The prenatal period is a particularly sensitive window of time where development can be impacted by many agents including environmental insults. Exposure to certain chemical, biological, and physical stressors during gestation have been shown to produce adverse health effects in the developing human fetus, as well as throughout an individual’s lifetime. The overall goals of this dissertation were to investigate the impacts of prenatal exposures of environmental stressors on developmental outcomes, including birthweight and neurobehavior, and to explore technologies that can be used to more quickly identify chemicals that may impact the developing fetus. First, we characterize the effects of prenatal air pollution exposure on neurobehavior in childhood. Our work demonstrates the potential impact of prenatal ambient air pollution exposure measured as fine particulate matter (particulate matter ≤ 2.5 μm; PM2.5) exposure on clinically relevant behaviors, specifically thought problems, and suggests that males may be more susceptible to air pollution-induced externalizing behavioral outcomes. In the second aim, we explore critical windows of susceptibility to the effects of various prenatal heat exposure measures on gestational growth. Our work finds that heat index variability is more strongly associated with gestational growth than other heat measures, with disproportionate effects observed in males and those experiencing homelessness during pregnancy. Our third aim compares the utilization and parameterization of New Approach Methodologies for deriving point of departure values that could serve as an alternative to the traditional hazard values derived from animal in vivo studies. Specifically, we examine pesticide chemicals with thyroid-based in vitro endpoints because of the potential impact these chemicals have on in-utero neurodevelopment. We demonstrate that pharmacokinetic model choice and dosing scenario have a substantial impact on predicted values, resulting in estimated values that can be much less restrictive than current values used in regulation. Our work highlights the need for regulators to carefully consider these choices when applying these data to hazard assessments, in order to not underestimate the potential for pesticides to impact maternal thyroid hormones. / 2023-01-24T00:00:00Z

La sélection des meilleurs candidats médicament se base sur des choix multiparamétriques réunissant l’efficacité potentielle, les caractéristiques ADME et le profil de sécurité des nouvelles entités chimiques. En ce sens, la prédiction précoce de la pharmacocinétique est élémentaire pour orienter les prises de décision et donner un cap pertinent aux projets. En raison de son rôle central dans le devenir des médicaments, la clairance métabolique médiée principalement par le foie est l’un des paramètres les plus importants. L’objectif de ce projet était d’améliorer la prédiction de la clairance en se concentrant notamment sur les molécules présentant une bonne stabilité métabolique et qui sont de ce fait encore difficiles à étudier. Les travaux menés dans cette thèse nous ont permis d’étoffer nos connaissances sur les modèles hépatiques in vitro et les méthodes d’extrapolation usuelles mais aussi de découvrir et de développer de nouvelles stratégies de prédiction. Nous nous sommes concentrés en profondeur sur la clairance métabolique et à tout ce qui impacte les prédictions. Le modèle de co-culture microorganisée (MPCC) HepatopacTM qui permet de stabiliser les hépatocytes humains sur plusieurs semaines a ainsi été identifié comme une alternative judicieuse aux modèles de routine lorsque les molécules ne peuvent pas être étudiées en culture 2D classique. L’étude de la fraction libre plasmatique et l’intégration de nouvelles hypothèses physiologiques telles que la théorie de « l’uptake facilité par l’albumine » ont également participé à améliorer les prédictions. Compte tenu des performances du modèle HepatopacTM, nous avons développé une approche innovante basée sur le spotting de précision afin de produire tous types de co-cultures microorganisées. Les co-cultures fabriquées grâce à cette technique démontrent que la méthode est robuste, accessible et simple à mettre en œuvre. Notre méthode de spotting a ensuite été utilisée pour faire évoluer le modèle MPCC et l’ouvrir à de nouvelles applications. / The selection of the best drug candidates is based on multiparametric choices combining the potential efficacy, ADME characteristics and the safety profile of the new chemical entities. In this sense, the early prediction of pharmacokinetic is essential to guide decision-making and provide a relevant course for projects. Because of its central role in drug disposition, metabolic clearance mediated primarily by the liver is one of the most important parameters. The objective of this project was to improve clearance prediction by focusing on low clearance compounds that are still difficult to study. This work allowed us to expand our knowledge on in vitro liver models and usual extrapolation methods but also to discover and develop new prediction strategies. We focused on metabolic clearance and all parameters that impact the predictions. Micropatterned co-cultures (MPCCs) of primary human hepatocytes (HepatopacTM), which stabilizes hepatocytes over several weeks, has been identified as a judicious alternative to routine models when the molecules cannot be studied in conventional monolayer culture. The study of plasma protein binding and the integration of new physiological hypothesis such as the "Albumin-Facilitated Uptake" also contributed to improve the predictions. Given the performance of the HepatopacTM model, we have developed an innovative approach using a digital dispensing system to spot collagen and produce all types of micropatterned co-cultures. Co-cultures manufactured by this technique demonstrate that the method is robust, accessible and easy to use. Our spotting method was used to evolve the MPCC model and explore new applications.

