The pharmacokinetics of rifampicin, isoniazid, pyrazinamide and ethambutol in TB/HIV coinfected patients recruited in two phase III clinical trials (61 patients in TB-HAART and 222 patients in RAFA study) were described using nonlinear mixed-effects modelling. Concentration-time data for rifampicin (TB-HAART study) was used to develop a semimechanistic pharmacokinetic model incorporating autoinduction and saturable pharmacokinetics. A model describing the pharmacokinetics of pyrazinamide (TB-HAART study) was developed and used to evaluate the 24-hour area under the concentration-time curve (AUC0–24), and maximum concentrations (Cmax) achieved with the currently recommended weight-adjusted doses for drug-susceptible and -resistant tuberculosis. Concentration-time data from the RAFA study were used to characterise the pharmacokinetics of the four drugs of the fixed dose combination (FDC) therapy including desacetyl-rifampicin, and acetyl-isoniazid. Binary recursive techniques were applied in the conditional inference framework to determine predictors including drug exposure of time-to-stable culture conversion and poor long-term treatment outcomes. The model describing the pharmacokinetics of rifampicin predicted that increasing the dose results in a more than proportional increase in exposure. Clearance of rifampicin increased by 90% from baseline to steady-state due to autoinduction and the process takes up to 21 days. Monte Carlo simulations showed that rifampicin doses of at least 25 mg/kg would be required to achieve an AUC0–24/MIC ratio of at least 271. Based on the model describing the pharmacokinetics of isoniazid, co-administration of isoniazid and efavirenz-based antiretroviral therapy results in a 54% reduction in isoniazid exposure only in fast acetylators. There were disparities in exposure across weight bands for all the four drugs: patients with lower weight had reduced exposure. To match drug exposure across the weight bands, we recommend the addition of one FDC tablet to patients with weight less than 55 kg. There is need to explore the use of fat-free mass-adjusted dosing since cumulative evidence shows its superiority over total body weight in driving exposure via allometric scaling for all first-line antituberculosis drugs. Individual drug exposures were not predictive of either time-to-stable culture conversion or long-term tuberculosis treatment outcomes. Baseline X-ray grading, HIV stage as TB diagnosis, and treatment arm were predictive of time-to-stable culture conversion while the presence of cavities, patient’s level of physical activity and CD4 count were the drivers of long-term treatment outcomes.

Le développement des méthodes de séquençage de nouvelle génération a permis d’approfondir les connaissances sur le rôle des communautés bactériennes commensales pour la santé de leur hôte, et l’impact négatif de la perturbation de leur équilibre. Les antibiotiques sont les principaux perturbateurs de cet équilibre, mais leur impact n’a pas été quantifié précisément.Nous avons quantifié la relation entre les concentrations fécales d’antibiotiques et la perturbation de la diversité bactérienne au sein du microbiote intestinal, et modélisé le lien entre la perte de diversité bactérienne et la probabilité de décès dans un modèle animal de colite à Clostridium difficile induite par les antibiotiques. Nous avons montré que l’indice de diversité de Shannon et la distance UniFac non pondérée étaient les indices de diversité qui étaient le plus prédictif du décès dans ce modèle d’infection.Chez des volontaires sains, nous avons développé un modèle mathématique semimécanistique de l’évolution de la diversité au sein du microbiote, mesurée par deux indices de diversité, après perturbation antibiotique, et quantifié la relation entre l’exposition individuelle plasmatique et fécale à un antibiotique, et son effet sur la perturbation de la diversité bactérienne au cours du temps. Nous avons également analysé le rôle de la voie d’élimination des antibiotiques pour la limitation de l’impact d’un antibiotique sur le microbiote. Ces travaux nous ont permis de montrer que le microbiote intestinal présente une grande sensibilité aux antibiotiques, et que la voie d’élimination ne semble de ce fait pas jouer un rôle prépondérant dans la perspective de limiter l’impact des antibiotiques sur le microbiote intestinal. / The development of next generation sequencing broadened our knowledge on the role of commensal bacterial communities on their host’s health, and the negative impact of their disruption. Antibiotics are the main disrupting factor, but their impact has not been precisely quantified.We quantified the relationship between antibiotic fecal concentrations and the loss of bacterial diversity in the intestinal microbiota, and modelled the link between the loss of diversity and mortality in a hamster model of antibiotic-induced Clostridium difficile infection. We showed that the Shannon diversity index and the unweighted UniFrac distance are the 2 indices that best predict mortality in this model. In healthy volunteers, we developed a semi-mechanistic model of the evolution over time of bacterial diversity – measured by two indices – after an antibiotic perturbation, and quantified the relationship between antibiotic concentrations in plasma and feces and the loss of bacterial diversity in the intestinal microbiota. We also analyzed the role of the antibiotic elimination pathway in the reduction of their impact on the microbiota. In this work, we showed that the intestinal microbiota is highly susceptible to antibiotics, and that the elimination route doesn’t have a major role, in the perspective of limiting antibiotics’ impact on the intestinal microbiota.