Analysis of extracellular vesicles from patients with advanced pancreatic cancer identifies miRNAs with predictive value for treatment with gemcitabine + nab-paclitaxel

Pancreatic cancer (PC) is the seventh leading cause of cancer death. Despite recent therapy advancements, 5-year survival is 11%. Resistance to therapy is common, and no predictive factors, except for BRCA1/2 and PALB2 mutations, can drive treatment selection. Based on the easy isolation of extracellular vesicles (EVs) from blood and the role of EV-borne miRNAs in chemoresistance, we analyzed EVs and their miRNA content in order to identify predictive factors. First, we analyzed samples from 28 PC patients and 7 healthy subjects, in order to establish methods for isolation and analysis of EVs and their miRNA content. We observed a significantly different expression of 28 miRNAs, including oncogenic or tumor suppressor miRNAs, showing the ability of our approach to detect candidate biomarkers. Then, we analyzed samples of 21 advanced PC patients, collected before first-line treatment with gemcitabine + nab-paclitaxel, and compared findings in responders and non-responders. EVs have been analyzed with Nanoparticle tracking analysis, flow cytometry and RNA-Seq; then, laboratory results have been matched with clinical data. Nanoparticle tracking analysis did not show any significant difference. Flow cytometry showed a lower expression of SSE4 and CD81 in responders. Finally, miRNA analysis showed 25 upregulated and 19 downregulated miRNAs in responders. In particular, in responders we observed upregulation of miR-141-3p, miR-141-5p, miR-200a-3p, miR-200b-3p, miR-200c-3p, miR-375-3p, miR-429, miR-545-5p. These miRNAs have targets with a previously reported role in PC. In conclusion, we show the feasibility of the proposed approach to identify EV-derived biomarkers with predictive value for therapy with gemcitabine + nab-paclitaxel in PC. Our findings highlight the possibility to exploit liquid biopsy for personalized treatment in PC, in order to maximize chances of response and patients’ outcome. These findings are worthy of further investigation: in the same setting, with different chemotherapy schedules, and in different disease settings such as preoperative therapy.

The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.

Fluid dynamic analysis and modelling of industrial chemical equipment

The research is aimed at contributing to the identification of reliable fully predictive Computational Fluid Dynamics (CFD) methods for the numerical simulation of equipment typically adopted in the chemical and process industries. The apparatuses selected for the investigation, specifically membrane modules, stirred vessels and fluidized beds, were characterized by a different and often complex fluid dynamic behaviour and in some cases the momentum transfer phenomena were coupled with mass transfer or multiphase interactions. Firs of all, a novel modelling approach based on CFD for the prediction of the gas separation process in membrane modules for hydrogen purification is developed. The reliability of the gas velocity field calculated numerically is assessed by comparison of the predictions with experimental velocity data collected by Particle Image Velocimetry, while the applicability of the model to properly predict the separation process under a wide range of operating conditions is assessed through a strict comparison with permeation experimental data. Then, the effect of numerical issues on the RANS-based predictions of single phase stirred tanks is analysed. The homogenisation process of a scalar tracer is also investigated and simulation results are compared to original passive tracer homogenisation curves determined with Planar Laser Induced Fluorescence. The capability of a CFD approach based on the solution of RANS equations is also investigated for describing the fluid dynamic characteristics of the dispersion of organics in water. Finally, an Eulerian-Eulerian fluid-dynamic model is used to simulate mono-disperse suspensions of Geldart A Group particles fluidized by a Newtonian incompressible fluid as well as binary segregating fluidized beds of particles differing in size and density. The results obtained under a number of different operating conditions are compared with literature experimental data and the effect of numerical uncertainties on axial segregation is also discussed.

Model predictive flight control systems for rotorcraft UAS

Constraints are widely present in the flight control problems: actuators saturations or flight envelope limitations are only some examples of that. The ability of Model Predictive Control (MPC) of dealing with the constraints joined with the increased computational power of modern calculators makes this approach attractive also for fast dynamics systems such as agile air vehicles. This PhD thesis presents the results, achieved at the Aerospace Engineering Department of the University of Bologna in collaboration with the Dutch National Aerospace Laboratories (NLR), concerning the development of a model predictive control system for small scale rotorcraft UAS. Several different predictive architectures have been evaluated and tested by means of simulation, as a result of this analysis the most promising one has been used to implement three different control systems: a Stability and Control Augmentation System, a trajectory tracking and a path following system. The systems have been compared with a corresponding baseline controller and showed several advantages in terms of performance, stability and robustness.

