Spontaneous emergence of multiple drug resistance in tuberculosis before and during therapy

Mathematical models of drug-resistant tuberculosis lack bacterial heterogeneity: A systematic review

Drug-resistant tuberculosis (DR-TB) threatens progress in the control of TB. Mathematical models are increasingly being used to guide public health decisions on managing both antimicrobial resistance (AMR) and TB. It is important to consider bacterial heterogeneity in models as it can have consequences for predictions of resistance prevalence, which may affect decision-making. We conducted a systematic review of published mathematical models to determine the modelling landscape and to explore methods for including bacterial heterogeneity. Our first objective was to identify and analyse the general characteristics of mathematical models of DR-mycobacteria, including M. tuberculosis. The second objective was to analyse methods of including bacterial heterogeneity in these models. We had different definitions of heterogeneity depending on the model level. For between-host models of mycobacterium, heterogeneity was defined as any model where bacteria of the same resistance level were further differentiated. For bacterial population models, heterogeneity was defined as having multiple distinct resistant populations. The search was conducted following PRISMA guidelines in five databases, with studies included if they were mechanistic or simulation models of DR-mycobacteria. We identified 195 studies modelling DR-mycobacteria, with most being dynamic transmission models of non-treatment intervention impact in M. tuberculosis (n = 58). Studies were set in a limited number of specific countries, and 44% of models (n = 85) included only a single level of "multidrug-resistance (MDR)". Only 23 models (8 between-host) included any bacterial heterogeneity. Most of these also captured multiple antibiotic-resistant classes (n = 17), but six models included heterogeneity in bacterial populations resistant to a single antibiotic. Heterogeneity was usually represented by different fitness values for bacteria resistant to the same antibiotic (61%, n = 14). A large and growing body of mathematical models of DR-mycobacterium is being used to explore intervention impact to support policy as well as theoretical explorations of resistance dynamics. However, the majority lack bacterial heterogeneity, suggesting that important evolutionary effects may be missed.

Unraveling resistance mechanisms in combination therapy: A comprehensive review of recent advances and future directions

Antimicrobial resistance is a global health threat. Misuse and overuse of antimicrobials are the main drivers in developing drug-resistant bacteria. The emergence of the rapid global spread of multi-resistant bacteria requires urgent multisectoral action to generate novel treatment alternatives. Combination therapy offers the potential to exploit synergistic effects for enhanced antibacterial efficacy of drugs. Understanding the complex dynamics and kinetics of drug interactions in combination therapy is crucial. Therefore, this review outlines the current advances in antibiotic resistance's evolutionary and genetic dynamics in combination therapies-exposed bacteria. Moreover, we also discussed four pivotal future research areas to comprehend better the development of antibiotic resistance in bacteria treated with combination strategies.

Trends of Mycobacterium tuberculosis and Rifampicin resistance in Northwest Ethiopia: Xpert® MTB/RIF assay results from 2015 to 2021

BACKGROUND

Tuberculosis (TB) remains one of the leading causes of morbidity and mortality worldwide, particularly in countries with limited resources. The emergence of drug resistance in mycobacterium tuberculosis (MTB), particularly rifampicin (RIF) resistance, hindered TB control efforts. Continuous surveillance and regular monitoring of drug-resistant TB, including rifampicin resistance (RR), are required for effective TB intervention strategies and prevention and control measures.

OBJECTIVE

Determine the trend of TB and RR-TB among presumptive TB patients in Northwest Ethiopia.

METHOD

A retrospective study was conducted at the University of Gondar Comprehensive Specialized Hospital (UoG-CSH). The study included TB registration logbook data from all patients who visited the hospital and were tested for MTB using the Xpert® MTB/RIF assay between 2015 and 2021. The SPSS version 26 software was used to enter, clean, and analyze the laboratory-based data.

RESULTS

A total of 18,787 patient results were included, with 93.8% (17,615/18787) of them being successful, meaning they were not invalid, error, or aborted. About 10.5% (1846/17615) of the 17,615 results were MTB-positive, with 7.42% (137/1846) RIF resistant. Age, anti-TB treatment history, and diagnosis year were associated with the presence of MTB and RR-MTB. Tuberculosis (TB) prevalence was higher in productive age groups, whereas RR-TB prevalence was higher in the elderly. Regarding diagnosis year, the prevalence of TB and RR-TB showed a declining trend as the year progressed. While MTB was detected in 12.8% (471/3669) of new and 22.2% (151/679) of re-treatment presumptive TB patients, RR-MTB was detected in 8.5% (40/471) of new and 18.5% (28/151) of re-treatment TB cases.

CONCLUSION

The prevalence of TB and RR-TB in the study area showed a declining trend over the years. While TB was more prevalent in productive age groups (15 to 45 years), RR-TB was more prevalent in older populations (over 45 years), than others. Moreover, patients with a history of anti-TB drug exposure were more likely to be positive for DR-TB, highlighting the need to strengthen DOT programs for proper management of TB treatment.

An insight to the recent advancements in detection of Mycobacterium tuberculosis using biosensors: A systematic review

Since ancient times, Tuberculosis (TB) has been a severe invasive illness that has been prevalent for thousands of years and is also known as "consumption" or phthisis. TB is the most common chronic lung bacterial illness in the world, killing over 2 million people each year, caused by Mycobacterium tuberculosis (MTB). As per the reports of WHO, in spite of technology advancements, the average rate of decline in global TB infections from 2000-2018 was only 1.6% per year, and the worldwide reduction in TB deaths was only 11%. In addition, COVID-19 pandemic has reversed years of global progress in tackling TB with fewer diagnosed cases. The majority of undiagnosed patients of TB are found in low- and middle-income countries where the GeneXpert MTB/RIF assay and sputum smear microscopy have been approved by the WHO as reference procedures for quickly detecting TB. Biosensors, like other cutting-edge technologies, have piqued researchers' interest since they offer a quick and accurate way to identify MTB. Modern integrated technologies allow for the rapid, low-cost, and highly precise detection of analytes in extremely little amounts of sample by biosensors. Here in this review, we outlined the severity of tuberculosis (TB) and the most recent developments in the biosensors sector, as well as their various kinds and benefits for TB detection. The review also emphasizes how widespread TB is and how it needs accurate diagnosis and effective treatment.

The second national anti-tuberculosis drug resistance survey in Tanzania, 2017-2018

OBJECTIVE

To determine the levels and patterns of resistance to first- and second-line anti-tuberculosis (TB) drugs among new and previously treated sputum smear positive pulmonary TB (PTB) patients.

METHODS

We conducted a nationally representative cross-sectional facility-based survey in June 2017-July 2018 involving 45 clusters selected based on probability proportional to size. The survey aimed to determine the prevalence of anti-TB drug resistance and associated risk factors among smear positive PTB patients in Tanzania. Sputum samples were examined using smear microscopy, Xpert MTB/RIF, culture and drug susceptibility testing (DST). Logistic regression was used to account for missing data and sampling design effects on the estimates and their standard errors.

RESULTS

We enrolled 1557 TB patients, including 1408 (90.4%) newly diagnosed and 149 (9.6%) previously treated patients. The prevalence of multidrug-resistant TB (MDR-TB) was 0.85% [95% confidence interval (CI): 0.4-1.3] among new cases and 4.6% (95% CI: 1.1-8.2) among previously treated cases. The prevalence of Mycobacterium tuberculosis strains resistant to any of the four first-line anti-TB drugs (isoniazid, rifampicin, streptomycin and ethambutol) was 1.7% among new TB patients and 6.5% among those previously treated. Drug resistance to all first-line drugs was similar (0.1%) in new and previously treated patients. None of the isolates displayed poly-resistance or extensively drug-resistant TB (XDR-TB). The only risk factor for MDR-TB was history of previous TB treatment (odds ratio = 5.7, 95% CI: 1.9-17.2).

