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1.
Artigo em Inglês | MEDLINE | ID: mdl-39012226

RESUMO

RATIONALE: Optimizing pyrazinamide dosing is critical to improve treatment efficacy while minimizing toxicity during tuberculosis treatment. Study 31/ACTG A5349 represents the largest Phase 3 randomized controlled therapeutic trial to date for such investigation. OBJECTIVES: We sought to report pyrazinamide pharmacokinetic parameters, risk factors for lower pyrazinamide exposure, and relationships between pyrazinamide exposure with efficacy and safety outcomes. We aimed to determine pyrazinamide dosing strategies that optimize risks and benefits. METHODS: We analyzed pyrazinamide steady-state pharmacokinetic data using population nonlinear mixed-effects models. We evaluated the contribution of pyrazinamide exposure to long-term efficacy using parametric time-to-event models and safety outcomes using logistic regression. We evaluated optimal dosing with therapeutic windows targeting ≥95% durable cure and safety within the observed proportion of the primary safety outcome. MEASUREMENTS AND MAIN RESULTS: Among 2255 participants with 6978 plasma samples, pyrazinamide displayed 7-fold exposure variability (151-1053 mg·h/L). Body weight was not a clinically relevant predictor of drug clearance and thus did not justify the need for weight-banded dosing. Both clinical and safety outcomes were associated with pyrazinamide exposure, resulting in a therapeutic window of 231-355 mg·h/L for the control and 226-349 mg·h/L for the rifapentine-moxifloxacin regimen. Flat dosing of pyrazinamide at 1000 mg would have permitted an additional 13.1% (n=96) participants allocated to the control and 9.2% (n=70) to the rifapentine-moxifloxacin regimen dosed within the therapeutic window, compared to the current weight-banded dosing. CONCLUSIONS: Flat dosing of pyrazinamide at 1000 mg daily would be readily implementable and could optimize treatment outcomes in drug-susceptible tuberculosis. Clinical trial registration available at www. CLINICALTRIALS: gov, ID: NCT02410772. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

2.
Clin Infect Dis ; 78(6): 1680-1689, 2024 Jun 14.
Artigo em Inglês | MEDLINE | ID: mdl-38462673

RESUMO

BACKGROUND: The optimal dosing strategy for rifampicin in treating drug-susceptible tuberculosis (TB) is still highly debated. In the phase 3 clinical trial Study 31/ACTG 5349 (NCT02410772), all participants in the control regimen arm received 600 mg rifampicin daily as a flat dose. Here, we evaluated relationships between rifampicin exposure and efficacy and safety outcomes. METHODS: We analyzed rifampicin concentration time profiles using population nonlinear mixed-effects models. We compared simulated rifampicin exposure from flat- and weight-banded dosing. We evaluated the effect of rifampicin exposure on stable culture conversion at 6 months; TB-related unfavorable outcomes at 9, 12, and 18 months using Cox proportional hazard models; and all trial-defined safety outcomes using logistic regression. RESULTS: Our model-derived rifampicin exposure ranged from 4.57 mg · h/L to 140.0 mg · h/L with a median of 41.8 mg · h/L. Pharmacokinetic simulations demonstrated that flat-dosed rifampicin provided exposure coverage similar to the weight-banded dose. Exposure-efficacy analysis (n = 680) showed that participants with rifampicin exposure below the median experienced similar hazards of stable culture conversion and TB-related unfavorable outcomes compared with those with exposure above the median. Exposure-safety analysis (n = 722) showed that increased rifampicin exposure was not associated with increased grade 3 or higher adverse events or serious adverse events. CONCLUSIONS: Flat-dosing of rifampicin at 600 mg daily may be a reasonable alternative to the incumbent weight-banded dosing strategy for the standard-of-care 6-month regimen. Future research should assess the optimal dosing strategy for rifampicin, at doses higher than the current recommendation.


Assuntos
Rifampina , Tuberculose , Rifampina/farmacocinética , Rifampina/administração & dosagem , Humanos , Masculino , Adulto , Feminino , Pessoa de Meia-Idade , Tuberculose/tratamento farmacológico , Adulto Jovem , Antituberculosos/farmacocinética , Antituberculosos/administração & dosagem , Antituberculosos/efeitos adversos , Resultado do Tratamento , Adolescente , Relação Dose-Resposta a Droga , Idoso
3.
medRxiv ; 2024 Aug 20.
Artigo em Inglês | MEDLINE | ID: mdl-39228708

RESUMO

Radiology may better define tuberculosis (TB) severity and guide duration of treatment. We aimed to systematically study baseline chest X-rays (CXR) and their association with TB treatment outcome using real-world data. We used logistic regression to associate TB treatment outcomes with CXR findings, including percent of lung involved in disease (PLI), cavitation, and Timika score, alone or in combination with other clinical characteristics, stratifying by drug resistance status and HIV (n = 2,809). We fine-tuned convolutional neural nets (CNN) to automate PLI measurement from the CXR DICOM images (n = 5,261). PLI is the only CXR finding associated with unfavorable outcome across drug resistance and HIV subgroups [Rifampicin-susceptible disease without HIV, adjusted odds ratio (aOR) 1·11 (1·01, 1·22), P-value 0·025]. The most informed model of baseline characteristics tested predicts outcome with a validation mean area under the curve (AUC) of 0·769. PLI and Timika (AUC 0·656 and 0·655 respectively) predict unfavorable outcomes better than cavitary information (best AUC 0·591). The addition of PLI improves prediction compared to sex and age alone (AUC 0·680 and 0·627, respectively).PLI>25% provides a better separation of favorable and unfavorable outcomes compared to PLI>50%. The best performing ensemble of CNNs has an AUC 0·850 for PLI>25% and mean absolute error of 11·7% for the PLI value. PLI is better than cavitation for predicting unfavorable treatment outcome in pulmonary TB in non-clinical trial settings and it can be accurately and automatically predicted with CNNs. One Sentence Summary: The percent of lung involved in disease improves prediction of unfavorable outcomes in pulmonary tuberculosis when added to clinical characteristics.

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