Depot intramuscularly administered testosterone cypionate (TC) is indicated for treatment of hypogonadism in males. However, illegal use of TC and other anabolic steroids in athletic competition has been occurring for over 50 years. A randomized three-arm clinical trial was conducted to investigate side effects of long-term abuse of testosterone cypionate. The objective of the thesis is to apply modeling approach to characterize pharmacokinetics of long-term TC injections as well as identify its side effects on healthy male volunteers. A linear one-compartment model with first-order absorption best described the concentration-time profile of testosterone obtained from 31 healthy males. The population clearance estimates for total and free testosterone were 2.42*103 and 6.03*105 L/day, respectively. Weight and albumin were identified as significant covariates for total testosterone. Given the known inhibitory effect of testosterone on HPG axis, an indirect effect model was applied to describe the suppression of luteinizing hormone and spermatogenesis. The estimated potency of total testosterone with respect to LH suppression was 9.38ng/ml. Model simulation showed that suppression of luteinizing hormone and spermatogenesis after TC injection was more severe and of greater duration in the highest dose level. A polynomial change point mixed effects model was successfully built to describe the change in weight and lipid profiles after weekly injection of testosterone cypionate. Model simulation showed that both 250mg and 500mg would incur an average increase of body weight of 3.5kg at 8 weeks after dosing. A polynomial change point model also identifies that there is a tendency for lipid decrease after TC administration. However, no difference was found in the lipid change between three dose groups, which precludes any definite conclusion on the effect of long-term TC administration on lipid profiles.

Phenytoin is a relatively old anticonvulsant, but it has been commonly prescribed for more than half a century. The variability of phenytoin pharmacokinetic characteristics presents a challenge in therapeutic drug monitoring; hence, in the past twenty years its pharmacokinetic characteristics have been studied extensively. Up to now the studies were done with either healthy individuals or patients with normal liver functions. In this study a multifactorial scale of liver function, Pugh-Modified CTC (Child-Turcotte Criteria), has been incorporated to develop and evaluate a population pharmacokinetic model for phenytoin to be used in patients with liver dysfunction. Nonlinear Mixed Effects Model (NONMEM), a regression computer program, was utilized to develop the population pharmacokinetic model on the data of this study. The predictive performance of this model was evaluated by means of bootstrapping of the prediction error (PE) with the improved prediction-error (PE imp ) serving as an estimate of internal validity. The developed and validated final population pharmacokinetic model for phenytoin in patients with liver dysfunction is presented as follows: [special characters omitted] where Vmax is the maximum metabolic rate (mg/h); &thetas; 1 , the intercept for Vmax, is 7.41 mg/h; WT is the body weight (Kg); LS indicates one of three liver statuses: normal (CTC ≤ 6), mild dysfunction (CTC scores of 7–9), and moderate dysfunction (CTC scores of 10–12); Vd, the apparent volume of distribution (L), is 184 L; &thetas; WT is 0.126 and &thetas; LS is 2.14 for moderate liver-dysfunction. The maximum metabolic rate increased in patients with liver dysfunction, and there was weak statistical evidence that Vmax might increase in patients with chronic alcohol abuse. Based on the aforementioned longitudinal (population) pharmacokinetic model, a dosing method was also developed. By utilizing the dosing method, it may be possible to improve phenytoin dosage regimens, initial doses, and Bayesian estimates of pharmacokinetic parameters. Improved initial doses and more accurate estimates of pharmacokinetic parameters may lead to fewer required measured phenytoin concentrations and fewer dose changes. A decrease in the number of dose changes should result in less time expended in the writing and processing prescriptions and orders, and there may also be fewer wasted doses. Additionally, the improved initial doses should result in concentrations more frequently in the therapeutic window; thereby, resulting in less toxicity, greater efficacy, and improved patient compliance. All of these effects should decrease the cost of therapy in patients receiving phenytoin, a factor which is an important consideration in this age of cost containment and managed care.

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.

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.

