The kidneys have a significant role in drug elimination through both metabolic and excretory routes. Despite a recent paradigm shift towards systems pharmacology approaches, prediction of renal drug disposition using 'bottom-up' and mechanistic modelling approaches remains underdeveloped. Lack of 'gold-standard' in vitro assays and corresponding in vitro-in vivo extrapolation (IVIVE) approaches for prediction of renal metabolic (CLR,met) and excretory (CLR) clearances contribute to this. A comprehensive literature analysis of quantitative physiological data to inform renal IVIVE scaling factors and systems parameters relevant for physiologically based pharmacokinetic (PBPK) kidney models was initially performed to identify existing knowledge gaps. Following this, microsomal protein content in dog kidney cortex (MPPGK) and liver (MPPGL) were measured in 17 samples from the same animal. Mean dog MPPGK (44.0 mg/ g kidney) and MPPGL (63.6 mg/ g liver) obtained using glucose-6-phosphatase activity as the microsomal protein marker where systematically higher than when CYP content was used as the marker (33.9 mg/ g kidney and 41.1 mg/ g liver respectively). Dog MPPGK was lower than MPPGL, with no direct correlation between the organs. In addition to dog, MPPGK and cytosolic protein per gram kidney (CPPGK) were obtained from 31 human samples, which represent the largest dataset currently available. Mean human MPPGK (25.7 mg/ g kidney) and CPPGK (52.7 mg/ g kidney), were measured using glucose-6-phosphatase and glutathione-S-transferase activities as recovery markers, respectively. Activity of prepared kidney microsomes was assessed using mycophenolic acid glucuronidation as a marker. Novel scaling factor of 25.7 mg/ g kidney was applied for IVIVE of mycophenolic acid microsomal glucuronidation data, resulting in a 2-fold increase in scaled intrinsic clearance compared with data scaled by the commonly used literature MPPGK value (12.8 mg/ g kidney). In addition to the microsomal scaling factor, several elements of a modified stereology method were developed for quantifying human proximal tubule cellularity. The methods included implementation of a systematic uniform random sampling protocol and investigation of tinctorial and immunohistochemistry based staining approaches that could be used identify and count proximal tubule cells in histology sections. A range of mechanistic models for prediction of CLR via either tubular reabsorption or active secretion were developed. A novel 5-compartment model for prediction of tubular reabsorption and CLR from Caco-2 apparent permeability data was developed. This model accounted for relevant physiological complexities of the kidney, such as regional differences in tubular filtrate flow rates and tubular surface area, including consideration of the impact of microvilli. The model predicted the CLR of 45 drugs with overall good accuracy (geometric mean fold error of 1.96), although a systematic under-prediction was noted for basic drugs. The novel 5-compartment model represents an important addition to the IVIVE toolbox for physiologically-based prediction of renal tubular reabsorption and CLR and can be implemented in the more complex mechanistic kidney models, as shown in the case of prediction of urine flow dependent CLR of theophylline and caffeine. Final part of the Thesis focused on the refinement of digoxin PBPK kidney model and its ability to predict effect of aging and renal impairment on digoxin CLR. The analysis has identified that reducing either the proximal tubule cellularity or OATP4C1 abundance parameters in the mechanistic model recovers well observed reduced tubular secretion and CLR of digoxin in renal impairment populations whereas no effect of modification of P-gp abundance was observed. Conversely, reducing the proximal tubule cellularity, OATP4C1 abundance or P-gp abundance parameters in the model resulted in negligible change, decreased or increased accumulation of digoxin in proximal tubule cells, respectively. In conclusion, the current study provides to date the most comprehensive kidney microsomal and cytosolic metabolic scaling factors, together with revised database on renal physiological data necessary for quantitative prediction of renal drug disposition. Mechanistic modelling work shown here has highlighted a need for physiological data from different population groups to inform kidney model parameters, in order to improve the scope and utility of such models within the systems pharmacology paradigm.