CONCLUSION

The burden of MDR-TB in the country was relatively low with no evidence of XDR-TB. Given the overall small number of MDR-TB cases in this survey, it will be beneficial focusing efforts on intensified case detection including universal DST.

Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations

Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotic's mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that both bacteriostatic and bactericidal agents can be equally effective at suppressing the selection of resistant mutants, but that key determinants of resistance selection are the relationships between the number of drug-inactivated targets within a cell and the rates of cellular growth and death. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strain's minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this "secondary mutation selection window" could safeguard against the emergence of high-fitness resistant strains during treatment.

Factors associated with treatment outcome of MDR/RR-TB patients treated with shorter injectable based regimen in West Java Indonesia

Background and aims

Multi drug or rifampicin resistant tuberculosis (MDR/RR-TB) is a major burden to TB prevention and eradication globally. Since 2016, WHO guidelines have included options for treating MDR/RR-TB with a standard regimen of 9 to 11 months duration (the ’shorter regimen’) rather than an individual regimen of at least 20 months. This regimen has been introduced in Indonesia since September 2017. Therefore, we aimed to determine the success rate and factors associated with the treatment outcome of shorter injectable based regimen in West Java province, Indonesia.

Methods

This was a retrospective cohort study of MDR/RR-TB patients aged over 18 years old who received the shorter injectable based regimen between September 2017 and December 2020. We defined successful outcomes as the combined proportion of patients who were cured or had complete treatment. While, unsuccessful outcomes were defined as the combined proportion of patients who died from any causes, failure, and loss to follow-up (LTFU).

Results

A total of 315 patients were included in this study. The success rate was 64.5%. Multivariate analysis showed male gender (aRR = 1.18, 95% CI 1.04 to 1.34) increased the chance of successful outcome, while malnutrition (aRR = 0.78, 95% CI 0.68 to 0.89), history of previous TB treatment (aRR = 0.80%CI 0.68 to 0.94), and time of culture conversion >2 months (aRR = 0.72 (95% CI 0.59 to 0.87) decreased the chance of successful outcome.

Conclusion

History of previous TB treatment, time of culture conversion >2 months, and malnutrition were independent factors that decrease the chance for success rate, while male gender increase the likelihood for success rate of patients treated by the shorter injectable based regimen.

Advancing Immunotherapeutic Vaccine Strategies Against Pulmonary Tuberculosis

Chemotherapeutic intervention remains the primary strategy in treating and controlling tuberculosis (TB). However, a complex interplay between therapeutic and patient-related factors leads to poor treatment adherence. This in turn continues to give rise to unacceptably high rates of disease relapse and the growing emergence of drug-resistant forms of TB. As such, there is considerable interest in strategies that simultaneously improve treatment outcome and shorten chemotherapy duration. Therapeutic vaccines represent one such approach which aims to accomplish this through boosting and/or priming novel anti-TB immune responses to accelerate disease resolution, shorten treatment duration, and enhance treatment success rates. Numerous therapeutic vaccine candidates are currently undergoing pre-clinical and clinical assessment, showing varying degrees of efficacy. By dissecting the underlying mechanisms/correlates of their successes and/or shortcomings, strategies can be identified to improve existing and future vaccine candidates. This mini-review will discuss the current understanding of therapeutic TB vaccine candidates, and discuss major strategies that can be implemented in advancing their development.

Mathematical model and tool to explore shorter multi-drug therapy options for active pulmonary tuberculosis

Standard treatment for active tuberculosis (TB) requires drug treatment with at least four drugs over six months. Shorter-duration therapy would mean less need for strict adherence, and reduced risk of bacterial resistance. A system pharmacology model of TB infection, and drug therapy was developed and used to simulate the outcome of different drug therapy scenarios. The model incorporated human immune response, granuloma lesions, multi-drug antimicrobial chemotherapy, and bacterial resistance. A dynamic population pharmacokinetic/pharmacodynamic (PK/PD) simulation model including rifampin, isoniazid, pyrazinamide, and ethambutol was developed and parameters aligned with previous experimental data. Population therapy outcomes for simulations were found to be generally consistent with summary results from previous clinical trials, for a range of drug dose and duration scenarios. An online tool developed from this model is released as open source software. The TB simulation tool could support analysis of new therapy options, novel drug types, and combinations, incorporating factors such as patient adherence behavior.

A comprehensive in-silico model of pulmonary tuberculosis successfully predicted previous clinical trials and could simulate future therapeutics.

Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis

Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.

Characteristics of pulmonary multidrug-resistant tuberculosis patients in Tigray Region, Ethiopia: A cross-sectional study

BACKGROUND

Tuberculosis (TB) is among the top 10 causes of mortality and the first killer among infectious diseases worldwide. One of the factors fuelling the TB epidemic is the global rise of multidrug resistant TB (MDR-TB). The aim of this study was to determine the magnitude and factors associated with MDR-TB in the Tigray Region, Ethiopia.

METHOD

This study employed a facility-based cross-sectional study design, which was conducted between July 2018 and August 2019. The inclusion criteria for the study participants were GeneXpert-positive who were not under treatment for TB, PTB patients' ≥15 years of age and who provided written informed consent. A total of 300 participants were enrolled in the study, with a structured questionnaire used to collect data on clinical, sociodemographic and behavioral factors. Sputum samples were collected and processed for acid-fast bacilli staining, culture and drug susceptibility testing. Drug susceptibility testing was performed using a line probe assay. Logistic regression was used to analyze associations between outcome and predictor variables.

RESULTS

The overall proportion of MDR-TB was 16.7% (11.6% and 32.7% for new and previously treated patients, respectively). Of the total MDR-TB isolates, 5.3% were pre-XDR-TB. The proportion of MDR-TB/HIV co-infection was 21.1%. A previous history of TB treatment AOR 3.75; 95% CI (0.7-2.24), cigarette smoking AOR 6.09; CI (1.65-2.50) and patients who had an intermittent fever (AOR = 2.54, 95% CI = 1.21-5.4) were strongly associated with MDR-TB development.

CONCLUSIONS

The magnitude of MDR-TB observed among new and previously treated patients is very alarming, which calls for an urgent need for intervention. The high proportion of MDR-TB among newly diagnosed cases indicates ongoing transmission, which suggests the need for enhanced TB control program performance to interrupt transmission. The increased proportion of MDR-TB among previously treated cases indicates a need for better patient management to prevent the evolution of drug resistance. Assessing the TB control program performance gaps and an optimal implementation of the WHO recommended priority actions for the management of drug-resistant TB, is imperative to help reduce the current high MDR-TB burden in the study region.

