Factors associated with tracheostomy-associated infection treatment: A multicenter observational study.

OBJECTIVE: To characterize factors that influence the decision to treat suspected pediatric bacterial tracheostomy-associated respiratory infections (bTRAINs; e.g., pneumonia, tracheitis). METHODS: We conducted a multicenter, prospective cohort study of children with pre-existing tracheostomy hospitalized at six children's hospitals for a suspected bTRAIN (receipt of respiratory culture plus ≥1 doses of an antibiotic within 48 h). The primary predictor was respiratory culture growth categorized as Pseudomonas aeruginosa, P. aeruginosa + ≥1 other bacterium, other bacteria alone, or normal flora/no growth. Our primary outcome was bTRAIN treatment with a complete course of antibiotics as documented by the discharge team. We used logistic regression with generalized estimating equations to identify the association between our primary predictor and outcome and to identify demographic, clinical, and diagnostic testing factors associated with treatment. RESULTS: Of the 440 admissions among 289 patients meeting inclusion criteria, 307 (69.8%) had positive respiratory culture growth. Overall, 237 (53.9%) of admissions resulted in bTRAIN treatment. Relative to a negative culture, a culture positive for P. aeruginosa plus ≥1 other organism (adjusted odds ratio [aOR] 2.3; 95% confidence interval [CI] 1.02-5.0)] or ≥1 other organism alone (aOR: 2.8; 95% CI: 1.4-5.6)] was associated with treatment. Several clinical and diagnostic testing (respiratory Gram-stain and chest radiograph) findings were also associated with treatment. Positive respiratory viral testing was associated with reduced odds of treatment (aOR: 0.5; 95% CI: 0.2-0.9). CONCLUSIONS: Positive respiratory cultures as well as clinical indicators of acute illness and nonculture test results were associated with bTRAIN treatment. Clinicians may be more comfortable withholding antibiotics when a virus is identified during testing.

Transcriptome profiles of macrophages upon infection by morphotypic smooth and rough variants of Mycobacterium abscessus.

Mycobacterium abscessus (Mab) infection can be deadly in patients with chronic lung diseases like cystic fibrosis (CF). In vitro and in vivo, Mab may adopt a smooth (S) or rough (R) morphotype, the latter linked to more severe disease conditions. In vitro studies revealed differences in pathogenicity and immune response to S and R morphotypes. We propose that in vivo both morphotypes exist and may transiently switch depending on the environment, having important pathogenic and immunologic consequences. This can be modeled by morphotypic S and R variants of Mab selected based on in vitro growth conditions. Here, we report the first analysis of early transcriptional events in mouse bone marrow derived macrophages (BMDMs) upon infection with media-selected interchangeable Mab-S and Mab-R morphotypes. The early transcriptional events after infection with both morphotypes showed considerable overlap of the pro-inflammatory genes that were differentially regulated compared to the uninfected macrophages. We also observed signature genes significantly differentially regulated in macrophages during infection of media-selected morphotypic Mab-S and Mab-R variants. In conclusion, media-selected Mab-S and Mab-R behave in a similar fashion to stable S and R types with respect to pathogenesis and immune response, serving as a useful model for environmentally influenced morphotype selection.

RPEM: Randomized Monte Carlo parametric expectation maximization algorithm.

Inspired from quantum Monte Carlo, by sampling discrete and continuous variables at the same time using the Metropolis-Hastings algorithm, we present a novel, fast, and accurate high performance Monte Carlo Parametric Expectation Maximization (MCPEM) algorithm. We named it Randomized Parametric Expectation Maximization (RPEM). We compared RPEM with NONMEM's Importance Sampling Method (IMP), Monolix's Stochastic Approximation Expectation Maximization (SAEM), and Certara's Quasi-Random Parametric Expectation Maximization (QRPEM) for a realistic two-compartment voriconazole model with ordinary differential equations using simulated data. We show that RPEM is as fast and as accurate as the algorithms IMP, QRPEM, and SAEM for the voriconazole model in reconstructing the population parameters, for the normal and log-normal cases.

Genetic predisposition and high exposure to colistin in the early treatment period as independent risk factors for colistin-induced nephrotoxicity.

