Analysis of combination drug therapy to develop regimens with shortened duration of treatment for tuberculosis

Efficacies of three drug regimens containing omadacycline to treat Mycobacteroides abscessus disease

Mycobacteroides abscessus (Mab, also known as Mycobacterium abscessus) causes opportunistic pulmonary and soft tissue infections that are difficult to cure with existing treatments. Omadacycline, a new tetracycline antibiotic, exhibits potent in vitro and in vivo activity against Mab. As regimens containing multiple antibiotics are required to produce a durable cure for Mab disease, we assessed efficacies of three three-drug combinations in a pre-clinical mouse model of pulmonary Mab disease to identify companion drugs with which omadacycline exhibits the highest efficacy. Additionally, we assessed the susceptibility of Mab recovered from mouse lungs after four weeks of exposure to the three triple-drug regimens. Among the three-drug regimens, omadacycline + imipenem + amikacin produced the largest reduction in Mab burden, whereas omadacycline + imipenem + linezolid exhibited the most effective early bactericidal activity. Omadacycline + linezolid + clofazimine, a regimen that can be administered orally, lacked early bactericidal activity but produced a gradual reduction in the lung Mab burden over time. The robust efficacy exhibited by these three regimens in the mouse model supports their further evaluation in patients with Mab lung disease. As we were unable to isolate drug-resistant Mab mutants at the completion of four weeks of treatment, these triple-drug combinations show promise of producing durable cure and minimizing selection of resistant mutants.

Two-way pharmacodynamic modeling of drug combinations and its application to pairs of repurposed Ebola and SARS-CoV-2 agents

Existing pharmacodynamic (PD) mathematical models for drug combinations discriminate antagonistic, additive, multiplicative, and synergistic effects, but fail to consider how concentration-dependent drug interaction effects may vary across an entire dose-response matrix. We developed a two-way pharmacodynamic (TWPD) model to capture the PD of two-drug combinations. TWPD captures interactions between upstream and downstream drugs that act on different stages of viral replication, by quantifying upstream drug efficacy and concentration-dependent effects on downstream drug pharmacodynamic parameters. We applied TWPD to previously published in vitro drug matrixes for repurposed potential anti-Ebola and anti-SARS-CoV-2 drug pairs. Depending on the drug pairing, the model recapitulated combined efficacies as or more accurately than existing models and can be used to infer efficacy at untested drug concentrations. TWPD fits the data slightly better in one direction for all drug pairs, meaning that we can tentatively infer the upstream drug. Based on its high accuracy, TWPD could be used in concert with PK models to estimate the therapeutic effects of drug pairs in vivo.

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.

In Vitro antibiotic combinations of Colistin, Meropenem, Amikacin, and Amoxicillin/clavulanate against multidrug-resistant Klebsiella pneumonia isolated from patients with ventilator-associated pneumonia

BACKGROUND

Hospital infections such as ventilator-associated pneumonia (VAP) due to multidrug-resistant Klebsiella pneumoniae (MDR-KP) strains have increased worldwide. In addition, biofilm production by these resistant isolates has confronted clinicians with higher treatment failure and infection recurrence. Given the paucity of new agents and limited data on combination therapy for MDR-KPs, the present study sought to evaluate the in vitro activity of several antibiotic combinations against planktonic and biofilm MDR-KPs isolated from patients with VAP.

RESULTS

All 10 carbapenem-resistant Klebsiella pneumoniae (CRKP) isolates demonstrated multidrug resistance against the tested antibiotics. At planktonic mode, combinations of colistin-meropenem and amoxicillin/clavulanate in combination with meropenem, colistin, or amikacin showed synergism against 60-70% isolates. On the other hand, in the biofilm state, colistin-based combinations exhibited synergism against 50-70% isolates and the most effective combination was colistin-amikacin with 70% synergy.

CONCLUSIONS

The results revealed that combinations of amoxicillin/clavulanate with colistin, meropenem, or amikacin in the planktonic mode and colistin with amoxicillin/clavulanate, meropenem, or amikacin in the biofilm mode could effectively inhibit CRKP isolates, and thus could be further explored for the treatment of CRKPs.

Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens

Tuberculosis (TB) continues to be one of the deadliest infectious diseases in the world, causing ~1.5 million deaths every year. The World Health Organization initiated an End TB Strategy that aims to reduce TB-related deaths in 2035 by 95%. Recent research goals have focused on discovering more effective and more patient-friendly antibiotic drug regimens to increase patient compliance and decrease emergence of resistant TB. Moxifloxacin is one promising antibiotic that may improve the current standard regimen by shortening treatment time. Clinical trials and in vivo mouse studies suggest that regimens containing moxifloxacin have better bactericidal activity. However, testing every possible combination regimen with moxifloxacin either in vivo or clinically is not feasible due to experimental and clinical limitations. To identify better regimens more systematically, we simulated pharmacokinetics/pharmacodynamics of various regimens (with and without moxifloxacin) to evaluate efficacies, and then compared our predictions to both clinical trials and nonhuman primate studies performed herein. We used GranSim, our well-established hybrid agent-based model that simulates granuloma formation and antibiotic treatment, for this task. In addition, we established a multiple-objective optimization pipeline using GranSim to discover optimized regimens based on treatment objectives of interest, i.e., minimizing total drug dosage and lowering time needed to sterilize granulomas. Our approach can efficiently test many regimens and successfully identify optimal regimens to inform pre-clinical studies or clinical trials and ultimately accelerate the TB regimen discovery process.

Coping with the ESKAPE pathogens: Evolving strategies, challenges and future prospects

Globally, the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are the major cause of nosocomial infections. These pathogens are multidrug resistant, and their negative impacts have brought serious health challenges and economic burden on many countries worldwide. Thus, this narrative review exploits different emerging alternative therapeutic strategies including combination antibiotics, antimicrobial peptides ((AMPs), bacteriophage and photodynamic therapies used in the treatment of the ESKAPE pathogens, their merits, limitations, and future prospects. Our findings indicate that ESKAPE pathogens exhibit resistance to drug using different mechanisms including drug inactivation by irreversible enzyme cleavage, drug-binding site alteration, diminution in permeability of drug or drug efflux increment to reduce accumulation of drug as well as biofilms production. However, the scientific community has shown significant interest in using these novel strategies with numerous benefits although they have some limitations including but not limited to instability and toxicity of the therapeutic agents, or the host developing immune response against the therapeutic agents. Thus, comprehension of resistance mechanisms of these pathogens is necessary to further develop or modify these approaches in order to overcome these health challenges including the barriers of bacterial resistance.

Combination Therapy to Kill Mycobacterium tuberculosis in Its Nonreplicating Persister Phenotype

Mycobacterium tuberculosis (Mtb) exists in various metabolic states, including a nonreplicating persister (NRP) phenotype which may affect response to therapy. We have adopted a model-informed strategy to accelerate discovery of effective Mtb treatment regimens and previously found pretomanid (PMD), moxifloxacin (MXF), and bedaquiline (BDQ) to readily kill logarithmic- and acid-phase Mtb. Here, we studied multiple concentrations of each drug in flask-based, time-kill studies against NRP Mtb in single-, two- and three-drug combinations, including the active M2 metabolite of BDQ. We used nonparametric population algorithms in the Pmetrics package for R to model the data and to simulate the 95% confidence interval of bacterial population decline due to the two-drug combination regimen of PMD + MXF and compared this to observed declines with three-drug regimens. PMD + MXF at concentrations equivalent to average or peak human concentrations effectively eradicated Mtb. Unlike other states for Mtb, we observed no sustained emergence of less susceptible isolates for any regimen. The addition of BDQ as a third drug significantly (P < 0.05) shortened time to total bacterial suppression by 3 days compared to the two-drug regimen, similar to our findings for Mtb in logarithmic or acid growth phases.

