Adrenal steroid hormones, which regulate a plethora of physiological functions, are produced via tightly controlled pathways. Adrenal hormone excess associates with clinical conditions impacting metabolism and cardiovascular and immune function. Aldosterone and cortisol producing enzymes, CYP11B2 and CYP11B1, share 93% homology requiring highly selective drugs for pharmacological treatment. Investigations of these pathways, based on experimental data, can be facilitated by computational modeling for calculations of metabolic rate alterations. Such systems can be utilized in a variety of applications including investigating effects of endocrine-disrupting chemicals, drugs, gene manipulations. On a human level, regression modelling involving use of clinical data can contribute in defining effects of such diseases or providing reference data for characterizing normal values of physiological processes. The main subject of this thesis was the development of modeling techniques that would benefit basic and clinical research by supplying means for investigating adrenal related dysfunctions and disease. As a first approach, we used a model system, based on mass balance and mass reaction equations, to kinetically evaluate adrenal steroidogenesis in human adrenal cortex-derived NCI H295R cells. For this purpose a panel of 10 steroids was measured by liquid chromatographic-tandem mass spectrometry. Time-dependent changes in cell incubate concentrations of steroids were measured after incubation with angII, forskolin and abiraterone. Model parameters were estimated based on experimental data using weighted least square fitting. Time-dependent angII- and forskolin-induced changes were observed for incubate concentrations of precursor steroids with peaks that preceded maximal increases in aldosterone and cortisol. Inhibition of 17-alpha-hydroxylase/17,20-lyase with abiraterone resulted in increases in upstream precursor steroids and decreases in downstream products. Derived model parameters, including rate constants of enzymatic processes, appropriately quantified observed and expected changes in metabolic pathways at multiple conversion steps. Our data from our first approach demonstrated limitations of single time point measurements and the importance of assessing pathway dynamics in studies of adrenal cortical cell line steroidogenesis. Despite the benefits from a computational approach in comparison to the single time point studies, this kind of modeling demonstrated certain limitations regarding effort, reproducibility and costs. Therefor a second study was conducted in order to address such limitations. As a second approach we introduced an effective in vitro assay for evaluation of steroidogenic enzyme kinetics based on intracellular flux calculations. H295RA cells were cultured in chambers (µ-Slide, Ibidi) under constant medium flow. Four hourly samples were collected (control samples), followed by collections over an additional four hours after treatment with either fadrozole (10nM), metyrapone (10uM), ASI_191 (5nM), a novel CYP11B2 inhibitor or ASI_254 (100nM), a newly synthesized CYP17 inhibitor. Mass spectrometric measurements of multiple steroids combined with linear system computational modeling facilitated calculation of intracellular flux rates at different steroidogenic pathway steps and assessment of the selectivity of drugs for those specific steps. While treatment with fadrozole, metyrapone and ASI_191 all resulted in reductions in fluxes of aldosterone, corticosterone and cortisol production, treatment with ASI_254 led to increased flux through the mineralocorticoid pathway and increased production of aldosterone with reduced production of steroids downstream of CYP17. Comparisons of changes in intracellular fluxes revealed much higher selectivity of ASI_191 for CYP11B2 over CYP11B1 compared to fadrozole or metyrapone. Our study demonstrates the advantages of continuous culture systems over static systems for studying effects of steroidogenic inhibitors. By culturing cells under perfusion the methodology establishes a more realistic model for investigating drug effects, provides for simple and rapid calculations of intracellular fluxes and offers a robust method for drug screening or in vitro investigations of metabolic mechanisms. As a third approach we utilized LC-MS/MS derived plasma concentrations for each of 525 normotensive and hypertensive volunteers with (n=227) and without (n=298) hypertension in combination with regression modeling for the extraction of age and gender-adjusted reference intervals. Values of 8 steroids (pregnenolone, 11-deoxycorticosterone, corticosterone, 17-hydroxyprogesterone, cortisone, dehydroepiandrosterone, dehydroepiandrosterone-sulfate, androstenedione) versus age and gender were modelled via multivariate fractional polynomial analysis successfully providing with 0.5 and 99.5% reference intervals as a function of age and gender.:Contents
