Existing therapeutic options for the alleviation of nicotine addiction have been largely ineffective at stemming the tide of tobacco use. Immunopharmacotherapy, or vaccination, is a promising, alternate therapy that is currently being explored. Results from previous studies indicate that nicotine vaccines (NVs) are effective in subjects that achieve high drug-specific antibody titers, though overall efficacy has not been observed. Consequently, improvement of these vaccines is necessary before they can achieve approval for human use. In this report, three separate approaches towards NV potentiation are explored. The first approach applied physiologically-based pharmacokinetic (PBPK) modeling to better assess NV potential. Rat and human physiological and pharmacological parameters were obtained from literature and used to construct compartmentalized models for nicotine and cotinine distribution. These models were then calibrated and validated using data obtained from literature. The final models verified the therapeutic potential of the NV concept, identified four key parameters associated with vaccine success, and established correlates for success that could be used to evaluate future NVs prior to clinical trials. In the second approach, conjugate NV scaffoldings were engineered by using wild-type (WT) and chimeric human papilloma (HPV) 16 L1 protein virus-like particles (VLPs). The chimeric protein was created by removing the last 34 C-terminal residues from the WT protein and then incorporating a multi-epitope insert that could universally target major histocompatibility complex (MHC) class II molecules. The proteins were subsequently expressed in E. coli and purified using a multi-step process. Comparisons between the separation outcomes revealed that the insert was able to modulate individual process outcomes and improve overall yield without inhibiting VLP assembly. In the third approach, commonly used carrier proteins were computationally mined for their MHC class II epitope content using human leukocyte antigen (HLA) population frequency data and MHC epitope prediction software. The most immunogenic epitopes were concatenated with interspacing cathepsin cleavage sequences and the resulting protein was re-evaluated using the earlier methods. This work represents the first ever in silico design of chimeric antigens that could potentially target all of the major HLA DQ and HLA DR allotypes found in humans. / Doctor of Philosophy / Existing treatment options for addressing nicotine addiction have been largely ineffective at preventing tobacco use. Vaccination, on the other hand, is a promising, alternate treatment option that is currently being explored. Previous studies have shown that nicotine vaccines (NVs) are effective in the subjects that respond well to the vaccine. Effectiveness in the majority of vaccine recipients, however, has not been observed. Consequently, improvement of these vaccines is necessary before they can be used in humans. In this report, three separate approaches for improving NV effectiveness are explored. The first approach applied physiologically-based pharmacokinetic (PBPK) modeling to better assess NV potential. Parameters were obtained from literature and used to construct models that could predict NV effectiveness in rats and humans. These models were then calibrated and validated using data obtained from literature. The final models verified that NVs could work if certain conditions were met, identified four key parameters associated with vaccine success, and allowed for estimation of NV efficacy prior to their evaluation in humans. In the second approach, protein carriers for conjugate NVs were constructed using the human papilloma (HPV) 16 L1 protein. This protein is known for its ability to form virus-like particles (VLPs). Both a modified and an unmodified (wild-type) protein were constructed. The modified HPV 16 L1 protein was created by replacing the last 34 C-terminal amino acids with a polypeptide insert that could enhance the immunogenicity of the vaccine. The modified and unmodified proteins were then expressed in E. coli and purified. Results indicated that the insert was able to modulate individual process outcomes and improve overall process yield without preventing VLP assembly. In the third approach, commonly used carrier proteins were computationally mined for their MHC class II epitope content using human gene frequency data and MHC epitope prediction software. The epitopes that were predicted to be the most immunogenic were linked together with interspacing protease recognition sequences and the immunogenicity of the resulting protein was re-evaluated using the prediction software. This work represents the first computational design of antigens that could potentially allow a vaccine to be effective in a large portion of human population regardless of the genetic variability.