Colistin is known to cause nephrotoxicity due to its extensive reabsorption and accumulation in renal tubules. In vitro studies have identified the functional role of colistin transporters such as OCTN2, PEPT2, megalin, and P-glycoprotein. However, the role of these transporter gene variants in colistin-induced nephrotoxicity has not been studied. Utilizing targeted next-generation sequencing, we screened for genetic polymorphisms covering the colistin transporters (SLC15A1, SLC15A2, SLC22A5, LRP2, and ABCB1) in 42 critically ill patients who received colistimethate sodium. The genetic variants rs2257212 ((NM_021082.4):c.1048C>G) and rs13397109 ((NM_004525.3):C.7626C > T) were identified as being associated with an increased incidence of acute kidney injury (AKI) on Day 7. Colistin area under the curve (AUC) was predicted using a previously published pharmacokinetic model of colistin. Using logistic regression analysis, the predicted 24-h AUC of colistin was identified as an important contributor for increased odds of AKI on Day 7. Among 42 patients, 4 (9.5%) were identified as having high predisposition to colistin-induced AKI based on the presence of predisposing genetic variants. Determination of the presence of the abovementioned genetic variants and early therapeutic drug monitoring may reduce or prevent colistin-induced nephrotoxicity and facilitate dose optimization of colistimethate sodium.

Time-dependent pharmacodynamics of amikacin on Mycobacterium abscessus growth and resistance emergence.

Mycobacterium abscessus pulmonary disease is increasing in prevalence globally, particularly for individuals with cystic fibrosis. These infections are challenging to treat due to a high rate of resistance. Amikacin is critical to treatment, but the development of toxicity, amikacin resistance, and treatment failure are significant challenges. Amikacin has been characterized previously as peak-dependent and extended-interval dosing is commonly used. In our hollow fiber infection model of M. abscessus, amikacin exhibited time-dependent rather than the expected peak-dependent pharmacodynamics. Humanized amikacin exposures with more frequent, short-interval dosing (continuous infusion or every 12 hours) yielded improved microbiological response compared to extended-interval dosing (every 24 hours or 1-3 times per week). Short-interval dosing inhibited growth with a mean (SD) maximum Δlog10 colony forming units of -4.06 (0.52), significantly more than extended-interval dosing (P = 0.0013) every 24 hours, -2.40 (0.58), or 1-3 times per week, -2.39 (0.38). Growth recovery, an indicator of resistance emergence, occurred at 6.56 (0.70) days with short-interval dosing but was significantly earlier with extended-interval dosing (P = 0.0032) every 24 hours, 3.88 (0.85) days, and 1-3 times per week, 3.27 (1.72) days. Microbiological response correlated best with the pharmacodynamic index of %T > minimum inhibitory concentration (MIC), with an EC80 for growth inhibition of ~40%T > MIC. We used a previously published population model of amikacin to determine the probability of achieving 40%T > MIC and show that current dosing strategies are far below this target, which may partially explain why treatment failure remains so high for these infections. These data support a cautious approach to infrequent amikacin dosing for the treatment of M. abscessus.IMPORTANCEPulmonary disease caused by Mycobacterium abscessus complex (MABSC) is increasing worldwide, particularly in patients with cystic fibrosis. MABSC is challenging to treat due to high levels of antibiotic resistance. Treatment requires 2-4 antibiotics over more than 12 months and has a significant risk of toxicity but still fails to eradicate infection in over 50% of patients with cystic fibrosis. Antibiotic dosing strategies have been largely informed by common bacteria such as Pseudomonas aeruginosa. The "pharmacodynamic" effects of amikacin, a backbone of MABSC treatment, were thought to be related to maximum "peak" drug concentration, leading to daily or three times weekly dosing. However, we found that amikacin MABSC kill and growth recovery, an indicator of antibiotic resistance, are dependent on how long amikacin concentrations are above the minimum inhibitory concentration, not how high the peak concentration is. Therefore, we recommend a re-evaluation of amikacin dosing to determine if increased frequency can improve efficacy.

Individual meropenem epithelial lining fluid and plasma PK/PD target attainment.