Acknowledgements 4
Publication Note 5
Contents 6
Abbreviations 9
1. Introduction 10
1.1. The adrenal gland 10
1.1.1. Adrenal cortex steroids regulation 10
1.1.2. Physiological metabolic processes of steroidogenesis 13
1.2. Adrenal cortical dysfunction and therapeutic challenges 15
1.3. The adrenocortical cell line NCI –H295R 16
1.4. Chemical regulators of adrenal steroidogenesis 17
1.5. Computational mechanistic modelling of adrenal metabolism 18
1.5.1. Static cell culture models for characterizing pathway dynamics 18
1.5.2. Steady state as a tool for intracellular fluxes estimation 19
2. Hypothesis and aims 20
3. Materials and methods 22
3.1. Experimental overview 22
3.1.1. Computational mechanistic modeling of steroid metabolism 22
3.1.2 A steady state system for in vitro evaluation of steroidogenic pathway dynamics 24
3.1.3 Calculation of age and gender adjusted reference intervals for plasma adrenal steroids for healthy population 27
3.2 Liquid chromatography – tandem mass spectrometry 29
3.3. Static cell culture mechanistic model 29
3.3.1. Cell culture conditions 29
3.3.2. Computational model representation for steroid metabolism and cell proliferation using ODE systems 29
3.3.3. Metabolic pathways 30
3.3.4. Transport processes 32
3.3.5. Parameter estimation 34
3.4. Steady state model system for computation of pathway kinetics 35
3.4.1. Cell culture conditions for continuous flow culture system 35
3.4.2 Steroid secretion rates and intracellular fluxes calculation under steady state conditions 37
3.4.3 Statistical analysis 40
3.5. Regression and classification models for diagnostic clinical studies 40
3.5.1. Age and sex adjusted reference intervals for adrenal steroids in plasma – patient data collection 40
3.5.2. Mathematical description of multivariate fractional polynomial analysis 41
4. Results 43
4.1. Computational analysis of steroid profiling in NCI H295R cells 43
4.1.1. Transport and metabolic pathway modeling 43
4.1.2. Angiotensin II and forskolin stimulation 43
4.1.3. Abiraterone treatment 46
4.2. Steady state model for in vitro evaluation of steroidogenic pathway dynamics 48
4.2.1. Continuous flow culture steroid profiling 48
4.2.2. Secretion rates, intracellular flux rates and relative changes in rate constants 51
4.2.3. Cell number and viability 53
4.3. Reference intervals for adrenal steroid plasma concentrations 55
5. Discussion 61
Appendix A – Static culture model 70
A1. ODE system equations of the static culture model 70
A2. Jsim MML example code of the ODE system implementation of the static culture model 74
A3. Supplementary data for static culture model derived data 79
Appendix B – Steady state model 84
B1. Mathematical derivation of analytical solution for intracellular flux calculation of the steady state model 84
B2. Equations describing steroid production in cells and flowing medium of the steady state model 86
B3. Quadratic programming formulation and solution of upstream steroidogenic pathways system of the steady state model 91
B4. Calculation of reaction rate constant relative changes 92
B5. Python implementation of secretion rates, flux rates and rate constant relative changes calculation 94
B6. Numerical example of intracellular flux calculation. 98
B7. Supplementary material for steady state model derived data 103
Appendix C – Age and gender-adjusted reference intervals 110
Zusammenfasung 112
Summary 113
Literature 116
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:36496 |
| Date | 09 December 2019 |
| Creators | Mangelis, Anastasios |
| Contributors | Einsenhofer, Graeme, Morawietz, Henning, Technischen Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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