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Purpose: Cisplatin-induced hearing loss (CIHL) is common and permanent. As compared with earlier otoprotectants, we hypothesized N-acetylcysteine (NAC) offers potential for stronger otoprotection through stimulation of glutathione (GSH) production. This study tested the optimal dose, safety, and efficacy of NAC to prevent CIHL. Patients and Methods: In this nonrandomized, controlled phase Ia/Ib trial, children and adolescents newly diagnosed with nonmetastatic, cisplatin-treated tumors received NAC intravenously 4 hours post-cisplatin. The trial performed dose-escalation across three dose levels to establish a safe dose that exceeded the targeted peak serum NAC concentration of 1.5 mmol/L (as identified from preclinical models). Patients with metastatic disease or who were otherwise ineligible were enrolled in an observation-only/control arm. To evaluate efficacy, serial age-appropriate audiology assessments were performed. Integrated biology examined genes involved in GSH metabolism and post-NAC GSH concentrations. Results: Of 52 patients enrolled, 24 received NAC and 28 were in the control arm. The maximum tolerated dose was not reached; analysis of peak NAC concentration identified 450 mg/kg as the recommended phase II dose (RP2D). Infusion-related reactions were common. No severe adverse events occurred. Compared with the control arm, NAC decreased likelihood of CIHL at the end of cisplatin therapy [OR, 0.13; 95% confidence interval (CI), 0.021–0.847; P = 0.033] and recommendations for hearing intervention at end of study (OR, 0.082; 95% CI, 0.011–0.60; P = 0.014). NAC increased GSH; GSTP1 influenced risk for CIHL and NAC otoprotection. Conclusions: NAC was safe at the RP2D, with strong evidence for efficacy to prevent CIHL, warranting further development as a next-generation otoprotectant.

Background Annual mortality from neonatal sepsis is an estimated 430 000–680 000 infants globally, most of which occur in low- and middle-income countries (LMICs). The WHO currently recommends a narrow-spectrum β-lactam (e.g. ampicillin) and gentamicin as first-line empirical therapy. However, available epidemiological data demonstrate high rates of resistance to both agents. Alternative empirical regimens are needed. Flomoxef and amikacin are two off-patent antibiotics with potential for use in this setting. Objectives To assess the pharmacodynamics of flomoxef and amikacin in combination. Methods The pharmacodynamic interaction of flomoxef and amikacin was assessed in chequerboard assays and a 16-arm dose-ranged hollow-fibre infection model (HFIM) experiment. The combination was further assessed in HFIM experiments mimicking neonatal plasma exposures of clinically relevant doses of both drugs against five Enterobacterales isolates with a range of flomoxef/amikacin MICs. Results Flomoxef and amikacin in combination were synergistic in bacterial killing in both assays and prevention of emergence of amikacin resistance in the HFIM. In the HFIM assessing neonatal-like drug exposures, the combination killed 3/5 strains to sterility, (including 2/5 that monotherapy with either drug failed to kill) and failed to kill the 2/5 strains with flomoxef MICs of 32 mg/L. Conclusions We conclude that the combination of flomoxef and amikacin is synergistic and is a potentially clinically effective regimen for the empirical treatment of neonatal sepsis in LMIC settings and is therefore suitable for further assessment in a clinical trial.

Supplementary Fig. 1 Treatment schema & Sampling Timepoints

Supplementary Fig. 4 Evaluation of long-term storage of serum specimens (NAC assay)

Supplementary Fig. 3 Change in serum glutathione concentrations following NAC infusion

Supplementary Fig. 5 Progression-free survival in NAC treated and observation patients