Analysis and modelling of the performance of technologies for flue gas treatment in Waste-to-Energy processes

Waste management represents an important issue in our society and Waste-to-Energy incineration plants have been playing a significant role in the last decades, showing an increased importance in Europe. One of the main issues posed by waste combustion is the generation of air contaminants. Particular concern is present about acid gases, mainly hydrogen chloride and sulfur oxides, due to their potential impact on the environment and on human health. Therefore, in the present study the main available technological options for flue gas treatment were analyzed, focusing on dry treatment systems, which are increasingly applied in Municipal Solid Wastes (MSW) incinerators. An operational model was proposed to describe and optimize acid gas removal process. It was applied to an existing MSW incineration plant, where acid gases are neutralized in a two-stage dry treatment system. This process is based on the injection of powdered calcium hydroxide and sodium bicarbonate in reactors followed by fabric filters. HCl and SO2 conversions were expressed as a function of reactants flow rates, calculating model parameters from literature and plant data. The implementation in a software for process simulation allowed the identification of optimal operating conditions, taking into account the reactant feed rates, the amount of solid products and the recycle of the sorbent. Alternative configurations of the reference plant were also assessed. The applicability of the operational model was extended developing also a fundamental approach to the issue. A predictive model was developed, describing mass transfer and kinetic phenomena governing the acid gas neutralization with solid sorbents. The rate controlling steps were identified through the reproduction of literature data, allowing the description of acid gas removal in the case study analyzed. A laboratory device was also designed and started up to assess the required model parameters.

Outcome analysis of predictors in locally advanced pancreatic cancer

Guidelines report a wide range of options in locally advanced pancreatic cancer (LAPC): definitive chemotherapy or chemoradiotherapy or the emerging stereotactic body radiotherapy (SBRT) (+/- chemotherapy). On behalf of the AIRO (Italian Association of Radiation Oncology and Clinical Oncology) Gastrointestinal Study Group, we collected retrospective clinical data on 419 LAPC from 15 Italian centers. The study protocol (PAULA-1: Pooled Analysis in Unresectable Locally Advanced pancreatic cancer) was approved by institutional review board of S. Orsola-Malpighi Hospital (201/2015/O/OssN). From this large database we performed tree different studies. The first was a retrospective study about 56 LAPC treated with SBRT at a median biologically equivalent dose of 48 Gy +/- chemotherapy. We demonstrated a statistically significant impact of biologically equivalent dose based on an α/β ratio of 10Gy ≥ 48Gy for local control (LC) (p: 0.045) and overall survival (p: 0.042) in LAPC. The second was a retrospective matched-cohort case-control study comparing SBRT (40 patients) and chemoradiation (40 patients) in LAPC in terms of different endpoints. Our findings suggested an equivalence in terms of most outcomes among the two treatments and an advantage of SBRT in terms of LC (p: 0.017). The third study was a retrospective comparison of definitive chemotherapy, chemoradiotherapy and SBRT (+/- chemotherapy) in terms of different outcomes in LAPC. A predictive model for LC in LAPC was also developed reaching an AUC of 68% (CI 58,7%-77,4%). SBRT treatment emerged as a positive predictive factor for improved LC. Findings deriving from our three studies suggest that SBRT is comparable to standard of care (definitive chemotherapy and chemoradiotherapy) in terms of outcomes. SBRT seems to be an emerging therapeutic option in LAPC significantly improving local control. Furthermore, we have shown the potential of a predictive model for LC. Randomized trials are needed to compare these different therapeutic options in LAPC.

New methodologies in CT perfusion and MRI analysis to develop cancer imaging biomarkers

Quantitative imaging in oncology aims at developing imaging biomarkers for diagnosis and prediction of cancer aggressiveness and therapy response before any morphological change become visible. This Thesis exploits Computed Tomography perfusion (CTp) and multiparametric Magnetic Resonance Imaging (mpMRI) for investigating diverse cancer features on different organs. I developed a voxel-based image analysis methodology in CTp and extended its use to mpMRI, for performing precise and accurate analyses at single-voxel level. This is expected to improve reproducibility of measurements and cancer mechanisms’ comprehension and clinical interpretability. CTp has not entered the clinical routine yet, although its usefulness in the monitoring of cancer angiogenesis, due to different perfusion computing methods yielding unreproducible results. Instead, machine learning applications in mpMRI, useful to detect imaging features representative of cancer heterogeneity, are mostly limited to clinical research, because of results’ variability and difficult interpretability, which make clinicians not confident in clinical applications. In hepatic CTp, I investigated whether, and under what conditions, two widely adopted perfusion methods, Maximum Slope (MS) and Deconvolution (DV), could yield reproducible parameters. To this end, I developed signal processing methods to model the first pass kinetics and remove any numerical cause hampering the reproducibility. In mpMRI, I proposed a new approach to extract local first-order features, aiming at preserving spatial reference and making their interpretation easier. In CTp, I found out the cause of MS and DV non-reproducibility: MS and DV represent two different states of the system. Transport delays invalidate MS assumptions and, by correcting MS formulation, I have obtained the voxel-based equivalence of the two methods. In mpMRI, the developed predictive models allowed (i) detecting rectal cancers responding to neoadjuvant chemoradiation showing, at pre-therapy, sparse coarse subregions with altered density, and (ii) predicting clinically significant prostate cancers stemming from the disproportion between high- and low- diffusivity gland components.