Background: Elucidating the role of intestinal drug transporter function in drug development is crucial, as transporter proteins can impact on drug absorption, efficacy and adverse events. In Vitro-In Vivo Extrapolation linked to Physiologically-Based Pharmacokinetic (IVIVE-PBPK) models aim to predict the in vivo impact of transporters from in vitro cell–based transport data and expression-based scaling factors. Currently, these models depend on relative measurements of transporter expression i.e., mRNA or immunoblotting. There is a critical need for physiologically relevant measures of transporter protein abundance to populate these biological frameworks. Objectives: The key objectives were to develop and validate a targeted proteomics workflow to quantify transporter protein abundances in human enterocytes and Caco-2 cells with a QconCAT technique. A cross-laboratory comparison on matched samples was also performed to assess between-laboratory bias in abundance determination. Together with abundance data from each laboratory, BCRP and P-gp transporter activities from Caco-2 cells were used to identify function-abundance relationships, to facilitate the potential development of abundance-function scaling factors. Results: Development of a differential centrifugation technique to obtain plasma membranes was undertaken using MDCK-II and Caco-2 cells. The plasma membrane fraction showed little enrichment from the preceding total membrane fraction and was contaminated with endoplasmic reticulum, as assessed by marker enzyme activities. There were also no differences in Na/K-ATPase, BCRP and P-gp abundances between plasma and total membrane fractions in Caco-2 cells. This may be due to losses of protein from the target membrane fraction, thus, a theoretical framework combining protein assay (BCA) and transporter abundance determinations was proposed. Pilot data on the generation of recovery correction factors using Villin and Na/K-ATPase abundances, to account for protein losses is also presented. The abundances of 6 transporters in jejunal enterocyte membranes (n=3), including the key efflux proteins BCRP (2.56±0.82 fmol/μg), P-gp (1.89±1.07 fmol/μg) and MRP2 (0.59±0.246 fmol/μg) were determined with precision. In addition, peptide losses during protein digestion stages were accounted for in abundance determinations. A cross laboratory comparison of transporter abundances from intestinal (n=4) and Caco-2 cells (n=7) measured in our laboratory and Bertin Pharma (BPh), showed that P-gp abundances were highly correlated (rs=0.72), yet BPh abundances were systematically lower than determined in our laboratory (2.0±2.08 versus. 4.8±3.51 fmol/μg, respectively). No differences or correlations were found for Na/K-ATPase and BCRP abundances between laboratories. A jejunal-Caco-2 cell relative expression factor (REF) for each protein for both laboratories was generated. The P-gp REF was similar for BPh and our laboratory (0.37 vs. 0.4, respectively) however, for BCRP there was a distinct difference (1.11 versus 2.22, respectively). These findings provide the first evidence that employing expression factors generated from abundances quantified in different laboratories may produce altered IVIVE-PBPK outcomes. Functional studies in Caco-2 cells using E-3-S and vinblastine as probes for BCRP and P-gp, respectively, show that protein abundance is more closely correlated to transporter activity than mRNA expression. In addition, it was only possible to verify that increasing P-gp abundances in Caco-2 cells were ranked alongside vinblastine intrinsic clearance, as there was little consistency when estimating Km between the different Caco-2 cell models expressing increasing P-gp abundances, which may be attributed to limited absorptive transport saturation. Thus, forming any conclusions with confidence on concentration dependent abundance-activity relationships was difficult. These data suggest the value of REF scaling factors based on protein abundances, but emphasises the need to generate these from both in vitro and in vivo samples, using the same proteomic workflow. Further work to verify abundance-function relationships is required. Conclusion: A targeted proteomic workflow has been developed allowing quantification of protein abundances for key drug transporters in human gut tissues and cell models. The study has highlighted important areas including losses of targeted proteins, contamination of plasma membrane fractions and standardisation between laboratories that need to be addressed before implementation of transporter abundances into PBPK models is undertaken. Nevertheless, the evidence for a close relationship between transporter abundance and function indicate the potential value of this data for generation of robust REF scaling factors.