FIRST LINE ANTI-TUBERCULOSIS DRUG RESISTANCE PATTERN IN MULTIDRUG-RESISTANT PULMONARY TUBERCULOSIS PATIENTS CORRELATE WITH ACID FAST BACILLI MICROSCOPY GRADING

Multidrug-resistant tuberculosis (MDR-TB) is a global public health crisis. Acid-fast bacilli (AFB) gradation in sputum examination is an important component in Pulmonary Tuberculosis (PTB) diagnosis and treatment outcome monitoring. Previously treated pulmonary TB patients with a higher AFB smear gradation may have higher rates of acquired resistance. Patients with a higher AFB grade indicate a higher bacillary load and had higher rates of acquired resistance. This study aims to evaluate the correlation between AFB gradation and first-line anti-TB drug resistance patterns in MDR pulmonary TB patients. This was a retrospective study conducted from August 2009 to April 2018 in Dr. Soetomo Hospital. Sputum samples were taken from MDR PTB patients. Sputum smear examination was done using Ziehl–Neelsen staining and gradation was measured according to IUATLD criteria. Samples with positive smear were evaluated for resistance patterns based on culture and resistance tests using the MGIT 960 BACTEC System. There were 433 sputum samples with AFB positive collected from MDR PTB patients. Resistance to RHES was found in 22 (14%) AFB +1, 19 (15%) AFB +2, and 29 (20%) AFB +3. Resistance to RHS was found in 22 (14%) AFB +1, 12 (9%) AFB +2, and 13 (9%) AFB +3. Resistance to RHE was found in 39 (25%) AFB +1, 38 (29%) AFB +2, and 35 (24%) AFB +3. Resistance to RH was found in 74 (47%) AFB +1, 61 (47%) AFB +2, and 69 (47%) AFB +3. Statistic analysis by Spearman test showed that there was no significant correlation between AFB gradation and first-line anti-TB drug resistance patterns. Acquired resistance to RHES can also found in lower bacillary load AFB +1.

Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells

The emergence of antibiotic resistance poses a critical threat to the efficacy of antibiotic treatments. A resistant bacterial population must originally arise from a single cell that mutates or acquires a resistance gene. This single cell may, by chance, fail to successfully reproduce before it dies, leading to loss of the nascent resistant lineage. Here, we show that antibiotic concentrations that selectively favor resistance are nonetheless sufficient to reduce the chance of outgrowth from a single cell to a very low probability. Our findings suggest that lower antibiotic concentrations than those required to clear a large resistant population may be sufficient to prevent, with high probability, outgrowth of initially rare resistant mutants.

A better understanding of how antibiotic exposure impacts the evolution of resistance in bacterial populations is crucial for designing more sustainable treatment strategies. The conventional approach to this question is to measure the range of concentrations over which resistant strain(s) are selectively favored over a sensitive strain. Here, we instead investigate how antibiotic concentration impacts the initial establishment of resistance from single cells, mimicking the clonal expansion of a resistant lineage following mutation or horizontal gene transfer. Using two Pseudomonas aeruginosa strains carrying resistance plasmids, we show that single resistant cells have <5% probability of detectable outgrowth at antibiotic concentrations as low as one-eighth of the resistant strain’s minimum inhibitory concentration (MIC). This low probability of establishment is due to detrimental effects of antibiotics on resistant cells, coupled with the inherently stochastic nature of cell division and death on the single-cell level, which leads to loss of many nascent resistant lineages. Our findings suggest that moderate doses of antibiotics, well below the MIC of resistant strains, may effectively restrict de novo emergence of resistance even though they cannot clear already-large resistant populations.

Effect of drug dose and timing of treatment on the emergence of drug resistance in vivo in a malaria model

BACKGROUND AND OBJECTIVES

There is a significant interest in identifying clinically effective drug treatment regimens that minimize the de novo evolution of antimicrobial resistance in pathogen populations. However, in vivo studies that vary treatment regimens and directly measure drug resistance evolution are rare. Here, we experimentally investigate the role of drug dose and treatment timing on resistance evolution in an animal model.

METHODOLOGY

In a series of experiments, we measured the emergence of atovaquone-resistant mutants of Plasmodium chabaudi in laboratory mice, as a function of dose or timing of treatment (day post-infection) with the antimalarial drug atovaquone.

RESULTS

The likelihood of high-level resistance emergence increased with atovaquone dose. When varying the timing of treatment, treating either very early or late in infection reduced the risk of resistance. When we varied starting inoculum, resistance was more likely at intermediate inoculum sizes, which correlated with the largest population sizes at time of treatment.

CONCLUSIONS AND IMPLICATIONS

(i) Higher doses do not always minimize resistance emergence and can promote the emergence of high-level resistance. (ii) Altering treatment timing affects the risk of resistance emergence, likely due to the size of the population at the time of treatment, although we did not test the effect of immunity whose influence may have been important in the case of late treatment. (iii) Finding the 'right' dose and 'right' time to maximize clinical gains and limit resistance emergence can vary depending on biological context and was non-trivial even in our simplified experiments.

LAY SUMMARY

In a mouse model of malaria, higher drug doses led to increases in drug resistance. The timing of drug treatment also impacted resistance emergence, likely due to the size of the population at the time of treatment.

Mycobacterium tuberculosis clinical isolates carry mutational signatures of host immune environments

Mycobacterium tuberculosis (Mtb) infection results in a spectrum of clinical and histopathologic manifestations. It has been proposed that the environmental and immune pressures associated with different contexts of infection have different consequences for the associated bacterial populations, affecting drug susceptibility and the emergence of resistance. However, there is little concrete evidence for this model. We prospectively collected sputum samples from 18 newly diagnosed and treatment-naïve patients with tuberculosis and sequenced 795 colony-derived Mtb isolates. Mutant accumulation rates varied considerably between different bacilli isolated from the same individual, and where high rates of mutation were observed, the mutational spectrum was consistent with reactive oxygen species-induced mutagenesis. Elevated bacterial mutation rates were identified in isolates from HIV-negative but not HIV-positive individuals, suggesting that they were immune-driven. These results support the model that mutagenesis of Mtb in vivo is modulated by the host environment, which could drive the emergence of variants associated with drug resistance in a host-dependent manner.

Focusing on DNA Repair and Damage Tolerance Mechanisms in Mycobacterium tuberculosis: An Emerging Therapeutic Theme

Tuberculosis (TB) is one such disease that has become a nuisance in the world scenario and one of the most deadly diseases of the current times. The etiological agent of tuberculosis, Mycobacterium tuberculosis (M. tb) kills millions of people each year. Not only 1.7 million people worldwide are estimated to harbor M. tb in the latent form but also 5 to 15 percent of which are expected to acquire an infection during a lifetime. Though curable, a long duration of drug regimen and expense leads to low patient adherence. The emergence of multi-, extensive- and total- drug-resistant strains of M. tb further complicates the situation. Owing to high TB burden, scientists worldwide are trying to design novel therapeutics to combat this disease. Therefore, to identify new drug targets, there is a growing interest in targeting DNA repair pathways to fight this infection. Thus, this review aims to explore DNA repair and damage tolerance as an efficient target for drug development by understanding M. tb DNA repair and tolerance machinery and its regulation, its role in pathogenesis and survival, mutagenesis, and consequently, in the development of drug resistance.

Multidrug-resistant tuberculosis outbreak associated with poor treatment adherence and delayed treatment: Arua District, Uganda, 2013-2017

BACKGROUND

In August 2017, the Uganda Ministry of Health was notified of increased cases of multidrug-resistant tuberculosis (MDR-TB) in Arua District, Uganda during 2017. We investigated to identify the scope of the increase and risk factors for infection, evaluate health facilities' capacity to manage MDR-TB, and recommend evidence-based control measures.