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The purpose of the project was to investigate the impact of IV and PO routes difference for MDZ, a prototypical CYP3A substrate, and two CYP3A inhibitors (CYP3AI) -FLZ and ERY-, on the magnitude and time course of their inhibitory metabolic DDI. Individual semi-PBPK models for MDZ, FLZ and ERY were developed and validated separately, using pharmacokinetic (PK) parameters from clinical/in-vitro studies and published physiological parameters. Subsequently, DDI sub-models between MDZ and CYP3AIs incorporated non-competitive and mechanism-based inhibition (MBI) for FLZ and ERY, respectively, on hepatic and gut wall (GW) CYP3A metabolism of MDZ, using available in-vitro/in-vivo information. Model-simulated MDZ PK profiles were compared with observed data from available clinical PK and DDI studies, by visual predictive check and exposure metrics comparison. DDI magnitude and time course for CYP3AI (IV vs. PO) followed by MDZ (IV vs. PO) at various time points were predicted by the validated semi-PBPK-DDI models. Two hypothetical CYP3A substrates and four CYP3AI (derived from MDZ, FLZ and ERY, with GW metabolism removed, hepatic metabolism reduced, or oral bioavailability (Foral) and/or elimination half-life (t1/2) modified) were also simulated to generalize conclusions. The final semi-PBPK-DDI models predict well the PK profiles for IV/PO MDZ in absence/presence of IV/PO CYP3AI, with deviations between model-predicted and observed exposure metrics within 30%. Prospective simulations demonstrate that: 1) CYP3A substrates, e.g., MDZ, are consistently more sensitive to metabolic inhibition after PO than after IV administration, due to pre-systemic hepatic and/or GW metabolism. For substrates without GW metabolism and limited hepatic metabolism, only a marginal route difference for substrate administration is observed. 2) For high-Foral CYP3AIs, e.g., FLZ, no inhibitor IV-PO route DDI differences are expected, unless they are given simultaneously with PO MDZ. 3) For low-Foral CYP3AIs, e.g., ERY, greater inhibition is expected after IV than after PO administration for IV MDZ, but is difficult to predict for PO MDZ. 4) In addition to Foral and plasma t1/2 of CYP3AIs, the DDI onset, peak and duration are determined by their oral absorption rate and by the resulting hepatic and/or GW concentration profiles relative to Ki for noncompetitive CYP3AIs, but by CYP3A kinetics (synthesis, degradation rate) for MBI CYP3AIs.

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.

The majority of drugs available on the market are intended to be administered through the oral route. To achieve the desired therapeutic effect, an orally administered drug must first reach the systemic circulation and then its site of action. The fraction of the administered drug that reaches the systemic circulation is known as oral bioavailability and it is the product of the absorption and first-pass metabolism processes occurring in both the GI tract and the liver. The factors controlling bioavailability are manifold –both drug and physiologically related - and their complex interplay is key to defining a drug’s oral bioavailability. In drug discovery and development it is therefore pivotal to anticipate and understand the bioavailability of a drug candidate; a far from simple task, considering the multifactorial nature of the process. For that reason, the overall aim of this thesis was to provide different modelling and simulation (M&S) strategies that can be used for the prediction of oral bioavailability that can be of use in drug discovery and development. The first part of this thesis was focused on the evaluation of the use of bioavailability data obtained from pre-clinical species as a predictor of the human value, in a more traditional approach. In particular, the aim was to evaluate models that can quantitatively and qualitatively provide a relationship between animal and human bioavailability, by analysing trends in a large bioavailability dataset. This section demonstrated that although pre-clinical species cannot quantitatively predict bioavailability, the data obtained from them can be used for qualitative prediction of the human value. Nevertheless, such a modelling approach does not provide a mechanistic rationale of the factors affecting the bioavailability differences. Consequently, the second part of this thesis was focused on such mechanistic predictions. Particularly, we investigated the impact that drug release patterns can have on drug absorption and intestinal first pass metabolism, taking into account the physiological differences observed across the length of the human gastrointestinal (GI) tract. These release patterns are suspected to lead to bioavailability differences due to changes in the first-pass metabolism, especially for CYP3A substrates. Therefore we investigated this phenomenon applying a physiologically-based pharmacokinetic (PBPK) M&S approach: firstly, from the discovery point of view, using PBPK models in a prospective fashion to investigating the drug-related factors that might lead to such differences and secondly, from the development point of view, to predict the mechanistic differences in absorption and metabolism of oxybutynin, a drug known for its higher bioavailability when formulated as a modified release (MR) product. The latter was done by developing and applying a novel simplified PBPK model to predict such differences. The results of this work showed that the intestinal metabolism can be significantly reduced when having MR formulations of CYP3A substrates which, in some cases, can lead to higher relative bioavailability. Additionally, this thesis provided novel methods and models that have the potential to improve bioavailability predictions when using PBPK models, in particular for drugs formulated as MR.