Supplementary Fig. 2 Cumulative incidence of SIOP Grade ≥2 hearing loss

Supplemental Tables and Files

Purpose:Cisplatin-induced hearing loss (CIHL) is common and permanent. As compared with earlier otoprotectants, we hypothesized N-acetylcysteine (NAC) offers potential for stronger otoprotection through stimulation of glutathione (GSH) production. This study tested the optimal dose, safety, and efficacy of NAC to prevent CIHL.Patients and Methods:In this nonrandomized, controlled phase Ia/Ib trial, children and adolescents newly diagnosed with nonmetastatic, cisplatin-treated tumors received NAC intravenously 4 hours post-cisplatin. The trial performed dose-escalation across three dose levels to establish a safe dose that exceeded the targeted peak serum NAC concentration of 1.5 mmol/L (as identified from preclinical models). Patients with metastatic disease or who were otherwise ineligible were enrolled in an observation-only/control arm. To evaluate efficacy, serial age-appropriate audiology assessments were performed. Integrated biology examined genes involved in GSH metabolism and post-NAC GSH concentrations.Results:Of 52 patients enrolled, 24 received NAC and 28 were in the control arm. The maximum tolerated dose was not reached; analysis of peak NAC concentration identified 450 mg/kg as the recommended phase II dose (RP2D). Infusion-related reactions were common. No severe adverse events occurred. Compared with the control arm, NAC decreased likelihood of CIHL at the end of cisplatin therapy [OR, 0.13; 95% confidence interval (CI), 0.021–0.847; P = 0.033] and recommendations for hearing intervention at end of study (OR, 0.082; 95% CI, 0.011–0.60; P = 0.014). NAC increased GSH; GSTP1 influenced risk for CIHL and NAC otoprotection.Conclusions:NAC was safe at the RP2D, with strong evidence for efficacy to prevent CIHL, warranting further development as a next-generation otoprotectant.

Primer sequences used for rtPCR.

Background Neonatal sepsis is a serious bacterial infection of neonates, globally killing up to 680 000 babies annually. It is frequently complicated by antimicrobial resistance, particularly in low- and middle-income country (LMIC) settings with widespread resistance to the WHO’s recommended empirical regimen of ampicillin and gentamicin. Objectives We assessed the utility of flomoxef and fosfomycin as a potential alternative empirical regimen for neonatal sepsis in these settings. Methods We studied the combination in a 16-arm dose-ranged hollow-fibre infection model (HFIM) experiment and chequerboard assays. We further assessed the combination using clinically relevant regimens in the HFIM with six Enterobacterales strains with a range of flomoxef/fosfomycin MICs. Results Pharmacokinetic/pharmacodynamic modelling of the HFIM experimental output, along with data from chequerboard assays, indicated synergy of this regimen in terms of bacterial killing and prevention of emergence of fosfomycin resistance. Flomoxef monotherapy was sufficient to kill 3/3 strains with flomoxef MICs ≤0.5 mg/L to sterility. Three of three strains with flomoxef MICs ≥8 mg/L were not killed by fosfomycin or flomoxef monotherapy; 2/3 of these were killed with the combination of the two agents. Conclusions These data suggest that flomoxef/fosfomycin could be an efficacious and synergistic regimen for the empirical treatment of neonatal sepsis in LMIC settings with prevalent antimicrobial resistance. Our HFIM results warrant further assessment of the flomoxef/fosfomycin combination in clinical trials.

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.

Childhood vaccine hesitancy has been studied extensively before the COVID-19 pandemic. The pandemic presented new barriers to pediatric vaccinations. Furthermore, the development of COVID-19 vaccines has complicated factors underlying vaccine hesitancy. We performed a cross-sectional mobile phone-based survey at Children’s Hospital Los Angeles querying parents regarding perspectives on vaccines before and during the pandemic. Our primary aim was to understand the impact of the pandemic on routine childhood vaccine hesitancy. Secondarily, we examined intent to vaccinate, COVID-19 vaccine hesitancy, and key contributing demographic factors. Among 252 participants, we found overall increased childhood vaccine hesitancy (p = 0.006), increased risk perception (p = 0.006), and unchanged vaccine confidence during the COVID-19 pandemic. Increased hesitancy did not translate into decreased intent to vaccinate with routine childhood vaccines or influenza vaccines. During the pandemic, households with higher income (50–99 K, > 100 K) correlated with decreased routine childhood vaccine hesitancy, while Hispanic ethnicity and African American race had increased risk perception. For COVID-19 vaccine hesitancy, households with higher income (> 100 K) correlated with decreased hesitancy, while non-White ethnicity and race had increased risk perception. We found that routine childhood vaccine hesitancy increased during the COVID-19 pandemic, mainly due to increased risk perception. Key contributing demographic factors behind both childhood vaccine hesitancy and COVID-19 vaccine hesitancy included household income and race. Understanding factors behind routine childhood vaccine hesitancy is crucial to maintaining pediatric vaccination rates and promoting vaccine confidence during and after the COVID-19 pandemic.