O bussulfano (Bu) é um agente alquilante usado no condicionamento que precede o transplante de células-tronco hematopoiéticas (TCTH) em crianças. Sua farmacocinética (FC) apresenta uma grande variabilidade interindivíduo, que pode ser parcialmente explicada pelas variantes genéticas de GSTA1, gene da enzima glutationa S-transferase α1, crucial para o metabolismo do Bu. Vários métodos de predição da FC do Bu são usados para calcular sua dose, essencialmente com base na idade e peso do paciente. Até o momento, apenas um modelo adulto incorporou as variantes de GSTA1 no cálculo da sua dose do Bu. No presente trabalho, avaliou-se, inicialmente, o desempenho de métodos atualmente disponíveis em pediatria, em função das variantes genéticas de GSTA1. Foram avaliados os parâmetros de FC da primeira dose de 101 crianças e adolescentes submetidos a TCTH alogênico no CHU Sainte-Justine, Montreal, Canadá, após regime de condicionamento que incluía Bu intravenoso (BuCR, do inglês busulfan-containing regimen). Os haplótipos GSTA1 foram interpretados em pares (diplótipos) e depois classificados em três grupos com base nos seus diferentes potenciais de expressão enzimática. As AUCs (area under the curve) medidas e as AUCs calculadas a partir de doses de Bu preditas por 11 modelos diferentes foram classificadas de acordo com a sua capacidade para atingir a AUC-alvo (900 a 1.500 μM.min). Também foram calculados os erros de previsão do clearance do Bu. Após a primeira dose, as AUCs medidas atingiram a AUC-alvo em 38,7%. Os diplótipos de GSTA1 relacionados ao metabolismo lento (G3) e regimes contendo fludarabina (FluCR, do inglês fludarabine-containing regimen) foram os únicos fatores associados à AUC no alvo (OR 4,7, IC 95%, 1,1 - 19,8, p = 0,04 e OR 9,9, IC 95%, 1,6 - 61,7, p = 0,01, respectivamente). Utilizando os outros métodos para o cálculo da dose, a percentagem de AUC no alvo variou de 16% a 74%. G3 e FluCR foram, em alguns modelos, associados à AUC no alvo ou na faixa tóxica, enquanto que os metabolizadores rápidos (G1) foram por vezes associados a AUCs subterapêuticas. Essas associações foram confirmadas na análise de predição do clearance, em que os diplótipos da GSTA1 e o regime de condicionamento influenciaram significativamente a maioria dos erros de previsão dos métodos testados. Uma vez que GSTA1 mostrou influenciar significativamente os algoritmos disponíveis, pretendeu-se desenvolver um modelo de FC de população que incluísse variantes genéticas de GSTA1 como um fator no cálculo de dose do Bu. Para tanto, foram analisados os dados de concentração-tempo de 112 crianças e adolescentes que receberam um BuCR mieloablativo antes de 115 TCTH (autólogos e alogênicos), realizados também no CHU Sainte-Justine. Para a construção do modelo de FC de população, utilizou-se uma análise mista não linear. Sexo, doença de base (maligna vs. não maligna), idade pós-menstrual (PMA) ou idade cronológica, regime de condicionamento e diplótipos de GSTA1 foram avaliados como fatores potenciais. Um modelo de um compartimento com eliminação de primeira ordem foi o que melhor descreveu os dados disponíveis. Um fator de maturação do metabolismo de Bu (Fmat) e o peso elevado a exponencial alométrico teórico foram incluídos no modelo de base. A análise dos fatores revelou PMA (ΔOFV = -26,7, p = 2,3x10-7) e grupos de diplótipos de GSTA1 (ΔOFV = -11,7, p = 0,003) como fatores significativamente associados, respectivamente, ao volume e ao CL do Bu. Os CL dos metabolizadores rápidos (G1) foram preditos como sendo 7% mais elevados que os definidos como metabolizadores normais (G2), enquanto que os metabolizadores lentos (G3) foram descritos com CL 12% menor que os G2. Em conclusão, após se evidenciar que os métodos disponíveis para o cálculo de dose do Bu não são adequados para todos os grupos de diplótipos de GSTA1, propôs-se o primeiro algoritmo de cálculo de dose de Bu em pediatria baseado em farmacogenética. Seu uso pode contribuir para uma melhor previsibilidade da FC do Bu e, desta forma, melhor predizer a exposição de crianças e adolescentes à droga, de acordo com a capacidade metabólica de cada indivíduo. / Busulfan (Bu) is an alkylating agent used in the conditioning before hematopoietic stem cells transplantation (HSCT) in children. Its pharmacokinetics (PK) presents a great inter-individual variability, which can be partially explained by GSTA1 genetic variants, gene coding for the enzyme glutathione s-tranferase α1, crucial for Bu metabolism. Several methods of predicting PK are available and are used to calculate the Bu dose, based essentially on patients’ age and anthropometric characteristics. So far, a single adult model successfully incorporated this factor into the Bu dose calculation. In the present work, we initially evaluate the performance of the currently available guidelines across the different GSTA1 genetic variants. The PK parameters from the Bu first doses from 101 children and adolescents who have undergone allogenic SCT at the CHU Sainte-Justine, Montreal, Canada following a IV Bu-containing conditioning regimen (BuCR). GSTA1 haplotypes were interpreted in pairs (diplotypes) and then classified in 3 groups