This dissertation aimed at developing an inhibitor strategy to improve the oral bioavailability (Foral) and systemic exposure (AUC∞) of buprenorphine (BUP) as well as reduce the variability associated with them. Twenty-seven generally recognized as safe (GRAS) compounds or dietary substances were evaluated for their potential to inhibit the oxidative and conjugative metabolism of BUP, using pooled human intestinal and liver microsomes. In both the organs, oxidation appeared to be the major metabolic pathway with a 6 fold (intestine) and 4 fold (liver) higher intrinsic clearance than glucuronidation. Buprenorphine was predicted to show low and variable Foral, AUC∞, and a large total clearance. The biorelevant solubilities of 5 preferred inhibitors were incorporated in the final model. An inhibitor dosing strategy was identified to increase Foral and reduce the variability in oral BUP AUC∞. These results demonstrate the feasibility of the approach of using GRAS or dietary compounds to inhibit the presystemic metabolism of buprenorphine and thus improve its oral bioavailability. This inhibitor strategy has promising applicability to a variety of drugs suffering from low and variable oral bioavailability due to extensive presystemic oxidative and conjugative metabolism.

Acetaminophen, known as paracetamol in the UK and Tylenol in the United States, is a widespread and commonly used painkiller all over the world. Taken in large enough doses, however, it can cause fatal liver damage. In the U.S., 56000 people are admitted to hospital each year due to acetaminophen overdose and its related effects, at great cost to healthcare services. In this thesis we present a number of different models of acetaminophen metabolism and toxicity. Previously, models of acetaminophen toxicity have been complex and due to this complexity, do not lend themselves well to more advanced mathematical analysis such as the perturbation analysis presented later in this thesis. We begin with a simple model of acetaminophen metabolism, studying a single liver cell and performing numerical and sensitivity analysis to further understand the most important mechanisms and pathways of the model. Through this we identify key parameters that affect the total toxicity in our model. We then proceed to perform singular perturbation analysis, studying the behaviour of the model over different timescales, finding a number of key timescales for the depletion and subsequent recovery of various cofactors as well as critical dose above which we see toxicity occurring. Later in the thesis, this model is used to model metabolism in a spheroid cell culture, examining the difference spatial effects have on metabolism across a 3D cell culture. We then present a more complex model, examining the difference the addition of an adaptive response to acetaminophen overdose from the Nrf2 signalling pathway, has on our results. We aim to reproduce an unexplained result in the experimental data of our colleagues, and so analyse the steady states of our model when subjected to an infused dose, rather than a bolus one. We identify another critical dose which leads to GSH depletion in the infused dose case and find that Nrf2 adaptation decreases toxicity and model sensitivity. This model is then used as part of a whole-body PBPK model, exploring the effects that the distribution of the drug across the bloodstream and different organs has. We explore the affects of that a delay in up-regulation from the Nrf2 pathway has on the model, and find that with rescaled parameters we can qualitatively reproduce the results of our collaborators. Finally, we present the results of in vitro work that we have undertaken, the aim of which was to find new parameters for the model in human hepatocytes, rather than from rodent models, and find a new value for a parameter in our model from human cell lines.