Pharmacokinetic and pharmacodynamic models can be applied to clinical study data using various modelling approaches depending on the aim of the analysis. In population pharmacokinetics for instance, simple compartmental models can be employed to describe concentration-time data, identify prognostic factors and interpolate within well-defined experimental conditions. The first objective of this thesis was to illustrate such a ‘semi-mechanistic’ pharmacokinetic modelling approach using mavoglurant as an example of a compound under clinical development. In particular, methods to accurately characterise complex oral pharmacokinetic profiles and evaluate the impact of absorption factors were investigated. When the purpose of the model-based analysis is to further extrapolate beyond the experimental conditions in order to guide the design of subsequent clinical trials, physiologically-based pharmacokinetic (PBPK) models are more valuable as they incorporate information not only on the drug but also on the system, i.e. on mammillary anatomy and physiology. The combination of such mechanistic models with statistical modelling techniques in order to analysis clinical data has been widely applied in toxicokinetics but has only recently received increasing interest in pharmacokinetics. This is probably because, due to the higher complexity of PBPK models compared to conventional pharmacokinetic models, additional efforts are required for adequate population data analysis. Hence, the second objective of this thesis was to explore methods to allow the application of PBPK models to clinical study data, such as the Bayesian approach or model order reduction techniques, and propose a general mechanistic modelling workflow for population data analysis. In pharmacodynamics, mechanistic modelling of clinical data is even less common than in pharmacokinetics. This is probably because our understanding of the interaction between therapeutic drugs and biological processes is limited and also because the types of data to analyse are often more complex than pharmacokinetic data. In oncology for instance, the most widely used clinical endpoint to evaluate the benefit of an experimental treatment is survival of patients. Survival data are typically censored due to logistic constraints associated with patient follow-up. Hence, the analysis of survival data requires specific statistical techniques. Longitudinal tumour size data have been increasingly used to assess treatment response for solid tumours. In particular, the survival prognostic value of measures derived from such data has been recently evaluated for various types of cancer although not for pancreatic cancer. The last objective of this thesis was therefore to investigate different modelling approaches to analyse survival data of pancreatic cancer patients treated with gemcitabine, and compare tumour burden measures with other patient clinical characteristics and established risk factors, in terms of predictive value for survival.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22036号 / 医博第4521号 / 新制||医||1038(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 滝田 順子, 教授 松田 道行, 教授 山田 亮 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Este trabalho introduz um modelo fisiológico para o reflexo pupilar em função das condições de iluminação (Pupil Light Reflex - PLR), e um modelo baseado em imagem para deformação dos padrões da íris. O modelo para PLR expressa o diâmetro da pupila ao longo do tempo e em função da iluminação ambiental, sendo descrito por uma equação diferencial com atraso, adaptando naturalmente o tamanho da pupila a mudanças bruscas de iluminação. Como os parâmetros do nosso modelo são derivados a partir de modelos baseados em experimentos científicos, ele simula corretamente o comportamento da pupila humana para um indivíduo médio. O modelo é então estendido para dar suporte a diferenças individuais e a hippus, além de utilizar modelos para latência e velocidade de dilatação e contração. Outra contribuição deste trabalho é um modelo para deformação realista dos padrões da íris em função da contração e dilatação da pupila. Após capturar várias imagens de íris de diversos voluntários durante diferentes estágios de dilatação, as trajetórias das estruturas das íris foram mapeadas e foi identificado um comportamento médio para as mesmas. Demonstramos a eficácia e qualidade dos resultados obtidos, comparando-os com fotografias e vídeos capturados de íris reais. Os modelos aqui apresentados produzem efeitos foto-realistas e podem ser utilizados para produzir animações preditivas da pupila e da íris em tempo real, na presença