based on different potentials of enzyme expression. Measured AUCs and AUCs calculated from Bu doses predicted by 11 different models were classified according to their ability to achieve the AUC target (900 and 1500μM.min). Clearance prediction errors were also calculated. After the first dose, measured AUCs achieved the target in 38.7%. GSTA1 diplotypes groups related to poor Bu metabolism (G3) and fludarabine-containing regimens (FluCR) were the only factors associated with AUC within target (OR 4.7, 95% CI, 1.1 - 19.8, p=0.04 and OR 9.9, 95% CI, 1.6 - 61.7, p=0.01, respectively). Using other methods for dose calculation, percentage of AUCs within target varied from 16% to 74%. G3 and FluCR were, in some models, associated to AUC within the target and in the toxic range, whereas rapid-metabolizers (G1) were correlated with sub therapeutic AUCs. These associations were confirmed in clearance-prediction analysis, where GSTA1 diplotypes groups and conditioning regimen consistently influenced methods’ most prediction errors. Once GSTA1 status was demonstrated to influence significantly the available Bu dosing algorithms, we aimed to develop a population PK (PPK) model which included GSTA1 genetic variants as a covariate. For that, concentration-time data from 112 children and adolescents receiving IV Bu as a component of the conditioning regimen for 115 stem cell transplantations (autologous and allogenic) performed at CHU Sainte-Justine were analyzed. Non-linear mixed effects analysis was used to build a PPK model. Sex, baseline disease (malignant vs. non-malignant), post-menstrual age (PMA) or chronological age, conditioning regimen and GSTA1 diplotypes groups were evaluated as potential covariates. A one-compartment model with first-order elimination best described the data. A factor of Bu metabolism maturation (Fmat) and theoretical allometric scaling of weight were included in the base model. Covariate analysis revealed PMA (ΔOFV=-26.7, p=2.3x10-7) and GSTA1 diplotypes groups (ΔOFV=-11.7, p=0.003), as significant factors on volume and clearance (CL), respectively. CL of rapid metabolizers (G1) were predicted as being 7% higher and that of poor ones (G3) 12% lower than CL of those defined as normal metabolizers (G2). In conclusion, after evidencing that available Bu dosing methods are not suitable for all GSTA1 diplotypes groups, we have proposed the first pharmacogenomics-based dosing algorithm for Bu to be used in a pediatrics. Its use may contribute considerably to better predict Bu exposure in children and adolescents tailoring the dose according to individual metabolic capacity.

O bussulfano (Bu) é um agente alquilante usado no condicionamento que precede o transplante de células-tronco hematopoiéticas (TCTH) em crianças. Sua farmacocinética (FC) apresenta uma grande variabilidade interindivíduo, que pode ser parcialmente explicada pelas variantes genéticas de GSTA1, gene da enzima glutationa S-transferase α1, crucial para o metabolismo do Bu. Vários métodos de predição da FC do Bu são usados para calcular sua dose, essencialmente com base na idade e peso do paciente. Até o momento, apenas um modelo adulto incorporou as variantes de GSTA1 no cálculo da sua dose do Bu. No presente trabalho, avaliou-se, inicialmente, o desempenho de métodos atualmente disponíveis em pediatria, em função das variantes genéticas de GSTA1. Foram avaliados os parâmetros de FC da primeira dose de 101 crianças e adolescentes submetidos a TCTH alogênico no CHU Sainte-Justine, Montreal, Canadá, após regime de condicionamento que incluía Bu intravenoso (BuCR, do inglês busulfan-containing regimen). Os haplótipos GSTA1 foram interpretados em pares (diplótipos) e depois classificados em três grupos com base nos seus diferentes potenciais de expressão enzimática. As AUCs (area under the curve) medidas e as AUCs calculadas a partir de doses de Bu preditas por 11 modelos diferentes foram classificadas de acordo com a sua capacidade para atingir a AUC-alvo (900 a 1.500 μM.min). Também foram calculados os erros de previsão do clearance do Bu. Após a primeira dose, as AUCs medidas atingiram a AUC-alvo em 38,7%. Os diplótipos de GSTA1 relacionados ao metabolismo lento (G3) e regimes contendo fludarabina (FluCR, do inglês fludarabine-containing regimen) foram os únicos fatores associados à AUC no alvo (OR 4,7, IC 95%, 1,1 - 19,8, p = 0,04 e OR 9,9, IC 95%, 1,6 - 61,7, p = 0,01, respectivamente). Utilizando os outros métodos para o cálculo da dose, a percentagem de AUC no alvo variou de 16% a 74%. G3 e FluCR foram, em alguns modelos, associados à AUC no alvo ou na faixa tóxica, enquanto que os metabolizadores rápidos (G1) foram por vezes associados a AUCs subterapêuticas. Essas associações foram confirmadas na análise de predição do clearance, em que os diplótipos da GSTA1 e o regime de condicionamento influenciaram significativamente a maioria dos erros de previsão dos métodos testados. Uma vez que GSTA1 mostrou influenciar significativamente os algoritmos disponíveis, pretendeu-se desenvolver um modelo de FC de população que incluísse variantes genéticas de GSTA1 como um fator no cálculo de dose do Bu. Para tanto, foram