Preclinical models to optimize treatment of tuberculous meningitis - A systematic review

Tuberculous meningitis (TBM) is the most devastating form of TB, resulting in death or neurological disability in up to 50% of patients affected. Treatment is similar to that of pulmonary TB, despite poor cerebrospinal fluid (CSF) penetration of the cornerstone anti-TB drug rifampicin. Considering TBM pathology, it is critical that optimal drug concentrations are reached in the meninges, brain and/or the surrounding CSF. These type of data are difficult to collect in TBM patients. This review aims to identify and describe a preclinical model representative for human TBM which can provide the indispensable data needed for future pharmacological characterization and prioritization of new TBM regimens in the clinical setting. We reviewed existing literature on treatment of TBM in preclinical models: only eight articles, all animal studies, could be identified. None of the animal models completely recapitulated human disease and in most of the animal studies key pharmacokinetic data were missing, making the comparison with human exposure and CNS distribution, and the study of pharmacokinetic-pharmacodynamic relationships impossible. Another 18 articles were identified using other bacteria to induce meningitis with treatment including anti-TB drugs (predominantly rifampicin, moxifloxacin and levofloxacin). Of these articles the pharmacokinetics, i.e. plasma exposure and CSF:plasma ratios, of TB drugs in meningitis could be evaluated. Exposures (except for levofloxacin) agreed with human exposures and also most CSF:plasma ratios agreed with ratios in humans. Considering the lack of an ideal preclinical pharmacological TBM model, we suggest a combination of 1. basic physicochemical drug data combined with 2. in vitro pharmacokinetic and efficacy data, 3. an animal model with adequate pharmacokinetic sampling, microdialysis or imaging of drug distribution, all as a base for 4. physiologically based pharmacokinetic (PBPK) modelling to predict response to TB drugs in treatment of TBM.