres J, Zadorozhny V, Kim MS, Hoshide M, Catanzaro D, Jackson L, Lin G, Desmond E, Rodrigues C, Eisenach K, Victor TC, Ismail N, Crudu V, Gler MT, Catanzaro A. Predicting extensively drug-resistant Mycobacterium tuberculosis phenotypes with genetic mutations. Journal of Clinical Microbiology. 2014;52 :781-789. DOI: 10.1128/JCM.02701-13 - 4.
Naidoo CC, Pillay M. Increased in vitro fitness of multi- and extensively drug-resistant F15/LAM4/KZN strains ofMycobacterium tuberculosis . Clinical Microbiology and Infection. 2014;20 :O361-O369. DOI: 10.1111/1469-0691.12415 - 5.
Manson AL, Cohen KA, Abeel T, Desjardins CA, Armstrong DT, Barry CE 3rd, Brand J, TBResist Global Genome Consortium, Chapman SB, Cho SN, Gabrielian A, Gomez J, Jodals AM, Joloba M, Jureen P, Lee JS, Malinga L, Maiga M, Nordenberg D, Noroc E, Romancenco E, Salazar A, Ssengooba W, Velayati AA, Winglee K, Zalutskaya A, Via LE, Cassell GH, Dorman SE, Ellner J, Farnia P, Galagan JE, Rosenthal A, Crudu V, Homorodean D, Hsueh PR, Narayanan S, Pym AS, Skrahina A, Swaminathan S, Van der Walt M, Alland D, Bishai WR, Cohen T, Hoffner S, Birren BW, Earl AM. Genomic analysis of globally diverse Mycobacterium tuberculosis strains provides insights into emergence and spread of multidrug resistance. Nature Genetics. 2017;49 :395-402. DOI: 10.1038/ng.3767 - 6.
Cabibbe AM, Cirillo DM. Best approaches to drug-resistance surveillance at the country level. International Journal of Mycobacteriology. 2016; 5 :S40-S41. DOI: 10.1016/j.ijmyco.2016.09.010 - 7.
Ilin AI, Kulmanov ME, Korotetskiy IS, Islamov RA, Akhmetova GK, Lankina MV, Reva ON. Genomic insight into mechanisms of reversion of antibiotic resistance in multidrug resistant Mycobacterium tuberculosis induced by a nanomolecular iodine-containing complex FS-1. Frontiers in Cellular and Infection Microbiology. 2017;7 :151. DOI: 10.3389/fcimb.2017.00151 - 8.
Zhang Y, Yew W-W. Mechanisms of drug resistance in Mycobacterium tuberculosis : Update 2015. The International Journal of Tuberculosis and Lung Disease. 2015;19 :1276-1289. DOI: 10.5588/ijtld.15.0389 - 9.
Trauner A, Borrell S, Reither K, Gagneux S. Evolution of drug resistance in tuberculosis: Recent progress and implications for diagnosis and therapy. Drugs. 2014; 74 :1063-1072. DOI: 10.1007/s40265-014-0248-y - 10.
Sandgren A, Strong M, Muthukrishnan P, Weiner BK, Church GM, Murray MB. Tuber-culosis drug resistance mutation database. PLoS Medicine. 2009; 6 :e2. DOI: 10.1371/journal.pmed.1000002 - 11.
Fonseca JD, Knight GM, McHugh TD. The complex evolution of antibiotic resistance in Mycobacterium tuberculosis . International Journal of Infectious Diseases. 2015;32 :94-100. DOI: 10.1016/j.ijid.2015.01.014 - 12.
Cohen T, Murray M. Modeling epidemics of multidrug-resistant M. tuberculosis of heterogeneous fitness. Nature Medicine. 2004;10 :1117-1121. DOI: 10.1038/nm1110 - 13.
Müller B, Borrell S, Rose G, Gagneux S. The heterogeneous evolution of multidrug-resistant Mycobacterium tuberculosis . Trends in Genetics. 2013;29 :160-169. DOI: 10.1016/j.tig.2012.11.005 - 14.
Kolyva A, Old KP. New TB drugs: Mechanisms of action and resistance. In: Understanding Tuberculosis—New Approaches to Fighting against Drug Resistance. Rijeka, Croatia: InTech; 2012. pp. 210-232. DOI: 10.5772/30992 - 15.
Aldred KJ, Blower TR, Kerns RJ, Berger JM, Osheroff N. Fluoroquinolone interactions with Mycobacterium tuberculosis gyrase: Enhancing drug activity against wild-type and resistant gyrase. Proceedings of the National Academy of Sciences. 2016;113 :E839-E846. DOI: 10.1073/pnas.1525055113 - 16.