Analysis of the main phenomena affecting solid rocket motors internal ballistics

In solid rocket motors, the absence of combustion controllability and the large amount of financial resources involved in full-scale firing tests, increase the importance of numerical simulations in order to asses stringent mission thrust requirements and evaluate the influence of thrust chamber phenomena affecting the grain combustion. Among those phenomena, grain local defects (propellant casting inclusions and debondings), combustion heat accumulation involving pressure peaks (Friedman Curl effect), and case-insulating thermal protection material ablation affect thrust prediction in terms of not negligible deviations with respect to the nominal expected trace. Most of the recent models have proposed a simplified treatment to the problem using empirical corrective functions, with the disadvantages of not fully understanding the physical dynamics and thus of not obtaining predictive results for different configurations of solid rocket motors in a boundary conditions-varied scenario. This work is aimed to introduce different mathematical approaches to model, analyze, and predict the abovementioned phenomena, presenting a detailed physical interpretation based on existing SRMs configurations. Internal ballistics predictions are obtained with an in-house simulation software, where the adoption of a dynamic three-dimensional triangular mesh together with advanced computer graphics methods, allows the previous target to be reached. Numerical procedures are explained in detail. Simulation results are carried out and discussed based on experimental data.

Predictive And Prognostic Role Of Biological Markers In Neuroendocrine Neoplasia And Evaluation Of Activity And Safety Of Second Line Treatments In Neuroendocrine Carcinoma Patients

BACKGROUND Neuroendocrine neoplasia (NEN) are divided in well differentiated G1,G2 and G3 neuroendocrine tumors (NETs) and G3 neuroendocrine carcinomas (NECs). For the latter no standard therapy in second-line is available and prognosis is poor. METHODS Primary aim was to evaluate new prognostic and predictive biomarkers (WP1-3). In WP4 we explored the activity of FOLFIRI and CAPTEM as second-line in NEC patients in a multicenter non-comparative phase II trial RESULTS In WP1-2 we found that 4 of 6 GEP-NEC patients with a negative 68Ga-PET/CT had a loss of expression of RB1. In WP3 on 47 GEP-NENs patients the presence of DLL3 in 76.9% of G3 NEC correlate with RB1-loss (p<0.001), negative 68Ga-PET/CT(p=0.001) and a poor prognosis. In the WP4 we conducted a multicenter non-comparative phase II trial to explore the activity of FOLFIRI or CAPTEM in terms of DCR, PFS and OS given as second-line in NEC patients. From 06/03/2017 to 18/01/2021 53 out of 112 patients were enrolled in 17 of 23 participating centers. Median follow-up was 10.8 (range 1.4 – 38.6) months. The 3-month DCR was 39.3% in the FOLFIRI and 32.0 % in the CAPTEM arm. The 6-months PFS rate was 34.6% ( 95%CI 17.5-52.5) in FOLFIRI and 9.6% (95%CI 1.8-25.7) in CAPTEM group. In the FOLFIRI subgroup the 6-months and 12-months OS rate were 55.4% (95%CI 32.6-73.3) and 30.3% (CI 11.1-52.2) respectively. In CAPTEM arm the 6-months and 12-months OS rate were 57.2% (95%34.9-74.3) and 29.0% (95%10.0-43.3). The miRNA analysis of 20 patients compared with 20 healthy subjects shows an overexpression of miRNAs involved in staminality , neo-angiogenesis and mitochontrial anaerobic glycolysis activation. CONCLUSION WP1-3 support the hypothesis that G3NECs carrying RB1 loss is associated with a DLL3 expression highlighting a potential therapeutic opportunity. Our study unfortunately didn’t met the primary end–point but the results are promising

Integrating Machine Learning Paradigms for Predictive Maintenance in the Fourth Industrial Revolution era