analisados os dados de concentração-tempo de 112 crianças e adolescentes que receberam um BuCR mieloablativo antes de 115 TCTH (autólogos e alogênicos), realizados também no CHU Sainte-Justine. Para a construção do modelo de FC de população, utilizou-se uma análise mista não linear. Sexo, doença de base (maligna vs. não maligna), idade pós-menstrual (PMA) ou idade cronológica, regime de condicionamento e diplótipos de GSTA1 foram avaliados como fatores potenciais. Um modelo de um compartimento com eliminação de primeira ordem foi o que melhor descreveu os dados disponíveis. Um fator de maturação do metabolismo de Bu (Fmat) e o peso elevado a exponencial alométrico teórico foram incluídos no modelo de base. A análise dos fatores revelou PMA (ΔOFV = -26,7, p = 2,3x10-7) e grupos de diplótipos de GSTA1 (ΔOFV = -11,7, p = 0,003) como fatores significativamente associados, respectivamente, ao volume e ao CL do Bu. Os CL dos metabolizadores rápidos (G1) foram preditos como sendo 7% mais elevados que os definidos como metabolizadores normais (G2), enquanto que os metabolizadores lentos (G3) foram descritos com CL 12% menor que os G2. Em conclusão, após se evidenciar que os métodos disponíveis para o cálculo de dose do Bu não são adequados para todos os grupos de diplótipos de GSTA1, propôs-se o primeiro algoritmo de cálculo de dose de Bu em pediatria baseado em farmacogenética. Seu uso pode contribuir para uma melhor previsibilidade da FC do Bu e, desta forma, melhor predizer a exposição de crianças e adolescentes à droga, de acordo com a capacidade metabólica de cada indivíduo. / Busulfan (Bu) is an alkylating agent used in the conditioning before hematopoietic stem cells transplantation (HSCT) in children. Its pharmacokinetics (PK) presents a great inter-individual variability, which can be partially explained by GSTA1 genetic variants, gene coding for the enzyme glutathione s-tranferase α1, crucial for Bu metabolism. Several methods of predicting PK are available and are used to calculate the Bu dose, based essentially on patients’ age and anthropometric characteristics. So far, a single adult model successfully incorporated this factor into the Bu dose calculation. In the present work, we initially evaluate the performance of the currently available guidelines across the different GSTA1 genetic variants. The PK parameters from the Bu first doses from 101 children and adolescents who have undergone allogenic SCT at the CHU Sainte-Justine, Montreal, Canada following a IV Bu-containing conditioning regimen (BuCR). GSTA1 haplotypes were interpreted in pairs (diplotypes) and then classified in 3 groups based on different potentials of enzyme expression. Measured AUCs and AUCs calculated from Bu doses predicted by 11 different models were classified according to their ability to achieve the AUC target (900 and 1500μM.min). Clearance prediction errors were also calculated. After the first dose, measured AUCs achieved the target in 38.7%. GSTA1 diplotypes groups related to poor Bu metabolism (G3) and fludarabine-containing regimens (FluCR) were the only factors associated with AUC within target (OR 4.7, 95% CI, 1.1 - 19.8, p=0.04 and OR 9.9, 95% CI, 1.6 - 61.7, p=0.01, respectively). Using other methods for dose calculation, percentage of AUCs within target varied from 16% to 74%. G3 and FluCR were, in some models, associated to AUC within the target and in the toxic range, whereas rapid-metabolizers (G1) were correlated with sub therapeutic AUCs. These associations were confirmed in clearance-prediction analysis, where GSTA1 diplotypes groups and conditioning regimen consistently influenced methods’ most prediction errors. Once GSTA1 status was demonstrated to influence significantly the available Bu dosing algorithms, we aimed to develop a population PK (PPK) model which included GSTA1 genetic variants as a covariate. For that, concentration-time data from 112 children and adolescents receiving IV Bu as a component of the conditioning regimen for 115 stem cell transplantations (autologous and allogenic) performed at CHU Sainte-Justine were analyzed. Non-linear mixed effects analysis was used to build a PPK model. Sex, baseline disease (malignant vs. non-malignant), post-menstrual age (PMA) or chronological age, conditioning regimen and GSTA1 diplotypes groups were evaluated as potential covariates. A one-compartment model with first-order elimination best described the data. A factor of Bu metabolism maturation (Fmat) and theoretical allometric scaling of weight were included in the base model. Covariate analysis revealed PMA (ΔOFV=-26.7, p=2.3x10-7) and GSTA1 diplotypes groups (ΔOFV=-11.7, p=0.003), as significant factors on volume and clearance (CL), respectively. CL of rapid metabolizers (G1) were predicted as being 7% higher and that of poor ones (G3) 12% lower than CL of those defined as normal metabolizers (G2). In conclusion, after evidencing that available Bu dosing methods are not suitable for all GSTA1 diplotypes groups, we have proposed the first pharmacogenomics-based dosing algorithm for Bu to be used in a pediatrics. Its use may contribute considerably to better predict Bu exposure in children and adolescents tailoring the dose according to individual metabolic capacity.

