Standards for model-based early bactericidal activity analysis and sample size determination in tuberculosis drug development

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-EBA0-14 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 be considered.Conclusion: In conclusion, a robust standardized pharmacometric model-based EBA analysis approach was established in close collaboration between microbiologists, clinicians and pharmacometricians. The work illustrates the importance of accounting for covariates and drug exposure in EBA analysis in order to increase the power of detecting early bactericidal activity for a single treatment arm as well as differences in EBA between treatments arms in Phase 2a trials of TB drug development

Drug Susceptibility Patterns of Mycobacterium Tuberculosis from Adults With Multidrug-Resistant Tuberculosis and Implications for a Household Contact Preventive Therapy Trial

BACKGROUND: Drug susceptibility testing (DST) patterns of Mycobacterium tuberculosis (MTB) from patients with rifampicin-resistant tuberculosis (RR-TB) or multidrug-resistant TB (MDR-TB; or resistant to rifampicin and isoniazid (INH)), are important to guide preventive therapy for their household contacts (HHCs). METHODS: As part of a feasibility study done in preparation for an MDR-TB preventive therapy trial in HHCs, smear, Xpert MTB/RIF, Hain MTBDRplus, culture and DST results of index MDR-TB patients were obtained from routine TB programs. A sputum sample was collected at study entry and evaluated by the same tests. Not all tests were performed on all specimens due to variations in test availability. RESULTS: Three hundred eight adults with reported RR/MDR-TB were enrolled from 16 participating sites in 8 countries. Their median age was 36 years, and 36% were HIV-infected. Routine testing on all 308 were confirmed as having RR-TB, but only 75% were documented as having MDR-TB. The majority of those not classified as having MDR-TB were because only rifampicin resistance was tested. At study entry (median 59 days after MDR-TB treatment initiation), 280 participants (91%) were able to produce sputum for the study, of whom 147 (53%) still had detectable MTB. All but 2 of these 147 had rifampicin DST done, with resistance detected in 89%. Almost half (47%) of the 147 specimens had INH DST done, with 83% resistance. Therefore, 20% of the 280 study specimens had MDR-TB confirmed. Overall, DST for second-line drugs were available in only 35% of the 308 routine specimens and 15% of 280 study specimens. CONCLUSIONS: RR-TB was detected in all routine specimens but only 75% had documented MDR-TB, illustrating the need for expanded DST beyond Xpert MTB/RIF to target preventive therapy for HHC

Power to identify exposure‐response relationships in phase IIa pulmonary tuberculosis trials with multi‐dimensional bacterial load modeling

Abstract Adequate power to identify an exposure‐response relationship in a phase IIa clinical trial for pulmonary tuberculosis (TB) is important for dose selection and design of follow‐up studies. Currently, it is not known what response marker provides the pharmacokinetic‐pharmacodynamic (PK‐PD) model more power to identify an exposure‐response relationship. We simulated colony‐forming units (CFU) and time‐to‐positivity (TTP) measurements for four hypothetical drugs with different activity profiles for 14 days. The power to identify exposure‐response relationships when analyzing CFU, TTP, or combined CFU + TTP data was determined at 60 total participants, or with 25 out of 60 participants in the lowest and highest dosing groups (unbalanced design). For drugs with moderate bactericidal activity, power was low (<59%), irrespective of the data analyzed. Power was 1.9% to 29.4% higher when analyzing TTP data compared to CFU data. Combined analysis of CFU and TTP further improved the power, on average by 4.2%. For a drug with a medium‐high activity, the total sample size needed to achieve 80% power was 136 for CFU, 72 for TTP, and 68 for combined CFU + TTP data. The unbalanced design improved the power by 16% over the balanced design. In conclusion, the power to identify an exposure‐response relationship is low for TB drugs with moderate bactericidal activity or with a slow onset of activity. TTP provides the PK‐PD model with more power to identify exposure‐response relationships compared to CFU, and combined analysis or an unbalanced dosing group study design offers modest further improvement

Seasonal influence on respiratory tract infection severity including COVID‐19 quantified through Markov Chain modeling

Abstract 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

Reproducibility in pharmacometrics applied in a phase III trial of BCG-vaccination for COVID-19

Abstract 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-Guérin (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

Population Pharmacokinetics of Delamanid and its Main Metabolite DM-6705 in Drug-Resistant Tuberculosis Patients Receiving Delamanid Alone or Coadministered with Bedaquiline

