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(1) Background: Some individuals are more susceptible to developing respiratory tract infections (RTIs) or coronavirus disease (COVID-19) than others. The aim of this work was to identify risk factors for symptomatic RTIs including COVID-19 and symptomatic COVID-19 during the coronavirus pandemic by using infection incidence, participant baseline, and regional COVID-19 burden data. (2) Methods: Data from a prospective study of 1000 frontline healthcare workers randomized to Bacillus Calmette-Gu & eacute;rin vaccination or placebo, and followed for one year, was analyzed. Parametric time-to-event analysis was performed to identify the risk factors associated with (a) non-specific symptomatic respiratory tract infections including COVID-19 (RTIs+COVID-19) and (b) symptomatic RTIs confirmed as COVID-19 using a polymerase chain reaction or antigen test (COVID-19). (3) Results: Job description of doctor or nurse (median hazard ratio [HR] 1.541 and 95% confidence interval [CI] 1.299-1.822), the reported COVID-19 burden (median HR 1.361 and 95% CI 1.260-1.469 for 1.4 COVID-19 cases per 10,000 capita), or a BMI > 30 kg/m(2) (median HR 1.238 and 95% CI 1.132-1.336 for BMI of 35.4 kg/m(2)) increased the probability of RTIs+COVID-19, while positive SARS-CoV-2 serology at enrollment (median HR 0.583 and 95% CI 0.449-0.764) had the opposite effect. The reported COVID-19 burden (median HR 2.372 and 95% CI 2.116-2.662 for 1.4 COVID-19 cases per 10,000 capita) and a job description of doctor or nurse (median HR 1.679 and 95% CI 1.253-2.256) increased the probability of developing COVID-19, while smoking (median HR 0.428 and 95% CI 0.284-0.648) and positive SARS-CoV-2 serology at enrollment (median HR 0.076 and 95% CI 0.026-0.212) decreased it. (4) Conclusions: Nurses and doctors with obesity had the highest probability of developing RTIs including COVID-19. Non-smoking nurses and doctors had the highest probability of developing COVID-19 specifically. The reported COVID-19 b

A milestone in the development of novel antituberculosis drugs is the demonstration of early bactericidal activity (EBA) in a phase IIa clinical trial. The significant variability in measurements of bacterial load complicates data analysis in these trials. A systematic review and evaluation of methods for determination of EBA in pulmonary tuberculosis studies was undertaken. Bacterial load quantification biomarkers, reporting intervals, calculation methods, statistical testing, and handling of negative culture results were extracted. In total, 79 studies were identi-fied in which EBA was determined. Colony-forming units on solid culture media and/or time-to-positivity in liquid media were the biomarkers used most often, reported in 72 (91%) and 34 (43%) studies, respec-tively. Twenty-two different reporting intervals were presented, and 12 different calculation methods for EBA were identified. Statistical testing for a significant EBA compared with no change was performed in 54 (68%) studies, and between-group testing was performed in 32 (41%) studies. Negative culture result handling was discussed in 34 (43%) studies. Notable variation was found in the analysis methods and reporting of EBA studies. A standardized and clearly reported analysis method, accounting for different levels of variability in the data, could aid the generalization of study results and facilitate comparison between drugs/regimens.

Background: A critical step in tuberculosis (TB) drug development is the Phase 2a early bactericidal activity (EBA) study which informs if a new drug or treatment has short-term activity in humans. The aim of this work was to present a standardized pharmacometric model-based early bactericidal activity analysis workflow and determine sample sizes needed to detect early bactericidal activity or a difference between treatment arms. Methods: Seven different steps were identified and developed for a standardized pharmacometric model-based early bactericidal activity analysis approach. Non-linear mixed effects modeling was applied and different scenarios were explored for the sample size calculations. The sample sizes needed to detect early bactericidal activity given different TTP slopes and associated variability was assessed. In addition, the sample sizes needed to detect effect differences between two treatments given the impact of different TTP slopes, variability in TTP slope and effect differences were evaluated. Results: The presented early bactericidal activity analysis approach incorporates estimate of early bactericidal activity with uncertainty through the model-based estimate of TTP slope, variability in TTP slope, impact of covariates and pharmacokinetics on drug efficacy. Further it allows for treatment comparison or dose optimization in Phase 2a. To detect early bactericidal activity with 80% power and at a 5% significance level, 13 and 8 participants/arm were required for a treatment with a TTP-EBA(0-14) as low as 11 h when accounting for variability in pharmacokinetics and when variability in TTP slope was 104% [coefficient of variation (CV)] and 22%, respectively. Higher sample sizes are required for smaller early bactericidal activity and when pharmacokinetics is not accounted for. Based on sample size determinations to detect a difference between two groups, TTP slope, variability in TTP slope and effect difference between two treatment arms needs to

