Cutting Edge PBPK Models and Analyses: Providing the Basis for Future Modeling Efforts and Bridges to Emerging Toxicology Paradigms

Physiologically based Pharmacokinetic (PBPK) models are used for predictions of internal or target dose from environmental and pharmacologic chemical exposures. Their use in human risk assessment is dependent on the nature of databases (animal or human) used to develop and test them, and includes extrapolations across species, experimental paradigms, and determination of variability of response within human populations. Integration of state-of-the science PBPK modeling with emerging computational toxicology models is critical for extrapolation between in vitro exposures, in vivo physiologic exposure, whole organism responses, and long-term health outcomes. This special issue contains papers that can provide the basis for future modeling efforts and provide bridges to emerging toxicology paradigms. In this overview paper, we present an overview of the field and introduction for these papers that includes discussions of model development, best practices, risk-assessment applications of PBPK models, and limitations and bridges of modeling approaches for future applications. Specifically, issues addressed include: (a) increased understanding of human variability of pharmacokinetics and pharmacodynamics in the population, (b) exploration of mode of action hypotheses (MOA), (c) application of biological modeling in the risk assessment of individual chemicals and chemical mixtures, and (d) identification and discussion of uncertainties in the modeling process

Update on a Pharmacokinetic-Centric Alternative Tier II Program for MMT—Part I: Program Implementation and Lessons Learned

Concerns have been raised regarding environmental manganese exposure since high exposures have been associated with neurological disorders. The USA Environmental Protection Agency most recent human health risk assessment of inhaled manganese conducted in 1993 identified specific areas of uncertainty regarding manganese pharmacokinetics. This led to the development of a test rule under the USA Clean Air Act that required the generation of pharmacokinetic information on the inorganic manganese combustion products of the organometallic fuel additive methylcyclopentadienyl manganese tricarbonyl (MMT). The Alternative Tier 2 testing program for MMT, described in this paper, has yielded substantial pharmacokinetic data and has enabled the generation of physiologically based pharmacokinetic (PBPK) models for manganese. These models are capable of predicting tissue manganese concentrations across a variety of dose routes, levels, and durations while accounting for factors such as age, gender, and reproductive status, enabling the consideration of tissue dosimetry in future risk assessments

Microbiota-based Models Enhance Detection of Colorectal Cancer.

Colorectal cancer (CRC) is the second leading cause of death among cancers in the United States. Although individuals diagnosed early have a greater than 90% chance of survival, more than one-third of individuals do not adhere to screening recommendations partly because the standard diagnostics, colonoscopy and sigmoidoscopy, are expensive and invasive. Thus, there is a great need to improve the sensitivity of non-invasive tests to detect early stage cancers and adenomas. Numerous studies have demonstrated a causal link between the formation of colonic lesions and the activity of the gut microbiota in tissue culture and animal models. These findings have been complemented by studies in human populations identifying shifts in the composition of the gut microbiota associated with the progression of colorectal cancer. These results suggest that the gut microbiota may represent a reservoir of biomarkers that would complement existing non-invasive methods such as the widely used fecal immunochemical test (FIT). Using stool samples from 490 patients we developed a cross-validated random forest classification model that detects colonic lesions using the relative abundance of gut microbiota and the concentration of hemoglobin in stool. The microbiota-based model had significantly higher sensitivity for lesions compared to FIT alone, detecting the majority of lesions that were missed by FIT. Furthermore, we demonstrated that microbial DNA isolated from the residual buffer of FIT cartridges could be used in place of stool samples for microbiota characterization. These findings demonstrate the potential for microbiota analysis to be combined with existing screening methods to improve detection of colonic lesions.PhDMicrobiology and ImmunologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133364/1/ntbaxter_1.pd