Pre-Clinical Tools for Predicting Drug Efficacy in Treatment of Tuberculosis

Combination therapy has, to some extent, been successful in limiting the emergence of drug-resistant tuberculosis. Drug combinations achieve this advantage by simultaneously acting on different targets and metabolic pathways. Additionally, drug combination therapies are shown to shorten the duration of therapy for tuberculosis. As new drugs are being developed, to overcome the challenge of finding new and effective drug combinations, systems biology commonly uses approaches that analyse mycobacterial cellular processes. These approaches identify the regulatory networks, metabolic pathways, and signaling programs associated with M. tuberculosis infection and survival. Different preclinical models that assess anti-tuberculosis drug activity are available, but the combination of models that is most predictive of clinical treatment efficacy remains unclear. In this structured literature review, we appraise the options to accelerate the TB drug development pipeline through the evaluation of preclinical testing assays of drug combinations.

Evaluating the effect of clofazimine against Mycobacterium tuberculosis when given alone or in combination with pretomanid, bedaquiline or linezolid

Clofazimine (CFZ) has been regaining prominence for treating tuberculosis in recent years. However, as a single drug, it shows limited efficacy and optimal combination partners have not been identified. Therefore, the objective of our analysis was to evaluate the efficacy of CFZ-containing two-drug regimen with pretomanid (PMD), bedaquiline (BDQ) or linezolid (LZD) by determining: i) their pharmacodynamic (PD) mode of interaction against Mycobacterium tuberculosis (Mtb) strain H37Rv in log- and acid-metabolic states, and Mtb strain 18b in a non-replicating persister metabolic state, ii) to predict bacterial cell kill of the drugs alone and in combination, and iii) to evaluate the relationship between the interaction mode and bacterial cell kill amount. The results of our Greco universal response surface analysis showed that CFZ was at least additive with a clear trend towards synergy when combined with PMD, BDQ, and LZD against Mtb in all explored metabolic states under in vitro checkerboard assay conditions. They further showed that all 2-drug combination regimens exerted more bacterial kill than any of the drugs alone. CFZ alone showed the least antimicrobial efficacy amongst the evaluated drugs and there was a lack of correlation between the mode of interaction and the amount of bacterial kill. However, we may underestimate the effect of CFZ in this screening approach due to limited in vitro study duration and neglect of target site accumulation.

Building optimal 3-drug combination chemotherapy regimens to eradicate Mycobacterium tuberculosis in its slow growth acid phase

Mycobacterium tuberculosis (Mtb) metabolic state affects the response to therapy. Quantifying the effect of antimicrobials in the acid- and nonreplicating-metabolic phases of Mtb growth will help to optimize therapy for tuberculosis. As a brute-force approach to all possible drug combinations against Mtb in all different metabolic states is impossible, we have adopted a model-informed strategy to accelerate the discovery. Using multiple concentrations of each drug in time kill studies, we examined single-, two- and three-drug combinations of pretomanid, moxifloxacin, and bedaquiline plus its active metabolite against Mtb in its acid-phase metabolic state. We used a nonparametric modeling approach to generate full distributions of interaction terms between pretomanid and moxifloxacin for susceptible and less-susceptible populations. From the model, we could predict the 95% confidence interval of the simulated total bacterial population decline due to the 2-drug combination regimen of pretomanid and moxifloxacin and compare this to observed declines with 3 drug regimens. We found that the combination of pretomanid and moxifloxacin at concentrations equivalent to average or peak human concentrations effectively eradicated Mtb in its acid growth phase and prevented emergence of less susceptible isolates. The addition of bedaquiline as a third drug shortened time to total and less susceptible bacterial suppression by 8 days compared to the 2-drug regimen, which was significantly faster than the 2-drug kill.

Clinical efficacy and safety of ultra-short-course chemotherapy in treatment of spinal tuberculosis after complete debridement: an observational study

Objective

To evaluate the clinical efficacy and safety of ultra-short-course chemotherapy (<4 months) in treating spinal tuberculosis following complete debridement.

Methods

Clinical data of patients diagnosed with spinal tuberculosis, who underwent surgery with postoperative chemotherapy for < 4 months at the General Hospital of Ningxia Medical University between January 2005 and March 2015, were retrospectively analysed. Clinical manifestations, American Spinal Injury Association grades, states of bone fusion and lesion healing, deformity correction, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) levels and adverse drug reactions, observed before and after surgery and at the final follow-up, were assessed.

Results

Sixty patients were included, comprising 26 male and 34 female patients aged 16–78 years (mean, 40.85 years). Patients received postoperative chemotherapy for 3–4 months (mean, 3.61 months) and were followed for 25–129 months (mean, 70.61 months). Spinal tuberculosis recurred after surgery in one patient, who was cured by subsequent surgery. At the final follow-up, no symptoms of tuberculosis, local pain, abscess or sinus were observed. Daily life and working abilities were almost recovered in all patients. ESR and CRP levels were restored to normal, bone grafts fused, lesions healed and neurological functions were recovered. Postoperative chemotherapy-induced complications occurred in 10 patients (16.67%).

Conclusions

Complete debridement plus ultra-short-course chemotherapy for 3–4 months may be safe and efficacious in treating spinal tuberculosis, and requires further investigation.

Combating Multidrug-Resistant Bacteria by Integrating a Novel Target Site Penetration and Receptor Binding Assay Platform Into Translational Modeling

Multidrug-resistant bacteria are causing a serious global health crisis. A dramatic decline in antibiotic discovery and development investment by pharmaceutical industry over the last decades has slowed the adoption of new technologies. It is imperative that we create new mechanistic insights based on latest technologies, and use translational strategies to optimize patient therapy. Although drug development has relied on minimal inhibitory concentration testing and established in vitro and mouse infection models, the limited understanding of outer membrane permeability in Gram-negative bacteria presents major challenges. Our team has developed a platform using the latest technologies to characterize target site penetration and receptor binding in intact bacteria that inform translational modeling and guide new discovery. Enhanced assays can quantify the outer membrane permeability of β-lactam antibiotics and β-lactamase inhibitors using multiplex liquid chromatography tandem mass spectrometry. While β-lactam antibiotics are known to bind to multiple different penicillin-binding proteins (PBPs), their binding profiles are almost always studied in lysed bacteria. Novel assays for PBP binding in the periplasm of intact bacteria were developed and proteins identified via proteomics. To characterize bacterial morphology changes in response to PBP binding, high-throughput flow cytometry and time-lapse confocal microscopy with fluorescent probes provide unprecedented mechanistic insights. Moreover, novel assays to quantify cytosolic receptor binding and intracellular drug concentrations inform target site occupancy. These mechanistic data are integrated by quantitative and systems pharmacology modeling to maximize bacterial killing and minimize resistance in in vitro and mouse infection models. This translational approach holds promise to identify antibiotic combination dosing strategies for patients with serious infections.

Higher Dosing of Rifamycins Does Not Increase Activity against Mycobacterium tuberculosis in the Hollow-Fiber Infection Model

Improvements in the translational value of preclinical models can allow more-successful and more-focused research on shortening the duration of tuberculosis treatment. Although the hollow-fiber infection model (HFIM) is considered a valuable addition to the drug development pipeline, its exact role has not been fully determined yet. Since the strategy of increasing the dose of rifamycins is being evaluated for its treatment-shortening potential, additional in vitro modeling is important. Therefore, we assessed increased dosing of rifampin and rifapentine in our HFIM in order to gain more insight into the place of the HFIM in the drug development pipeline. Total and free-fraction concentrations corresponding to daily dosing of 2.7, 10, and 50 mg of rifampin/kg of body weight, as well as 600 mg and 1,500 mg rifapentine, were assessed in our HFIM using the Mycobacterium tuberculosis H37Rv strain. Drug activity and the emergence of drug resistance were assessed by CFU counting and subsequent mathematical modeling over 14 days, and pharmacokinetic exposures were checked. We found that increasing rifampin exposure above what is expected with the standard dose did not result in higher antimycobacterial activity. For rifapentine, only the highest concentration showed increased activity, but the clinical relevance of this observation is questionable. Moreover, for both drugs, the emergence of resistance was unrelated to exposure. In conclusion, in the simplest experimental setup, the results of the HFIM did not fully correspond to preexisting clinical data. The inclusion of additional parameters and readouts in this preclinical model could be of interest for proper assessment of the translational value of the HFIM.

