Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas

Novel automated AIMLAM for diagnosis of Mycobacterium tuberculosis

Aim: A rapid and precise diagnostic method is crucial for timely intervention and management of tuberculosis. The present study compared the diagnostic accuracy of a novel lipoarabinomannan (LAM) antigen test, AIMLAM, for tuberculosis in urine samples. Methodology: The study subjected 106 TB suspects to smear microscopy, MGIT, GeneXpert and AIMLAM. Results: Among 106, smear microscopy identified 36 as positive (33%) (sensitivity; 70.93%, 95% CI (60.14-80.22%), while MGIT showed 38 positive (36.8%). GeneXpert detected 59 positives (sensitivity; 96.83, 95% CI (89.00-99.61%)). AIMLAM declared 61 as positive (57.5%) (sensitivity; 100.00, 95% CI (94.13-100.00%) and 45 as negative (42.5%). Conclusion: Overall, AIMLAM demonstrated better diagnostic accuracy than GeneXpert Assay, smear microscopy and MGIT liquid culture in urine samples.

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.

Correlation between serum isoniazid concentration and therapeutic response in patients of pulmonary tuberculosis in Central India: A prospective observational study

BACKGROUND

Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis is one of the top ten causes of death worldwide. Isoniazid (INH) is an important component of anti-tuberculosis therapy (ATT). Low isoniazid levels can serve as a risk factor for the development of treatment failure, relapse of disease and acquired secondary resistance. Hence, serum level of isoniazid becomes a critical factor in determining the treatment outcome of patients on ATT. This study aimed to evaluate the correlation between serum isoniazid concentration and therapeutic response in patients of pulmonary tuberculosis in Central India.

METHODS

This was a prospective single cohort observational study conducted at a tertiary care hospital. Therapeutic response in newly diagnosed patients of pulmonary TB was determined based the microbiological, clinical and radiological parameters. Serum INH levels were estimated based on a spectrophotometric method using nano-spectrophotometer.

RESULTS

In this study, patients had a significant improvement in treatment outcome as evident by a significant decrease in the TB score I at end of IP (p = 0.001) and a significant decline in the Timika score at end of CP (p = 0.001). Although all patients converted to sputum negative at end of CP, 20% remained positive at end of IP. Lower INH levels were seen in 13.3% of the study population. Higher INH levels were observed in sputum converters, patients with low TB score I and low Timika score, although no statistically significant difference was noted (p > 0.05).

CONCLUSION

In this study, we could not find any statistically significant association between serum INH levels and therapeutic outcome of the patients. Further studies on a larger population could provide better understanding about the prevalence of low serum isoniazid levels among the Indian population and establish its relationship with therapeutic outcome. Also, the usage of a comparatively less expensive spectrophotometric method of analysis makes this feasible in almost every district hospital without the need of high-performance liquid chromatography which is costlier and needs more expertise.

A systematic efficacy analysis of tuberculosis treatment with BPaL-containing regimens using a multiscale modeling approach

Tuberculosis (TB) is a life-threatening infectious disease. The standard treatment is up to 90% effective; however, it requires the administration of four antibiotics (isoniazid, rifampicin, pyrazinamide, and ethambutol [HRZE]) over long time periods. This harsh treatment process causes adherence issues for patients because of the long treatment times and a myriad of adverse effects. Therefore, the World Health Organization has focused goals of shortening standard treatment regimens for TB in their End TB Strategy efforts, which aim to reduce TB-related deaths by 95% by 2035. For this purpose, many novel and promising combination antibiotics are being explored that have recently been discovered, such as the bedaquiline, pretomanid, and linezolid (BPaL) regimen. As a result, testing the number of possible combinations with all possible novel regimens is beyond the limit of experimental resources. In this study, we present a unique framework that uses a primate granuloma modeling approach to screen many combination regimens that are currently under clinical and experimental exploration and assesses their efficacies to inform future studies. We tested well-studied regimens such as HRZE and BPaL to evaluate the validity and accuracy of our framework. We also simulated additional promising combination regimens that have not been sufficiently studied clinically or experimentally, and we provide a pipeline for regimen ranking based on their efficacies in granulomas. Furthermore, we showed a correlation between simulation rankings and new marmoset data rankings, providing evidence for the credibility of our framework. This framework can be adapted to any TB regimen and can rank any number of single or combination regimens.