Evaluating Management Options to Increase Roadside Carbon Sequestration

We estimated the amount of carbon sequestered along Montana Department of Transportation (MDT) roads and tested 3 different highway right-of-way (ROW) management techniques to increase carbon stocks. Using Geographic Information System techniques, the total ROW acreage owned by MDT was found to sequester 75,292 metric tons of carbon per year and to consist mostly of grasslands (70%). From 2016-2018 we tested 3 ROW management techniques to increase carbon stocks- increase mowing height, plant woody shrubs, or add legumes to reclamation seed mixes of disturbed soils - at 3 sites (Three Forks [3F], Bear Canyon [BC], and Bozeman Pass [BP]) along Interstate 90 in southwestern Montana. Soil samples generally averaged 0.75–1.5% soil organic carbon (SOC) at the 3F site, 2.5–4% SOC at the BC site, and 1.5–2.5% SOC at the BP site. Average SOC levels were always lower in 2018 than in 2016. Soil respiration rates were generally highest in June or July at the BC site, averaging ~4 μmol CO2 m-2 second-1. Soil respiration rates were lower at the BC site in November 2016, at the BP site in June 2018, and at the 3F site in July 2018 (all ~2–3 μmol CO2 m-2 s-1). Aboveground biomass carbon estimates generally mirrored belowground SOC estimates. Taken together, our findings suggest that of the three treatments implemented (raised mowing height, shrub planting, and disturbance), minimizing disturbance to soils likely makes the greatest contribution to the medium- and long-term carbon-storage potential of these roadside soils

Exploration of the biochemical differences between high and low dose methadone clients on stable maintenance therapy

There is large variability in the dose of methadone required to prevent withdrawal symptoms in chronic, stable methadone users. The difference in dose between low-dose and high-dose patients may vary >50 fold, and could be as low as 300 mg/day. Our Objective was to identify factors which account for the difference in biochemical response of patients to low- and high-dose administration of methadone. We hypothesized that differences in high dose vs. low dose methadone clients are due to lower number of human μ-opioid receptors (hMORs) in high dose maintenance therapy patients than in those on lower doses, and/or desensitization down-stream from the opioid receptor that manifests as an attenuated cyclic AMP (cAMP) response to opioid agonists. We also hypothesized that concurrent drug use as well as Pglycoprotein levels may influence dosing requirements. Using white blood cells as a model, we measured hMOR expression, in vivo cAMP levels, cAMP levels in response to exposure to increasing levels of methadone, P-GP expression and the presence of other drugs. Our findings indicated that hMOR numbers on lymphocytes, granulocytes and monocytes did not vary for controls, low-dose, and high-dose methadonetreated patients. Baseline levels of cAMP in white blood cells were higher in controls than in low-dose methadone patients, and significantly lower in highdose patients than either controls or low-dose patients. Increasing concentrations of methadone exposure for control leucocytes resulted in a dose-related reduction in cAMP. In contrast, increasing doses of methadone treatment had no iii effect on cAMP levels in white cells of either low- or high-dose methadone patients. P-glycoprotein levels did not correlate with dose requirements. Concurrent drug use was detected in a high percentage of patients. In conclusion, the dose of methadone required to prevent withdrawal symptoms in high-dose and low-dose methadone patients is not related to changes in hMOR number. In contrast, baseline cAMP levels were significantly lower in high-dose patients than in low-dose patients. Chronic treatment also abolished the methadone dose-related reduction in cAMP in-vitro in lymphocytes, indicating desensitization. Concurrent drug use may play some part in dosing requirements; however P-glycoprotein levels appeared not to. It is possible that mechanisms of the hMOR signal transduction cascade are responsible for these dosing discrepancies as related to of methadone-treated patients, however, more research is required to determine exact mechanism

Numerical simulations of gas transport in argillaceous rocks: A molecular dynamics and pore-scale simulation study