Background: Delamanid is a nitroimidazole, a novel class of drug for treating tuberculosis. Delamanid is primarily metabolized by albumin into the metabolite DM-6705. The aims of this analysis were to develop a population pharmacokinetic (PK) model to characterize the concentration-time course of delamanid and DM-6705 in adults with drug-resistant tuberculosis and to explore a potential drug-drug interaction with bedaquiline when co-administered.  Methods: Delamanid and DM-6705 concentrations after oral administration, from 52 participants (of whom 26 took bedaquiline concurrently and 20 were HIV-1 positive) enrolled in the DELIBERATE trial were analyzed using nonlinear mixed-effects modeling. Results: Delamanid PK was described by a one-compartment disposition model with transit compartment absorption (mean absorption time of 1.45 h (95% confidence interval 0.501–2.20)) and linear elimination. The PK of DM-6705 metabolite, was described by a one-compartment disposition model with delamanid clearance as input and linear elimination. Predicted terminal half-life values for delamanid and DM-6705 were 15.1 hours and 7.8 days, respectively. The impact of plasma albumin concentrations on delamanid metabolism was not significant. Bedaquiline co-administration did not affect delamanid PK. Other than allometric scaling with body weight, no patients’ demographics were significant (including HIV).  Conclusions: This is the first published joint PK model of delamanid and its DM-6705 metabolite. As such, it can be utilized in future exposure-response or exposure-safety analyses. Importantly, albumin concentrations, bedaquiline co-administration, and HIV co-infection (dolutegravir co-administration) did not have an effect on delamanid and DM-6705 PK

Early bactericidal activity studies for pulmonary tuberculosis : A systematic review of methodological aspects

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

DataSheet1_Standards for model-based early bactericidal activity analysis and sample size determination in tuberculosis drug development.docx

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-EBA0-14 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 be considered.Conclusion: In conclusion, a robust standardized pharmacometric model-based EBA analysis approach was established in close collaboration between microbiologists, clinicians and pharmacometricians. The work illustrates the importance of accounting for covariates and drug exposure in EBA analysis in order to increase the power of detecting early bactericidal activity for a single treatment arm as well as differences in EBA between treatments arms in Phase 2a trials of TB drug development.</p

Drug susceptibility patterns of Mycobacterium tuberculosis from adults with multidrug-resistant tuberculosis and implications for a household contact preventive therapy trial

Demers, A. M., et al. 2021. Drug susceptibility patterns of Mycobacterium tuberculosis from adults with multidrug-resistant tuberculosis and implications for a household contact preventive therapy trial. BMC Infectious Diseases, 21:205, doi:10.1186/s12879-021-05884-4Background: Drug susceptibility testing (DST) patterns of Mycobacterium tuberculosis (MTB) from patients with rifampicin-resistant tuberculosis (RR-TB) or multidrug-resistant TB (MDR-TB; or resistant to rifampicin and isoniazid (INH)), are important to guide preventive therapy for their household contacts (HHCs). Methods: As part of a feasibility study done in preparation for an MDR-TB preventive therapy trial in HHCs, smear, Xpert MTB/RIF, Hain MTBDRplus, culture and DST results of index MDR-TB patients were obtained from routine TB programs. A sputum sample was collected at study entry and evaluated by the same tests. Not all tests were performed on all specimens due to variations in test availability. Results: Three hundred eight adults with reported RR/MDR-TB were enrolled from 16 participating sites in 8 countries. Their median age was 36 years, and 36% were HIV-infected. Routine testing on all 308 were confirmed as having RR-TB, but only 75% were documented as having MDR-TB. The majority of those not classified as having MDR-TB were because only rifampicin resistance was tested. At study entry (median 59 days after MDR-TB treatment initiation), 280 participants (91%) were able to produce sputum for the study, of whom 147 (53%) still had detectable MTB. All but 2 of these 147 had rifampicin DST done, with resistance detected in 89%. Almost half (47%) of the 147 specimens had INH DST done, with 83% resistance. Therefore, 20% of the 280 study specimens had MDR-TB confirmed. Overall, DST for second-line drugs were available in only 35% of the 308 routine specimens and 15% of 280 study specimens. Conclusions: RR-TB was detected in all routine specimens but only 75% had documented MDR-TB, illustrating the need for expanded DST beyond Xpert MTB/RIF to target preventive therapy for HHC.https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-021-05884-4Publisher's versio