Zhao X, Yu H, Yu S, Wang F, Sacchettini JC, Magliozzo RS. Hydrogen peroxide-mediated isoniazid activation catalyzed by Mycobacterium tuberculosis catalase-peroxidase (KatG) and its S315T mutant. Biochemistry. 2006;45 :4131-4140. DOI: 10.1021/bi051967o - 17.
Chernyaeva EN, Shulgina MV, Rotkevich MS, Dobrynin PV, Simonov SA, Shitikov EA, Ischenko DS, Karpova IY, Kostryukova ES, Ilina EN, Govorun VM, Zhuravlev VY, Manicheva OA, Yablonsky PK, Isaeva YD, Nosova EY, Mokrousov IV, Vyazovaya AA, Narvskaya OV, Lapidus AL, O'Brien SJ. Genome-wide Mycobacterium tuberculosis variation (GMTV) database: A new tool for integrating sequence variations and epidemiology. BMC Genomics. 2014;15 :308. DOI: 10.1186/1471-2164-15-308 - 18.
Gagneux S. Host-pathogen coevolution in human tuberculosis. Philosophical Tran-sactions of the Royal Society of London. Series B, Biological Sciences. 2012; 367 :850-859. DOI: 10.1098/rstb.2011.0316 - 19.
Comas I, Coscolla M, Luo T, Borrell S, Holt KE, Kato-Maeda M, Parkhill J, Malla B, Berg S, Thwaites G, Yeboah-Manu D, Bothamley G, Mei J, Wei L, Bentley S, Harris SR, Niemann S, Diel R, Aseffa A, Gao Q, Young D, Gagneux S. Out-of-Africa migration and Neolithic co-expansion of Mycobacterium tuberculosis with modern humans. Nature Genetics. 2014;45 :1176-1182. DOI: 10.1038/ng.2744 - 20.
Hershberg R, Lipatov M, Small PM, Sheffer H, Niemann S, Homolka S, Roach JC, Kremer K, Petrov DA, Feldman MW, Gagneux S. High functional diversity in Mycobacterium tuberculosis driven by genetic drift and human demography. PLoS Biology. 2008;6 :e311. DOI: 10.1371/journal.pbio.0060311 - 21.
Banuls A-L, Sanou A, Van Anh NT, Godreuil S. Mycobacterium tuberculosis : Ecology and evolution of a human bacterium. Journal of Medical Microbiology. 2015;64 :1261-1269. DOI: 10.1099/jmm.0.000171 - 22.
Reiling N, Homolka S, Walter K, Brandenburg J, Niwinski L, Ernst M, Herzmann C, Lange C, Diel R, Ehlers S, Niemann S. Clade-specific virulence patterns of Mycobacterium tuberculosis complex strains in human primary macrophages and aerogenically infected mice. MBio. 2013;4 :pii: e00250-pii: e00213. DOI: 10.1128/mBio.00250-13 - 23.
Hanekom M, Gey van Pittius NC, McEvoy C, Victor TC, Van Helden PD, Warren RM. Mycobacterium tuberculosis Beijing genotype: A template for success. Tuberculosis. 2011;91 :510-523. DOI: 10.1016/j.tube.2011.07.005 - 24.
Köser CU, Bryant JM, Becq J, Török EM, Ellington MJ, Marti-Renom M, Carmichael AJ, Parkhill J, Smith GP, Peacock J. Whole-genome sequencing for rapid susceptibility testing of M. tuberculosis . The New England Journal of Medicine. 2013;369 :290-292. DOI: 10.1056/NEJMc1215305 - 25.
Falush D, Bowden R. Genome-wide association mapping in bacteria? Trends in Microbiology. 2006; 14 :353-355. DOI: 10.1016/j.tim.2006.06.003 - 26.
Falush D. Bacterial genomics: Microbial GWAS coming of age. Nature Microbiology. 2016; 1 :16059. DOI: 10.1038/nmicrobiol.2016.59 - 27.
Earle SG, Wu CH, Charlesworth J, Stoesser N, Gordon NC, Walker TM, Spencer CCA, Iqbal Z, Clifton DA, Hopkins KL, Woodford N, Smith EG, Ismail N, Llewelyn MJ, Peto TE, Crook DW, McVean G, Walker AS, Wilson DJ. Identifying lineage effects when controlling for population structure improves power in bacterial association studies. Nature Microbiology. 2016; 1 :16041. DOI: 10.1038/nmicrobiol.2016.41 - 28.
Coll F, McNerney R, Guerra-Assunção JA, Glynn JR, Perdigão J, Viveiros M, Portugal I, Pain A, Martin N, Clark TG. A robust SNP barcode for typing Mycobacterium tuberculosis complex strains. Nature Communications. 2014;5 :4812. DOI: 10.1038/ncomms5812 - 29.