It is unclear whether plasma is a reliable surrogate for target attainment in the epithelial lining fluid (ELF). The objective of this study was to characterize meropenem target attainment in plasma and ELF using prospective samples. The first 24-hour T>MIC was evaluated vs 1xMIC and 4xMIC targets at the patient (i.e., fixed MIC of 2 mg/L) and population [i.e., cumulative fraction of response (CFR) according to EUCAST MIC distributions] levels for both plasma and ELF. Among 65 patients receiving ≥24 hours of treatment, 40% of patients failed to achieve >50% T>4xMIC in plasma and ELF, and 30% of patients who achieved >50% T>4xMIC in plasma had <50% T>4xMIC in ELF. At 1xMIC and 4xMIC targets, 3% and 25% of patients with >95% T>MIC in plasma had <50% T>MIC in ELF, respectively. Those with a CRCL >115 mL/min were less likely to achieve >50%T>4xMIC in ELF (P < 0.025). In the population, CFR for Escherichia coli at 1xMIC and 4xMIC was >97%. For Pseudomonas aeruginosa, CFR was ≥90% in plasma and ranged 80%-85% in ELF at 1xMIC when a loading dose was applied. CFR was reduced in plasma (range: 75%-81%) and ELF (range: 44%-60%) in the absence of a loading dose at 1xMIC. At 4xMIC, CFR for P. aeruginosa was 60%-86% with a loading dose and 18%-62% without a loading dose. We found that plasma overestimated ELF target attainment inup to 30% of meropenem-treated patients, CRCL >115 mL/min decreased target attainment in ELF, and loading doses increased CFR in the population.

Optimizing Vancomycin Therapy in Critically Ill Children: A Population Pharmacokinetics Study to Inform Vancomycin Area under the Curve Estimation Using Novel Biomarkers.

Area under the curve (AUC)-directed vancomycin therapy is recommended, but Bayesian AUC estimation in critically ill children is difficult due to inadequate methods for estimating kidney function. We prospectively enrolled 50 critically ill children receiving IV vancomycin for suspected infection and divided them into model training (n = 30) and testing (n = 20) groups. We performed nonparametric population PK modeling in the training group using Pmetrics, evaluating novel urinary and plasma kidney biomarkers as covariates on vancomycin clearance. In this group, a two-compartment model best described the data. During covariate testing, cystatin C-based estimated glomerular filtration rate (eGFR) and urinary neutrophil gelatinase-associated lipocalin (NGAL; full model) improved model likelihood when included as covariates on clearance. We then used multiple-model optimization to define the optimal sampling times to estimate AUC24 for each subject in the model testing group and compared the Bayesian posterior AUC24 to AUC24 calculated using noncompartmental analysis from all measured concentrations for each subject. Our full model provided accurate and precise estimates of vancomycin AUC (bias 2.3%, imprecision 6.2%). However, AUC prediction was similar when using reduced models with only cystatin C-based eGFR (bias 1.8%, imprecision 7.0%) or creatinine-based eGFR (bias -2.4%, imprecision 6.2%) as covariates on clearance. All three model(s) facilitated accurate and precise estimation of vancomycin AUC in critically ill children.

Model-Informed Rationale for Early Therapeutic Drug Monitoring of Colistin in Critically Ill Patients.

Adequate colistin exposure is important for microbiological clearance. This study was performed in critically ill patients >18 years old to develop a simplified nonparametric pharmacokinetic (PK) model of colistin for routine clinical use and to determine the role of dose optimization. The Non-Parametric Adaptive Grid algorithm within the Pmetrics software package for R was used to develop a PK model from 47 patients, and external validation of the final model was performed in 13 patients. A 1-compartment multiplicative gamma error model with 0-order input and first-order elimination of colistin was developed with creatinine clearance and serum albumin as covariates on elimination rate constant. An R2 for observed vs individual predicted colistin concentrations of 0.92 was obtained in the validation cohort. High interindividual variability in colistin steady-state area under the plasma concentration-time curve (AUC) from from 120 hours to 144 hours (coefficient of variation = 80.1%) and a high interoccasion variability (median coefficient of variation of AUC from time 0 to hours predicted every 8 hours for initial 96 hours after starting colistin = 23.8) was predicted in patients who received this antibiotic for a period of over 152 hours (n = 22). With the model-suggested dose regimen, only 20% of simulated profiles achieved AUC from time 0 to 24 hours in the range of 50 to 60 mg â€¢ h/L due to high variability in population PK. In this group of patients, steady-state colistin concentrations were predicted to be achieved >96 hours after initiation of colistimethate sodium. This study advocates the need for early and repeated therapeutic drug monitoring and dose optimization in critically ill patients to achieve adequate therapeutic concentration of colistin.