Nearly all international regimens for pediatric acute lymphoblastic leukemia (ALL) incorporate intravenous "high dose" methotrexate (HDMTX, ≥1 gram/m2 ) to penetrate the central nervous system. Dosing is routinely adjusted for body surface area (BSA) but limited data describe the pharmacokinetics of HDMTX, particularly in obese and/or large patients. To understand the impact of body size (BSA) and body fat percentage (BFP) on HDMTX pharmacokinetics, we performed a secondary analysis of 36 children and adolescents 10-21 years old treated for newly diagnosed ALL and who were enrolled in a prospective study examining body composition. All patients received 5 grams/m2 of HDMTX infused over 24 hours. Plasma methotrexate concentrations were measured at +24, +42, and +48 hours. At 48 hours, ≥0.4 μmol/L was defined as "delayed elimination" necessitating prolonged supportive care. BFP was measured using dual-energy x-ray absorptiometry. A nonparametric population pharmacokinetic model was constructed with subsequent simulations to explore effects of BSA and BFP extremes. Despite standard BSA-adjusted dosing, we found significant intra-patient variability in mean MTX concentration (38%, range 1.2-86%). BSA and BFP were not linearly associated with increased area under the curve (AUC, p = 0.74 and p = 0.12), but both larger size (BSA) and greater obesity (BFP) were associated with an approximately twofold higher risk for delayed elimination at 48 hours. HDMTX AUC was not associated with toxicity. MTX pharmacokinetics vary among and even within patients despite BSA-adjusted dosing. Obesity and large size are identified as new risk factors for delayed elimination requiring further investigation. This article is protected by copyright. All rights reserved.

Refereed/Peer-reviewed Individualization of vancomycin dosing based on therapeutic drug monitoring (TDM) data is known to improve patient outcomes compared with fixed or empirical dosing strategies. There is increasing evidence to support area-under-the-curve (AUC24)-guided TDM to inform vancomycin dosing decisions for patients receiving therapy for more than 48 hours. It is acknowledged that there may be institutional barriers to the implementation of AUC24-guided dosing, and additional effort is required to enable the transition from trough-based to AUC24-based strategies. Adequate documentation of sampling, correct storage and transport, accurate laboratory analysis, and pertinent data reporting are required to ensure appropriate interpretation of TDM data to guide vancomycin dosing recommendations. Ultimately, TDM data in the clinical context of the patient and their response to treatment should guide vancomycin therapy. Endorsed by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology, the IATDMCT Anti-Infectives Committee, provides recommendations with respect to best clinical practice for vancomycin TDM.

