Lénaïg Tanneau (1), Mats O Karlsson (1), Andreas H Diacon (2), Justin Shenje (3), Jorge De Los Rios (4), Kelly E Dooley (5), Gary Maartens (6), Elin M Svensson (1, 7)
(1) Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden (2) TASK Applied Science, Cape Town, South Africa (3) SATVI, University of Cape Town, South Africa (4) Barranco Clinical Research Site, Asociacion Civil Impacta Salud y Educacion, Lima, Peru (5) Center for Tuberculosis Research, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA (6) Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa (7) Department of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
Objectives: Tuberculosis (TB) is an infectious disease affecting principally the lungs, caused by Mycobacterium tuberculosis. With approximately 10 million new TB cases and 1.5 million TB related deaths reported in 2018, TB is the leading infectious cause of death worldwide [1]. The public threat is aggravated by resistance development to first line treatment and good therapeutic options for drug-resistant (DR) TB patients are limited. A few new drugs have been registered for the treatment of DR TB, such as delamanid. Approved in 2014 by EMA, delamanid has been shown to increase the 2-month sputum culture conversion rates when added to an optimized background regimen in drug-resistant TB patients [2]. Delamanid is a nitromidazole agent primarily metabolized by albumin into DM-6705. This metabolite appears to be associated with QTc prolongation [3]. The aims of this analysis were to 1) develop a population pharmacokinetics (PK) model to describe observed delamanid and DM-6705 concentrations over time, 2) test possible covariate parameter relationships that could influence absorption, distribution and clearance, and 3) to derive PK metrics to be used in a future exposure-safety analysis of QTc prolongation.
Methods: PK data from a randomized, open-label, phase 2 study conducted to assess safety of delamanid and bedaquiline co-administration were analyzed [4]. In this work, we focused on participants who received delamanid or both delamanid and bedaquiline (28 DR-TB patients in each group), together with a multidrug background treatment. Delamanid was dosed at 100mg twice daily. Assessment of delamanid and DM-6705 PK was performed with data up to week 8 in 52 patients (2 patients in each group were ignored because they did not have any PK samples). Rich blood samples were collected over 24 hours (6 samples) at weeks 2 and 8, and pre-dose samples were drawn every two weeks at all other visits.
Results: Delamanid pharmacokinetic was best described by a one-compartment allometric disposition model with transit compartment absorption and linear elimination. The formation of the metabolite DM-6705 and its pharmacokinetic was described by a two-compartment allometric disposition model with a linear elimination from the central compartment. Population estimates for mean absorption time (MAT), delamanid CLDLM, VDLM and DM-6705 CLDM-6705, V DM-6705, Q DM-6705,V2 DM-6705 were 0.87 h (RSE 30.3%), 31 L/h (RSE 4%), 3150 L (RSE 11.9%), 79.5 L/h (RSE 5.4%), 158 L (RSE 90.3%), 121 L/h (RSE 37%), 16900 L (RSE 19.2%) respectively. Predicted terminal half-life values for delamanid and DM-6705 were 2.9 days and 10.2 days, respectively. Inter-individual variability was included on CLDLM, VDLM, CLDM-6705 and on the fraction of delamanid metabolized to DM-6705. Inter-occasion variability was included on relative bioavailability (F) relative to the typical value and on MAT. Additional to allometry, gender, baseline body weight, age, baseline albumin, race, HIV co-infection, TB type and co-administration of bedaquiline were used as covariates in a FREM approach[5,6], none of these reduced more than marginally the remaining unexplained parameter variability.
Conclusions: The PK of delamanid and DM-6705 was adequately described simultaneously using one combined model. None of the investigated covariates beyond weight appear to have a clinically relevant impact on the delamanid or DLM-6705 PK. This is the first public presentation of a delamanid population PK model and it can be utilized in future exposure-response analyzes.
References:
[1]. World Health Organization. WHO | Global tuberculosis report 2019 [Internet]. WHO. 2019. Available from: http://www.who.int/tb/publications/global_report/en/
[2]. Gler MT, Skripconoka V, Sanchez-Garavito E, Xiao H, Cabrera-Rivero JL, Vargas-Vasquez DE, Gao M, Awad M, Park S-K, Shim TS, Suh GY, Danilovits M, Ogata H, Kurve A, Chang J, Suzuki K, Tupasi T, Koh W-J, Seaworth B, Geiter LJ, Wells CD. Delamanid for Multidrug-Resistant Pulmonary Tuberculosis. New England Journal of Medicine. 2012 Jun 7;366(23):2151–60.
[3]. Liu Y, Matsumoto M, Ishida H, Ohguro K, Yoshitake M, Gupta R, Geiter L, Hafkin J. Delamanid: From discovery to its use for pulmonary multidrug-resistant tuberculosis (MDR-TB). Tuberculosis. 2018 Jul 1;111:20–30.
[4]. Evaluating the Safety, Tolerability, and Pharmacokinetics of Bedaquiline and Delamanid, Alone and in Combination, For Drug-Resistant Pulmonary Tuberculosis – Full Text View – ClinicalTrials.gov [Internet]. Available from: https://clinicaltrials.gov/ct2/show/NCT02583048
[5]. Yngman G, Nyberg J, Jonsson EN, Karlsson MO. Practical considerations for using the full random effects modeling (FREM) approach to covariate modeling. PAGE 26 (2017) Abstr 7365 [www.page-meeting.org/?abstract=7365].
[6]. Karlsson MO. A full model approach based on the covariance matrix of parameters and covariates. PAGE 21 (2012) Abstr 2455 [www.page-meeting.org/?abstract=2455].
Reference: PAGE () Abstr 9546 [www.page-meeting.org/?abstract=9546]
Poster: Drug/Disease Modelling - Infection