The Funnel: a Screening Technique for Identifying Optimal Two-Drug Combination Chemotherapy Regimens

The Mycobacterium tuberculosis drug discovery effort has generated a substantial number of new/repurposed drugs for therapy for this pathogen. The arrival of these drugs is welcome, but another layer of difficulty has emerged. Single agent therapy is insufficient for patients with late-stage tuberculosis because of resistance emergence. To achieve our therapeutic ends, it is requisite to identify optimal combination regimens. These regimens go through a lengthy and expensive evaluative process. If we have a modest group of 6 to 8 new or repurposed agents, this translates into 15 to 28 possible 2-drug combinations. There is neither time nor resources to give an extensive evaluation for all combinations. We sought a screening procedure that would identify combinations that had a high likelihood of achieving good bacterial burden decline. We examined pretomanid, moxifloxacin, linezolid, and bedaquiline in log-phase growth, acid-phase growth, and nonreplicative persister (NRP) phase in the Greco interaction model. We employed the interaction term α and the calculated bacterial burden decline as metrics to rank different regimens in different metabolic states. No relationship was found between α and bacterial kill. We chose bacterial kill as the prime metric. The combination of pretomanid plus moxifloxacin emerged as the clear frontrunner, as the largest bacterial declines were seen in log phase and acid phase with this regimen and it was second best in NRP phase. Bedaquiline also produced good kill. This screening process may identify optimal combinations that can be further evaluated in both the hollow-fiber infection model and in animal models of Mycobacterium tuberculosis infection.

An Algorithm for Nonparametric Estimation of a Multivariate Mixing Distribution with Applications to Population Pharmacokinetics

Population pharmacokinetic (PK) modeling has become a cornerstone of drug development and optimal patient dosing. This approach offers great benefits for datasets with sparse sampling, such as in pediatric patients, and can describe between-patient variability. While most current algorithms assume normal or log-normal distributions for PK parameters, we present a mathematically consistent nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions without any assumption about the shape of the distribution. This approach can handle distributions with any shape for all PK parameters. It is shown in convexity theory that the NPML estimator is discrete, meaning that it has finite number of points with nonzero probability. In fact, there are at most N points where N is the number of observed subjects. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. In the simplest case, each point essentially represents the set of PK parameters for one patient. The probability of the points is found by a primal-dual interior-point method; the location of the support points is found by an adaptive grid method. Our method is able to handle high-dimensional and complex multivariate mixture models. An important application is discussed for the problem of population pharmacokinetics and a nontrivial example is treated. Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics. Thereby, this approach presents an important addition to the pharmacometric toolbox for drug development and optimal patient dosing.

Developing New Drugs for Mycobacterium tuberculosis Therapy: What Information Do We Get from Preclinical Animal Models?

Preclinical animal models of infection are employed to develop new agents but also to screen among molecules to rank them. There are often major differences between human pharmacokinetic (PK) profiles and those developed by animal models of infection, and these may lead to substantial differences in efficacy relative to that seen in humans. Linezolid is a repurposed agent employed to great effect for therapy of Mycobacterium tuberculosis In this study, we used the hollow-fiber infection model (HFIM) to evaluate the impact of different pharmacokinetic profiles of mice and nonhuman primates (NHP) versus humans on bacterial cell kill as well as resistance suppression. We examined both plasma and epithelial lining fluid (ELF) profiles. We examined simulated exposures equivalent to 600 mg and 900 mg daily of linezolid in humans. For both plasma and ELF exposures, the murine PK profile provided estimates of effect that were biased low relative to human and NHP PK profiles. Mathematical modeling identified a linkage between minimum concentrations (C _min) and bacterial kill and peak concentrations (C _peak) and resistance suppression, with the latter being supported by a prospective validation study. Finding new agents with novel mechanisms of action against M. tuberculosis is difficult. It would be a tragedy to discard a new agent because of a biased estimate of effect in a preclinical animal system. The HFIM provides a system to benchmark evaluation of new compounds in preclinical animal model systems against human PK effects (species scale-up estimates of PK), to safeguard against unwarranted rejection of promising new agents.

Building Optimal Three-Drug Combination Chemotherapy Regimens

Multidrug therapy is often required. Examples include antiviral therapy, nosocomial infections, and, most commonly, anti-Mycobacterium tuberculosis therapy. Our laboratory previously identified a mathematical approach to identify 2-drug regimens with a synergistic or additive interaction using a full factorial study design. Our objective here was to generate a method to identify an optimal 3-drug therapy. We studied M. tuberculosis isolate H37Rv in log-phase growth in flasks. Pretomanid and moxifloxacin were chosen as the base 2-drug regimen. Bedaquiline (plus M2 metabolite) was chosen as the third drug for evaluation. Total bacterial burden and bacterial burden less-susceptible to study drugs were enumerated. A large mathematical model was fit to all the data. This allowed extension to evaluation of the 3-drug regimen by employing a Monte Carlo simulation. Pretomanid plus moxifloxacin demonstrated excellent bacterial kill and suppressed amplification of less-susceptible pathogens. Total bacterial burden was driven to extinction in 3 weeks in 6 of 9 combination therapy evaluations. Only the lowest pretomanid/moxifloxacin exposures in combination did not extinguish the bacterial burden. No combination regimen allowed resistance amplification. Generation of 95% credible intervals about estimates of the interaction parameters α (αs, αr-p, and αr-m) by bootstrapping showed the interaction was near synergistic. The addition of bedaquiline/M2 metabolite was evaluated by forming a 95% confidence interval regarding the decline in bacterial burden. The addition of bedaquiline/M2 metabolite shortened the time to eradication by 1 week and was significantly different. A model-based system approach to evaluating combinations of 3 agents shows promise to rapidly identify the most promising combinations that can then be trialed.

Can phenotypic data complement our understanding of antimycobacterial effects for drug combinations?

Objectives

To demonstrate how phenotypic cell viability data can provide insight into antimycobacterial effects for the isoniazid/rifampicin treatment backbone.

Methods

Data from a Mycobacterium komossense hollow-fibre infection model comprising a growth control group, rifampicin at three different exposures (C_max = 0.14, 0.4 and 1.47 mg/L with t_½ = 1.57 h and τ = 8 h) and rifampicin plus isoniazid (C_max rifampicin = 0.4 mg/L and C_max isoniazid = 1.2 mg/L with t_½ = 1.57 h and τ = 8 h) were used for this investigation. A non-linear mixed-effects modelling approach was used to fit conventional cfu data, quantified using solid-agar plating. Phenotypic proportions of respiring (alive), respiring but with damaged cell membrane (injured) and ‘not respiring’ (dead) cells data were quantified using flow cytometry and Sytox Green™ (Sigma–Aldrich, UK) and resazurin sodium salt staining and fitted using a multinomial logistic regression model.

Results

Isoniazid/rifampicin combination therapy displayed a decreasing overall antimicrobial effect with time (θTime1/2 = 438 h) on cfu data, in contrast to rifampicin monotherapy where this trend was absent. In the presence of isoniazid a phenotype associated with cell injury was displayed, whereas with rifampicin monotherapy a pattern of phenotypic cell death was observed. Bacterial killing onset time on cfu data correlated negatively (θTime50 = 28.9 h, θLAGRIF50 = 0.132 mg/L) with rifampicin concentration up to 0.165 mg/L and this coincided with a positive relationship between rifampicin concentration and the probability of phenotypic cell death.