In the last decade, manufacturing companies have been facing two significant challenges. First, digitalization imposes adopting Industry 4.0 technologies and allows creating smart, connected, self-aware, and self-predictive factories. Second, the attention on sustainability imposes to evaluate and reduce the impact of the implemented solutions from economic and social points of view. In manufacturing companies, the maintenance of physical assets assumes a critical role. Increasing the reliability and the availability of production systems leads to the minimization of systems’ downtimes; In addition, the proper system functioning avoids production wastes and potentially catastrophic accidents. Digitalization and new ICT technologies have assumed a relevant role in maintenance strategies. They allow assessing the health condition of machinery at any point in time. Moreover, they allow predicting the future behavior of machinery so that maintenance interventions can be planned, and the useful life of components can be exploited until the time instant before their fault. This dissertation provides insights on Predictive Maintenance goals and tools in Industry 4.0 and proposes a novel data acquisition, processing, sharing, and storage framework that addresses typical issues machine producers and users encounter. The research elaborates on two research questions that narrow down the potential approaches to data acquisition, processing, and analysis for fault diagnostics in evolving environments. The research activity is developed according to a research framework, where the research questions are addressed by research levers that are explored according to research topics. Each topic requires a specific set of methods and approaches; however, the overarching methodological approach presented in this dissertation includes three fundamental aspects: the maximization of the quality level of input data, the use of Machine Learning methods for data analysis, and the use of case studies deriving from both controlled environments (laboratory) and real-world instances.

Big data analytics for proactive and predictive maintenance in electric car battery packs

The idea behind the project is to develop a methodology for analyzing and developing techniques for the diagnosis and the prediction of the state of charge and health of lithium-ion batteries for automotive applications. For lithium-ion batteries, residual functionality is measured in terms of state of health; however, this value cannot be directly associated with a measurable value, so it must be estimated. The development of the algorithms is based on the identification of the causes of battery degradation, in order to model and predict the trend. Therefore, models have been developed that are able to predict the electrical, thermal and aging behavior. In addition to the model, it was necessary to develop algorithms capable of monitoring the state of the battery, online and offline. This was possible with the use of algorithms based on Kalman filters, which allow the estimation of the system status in real time. Through machine learning algorithms, which allow offline analysis of battery deterioration using a statistical approach, it is possible to analyze information from the entire fleet of vehicles. Both systems work in synergy in order to achieve the best performance. Validation was performed with laboratory tests on different batteries and under different conditions. The development of the model allowed to reduce the time of the experimental tests. Some specific phenomena were tested in the laboratory, and the other cases were artificially generated.

Research of predictive biomarkers to anti-CD22 antibody-drug conjugate treatment in B-cell acute lymphoblastic leukemia

Background: The treatment of B-cell acute lymphoblastic leukemia (B-ALL) has been enriched by novel agents targeting surface markers CD19 and CD22. Inotuzumab ozogamicin (INO) is a CD22-calicheamicin conjugated monoclonal antibody approved in the setting of relapse/refractory (R/R) B-ALL able to induce a high rate of deep responses, not durable over time. Aims: This study aims to identify predictive biomarkers to INO treatment in B- ALL by flow cytometric analysis of CD22 expression and gene expression profile. Materials and methods: Firstly, the impact on patient outcome in 30 R/R B-ALL patients of baseline CD22 expression in terms of CD22 blast percentage and CD22 fluorescent intensity (CD22-FI) was explored. Secondly, baseline gene expression profile of 18 R/R B-ALL patient samples was analyzed. For statistical analysis of differentially expressed genes (DEGs) patients were divided in non-responders (NR), defined as either INO-refractory or with duration of response (DoR) < 3 months, and responders (R). Gene expression results were analyzed with Ingenuity pathway analysis (IPA). Results: In our patient set higher CD22-FI, defined as higher quartiles (Q2-Q4), correlated with better patient outcome in terms of CR rate, OS and DoR, compared to lower CD22-FI (Q1). CD22 blast percentage was less able to discriminate patients’ outcome, although a trend for better outcome in patients with CD22 ≥ 90% could be appreciated. Concerning gene expression profile, 32 genes with corrected p value <0.05 and absolute FC ≥2 were differentially expressed in NR as compared to R. IPA upstream regulator and regulator effect analysis individuated the inhibition of tumor suppressor HIPK2 as causal upstream condition of the downregulation of 6 DEGs. Conclusions: CD22-FI integrates CD22-percentage on leukemic blasts for a more comprehensive target pre-treatment evaluation. Moreover, a unique pattern of gene expression signature based on HIPK2 downregulation was identified, providing important insights in mechanisms of resistance to INO.