This thesis focuses on the measurement of diffusion coefficient of dialysis membrane. The first part describes possibilities of membrane modelling. Basic models, which allow us to determine the basic characteristics of dialysis membranes like permeability and diffusion coefficient, are described. Next chapter deals with basic types and properties of membranes. The main part focuses on making the experimental installation, which is used to simulate permeance of contrast agent, used in DCE-MRI, through dialysis membrane. The last theoretical chapter describes calculations used to estimate diffusion coefficient. Practical part of this thesis uses a designed experimental installation for estimation of diffusion coefficient for two contrast agents Gadovist® and Multihance®.

No description available.

One of the major challenges faced by the pharmaceutical industry is to accelerate the product innovation process and reduce the time-to-market for new drug developments. This involves billions of dollars of investment due to the large amount of experimentation and validation processes involved. A computational modeling approach, which could explore the design space rapidly, reduce uncertainty and make better, faster and safer decisions, fits into the overall goal and complements the product development process. Our research focuses on the early preclinical stage of the drug development process involving lead selection, optimization and candidate identification steps. Our work helps in screening the most favorable candidates based on the biopharmaceutical and pharmacokinetic properties. This helps in precipitating early development failures in the early drug discovery and candidate selection processes and reduces the rate of late-stage failures, which is more expensive. In our research, we successfully integrated two well-known models, namely the drug release model (dissolution model) with a drug transport model (compartmental absorption and transit (CAT) model) to predict the release, distribution, absorption and elimination of an oral drug through the gastrointestinal (GI) tract of the human body. In the CAT model, the GI tract is envisioned as a series of compartments, where each compartment is assumed to be a continuous stirred tank reactor (CSTR). We coupled the drug release model in the form of partial differential equations (PDE's) with the CAT model in the form of ordinary differential equations (ODE's). The developed model can also be used to design the drug tablet for target pharmacokinetic characteristics. The advantage of the suggested approach is that it includes the mechanism of drug release and also the properties of the polymer carrier into the model. The model is flexible and can be adapted based on the requirements of the clients. Through this model, we were also able to avoid depending on commercially available software which are very expensive. In the drug discovery and development process, the tablet formulation (oral drug delivery) is an important step. The tablet consists of active pharmaceutical ingredient (API), excipients and polymer. A controlled release of drug from this tablet usually involves swelling of the polymer, forming a gel layer and diffusion of drug through the gel layer into the body. The polymer is mainly responsible for controlling the release rate (of the drug from the tablet), which would lead to a desired therapeutic effect on the body. In our research, we also developed a molecular design strategy for generating molecular structures of polymer candidates with desired properties. Structure-property relationships and group contributions are used to estimate the polymer properties based on the polymer molecular structure, along with a computer aided technique to generate molecular structures of polymers having desired properties. In greater detail, we utilized group contribution models to estimate several desired polymer properties such as grass transition temperature (Tg), density (ρ) and linear expansion coefficient (α). We subsequently solved an optimization model, which generated molecular structures of polymers with desired property values. Some examples of new polymer repeat units are - [CONHCH₂ - CH₂NHCO]n -, - [CHOH - COO]n -. These repeat-units could potentially lead to novel polymers with interesting characteristics; a polymer chemist could further investigate these. We recognize the need to develop group contribution models for other polymer properties such as porosity of the polymer and diffusion coefficients of water and drug in the polymer, which are not currently available in literature. The geometric characteristics and the make-up of the drug tablet have a large impact on the drug release profile in the GI tract. We are exploring the concept of tablet customization, namely designing the dosage form of the tablet based on a desired release profile. We proposed tablet configurations which could lead to desired release profiles such as constant or zero-order release, Gaussian release and pulsatile release. We expect our work to aid in the product innovation process. / Ph. D.

