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.

Un problème majeur du cancer est l'apparition de métastases, difficiles à détecter par l'imagerie médicale et qui peuvent progresser rapidement. Par le biais de la modélisation mathématique, nous espérons développer de nouveaux outils capables d'anticiper l'état métastatique d'un patient.Les deux premières parties de cette thèse sont dédiées au développement d'un tel outil, l'objectif étant sonutilisation chez l'animal voire en clinique. Dû aux variabilités intra- et inter-individuelles, nous sommes amenés à utiliser des modèles statistiques coûteux en temps de calcul.Dans la partie 1, nous étendons une approche introduite par Iwata et al. et développée dans l'équipe. Nousproposons une résolution numérique plus efficace basée sur la reformulation du modèle sous formed'équation intégrale de Volterra de type convolution, qui s'avère également utile pour montrer despropriétés théoriques du modèle. En outre, nous étudions une extension stochastique de ce modèle déterministe.Dans la partie 2, nous montrons que notre approche est adaptée à la description de données souris. Utilisant le cadre statistique des modèles nonlinéaires à effets mixtes, nous construisons un modèle métastatique identifiable à partir des données et nous interprétons les résultats biologiquement.La partie 3 regroupe des résultats issus de collaborations avec des biologistes. Nous avons commencé àmodéliser la croissance tumorale à partir d'observations par imagerie SPECT en utilisant un modèle deGyllenberg et Webb. D'autre part, afin d'améliorer la précision des observations SPECT, nous testons des techniques dedétection de contours via des méthodes volumes finis basées sur des schémas DDFV. / Metastasis is one of the major problems of cancer because metastases areoften difficult to detect by clinical imaging and may develop rapidly. With the help of mathematical modelling, we hope to developnew tools capable of anticipating the metastatic state of a patient.The first two parts of this thesis are dedicated to developing such a tool, destined for a preclinical oreven clinical use. As tumour growth dynamics vary strongly between individuals and since observations are often sparse andnoisy, we need to consider computationally expensive statistical tools.In the first part, we extend an approach introduced by Iwata et al. and developed by Barbolosi et al. In particular, wepropose a more efficient numerical resolution based on a model reformulation into a Volterra integral equation of convolutiontype. This reformulation also permits to prove theoretical model properties (regularity and identifiability). Moreover, we study a stochastic generalisation of this deterministic model.In the second part, we will show that our approach is suitable for the description of experimental data on tumour-bearing mice.Using the statistical framework of nonlinear mixed-effects modelling, we build a metastatic model that is identifiable fromour data. We then interpret the results biologically.The last part of this thesis contains several results obtained in collaboration with biologists. We have started to model tumourgrowth with data obtained from SPECT imaging, using a model by Gyllenberg and Webb. Also, in order to improve the precision ofSPECT data, we have tested contour detection methods via finite volume methods based on DDFV schemes.

Tuberculosis is one the leading causes of death globally and was before the COVID-19 pandemic the leading cause of death from a single infectious agent. Developing active tuberculosis is life threatening and therefore is the rise of drug-resistant tuberculosis alarming as this risk causing current treatments to become ineffective. Linezolid is a promising drug for treatment of drug-resistant pulmonary tuberculosis, but the effect of linezolid treatment for pulmonary tuberculosis subjects is still not understood well enough and the World Health Organization has requested this knowledge gap to be filled. In this project we support the closing of this knowledge gap by describing the pharmacokinetics of linezolid for treatment of pulmonary tuberculosis using data collected from a phase two clinical trial in a South African population. This was done by creating a pop-PK model and resulted in the PK of linezolid in pulmonary tuberculosis patients from South Africa was best described using a one-compartment model, with first-order absorption process preceded by a series of transit compartments and saturable elimination. However, the diagnostics of the model still show that there are room for improvements and future work is necessary to further optimize the model.

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.