Conclusions

Cell viability data provide structured information on the pharmacodynamic interaction between isoniazid and rifampicin that complements the understanding of the antibacillary effects of this mycobacterial treatment backbone.

Evaluation of pharmacokinetic-pharmacodynamic relationships and selection of drug combinations for tuberculosis

AIMS

Despite evidence of the efficacy of anti-tubercular drug regimens in clinical practice, the rationale underpinning the selection of doses and companion drugs for combination therapy remains empirical. Novel methods are needed to optimise the antibacterial activity in combination therapies. A drug-disease modelling framework for rational selection of dose and drug combinations in tuberculosis is presented here.

METHODS

A model-based meta-analysis was performed to assess the antibacterial activity of different combinations in infected mice. Data retrieved from the published literature were analysed using a two-state bacterial growth dynamics model, including fast- and slow-growing bacterial populations. The contribution of each drug to the overall antibacterial activity of the combination was parameterised as relative change to the potency of the backbone drug (EC₅₀ -F and/or EC₅₀ -S). Rifampicin and bedaquiline were selected as paradigm drugs to evaluate the predictive performance of the modelling approach.

RESULTS

Pyrazinamide increased the potency (EC₅₀ -F and EC₅₀ -S) of rifampicin (RZ) and bedaquiline (BZ) by almost two-fold. By contrast, pretomanid and isoniazid were found to worsen the antibacterial activity of BZ and RZ, respectively. Following extrapolation of in vivo pharmacokinetic-pharmacodynamic relationships, the dose of rifampicin showing maximum bactericidal effect in tuberculosis patients was predicted to be 70 mg·kg^-1 when given in combination with pyrazinamide.

CONCLUSIONS

The use of a drug-disease modelling framework may provide a more robust rationale for extrapolation and selection of dose and companion drugs in humans. Our analysis demonstrates that RZ and BZ should be considered as a backbone therapy in prospective novel combination regimens against tuberculosis.

Linezolid-based Regimens for Multidrug-resistant Tuberculosis (TB): A Systematic Review to Establish or Revise the Current Recommended Dose for TB Treatment

Linezolid has been successfully used for treatment of multidrug-resistant tuberculosis (MDR-TB). However, dose- and duration-related toxicity limit its use. Here, our aim was to search relevant pharmacokinetics (PK)/pharmacodynamics (PD) literature to identify the effective PK/PD index and to define the optimal daily dose and dosing frequency of linezolid in MDR-TB regimens. The systematic search resulted in 8 studies that met inclusion criteria. A significant PK variability was observed. Efficacy of linezolid seems to be driven by area under the concentration-time curve (AUC)/minimum inhibitory concentration (MIC). Literature is inconclusive about the preferred administration of a daily dose of 600 mg. To prevent development of drug resistance, an AUC/MIC ratio of 100 in the presence of a companion drug at relevant exposure is required. A daily dose of 600 mg seems appropriate to balance between efficacy and toxicity. Being a drug with a very narrow therapeutic window, linezolid treatment may benefit from a more personalized approach, that is, measuring actual MIC values and therapeutic drug monitoring.

Linezolid pharmacokinetics in MDR-TB: a systematic review, meta-analysis and Monte Carlo simulation

Objectives

The oxazolidinone linezolid is an effective component of drug-resistant TB treatment, but its use is limited by toxicity and the optimum dose is uncertain. Current strategies are not informed by clinical pharmacokinetic (PK)/pharmacodynamic (PD) data; we aimed to address this gap.

Methods

We defined linezolid PK/PD targets for efficacy (fAUC_0–24:MIC >119 mg/L/h) and safety (fC_min <1.38 mg/L). We extracted individual-level linezolid PK data from existing studies on TB patients and performed meta-analysis, producing summary estimates of fAUC_0–24 and fC_min for published doses. Combining these with a published MIC distribution, we performed Monte Carlo simulations of target attainment.

Results

The efficacy target was attained in all simulated individuals at 300 mg q12h and 600 mg q12h, but only 20.7% missed the safety target at 300 mg q12h versus 98.5% at 600 mg q12h. Although suggesting 300 mg q12h should be used preferentially, these data were reliant on a single centre. Efficacy and safety targets were missed by 41.0% and 24.2%, respectively, at 300 mg q24h and by 44.6% and 27.5%, respectively, at 600 mg q24h. However, the confounding effect of between-study heterogeneity on target attainment for q24h regimens was considerable.

Conclusions

Linezolid dosing at 300 mg q12h may retain the efficacy of the 600 mg q12h licensed dosing with improved safety. Data to evaluate commonly used 300 mg q24h and 600 mg q24h doses are limited. Comprehensive, prospectively obtained PK/PD data for linezolid doses in drug-resistant TB treatment are required.

Mimicking in-vivo exposures to drug combinations in-vitro: anti-tuberculosis drugs in lung lesions and the hollow fiber model of infection

Here, we evaluate protocol requirements to mimic therapeutically relevant drug concentrations at the site of infection (i.e. lung lesion) in an in-vitro hollow fibre model of infection using pulmonary tuberculosis as a paradigm. Steady-state pharmacokinetic profiles in plasma, lung tissue and lung lesion homogenate were simulated for isoniazid, rifampicin and pyrazinamide and moxifloxacin. An R-shiny User Interface was developed to support conversion of in-vivo pharmacokinetic C_MAX, T_MAX and T_1/2 estimates into pump settings. A monotherapy protocol mimicking isoniazid in lung lesion homogenate (isoniazid C_MAX = 1,200 ng/ml, T_MAX = 2.2 hr and T_1/2 = 4.7 hr), and two combination therapy protocols including drugs with similar (isoniazid and rifampicin (C_MAX = 400 ng/ml)) and different half-lives (isoniazid and pyrazinamide (C_MAX = 28,900 ng/ml and T_1/2 = 8.0 hr)) were implemented in a hollow-fiber system. Drug levels in the perfusate were analysed using ultra-high-performance liquid chromatographic-tandem mass spectrometric detection. Steady state pharmacokinetic profiles measured in the hollow fiber model were similar to the predicted in-vivo steady-state lung lesion homogenate pharmacokinetic profiles. The presented approach offers the possibility to use pharmacological data to study the effect of target tissue exposure for drug combinations. Integration with pharmacokinetics modelling principles through a web interface will provide access to a wider community interested in the evaluation of efficacy of anti-tubercular drugs.

Effects of a rifampicin pre-treatment on linezolid pharmacokinetics

Linezolid is an oxazolidinone antibiotic that effectively treats methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE). Since rifampicin induces other antibiotic effects, it is combined with linezolid in therapeutic regimes. However, linezolid blood concentrations are reduced by this combination, which increases the risk of the emergence of antibiotic-resistant bacteria. We herein demonstrated that the combination of linezolid with rifampicin inhibited its absorption and promoted its elimination, but not through microsomal enzymes. Our results indicate that the combination of linezolid with rifampicin reduces linezolid blood concentrations via metabolic enzymes.

Generating Robust and Informative Nonclinical In Vitro and In Vivo Bacterial Infection Model Efficacy Data To Support Translation to Humans

In June 2017, the National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health, organized a workshop entitled “Pharmacokinetics-Pharmacodynamics (PK/PD) for Development of Therapeutics against Bacterial Pathogens.” The aims were to discuss details of various PK/PD models and identify sound practices for deriving and utilizing PK/PD relationships to design optimal dosage regimens for patients. Workshop participants encompassed individuals from academia, industry, and government, including the United States Food and Drug Administration.