Les propriétés pharmacocinétiques d’un nouveau médicament et les risques d’interactions médicamenteuses doivent être investigués très tôt dans le processus de recherche et développement. L’objectif principal de cette thèse était de concevoir des approches prédictives de modélisation du devenir du médicament dans l’organisme en présence et en absence de modulation d’activité métabolique et de transport. Le premier volet de recherche consistait à intégrer dans un modèle pharmacocinétique à base physiologique (PBPK), le transport d’efflux membranaire gouverné par les glycoprotéines-P (P-gp) dans le cœur et le cerveau. Cette approche, basée sur des extrapolations in vitro-in vivo, a permis de prédire la distribution tissulaire de la dompéridone chez des souris normales et des souris déficientes pour les gènes codant pour la P-gp. Le modèle a confirmé le rôle protecteur des P-gp au niveau cérébral, et a suggéré un rôle négligeable des P-gp dans la distribution tissulaire cardiaque pour la dompéridone. Le deuxième volet de cette recherche était de procéder à l’analyse de sensibilité globale (ASG) du modèle PBPK précédemment développé, afin d’identifier les paramètres importants impliqués dans la variabilité des prédictions, tout en tenant compte des corrélations entre les paramètres physiologiques. Les paramètres importants ont été identifiés et étaient principalement les paramètres limitants des mécanismes de transport à travers la membrane capillaire. Le dernier volet du projet doctoral consistait à développer un modèle PBPK apte à prédire les profils plasmatiques et paramètres pharmacocinétiques de substrats de CYP3A administrés par voie orale à des volontaires sains, et de quantifier l’impact d’interactions médicamenteuses métaboliques (IMM) sur la pharmacocinétique de ces substrats. Les prédictions des profils plasmatiques et des paramètres pharmacocinétiques des substrats des CYP3A ont été très comparables à ceux mesurés lors d’études cliniques. Quelques écarts ont été observés entre les prédictions et les profils plasmatiques cliniques mesurés lors d’IMM. Cependant, l’impact de ces inhibitions sur les paramètres pharmacocinétiques des substrats étudiés et l’effet inhibiteur des furanocoumarins contenus dans le jus de pamplemousse ont été prédits dans un intervalle d’erreur très acceptable. Ces travaux ont contribué à démontrer la capacité des modèles PBPK à prédire les impacts pharmacocinétiques des interactions médicamenteuses avec une précision acceptable et prometteuse. / Early knowledge of pharmacokinetic properties of a new drug candidate and good characterization of the impact of drug-drug interaction (DDI) on those properties is of crucial importance in the process of drug research and development. The main objective of this thesis consisted in the conception of PBPK models able to predict the drug disposition in the absence and presence of metabolic and transport activity modulation. The first part of this work aimed to develop a PBPK model that incorporates the efflux function of P-gp expressed in various tissues, in order to predict the impact of P-gp activity modulation on drug distribution. This approach, based on in vivo-in vitro extrapolation for estimating the transport-related parameters, allowed the prediction of domperidone distribution in heart and brain of wild-type mice and P-gp deficient mice. The model pointed out the protective function of P-gp in brain whereas it showed the negligible protective effect of P-gp in heart. The second part of the project aimed to perform the global sensitivity analysis of the previous PBPK model, in order to investigate how the uncertainly and variability of the correlated physiological parameters influence the outcome of the drug distribution process. While a moderate variability of the model predictions was observed, this analysis confirmed the importance for a better quantitative characterization of parameters related to the transport processes trough the blood-tissue membrane. Accounting for the input correlation allowed the delineation of the true contribution of each input to the variability of the model outcome. The last part of the project consisted in predicting the pharmacokinetics of selected CYP3A substrates administered at a single oral dose to human, alone or with an inhibitor. Successful predictions were obtained for a single administration of the CYP3A substrates. Some deviations were observed between the predictions and in vivo plasma profiles in the presence of DDI. However, the impact of inhibition on the PK parameters of the selected substrates and the impact of grapefruit juice-mediated inhibition on the extent of intestinal pre-systemic elimination were predicted within a very acceptable error range. Overall, this thesis demonstrated the ability of PBPK models to predict DDI with promising accuracy.