METHODS

We defined an MDR-TB case-patient as a TB patient attending Arua Regional Referral Hospital (ARRH) during 2013-2017 with a sputum sample yielding Mycobacterium tuberculosis resistant to at least rifampicin and isoniazid, confirmed by an approved drug susceptibility test. We reviewed clinical records from ARRH and compared the number of MDR-TB cases during January-August 2017 with the same months in 2013-2016. To identify risk factors specific for MDR-TB among cases with secondary infection, we conducted a case-control study using persons with drug-susceptible TB matched by sub-county of residence as controls. We observed infection prevention and control practices in health facilities and community, and assessed health facilities' capacity to manage TB.

RESULTS

We identified 33 patients with MDR-TB, of whom 30 were secondary TB infection cases. The number of cases during January-August 2017 was 10, compared with 3-4 cases in January-August from 2013 to 2016 (p = 0.02). Men were more affected than women (6.5 vs 1.6/100,000, p < 0.01), as were cases ≥18 years old compared to those < 18 years (8.7 vs 0.21/100,000, p < 0.01). In the case-control study, poor adherence to first-line anti-TB treatment (aOR = 9.2, 95% CI: 2.3-37) and initiating treatment > 15 months from symptom onset (aOR = 11, 95% CI: 1.5-87) were associated with MDR-TB. All ten facilities assessed reported stockouts of TB commodities. All 15 ambulatory MDR-TB patients we observed were not wearing masks given to them to minimize community infection. The MDR-TB ward at ARRH capacity was 4 patients but there were 11 patients.

CONCLUSION

The number of cases during January-August in 2017 was significantly higher than during the same months in 2013-2016. Poor adherence to TB drugs and delayed treatment initiation were associated with MDR-TB infection. We recommended strengthening directly-observed treatment strategy, increasing access to treatment services, and increasing the number of beds in the MDR-TB ward at ARRH.

Competing evolutionary paths in growing populations with applications to multidrug resistance

Investigating the emergence of a particular cell type is a recurring theme in models of growing cellular populations. The evolution of resistance to therapy is a classic example. Common questions are: when does the cell type first occur, and via which sequence of steps is it most likely to emerge? For growing populations, these questions can be formulated in a general framework of branching processes spreading through a graph from a root to a target vertex. Cells have a particular fitness value on each vertex and can transition along edges at specific rates. Vertices represent cell states, say genotypes or physical locations, while possible transitions are acquiring a mutation or cell migration. We focus on the setting where cells at the root vertex have the highest fitness and transition rates are small. Simple formulas are derived for the time to reach the target vertex and for the probability that it is reached along a given path in the graph. We demonstrate our results on several scenarios relevant to the emergence of drug resistance, including: the orderings of resistance-conferring mutations in bacteria and the impact of imperfect drug penetration in cancer.

How long does it take for a treatment naive, growing bacterial colony to be able to survive exposure to a cocktail of antibiotics? En route to multidrug resistance, what order did the drugs become impotent in? Questions such as these that pertain to the emergence of a significant cell type in a growing population arise frequently. They are often investigated via mathematical modelling but biologically insightful results are challenging to obtain. Here we outline a general framework of a stochastically growing population spreading through a graph to study such questions and provide simple formulas as answers. The significant cell type appears upon the population reaching a target vertex. Due to their simplicity, the derived formulas are widely accessible and can be used to guide and develop intuition on a range of biological scenarios. We demonstrate this on several settings including: how a region where drugs cannot penetrate affects the emergence of resistance, and, the ordering of mutations that leads to drugs being ineffective.

Effect of the duration of antimicrobial exposure on the development of antimicrobial resistance (AMR) for macrolide antibiotics: protocol for a systematic review with a network meta-analysis

BACKGROUND

Antimicrobial resistance generates a huge health and economic burden and has the potential to become the leading cause of death globally, but its underlying drivers are yet to be fully described. The association between a microbe's exposure to antimicrobials and subsequent development of, or selection for, resistance is well documented, as are the exacerbating microbial and human factors. However, the nature and extent of this risk, and how it varies by antimicrobial class and duration of treatment, is poorly defined. The goal of our systematic review and network meta-analysis is to determine the relationship between the duration of antimicrobial exposure and selection for resistance. We will use macrolides as the antimicrobial class of interest and Streptococcus pneumoniae carriage as an indicator organism. Our secondary outcomes include duration of symptoms, risk of treatment failure and recurrence, and descriptions of resistance mechanisms.

METHODS

We will conduct a systematic review, selecting studies if they are published randomised controlled trials (RCTs) which report the relationship between taking a macrolide for any indication and incidence of resistant Streptococcus pneumoniae in patients of any age group. We will use a predefined search strategy to identify studies meeting these eligibility criteria in MEDLINE, Embase, Global Health and the Cochrane Central Register of RCTs. Two authors will independently screen titles and abstracts, review the full texts and undertake data extraction. We will use the Cochrane Collaboration's tool to assess the quality of included RCTs. If feasible, we will perform pair-wise meta-analysis modelling to determine the relationship between the duration of macrolide treatment and development of macrolide resistant Streptococcus pneumoniae. If the identified studies meet the assumptions for a network meta-analysis (NMA), we will additionally model this relationship using indirect comparisons. Our protocol utilises reporting guidance by Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and the extensions for protocols (PRISMA-P) and network meta-analyses (PRISMA for NMA). Our review will also report to these standards.

DISCUSSION

Establishing the relationship between the duration of antimicrobial exposure and development of, or selection for, resistance will inform the design of antimicrobial prescriptions, treatment guidelines and the behaviour of both physicians and patients. This work will therefore be a strong contribution towards the full realisation of current antimicrobial resistance stewardship strategies.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42018089275.

Insights into the processes that drive the evolution of drug resistance in Mycobacterium tuberculosis

At present, the successful transmission of drug-resistant Mycobacterium tuberculosis, including multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, in human populations, threatens tuberculosis control worldwide. Differently from many other bacteria, M. tuberculosis drug resistance is acquired mainly through mutations in specific drug resistance-associated genes. The panel of mutations is highly diverse, but depends on the affected gene and M. tuberculosis genetic background. The variety of genetic profiles observed in drug-resistant clinical isolates underlines different evolutionary trajectories towards multiple drug resistance, although some mutation patterns are prominent. This review discusses the intrinsic processes that may influence drug resistance evolution in M. tuberculosis, such as mutation rate, drug resistance-associated mutations, fitness cost, compensatory mutations and epistasis. This knowledge should help to better predict the risk of emergence of highly resistant M. tuberculosis strains and to develop new tools and strategies to limit the development and spread of MDR and XDR strains.

Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas

Drug resistant tuberculosis is increasing world-wide. Resistance against isoniazid (INH), rifampicin (RIF), or both (multi-drug resistant TB, MDR-TB) is of particular concern, since INH and RIF form part of the standard regimen for TB disease. While it is known that suboptimal treatment can lead to resistance, it remains unclear how host immune responses and antibiotic dynamics within granulomas (sites of infection) affect emergence and selection of drug-resistant bacteria. We take a systems pharmacology approach to explore resistance dynamics within granulomas. We integrate spatio-temporal host immunity, INH and RIF dynamics, and bacterial dynamics (including fitness costs and compensatory mutations) in a computational framework. We simulate resistance emergence in the absence of treatment, as well as resistance selection during INH and/or RIF treatment. There are four main findings. First, in the absence of treatment, the percentage of granulomas containing resistant bacteria mirrors the non-monotonic bacterial dynamics within granulomas. Second, drug-resistant bacteria are less frequently found in non-replicating states in caseum, compared to drug-sensitive bacteria. Third, due to a steeper dose response curve and faster plasma clearance of INH compared to RIF, INH-resistant bacteria have a stronger influence on treatment outcomes than RIF-resistant bacteria. Finally, under combination therapy with INH and RIF, few MDR bacteria are able to significantly affect treatment outcomes. Overall, our approach allows drug-specific prediction of drug resistance emergence and selection in the complex granuloma context. Since our predictions are based on pre-clinical data, our approach can be implemented relatively early in the treatment development process, thereby enabling pro-active rather than reactive responses to emerging drug resistance for new drugs. Furthermore, this quantitative and drug-specific approach can help identify drug-specific properties that influence resistance and use this information to design treatment regimens that minimize resistance selection and expand the useful life-span of new antibiotics.

Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach

Granulomas are complex lung lesions that are the hallmark of tuberculosis (TB). Understanding antibiotic dynamics within lung granulomas will be vital to improving and shortening the long course of TB treatment. Three fluoroquinolones (FQs) are commonly prescribed as part of multi-drug resistant TB therapy: moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date, insufficient data are available to support selection of one FQ over another, or to show that these drugs are clinically equivalent. To predict the efficacy of MXF, LVX and GFX at a single granuloma level, we integrate computational modeling with experimental datasets into a single mechanistic framework, GranSim. GranSim is a hybrid agent-based computational model that simulates granuloma formation and function, FQ plasma and tissue pharmacokinetics and pharmacodynamics and is based on extensive in vitro and in vivo data. We treat in silico granulomas with recommended daily doses of each FQ and compare efficacy by multiple metrics: bacterial load, sterilization rates, early bactericidal activity and efficacy under non-compliance and treatment interruption. GranSim reproduces in vivo plasma pharmacokinetics, spatial and temporal tissue pharmacokinetics and in vitro pharmacodynamics of these FQs. We predict that MXF kills intracellular bacteria more quickly than LVX and GFX due in part to a higher cellular accumulation ratio. We also show that all three FQs struggle to sterilize non-replicating bacteria residing in caseum. This is due to modest drug concentrations inside caseum and high inhibitory concentrations for this bacterial subpopulation. MXF and LVX have higher granuloma sterilization rates compared to GFX; and MXF performs better in a simulated non-compliance or treatment interruption scenario. We conclude that MXF has a small but potentially clinically significant advantage over LVX, as well as LVX over GFX. We illustrate how a systems pharmacology approach combining experimental and computational methods can guide antibiotic selection for TB.

Tuberculosis (TB) is caused by infection with the bacterium Mycobacterium tuberculosis (Mtb) and kills 1.5 million people each year. TB requires at least 6 months of treatment with up to four drugs, and is characterized by formation of granulomas in patient lungs. Granulomas are spherical collections of host cells and bacteria. Fluoroquinolones (FQs) are a class of drug that could help shorten TB treatment. Three FQs that are used to treat TB are: moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date, it is unclear if one FQ is better than the others at treating TB, in part because little is known about how these drugs distribute and work inside the lung granulomas. We use computer simulations of Mtb infection and FQ treatment within granulomas to predict which FQ is better and why. Our computer model is calibrated to multiple experimental data sets. We compare the three FQs by multiple metrics, and predict that MXF is better than LVX and GFX because it kills bacteria more quickly, and it works better when patients miss doses. However, all three FQs are unable to kill a part of the bacterial population living in the center of granulomas. Our results can now inform future experimental studies.

A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment

Tuberculosis (TB) is an ancient and deadly disease characterized by complex host-pathogen dynamics playing out over multiple time and length scales and physiological compartments. Computational modeling can be used to integrate various types of experimental data and suggest new hypotheses, mechanisms, and therapeutic approaches to TB. Here, we offer a first-time comprehensive review of work on within-host TB models that describe the immune response of the host to infection, including the formation of lung granulomas. The models include systems of ordinary and partial differential equations and agent-based models as well as hybrid and multi-scale models that are combinations of these. Many aspects of M. tuberculosis infection, including host dynamics in the lung (typical site of infection for TB), granuloma formation, roles of cytokine and chemokine dynamics, and bacterial nutrient availability have been explored. Finally, we survey applications of these within-host models to TB therapy and prevention and suggest future directions to impact this global disease.

Estimating the future burden of multidrug-resistant and extensively drug-resistant tuberculosis in India, the Philippines, Russia, and South Africa: a mathematical modelling study

BACKGROUND

Multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis are emerging worldwide. The Green Light Committee initiative supported programmatic management of drug-resistant tuberculosis in 90 countries. We used estimates from the Preserving Effective TB Treatment Study to predict MDR and XDR tuberculosis trends in four countries with a high burden of MDR tuberculosis: India, the Philippines, Russia, and South Africa.

METHODS

We calibrated a compartmental model to data from drug resistance surveys and WHO tuberculosis reports to forecast estimates of incident MDR and XDR tuberculosis and the percentage of incident MDR and XDR tuberculosis caused by acquired drug resistance, assuming no fitness cost of resistance from 2000 to 2040 in India, the Philippines, Russia, and South Africa.

FINDINGS

The model forecasted the percentage of MDR tuberculosis among incident cases of tuberculosis to increase, reaching 12·4% (95% prediction interval 9·4-16·2) in India, 8·9% (4·5-11·7) in the Philippines, 32·5% (27·0-35·8) in Russia, and 5·7% (3·0-7·6) in South Africa in 2040. It also predicted the percentage of XDR tuberculosis among incident MDR tuberculosis to increase, reaching 8·9% (95% prediction interval 5·1-12·9) in India, 9·0% (4·0-14·7) in the Philippines, 9·0% (4·8-14·2) in Russia, and 8·5% (2·5-14·7) in South Africa in 2040. Acquired drug resistance would cause less than 30% of incident MDR tuberculosis during 2000-40. Acquired drug resistance caused 80% of incident XDR tuberculosis in 2000, but this estimate would decrease to less than 50% by 2040.

INTERPRETATION

MDR and XDR tuberculosis were forecast to increase in all four countries despite improvements in acquired drug resistance shown by the Green Light Committee-supported programmatic management of drug-resistant tuberculosis. Additional control efforts beyond improving acquired drug resistance rates are needed to stop the spread of MDR and XDR tuberculosis in countries with a high burden of MDR tuberculosis.

FUNDING

US Agency for International Development and US Centers for Disease Control and Prevention, Division of Tuberculosis Elimination.

Acquisition of Rifampin Resistance in Pulmonary Tuberculosis

Mycobacterium tuberculosis strains with spontaneous mutations conferring resistance to rifampin (RIF) are exceedingly rare, and fixed drug combinations typically prevent augmentation of resistance to single drugs. Fourteen newly diagnosed tuberculosis patients were treated with RIF alone for 14 days, and bacterial loads, including mutation frequencies, were determined. A statistical model estimated that 1% of the remaining viable mycobacteria could be RIF resistant after 30 days of monotherapy. This indicates that temporal and spatial windows of RIF monotherapy due to uneven drug distribution within lung lesions could contribute to the acquisition of resistance to RIF.

Multidrug resistant tuberculosis in Ethiopian settings and its association with previous history of anti-tuberculosis treatment: a systematic review and meta-analysis

Background

Efforts to control the global burden of tuberculosis (TB) have been jeopardized by the rapid evolution of multi-drug resistant Mycobacterium tuberculosis (MTB), which is resistant to at least isoniazid and rifampicin. Previous studies have documented variable prevalences of multidrug-resistant tuberculosis (MDR-TB) and its risk factors in Ethiopia. Therefore, this meta-analysis is aimed, firstly, to determine the pooled prevalence of MDR-TB among newly diagnosed and previously treated TB cases, and secondly, to measure the association between MDR-TB and a history of previous anti-TB drugs treatment.