Interchangeability of generic drugs for subpopulations: Bioequivalence simulation from a nonparametric PK model of gabapentin generic drugs.

Patients are often switched between generic formulations of the same drug, but in some cases generic interchangeability is questioned. For generic drugs to be approved, bioequivalence with the innovator drug should be demonstrated, but evidence of bioequivalence is not required in the intended patient population or relative to other approved generics. AIM: We aim to identify pathophysiological pharmacokinetic subpopulations for whom there is a difference in comparative bioavailability compared to a healthy population. METHODS: We used simulated exposures from a nonparametric model of multiple generics and the originator gabapentin. Exposure was simulated for virtual populations with pharmacokinetic characteristics beyond those of healthy subjects with regard to rate of absorption, volume of distribution and reduced renal function. Virtual parallel design bioequivalence studies were performed using a random sample of 24 simulated subjects, with standard acceptance criteria. RESULTS: Results indicated increased pharmacokinetic variability for patient populations with a lower rate of absorption or a reduced renal function, but no change in the average comparable bioavailability ratio. This increased variability results in a reduced likelihood of demonstrating bioequivalence. Observations were similar for comparisons between all different formulations, as well as between subjects who received the identical formulation in a repeated fashion. No relevant effect was observed for simulations with increased volume of distribution. CONCLUSION: Our simulations indicate that the reduced likelihood of demonstrating bioequivalence for subjects with altered pharmacokinetics is not influenced by a formulation switch, nor does the average comparable bioavailability ratio change, therefore these results support generic interchangeability and current approval requirements for generics.

Individual target pharmacokinetic/pharmacodynamic attainment rates among meropenem-treated patients admitted to the ICU with hospital-acquired pneumonia.

OBJECTIVES: Critical illness reduces ß-lactam pharmacokinetic/pharmacodynamic (PK/PD) attainment. We sought to quantify PK/PD attainment in patients with hospital-acquired pneumonia. METHODS: Meropenem plasma PK data (n = 70 patients) were modelled, PK/PD attainment rates were calculated for empirical and definitive targets, and between-patient variability was quantified [as a coefficient of variation (CV%)]. RESULTS: Attainment of 100% T>4×MIC was variable for both empirical (CV% = 92) and directed (CV% = 33%) treatment. CONCLUSIONS: Individualization is required to achieve suggested PK/PD targets in critically ill patients.

Pharmacokinetic modelling of caspofungin to develop an extended dosing regimen in paediatric patients.

BACKGROUND: Echinocandins are commonly used in treatment and prophylaxis of invasive fungal diseases. Intravenous daily dosing for prophylaxis in the outpatient setting can however become a hurdle for adequate compliance in the paediatric population. OBJECTIVES: Simulations were performed to assess extended twice-weekly dosing for antifungal prophylaxis using caspofungin. METHODS: A population pharmacokinetic model was developed based on previously published data from children aged 3 months to 17 years. Using the final model, Monte Carlo simulations were performed to assess the dose needed for adequate exposure in a twice-weekly setting. Mean weekly AUC0-24 h/MIC together with reported AUC0-24 h from previously reported paediatric trials were used to guide adequate exposure. RESULTS AND CONCLUSIONS: A two-compartment model with linear elimination and allometric scaling using fixed exponents was found most adequate to describe the given paediatric populations. Simulations showed that a 200 mg/m2 twice-weekly regimen with maximal 200 mg total dose should result in exposures matching registered daily dosing as well as commonly used pharmacokinetic/pharmacodynamic targets.

Cefepime Precision Dosing Tool: from Standard to Precise Dose Using Nonparametric Population Pharmacokinetics.