Understanding exposures of cefepime, a β-lactam antibiotic, is crucial for developing regimens to achieve optimal exposure and improved clinical outcomes. This study sought to develop and evaluate a unified population pharmacokinetic model in both pediatric and adult patients receiving cefepime treatment. Multiple physiologically relevant models were fit to pediatric and adult subject data. To evaluate the final model performance, a withheld group of twelve pediatric and two separate adult populations were assessed. Seventy subjects with a total of 604 cefepime concentrations were included in this study. All adults (n=34) on average weighed 82.7 kg and displayed a mean creatinine clearance (CrCL) of 106.7 mL/min. All pediatric subjects (n=36) had mean weight and CrCL of 16.0 kg and 195.64 mL/min, respectively. A covariate-adjusted two compartment model described the observed concentrations well (population model R2, 87.0%; Bayesian model R2, 96.5%). In the evaluation subsets, the model performed similarly well (population R2, 84.0%; Bayesian R2, 90.2%). The identified model serves well for population dosing and as a Bayesian prior for precision dosing.Key pointsA unified cefepime population pharmacokinetic model has been developed from adult and pediatric patients and evaluates well in independent populations.When paired with real time beta-lactam assays, precision dosing approach will optimize drug exposure and improve clinical outcomes.

Linezolid is an antibiotic used to treat infections caused by drug-resistant gram-positive organisms, including vancomycin-resistant Enterococcus faecium, multi-drug resistant Streptococcus pneumoniae, and methicillin-resistant Staphylococcus aureus. The adverse effects of linezolid can include thrombocytopenia and neuropathy, which are more prevalent with higher exposures and longer treatment durations. Although linezolid is traditionally administered at a standard 600 mg dose every 12 hours, the resulting exposure can vary greatly between patients and can lead to treatment failure or toxicity. The efficacy and toxicity of linezolid are determined by the exposure achieved in the patient; numerous clinical and population pharmacokinetics (popPK) studies have identified threshold measurements for both parameters. Several special populations with an increased need for linezolid dose adjustments have also been identified. Therapeutic Drug Monitoring (TDM) is a clinical strategy that assesses the response of an individual patient and helps adjust the dosing regimen to maximize efficacy while minimizing toxicity. Adaptive feedback control and model-informed precision dosing are additional strategies that use Bayesian algorithms and PK models to predict patient-specific drug exposure. TDM is a very useful tool for patient populations with sparse clinical data or known alterations in pharmacokinetics, including children, patients with renal insufficiency or those receiving renal replacement therapy, and patients taking co-medications known to interact with linezolid. As part of the clinical workflow, clinicians can use TDM with the thresholds summarized from the current literature to improve linezolid dosing for patients and maximize the probability of treatment success.

Imagine it is 2030, and the drug label is in the cloud, is interactive, and can provide model-informed precision dosing support based on an individual's genomic and physiologic makeup that is uploaded via a user-friendly interface. Precision medicine has vastly improved our ability to provide tailored therapeutics, and groundbreaking advances in noninvasive systems have generated smart wearable devices that can follow our physiologic state and drug exposure in real time. One device consists of a microfluidic drug biosensor with a transmitter attached to the skin and a mobile app that displays concentration values and trends while issuing alerts and providing suggestions on dose changes when out of range. This sounds compelling, but why has model-informed precision dosing not yet become common clinical reality in 2020?

BACKGROUND: Up to 90% of children develop Pseudomonas aeruginosa (Pa)-positive respiratory cultures after tracheotomy. OBJECTIVE: To identify factors associated with chronic Pa-positive respiratory cultures in the first two years after tracheotomy. METHOD: We conducted a retrospective cohort study of 210 children ≤18 years old who underwent tracheotomy at a single freestanding children’s hospital who had two or more years of respiratory cultures post-tracheotomy available for analysis. We conducted multivariable logistic regression to test the association between demographic and clinical factors to our primary outcome of chronic Pa infection, defined as >75% of respiratory cultures positive for Pa in the first two years after tracheotomy. RESULTS: Of the primarily male (61%), Hispanic (68%) and publicly-insured (88%) cohort, 18% (n=37) developed chronic Pa-positive respiratory cultures in the first two years. On multivariable logistic regression, pre-tracheotomy Pa-positive respiratory culture (aOR=11.3; 95% CI: 4-1.5) and discharge on beta-agonist (aOR=6.3; 95% CI: 1.1-36.8) were independently associated with chronic Pa-positive respiratory cultures while discharge on chronic mechanical ventilation was associated with decreased odds (aOR=0.3; 95% CI: 0.1-0.7). On sensitivity analysis examining those without a pre-tracheotomy Pa-positive respiratory culture, discharge on MV continued to be associated with decreased odds of chronic Pa (aOR 0.1; 95% CI 0.02-0.4) and three other variables (male gender, chronic lung disease, discharge on inhaled corticosteroids) were associated with increased odds of chronic Pa. CONCLUSION: Because pre-tracheotomy Pa growth on respiratory culture is associated with post-tracheotomy chronic Pa-positive respiratory cultures, future research should examine pre-tracheotomy Pa eradication or suppression protocols.