The elimination of drugs from the body is in many cases performed by the liver. Much could be gained if an accurate prediction of this process could be made early in the development of new drugs. However, for the elimination to occur, the drug molecule needs first to get inside the liver cell. Disposition is the expression used to encapsulate both elimination and distribution. This thesis presents novel approaches and models based on simple in vitro systems for the investigation of processes involved in the hepatic drug disposition. An approach to the estimation of enzyme kinetics based on substrate depletion data from cell fractions was thoroughly evaluated through experiments and simulations. The results that it provided were confirmed to be accurate and robust. In addition, a new experimental setup suitable for a screening environment, i.e., for a reduced number of samples, was generated through optimal experimental design. The optimization suggested that sampling at late time points over a wide range of concentration was the most advantageous. A model, based on data from primary hepatocytes in suspension, for the investigation of cellular disposition of metabolized drugs was developed. Information on the relative importance of metabolism and membrane protein related distribution was obtained by analysis of changes in the kinetics by specific inhibition of the various processes. The model was evaluated by comparing the results to those obtained from an in vivo study analyzed with an especially constructed mechanistic PBPK model. These investigations showed that the suggested model produced good predictions of the relative importance of metabolism and carrier mediated membrane transport for hepatic disposition. In conclusion, new approaches for the investigation of processes involved in hepatic disposition were developed. These methods were shown to be robust and increased the output of information from already commonly implemented in vitro systems.

Les propriétés pharmacocinétiques d’un nouveau médicament et les risques d’interactions médicamenteuses doivent être investigués très tôt dans le processus de recherche et développement. L’objectif principal de cette thèse était de concevoir des approches prédictives de modélisation du devenir du médicament dans l’organisme en présence et en absence de modulation d’activité métabolique et de transport. Le premier volet de recherche consistait à intégrer dans un modèle pharmacocinétique à base physiologique (PBPK), le transport d’efflux membranaire gouverné par les glycoprotéines-P (P-gp) dans le cœur et le cerveau. Cette approche, basée sur des extrapolations in vitro-in vivo, a permis de prédire la distribution tissulaire de la dompéridone chez des souris normales et des souris déficientes pour les gènes codant pour la P-gp. Le modèle a confirmé le rôle protecteur des P-gp au niveau cérébral, et a suggéré un rôle négligeable des P-gp dans la distribution tissulaire cardiaque pour la dompéridone. Le deuxième volet de cette recherche était de procéder à l’analyse de sensibilité globale (ASG) du modèle PBPK précédemment développé, afin d’identifier les paramètres importants impliqués dans la variabilité des prédictions, tout en tenant compte des corrélations entre les paramètres physiologiques. Les paramètres importants ont été identifiés et étaient principalement les paramètres limitants des mécanismes de transport à travers la membrane capillaire. Le dernier volet du projet doctoral consistait à développer un modèle PBPK apte à prédire les profils plasmatiques et paramètres pharmacocinétiques de substrats de CYP3A administrés par voie orale à des volontaires sains, et de quantifier l’impact d’interactions médicamenteuses métaboliques (IMM) sur la pharmacocinétique de ces substrats. Les prédictions des profils plasmatiques et des paramètres pharmacocinétiques des substrats des CYP3A ont été très comparables à ceux mesurés lors d’études cliniques. Quelques écarts ont été observés entre les prédictions et les profils plasmatiques cliniques mesurés lors d’IMM. Cependant, l’impact de ces inhibitions sur les paramètres pharmacocinétiques des substrats étudiés et l’effet inhibiteur des furanocoumarins contenus dans le jus de pamplemousse ont été prédits dans un intervalle d’erreur très acceptable. Ces travaux ont contribué à démontrer la capacité des modèles PBPK à prédire les impacts pharmacocinétiques des interactions médicamenteuses avec une précision acceptable et prometteuse. / Early knowledge of pharmacokinetic properties of a new drug candidate and good characterization of the impact of drug-drug interaction (DDI) on those properties is of crucial importance in the process of drug research and development. The main objective of this thesis consisted in the conception of PBPK models able to predict the drug disposition in the absence and presence of metabolic and transport activity