Cefepime is the second most common cephalosporin used in U.S. hospitals. We aim to develop and validate a cefepime population pharmacokinetic (PK) model and integrate it into a precision dosing tool for implementation. Two data sets (680 patients) were used to build the cefepime PK model in Pmetrics, and three data sets (34 patients) were used for the validation. A separate application data set (115 patients) was used for the implementation and validation of a precision dosing tool. The model support points and covariates were used to generate the optimal initial dose (OID). Cefepime PK was described by a two-compartment model including weight and creatinine clearance (CrCl) as covariates. The median rate of elimination was 0.30 h-1 (adults) and 0.96 h-1 (children), the central volume of distribution was 13.85 L, and the rate of transfer from the central to the peripheral compartments was 1.22 h-1 and from the peripheral to the central compartments was 1.38 h-1. After integration in BestDose, the observed versus predicted cefepime concentration fit using the application data set was excellent (R2 > 0.98), and the median difference between what was observed and what BestDose predicted on a second occasion was 4%. For the OID, cefepime at a 0.5- to 1-g 4-h infusion every 8 to 24 h (q8 to 24 h) with a CrCl of <70 mL/min was needed to achieve a target range of free trough:MIC 1 to 4 at a MIC of 8 mg/L, while continuous infusion was needed for higher CrCl and weight values. In conclusion, we developed and validated a cefepime model for clinical application. The model was integrated in a precision dosing tool for implementation, and the median concentration prediction bias was 4%. The OID algorithm was provided.

Isavuconazole Pharmacokinetics and Pharmacodynamics in Children.

Isavuconazole is a broad-spectrum azole anti-fungal not yet approved in children. We conducted a retrospective, single-center review of isavuconazole use and routine therapeutic drug monitoring in pediatric patients, extracting demographic, dosing, concentration, mortality and hepatoxicity data. We constructed a nonparametric population model using Pmetrics. Of 26 patients, 19 (73%) were male. The mean (SD) age and weight were 12.7 (5.5) years and 50.9 (26.8) kg. Eighty percent received between 9.7 and 10.6 mg/kg per dose. Ten (38%) subjects had proven fungal disease and eight (31%) had probable disease, mostly with Candida and Aspergillus spp. The predicted steady-state isavuconazole concentrations in our patients were similar to previous reports in children and adults, and simulations with the proposed dosing of 10 mg/kg/dose every 8 h for 2 days followed by once daily maintenance matched effective adult exposures. Attributable mortality (5 of 11 deaths) was associated with steady-state daily AUC < 60 mg∗h/L and higher AST/ALT with trough concentrations > 5 mg/L. Neither dose nor trough alone correlated well with AUC, but AUC can be estimated with one sample 10 h after the first maintenance dose or a trough concentration, if combined with a Bayesian approach or a peak and trough without a Bayesian approach.

Parametric and Nonparametric Population Pharmacokinetic Models to Assess Probability of Target Attainment of Imipenem Concentrations in Critically Ill Patients.

Population pharmacokinetic modeling and simulation (M&S) are used to improve antibiotic dosing. Little is known about the differences in parametric and nonparametric M&S. Our objectives were to compare (1) the external validation of parametric and nonparametric models of imipenem in critically ill patients and (2) the probability of target attainment (PTA) calculations using simulations of both models. The M&S software used was NONMEM 7.2 (parametric) and Pmetrics 1.5.2 (nonparametric). The external predictive performance of both models was adequate for eGFRs ≥ 78 mL/min but insufficient for lower eGFRs, indicating that the models (developed using a population with eGFR ≥ 60 mL/min) could not be extrapolated to lower eGFRs. Simulations were performed for three dosing regimens and three eGFRs (90, 120, 150 mL/min). Fifty percent of the PTA results were similar for both models, while for the other 50% the nonparametric model resulted in lower MICs. This was explained by a higher estimated between-subject variability of the nonparametric model. Simulations indicated that 1000 mg q6h is suitable to reach MICs of 2 mg/L for eGFRs of 90-120 mL/min. For MICs of 4 mg/L and for higher eGFRs, dosing recommendations are missing due to largely different PTA values per model. The consequences of the different modeling approaches in clinical practice should be further investigated.

Growth medium and nitric oxide alter Mycobacterium abscessus morphotype and virulence.