Large clinical trials often generate complex and large datasets which need to be presented frequently throughout the trial for interim analysis or to inform a data safety monitory board (DSMB). In addition, reliable and traceability are required to ensure reproducibility in pharmacometric data analysis. A reproducible pharmacometric analysis workflow was developed during a large clinical trial involving 1000 participants over one year testing Bacillus Calmette-Guerin (BCG) (re)vaccination in coronavirus disease 2019 (COVID-19) morbidity and mortality in frontline health care workers. The workflow was designed to review data iteratively during the trial, compile frequent reports to the DSMB, and prepare for rapid pharmacometric analysis. Clinical trial datasets (n = 41) were transferred iteratively throughout the trial for review. An RMarkdown based pharmacometric processing script was written to automatically generate reports for evaluation by the DSMB. Reports were compiled, reviewed, and sent to the DSMB on average three days after the data cut-off, reflecting the trial progress in real-time. The script was also utilized to prepare for the trial pharmacometric analyses. The same source data was used to create analysis datasets in NONMEM format and to support model script development. The primary endpoint analysis was completed three days after data lock and unblinding, and the secondary endpoint analyses two weeks later. The constructive collaboration between clinical, data management, and pharmacometric teams enabled this efficient, timely, and reproducible pharmacometrics workflow.

Respiratory tract infections (RTIs) are a burden to global health, but their characterization is complicated by the influence of seasonality on incidence and severity. The Re-BCG-CoV-19 trial (NCT04379336) assessed BCG (re)vaccination for protection from coronavirus disease 2019 (COVID-19) and recorded 958 RTIs in 574 individuals followed over 1 year. We characterized the probability of RTI occurrence and severity using a Markov model with health scores (HSs) for four states of symptom severity. Covariate analysis on the transition probability between HSs explored the influence of demographics, medical history, severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), or influenza vaccinations, which became available during the trial, SARS-CoV-2 serology, and epidemiology-informed seasonal influence of infection pressure represented as regional COVID-19 pandemic waves, as well as BCG (re)vaccination. The infection pressure reflecting the pandemic waves increased the risk of RTI symptom development, whereas the presence of SARS-CoV-2 antibodies protected against RTI symptom development and increased the probability of symptom relief. Higher probability of symptom relief was also found in participants with African ethnicity and with male biological gender. SARS-CoV-2 or influenza vaccination reduced the probability of transitioning from mild to healthy symptoms. Model diagnostics over calendar-time indicated that COVID-19 cases were under-reported during the first wave by an estimated 2.76-fold. This trial was performed during the initial phase of the COVID-19 pandemic in South Africa and the results reflect that situation. Using this unique clinical dataset of prospectively studied RTIs over the course of 1 year, our Markov Chain model was able to capture risk factors for RTI development and severity, including epidemiology-informed infection pressure.

