A pharmacometric multistate model for analyzing long-term outcomes of patients with drug-resistant tuberculosis under bedaquiline treatment
Yu-Jou Lin (1), Yuanxi Zou (1), Mats O. Karlsson (1), Elin M. Svensson (1,2)
(1) Department of Pharmacy, Uppsala University, Sweden, (2) Department of Pharmacy, Radboud university medical center, The Netherlands
Objectives: Tuberculosis (TB) is one of the deadliest infectious diseases worldwide. Bedaquiline (BDQ), a drug used as part of combination therapy for patients with multi-drug resistant (MDR) TB, is approved for a 24-week treatment duration. While exposure-response-safety relationships have already been established on early treatment response [1–5], only data in treatment period were used. This project aimed to develop a pharmacometric multistate model and evaluate the link between long-term outcomes and early treatment response via multiple predictors.
Methods: Data were obtained from two clinical trials (C208 and C209) sponsored by Janssen Pharmaceuticals [6,7]. In the C208 study, patients were receiving either placebo or BDQ, while in the C209 study, all patients received BDQ. In both trials, patients were treated with a multi-drug background regimen and followed until 120 weeks. In our analysis, patients were assigned to different states over time:
S1 – Active infection
S2 – Conversion (2 negative sputum cultures collected at least 25 days apart, not intervened by positive cultures)
S3 – Recurrence (2 consecutive positive sputum cultures or a single positive result before the patient completed or discontinued from the trial, after conversion)
S4 – Dropout (lost to follow-up in the study but not death)
S5 – Death
All patients were in active infection state (S1) at the start of treatment. During the study, patients in S1 could have sputum conversion (S2) and further develop recurrence (S3). Those in S3 who reached sputum conversion again moved back to S2. Patients could drop out from the study or die from any state. Transition rates λij from state i to state j were used to describe the observed data and estimated via different hazard functions (constant, Weibull and a surge function).
Potential predictors including baseline demographics (sex, weight, age, race), type of drug-resistant TB, concomitant HIV-infection, cavitation (Y/N), baseline mean time-to-positivity (MTTP), BDQ (Y/N), study pre-treatment (Y/N), and study (C208/C209) were explored on each transition rate. Post-baseline dynamic predictors, such as mycobacterial load (MBLt) and half-life of bacterial clearance (HLt) were derived from the previous developed PKPD model [2,4] based on the observations up to week 2, 4, 8 and 24 (t = 2, 4, 8, 24) and investigated prospectively in the multistate model. Additionally, time to sputum culture conversion and culture status at month 2 were evaluated on transitions from S2 or S3. The predictors were selected based upon the objective function value (OFV) and diagnostic plots.
Results: Data from 402 patients (51% from C208 and 49% from C209) with 6984 observations were included in the analysis. The proportion of patients with drug-resistant TB was 82%. The transition from active infection to conversion (λ12) followed a surge function and baseline MTTP and HL2 were jointly significant predictors of λ12: a higher baseline MTTP (i.e., lower bacterial load) and shorter HL2 (faster clearance of bacteria) increased the probability to achieve early conversion. The hazards from conversion to recurrence (λ23) and recurrence to conversion (λ32) were constant over time. Being male and not receiving BDQ independently increased the transition hazard of λ23. The hazard of dropout from active infection (λ14)was constant, whereas the hazard from conversion and recurrence to dropout (λ24, λ34) followed a Weibull distribution and decreased over time since entering the state. λ24 and λ34 were set to the same value as no significant difference was found. Enrolled in the C208 study was associated with a higher risk of dropout, and patients with younger age had increased λ24/34. Similarly, the transition from active infection and recurrence to death were set to the same with increasing hazard over time (Weibull distribution), while the hazard from conversion to death was constant. Patients with lower weight were found to have a higher risk of death. The visual predictive checks (VPCs) demonstrated that the model could describe the observed transitions well.
Conclusions: The developed multistate model successfully described long-term MDR-TB treatment outcomes with BDQ therapy. Baseline covariates were identified as predictors on selected transition rates, and model-derived treatment response during the first two weeks was associated with λ12.
References:
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