L’objectif principal de nos études a été de caractériser la pharmacocinétique du baclofène chez le patient alcoolo-dépendant et d’étudier la variation du craving en fonction de l'exposition au baclofène pour objectif de comprendre s’il existait des sujets répondeurs et des sujets non répondeurs. Nous nous sommes intéressés à la sécurité d’emploi du baclofène, à l’influence que pourraient avoir les paramètres démographiques et biologiques ainsi que la consommation de tabac concomitante. Nous avons pu mettre en évidence que le baclofène présentait une pharmacocinétique linéaire avec une relation proportionnelle de 30 à 240 mg par jour avec une importante variabilité interindividuelle. Une modélisation pharmacocinétique/pharmacodynamique par approche de population nous a permis de définir la relation entre l’exposition au baclofène et le craving à l’alcool. Nous avons constaté que le baclofène permettait de diminuer le craving à l’alcool pour l’ensemble des patients traités, et nous avons pu élaborer l’hypothèse qu’il existait deux sous-populations de patients différenciés par leur rapidité de réponse. Bien que chez les patients non-répondeurs (répondeurs tardifs) les taux sanguins de créatinine et de phosphatases alcalines étaient significativement plus élevés laissant supposer que les patients sévèrement malades répondaient moins au traitement, le faible nombre de patients (n=50) et l’absence de placebo ne permettent pas de conclure. Des analyses préliminaires des données de craving à l’alcool et de consommation d’alcool suggèrent qu’il existe une relation entre craving et consommation d’alcool. Des analyses complémentaires sont nécessaires pour confirmer ces résultats. / The main objective of our studies was to characterize the pharmacokinetics of baclofen in alcohol-dependent patients and to investigate the variation of craving as a function of exposure with a secondary objective which was to explore the possible existence of baclofen responders and non-responders. We investigated baclofen safety, the potential influence of demographic and biological parameters as well as the concomitant use of tobacco. We observed that baclofen showed linear pharmacokinetics with a proportional relationship from 30 to 240 mg per day with a high inter-individual variability. A pharmacokinetic/pharmacodynamic population approach has enabled us to define the relationship between baclofen exposure and alcohol craving. A wide inter-individual variability in response was depicted but could not be explained by any of the covariates studied. We found that baclofen could possibly reduce alcohol craving in all the patients treated, and we drew up the hypothesis of two subpopulations of patients differentiated by their speed of response. Although in non-responders (late responders) blood levels of creatinine and alkaline phosphatase were significantly higher than in responders, suggesting that seriously ill patients could be less responsive to baclofen treatment, the low number of patients (n = 50) and the absence of a placebo group renders this results inconclusive. Preliminary analyzes of alcohol craving and alcohol consumption data suggest that a relationship exists between craving and alcohol consumption. Additional analyzes are needed to confirm these results.