Pour les patients diagnostiqués avec un cancer au stade précoce, les décisions de traitement dépendent de l’évaluation du risque de rechute métastatique. Les outils de pronostic actuels sont fondés sur des approches purement statistiques, sans intégrer les connaissances disponibles sur les processus biologiques à l’oeuvre. L’objectif de cette thèse est de développer des modèles prédictifs du processus métastatique en utilisant une approche de modélisation mécaniste et la modélisation à effets mixtes. Dans la première partie, nous étendons un modèle mathématique du processus métastatique pour décrire la croissance de la tumeur primaire et de la masse métastatique totale chez des souris traitées avec le sunitinib (un inhibiteur de tyrosine kinase ayant une action anti-angiogénique) administré comme traitement néoadjuvant (i.e. avant exérèse de la tumeur primaire). Le modèle est utilisé pour tester des hypothèses expliquant les effets différentiels du sunitinib sur la tumeur primaire et les métastases. Des algorithmes d’apprentissage statistique sont utilisés pour évaluer la valeur prédictive des biomarqueurs sur les paramètres du modèle.Dans la deuxième partie de cette thèse, nous développons un modèle mécaniste pour la prédiction du temps de rechute métastatique et le validons sur des données cliniques des patientes atteintes d’un cancer du sein localisé. Ce modèle offre des prédictions personnalisées des métastases invisibles au moment du diagnostic, ainsi que des simulations de la croissance métastatique future, et il pourrait être utilisé comme un outil de prédiction individuelle pour aider à la gestion des patientes atteintes de cancer du sein. / For patients diagnosed with early-stage cancer, treatment decisions depend on the evaluation of the risk of metastatic relapse. Current prognostic tools are based on purely statistical approaches that relate predictor variables to the outcome, without integrating any available knowledge of the underlying biological processes. The purpose of this thesis is to develop predictive models of the metastatic process using an established mechanistic modelling approach and the statistical mixed-effects modelling framework.In the first part, we extend the mathematical metastatic model to describe primary tumour and metastatic dynamics in response to neoadjuvant sunitinib in clinically relevant mouse models of spontaneous metastatic breast and kidney cancers. The calibrated model is then used to test possible hypothesis for the differential effects of sunitinib on primary tumour and metastases, and machine learning algorithms are applied to assess the predictive power of biomarkers on the model parameters.In the second part of this thesis, we develop a mechanistic model for the prediction of the time to metastatic relapse and validate it on a clinical dataset of breast cancer patients. This model offers personalised predictions of the invisible metastatic burden at the time of diagnosis, as well as forward simulations of metastatic growth, and it could be used as a personalised prediction tool to assist in the routine management of breast cancer patients.

Biological invasions are reportedly one of the major contributory factors to biodiversity loss worldwide. The impacts of invasive alien plant (IAP) species on native communities are widely documented in the scientific literature, however, there is still a lack of detailed information on their impacts within the most vulnerable habitats. Riparian habitats are highly dynamic systems and naturally disturbed, making them particularly vulnerable to invasion. Climate change, directly or indirectly, is also predicted to adversely impact river systems, which may subsequently alter invasion rates and the impacts of IAPs. However, the interactions between climate and IAPs and their combined effects on vegetation have rarely been examined. To address these knowledge gaps, this thesis investigates: (1) the role of environmental variables, such as sediment loading or climate-related changes to river flow regime, on the abundance of IAPs within riparian zones; (2) how variation in IAP abundance impacts native vegetation, relative to the effects of native dominant plant species and (3) some of the mechanisms underlying the effects of IAPs in riparian habitats. Historic and recent field survey data were used to investigate changes in riparian vegetation on British rivers during the last 20 years. Analyses indicate that IAPs had a negative but small effect on native plant diversity. Overall, changes in land use and differences in flow regime between recording periods were the most important predictors of plant community change. Specifically, IAPs had a greater probability of being present along lowland rivers that experienced increased frequency of high flow events. On a local scale across rivers in Scotland, the abundance of IAPs was constrained by greater soil moisture in summer, whilst greater abundance was associated with tree-lined banks. Both native dominant species and IAPs negatively affected subordinate species abundance to a greater extent than species richness, although this effect varied spatially with bank elevation. Artificial turf mats were used to quantify viable propagules within riverine sediment deposited over-winter along invaded riverbanks. The data indicate that there is a legacy effect of IAP abundance, with the most invaded sites being associated with higher sediment loading the following year, though, contrary to the general pattern, 12 sediment associated propagules were scarcer at invaded sites. Moreover, lower above-ground native diversity was associated with sites which had been previously invaded. Plant species composition in the propagule bank and above-ground vegetation were highly dissimilar, particularly closest to the water’s edge at highly invaded sites. This suggests that mono-specific stands of IAPs proliferate best under less disturbed environmental conditions, although fluvial disturbance events may be required to create opportunities for initial establishment. The propagule bank contributed very little to the above-ground vegetation, nor did it limit invasion, suggesting that above-ground plant composition is largely dictated by competitive interactions. The findings presented in this thesis suggest that invasion by IAPs is an additional stressor for native vegetation within riparian habitats, modifying above-ground plant communities via competition and suppressing recruitment from the propagule bank. However, native dominant species common in riparian habitats also negatively impact, subordinate species via competition, in some cases equalling the effect of IAPs. Native dominant and IAP species are differently affected by environmental factors operating in the riparian zone, which may provide future opportunities for reducing and managing invasions.