A preclinical model of TB meningitis to determine drug penetration and activity at the sites of disease.
Antimicrob Agents Chemother
; 67(12): e0067123, 2023 12 14.
Article
em En
| MEDLINE
| ID: mdl-37966227
ABSTRACT
Tuberculosis meningitis (TBM) is essentially treated with the first-line regimen used against pulmonary tuberculosis, with a prolonged continuation phase. However, clinical outcomes are poor in comparison, for reasons that are only partially understood, highlighting the need for improved preclinical tools to measure drug distribution and activity at the site of disease. A predictive animal model of TBM would also be of great value to prioritize promising drug regimens to be tested in clinical trials, given the healthy state of the development pipeline for the first time in decades. Here, we report the optimization of a rabbit model of TBM disease induced via inoculation of Mycobacterium tuberculosis into the cisterna magna, recapitulating features typical of clinical TBM neurological deterioration within months post-infection, acid-fast bacilli in necrotic lesions in the brain and spinal cord, and elevated lactate levels in cerebrospinal fluid (CSF). None of the infected rabbits recovered or controlled the disease. We used young adult rabbits, the size of which allows for spatial drug quantitation in critical compartments of the central nervous system that cannot be collected in clinical studies. To illustrate the translational value of the model, we report the penetration of linezolid from plasma into the CSF, meninges, anatomically distinct brain areas, cervical spine, and lumbar spine. Across animals, we measured the bacterial burden concomitant with neurological deterioration, offering a useful readout for drug efficacy studies. The model thus forms the basis for building a preclinical platform to identify improved regimens and inform clinical trial design.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Tuberculose Meníngea
/
Mycobacterium tuberculosis
Limite:
Animals
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article