This dissertation investigates the gas transport and clay behavior within the context of deep geological disposal of nuclear waste. The repository for spent fuel and high-level waste can generate substantial amounts of gas through processes such as anaerobic corrosion of carbon steel, radiolysis of water, and radioactive decay in the waste. Likewise, gas production can occur in low and intermediate-level waste repositories due to chemical degradation of organic waste materials and corrosion of metals. If these gases cannot sufficiently escape from the vicinity of the repository, a localized build-up of gas pressure could compromise the integrity of the barriers and the safety design of the repository. Therefore, a thorough understanding of gas transport mechanisms and processes is crucial for assessing the repository’s performance. Diffusion is the primary mechanism governing solute and fluid transport in these clays due to their low permeability. While experiments can provide valuable transport parameters for de- signing the barrier materials, they may not fully capture the long-term evolution of transport processes and specific subsurface conditions. Consequently, numerical and computer simu- lations become indispensable for determining the transport mechanisms and exploring the behavior of the system beyond the limits of experimental detection. These simulations offer the opportunity to explain experimental results, probe scales, and processes that are below the detection limit of experiments, and enhance our understanding of the transport mechanisms involved. Gas diffusion simulation in fully saturated Na-montmorillonite (Na-MMT) was performed and the effects of pore size, gas species, and temperature were investigated. Classical molecular dy- namics simulations were utilized to study the diffusion coefficients of various gases (CO2, H2, CH4, He, Ar). The findings indicate that the diffusion coefficients are influenced by the pore size, with H2 and He demonstrating higher mobility compared to Ar, CO2, and CH4. The be- havior of gases is affected by the confinement and the structuring of water molecules near the clay surface, as evidenced by density profiles and radial distribution functions. The obtained diffusion coefficients for different gases and slit pore sizes were parameterized using a single empirical relationship, enabling their application in macroscopic simulations of gas transport. Considering the long-term desaturation and resaturation process, the study extends to simulate gas diffusion in partially saturated Na-MMT and investigates the partitioning of gas molecules between the gas-rich and water-rich phases. Classical molecular dynamics simulations were employed to explore the impact of gas-filled pore widths, temperature, gas mean free path, gas size, and gas molecular weights on diffusion coefficients and partitioning coefficients. The re- sults demonstrate that the diffusion coefficient in the gas phase increases with larger gas-filled pore widths and eventually converges asymptotically towards the diffusion coefficient in the bulk state. Partitioning coefficients were found to be strongly dependent on temperature and gas molecular weights. Furthermore, non-equilibrium molecular dynamics simulations were conducted to investigate the mobility of gases in a pressure-driven flow within a partially sat- urated Na-MMT mesopore. The results reveal the presence of slip boundary conditions at the microscale, which challenges the assumptions made in continuum models. To predict the dif- fusion coefficient and dynamic viscosity of the gas, a Bosanquet-type equation was developed as a function of the average pore width, gas mean free path, geometric factor, and thickness of the adsorbed water film. Na-montmorillonite, being a swelling clay, undergoes changes in its swelling behavior when exposed to different chemical species like gas due to variations in chemical potential. These alterations can subsequently impact the hydraulic properties and transport mechanism of the clay. Consequently, we investigated the influence of gas presence on the swelling pressure of Na-MMT. To achieve this, classical molecular dynamics simulations were employed as a methodology to examine the effect of gas on swelling pressure. The findings indicate that gas molecules cause an increase in the swelling pressure of Na-montmorillonite, with an approx- imate rise of 3 MPa. The specific behavior observed is influenced by factors such as the dry density and the characteristics of the gas species. Additionally, the analysis includes a com- prehensive exploration of structural transformations occurring within the clay interlayer, pro- viding insights into the discrepancies observed between experimental and simulated curves, particularly at high levels of compaction. The thesis delves into pore-scale modeling to determine diffusion coefficients of water in com- pacted porous smectite clay structures. This exploration is motivated by the limitations inher- ent in conventional approaches used to obtain transport parameters, which tend to oversim- plify the intricate porous nature of clay media by treating them as a continuum. This oversim- plification neglects the behaviors occurring at smaller scales. To overcome this limitation, the thesis employs various techniques such as random walk simulations, lattice Boltzmann mod- eling, and large-scale molecular dynamics simulations to investigate transport mechanisms. These advanced modeling techniques take into account local diffusivities within the represen- tative elementary volume, allowing for a more accurate understanding of transport phenom- ena. By considering local diffusivities, particularly near chemically reactive clay surfaces, this approach sheds light on the significance of accurately comprehending transport phenomena in porous materials. By overcoming the limitations of conventional approaches, the thesis provides valuable insights into the diffusion coefficients of water within compacted porous smectite clay structures. This thesis offers a comprehensive exploration of gas transport and clay behavior, focusing on their relevance to deep geological disposal of nuclear waste and energy storage. By establishing connections between simulations conducted under fully saturated and partially saturated con- ditions, examining the influence of gases on swelling pressure, and incorporating pore-scale modeling, this research provides valuable insights into diffusion, swelling, and pore-scale pro- cesses. These findings contribute to the development of effective barrier materials and enhance our understanding of waste management strategies in complex geological environments. The knowledge gained from this study has practical implications for improving the safety and effi- ciency of deep geological disposal systems and advancing energy storage technologies