Within-Host Mathematical Models of Antibiotic Resistance

Mathematical models have been used to study the spread of infectious diseases from person to person. More recently studies are developing within-host modeling which provides an understanding of how pathogens-bacteria, fungi, parasites, or viruses-develop, spread, and evolve inside a single individual and their interaction with the host's immune system.Such models have the potential to provide a more detailed and complete description of the pathogenesis of diseases within-host and identify other influencing factors that may not be detected otherwise. Mathematical models can be used to aid understanding of the global antibiotic resistance (ABR) crisis and identify new ways of combating this threat.ABR occurs when bacteria respond to random or selective pressures and adapt to new environments through the acquisition of new genetic traits. This is usually through the acquisition of a piece of DNA from other bacteria, a process called horizontal gene transfer (HGT), the modification of a piece of DNA within a bacterium, or through. Bacteria have evolved mechanisms that enable them to respond to environmental threats by mutation, and horizontal gene transfer (HGT): conjugation; transduction; and transformation. A frequent mechanism of HGT responsible for spreading antibiotic resistance on the global scale is conjugation, as it allows the direct transfer of mobile genetic elements (MGEs). Although there are several MGEs, the most important MGEs which promote the development and rapid spread of antimicrobial resistance genes in bacterial populations are plasmids and transposons. Each of the resistance-spread-mechanisms mentioned above can be modeled allowing us to understand the process better and to define strategies to reduce resistance.

Evaluating the Sensitivity of Different Molecular Techniques for Detecting Mycobacterium tuberculosis Complex in Patients with Pulmonary Infection

This study aimed to evaluate the accuracy of detecting drug-resistant Mycobacterium tuberculosis complex (MTBC)-specific DNA in sputum specimens from 48 patients diagnosed with pulmonary tuberculosis. The presence of MTBC DNA in the specimens was validated using the GeneXpert MTB/RIF system and compared with a specific PCR assay targeting the IS6110 and the mtp40 gene sequence fragments. Additionally, the results obtained by multiplex PCR assays to detect the most frequently encountered rifampin, isoniazid, and ethambutol resistance-conferring mutations were matched with those obtained by GeneXpert and phenotypic culture-based drug susceptibility tests. Of the 48 sputum samples, 25 were positive for MTBC using the GeneXpert MTB/RIF test. Nevertheless, the IS6110 and mtp40 single-step PCR revealed the IS6110 in 27 of the 48 sputum samples, while the mtp40 gene fragment was found in only 17 of them. Furthermore, multiplex PCR assays detected drug-resistant conferring mutations in 21 (77.8%) of the 27 samples with confirmed MTBC DNA, 10 of which contained single drug-resistant conferring mutations towards ethambutol and two towards rifampin, and the remaining nine contained double-resistant mutations for ethambutol and rifampin. In contrast, only five sputum specimens (18.5%) contained drug-resistant MTBC isolates, and two contained mono-drug-resistant MTBC species toward ethambutol and rifampin, respectively, and the remaining three were designated as multi-drug resistant toward both drugs using GeneXpert and phenotypic culture-based drug susceptibility tests. Such discrepancies in the results emphasize the need to develop novel molecular tests that associate with phenotypic non-DNA-based assays to improve the detection of drug-resistant isolates in clinical specimens in future studies.

A Credibility Assessment Plan for an In Silico Model that Predicts the Dose-Response Relationship of New Tuberculosis Treatments

Tuberculosis is one of the leading causes of death in several developing countries and a public health emergency of international concern. In Silico Trials can be used to support innovation in the context of drug development reducing the duration and the cost of the clinical experimentations, a particularly desirable goal for diseases such as tuberculosis. The agent-based Universal Immune System Simulator was used to develop an In Silico Trials environment that can predict the dose-response of new therapeutic vaccines against pulmonary tuberculosis, supporting the optimal design of clinical trials. But before such in silico methodology can be used in the evaluation of new treatments, it is mandatory to assess the credibility of this predictive model. This study presents a risk-informed credibility assessment plan inspired by the ASME V&V 40-2018 technical standard. Based on the selected context of use and regulatory impact of the technology, a detailed risk analysis is described together with the definition of all the verification and validation activities and related acceptability criteria. The work provides an example of the first steps required for the regulatory evaluation of an agent-based model used in the context of drug development.

Cumulative human health risk analysis of trihalomethanes exposure in drinking water systems

Chlorinated compounds on reaction with natural organic substances present in water leads to the formation of trihalomethanes (THMs), a major type of disinfection by-products (DBPs). Trihalomethanes (THMs) are the most widely investigated DBPs in drinking water systems because of their carcinogenic potential and subsequent adverse effects on human health. This study investigated the effect of gastro-intestinal absorption factor on human health risk assessment. Monitoring and analysis of water quality parameters and THMs levels in drinking water treatment plants revealed that the average values (306.5 μg/L) exceeded the recommended US EPA guidelines of 80 μg/L. Spearman rank (rho) correlation coefficient indicated that dissolved organic carbon is the major parameter influencing THMs formation. Monte Carlo simulations base risk assessment study was conducted for three different exposure pathways. The observed human health risk exposure effects due to THMs were below the recommended USEPA level (1.0 × 10^-6) for both the drinking water treatment plants. Seasonal disparity on risk estimation analysis revealed higher risk in summer season followed by autumn which is principally due to high concentration of THMs in summers.