Methods

PubMed, Embase and Google Scholar databases were searched. Studies that reported a prevalence of MDR-TB among new and previously treated TB patients were selected. Studies or surveys conducted at national or sub-national level, with reported MDR-TB prevalence or sufficient data to calculate prevalence were considered for the analysis. Two authors searched and reviewed the studies for eligibility and extracted the data in pre-defined forms. Forest plots of all prevalence estimates were performed and summary estimates were also calculated using random effects models. Associations between previous TB treatment and MDR-MTB infection were examined through subgroup analyses stratified by new and previously treated patients.

Results

We identified 16 suitable studies and found an overall prevalence of MDR-TB among newly diagnosed and previously treated TB patients to be 2% (95% CI 1% - 2%) and 15% (95% CI 12% - 17%), respectively. The observed difference was statistically significant (P < 0.001) and there was an odds ratio of 8.1 (95% CI 7.5–8.7) for previously treated TB patients to develop a MDR-MTB infection compared to newly diagnosed cases. For the past 10 years (2006 to 2014) the overall MDR-TB prevalence showed a stable time trend.

Conclusions

The burden of MDR-TB remains high in Ethiopian settings, especially in previously treated TB cases. Previous TB treatment was the most powerful predictor for MDR-MTB infection. Strict compliance with anti-TB regimens and improving case detection rate are the necessary steps to tackle the problem in Ethiopia.

The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis

Global tuberculosis incidence has declined marginally over the past decade, and tuberculosis remains out of control in several parts of the world including Africa and Asia. Although tuberculosis control has been effective in some regions of the world, these gains are threatened by the increasing burden of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis. XDR tuberculosis has evolved in several tuberculosis-endemic countries to drug-incurable or programmatically incurable tuberculosis (totally drug-resistant tuberculosis). This poses several challenges similar to those encountered in the pre-chemotherapy era, including the inability to cure tuberculosis, high mortality, and the need for alternative methods to prevent disease transmission. This phenomenon mirrors the worldwide increase in antimicrobial resistance and the emergence of other MDR pathogens, such as malaria, HIV, and Gram-negative bacteria. MDR and XDR tuberculosis are associated with high morbidity and substantial mortality, are a threat to health-care workers, prohibitively expensive to treat, and are therefore a serious public health problem. In this Commission, we examine several aspects of drug-resistant tuberculosis. The traditional view that acquired resistance to antituberculous drugs is driven by poor compliance and programmatic failure is now being questioned, and several lines of evidence suggest that alternative mechanisms-including pharmacokinetic variability, induction of efflux pumps that transport the drug out of cells, and suboptimal drug penetration into tuberculosis lesions-are likely crucial to the pathogenesis of drug-resistant tuberculosis. These factors have implications for the design of new interventions, drug delivery and dosing mechanisms, and public health policy. We discuss epidemiology and transmission dynamics, including new insights into the fundamental biology of transmission, and we review the utility of newer diagnostic tools, including molecular tests and next-generation whole-genome sequencing, and their potential for clinical effectiveness. Relevant research priorities are highlighted, including optimal medical and surgical management, the role of newer and repurposed drugs (including bedaquiline, delamanid, and linezolid), pharmacokinetic and pharmacodynamic considerations, preventive strategies (such as prophylaxis in MDR and XDR contacts), palliative and patient-orientated care aspects, and medicolegal and ethical issues.

Ribosomal mutations promote the evolution of antibiotic resistance in a multidrug environment

Antibiotic resistance arising via chromosomal mutations is typically specific to a particular antibiotic or class of antibiotics. We have identified mutations in genes encoding ribosomal components in Mycobacterium smegmatis that confer resistance to several structurally and mechanistically unrelated classes of antibiotics and enhance survival following heat shock and membrane stress. These mutations affect ribosome assembly and cause large-scale transcriptomic and proteomic changes, including the downregulation of the catalase KatG, an activating enzyme required for isoniazid sensitivity, and upregulation of WhiB7, a transcription factor involved in innate antibiotic resistance. Importantly, while these ribosomal mutations have a fitness cost in antibiotic-free medium, in a multidrug environment they promote the evolution of high-level, target-based resistance. Further, suppressor mutations can then be easily acquired to restore wild-type growth. Thus, ribosomal mutations can serve as stepping-stones in an evolutionary path leading to the emergence of high-level, multidrug resistance.

DOI:http://dx.doi.org/10.7554/eLife.20420.001

The rise of antibiotic resistant bacteria is challenging clinicians, and some infections are now resistant to almost all of the drugs that are currently available. Some types of bacteria – such as mycobacteria, which include the bacteria that cause tuberculosis and leprosy – can only acquire antibiotic resistance from mutations that alter their existing genes. The process by which bacteria develop resistance to multiple drugs is generally viewed as a stepwise accumulation of different mutations. However, the role of individual mutations that increase a bacterium’s resistance to multiple antibiotics has not been fully explored.

Gomez, Kaufmann-Malaga et al. exposed bacteria from the species Mycobacterium smegmatis, a cousin of the bacterium that causes tuberculosis, to a mixture of relatively low concentrations of different antibiotics that should kill the bacteria relatively slowly. Hundreds of small bacteria cultures were grown in parallel, and only a fraction of them developed antibiotic-resistant members. Gomez, Kaufmann-Malaga et al. identified mutations in these bacteria that unexpectedly gave the bacteria resistance to several unrelated classes of antibiotics.

Individual mutants carried single mutations in different components of the ribosome, a complex molecular machine that helps to build proteins inside cells. As well as increasing their resistance to antibiotics, these mutations also reduced the growth rate of the bacteria. This meant that when the bacteria were grown in an antibiotic-free environment they survived less well than non-mutant bacteria. However, the mutations gave the bacteria an advantage in environments that contained many different antibiotics, as they could more easily develop mutations that made them more resistant to other drugs. Thus, the mutant bacteria can serve as stepping-stones toward the development of high-level resistance to multiple drugs.

Further work will now explore whether this phenomenon occurs in a range of other bacterial species, including the bacteria that cause tuberculosis. While new antibiotics are desperately needed, a better understanding of how bacteria evolve the ability to resist the effects of antibiotics will help us to preserve the usefulness of existing and future drugs.

DOI:http://dx.doi.org/10.7554/eLife.20420.002

Population Heterogeneity in Mutation Rate Increases the Frequency of Higher-Order Mutants and Reduces Long-Term Mutational Load

Mutation rate is a crucial evolutionary parameter that has typically been treated as a constant in population genetic analyses. However, the propensity to mutate is likely to vary among co-existing individuals within a population, due to genetic polymorphisms, heterogeneous environmental influences, and random physiological fluctuations. We review the evidence for mutation rate heterogeneity and explore its consequences by extending classic population genetic models to allow an arbitrary distribution of mutation rate among individuals, either with or without inheritance. With this general new framework, we rigorously establish the effects of heterogeneity at various evolutionary timescales. In a single generation, variation of mutation rate about the mean increases the probability of producing zero or many simultaneous mutations on a genome. Over multiple generations of mutation and selection, heterogeneity accelerates the appearance of both deleterious and beneficial multi-point mutants. At mutation-selection balance, higher-order mutant frequencies are likewise boosted, while lower-order mutants exhibit subtler effects; nonetheless, population mean fitness is always enhanced. We quantify the dependencies on moments of the mutation rate distribution and selection coefficients, and clarify the role of mutation rate inheritance. While typical methods of estimating mutation rate will recover only the population mean, analyses assuming mutation rate is fixed to this mean could underestimate the potential for multi-locus adaptation, including medically relevant evolution in pathogenic and cancerous populations. We discuss the potential to empirically parameterize mutation rate distributions, which have to date hardly been quantified.