In June 2017, the National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health, organized a workshop entitled “Pharmacokinetics-Pharmacodynamics (PK/PD) for Development of Therapeutics against Bacterial Pathogens.” The aims were to discuss details of various PK/PD models and identify sound practices for deriving and utilizing PK/PD relationships to design optimal dosage regimens for patients. Workshop participants encompassed individuals from academia, industry, and government, including the United States Food and Drug Administration. This and the accompanying review on clinical PK/PD summarize the workshop discussions and recommendations. Nonclinical PK/PD models play a critical role in designing human dosage regimens and are essential tools for drug development. These include in vitro and in vivo efficacy models that provide valuable and complementary information for dose selection and translation from the laboratory to human. It is crucial that studies be designed, conducted, and interpreted appropriately. For antibacterial PK/PD, extensive published data and expertise are available. These have been leveraged to develop recommendations, identify common pitfalls, and describe the applications, strengths, and limitations of various nonclinical infection models and translational approaches. Despite these robust tools and published guidance, characterizing nonclinical PK/PD relationships may not be straightforward, especially for a new drug or new class. Antimicrobial PK/PD is an evolving discipline that needs to adapt to future research and development needs. Open communication between academia, pharmaceutical industry, government, and regulatory bodies is essential to share perspectives and collectively solve future challenges.

Evaluation of Polyphenolic Profile and Antibacterial Activity of Pomegranate Juice in Combination with Rifampin (R) against MDR-TB Clinical Isolates

BACKGROUND

The global rise of multi-drug resistant M. tuberculosis demands unconventional treatment to enhance the efficiency of current drugs. Punica granatum, which is known as pomegranate, is considered as a member of the Punicaceae family. Pomegranate, which is broadly documented for its activity against a wide spectrum of bacterial pathogens, deserves further scrutiny in this respect.

METHODS

Within this scope, this study investigated the effect of fresh pomegranate juice (FPJ) on the antibacterial activity of anti-tuberculosis drugs (Rifampin (R) and Isoniazid (INH)) against MDR-TB clinical isolates. The drug resistance profiles in M. tuberculosis clinical isolates were determined by susceptibility test using BACTEC MGIT 960 system. Four concentrations of fresh pomegranate juice (FPJ) (5%, 10%, 15%, and 20%) were evaluated in combination with R and INH at a dose range of (1.0 µg/ml) and (0.1 µg/ml), respectively against the MDR-TB isolates by the BACTEC MGIT 960 system. Moreover, this study scrutinized individual phenolic compounds of FPJ by using highperformance liquid chromatography (HPLC). The total polyphenols (TP), total flavonoid (TF), total anthocyanins content (TAC), and the antioxidant capacity were also assessed in FPJ.

RESULTS

Synergistic effects were observed between R and INH with FPJ against all tested strains. However, combination therapy of rifampin was more effective than isoniazid one. Therefore, the combination of R and FPJ has been used against (27) MDR-TB clinical isolates. 5% of FPJ plus R (1.0 µg/ml) were found to suppress the growth of one isolates for first group (INH and R resistant). However, 5% of FPJ demonstrated no synergistic impact with R for second (SM, R and INH resistant) and third group (INH, EMB, R and SM resistant). Moreover, 10% of FPJ and R (1.0 μg/ml) inhibited the bacterial growth of three isolates of first group and two isolates and one isolate for second and third group, respectively. Remarkably, 15% of FPJ plus R (1.0 µg/ml) appeared to inhibit the growth of MDR-TB isolates for all tested groups indicating a strong synergistic effect. Regarding H37RV, the complete inhibition of the bacterial growth was found to occur at 15% and 20% concentrations of FPJ only. Minimum inhibitory concentration (MIC) of FPJ ranged from (4% to13%) for first group and from (10% to15%) for second and third group. Thus, FPJ at 15% inhibited 100% of bacteria for all tested isolates (MIC100% =15%). Phenolic compounds identified in FPJ were gallic acid, benzoic acid, syringic, folic acid, pelargonidin, naringin+ellagic acid, naringenin, chlorogenic acid, caffeic acid, catechin, myricetin, kaempferol, quercetin, cyanidin-3-glycoside, p-cummaric acid, ferulic acid, and rutin. Total phenolic (TP), total flavonoid (TF), and total anthocyanin (TA) content were 841.5 mg/L, 638.73 mg RE/L, and 47.43 mg/L, accordingly.

CONCLUSION

Overall, FPJ displayed synergistic effect with R against MDR-TB clinical isolates due to its high content of polyphenol and antioxidant capability.

Artificial intelligence in nanomedicine

The field of nanomedicine has made substantial strides in the areas of therapeutic and diagnostic development. For example, nanoparticle-modified drug compounds and imaging agents have resulted in markedly enhanced treatment outcomes and contrast efficiency. In recent years, investigational nanomedicine platforms have also been taken into the clinic, with regulatory approval for Abraxane® and other products being awarded. As the nanomedicine field has continued to evolve, multifunctional approaches have been explored to simultaneously integrate therapeutic and diagnostic agents onto a single particle, or deliver multiple nanomedicine-functionalized therapies in unison. Similar to the objectives of conventional combination therapy, these strategies may further improve treatment outcomes through targeted, multi-agent delivery that preserves drug synergy. Also, similar to conventional/unmodified combination therapy, nanomedicine-based drug delivery is often explored at fixed doses. A persistent challenge in all forms of drug administration is that drug synergy is time-dependent, dose-dependent and patient-specific at any given point of treatment. To overcome this challenge, the evolution towards nanomedicine-mediated co-delivery of multiple therapies has made the potential of interfacing artificial intelligence (AI) with nanomedicine to sustain optimization in combinatorial nanotherapy a reality. Specifically, optimizing drug and dose parameters in combinatorial nanomedicine administration is a specific area where AI can actionably realize the full potential of nanomedicine. To this end, this review will examine the role that AI can have in substantially improving nanomedicine-based treatment outcomes, particularly in the context of combination nanotherapy for both N-of-1 and population-optimized treatment.

Effect of Moxifloxacin plus Pretomanid against Mycobacterium tuberculosis in Log Phase, Acid Phase, and Nonreplicating-Persister Phase in an In Vitro Assay

Combination therapy is a successful approach to treat tuberculosis in patients with susceptible strains of Mycobacterium tuberculosis However, the emergence of resistant strains requires identification of new, effective therapies. Pretomanid (PA824) and moxifloxacin (MXF) are promising options currently under evaluation in clinical trials for the treatment of susceptible and resistant mycobacteria. We applied our recently described screening strategy to characterize the interaction between PA824 and MXF toward the killing of M. tuberculosis in logarithmic growth phase (log phase), acid phase, and nonreplicating-persister (NRP) phase. Respective in vitro data generated for the H37Rv and 18b strains were evaluated in a microdilution plate system containing both drugs in combination. The Universal Response Surface Approach model from Greco et al. (W. R. Greco, G. Bravo, and J. C. Parsons, Pharmacol Rev 47:331-385, 1995) was used to characterize the nature of the interaction between both drugs; synergistic or additive combinations would prompt additional evaluation in the hollow-fiber infection model (HFIM) and in animal studies. The interaction between MXF and PA824 was additive against M. tuberculosis organisms in acid phase (interaction parameter [α] = 5.56e-8 [95% confidence interval {CI} = -0.278 to 0.278] and α = 0.408 [95% CI = 0.105 to 0.711], respectively), NRP phase (α = 0.625 [95% CI = -0.556 to 1.81] and α = 2.92 [95% CI = 0.215 to 5.63], respectively), and log phase (α = 1.57e-6 [95% CI = -0.930 to 0.930] and α = 1.83e-6 [95% CI = -0.929 and 0.929], respectively), prompting further testing of this promising combination for the treatment of tuberculosis in the HFIM and in animal studies.