Avec plus de 350 000 produits chimiques utilisés et de nouveaux arrivant sur le marché chaque année, des outils rapides et à coûts réduits sont nécessaires pour l'étude de ces produits. L’évaluation des risques pour ces produits est généralement faite à partir d’études animales, mais celles-ci présentent plusieurs limitations. Par exemple, évaluer le potentiel cancérogène d’une substance prendre jusqu'à trois ans et coûter six millions de dollars. En outre, il a été démontré que les modèles animaux n'ont qu'un faible pouvoir prédictif par rapport aux effets chez l’humain. Pour surmonter ces obstacles, on assiste actuellement à un mouvement mondial en faveur du développement et de l'acceptation de nouvelles approches méthodologiques (NAM) pour la priorisation des produits chimiques et l'évaluation des risques. Notre objectif était d’élaborer et d'évaluer une nouvelle approche d’extrapolation in vitro à in vivo (IVIVE) pour établir des niveaux d'exposition acceptables chez l'homme en combinant des des études in vitro et des outils de modélisation toxicologique. À cette fin, nous avons développé et évalué un outil informatique utilisé dans l'approche IVIVE et mené des études de cas sur deux produits chimiques pour lesquels étaient disponibles des données in vitro, des modèles d'exposition, et des études épidémiologiques associant l’exposition à des effets néfastes chez l’humain. Dans le premier article, nous avons développé et évalué un modèle de bilan de masse dynamique (IV-MBM DP v1.0) pour estimer les concentrations intracellulaires au cours d'expériences in vitro avec administration répétée, incluant une description du transport facilité. Pour évaluer la précision du modèle, nous avons paramétré et appliqué le modèle à des scénarios de dose unique et de doses répétées, et évalué les concentrations estimées aux données empiriques. En outre, nous avons simulé des scénarios de dosage répété pour des produits chimiques organiques représentant une diversité de caractéristiques physico-chimiques, et nous avons comparé leur dispersion dans le système au fil du temps. Dans l'ensemble, pour les scénarios de dosage unique et répété, la concordance entre les données simulées et expérimentales a illustré le pouvoir prédictif du modèle. i Dans les deuxième et troisième articles, nous nous sommes concentrés sur l'utilisation et l'évaluation de notre nouvelle approche IVIVE en faisant deux études de cas impliquant l'exposition placentaire et lactationnelle à des polluants organiques persistants. La première étape de notre méthodologie a été la sélection d'un point de départ à partir d'une étude in vitro utilisant des cellules humaines. Ensuite, nous avons appliqué la modélisation benchmark dose pour obtenir la concentration associée à un changement relatif de 5% de la réponse par rapport au contrôle. Nous avons ensuite appliqué la modélisation de de bilan de masse pour déterminer la concentration cellulaire pour un point de départ conduisant à un changement de réponse de 5%. Un modèle toxicocinétique pour le transfert placentaire et par l’allaitement a ensuite été utilisé pour calculer la dose équivalente administrée et la concentration plasmatique associée, et des facteurs d'incertitude (variabilité interindividuelle (10) et sous-chronique à chronique (10)) ont été appliqués pour calculer les apports quotidiens tolérables et les équivalents de biosurveillance. Les équivalents de biosurveillance ont été comparés aux concentrations dans le sang de la mère et du cordon ombilical mesurées dans les études épidémiologiques. Nos études de cas portaient sur la neurotoxicité développementale du 2,2',4,4'-tétrabromodiphényléther (BDE-47) et sur l'obésogénicité du dichlorodiphényldichloroéthylène (p,p'-DDE). Pour les deux études, les apports quotidiens tolérables calculés en tenant compte des facteurs d’incertitude étaient plus faibles que les valeurs toxicologiques de référence déterminées sur la base d’études animales. En outre, les deux études de cas ont produit des équivalents de biosurveillance se situant dans la gamme des concentrations maternelles et du cordon ombilical mesurées dans les études épidémiologiques. Dans l'ensemble, l'évaluation de notre modèle de bilan de masse, ainsi que les valeurs conservatrices générées par l'approche IVIVE dans nos études de cas, renforcent la confiance dans les NAM, ce qui est essentiel pour leur adoption future par les organismes de réglementation. / With over 350,000 chemicals in use and more entering the market every year, cost-effective and time-efficient tools are necessary for the investigation of these products. Whole animal models are traditionally used and accepted by regulatory agencies; however, animal models carry multiple limitations. Specifically, animal models may take up to three years and six million dollars to investigate the carcinogenicity of a compound. Additionally, animal models have been shown to have poor predictive power for human safety. To overcome these obstacles, a global movement toward the development and acceptance of new alternative methods (NAMs) for chemical prioritization and risk assessment is taking place. Our objective was to develop and evaluate a novel in vitro to in vivo (IVIVE) approach to establish acceptable exposure levels in humans by combining novel in vitro and biological/computational modeling technologies for chemical safety assessment. To this end, we tested and evaluated a computational tool utilized in the IVIVE approach, and conducted proof-of-concept studies on two case chemicals with publicly available in vitro data, exposure models, and epidemiological studies demonstrating adverse health effects. In the first paper, we aimed to develop and evaluate a dynamic partitioning mass-balance model (IV-MBM DP v1.0) to estimate intracellular concentrations during in vitro experiments of repeat dosing, and incorporate facilitated transport into the model. To evaluate the model accuracy, we parametrized and applied the model to single dose and repeat dosing scenarios and assessed the