modulation. The first part of this work aimed to develop a PBPK model that incorporates the efflux function of P-gp expressed in various tissues, in order to predict the impact of P-gp activity modulation on drug distribution. This approach, based on in vivo-in vitro extrapolation for estimating the transport-related parameters, allowed the prediction of domperidone distribution in heart and brain of wild-type mice and P-gp deficient mice. The model pointed out the protective function of P-gp in brain whereas it showed the negligible protective effect of P-gp in heart. The second part of the project aimed to perform the global sensitivity analysis of the previous PBPK model, in order to investigate how the uncertainly and variability of the correlated physiological parameters influence the outcome of the drug distribution process. While a moderate variability of the model predictions was observed, this analysis confirmed the importance for a better quantitative characterization of parameters related to the transport processes trough the blood-tissue membrane. Accounting for the input correlation allowed the delineation of the true contribution of each input to the variability of the model outcome. The last part of the project consisted in predicting the pharmacokinetics of selected CYP3A substrates administered at a single oral dose to human, alone or with an inhibitor. Successful predictions were obtained for a single administration of the CYP3A substrates. Some deviations were observed between the predictions and in vivo plasma profiles in the presence of DDI. However, the impact of inhibition on the PK parameters of the selected substrates and the impact of grapefruit juice-mediated inhibition on the extent of intestinal pre-systemic elimination were predicted within a very acceptable error range. Overall, this thesis demonstrated the ability of PBPK models to predict DDI with promising accuracy.

Infusion pumps administer medications like morphine to pediatric patients in order to manage pain. Drug delivery delays can be the result of flow rate variabilities in the infusion pump system. Due to the risk of over-or underdosing, this could have a high impact on the pediatric population. This study’s aim is to investigate the effect of drug dilution and dosing delays by investigating which factors affect the morphine concentration in the patient. Implementation of a previously developed population pharmacokinetic model was performed in MATLAB. Then combining it with a self-developed model of the infusion pump system, a model which included the infusion pump and the system between the pump and the patient. Simulations were performed to investigate the contributing factors. The results show that dosing delays decrease with an increasing patient’s age. There are larger morphine concentration variations with lower syringe flow rates. A decrease in dosage and a smaller syringe volume result in a shorter time of reaching a steady state concentration. Using the wrong syringe which is not compatible with the machine will lead to an increasing morphine concentration in the patient that does not reach a steady state. A limitation of the study was that no clinical data was used for the simulations. These results are useful for clinicians when making decisions regarding intravenous administration of morphine, potentially leading to fewer medication errors. / Infusionspumpar administrerar läkemedel som morfin till pediatriska patienter för smärtlindring. Fördröjning av läkemedelstillförsel kan vara resultatet av flödeshastighetsvariationer i infusionspumpsystemet. På grund av risken för över- eller underdosering kan detta ha en stor inverkan på den pediatriska populationen. Denna studies syfte är att undersöka effekten av läkemedels- utspädning och -fördröjning genom att undersöka vilka faktorer som påverkar koncentrationen av morfin i patienten. Implementering av en tidigare utvecklad populationsfarmakokinetisk modell gjordes i MATLAB . För att sedan kombinera den med en egenutvecklad modell av infusionspumpsystemet, en modell som inkluderade infusionspumpen och systemet mellan pumpen och patienten. Simuleringar utfördes för att undersöka de bidragande faktorerna. Resultaten visar att doseringsfördröjningar minskar med patientens stigande ålder. Det finns större koncentrationsvariationer med lägre sprutflödeshastig- heter. En minskning av dosen och en mindre sprutvolym resulterar i en kortare tid för att uppnå en steady state-koncentration. Användning av fel spruta som inte är kompatibel med maskinen kommer att leda till en ökad morfinkon- centration hos patienten som inte når ett stabilt tillstånd. En begränsning med studien var att inga klinisk data användes för simuleringarna. Dessa resultat är användbara för läkare när de fattar beslut om intravenös administrering av morfin, vilket potentiellt kan leda till färre medicineringsfel.