L’évaluation de la bioéquivalence (BE) de formulations génériques (Test) et de marques commerciales (Référence) du dabigatran représente un défi de taille pour les sociétés pharmaceutiques en raison de la grande variabilité intra-individuelle et de la puissante relation concentration-effet du médicament. Soutenu par l'approche de modélisation pharmacocinétique de population (pop-PK), ce projet examine le potentiel d'évaluer la BE, avec des résultats comparables au paradigme actuel de BE, en utilisant un nombre réduit d'échantillons requis. Une étude de BE portant sur deux formulations de dabigatran (Test et Référence), comprenant 16 sujets et incluant 640 concentrations plasmatiques a été utilisée rétrospectivement pour l'analyse pop-PK. Un modèle pop-PK a été développé pour chaque formulation en suivant les techniques de modélisation standards. Des scénarios d'échantillonnage comportant un ensemble décroissant de prélèvements ont été sélectionnés selon une stratégie progressive et prudente basée sur les propriétés pharmacocinétiques (PK) connues du dabigatran ainsi que de connaissances cliniques acquises. Les modèles pop-PK Test et Référence ont été ajustés à chacun des scénarios d'échantillonnage réduits et leurs profils PK ont été simulés. Ensuite, des tests de BE ont été effectués pour identifier le scénario préservant les conclusions BE obtenues à partir du jeu de données d'origine tout en incluant le nombre minimal de prélèvements. Un modèle à deux compartiments avec élimination de premier ordre et absorption retardée décrivait le mieux les données de concentration plasmatique du dabigatran. Le sexe a été identifié en tant que covariable significative pour la biodisponibilité. Pour les scénarios d'échantillonnage réduit, tous les profils de PK simulés étaient similaires et robustes en termes de valeurs de paramètres PK et de courbes de concentrations, à l'exception des valeurs de Cmax. Les résultats ont également prouvé que le verdict de BE pouvait être maintenu jusqu'à un scénario d'échantillonnage réduit de cinq prélèvements en utilisant les normes et critères de BE réglementaires en vigueur. L’approche de modélisation pop-PK pourrait réduire le nombre d’échantillons utilisés pour l’évaluation de la BE du dabigatran, diminuant donc les coûts des futurs essais de BE cliniques en plus de représenter un bénéfice pour les participants de l’étude. / The bioequivalence (BE) assessment of generic (Test) and brand name (Reference) formulations of dabigatran, a drug with a steep exposure-response relationship exhibiting very high pharmacokinetic (PK) variability, represents an expensive challenge for pharmaceutical companies. Supported by the modeling approach of population pharmacokinetics (pop-PK), the present study investigates the potential of assessing with results comparable to the current BE paradigm BE using a reduced required number of samples. A BE study of two formulations of dabigatran (Test and Reference), comprising 16 subjects and including 640 plasma concentrations, was used retrospectively for the pop-PK analysis. Using standard modeling techniques, a pop-PK model was accordingly developed for each formulation. Sampling scenarios with reduced sampling time points were selected in a progressive and cautious strategy based on dabigatran’s known PK properties and clinical knowledge. The developed pop-PK model of the Reference and Test formulations were refitted on each reduced sampling dataset. All these models were simulated to generate virtual PK profiles for BE purpose. Then, following the standard BE test procedure, the task concluded by identifying the scenario preserving BE conclusions obtained from the original dataset, while including the minimum number of samples. A two-compartment model with first order elimination and a lagged absorption best described the plasma concentration data for dabigatran, and sex was identified as a significant covariate for bioavailability. For the reduced sampling scenarios, all simulated PK profiles were similar to the PK profile generated from the complete sampling in terms of PK parameters values and robustness, except for the Cmax values. The results also proved that the BE verdict could be maintained with a reduced sampling scenario of only five blood samples using the current regulatory BE standards and criteria. The pop-PK modeling approach can be an efficient tool that requires only a reduced number of samplings for the BE assessment of dabigatran, thus can potentially lower the costs in future BE trials and benefit the participants of the study.