output against empirical data. In addition, we simulated repeat dosing scenarios for organic chemicals with different properties and compared their dispersion within the system over time. Overall, for single and repeat dosing scenarios, concordance between simulated and experimental data illustrated the predictive power of the model. In the second and third papers, we focused on the use and evaluation of our novel IVIVE approach through case studies involving placental and lactational exposure to persistent organic pollutants. The first step of our methodology was the selection of a point of departure from an in vitro study utilizing human cells. Next, we applied benchmark dose modeling to obtain the nominal iv concentration at a 5% relative change in response from control. We subsequently applied mass- balance modeling to determine the cellular concentration for the POD leading to a 5% change in response. A toxicokinetic model for placental transfer and lactation was then used to calculate the administered equivalent dose and associated maternal and cord plasma concentration, and uncertainty factors (interindividual variability (10) and subchronic to chronic (10)) were applied to calculate tolerable daily intakes and biomonitoring equivalents. Biomonitoring equivalents were compared to concentrations in maternal and cord blood measured in epidemiological studies. Our case studies were on 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) developmental neurotoxicity and dichlorodiphenyldichloroethylene (p,p’-DDE) obesogenicity. For both studies, calculated tolerable daily intakes accounting for uncertainty factors were more conservative than the reference doses determined through the use of whole animal models. Moreover, both case studies produced biomonitoring equivalents within the range of maternal and cord levels measured in epidemiological studies. Overall, assessment of our IV-MBM DP v1.0 mass-balance model, as well as the demonstrated protective quality of the IVIVE approach in our case studies, enhances confidence in NAMs, which is essential for their future adoption by regulatory agencies.

Tuberculosis, the leading cause of death by a single infection disease caused by bacteria, requires long treatments and the bacteria are prone to develop drug resistance. Therefore, new efficient treatment regiments needs developing, which requires new tools for drug development. A major reason for discontinuance of a drug under development is undesired pharmacokinetic properties. Therefore, it is important to have early information of this, preferably the first time the drug is tested in animals. The first in vivo pharmacokinetic experiment is often done in mice and the only information present at this stage are often in vitro values and physicochemical properties. Physiological-based pharmacokinetic modelling can be used to extrapolate from in vitro to in vivo values. From this, the first in vivo pharmacokinetic experiment can be designed, often with the goal of reducing the amount of mice. This goal is one of the three R.s and it is called Reduction. To explore the Reduction of an experiment population pharmacokinetic modelling can be utilized via exploration of the imprecision, bias and probability of an informative experiment to evaluate if a design meets the goal of Reduction. In this report a recommendation of the first in vivo pharmacokinetic experiment is presented. This is based on in vitro values and physicochemical properties that are common in anti-tuberculosis drugs. If the probability of an informative experiment is critical, a terminal sampling of 40 mice is recommended. If imprecision and bias are necessary, zipper sampling of 10 mice is recommended.

Predicting drug concentrations in the blood and at the site of action is the hottest topic in pharmacokinetics (PK). In vitro-in vivo extrapolation (IVIVE) and physiological based pharmacokinetics (PBPK) models are two major PK prediction strategies. However, both IVIVE and PBPK models are considered as immature methodologies due to their poor predictability. The goal of the research is to investigate the discrepancies within IVIVE and PBPK predictions according to first-principles: convection, diffusion, metabolism, and carrier-mediated transport. In Chapter 2, non-permeability limited hepatic elimination under perfusion steady state is examined. The well-stirred model is re-derived from the convection-dispersion-elimination equation when both dispersion and concentration gradient are ignored and re-named as the zero-gradient model. Pang and Rowland’s lidocaine data are re-analyzed. Their data analysis was based on an unfair comparison of the zero-gradient and parallel- tube models at two different efficiency number ranges. The interference of sensitivity greatly biased the comparison. I also show that both theoretical discussions and experimental results indicate that apparent intrinsic clearance and intrinsic clearance could be affected by blood flow and protein binding. In Chapter 3, I discuss permeability limited hepatic elimination under perfusion steady state. Permeability limited elimination is classified to diffusion dominated, carrier-mediated transport mediated, and mixed effects based on drug passage mechanisms. Each of these three drug passage classes is sub-divided to sink condition and finite volume condition based on the boundary conditions of drug passage. In Chapter 4, the discrepancies within IVIVE for both non-permeability limited and permeability limited drugs are explored. The deficiencies in assay design and data analysis of common in vitro metabolism assays are investigated. The scaling/converting equations for both non-permeability limited and permeability limited drugs are derived. In Chapter 5, I focus on transient PK models. Numerical analysis using finite difference and finite volume methods are introduced into the derivation and discussion of transient PBPK models. In addition, the use of partition coefficient in the non-eliminating tissue/organ models is discussed.