Diverse drug-resistant subpopulations of Mycobacterium tuberculosis are sustained in continuous culture

Drug resistance to tuberculosis (TB) has become more widespread over the past decade. As such, understanding the emergence and fitness of antibiotic-resistant subpopulations is crucial for the development of new interventions. Here we use a simple mathematical model to explain the differences in the response to isoniazid (INH) of Mycobacterium tuberculosis cells cultured under two growth rates in a chemostat. We obtain posterior distributions of model parameters consistent with data using a Markov chain Monte Carlo (MCMC) method. We explore the dynamics of diverse INH-resistant subpopulations consistent with these data in a multi-population model. We find that the simple model captures the qualitative behaviour of the cultures under both dilution rates and also present testable predictions about how diversity is maintained in such cultures.

Genomic diversity in autopsy samples reveals within-host dissemination of HIV-associated Mycobacterium tuberculosis

Mycobacterium tuberculosis remains a leading cause of death worldwide, especially among individuals infected with HIV. Whereas phylogenetic analysis has revealed M. tuberculosis spread throughout history and in local outbreaks, much less is understood about its dissemination within the body. Here we report genomic analysis of 2,693 samples collected post mortem from lung and extrapulmonary biopsies of 44 subjects in KwaZulu-Natal, South Africa, who received minimal antitubercular treatment and most of whom were HIV seropositive. We found that purifying selection occurred within individual patients, without the need for patient-to-patient transmission. Despite negative selection, mycobacteria diversified within individuals to form sublineages that co-existed for years. These sublineages, as well as distinct strains from mixed infections, were differentially distributed throughout the lung, suggesting temporary barriers to pathogen migration. As a consequence, samples taken from the upper airway often captured only a fraction of the population diversity, challenging current methods of outbreak tracing and resistance diagnostics. Phylogenetic analysis indicated that dissemination from the lungs to extrapulmonary sites was as frequent as between lung sites, supporting the idea of similar migration routes within and between organs, at least in subjects with HIV. Genomic diversity therefore provides a record of pathogen diversification and repeated dissemination across the body.

Negligible risk of inducing resistance in Mycobacterium tuberculosis with single-dose rifampicin as post-exposure prophylaxis for leprosy

Post-exposure prophylaxis (PEP) for leprosy is administered as one single dose of rifampicin (SDR) to the contacts of newly diagnosed leprosy patients. SDR reduces the risk of developing leprosy among contacts by around 60 % in the first 2–3 years after receiving SDR. In countries where SDR is currently being implemented under routine programme conditions in defined areas, questions were raised by health authorities and professional bodies about the possible risk of inducing rifampicin resistance among the M. tuberculosis strains circulating in these areas. This issue has not been addressed in scientific literature to date. To produce an authoritative consensus statement about the risk that SDR would induce rifampicin-resistant tuberculosis, a meeting was convened with tuberculosis (TB) and leprosy experts. The experts carefully reviewed and discussed the available evidence regarding the mechanisms and risk factors for the development of (multi) drug-resistance in M. tuberculosis with a view to the special situation of the use of SDR as PEP for leprosy. They concluded that SDR given to contacts of leprosy patients, in the absence of symptoms of active TB, poses a negligible risk of generating resistance in M. tuberculosis in individuals and at the population level. Thus, the benefits of SDR prophylaxis in reducing the risk of developing leprosy in contacts of new leprosy patients far outweigh the risks of generating drug resistance in M. tuberculosis.

Biosensor-based detection of tuberculosis

Tuberculosis (TB), caused byMycobacterium tuberculosis(M.tb.), is one of the most prevalent and serious infectious diseases worldwide with an estimated annual global mortality of 1.4 million in 2010.

Dynamics of Mycobacteriophage-Mycobacterial Host Interaction: Evidence for Secondary Mechanisms for Host Lethality

Mycobacteriophages infect mycobacteria, resulting in their death. Therefore, the possibility of using them as therapeutic agents against the deadly mycobacterial disease tuberculosis (TB) is of great interest. To obtain better insight into the dynamics of mycobacterial inactivation by mycobacteriophages, this study was initiated using mycobacteriophage D29 and Mycobacterium smegmatis as the phage-host system. Here, we implemented a goal-oriented iterative cycle of experiments on one hand and mathematical modeling combined with Monte Carlo simulations on the other. This integrative approach lends valuable insight into the detailed kinetics of bacterium-phage interactions. We measured time-dependent changes in host viability during the growth of phage D29 in M. smegmatis at different multiplicities of infection (MOI). The predictions emerging out of theoretical analyses were further examined using biochemical and cell biological assays. In a phage-host interaction system where multiple rounds of infection are allowed to take place, cell counts drop more rapidly than expected if cell lysis is considered the only mechanism for cell death. The phenomenon could be explained by considering a secondary factor for cell death in addition to lysis. Further investigations reveal that phage infection leads to the increased production of superoxide radicals, which appears to be the secondary factor. Therefore, mycobacteriophage D29 can function as an effective antimycobacterial agent, the killing potential of which may be amplified through secondary mechanisms.

Quantifying the impact of drug combination regimens on TB treatment efficacy and multidrug resistance probability

OBJECTIVES

TB patients' non-adherence to the multidrug treatment regimen is thought to be the main cause of the emergence of drug resistance. The purpose of this study was to quantify the impacts of two-drug combination regimens and non-adherence to these regimens on treatment efficacy and drug resistance probability.

METHODS

A drug treatment modelling strategy was developed by incorporating a pharmacokinetic/pharmacodynamic model into a bacterial population dynamic model to explore the dynamics of TB bacilli and evolution of resistance during multidrug combination therapy, with an emphasis on non-adherence. A Hill-equation-based pharmacodynamic model was used to assess the bactericidal efficacy of single drugs and to estimate drug interactions.

RESULTS

Non-adherence to the treatment regimen increased treatment duration by nearly 1.6- and 3.4-fold relative to compliance with treatment. Symptom-based intermittent treatment, a form of non-adherence, might lead to treatment failure and accelerated growth and evolution of resistant mutants, resulting in a dramatically higher probability of 4.17 × 10(-3) (95% CI 2.10 × 10(-4)-1.28 × 10(-2)) for the emergence of MDR TB. Overall, determination of the optimal treatment regimen depended on the different types of medication adherence.

CONCLUSIONS

Our model not only predicts evolutionary dynamics, but also quantifies treatment efficacy. More broadly, our model provides a quantitative framework for improving treatment protocols and establishing an emergence threshold of resistance that can be used to prevent drug resistance.