Contexte: L’insuffisance cardiaque (IC) est un syndrome clinique complexe regroupant un large spectre de mécanismes pathologiques qui peuvent altérer le fonctionnement de multiples organes, affectant ainsi la pharmacocinétique (PK) des médicaments. La modélisation pharmacocinétique de population (Pop-PK) consiste à appliquer des modèles non linéaires à effets mixtes dans le but de décrire l’exposition au traitement et quantifier la variabilité au niveau des paramètres PK. Objectif: Ce travail vise à évaluer par approche populationnelle la PK du candesartan en IC et à déterminer les covariables décrivant d’une façon statistiquement et cliniquement significative la variabilité au niveau de la clairance. Méthodes: Les données d’une étude pharmacogénomique ouverte, multicentrique et prospective ont été récupérées pour amorcer notre analyse. Le processus de modélisation et les simulations nécessaires sont réalisés à l’aide du logiciel NONMEM (Nonlinear Mixed Effects Modeling). Les covariables préliminaires ont été sélectionnées par des tests statistiques tels que la régression linéaire et l’ANOVA. Enfin, l’élaboration du modèle final est effectuée en utilisant le processus de sélection séquentielle « forward/backward ». Résultats: Un total de 281 patients caucasiens ont été inclus pour développer le modèle Pop-PK. Les données du candesartan ont été caractérisées par un modèle à un compartiment avec absorption de premier ordre et temps de latence. Le poids, l'âge, la fraction N-terminale du pro-peptide natriurétique de type b (NT_proBNP), le débit de filtration glomérulaire (DFG), le diabète, l'utilisation du furosémide et le sexe étaient les covariables sélectionnées préliminairement pour la clairance apparente (CL/F). Le modèle final développé pour la clairance apparente est représenté par l'équation suivante : CL/F (L/h) = 8.63*(Poids/82.45)0.963 * (DFG/74)0.56 * (0.682) Diabète * EXP0.138 Les simulations ont révélé qu'une diminution importante de la clairance orale (diminution de plus que 25 %) est obtenue en combinant les facteurs significatifs retenus dans le modèle final (patients ayant un faible poids corporel avec une insuffisance rénale modérée à sévère et patients diabétiques avec une insuffisance rénale faible à modérée). Nous avons constaté que les patients ayant ces combinaisons dans notre base de données présentaient des concentrations comparables à celles des autres patients malgré qu’ils aient toléré de plus faibles doses pendant la titration. Conclusion: La modélisation PK de population a servi comme une approche efficace pour caractériser la PK du candesartan en IC et pour identifier une sous-population à risque d’une exposition élevée. Le poids, le DFG et le diabète sont des prédicteurs indépendants de la clairance du candesartan en IC. Considérant ces facteurs, une approche plus individualisée de l'administration du candesartan est nécessaire chez les patients atteints d’IC. / Context: Heart failure (HF) is a clinical condition that causes pathological changes all over the body affecting hence the pharmacokinetic of drugs. Population pharmacokinetic modeling (Pop-PK) consists in applying non-linear mixed-effects models to characterize treatment exposure and quantify PK parameters variability. Objective: The aim of this study was to investigate the pharmacokinetic (PK) of candesartan in HF patients while examining statistically and clinically significant covariates on estimated clearance using population pharmacokinetics (Pop-PK) modeling approach. Methods: Data from a prospective, multicenter, open label, pharmacogenomic study were available for this analysis. Modeling and simulations were conducted using Nonlinear Mixed-Effect Modeling software NONMEM. Preliminary selection of covariates was accomplished with statistical tests (linear regression and ANOVA). Final model development was performed using forward/backward selection approach on the preliminarily selected covariates. Results: A total of 281 Caucasian patients were included to develop the Pop-PK model. Candesartan data were characterized by a 1 compartment model with first order absorption and lag time. Weight, age, N-terminal pro b-type natriuretic peptide (NT_proBNP), estimated glomerular filtration rate (eGFR), diabetes, use of furosemide and sex were the preliminarily selected covariates for apparent clearance (CL/F). The final model developed for apparent clearance is represented by the following equation: CL/F (L/h) = 8.63*(Weight/82.45)0.963 * (eGFR/74)0.56 * (0.682) Diabetes * EXP0.138 Simulations revealed that an important decrease in oral clearance (decrease of more than 25%) is obtained with the combination of the significant factors retained in the final model (patients having low weight with moderately to severely impaired renal function and diabetic with mildly to moderately impaired renal function). Patients having these combinations in our database were found to achieve comparable concentrations to the rest of patients despite tolerating only lower doses. Conclusion: Population pharmacokinetic modeling provided an effective approach to characterize the PK of candesartan in HF and to identify a subpopulation at potential risk of high exposure. Weight, eGFR and diabetes are independent predictors of candesartan clearance in patients with HF. Considering these factors, a more individualized approach of candesartan dosing is needed in HF patients.