Population pharmacokinetics consists in analyzing pharmacokinetic (PK) data collected in groups of individuals. Population PK is widely used to guide drug development and to inform dose adjustment via therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD). There are two main types of population PK methods: parametric (P) and nonparametric (NP). The characteristics of P and NP population methods have been previously reviewed. The aim of this article is to answer some frequently asked questions that are often raised by scholars, clinicians and researchers about P and NP population PK methods. The strengths and limitations of both approaches are explained, and the characteristics of the main software programs are presented. We also review the results of studies that compared the results of both approaches in the analysis of real data. This opinion paper may be informative for potential users of population methods in PK and guide them in the selection and use of those tools. It also provides insights on future research in this area. This article is protected by copyright. All rights reserved.

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.

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., We set forth a pharmacokinetics‐based clinical laboratory procedure and decision‐making algorithm for the therapeutic monitoring of atorvastatin concentrations at any time that has passed from drug intake to sample collection.

Individualization of vancomycin dosing based on therapeutic drug monitoring (TDM) data is known to improve patient outcomes compared with fixed or empirical dosing strategies. There is increasing evidence to support area-under-the-curve (AUC24)-guided TDM to inform vancomycin dosing decisions for patients receiving therapy for more than 48 hours. It is acknowledged that there may be institutional barriers to the implementation of AUC24-guided dosing, and additional effort is required to enable the transition from trough-based to AUC24-based strategies. Adequate documentation of sampling, correct storage and transport, accurate laboratory analysis, and pertinent data reporting are required to ensure appropriate interpretation of TDM data to guide vancomycin dosing recommendations. Ultimately, TDM data in the clinical context of the patient and their response to treatment should guide vancomycin therapy. Endorsed by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology, the IATDMCT Anti-Infectives Committee, provides recommendations with respect to best clinical practice for vancomycin TDM.

BackgroundVancomycin is a first line antibiotic for many common infectious diseases and is the most commonly prescribed antibiotic in the United States hospital setting. Vancomycin is also well known to cause kidney injury; two recent prospective studies have identified that increasing vancomycin area under the concentration curve predicts vancomycin induced kidney injury (VIKI). However, outside of clinical trials, it is unclear if pre-clinical data can quantitatively describe VIKI in patients.MethodsData were simultaneously analyzed from a pre-clinical rat model and two prospective clinical studies. Logged vancomycin area under the concentration curve (AUC) data for rats (n=48) and patients from PROVIDE (n=263) and CAMERA2 (n=291) were included. VIKI was defined as urinary KIM-1 concentrations ≥9.42 ng/mL in the rat and according to KDIGO stage 1 kidney injury for all human patients. Multiple generalized linear models were explored, and the order of magnitude was calculated between the probability of acute kidney injury (AKI) from the average obtained in the clinical studies (i.e. CAMERA2 and PROVIDE) and the rat for 0.1 increments in Log10AUC bounded common concentrations obtained in the therapeutic range (i.e. ~200 −800 mg*24h/L).ResultsA logit link model best fit the data. When calculating the multiplicative factors between the studies therapeutic range AUCs, the rat was an average 2.7 to 4.2 times more sensitive to AKI between AUCs of 199.5 (i.e. log 10 AUC=2.3) and 794.3 mg*h/L (i.e. log 10 AUC=2.9), respectively.ConclusionsA pre-clinical rat model was quantitatively linked to toxicity data from two large human studies. The rat is an attractive pre-clinical model to explore exposure toxicity relationships with vancomycin. External validation is required.