Exploratory analysis of the role of circulating microRNA in metastatic melanoma patients

MicroRNAs act as oncogene or tumor suppressor gene regulators and are actively released from tumor cells in the circulation. Specific microRNAs can be isolated and quantified in the blood, usually in serum or plasma fractions, where they are uncommonly stable. Cell-free microRNAs serve many, and possibly yet unexplored, functional roles and microRNA levels reflect underlying conditions and have been associated with skin cancer presence, stage and evolution. However, the clinical potential of circulating miRNAs in metastatic melanoma remains largely undefined. From May 2020 to September 2022, we conducted a spontaneous, monocentric, exploratory study on human tissues in vitro, which aimed to evaluate the prognostic and predictive role of circulating miRNAs in metastatic melanoma patients. At the Medical Oncology Unit of Policlinico Sant’Orsola-Malpighi of Bologna, peripheral venous blood samples from patients with metastatic melanoma treated with checkpoint inhibitors (CPI) were collected before the start of CPI (baseline, T0) and longitudinally, approximately every 3 months (T1, T2, etc). Circulating miRNA quantification was performed by droplet digital PCR (Biorad) using an EvaGreen and LNA primer-based assays. QuantaSoft Program (Biorad) calculated the absolute quantifications of each miRNA, indicated as copies/µL. After analysis of the literature, we chose to analyze miR-155-5p, miR-320a and miR-424-5p level. All miRNAs except miR-424-5p show a significantly higher level in plasma of patients who are alive after 1 year of follow-up. High/low levels of baseline miR-155-5p, miR-320a and miR-424-5p are significantly associated with overall survival and progression-free survival. Furthermore, a preliminary analysis on the group of patients who received first-line with anti-PD-1 (N=7), baseline miR-155-5p shows higher levels in responder vs. non responder patients (p 0.06). These data, though promising, are preliminary and need to be further investigated in a larger cohort of patients.

Efficient and low-computational predictive models for spectral sensors

Spectral sensors are a wide class of devices that are extremely useful for detecting essential information of the environment and materials with high degree of selectivity. Recently, they have achieved high degrees of integration and low implementation cost to be suited for fast, small, and non-invasive monitoring systems. However, the useful information is hidden in spectra and it is difficult to decode. So, mathematical algorithms are needed to infer the value of the variables of interest from the acquired data. Between the different families of predictive modeling, Principal Component Analysis and the techniques stemmed from it can provide very good performances, as well as small computational and memory requirements. For these reasons, they allow the implementation of the prediction even in embedded and autonomous devices. In this thesis, I will present 4 practical applications of these algorithms to the prediction of different variables: moisture of soil, moisture of concrete, freshness of anchovies/sardines, and concentration of gasses. In all of these cases, the workflow will be the same. Initially, an acquisition campaign was performed to acquire both spectra and the variables of interest from samples. Then these data are used as input for the creation of the prediction models, to solve both classification and regression problems. From these models, an array of calibration coefficients is derived and used for the implementation of the prediction in an embedded system. The presented results will show that this workflow was successfully applied to very different scientific fields, obtaining autonomous and non-invasive devices able to predict the value of physical parameters of choice from new spectral acquisitions.

Enhancing predictive capability of models for solubility and permeability in polymers and composites

The interpretation of phase equilibrium and mass transport phenomena in gas/solvent - polymer system at molten or glassy state is relevant in many industrial applications. Among tools available for the prediction of thermodynamics properties in these systems, at molten/rubbery state, is the group contribution lattice-fluid equation of state (GCLF-EoS), developed by Lee and Danner and ultimately based on Panayiotou and Vera LF theory. On the other side, a thermodynamic approach namely non-equilibrium lattice-fluid (NELF) was proposed by Doghieri and Sarti to consistently extend the description of thermodynamic properties of solute polymer systems obtained through a suitable equilibrium model to the case of non-equilibrium conditions below the glass transition temperature. The first objective of this work is to investigate the phase behaviour in solvent/polymer at glassy state by using NELF model and to develop a predictive tool for gas or vapor solubility that could be applied in several different applications: membrane gas separation, barrier materials for food packaging, polymer-based gas sensors and drug delivery devices. Within the efforts to develop a predictive tool of this kind, a revision of the group contribution method developed by High and Danner for the application of LF model by Panayiotou and Vera is considered, with reference to possible alternatives for the mixing rule for characteristic interaction energy between segments. The work also devotes efforts to the analysis of gas permeability in polymer composite materials as formed by a polymer matrix in which domains are dispersed of a second phase and attention is focused on relation for deviation from Maxwell law as function of arrangement, shape of dispersed domains and loading.