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.

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.

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.

Understanding the processes that govern pre-systemic drug absorption and elimination is of high importance in pharmaceutical research and development, and clinical pharmacotherapy, as the oral route remains the most frequently used route of drug administration. The emergence of systems pharmacology has enabled the utilisation of in silico physiologically-based pharmacokinetic (PBPK) modelling and simulation (M&S) coupled to in vitro-in vivo extrapolation in order to perform extrapolation and exploratory M&S in special populations and scenarios were concerns regarding alterations in oral drug exposure may arise, such as following gastrointestinal (GI) surgery or metabolic drug-drug interactions (DDIs).Due to the multi-factorial physiological implications of bariatric surgery, resulting in the partial resection of the GI tract, the inability to rationalise and predict trends in oral drug bioavailability (Foral) following surgery present considerable pharmacotherapeutical challenges. PBPK M&S is a highly implemented approach for the prediction of DDIs. Reoccurring issues have emerged with regards to predictions of the magnitude of mechanism-based inhibition (MBI) where overestimations of DDIs have repeatedly been reported for drugs exhibiting high intestinal extraction. The aim of this thesis was to explore the interplay between oral drug absorption and metabolism occurring in the GI tract through the exploration of the impact of bariatric surgery on oral drug exposure and by theoretically examining the nesting and hierarchy of enterocyte and enzyme turnover and its impact on MBIs in the small intestine. This would be carried out by utilising a systems pharmacology PBPK M&S approach under a general model development framework of identification and characterisation of critical intrinsic factors and parameters, model implementation and validation. Developed post bariatric surgery PBPK models allow a framework to theoretically explore physiological mechanisms associated with altered oral drug exposure pre to post surgery, which could be assigned to the interplay between dissolution, absorption and gut-wall metabolism, where dissolution and formulation properties emerged as the perhaps most important parameters in predicting the drug disposition following surgery. Model validation identified missing critical factors that are essential for additional model refinement. Developed post bariatric surgery PBPK models have the potential of aiding clinical pharmacotherapy and decision-making following surgery. A mechanistic PBPK model was developed to describe the hierarchical dependency of enzyme and enterocyte turnover in the small intestine. Predicted enzyme recovery using the nested enzyme-within-enterocyte turnover model may potentially account for reported overpredictions of mechanism-based inhibition. Developed models in this thesis showcase the advantage of PBPK M&S in the extrapolation of oral drug exposure to special population and the potential of a PBPK approach in understanding underlying the underlying mechanism governing Foral and additionally highlight the need for generation of interdisciplinary data to support model development.

Charles University Faculty of Pharmacy in Hradec Kr'alov'e Department of Biophysics and Physical Chemistry Candidate: Yestay Rakhimov Supervisor: doc. Erik Jurjen Duintjer Tebbens, Ph.D. Title of diploma thesis: Mathematics and implementations of physiologically based phar- macokinetic modeling The thesis addresses some basic aspects of pharmacokinetic modeling, which is used to describe pharmacokinetic processes. Understanding these processes is important for example to determine optimal concentrations of drugs dosing. The thesis focuses on mathematical proofs of a number of pharmacokinetic equa- tions, which are often not given in standard books. The derived equations are illustrated with numerical experiments for a particular drug in the software PharmCalcCl and MAT- LAB. 4