Isavuconazole, administered as the water-soluble prodrug isavuconazonium sulfate, is a new triazole agent used to treat invasive fungal infections. This phase 1 study evaluated the pharmacokinetics (PK), safety, and tolerability of isavuconazole in 46 immunocompromised pediatric patients, stratified by age (1 to 80% and >76% of simulated pediatric patients following i.v. or oral administration, respectively. Intravenous and oral administration of isavuconazonium sulfate at the studied dosage of 10 mg/kg was well tolerated and resulted in exposure in pediatric patients similar to that in adults. (This study has been registered at ClinicalTrials.gov under identifier NCT03241550).

Antimicrobial resistance (particularly through extended-spectrum β-lactamase and aminoglycoside-modifying enzyme production) in neonatal sepsis is a global problem, particularly in low- and middle-income countries, with significant mortality rates. High rates of resistance are reported for the current WHO-recommended first-line antibiotic regimen for neonatal sepsis, i.e., ampicillin and gentamicin. We assessed the utility of fosfomycin and amikacin as a potential alternative regimen to be used in settings of increasingly prevalent antimicrobial resistance. The combination was studied in a 16-arm dose-ranged hollow-fiber infection model (HFIM) experiment. The combination of amikacin and fosfomycin enhanced bactericidal activity and prevented the emergence of resistance, compared to monotherapy with either antibiotic. Modeling of the experimental quantitative outputs and data from checkerboard assays indicated synergy. We further assessed the combination regimen at clinically relevant doses in the HFIM with nine Enterobacterales strains with high fosfomycin and amikacin MICs and demonstrated successful kill to sterilization for 6/9 strains. From these data, we propose a novel combination breakpoint threshold for microbiological success for this antimicrobial combination against Enterobacterales strains, i.e., MIC(F) × MIC(A) < 256 (where MIC(F) and MIC(A) are the fosfomycin and amikacin MICs, respectively). Monte Carlo simulations predict that a standard fosfomycin-amikacin neonatal regimen would achieve >99% probability of pharmacodynamic success for strains with MICs below this threshold. We conclude that the combination of fosfomycin with amikacin is a viable regimen for the empirical treatment of neonatal sepsis and is suitable for further clinical assessment in a randomized controlled trial.

In recent years, clofazimine (CFZ) has been regaining prominence for the treatment of tuberculosis. However, it shows limited efficacy as a single drug and optimal combination partners have not been identified. Therefore, the objective of our analysis was to evaluate the efficacy of CFZ-containing two-drug regimens with pretomanid (PMD), bedaquiline (BDQ) or linezolid (LZD) by: (i) determining their pharmacodynamic (PD) mode of interaction against Mycobacterium tuberculosis (Mtb) strain H37Rv in log- phase and acid-phase metabolic states, and against Mtb strain 18b in a non-replicating persister (NRP) metabolic state; (ii) predicting bacterial cell kill of the drugs alone and in combination; and (iii) evaluating the relationship between the interaction mode and the extent of bacterial cell kill. 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. The results further showed that all two-drug combination regimens exerted greater 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 extent of bacterial kill. However, we may underestimate the effect of CFZ in this screening approach owing to limited in vitro study duration and neglect of target site accumulation. Clofazimine; Pretomanid; Bedaquiline; Linezolid; Combination chemotherapy; Mycobacterium tuberculosis.

