2023 - A Coruña - Spain

PAGE 2023: Drug/Disease Modelling - Infection
Kamunkhwala Gausi

Bactericidal activity of pretomanid against drug-sensitive tuberculosis in the presence of rifamycins

Kamunkhwala Gausi (1), Elisa H. Ignatius(2), Bronwyn Hendricks(3), Nikhil Gupte(4), Kim Narunsky(3), Susan E. Dorman(5), Rodney Dawson(3), Kelly E. Dooley(6), and Paolo Denti(1) and, Assessing Pretomanid for Tuberculosis (APT) Study Team

1. Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa.; 2. School of Medicine, Johns Hopkins University, Baltimore, Maryland; 3. Division of Pulmonology, Department of Medicine and University of Cape Town Lung Institute, University of Cape Town, Cape Town, South Africa; 4. BJ Government Medical College, Pune, India; 5. Medical University of South Carolina, Charleston, South Carolina; 6. Vanderbilt University Medical Center, Nashville, Tennessee

Objectives: 

One of the challenges in tuberculosis (TB) control is the long duration of the treatment regimen(1). Drug-sensitive TB usually requires 6 months of therapy, hence shorter-duration regimens are desirable to improve adherence and limit the emergence of resistance. Pretomanid is a new anti-TB drug reported to be highly effective and proven to shorten the duration of drug-resistant TB treatment(2). This has raised interest in using pretomanid to shorten drug-sensitive TB treatment. The APT (Assessing Pretomanid for Tuberculosis) trial was designed to evaluate the bactericidal activity (pharmacodynamics) of a pretomanid-rifamycin-pyrazinamide regimen(3) against drug-sensitive TB, cognizant of the drug-drug interaction between rifamycins and pretomanid(4). As TB biomarkers, such as time to positivity (TTP), are known to be very noisy(5), it is crucial to perform quantitative measurements of bacterial load throughout the entire treatment duration. Therefore, our objective was to compare the bactericidal effectiveness of regimens containing pretomanid with the standard of care for drug-sensitive TB, by utilizing the entire TTP profile to evaluate bactericidal activity.

 

Methods: APT participants were randomized 1:1:1 to receive 12 weeks of study treatment: arm 3 (control) received standard-of-care rifampicin, isoniazid, pyrazinamide, and ethambutol (HRZE) for 8 weeks and HR 4 weeks. The E in arm 1 was replaced with pretomanid (Pa) and in arm 2 RE were replaced with rifabutin (Rb) and Pa respectively. Sputum was collected at baseline and weeks 1, 2, 3, 4, 6, 8, 10, 12, 16, and 24. The extent of bacterial killing was quantified by the change in time to culture positive (TTP), which was determined by a liquid culture system called Mycobacteria Growth Indicator Tube (MGIT). The upper limit of TTP was set to 42 days. Several empirical models were tested to describe the TTP time-kill curves: linear, broken stick, and exponential models. Random effects, assuming a log-normal distribution, were included on the parameters at occasion and/or subject level if statistically significant. A combination of additive and proportional error was used to model unexplained residual variability. The values above the upper limit were handled using the M3 method(6). In addition to study arm, the following covariates were tested: HIV status, lung cavitation, weight, smoking, age, and sex. The following safety biomarkers were captured during study treatment: complete blood counts, alanine transaminase (ALT), total bilirubin, and creatinine were collected at Weeks 1, 2, 3, 4, 6, 8, 10, and 12 after treatment initiation.

Results: 

157 participants were recruited, 56, 53, and 48 were randomized to arms 1, 2, and 3, respectively. A total of 118 (75%) were male, 125 (80%) had lung cavities, 6 (4%) were HIV positive, and 73 (47%) were current smokers. The median (IQR) weight was 55 (51 – 60) Kg, and age 30 (23 – 39) years. An exponential model with an intercept of 8.26 days and a rate of increase of TTP with time on treatment of 0.0339 TTP days/treatment day best described the TTP time-kill curves. The between-subject variability (BSV) in baseline TTP was box-cox transformed and had a moderated correlation (37%) with BSV in rate of increase of TTP with time on treatment. In the HRbZPa (arm 2) and HRZPa (arm 1) the TTP increased 48% and 31% faster compared to the standard of care arm (ΔOFV= 38, P.value < 0.001). Females were estimated to have a 30% faster rate of increase of TTP with time on treatment compared to males (ΔOFV= 11, P.value < 0.001), and an 8% decrease was estimated with a 10-years increase in age from 40 years (ΔOFV= 9, P.value = 0.003). No significant effect of lung cavitation or HIV status was observed on rate of increase of TTP with time on treatment and/or baseline TTP. Grade 3 or higher adverse events were observed in 3 (5%) participants of arm 1, 5 (9%) of arm 2, and 2 (4%) of arm 3.

Conclusions: 

The bactericidal activity (pharmacodynamics) of the pretomanid-rifamycin-pyrazinamide regimen is greater compared with the standard of care. The regimen with rifabutin performed better in terms of time-to-culture conversion compared to rifampicin, potentially due to the drug-drug interaction between rifampicin and pretomanid, which resulted in low pretomanid exposure. Further work is needed to better understand the risk factors associated with the adverse events observed in this study.



References:

  1. WHO. GLOBAL TUBERCULOSIS REPORT 2020 [Internet]. Geneva; 2020 [cited 2021 May 28]. Available from: http://apps.who.int/bookorders.
  2. TB Alliance. Pretomanid package insert. U.S. Food and Drug Administration. 2019.
  3. Dooley KE, Hendricks B, Gupte N, Barnes G, Narunsky K, Whitelaw C, et al. Assessing Pretomanid for Tuberculosis (APT), a Randomized Phase 2 Trial of Pretomanid-containing Regimens for Drug-sensitive TB: 12-Week Results. https://doi.org/101164/rccm202208-1475OC. 2022 Dec 1;
  4. Ignatius EH, Abdelwahab MT, Hendricks B, Gupte N, Narunsky K, Wiesner L, et al. Pretomanid Pharmacokinetics in the Presence of Rifamycins: Interim Results from a Randomized Trial among Patients with Tuberculosis. Antimicrob Agents Chemother [Internet]. 2021 Feb 20 [cited 2022 Nov 15];65(2). Available from: http://www.ncbi.nlm.nih.gov/pubmed/33229425
  5. Rockwood N, Du Bruyn E, Morris T, Wilkinson RJ. Assessment of treatment response in tuberculosis. Expert Rev Respir Med [Internet]. 2016 Jun 2 [cited 2020 May 27];10(6):643–54. Available from: http://www.tandfonline.com/doi/full/10.1586/17476348.2016.1166960
  6. Beal SL. Ways to fit a PK model with some data below the quantification limit. J Pharmacokinet Pharmacodyn. 2001;28(5):481–504.


Reference: PAGE 31 (2023) Abstr 10601 [www.page-meeting.org/?abstract=10601]
Poster: Drug/Disease Modelling - Infection
Top