The Combination of Fosfomycin plus Meropenem Is Synergistic for Pseudomonas aeruginosa PAO1 in a Hollow-Fiber Infection Model

Treating high-density bacterial infections is a challenging clinical problem. We have a paucity of new agents that can address this problem. Pseudomonas aeruginosa is a particularly difficult pathogen to treat effectively because of the plethora of resistance mechanisms it carries. Fosfomycin is an agent discovered circa 40 years ago. Recently, it has been resurrected in the United States and studied for intravenous therapy. We hypothesized that, to maximize its utility, it would require combination chemotherapy when used in a clinical circumstance in high-bacterial-burden infections. We chose to examine the combination of meropenem plus fosfomycin. These agents were studied in the hollow-fiber infection model. We utilized a fully factorial study design, looking at 2 doses of meropenem alone (1 and 2 g 8-hourly) and two doses of fosfomycin alone (6 and 8 g 8-hourly), as well as all possible combinations plus a no-treatment control. We used a high-dimensional model of 5 inhomogeneous differential equations with 5 system outputs to analyze all data simultaneously. Combination therapy outperformed all monotherapy regimens, with all combinations driving >6 log₁₀ CFU/ml of bacterial killing. Combination therapy was able to counterselect resistance emergence (meropenem mutants being killed by the combination, as well as fosfomycin mutants being killed by the combination) in all regimens studied. The analysis demonstrated that the combination was significantly synergistic for bacterial cell killing and resistance suppression. Meropenem plus fosfomycin is a promising combination for therapy of high-burden Pseudomonas aeruginosa infections and requires further study.

Linezolid Kills Acid-Phase and Nonreplicative-Persister-Phase Mycobacterium tuberculosis in a Hollow-Fiber Infection Model

The therapy for treatment of Mycobacterium tuberculosis infections is long and arduous. It has been hypothesized that the therapy duration is driven primarily by populations of organisms in different metabolic states that replicate slowly or not at all (acid-phase and nonreplicative-persister [NRP]-phase organisms). Linezolid is an oxazolidinone antimicrobial with substantial activity against Log-phase M. tuberculosis Here, we examined organisms in acid-phase growth and nonreplicative-persister-phenotype growth and determined the effect of differing clinically relevant exposures to linezolid in a hollow-fiber infection model (HFIM). The endpoints measured were bacterial kill over 29 days and whether organisms that were less susceptible to linezolid could be recovered during that period. In addition, we evaluated the effect of administration schedule on linezolid activity, contrasting daily administration with administration of twice the daily dose every other day. Linezolid demonstrated robust activity when administered daily against both acid-phase and NRP-phase organisms. We demonstrated a clear dose response, with 900 mg of linezolid daily generating ≥3 Log(CFU/ml) killing of acid-phase and NRP-phase M. tuberculosis over 29 days. Amplification of a population less susceptible to linezolid was not seen. Activity was reduced with every 48-h dosing, indicating that the minimum concentration (C_min)/MIC ratio drove the microbiological effect. We conclude that once-daily linezolid dosing has substantial activity against M. tuberculosis in acid-phase and NRP-phase metabolic states. Other studies have shown activity against Log-phase M. tuberculosis Linezolid is a valuable addition to the therapeutic armamentarium for M. tuberculosis and has the potential for substantially shortening therapy duration.

Effect of Linezolid plus Bedaquiline against Mycobacterium tuberculosis in Log Phase, Acid Phase, and Nonreplicating-Persister Phase in an In Vitro Assay

Tuberculosis is the ninth-leading cause of death worldwide. Treatment success is approximately 80% for susceptible strains and decreases to 30% for extensively resistant strains. Shortening the therapy duration for Mycobacterium tuberculosis is a major goal, which can be attained with the use of combination therapy. However, the identification of the most promising combination is a challenge given the quantity of older and newer agents available. Our objective was to identify promising 2-drug combinations using an in vitro strategy to ultimately be tested in an in vitro hollow fiber infection model (HFIM) and in animal models. We studied the effect of the combination of linezolid (LZD) and bedaquiline (BDQ) on M. tuberculosis strain H37Rv in log- and acid-phase growth and M. tuberculosis strain 18b in log- and nonreplicating-persister-phase growth in a plate system containing a 9-by-8 matrix of concentrations of both drugs alone and in combinations. A characterization of the interaction as antagonistic, additive, or synergistic was performed using the Greco universal response surface approach (URSA) model. Our results indicate that the interaction between LZD and BDQ is additive for bacterial killing in both strains for both of the metabolic states tested. This prescreen strategy was suitable to identify LZD and BDQ as a promising combination to be further tested in the HFIM. The presence of nonoverlapping mechanisms of drug action suggests each drug in the combination will likely be effective in suppressing the emergence of resistance by M. tuberculosis to the companion drug, which holds promise in improving treatment outcomes for tuberculosis.

Assessing the Combined Antibacterial Effect of Isoniazid and Rifampin on Four Mycobacterium tuberculosis Strains Using In Vitro Experiments and Response-Surface Modeling

While isoniazid and rifampin have been the cornerstone of tuberculosis therapy caused by drug-susceptible Mycobacterium tuberculosis for more than 40 years, their combined action has never been thoroughly assessed by modern quantitative pharmacology approaches. The aims of this work were to perform in vitro experiments and mathematical modeling of the antibacterial effect of isoniazid and rifampin alone and in combination against various strains of Mycobacterium tuberculosis After MIC determination of H37Rv and three strains belonging to the Beijing, Euro-American, and Indo-Oceanic lineages, the antibacterial effects of isoniazid and rifampin alone and in combination were studied in static time-kill experiments. A sigmoidal maximum effect model (Hill equation) and a response-surface model were used to describe the effect of the drugs alone and in combination, respectively. The killing effect of isoniazid and rifampin alone were well described by the Hill equation. Rifampin displayed a more concentration-dependent effect than isoniazid around the MIC. The pharmacodynamics parameters of each drug (maximal effect, median effect concentration, and coefficient of sigmoidicity) were quite similar between the four strains. The response-surface model from Minto et al. fit data of combined effect very well with low bias and imprecision (C. F. Minto, T. W. Schnider, T. G. Short, K. M. Gregg, A. Gentilini, Anesthesiology 92:1603-1616, 2000, https://doi.org/10.1097/00000542-200006000-00017). Response-surface modeling showed that the combined action of isoniazid and rifampin was synergistic for the H37Rv, Beijing, and Euro-American strains but only additive for the Indo-Oceanic strain. This study can serve as a motivating example for preclinical evaluation of combined action of antituberculous drugs.

Semi-mechanistic pharmacokinetic-pharmacodynamic modelling of antibiotic drug combinations

BACKGROUND

Deriving suitable dosing regimens for antibiotic combination therapy poses several challenges as the drug interaction can be highly complex, the traditional pharmacokinetic-pharmacodynamic (PKPD) index methodology cannot be applied straightforwardly, and exploring all possible dose combinations is unfeasible. Therefore, semi-mechanistic PKPD models developed based on in vitro single and combination experiments can be valuable to suggest suitable combination dosing regimens.

AIMS

To outline how the interaction between two antibiotics has been characterized in semi-mechanistic PKPD models. We also explain how such models can be applied to support dosing regimens and design future studies.

SOURCES

PubMed search for published semi-mechanistic PKPD models of antibiotic drug combinations.

CONTENT

Thirteen publications were identified where ten had applied subpopulation synergy to characterize the combined effect, i.e. independent killing rates for each drug and bacterial subpopulation. We report the various types of interaction functions that have been used to describe the combined drug effects and that characterized potential deviations from additivity under the PKPD model. Simulations from the models had commonly been performed to compare single versus combined dosing regimens and/or to propose improved dosing regimens.

IMPLICATIONS

Semi-mechanistic PKPD models allow for integration of knowledge on the interaction between antibiotics for various PK and PD profiles, and can account for associated variability within the population as well as parameter uncertainty. Decisions on suitable combination regimens can thereby be facilitated. We find the application of semi-mechanistic PKPD models to be essential for efficient development of antibiotic combination regimens that optimize bacterial killing and/or suppress resistance development.