Mycobacterium abscessus complex (MABC) infections cause significant morbidity and mortality among patients with chronic lung disease, like cystic fibrosis. MABC exists in smooth (S) and rough (R) morphotypes, but triggers of morphotype switching and associated pathogenicity or antimicrobial susceptibility are poorly understood. We demonstrate that M. abscessus subspecies abscessus (Mab), massiliense (Mms), and bolletii (Mbl) cultured in Middlebrook (MB) broth exhibit S morphotype, whereas the bacteria grown in Luria Bertani (LB) broth adopt the R morphotype, characterized by low glycopeptidolipid (GPL) expression. The components of broth that mediate this selection are complex, with albumin supplementation promoting growth of S morphotype, but not sufficient for complete selection. Consistent with the findings of other groups, R forms of Mab, Mms and Mbl selected by LB broth were internalized in RAW 264.7 macrophages with higher efficiency than S. Intracellular survival of broth-selected organisms was variable and was higher for S Mab, but lower for S Mms and Mbl. It is proposed that growth in R morphotype is induced during stress conditions, such as nutrient poor environments or during inflammation. One key component of inflammation is release of nitric oxide. We demonstrated that a nitric oxide donor (DETA-NONOate) appears to induce growth in an R morphotype, as indicated by reduced GPL expression of Mab. Mab treated with DETA-NONOate also enhanced susceptibility to azithromycin at sub-MIC concentrations. In conclusion, morphotype and macrophage intracellular bacterial load of MABC subspecies can be manipulated by growing the bacteria in different culture conditions. Nitric oxide may also drive morphotype selection and enhanced azithromycin activity against Mab and macrophage killing.

A pharmacokinetics-based approach to the monitoring of patient adherence to atorvastatin therapy.

The inadequate adherence of patients whose hyperlipidemia is treated with atorvastatin (ATR) to medical instructions presents a serious health risk. Our aim was to develop a flexible approach based on therapeutic drug monitoring (TDM), nonparametric population pharmacokinetic modeling, and Monte Carlo simulation to differentiate adherent patients from partially and nonadherent individuals in a nonrandomized, unicentric, observational study. Sixty-five subjects were enrolled. Nonparametric, mixed-effect population pharmacokinetic models of the sums of atorvastatin and atorvastatin lactone concentrations (ATR+ATRL) and of the concentrations of the acid and lactone forms of ATR and its 2- and 4-hydroxylated pharmacologically active metabolites (ATR+MET) were elaborated by including the TDM results obtained in 128 samples collected from thirty-nine subjects. Monte Carlo simulation was performed based on the elaborated models to establish the probabilities of attaining a specific ATR+ATRL or ATR+MET concentration in the range of 0.002-10 nmol (mg dose)-1 L-1 at 1-24 h postdose by adherent, partially adherent, and nonadherent patients. The results of the simulations were processed to allow the estimation of the adherence of further 26 subjects who were phlebotomized at sampling times of 2-20 h postdose by calculating the probabilities of attaining the ATR+ATRL and ATR+MET concentrations measured in these subjects in adherent, partially adherent, and nonadherent individuals. The best predictive values of the estimates of adherence could be obtained with sampling at early sampling times. 61.54% and 38.46% of subjects in the adherence testing set were estimated to be fully and partially adherent, respectively, while in all cases the probability of nonadherence was extremely low. The evaluation of patient adherence to ATR therapy based on pharmacokinetic modeling and Monte Carlo simulation has important advantages over the collection of trough samples and the use of therapeutic ranges.

Building Optimal Three-Drug Combination Chemotherapy Regimens To Eradicate Mycobacterium tuberculosis in Its Slow-Growth Acid Phase.

Mycobacterium tuberculosis metabolic state affects the response to therapy. Quantifying the effect of antimicrobials in the acid and nonreplicating metabolic phases of M. tuberculosis growth will help to optimize therapy for tuberculosis. As a brute-force approach to all possible drug combinations against M. tuberculosis 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 drugs and two- and three-drug combinations of pretomanid, moxifloxacin, and bedaquiline plus its active metabolite against M. tuberculosis 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 M. tuberculosis 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.

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.