<p>Autoinjectors are pen-like devices that typically deliver drug products of 2 mL or less. They shield the needle before and after use, reducing patient anxiety from needle phobia and mitigating the risk of needlestick injuries and accidental contamination. Additionally, automatic delivery ensures more consistent needle penetration depth and injection force than manual injection methods. </p> <p><br></p> <p>To optimize autoinjector design, this thesis presents experimentally validated computational models that describe the processes of needle insertion, drug delivery, and transport of subcutaneously administered therapeutic proteins in the body. A multi-objective optimization framework is also proposed to guide the design of autoinjectors.</p> <p><br></p> <p>This thesis focuses on spring-driven autoinjectors, the most common type of autoinjector. It begins with an overview of the interactions between the spring-driven autoinjector, tissue, and therapeutic proteins. Moving on to Chapter 2, a computational model is presented to accurately predict the kinematics of the syringe barrel and plunger during the needle insertion process.</p> <p><br></p> <p>In Chapter 3, we present a quasi-steady model for the drug delivery process, which considers the rheology of therapeutic proteins. The Carreau model is adopted to describe protein viscosity, and explicit relationships between flow rate and pressure drop in the needle are derived. Furthermore, the applicable regime for the power-law model for protein viscosity is identified.</p> <p><br></p> <p>Chapter 4 quantifies the impact of sloshing and cavitation on therapeutic proteins in the syringe. Additionally, a workflow is presented to integrate available simulation tools to predict the performance of spring-driven autoinjectors. The influence of each design parameter of spring-driven autoinjectors on their performance is also discussed. </p> <p><br></p> <p>The spring-driven autoinjector delivers therapeutic proteins through subcutaneous administration. To gain insights into the transport process of therapeutic proteins, Chapter 5 presents a physiologically-based pharmacokinetic model that has been validated against experimental data for humans and rats. The lymph flow rate significantly affects the bioavailability of therapeutic proteins. This finding highlights the importance of studying the transport of therapeutic proteins in the lymphatic system in future research.</p> <p><br></p> <p>Chapter 6 provides a multi-objective design optimization framework for the spring-driven autoinjector. The computational model is replaced with an accurate deep neural network surrogate to improve the computational efficiency.  Using this surrogate model, we conduct a sensitivity analysis to identify essential design parameters. After that, we perform multi-objective optimization to find promising design candidates.</p> <p><br></p> <p>Chapter 7 presents a model for bubble dynamics in a protein solution. An explicit expression for the bubble dissolution rate is derived, enabling extraction of the interfacial properties of the protein-coated interface from the measured bubble radii. Moreover, analytical solutions for the response of a protein-coated bubble to an imposed acoustic pressure are derived. This work provides insight into protein-coated bubbles, which are used as vehicles to deliver drugs, as active miniature tracers to probe the rheology of soft and biological materials, or as contrast agents to enhance the ultrasound backscatter in ultrasonic imaging.</p> <p><br></p> <p>At last, in Chapter 8, we introduce a model for laser-induced cavitation that considers several key factors, such as liquid compressibility, heat transfer, and non-equilibrium evaporation and condensation. Our model's predictions for the time-course of bubble radii have been validated with experimental data. Moreover, our model reveals that the reduction of the bubble's oscillation amplitude is primarily due to a decrease in the number of vapor molecules inside the bubble, highlighting the crucial role of phase change in laser-induced cavitation bubbles.</p> <p><br></p> <p>The developed computational models and framework provide crucial insights into the development of spring-driven autoinjectors and cavitation bubbles. These studies can also enhance the efficacy and safety of the delivery of therapeutic proteins, ultimately improving patient outcomes.</p>

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Paracetamol är ett vanligt använt läkemedel med analgesisk och antipyretisk effekt. Läkemedlet finns tillgängligt i ett flertal beredningsformer och doseringsstyrkor för användning både receptfritt och i sjukhusvården. Syftet med detta projekt var att undersöka påverkan av alternativ, off-label, dosering av paracetamol i pediatrisk vård, med hjälp av fysiologiskt baserad farmakokinetisk (PBPK) modellering. Modellen utvecklades först för en vuxen population genom integrering av in vitro, in vivo och in silico data för paracetamol. Efter detta extrapolerades concentrationskurvor till en pediatrisk population med hjälp av ontogeni-information. Modellen validerades i både vuxna och barn, och var tillförlitlig för både peroral och intravenös dosering. Efter valideringen utfördes simuleringar för nio olika åldersgrupper baserat på rekommenderade doseringsprotokoll i Sverige. Simuleringarna visade att perorala tablettdoseringen var jämförbar med formulering i lösningsform, med snarlika maximumkoncentrationer och area-under-kurvan (AUC) för exponering. Hastigheten av magtömning influerade maximumkoncentrationer men inte AUC. Ytterligare testades modellens förmåga att prediktera plasmakoncentrationer i blodet efter överdosering med paracetamol. Dessa prediktioner fungerade bättre när läkemedelsmetaboliserande enzymer lämnades oförändrade, eller ökade något i aktivitet. Slutligen, den utvecklade PBPK-modellen kan användas för att säkert undersöka olika doseringsprotokoll och för design av pediatriska kliniska studier. / Paracetamol, a widely used analgesic and antipyretic drug, can be found in various formulations and doses for both home and hospital use. The aim of this study was to investigate the impact of off-label dosing of paracetamol in pediatric clinical practice using physiologically based pharmacokinetic (PBPK) modeling. The model was initially developed for adults by integrating relevant in vitro, in vivo and in silico data of paracetamol, after which the model was extrapolated for pediatrics by adding ontogeny information. The model was successfully validated in both adult and pediatric populations, and it showed accuracy for both oral and intravenous administration routes. After validation, simulations were conducted across nine different age groups following the recommended doses in Sweden. These simulations showed that tablet dose is comparable to solution dosing, resulting in nearly identical maximum concentrations and area under the curve (AUC) values. Furthermore, it was observed that gastric emptying time, which reflects the fed state of individuals, significantly influences the maximum concentration, with longer gastric emptying times resulting in lower and delayed peak concentrations. However, the gastric emptying time had no effect on the AUC values. Lastly, the model’s performance on overdose data was evaluated, and it turned out that it performs better when liver enzymes were not affected, or they were only slightly elevated. Finally, the developed PBPK model can be further used for safe and effective way of exploring dose banding and designing clinical trials in pediatrics.

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