Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Cheminformatics and artificial intelligence for accelerating agrochemical discovery

The global cost-benefit analysis of pesticide use during the last 30 years has been characterized by a significant increase during the period from 1990 to 2007 followed by a decline. This observation can be attributed to several factors including, but not limited to, pest resistance, lack of novelty with respect to modes of action or classes of chemistry, and regulatory action. Due to current and projected increases of the global population, it is evident that the demand for food, and consequently, the usage of pesticides to improve yields will increase. Addressing these challenges and needs while promoting new crop protection agents through an increasingly stringent regulatory landscape requires the development and integration of infrastructures for innovative, cost- and time-effective discovery and development of novel and sustainable molecules. Significant advances in artificial intelligence (AI) and cheminformatics over the last two decades have improved the decision-making power of research scientists in the discovery of bioactive molecules. AI- and cheminformatics-driven molecule discovery offers the opportunity of moving experiments from the greenhouse to a virtual environment where thousands to billions of molecules can be investigated at a rapid pace, providing unbiased hypothesis for lead generation, optimization, and effective suggestions for compound synthesis and testing. To date, this is illustrated to a far lesser extent in the publicly available agrochemical research literature compared to drug discovery. In this review, we provide an overview of the crop protection discovery pipeline and how traditional, cheminformatics, and AI technologies can help to address the needs and challenges of agrochemical discovery towards rapidly developing novel and more sustainable products

Additive Manufacturing of Bio and Synthetic Polymers

Additive manufacturing technology offers the ability to produce personalized products with lower development costs, shorter lead times, less energy consumed during manufacturing and less material waste. It can be used to manufacture complex parts and enables manufacturers to reduce their inventory, make products on-demand, create smaller and localized manufacturing environments, and even reduce supply chains. Additive manufacturing (AM), also known as fabricating three-dimensional (3D) and four-dimensional (4D) components, refers to processes that allow for the direct fabrication of physical products from computer-aided design (CAD) models through the repetitious deposition of material layers. Compared with traditional manufacturing processes, AM allows the production of customized parts from bio- and synthetic polymers without the need for molds or machining typical for conventional formative and subtractive fabrication.In this Special Issue, we aimed to capture the cutting-edge state-of-the-art research pertaining to advancing the additive manufacturing of polymeric materials. The topic themes include advanced polymeric material development, processing parameter optimization, characterization techniques, structure–property relationships, process modelling, etc., specifically for AM

Academic Year 2019-2020 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

An excerpt from the Dean\u27s Message: There is no place like the Air Force Institute of Technology (AFIT). There is no academic group like AFIT’s Graduate School of Engineering and Management. Although we run an educational institution similar to many other institutions of higher learning, we are different and unique because of our defense-focused graduate-research-based academic programs. Our programs are designed to be relevant and responsive to national defense needs. Our programs are aligned with the prevailing priorities of the US Air Force and the US Department of Defense. Our faculty team has the requisite critical mass of service-tested faculty members. The unique composition of pure civilian faculty, military faculty, and service-retired civilian faculty makes AFIT truly unique, unlike any other academic institution anywhere

Application of LF-NMR measurements and supervised learning regression methods for improved characterization of heavy oils and bitumens

This work studies the physicochemical properties of unconventional hydrocarbon resources such as heavy oils and bitumens. The principal methods used in the research consisted of LF-NMR experiments, hypothesis testing, and statistical and data-driven modeling. The research output consists of several machine learning and analytical models capable of predicting heavy oil and bitumen viscosity and core sample water saturation with high accuracy. These results provide a strong case for in-situ LF-NMR applications in well logging

Computational Studies on Cellular Metabolism:From Biochemical Pathways to Complex Metabolic Networks

Biotechnology promises the biologically and ecologically sustainable production of commodity chemicals, biofuels, pharmaceuticals and other high-value products using industrial platform microorganisms. Metabolic engineering plays a key role in this process, providing the tools for targeted modifications of microbial metabolism to create efficient microbial cell factories that convert low value substrates to value-added chemicals. Engineering microbes for the bioproduction of chemicals has been practiced through three different approaches: (i) optimization of native pathways of a host organism; (ii) incorporation of heterologous pathways in an amenable organism; and finally (iii) design and introduction of synthetic pathways in an organism. So far, the progress that has been made in the biosynthesis of chemicals was mostly achieved using the first two approaches. Nevertheless, many novel biosynthetic pathways for the production of native and non-native compounds that have potential to provide near-theoretical yields and high specific production rates of chemicals remain yet to be discovered. Therefore, the third approach is crucial for the advancement of bio-based production of value-added chemicals. We need to fully comprehend and analyze the existing knowledge of metabolism in order to generate new hypotheses and design de novo pathways. In this thesis, through development and application of efficient computational methods, we took the research path to expand our understanding of cell metabolism with the aim to discover novel knowledge about metabolic networks. We analyze different aspects of metabolism through five distinct studies. In the first study, we begin with a holistic view of the enzymatic reactions across all the species, and we propose a computational approach for identifying all the theoretically possible enzymatic reactions based on the known biochemistry. We organize our results in a web-based database called âAtlas of biochemistryâ. In the second study, we focus on one of the most structurally diverse and ubiquitous constituents of metabolism, the lipid metabolism. Here we propose a computational framework for integrating lipid species with unknown metabolic/catabolic pathways into metabolic networks. In our next study, we investigate the full metabolic capacity of E. coli. We explore computationally all enzymatic potentials of this organism, and we introduce the âSuper E. coliâ, a new and advanced chassis for metabolic engineering studies. Our next contribution concentrates on the development of a new method for the atom-level description of metabolic networks. We demonstrate the significance of our approach through the reconstruction of atom-level map of the E. coli central metabolism. In the last study, we turn our focus on studying the thermodynamics of metabolism and we present our original approach for estimating the thermodynamic properties of an important class of metabolites. So far, the available thermodynamic properties either from experiments or the computational methods are estimated with respect to the standard conditions, which are different from typical biological conditions. Our workflow paves the way for reliable computing of thermochemical properties of biomolecules at biological conditions of temperature and pressure. Finally, in the conclusion chapter, we discuss the outlook of this work and the potential further applications of the computational methods that were developed in this thesis