Rationale: Carbapenems are recommended for treatment of drug-resistant tuberculosis. Optimal dosing remains uncertain. Objectives: To evaluate the 14-day bactericidal activity of meropenem, at different doses, with or without rifampin. Methods: Individuals with drug-sensitive pulmonary tuberculosis were randomized to one of four intravenous meropenem-based arms: 2 g every 8 hours (TID) (arm C), 2 g TID plus rifampin at 20 mg/kg once daily (arm D), 1 g TID (arm E), or 3 g once daily (arm F). All participants received amoxicillin/clavulanate with each meropenem dose. Serial overnight sputum samples were collected from baseline and throughout treatment. Median daily fall in colony-forming unit (CFU) counts per milliliter of sputum (solid culture) (EBA(CFU0-14)) and increase in time to positive culture (TTP) in liquid media were estimated with mixed-effects modeling. Serial blood samples were collected for pharmacokinetic analysis on Day 13. Measurements and Main Results: Sixty participants enrolled. Median EBA(CFU0-14) counts (2.5th-97.5th percentiles) were 0.22 (0.12-0.33), 0.12 (0.057-0.21), 0.059 (0.033-0.097), and 0.053 (0.035-0.081); TTP increased by 0.34 (0.21-0.75), 0.11 (0.052-037), 0.094 (0.034-0.23), and 0.12 (0.04-0.41) (log(10) h), for arms C-F, respectively. Meropenem pharmacokinetics were not affected by rifampin coadministration. Twelve participants withdrew early, many of whom cited gastrointestinal adverse events. Conclusions: Bactericidal activity was greater with the World Health Organization-recommended total daily dose of 6 g daily than with a lower dose of 3 g daily. This difference was only detectable with solid culture. Tolerability of intravenous meropenem, with amoxicillin/clavulanate, though, was poor at all doses, calling into question the utility of this drug in second-line regimens.

Delamanid and bedaquiline are two drugs approved to treat drug-resistant tuberculosis, and each have been associated with corrected QT interval (QTc) prolongation. We aimed to investigate the relationships between the drugs' plasma concentrations and the prolongation of observed QT interval corrected using Fridericia's formula (QTcF) and to evaluate their combined effects on QTcF, using a model-based population approach. Furthermore, we predicted the safety profiles of once daily regimens. Data were obtained from a trial where participants were randomized 1:1:1 to receive delamanid, bedaquiline, or delamanid + bedaquiline. The effect on QTcF of delamanid and/or its metabolite (DM- -6705) and the pharmacodynamic interactions under coadministration were explored based on a published model between bedaquiline's metabolite (M2) and QTcF. The metabolites of each drug were found to be responsible for the drug-related QTcF prolongation. The final drug-effect model included a competitive interaction between M2 and DM-6705 acting on the same cardiac receptor and thereby reducing each other's apparent potency, by 28% (95% confidence interval (CI), 22-40%) for M2 and 33% (95% CI, 24-54%) for DM-6705. The generated combined effect was not greater but close to "additivity" in the analyzed concentration range. Predictions with the final model suggested a similar QT prolonging potential with simplified, once-daily dosing regimens compared with the approved regimens, with a maximum median change from baseline QTcF increase of 20 milliseconds in both regimens. The concentrations-QTcF relationship of the combination of bedaquiline and delamanid was best described by a competitive binding model involving the two main metabolites. Model predictions demonstrated that QTcF prolongation with simplified once daily regimens would be comparable to currently used dosing regimens.

Bedaquiline and delamanid are two drugs approved to treat drug-resistant tuberculosis, and each have been associated with QTc prolongation. We aimed to investigate the relationships between the drugs’ plasma concentrations and observed QTcF prolongation and to evaluate their combined effects on QTcF, using a model-based population approach. Furthermore, we predicted the safety profiles of once daily regimens. Data were obtained from a trial where participants were randomized 1:1:1 to receive bedaquiline , delamanid or bedaquiline+delamanid. The effect on QTcF of delamanid and/or its metabolite (DM-6705) and the pharmacodynamic interactions under co-administration, were explored based on a published model between bedaquiline’s metabolite (M2) and QTcF. The metabolites of each drug were found to be responsible for the drug-related QTcF prolongation. The final drug-effect model included a competitive interaction between M2 and DM-6705 acting on the same cardiac receptor and thereby reducing each other’s apparent potency, by 28% (95CI 22%-40%) for M2 and 33% (95CI 24%-54%) for DM-6705. The generated combined effect was not greater but close to “additivity” in the analysed concentration range. Predictions with the final model suggested a similar QT prolonging potential with novel simplified regimens with novel once daily dosing compared to the approved regimens, with a maximum median change from baseline QTcF increase of 20 ms in both regimens. The concentrations-QTcF relationship of the combination of bedaquiline and delamanid was best described by a competitive binding model involving the two main metabolites, and predictions from the model support the use of these drugs together in once daily regimens.