DEGBOE: Discrete time Evolution modeling of Gene mutation through Bayesian inference using qualitative Observation of mutation Events

An important aspect of cancer progression concerns the way in which gene mutations accumulate in cellular lineages. Comprehensive efforts into cataloging cancer genes have revealed that tumors demonstrate variability in genes that accumulate mutations which depend on the presence or absence of other mutations. However, understanding the stochastic process by which mutations arise across the genome is an important open problem of this nature in biology due to modeling discrete variate time-series is the most challenging, and, as yet, least well-developed of all areas of research in time-series. In this paper, a DEGBOE framework is proposed to model the mutation time-series given the sequence data of the gene mutations. The method relates the discrete-time, nonlinear and nonstationary series of gene mutations to the time-varying autoregressive moving average model. It presents the observation as a nonlinear function dependent on two variables: gene mutation, and gene-gene interactions characterizing the effects of the varying presence or absence of other gene mutations on a mutations' occurrence and evolution. DEGBOE is applied to model the dynamics of frequently mutated genes in lung cancer, includingEGFR,KRAS, and TP53. The results of the model are analyzed and compared to the original simulated data of theDNAwalk, and experimental lung cancer mutations data. It identifies the driver role of TP53 mutations in lung cancer progression.

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs

Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

A Current Microbiological Picture of Mycobacterium Isolates from Istanbul, Turkey

Despite advances in diagnosis and treatment, tuberculosis (TB) continues to be one of the essential health problems throughout the world. Turkey is considered to be endemic for TB. In this study, we analyzed the distribution of Mycobacterium species, compare the diagnostic methods, and susceptibilities to anti-tuberculosis drugs of TB isolates. The aim was to document the current status and to provide a frame of reference for future studies. In this study, 278 Mycobacterium species isolated from 7,480 patients between September 2015 and June 2019 were included. Löwenstein-Jensen medium (LJ) and MGIT 960 were used for the isolation of strains. Susceptibility to 1^st-line anti-tuberculosis drugs was determined. Positivity rates in clinical samples were as follows: 1.4% for direct microscopic acid-fast bacilli (AFB) detection, 3.4% for growth on the LJ, and 3.7% for growth on MGIT-960. Two hundred thirty-three isolates were identified as Mycobacterium tuberculosis complex (MTBC) and 45 were non-tuberculous mycobacteria (NTMs). Eleven of the NTMs (24.4%) were Mycobacterium fortuitum group isolates, and eight NTMs (17.7%) were Mycobacterium abscessus complex isolates. A number of patients diagnosed with tuberculosis peaked twice between the ages of 20–31 and 60–71. A hundred and eighty-two MTBC isolates (78.1%) were susceptible to all 1^st-line anti-tuberculosis drugs, while 51 isolates (21.9%) were resistant to at least one drug tested. The multidrug-resistant tuberculosis rate was 13.7% among resistant strains and 3% in all strains. The liquid cultures were better for detection of both MTBC and NTMs isolates. The data demonstrate that MTBC continues to be challenge for this country and indicates the need for continued surveillance and full-spectrum services of mycobacteriology laboratory and infectious diseases.

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.

A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination

Mycobacterium tuberculosis (Mtb), the causative infectious agent of tuberculosis (TB), kills more individuals per year than any other infectious agent. Granulomas, the hallmark of Mtb infection, are complex structures that form in lungs, composed of immune cells surrounding bacteria, infected cells, and a caseous necrotic core. While granulomas serve to physically contain and immunologically restrain bacteria growth, some granulomas are unable to control Mtb growth, leading to bacteria and infected cells leaving the granuloma and disseminating, either resulting in additional granuloma formation (local or non-local) or spread to airways or lymph nodes. Dissemination is associated with development of active TB. It is challenging to experimentally address specific mechanisms driving dissemination from TB lung granulomas. Herein, we develop a novel hybrid multi-scale computational model, MultiGran, that tracks Mtb infection within multiple granulomas in an entire lung. MultiGran follows cells, cytokines, and bacterial populations within each lung granuloma throughout the course of infection and is calibrated to multiple non-human primate (NHP) cellular, granuloma, and whole-lung datasets. We show that MultiGran can recapitulate patterns of in vivo local and non-local dissemination, predict likelihood of dissemination, and predict a crucial role for multifunctional CD8+ T cells and macrophage dynamics for preventing dissemination.