La vancomycine est un antibiotique fréquemment utilisé dans le contexte hospitalier pour les infections cutanées et nosocomiales. Son utilisation nécessite un suivi thérapeutique pharmacocinétique (TDM) de la part du clinicien, étant donné l’index thérapeutique étroit et la variabilité de son profil pharmacocinétique entre les individus. Alors que le risque de néphrotoxicité associée à la vancomycine s’accroît avec sa durée de traitement, sa clairance et son volume de distribution deviennent difficiles à prédire dans le contexte des traitements prolongés, ce qui est souvent requis chez les patients avec infections ostéoarticulaires. Avec l’approche de modélisation pharmacocinétique de population (popPK), ce projet de maîtrise a cherché à évaluer les changements longitudinaux des paramètres pharmacocinétique de la vancomycine dans une population de patients atteints d’infections ostéoarticulaires. Dans un premier temps, nous avons décrit la pratique de TDM chez les patients qui recevaient de la vancomycine intraveineuse (IV) pour les infections ostéoarticulaires à l’Hôpital Général de Montréal entre décembre 2020 et décembre 2022. Dans un deuxième temps, nous avons identifié deux modèles popPK longitudinaux dans la littérature et évalué leur performance prédictive dans cette population. Bien que ces modèles proposent des approches intéressantes pour décrire les changements longitudinaux de la vancomycine, ils se sont avérés inadéquats pour décrire les paramètres pharmacocinétiques de cet antibiotique dans notre population. D’autres travaux seront nécessaires pour développer et valider des modèles longitudinaux de la vancomycine qui devront tenir compte des variables qui décrivent l’état inflammatoire du patient et des méthodes alternatives pour intégrer une structure longitudinale dans le modèle popPK. / Vancomycin is commonly used in the hospital setting to treat skin and soft tissues infections as well as nosocomial infections. As vancomycin has a small therapeutic window and its pharmacokinetic properties vary significantly across individuals, clinicians must ensure close therapeutic drug monitoring (TDM). As the risk of vancomycin induced nephrotoxicity increases with duration of therapy, clearance and distribution of vancomycin become difficult to predict in the context of long term treatment which is often required for osteoarticular infections. With the use of population pharmacokinetic (popPK) modeling, we aimed to examine the longitudinal changes in the pharmacokinetic properties of vancomycin in patients with osteoarticular infections. In the first part of this master’s project, we described the local practices of TDM in patients receiving intravenous (IV) vancomycin for osteoarticular infections at the Montreal General Hospital between December 2020 et December 2022. In the second part, we identified two longitudinal popPK models in the literature and assessed their predictive performance in this population. Although these models offer an interesting approach to integrate a longitudinal component in their structure, they were ultimately not applicable to our population. Further efforts to address the time related changes of vancomycin’s pharmacokinetics should take into account clinical factors such as the degree of systemic inflammation and consider alternative methods to integrate the duration of treatment and longitudinal components in the model structure.