SYSTEMATICS AND EVOLUTION IN THE TRIBE SCHIZOPETALAE (BRASSICACEAE): A MOLECULAR, MORPHOLOGICAL, AND ECOLOGICAL ANALYSIS OF THE DIVERSIFICATION OF AN ENDEMIC LINEAGE FROM THE ATACAMA DESERT (CHILE)

As aridity has been identified as an active promoter of diversification in deserts, attempts to test organismal differentiation in the Atacama Desert have resulted particularly challenging. Most limitations are related to the recent origin of the extreme aridity in the Atacama Desert, which have stimulated a rapid process of diversification and obscured evidence of interspecific divergence. Based on its favorable biological attributes and high endemicity, genera from the tribe Schizopetalae (Mathewsia and Schizopetalon) emerge as a practical study group to conduct studies of diversification under rapid and recent diversification. The present dissertation focuses on exploring this issue, 1) solving the phylogenetic relationships in the tribe Schizopetalae, 2) describing patterns of interspecific divergence in a well-defined lineage of Schizopetalon from the Atacama Desert, and 3) searching and testing multiple highly variable nuclear loci for phylogenetic and phylogeographic purposes. The results confirmed the monophyletic status of the tribe Schizopetalae and genus Schizopetalon; nevertheless, genus Mathewsia requires to be redefined because the exclusion of M. nivea. Patterns of interspecific differentiation suggest a process of allopatric divergence promoted by ecological niche differentiation between the Andes and coastal ranges in the Atacama Desert. While this result is consistent with previous hypotheses of divergence by habitat differentiation, elements of hybridization, incomplete lineage sorting, and phenotypic plasticity obscured the identification of species limits and precluded a better inference of lineage isolation. The analysis of available genomic resources demonstrated the suitability of obtaining multiple low copy nuclear loci from already available genomic data in Schizopetalon. However, the use of these markers is yet limited, as the detection of multiple copies implies that further analyses are needed to discard paralogous copies. Overall, this dissertation sets the foundation for more elaborated studies, as more available genomic resources and intricate pattern of divergence can result promising to explore the consequences of local patterns of extreme aridity in the diversification and evolution of species of Schizopetalae

Optimised analysis and visualisation of metabolic data using graph theoretical approaches