Tuberculosis (TB) is caused by infection with Mycobacterium tuberculosis (Mtb) and kills 3 people per minute worldwide. Granulomas, spherical structures composed of immune cells surrounding bacteria, are the hallmark of Mtb infection and sometimes fail to contain the bacteria and disseminate, leading to further granuloma growth within the lung environment. To date, the mechanisms that determine granuloma dissemination events have not been characterized. We present a computational multi-scale model of granuloma formation and dissemination within primate lungs. Our computational model is calibrated to multiple experimental datasets across the cellular, granuloma, and whole-lung scales of non-human primates. We match to both individual granuloma and granuloma-population datasets, predict likelihood of dissemination events, and predict a critical role for multifunctional CD8+ T cells and macrophage-bacteria interactions to prevent infection dissemination.

Both Pharmacokinetic Variability and Granuloma Heterogeneity Impact the Ability of the First-Line Antibiotics to Sterilize Tuberculosis Granulomas

Tuberculosis (TB) remains as one of the world's deadliest infectious diseases despite the use of standardized antibiotic therapies. Recommended therapy for drug-susceptible TB is up to 6 months of antibiotics. Factors that contribute to lengthy regimens include antibiotic underexposure in lesions due to poor pharmacokinetics (PK) and complex granuloma compositions, but it is difficult to quantify how individual antibiotics are affected by these factors and to what extent these impact treatments. We use our next-generation multi-scale computational model to simulate granuloma formation and function together with antibiotic pharmacokinetics and pharmacodynamics, allowing us to predict conditions leading to granuloma sterilization. In this work, we focus on how PK variability, determined from human PK data, and granuloma heterogeneity each quantitatively impact granuloma sterilization. We focus on treatment with the standard regimen for TB of four first-line antibiotics: isoniazid, rifampin, ethambutol, and pyrazinamide. We find that low levels of antibiotic concentration due to naturally occurring PK variability and complex granulomas leads to longer granuloma sterilization times. Additionally, the ability of antibiotics to distribute in granulomas and kill different subpopulations of bacteria contributes to their specialization in the more efficacious combination therapy. These results can inform strategies to improve antibiotic therapy for TB.

Global sensitivity analysis of biological multi-scale models

Mathematical models of biological systems need to both reflect and manage the inherent complexities of biological phenomena. Through their versatility and ability to capture behavior at multiple scales, multi-scale models offer a valuable approach. Due to the typically nonlinear and stochastic nature of multi-scale models as well as unknown parameter values, various types of uncertainty are present; thus, effective assessment and quantification of such uncertainty through sensitivity analysis is important. In this review, we discuss global sensitivity analysis in the context of multi-scale and multi-compartment models and highlight its value in model development and analysis. We present an overview of sensitivity analysis methods, approaches for extending such methods to a multi-scale setting, and examples of how sensitivity analysis can inform model reduction. Through schematics and references to past work, we aim to emphasize the advantages and usefulness of such techniques.

Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems

Rare events such as genetic mutations or cell-cell interactions are important contributors to dynamics in complex biological systems, eg, in drug-resistant infections. Computational approaches can help analyze rare events that are difficult to study experimentally. However, analyzing the frequency and dynamics of rare events in computational models can also be challenging due to high computational resource demands, especially for high-fidelity stochastic computational models. To facilitate analysis of rare events in complex biological systems, we present a multifidelity analysis approach that uses medium-fidelity analysis (Monte Carlo simulations) and/or low-fidelity analysis (Markov chain models) to analyze high-fidelity stochastic model results. Medium-fidelity analysis can produce large numbers of possible rare event trajectories for a single high-fidelity model simulation. This allows prediction of both rare event dynamics and probability distributions at much lower frequencies than high-fidelity models. Low-fidelity analysis can calculate probability distributions for rare events over time for any frequency by updating the probabilities of the rare event state space after each discrete event of the high-fidelity model. To validate the approach, we apply multifidelity analysis to a high-fidelity model of tuberculosis disease. We validate the method against high-fidelity model results and illustrate the application of multifidelity analysis in predicting rare event trajectories, performing sensitivity analyses and extrapolating predictions to very low frequencies in complex systems. We believe that our approach will complement ongoing efforts to enable accurate prediction of rare event dynamics in high-fidelity computational models.