Un nouveau règlement (CE) n° 1901/2006 établi par le Parlement européen et le Conseil de l’UE, relatif aux médicaments à usage pédiatrique, vise à améliorer la santé et la qualité de vie des enfants en Europe, en garantissant que les nouveaux médicaments pédiatriques et les médicaments déjà commercialisés seront pleinement adaptés à leurs besoins spécifiques. Ce règlement prévoit de nouvelles obligations pour l'industrie pharmaceutique, assorties de récompenses et d'incitations. Dans ce contexte, un plan d’investigation pédiatrique a été proposé pour l’ivabradine dans plusieurs sous-groupes de la population pédiatrique dans le traitement de l’insuffisance cardiaque chronique. L’ivabradine est une molécule déjà commercialisée chez l’adulte dans la prise en charge de l’angor, et de l’insuffisance cardiaque. Un premier travail a été d’aider au design de cette étude pédiatrique : évaluer la formulation pédiatrique, aider au choix de la dose initiale à administrer chez l’enfant, choisir le protocole de prélèvements et conseiller la méthode de prélèvements. Pour évaluer la formulation pédiatrique, une étude a été conduite pour déterminer la biodisponibilité relative de la formulation pédiatrique par rapport aux comprimés utilisés chez l’adulte. Une biodisponibilité relative similaire a été retrouvée entre les deux formulations. Une approche physiologique (PBPK « Physiollogically based PharmacoKineticsmodel ») a été utilisé pour prédire la dose initiale à administrer et pour proposer un protocole de prélèvements PK. La méthode DBS (Dried blood spot) consistant à collecter à chaque temps de prélèvement une goutte de sang (au pli du coude ou au bout du doigt) a été recommandée. La première dose à administrer chez l’enfant peut être également être déterminée par des modèles de population développés chez l’adulte et adaptés à l’enfant grâce à l’allométrie et à l’ajout de fonctions de maturation. Cette approche a été comparée au PBPK dans le cas de l’ivabradine et des résultats similaires ont été obtenus. Un deuxième travail a été réalisé après que l’étude clinique ait été conduite dans la population pédiatrique. L’étude a été menée chez 116 enfants (74 enfants recevant l’ivabradine, 42 recevant le placebo) âgés de 6 mois à 18 ans et les données ont été analysées. Tout d’abord, une relation a été établie entre les concentrations d’ivabradine plasmatiques et les concentrations d’ivabradine mesurées dans le sang total. Puis, afin de décrire les concentrations d’ivabradine et de son métabolite, un modèle de population prenant en compte l’effet de l’âge et du poids a été développé. En comparant les expositions plasmatiques, une dose par kilogramme plus élevée aurait été nécessaire chez les patients les plus jeunes pour atteindre un niveau d’exposition similaire aux patients plus âgés. Enfin, il a été monté que la relation PK/PD qui avait développé chez l’adulte était conservée dans la population pédiatrique. / New legislation governing the development and authorization of medicines for use in children was introduced in the European Union (EU) in January 2007. This Regulation aims to facilitate the development and accessibility of medicinal products for use in the paediatric population, to ensure that medicinal products used to treat the paediatric population are subject to ethical research of high quality and are appropriately authorised for use in the paediatric population, and to improve the information available on the use of medicinal products in the various paediatric populations. Several rewards and incentives for the development of paediatric medicines for children are available in the European Union (EU). In compliance with the paediatric European regulation, a study will be conducted in paediatric patients with CHF with the objective to determine the efficacious and safe dose of ivabradine, a compound already marketed in adults, and to assess its efficacy and safety in children over 1 year old. A first work was to help design a paediatric study for ivabradine focusing on: the paediatric formulation evaluation, the doses to be administered, the sampling design and the sampling technique. A study was conducted in order to assess the relative bioavailability (Frel) of the paediatric formulation and a similar Frel was observed between the paediatric formulation and the adult marketed tablet. PBPK modelling was used to predict initial doses to be administered in the paediatric study and to select the most appropriate sample time collections. The dried blood spot (DBS) technique was recommended in the clinical trial in children. A secondary objective was to perform a comparison of the prediction of ivabradine pharmacokinetics (PK) in children using a physiologically-based pharmacokinetic (PBPK) approach and allometric scaling of a population pharmacokinetic (PPK) model. Simulations obtained by both the PBPK approach and allometric scaling of a PPK model were compared a posteriori to the paediatric study observations. Both PPK and PBPK approaches allowed an adequate prediction of the PK of ivabradine and its metabolite in children. The second work was done after the study conduction in the paediatric population. The study was performed in 116 children (74 received ivabradine, 42 received the placebo) aged from 6 months to less than 18 years old and data were analysed. The relationship between blood and plasma concentrations was described using linear mixed effect models. In order to describe ivabradine and its metabolite blood concentrations in children, a joint population PK model was developed taking into account weight & age effects on PK parameters. Plasma exposure comparison indicated that higher dose/kg were necessary to achieve a similar exposure between younger and older children. The PK/PD relationship in adult patients is conserved in children.