Feuerriegel S, Koser CU, Niemann S. Phylogenetic polymorphisms in antibiotic resistance genes of the Mycobacterium tuberculosis complex. The Journal of Antimicrobial Chemotherapy. 2014;69 :1205-1210. DOI: 10.1093/jac/dkt535 - 30.
Feuerriegel S, Schleusener V, Beckert P, Kohl TA, Miotto P, Cirillo DM, Cabibbe AM, Niemann S, Fellenberg K. PhyResSE: A web tool delineating Mycobacterium tuberculosis antibiotic resistance and lineage from whole-genome sequencing data. Journal of Clinical Microbiology. 2015;53 :1908-1914. DOI: 10.1128/JCM.00025-15 - 31.
Cui ZJ, Yang QY, Zhang HY, Zhu Q, Zhang QY. Bioinformatics identification of drug resistance-associated gene pairs in Mycobacterium tuberculosis . International Journal of Molecular Sciences. 2016;17 :1417-1425. DOI: 10.3390/ijms17091417 - 32.
Mukhopadhyay S, Balaji KN. The PE and PPE proteins of Mycobacterium tuberculosis . Tuberculosis. 2017;91 :441-447. DOI: 10.1016/j.tube.2011.04.004 - 33.
Mortimer TD, Weber AM, Pepperell CS. Signatures of selection at drug resistance loci in Mycobacterium tuberculosis . mSystems 2018;3 :e00108-17. DOI: 10.1128/mSystems.00108-17 - 34.
Coll F, Prestona M, Guerra-Assunção JA, Hill-Cawthorn G, Harris D, Perdigão J, Viveiros M, Portugal I, Drobniewski F, Gagneux S, Glynn JR, Pain A, Parkhill J, McNerney R, Martin N, Clark TG. PolyTB: A genomic variation map for Mycobacterium tuberculosis . Tuberculosis. 2014;94 :346-354. DOI: 10.1016/j.tube.2014.02.005 - 35.
Faksri K, Tan JH, Chaiprasert A, Teo Y-Y, RT-H O. Bioinformatics tools and databases for whole genome sequence analysis of Mycobacterium tuberculosis . Infection, Genetics and Evolution. 2016;45 :359-368. DOI: 10.1016/j.meegid.2016.09.013 - 36.
Aminian M, Couvin D, Shabbeer A, Hadley K, Vandenberg S, Rastogi N, Bennett KP. Predicting Mycobacterium tuberculosis complex clades using knowledge-based Bayesian networks. BioMed Research International. 2014;2014 :1-11. DOI: 10.1155/2014/398484 - 37.
Baym M, Stone LK, Kishony R. Multidrug evolutionary strategies to reverse antibiotic resistance. Science. 2016; 351 :aad3292 - 38.
Cole ST, Brosch R, Parkhill J, Garnier T, Churcher C, Harris D, Gordon SV, Eiglmeier K, Gas S, Barry CE 3rd, Tekaia F, Badcock K, Basham D, Brown D, Chillingworth T, Connor R, Davies R, Devlin K, Feltwell T, Gentles S, Hamlin N, Holroyd S, Hornsby T, Jagels K, Krogh A, McLean J, Moule S, Murphy L, Oliver K, Osborne J, Quail MA, Rajandream MA, Rogers J, Rutter S, Seeger K, Skelton J, Squares R, Squares S, Sulston JE, Taylor K, Whitehead S, Barrell BG. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature. 1998;393 :537-544. DOI: 10.1038/31159 - 39.
Fleiss JL, Levin B, Paik MC. Statistical methods for rates and proportions. In: Shewart WA, Wilks SS, editors. Wiley Series in Probability and Statistics. 3rd ed. Wiley Online Library. Hoboken, New Jersey: John Wiley and Sons, Inc; 2004 - 40.
Heym B, Alzari PM, Honoré N, Cole ST. Missense mutations in the catalase-peroxidase gene, katG , are associated with isoniazid resistance inMycobacterium tuberculosis . Molecular Microbiology. 1995;15 :235-245. DOI: 10.1038/31159 - 41.
Mdluli K, Slayden RA, Zhu Y, Ramaswamy S, Pan X, Mead D, Crane DD, Musser JM, Barry CE 3rd. Inhibition of a Mycobacterium tuberculosis beta-ketoacyl ACP synthase by isoniazid. Science. 1998;280 :1607-1610. DOI: 10.1126/science.280.5369.1607 - 42.
Okamoto S, Tamaru A, Nakajima C, Nishimura K, Tanaka Y, Tokuyama S, Suzuki Y, Ochi K. Loss of a conserved 7-methylguanosine modification in 16S rRNA confers low-level streptomycin resistance in bacteria. Molecular Microbiology. 2007; 63 :1096-1106. DOI: 10.1111/j.1365-2958.2006.05585.x