de variações na iluminação. Combinados, os dois modelos permitem elevar a qualidade de animações faciais, mais especificamente, animações da íris humana. / This thesis introduces a physiologically-based model for pupil light reflex (PLR) and an image-based model for iridal pattern deformation. The PLR model expresses the pupil diameter as a function of the environment lighting, naturally adapting the pupil diameter even to abrupt changes in light conditions. Since the parameters of the PLR model were derived from measured data, it correctly simulates the actual behavior of the human pupil. The model is extended to include latency, constriction and dilation velocities, individual differences and some constrained random noise to model hippus. The predictability and quality of the simulations were validated through comparisons of modeled results against measured data derived from experiments also described in this work. Another contribution is a model for realist deformation of the iris pattern as a function of pupil dilation and constriction. The salient features of the iris are tracked in photographs, taken from several volunteers during an induced pupil-dilation process, and an average behavior of the iridal features is defined. The effectiveness and quality of the results are demonstrated by comparing the renderings produced by the models with photographs and videos captured from real irises. The resulting models produce high-fidelity appearance effects and can be used to produce real-time predictive animations of the pupil and iris under variable lighting conditions. Combined, the proposed models can bring facial animation to new photorealistic standards.

The medical use of marijuana is increasing, yet little is known about the exposure-response relationships resulting in its psychoactive effects. Δ9-tetrahydrocannabinol (THC) and its active metabolite (11-hydroxy-THC; THC-OH) are the principal psychoactive components in marijuana. It is well known that the plasma concentrations of the psychoactive components of marijuana do not directly relate to the observed psychoactive effects. The presence of a counter-clockwise hysteresis in the plasma concentrations-effect plot demonstrates a temporal delay between the plasma concentrations and observed effect following the intravenous administration of THC. The overarching objective of this research was to better understand the relationship between the plasma and brain concentrations of the psychoactive components (THC and THC-OH) and the observable psychoactive effects after intravenous administration of THC, utilizing model-based approaches. Specifically, the pharmacokinetics were explored using population pharmacokinetic (Pop PK) and physiologically-based pharmacokinetic (PBPK) modeling whereas the pharmacodynamics (PD) of the psychoactive effect (“highness”) were explored using effect-compartment modeling and linking the PD to the PBPK-derived concentrations predicted in the brain and an assumed effect-site. A “hypothetical” effect compartment model was developed to characterize the observed delay in peak “highness” ratings. A direct relationship was established between the reported psychoactive effects (“highness” or intoxication) and the predicted effect-site concentrations of both components (THC and THC-OH) using this effect-compartment modeling approach. The faster plasma to effect compartment equilibration for THC-OH indicated a more rapid equilibration of the active metabolite between plasma and the effect-site (biophase) than for the parent THC. In addition, a PBPK modeling approach was pursued to predict and relate the brain concentrations of THC and THC-OH to the psychoactive effect. The relationship between the effect and the predicted unbound brain concentration of THC indicated an indirect relationship, suggesting a temporal delay between brain concentrations of THC and observed effect. However, a direct relationship was observed between the observed effect and the unbound brain THC-OH concentrations. In addition, the unbound concentrations of THC-OH in the brain were predicted to be higher than the corresponding THC concentrations. These findings highlight the importance for the inclusion of THC-OH, in addition to THC, when relating the observed effect to the concentrations of the psychoactive components of marijuana. These models contribute to the understanding of the PK-PD relationships associated with marijuana use and are important steps in the prediction of the pharmacodynamic effects related to the psychoactive components in marijuana and establish an approach for investigating other THC-related effects.