Optimized droplet digital CFU assay (ddCFU) provides precise quantification of bacteria over a dynamic range of 6 logs and beyond

Standard digital assays need a large number of compartments for precise quantification of a sample over a broad dynamic range. We address this issue with an optimized droplet digital approach that uses a drastically reduced number of compartments for quantification. We generate serial logarithmic dilutions of an initial bacterial sample as an array of microliter-sized droplet plugs. In a subsequent step, these droplets are split into libraries of nanoliter droplets and pooled together for incubation and analysis. We show that our technology is at par with traditional dilution plate count for quantification of bacteria, but has the advantage of simplifying the experimental setup and reducing the manual workload. The method also has the potential to reduce the assay time significantly.

A Novel Small-Molecule Inhibitor of the Mycobacterium tuberculosis Demethylmenaquinone Methyltransferase MenG Is Bactericidal to Both Growing and Nutritionally Deprived Persister Cells

Active tuberculosis (TB) and latent Mycobacterium tuberculosis infection both require lengthy treatments to achieve durable cures. This problem has partly been attributable to the existence of nonreplicating M. tuberculosis “persisters” that are difficult to kill using conventional anti-TB treatments. Compounds that target the respiratory pathway have the potential to kill both replicating and persistent M. tuberculosis and shorten TB treatment, as this pathway is essential in both metabolic states. We developed a novel respiratory pathway-specific whole-cell screen to identify new respiration inhibitors. This screen identified the biphenyl amide GSK1733953A (DG70) as a likely respiration inhibitor. DG70 inhibited both clinical drug-susceptible and drug-resistant M. tuberculosis strains. Whole-genome sequencing of DG70-resistant colonies identified mutations in menG (rv0558), which is responsible for the final step in menaquinone biosynthesis and required for respiration. Overexpression of menG from wild-type and DG70-resistant isolates increased the DG70 MIC by 4× and 8× to 30×, respectively. Radiolabeling and high-resolution mass spectrometry studies confirmed that DG70 inhibited the final step in menaquinone biosynthesis. DG70 also inhibited oxygen utilization and ATP biosynthesis, which was reversed by external menaquinone supplementation. DG70 was bactericidal in actively replicating cultures and in a nutritionally deprived persistence model. DG70 was synergistic with the first-line TB drugs isoniazid, rifampin, and the respiratory inhibitor bedaquiline. The combination of DG70 and isoniazid completely sterilized cultures in the persistence model by day 10. These results suggest that MenG is a good therapeutic target and that compounds targeting MenG along with standard TB therapy have the potential to shorten TB treatment duration.

This study shows that MenG, which is responsible for the last enzymatic step in menaquinone biosynthesis, may be a good drug target for improving TB treatments. We describe the first small-molecule inhibitor (DG70) of Mycobacterium tuberculosis MenG and show that DG70 has characteristics that are highly desirable for a new antitubercular agent, including bactericidality against both actively growing and nonreplicating mycobacteria and synergy with several first-line drugs that are currently used to treat TB.

A Two-Locus Model of the Evolution of Insecticide Resistance to Inform and Optimise Public Health Insecticide Deployment Strategies

We develop a flexible, two-locus model for the spread of insecticide resistance applicable to mosquito species that transmit human diseases such as malaria. The model allows differential exposure of males and females, allows them to encounter high or low concentrations of insecticide, and allows selection pressures and dominance values to differ depending on the concentration of insecticide encountered. We demonstrate its application by investigating the relative merits of sequential use of insecticides versus their deployment as a mixture to minimise the spread of resistance. We recover previously published results as subsets of this model and conduct a sensitivity analysis over an extensive parameter space to identify what circumstances favour mixtures over sequences. Both strategies lasted more than 500 mosquito generations (or about 40 years) in 24% of runs, while in those runs where resistance had spread to high levels by 500 generations, 56% favoured sequential use and 44% favoured mixtures. Mixtures are favoured when insecticide effectiveness (their ability to kill homozygous susceptible mosquitoes) is high and exposure (the proportion of mosquitoes that encounter the insecticide) is low. If insecticides do not reliably kill homozygous sensitive genotypes, it is likely that sequential deployment will be a more robust strategy. Resistance to an insecticide always spreads slower if that insecticide is used in a mixture although this may be insufficient to outperform sequential use: for example, a mixture may last 5 years while the two insecticides deployed individually may last 3 and 4 years giving an overall 'lifespan' of 7 years for sequential use. We emphasise that this paper is primarily about designing and implementing a flexible modelling strategy to investigate the spread of insecticide resistance in vector populations and demonstrate how our model can identify vector control strategies most likely to minimise the spread of insecticide resistance.

Relationship between Fosfomycin Exposure and Amplification of Escherichia coli Subpopulations with Reduced Susceptibility in a Hollow-Fiber Infection Model

Understanding the relationship between antibiotic exposure and amplification of bacterial subpopulations with reduced drug susceptibility over time is important for evaluating the adequacy of dosing regimens. We utilized a hollow-fiber infection model to identify the fosfomycin intravenous dosing regimens that prevented the amplification of Escherichia coli bacterial subpopulations with reduced fosfomycin susceptibility. The challenge isolate was E. coli ATCC 25922 (agar MIC with glucose-6-phosphate, 1 mg/liter; agar MIC without glucose-6-phosphate, 32 mg/liter). The fosfomycin dosing regimens studied were 1 to 12 g every 8 h for 10 days to approximate that planned for clinical use. The studies included a no-treatment control regimen. Two bacterial subpopulations were identified, one with reduced susceptibility with agar MIC values ranging from 32 to 128 mg/liter and the other resistant with agar MIC values of 256 to >1,024 mg/liter on plates containing 5× and 256× the baseline MIC value, respectively. An inverted-U-shaped function best described the relationship between the amplification of the two bacterial subpopulations and drug exposure. The lowest fosfomycin dosing regimen that did not amplify a bacterial subpopulation with reduced susceptibility was 4 g administered every 8 h. Nearly immediate amplification of bacterial subpopulations with reduced susceptibility was observed with fosfomycin dosing regimens consisting of 1 to 2 g every 8 h. These data will be useful to support the selection of fosfomycin dosing regimens that minimize the potential for on-therapy amplification of bacterial subpopulations with reduced susceptibility.

Hollow-Fiber Methodology for Pharmacokinetic/Pharmacodynamic Studies of Antimalarial Compounds

Knowledge of pharmacokinetic/pharmacodynamic (PK/PD) relationships can enhance the speed and economy of drug development by enabling informed and rational decisions at every step, from lead selection to clinical dosing. For anti-infective agents in particular, dynamic in vitro hollow-fiber cartridge experiments permit exquisite control of kinetic parameters and the study of their consequent impact on pharmacodynamic efficacy. Such information is of great interest for the cost-restricted but much-needed development of new antimalarial drugs, especially since the major human pathogen Plasmodium falciparum can be cultivated in vitro but is not readily available in animal models. This protocol describes the materials and procedures for determining the PK/PD relationships of antimalarial compounds.

Toward an Evidence-Based Nonclinical Road Map for Evaluating the Efficacy of New Tuberculosis (TB) Drug Regimens: Proceedings of a Critical Path to TB Drug Regimens-National Institute of Allergy and Infectious Diseases In Vivo Pharmacology Workshop for TB Drug Development

Novel tuberculosis (TB) drug regimens are urgently needed, and their development will be enabled by improved preclinical approaches that more effectively inform and ensure safe selection of clinical candidates and drug combination/regimens. An evidence-based approach for the assessment of nonclinical models supporting TB drug development has been proposed by a joint partnership between the National Institute of Allergy and Infectious Diseases (NIAID) and the Critical Path to TB Drug Regimens (CPTR) Consortium.