The high interindividual variability in the pharmacokinetics (PK) of linezolid has been described, which results in an unacceptably high proportion of patients with either suboptimal or potentially toxic concentrations following the administration of a fixed regimen. The aim of this study was to develop a population pharmacokinetic model of linezolid and use this to build and validate alogorithms for individualized dosing. A retrospective pharmacokinetic analysis was performed using data from 338 hospitalized patients (65.4% male, 65.5 [±14.6] years) who underwent routine therapeutic drug monitoring for linezolid. Linezolid concentrations were analyzed by using high-performance liquid chromatography. Population pharmacokinetic modeling was performed using a nonparametric methodology with Pmetrics, and Monte Carlo simulations were employed to calculate the 100% time >MIC after the administration of a fixed regimen of 600 mg administered every 12 h (q12h) intravenously (i.v.). The dose of linezolid needed to achieve a PTA ≥ 90% for all susceptible isolates classified according to EUCAST was estimated to be as high as 2,400 mg q12h, which is 4 times higher than the maximum licensed linezolid dose. The final PK model was then used to construct software for dosage individualization, and the performance of the software was assessed using 10 new patients not used to construct the original population PK model. A three-compartment model with an absorptive compartment with zero-order i.v. input and first-order clearance from the central compartment best described the data. The dose optimization software tracked patients with a high degree of accuracy. The software may be a clinically useful tool to adjust linezolid dosages in real time to achieve prespecified drug exposure targets. A further prospective study is needed to examine the potential clinical utility of individualized therapy.

The need for precision dosing has been challenged on the basis of insufficient evidence. Herein, we argue that adequate evidence exists to conduct therapeutic drug monitoring (TDM) and precisely target antibiotic exposures. While achievement of any antibiotic concentration does not guarantee efficacy sans toxicity for any single patient, stochastic control optimizes the probability of achieving favourable responses across patients. We argue that variability in targets (such as the organism’s MIC) can be considered with models. That is, complexity alone does not relegate the decision-making framework to ‘clinician intuition’. We acknowledge the exposure–response relationships are modified by patient-specific factors (other drugs, baseline organ functional status etc.) and describe how precision dosing can inform clinical decision making rather than protocolize it. Finally, we call for randomized, controlled trials; however, we suggest that these trials are not necessary to make TDM standard of care for multiple classes of antibiotics.

Gentamicin is known to have concentration-dependent bactericidal activity, and its nephrotoxic effect is well described. We developed a population pharmacokinetic/pharmacodynamic model to optimize gentamicin dosing in pediatrics. Data were retrospectively collected for pediatric patients 1 month to 12 years of age, admitted to general pediatric wards or intensive care units and received gentamicin for suspected or proven Gram-negative infections at King Saud University Medical City, Riyadh, Saudi Arabia. A total of 306 gentamicin peak and trough concentrations sets from 107 patients were analyzed with mean (±standard deviation) patient age and weight of 4.5 ± 3.5 years and 16.7 ± 10.8 kg, respectively. Gentamicin pharmacokinetics were adequately described with a one compartment system (R = 0.82, bias = 1.75% and precision = 88% for population predictions and R = 0.94, bias = 5% and precision = 29% for individual predictions). The gentamicin pharmacokinetic parameters were as follows: volume of distribution = 8.9 L, total body clearance = 2.8 L/h for a 20-kg patient. Monte Carlo simulations showed that doses of 5-6 mg/kg/dose once daily are adequate only to treat infections with Gram-negative organisms having minimal inhibitory concentration less than 1 µg/mL. While, at minimal inhibitory concentration of 1 µg/mL, higher doses (7-8 mg/kg/dose once daily) are needed to maximize the efficacy of gentamicin. However, at minimal inhibitory concentration of 2 µg/mL, even a 10 mg/kg dose showed poor target attainment (52%). The finding of this study highlights the need to reevaluate the current breakpoints of gentamicin and also to assess the safety of higher doses of gentamicin in pediatrics.