The need to accelerate access to new drugs for multidrug-resistant tuberculosis

Approximately half a million people are thought to develop multidrug-resistant tuberculosis annually. Barely 20% of these people currently receive recommended treatment and only about 10% are successfully treated. Poor access to treatment is probably driving the current epidemic, via ongoing transmission. Treatment scale-up is hampered by current treatment regimens, which are lengthy, expensive, poorly tolerated and difficult to administer in the settings where most patients reside. Although new drugs provide an opportunity to improve treatment regimens, current and planned clinical trials hold little promise for developing regimens that will facilitate prompt treatment scale-up. In this article we argue that clinical trials, while necessary, should be complemented by timely, large-scale, operational research that will provide programmatic data on the use of new drugs and regimens while simultaneously improving access to life-saving treatment. Perceived risks - such as the rapid development of resistance to new drugs - need to be balanced against the high levels of mortality and transmission that will otherwise persist. Doubling access to treatment and increasing treatment success could save approximately a million lives over the next decade.

Totally drug-resistant tuberculosis and adjunct therapies

The first cases of totally drug-resistant (TDR) tuberculosis (TB) were reported in Italy 10 years ago; more recently, cases have also been reported in Iran, India and South Africa. Although there is no consensus on terminology, it is most commonly described as 'resistance to all first- and second-line drugs used to treat TB'. Mycobacterium tuberculosis (M.tb) acquires drug resistance mutations in a sequential fashion under suboptimal drug pressure due to monotherapy, inadequate dosing, treatment interruptions and drug interactions. The treatment of TDR-TB includes antibiotics with disputed or minimal effectiveness against M.tb, and the fatality rate is high. Comorbidities such as diabetes and infection with human immunodeficiency virus further impact on TB treatment options and survival rates. Several new drug candidates with novel modes of action are under late-stage clinical evaluation (e.g., delamanid, bedaquiline, SQ109 and sutezolid). 'Repurposed' antibiotics have also recently been included in the treatment of extensively drug resistant TB. However, because of mutations in M.tb, drugs will not provide a cure for TB in the long term. Adjunct TB therapies, including therapeutic vaccines, vitamin supplementation and/or repurposing of drugs targeting biologically and clinically relevant molecular pathways, may achieve better clinical outcomes in combination with standard chemotherapy. Here, we review broader perspectives of drug resistance in TB and potential adjunct treatment options.

Origin and proliferation of multiple-drug resistance in bacterial pathogens

SUMMARY

Many studies report the high prevalence of multiply drug-resistant (MDR) strains. Because MDR infections are often significantly harder and more expensive to treat, they represent a growing public health threat. However, for different pathogens, different underlying mechanisms are traditionally used to explain these observations, and it is unclear whether each bacterial taxon has its own mechanism(s) for multidrug resistance or whether there are common mechanisms between distantly related pathogens. In this review, we provide a systematic overview of the causes of the excess of MDR infections and define testable predictions made by each hypothetical mechanism, including experimental, epidemiological, population genomic, and other tests of these hypotheses. Better understanding the cause(s) of the excess of MDR is the first step to rational design of more effective interventions to prevent the origin and/or proliferation of MDR.

Magnitude and sources of bias in the detection of mixed strain M. tuberculosis infection

High resolution tests for genetic variation reveal that individuals may simultaneously host more than one distinct strain of Mycobacterium tuberculosis. Previous studies find that this phenomenon, which we will refer to as “mixed infection”, may affect the outcomes of treatment for infected individuals and may influence the impact of population-level interventions against tuberculosis. In areas where the incidence of TB is high, mixed infections have been found in nearly 20% of patients; these studies may underestimate the actual prevalence of mixed infection given that tests may not be sufficiently sensitive for detecting minority strains. Specific reasons for failing to detect mixed infections would include low initial numbers of minority strain cells in sputum, stochastic growth in culture and the physical division of initial samples into parts (typically only one of which is genotyped). In this paper, we develop a mathematical framework that models the study designs aimed to detect mixed infections. Using both a deterministic and a stochastic approach, we obtain posterior estimates of the prevalence of mixed infection. We find that the posterior estimate of the prevalence of mixed infection may be substantially higher than the fraction of cases in which it is detected. We characterize this bias in terms of the sensitivity of the genotyping method and the relative growth rates and initial population sizes of the different strains collected in sputum.

Evolutionary rescue: linking theory for conservation and medicine

Evolutionary responses that rescue populations from extinction when drastic environmental changes occur can be friend or foe. The field of conservation biology is concerned with the survival of species in deteriorating global habitats. In medicine, in contrast, infected patients are treated with chemotherapeutic interventions, but drug resistance can compromise eradication of pathogens. These contrasting biological systems and goals have created two quite separate research communities, despite addressing the same central question of whether populations will decline to extinction or be rescued through evolution. We argue that closer integration of the two fields, especially of theoretical understanding, would yield new insights and accelerate progress on these applied problems. Here, we overview and link mathematical modelling approaches in these fields, suggest specific areas with potential for fruitful exchange, and discuss common ideas and issues for empirical testing and prediction.

Emergence of resistance to atovaquone-proguanil in malaria parasites: insights from computational modeling and clinical case reports

The usefulness of atovaquone-proguanil (AP) as an antimalarial treatment is compromised by the emergence of atovaquone resistance during therapy. However, the origin of the parasite mitochondrial DNA (mtDNA) mutation conferring atovaquone resistance remains elusive. Here, we report a patient-based stochastic model that tracks the intrahost emergence of mutations in the multicopy mtDNA during the first erythrocytic parasite cycles leading to the malaria febrile episode. The effect of mtDNA copy number, mutation rate, mutation cost, and total parasite load on the mutant parasite load per patient was evaluated. Computer simulations showed that almost any infected patient carried, after four to seven erythrocytic cycles, de novo mutant parasites at low frequency, with varied frequencies of parasites carrying varied numbers of mutant mtDNA copies. A large interpatient variability in the size of this mutant reservoir was found; this variability was due to the different parameters tested but also to the relaxed replication and partitioning of mtDNA copies during mitosis. We also report seven clinical cases in which AP-resistant infections were treated by AP. These provided evidence that parasiticidal drug concentrations against AP-resistant parasites were transiently obtained within days after treatment initiation. Altogether, these results suggest that each patient carries new mtDNA mutant parasites that emerge before treatment but are killed by high starting drug concentrations. However, because the size of this mutant reservoir is highly variable from patient to patient, we propose that some patients fail to eliminate all of the mutant parasites, repeatedly producing de novo AP treatment failures.

The impact of drug resistance on Mycobacterium tuberculosis physiology: what can we learn from rifampicin?

The emergence of drug-resistant pathogens poses a major threat to public health. Although influenced by multiple factors, high-level resistance is often associated with mutations in target-encoding or related genes. The fitness cost of these mutations is, in turn, a key determinant of the spread of drug-resistant strains. Rifampicin (RIF) is a frontline anti-tuberculosis agent that targets the rpoB-encoded β subunit of the DNA-dependent RNA polymerase (RNAP). In Mycobacterium tuberculosis (Mtb), RIF resistance (RIF(R)) maps to mutations in rpoB that are likely to impact RNAP function and, therefore, the ability of the organism to cause disease. However, while numerous studies have assessed the impact of RIF(R) on key Mtb fitness indicators in vitro, the consequences of rpoB mutations for pathogenesis remain poorly understood. Here, we examine evidence from diverse bacterial systems indicating very specific effects of rpoB polymorphisms on cellular physiology, and consider these observations in the context of Mtb. In addition, we discuss the implications of these findings for the propagation of clinically relevant RIF(R) mutations. While our focus is on RIF, we also highlight results which suggest that drug-independent effects might apply to a broad range of resistance-associated mutations, especially in an obligate pathogen increasingly linked with multidrug resistance.