Suppression of Emergence of Resistance in Pathogenic Bacteria: Keeping Our Powder Dry, Part 2

We are in a crisis of bacterial resistance. For economic reasons, most pharmaceutical companies are abandoning antimicrobial discovery efforts, while, in health care itself, infection control and antibiotic stewardship programs have generally failed to prevent the spread of drug-resistant bacteria. At this point, what can be done? The first step has been taken. Governments and international bodies have declared there is a worldwide crisis in antibiotic drug resistance. As discovery efforts begin anew, what more can be done to protect newly developing agents and improve the use of new drugs to suppress resistance emergence? A neglected path has been the use of recent knowledge regarding antibiotic dosing as single agents and in combination to minimize resistance emergence, while also providing sufficient early bacterial kill. In this review, we look at the data for resistance suppression. Approaches include increasing the intensity of therapy to suppress resistant subpopulations; developing concepts of clinical breakpoints to include issues surrounding suppression of resistance; and paying attention to the duration of therapy, which is another important issue for resistance suppression. New understanding of optimizing combination therapy is of interest for difficult-to-treat pathogens like Pseudomonas aeruginosa, Acinetobacter spp., and multidrug-resistant (MDR) Enterobacteriaceae. These lessons need to be applied to our old drugs as well to preserve them and to be put into national and international antibiotic resistance strategies. As importantly, from a regulatory perspective, new chemical entities should have a resistance suppression plan at the time of regulatory review. In this way, we can make the best of our current situation and improve future prospects.

Clofazimine Prevents the Regrowth of Mycobacterium abscessus and Mycobacterium avium Type Strains Exposed to Amikacin and Clarithromycin

Multidrug therapy is a standard practice when treating infections by nontuberculous mycobacteria (NTM), but few treatment options exist. We conducted this study to define the drug-drug interaction between clofazimine and both amikacin and clarithromycin and its contribution to NTM treatment. Mycobacterium abscessus and Mycobacterium avium type strains were used. Time-kill assays for clofazimine alone and combined with amikacin or clarithromycin were performed at concentrations of 0.25× to 2× MIC. Pharmacodynamic interactions were assessed by response surface model of Bliss independence (RSBI) and isobolographic analysis of Loewe additivity (ISLA), calculating the percentage of statistically significant Bliss interactions and interaction indices (I), respectively. Monte Carlo simulations with predicted human lung concentrations were used to calculate target attainment rates for combination and monotherapy regimens. Clofazimine alone was bacteriostatic for both NTM. Clofazimine-amikacin was synergistic against M. abscessus (I = 0.41; 95% confidence interval [CI], 0.29 to 0.55) and M. avium (I = 0.027; 95% CI, 0.007 to 0.048). Based on RSBI analysis, synergistic interactions of 28.4 to 29.0% and 23.2 to 56.7% were observed at 1× to 2× MIC and 0.25× to 2× MIC for M. abscessus and M. avium, respectively. Clofazimine-clarithromycin was also synergistic against M. abscessus (I = 0.53; 95% CI, 0.35 to 0.72) and M. avium (I = 0.16; 95% CI, 0.04 to 0.35), RSBI analysis showed 23.5% and 23.3 to 53.3% at 2× MIC and 0.25× to 0.5× MIC for M. abscessus and M. avium, respectively. Clofazimine prevented the regrowth observed with amikacin or clarithromycin alone. Target attainment rates of combination regimens were >60% higher than those of monotherapy regimens for M. abscessus and M. avium. The combination of clofazimine with amikacin or clarithromycin was synergistic in vitro. This suggests a potential role for clofazimine in treatment regimens that warrants further evaluation.

Preclinical Evaluations To Identify Optimal Linezolid Regimens for Tuberculosis Therapy

Linezolid is an oxazolidinone with potent activity against Mycobacterium tuberculosis. Linezolid toxicity in patients correlates with the dose and duration of therapy. These toxicities are attributable to the inhibition of mitochondrial protein synthesis. Clinically relevant linezolid regimens were simulated in the in vitro hollow-fiber infection model (HFIM) system to identify the linezolid therapies that minimize toxicity, maximize antibacterial activity, and prevent drug resistance. Linezolid inhibited mitochondrial proteins in an exposure-dependent manner, with toxicity being driven by trough concentrations. Once-daily linezolid killed M. tuberculosis in an exposure-dependent manner. Further, 300 mg linezolid given every 12 hours generated more bacterial kill but more toxicity than 600 mg linezolid given once daily. None of the regimens prevented linezolid resistance. These findings show that with linezolid monotherapy, a clear tradeoff exists between antibacterial activity and toxicity. By identifying the pharmacokinetic parameters linked with toxicity and antibacterial activity, these data can provide guidance for clinical trials evaluating linezolid in multidrug antituberculosis regimens.

The emergence and spread of multidrug-resistant M. tuberculosis are a major threat to global public health. Linezolid is an oxazolidinone that is licensed for human use and has demonstrated potent activity against multidrug-resistant M. tuberculosis. However, long-term use of linezolid has shown to be toxic in patients, often resulting in thrombocytopenia. We examined therapeutic linezolid regimens in an in vitro model to characterize the exposure-toxicity relationship. The antibacterial activity against M. tuberculosis was also assessed for these regimens, including the amplification or suppression of resistant mutant subpopulations by the chosen regimen. Higher exposures of linezolid resulted in greater antibacterial activity, but with more toxicity and, for some regimens, increased resistant mutant subpopulation amplification, illustrating the trade-off between activity and toxicity. These findings can provide valuable insight for designing optimal dosage regimens for linezolid that are part of the long combination courses used to treat multidrug-resistant M. tuberculosis.

Pharmacodynamics of fosfomycin: insights into clinical use for antimicrobial resistance

The aim of this study was to improve the understanding of the pharmacokinetic-pharmacodynamic relationships of fosfomycin against extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli strains that have different fosfomycin MICs. Our methods included the use of a hollow fiber infection model with three clinical ESBL-producing E. coli strains. Human fosfomycin pharmacokinetic profiles were simulated over 4 days. Preliminary studies conducted to determine the dose ranges, including the dose ranges that suppressed the development of drug-resistant mutants, were conducted with regimens from 12 g/day to 36 g/day. The combination of fosfomycin at 4 g every 8 h (q8h) and meropenem at 1 g/q8h was selected for further assessment. The total bacterial population and the resistant subpopulations were determined. No efficacy was observed against the Ec42444 strain (fosfomycin MIC, 64 mg/liter) at doses of 12, 24, or 36 g/day. All dosages induced at least initial bacterial killing against Ec46 (fosfomycin MIC, 1 mg/liter). High-level drug-resistant mutants appeared in this strain in response to 12, 15, and 18 g/day. In the study arms that included 24 g/day, once or in a divided dose, a complete extinction of the bacterial inoculum was observed. The combination of meropenem with fosfomycin was synergistic for bacterial killing and also suppressed all fosfomycin-resistant clones of Ec2974 (fosfomycin MIC, 1 mg/liter). We conclude that fosfomycin susceptibility breakpoints (≤64 mg/liter according to CLSI [for E. coli urinary tract infections only]) should be revised for the treatment of serious systemic infections. Fosfomycin can be used to treat infections caused by organisms that demonstrate lower MICs and lower bacterial densities, although relatively high daily dosages (i.e., 24 g/day) are required to prevent the emergence of bacterial resistance. The ratio of the area under the concentration-time curve for the free, unbound fraction of fosfomycin versus the MIC (fAUC/MIC) appears to be the dynamically linked index of suppression of bacterial resistance. Fosfomycin with meropenem can act synergistically against E. coli strains in preventing the emergence of fosfomycin resistance.

A multi-scale approach to designing therapeutics for tuberculosis

Approximately one third of the world's population is infected with Mycobacterium tuberculosis. Limited information about how the immune system fights M. tuberculosis and what constitutes protection from the bacteria impact our ability to develop effective therapies for tuberculosis. We present an in vivo systems biology approach that integrates data from multiple model systems and over multiple length and time scales into a comprehensive multi-scale and multi-compartment view of the in vivo immune response to M. tuberculosis. We describe computational models that can be used to study (a) immunomodulation with the cytokines tumor necrosis factor and interleukin 10, (b) oral and inhaled antibiotics, and