New Paradigm for Translational Modeling to Predict Long-term Tuberculosis Treatment Response.
Clin Transl Sci
; 10(5): 366-379, 2017 Sep.
Article
en En
| MEDLINE
| ID: mdl-28561946
ABSTRACT
Disappointing results of recent tuberculosis chemotherapy trials suggest that knowledge gained from preclinical investigations was not utilized to maximal effect. A mouse-to-human translational pharmacokinetics (PKs) - pharmacodynamics (PDs) model built on a rich mouse database may improve clinical trial outcome predictions. The model included Mycobacterium tuberculosis growth function in mice, adaptive immune response effect on bacterial growth, relationships among moxifloxacin, rifapentine, and rifampin concentrations accelerating bacterial death, clinical PK data, species-specific protein binding, drug-drug interactions, and patient-specific pathology. Simulations of recent trials testing 4-month regimens predicted 65% (95% confidence interval [CI], 55-74) relapse-free patients vs. 80% observed in the REMox-TB trial, and 79% (95% CI, 72-87) vs. 82% observed in the Rifaquin trial. Simulation of 6-month regimens predicted 97% (95% CI, 93-99) vs. 92% and 95% observed in 2RHZE/4RH control arms, and 100% predicted and observed in the 35 mg/kg rifampin arm of PanACEA MAMS. These results suggest that the model can inform regimen optimization and predict outcomes of ongoing trials.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Tuberculosis
/
Investigación Biomédica Traslacional
/
Modelos Teóricos
Tipo de estudio:
Clinical_trials
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Clin Transl Sci
Año:
2017
Tipo del documento:
Article
País de afiliación:
Estados Unidos