Since the completion of the Human Genome Project in 2003, it has become increasingly apparent that while genomics has a major role to play in the understanding of human biology, information from other disciplines is necessary to explain the web of interacting signals that allow our bodies to function on a day to day basis and respond to rapid changes in our local environment. One such field, that of metabolomics, focuses on the study of the set of low molecular weight compounds (metabolites) involved in metabolism. Metabolomic studies aim to quantify the concentrations of each of these compounds within a subject under particular conditions, resulting in either information on the physiological effects of a disease or environmental factor (such as a toxin) on the organism, or the identification of metabolites or groups of metabolites that serve as biochemical markers for a state or illness. Whilst metabolite concentrations alone can give great insight into a chosen state, additional information can be obtained by considering the ways in which metabolites interact with each other as parts of a larger system. One method of tackling this problem, metabolic networks, is gaining popularity within the community as it offers a complementary approach to the traditional biological method for studying metabolism, the metabolic pathway. Construction methods are varied; ranging from the mapping of experimental data onto pathway diagrams, through the use of correlation-based techniques, to the analysis of time-series data of metabolic fluxes. However, while many attempts have been made to capture and visualise the complex web of reactions within an organism, few have yet succeeded in showing how they can be used to help identify the metabolites that are most significantly involved in the differences between groups of biological samples. This thesis discusses ways in which graphs may be used to aid researchers in both the visualisation and interpretation of metabolomic datasets, and provide a platform for more automated analysis techniques. To that end, it first presents the background to the relevant topics, metabolomics and graph theory, before moving on to show how metabolic correlation networks can be used to identify and visualise differences in metabolism between groups of subjects. It then introduces Linked Metabolites, a software package that has been developed to help researchers explain differences in metabolism by highlighting relationships between metabolites within the metabolic pathways, and to compile those relationships into directed metabolic graphs suitable for analysis using metrics from graph theory. Finally, the thesis explains how the directed metabolic graphs produced by Linked Metabolites could potentially be used to integrate data gathered from the same sample using different experimental techniques, refining the areas of the underlying biochemistry needing further investigation

The Dark side of Obesity: Multi-omics analysis of the dysmetabolic morbidities spectrum

Obesity is one of the most prevalent clinical conditions worldwide and is associated with a wide spectrum of dysmetabolic comorbidities. Complex cardio-metabolic disease cohorts, such as obesity cohorts are characterised by population heterogeneity, multiple underlying diseases status and different comorbidities’ treatment regiments. The systematic collection of multiple types of clinical and biological data from such cohorts and the data-analysis in an integrative manner is a challenging task due to the variables’ dimensionality and the lack of standardised know-how of post-processing.The main resource of this thesis has been the BARIA cohort, a detailed collection over time of multiple omics and demographic data from participants in bariatric surgery. BARIA datasets included plasma metabolites, RNA from hepatic, jejunal, mesenteric and subcutaneous adipose tissues and gut microbial metagenome, besides biometric data. The work presented in this thesis included the development of a systems biology integrative framework based on BARIA that (i) utilised unsupervised machine learning algorithms, self-organizing maps in particular, and multi-omics integrative frameworks, the DIABLO library, in order to stratify the BARIA heterogeneous obesity cohort and predict the bariatric surgery’s outcome. The thesis covered how BARIA can be the onset for (ii) studying molecular mechanisms related to type 2 diabetes (T2D) and G-protein coupled receptors (GPCRs) and for identifying a minimal set of biomarkers for obesity’s comorbidities such as (iii) non-alcoholic fatty liver disease (NAFL) and (iv) gallstones formation after bariatric surgery.The results indicated that the metabotypes comprising a bariatric surgery cohort exhibited a concrete metabolic status and different responses over time after the bariatric surgery. It has been demonstrated how obesity and T2D associated metabolites, such as 3-hydroxydecanoate, can increase inflammatory responses via GPCRs molecular activation and signalling. Last but not least, minimal sets of both evasive and non-evasive multi-omic discriminatory biomarkers for obesity’s dysmetabolic morbidities (NAFLD and gallstones after bariatric surgery) were obtained. Taking into consideration all the findings, this thesis presented how data-driven approaches can be used for studying in-depth heterogeneous cohorts, hereby facilitating early diagnosis and enabling potential preventive actions