The repurposed agent moxifloxacin has become an important addition to the physician’s armamentarium for the therapy of Mycobacterium tuberculosis. When a drug is administered, we need to have metrics for success. As for most antimicrobial chemotherapy, we contend that for Mycobacterium tuberculosis therapy, these metrics should be a decline in the susceptible bacterial burden and the suppression of amplification of less-susceptible populations. To achieve optimal outcomes relative to these metrics, a dose and schedule of administration need to be chosen. For large populations of patients, there are true between-patient differences in important pharmacokinetic parameters. These distributions of parameter values may have an impact on these metrics, depending on what measure of drug exposure drives the metrics. To optimize dose and schedule choice of moxifloxacin, we performed a dose fractionation experiment in the hollow fiber infection model. We examined 12-, 24-, and 48-h dosing intervals with doses of 200, 400, and 800 mg for each interval, respectively. Within each interval, we had an arm where half-lives of 12, 8, and 4 h were simulated. We attempted to keep the average concentration (C(avg)) or area under the concentration-time curve (AUC) constant across arms. We found that susceptible bacterial load decline was linked to C(avg), as we had indicated previously. Resistance suppression, a nonmonotonic function, had minimum concentration (C(min)) as the linked index. The 48-h interval with the 4-h half-life had the largest less-susceptible population. Balancing bacterial kill, resistance suppression, toxicity (linked to peak concentration [C(peak)]), and adherence, we conclude that the dose of 400 mg daily is optimal for moxifloxacin.

Vancomycin induces exposure-related acute kidney injury. However, the pharmacokinetic-toxicodynamic (PK-TD) relationship remains unclear. Sprague-Dawley rats received intravenous (i.v.) vancomycin doses of 300 mg/kg/day and 400 mg/kg/day, divided into once-, twice-, three-times-, or four-times-daily doses (i.e., QD, BID, TID, or QID) over 24 h. Up to 8 samples plus a terminal sample were drawn during the 24-h dosing period. Twenty-four-hour urine was collected and assayed for kidney injury molecule-1 (KIM-1). Vancomycin was quantified via liquid chromatography-tandem mass spectrometry (LC-MS/MS). Following terminal sampling, nephrectomy and histopathologic analyses were conducted. PK analyses were conducted using Pmetrics. PK exposures (i.e., area under the concentration-time curve from 0 to 24 h [AUC0-24] and maximum concentration from 0 to 24 h [Cmax0-24]) were calculated for each rat, and PK-TD relationships were discerned. A total of 53-rats generated PK-TD data. A 2-compartment model fit the data well (Bayesian observed versus predicted concentrations; R2 = 0.96). KIM-1 values were greater in QD and BID groups (P for QD versus TID

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.

Using pharmacokinetic (PK) models and Bayesian methods in dosing software facilitates the analysis of individual PK data and precision dosing. Several Bayesian methods are available for computing Bayesian posterior distributions using nonparametric population models. The objective of this study was to compare the performance of the maximum a posteriori (MAP) model, multiple model (MM), interacting MM (IMM), and novel hybrid MM(HMM) in estimating past concentrations and predicting future concentrations during therapy. Amikacin and vancomycin PK data were analyzed in older hospitalized patients using 2 strategies. First, the entire data set of each patient was fitted using each of the 4 methods implemented in BestDose software. Then, the 4 methods were used in each therapeutic drug monitoring occasion to estimate the past concentrations available at this time and to predict the subsequent concentrations to be observed on the next occasion. The bias and precision of the model predictions were compared among the methods. A total of 406 amikacin concentrations from 96 patients and 718 vancomycin concentrations from 133 patients were available for analysis. Overall, significant differences were observed in the predictive performance of the 4 Bayesian methods. The IMM method showed the best fit to past concentration data of amikacin and vancomycin, whereas the MM method was the least precise. However, MM best predicted the future concentrations of amikacin. The MAP and HMM methods showed a similar predictive performance and seemed to be more appropriate for the prediction of future vancomycin concentrations than the other models were. The richness of the prior distribution may explain the discrepancies between the results of the 2 drugs. Although further research with other drugs and models is necessary to confirm our findings, these results challenge the widely accepted assumption in PK modeling that a better data fit indicates better forecasting of future observations.