Identification of transformation products of emerging contaminants during tertiary treatment processes and their disposal in the environment by mass spectrometric techniques

Η διαθέσιμη βιβλιογραφία έχει καταδείξει την ατελή απομάκρυνση των αναδυόμενων ρύπων κατά τη βιολογική επεξεργασία που λαμβάνει χώρα στα Κέντρα Επεξεργασίας Λυμάτων (ΚΕΛ). Παρόλο που ο πρωταρχικός ρόλος της τριτοβάθμιας επεξεργασίας είναι η εξάλειψη των μικρορύπων, το παρεχόμενο οξειδωτικό μέσο αντιδρά με τους αναδυόμενους ρύπους, καταλήγοντας στο σχηματισμό άγνωστων προϊόντων μετασχηματισμού. Ο κύριος στόχος της παρούσας διπλωματικής εργασίας ήταν η διερεύνηση της απομάκρυνσης αναδυόμενων ρύπων καθώς και η ταυτοποίηση των προϊόντων μετασχηματισμού τους, τα οποία παράγονται κατά τις μεθόδους απολύμανσης που εφαρμόζονται σαν τριτοβάθμια επεξεργασία στα ΚΕΛ. Αρχικά παρουσιάζεται μια εισαγωγή για τους αναδυόμενους ρύπους και τον πιθανό μετασχηματισμό τους κατά τη διάρκεια των διεργασιών που εφαρμόζονται στα ΚΕΛ, και ιδιαιτέρως κατά τη διάρκεια της χλωρίωσης και οζόνωσης. Στη συνέχεια παρουσιάζονται συγκεκριμένες πορείες εργασίας και τεχνικές σχετικές με την ταυτοποίηση αναδυόμενων ρύπων και των προϊόντων μετασχηματισμού τους, οι οποίες βασίζονται σε αναλύσεις φασματομετρίας μάζας υψηλής διακριτικής ικανότητας. Το πειραματικό μέρος της παρούσας διπλωματικής εργασίας αποτελείται από τα ακόλουθα τρία μέρη: (α) Χλωρίωση βενζοτριαζολών και βενζοθειαζολών και ταυτοποίηση των προϊόντων μετασχηματισμού τους με υγροχρωματογραφία συζευγμένη με φασματομετρία μάζας υψηλής διακριτικής ικανότητας (Κεφάλαιο 3), (β) Οζόνωση ρανιτιδίνης: επίδραση των πειραματικών συνθηκών και ταυτοποίηση προϊόντων μετασχηματισμού (Κεφάλαιο 4) και (γ) Απομάκρυνση και μετασχηματισμός της σιταλοπράμης και τεσσάρων προϊόντων βιομετατροπής της κατά τη διάρκεια πειραμάτων οζόνωσης (Κεφάλαιο 5). Είναι πεποίθησή μας πως οι μελέτες αυτές θα συνεισφέρουν στην περαιτέρω ανάπτυξη της περιβαλλοντικής ανάλυσης, διεγείροντας το ενδιαφέρον των κανονιστικών αρχών, σχετικά με την ενσωμάτωση επιβλαβών προϊόντων μετασχηματισμού, τα οποία παράγονται κατά την τριτοβάθμια επεξεργασία των ΚΕΛ, σε ελέγχους ρουτίνας.The incomplete removal of emerging pollutants during the biological treatment applied in WWTPs is highly indicated by the existing literature. Although the primary purpose of tertiary treatment processes is the elimination of micropollutants, the provided oxidant agent reacts with emerging pollutants, leading to the formation of unknown transformation products. The main objectives of this thesis were the investigation of the removal of emerging pollutants and the identification of their transformation products which are produced during the disinfection methods that are applied as tertiary treatment is WWTPs. Initially, an introduction on emerging pollutants and their probable transformation during the processes that are applied in WWTPs, and especially during chlorination and ozonation, is presented. Specific workflows and techniques for the identification of emerging contaminants and their transformation products, based on high-resolution mass spectrometric analysis, are then presented. The experimental section of this thesis is constituted of the following three parts: (i) Chlorination of benzothiazoles and benzotriazoles and transformation products identification by LC-HR-MS/MS (Chapter 3), (ii) Ozonation of ranitidine: effect of experimental parameters and identification of transformation products (Chapter 4) and (iii) Removal and transformation of citalopram and four of its biotransformation products during ozonation experiments (Chapter 5). It is our strong belief that these studies will constitute a step forward in environmental analysis, by arousing the regulatory authorities concern, regarding that harmful transformation products produced during tertiary treatment processes in WWTPs, should also be incorporated in routine monitoring