Academic literature on the topic 'Chemo-Informatics'

Abstract The current study aimed to design and synthesize a novel series of fluorophenyl-isoxazole-carboxamide derivatives and evaluate their antiproliferative activities. Anticancer activities of the novel compounds were evaluated by MTS assay against four cancer cell lines, including liver (Hep3B, HepG2), cervical (HeLa), and breast (MCF-7), and α-fetoprotein tumor marker, cell cycle analysis, and annexin V tests. Chemo-informatics analysis showed that all synthesized derivatives 2a–2f obeyed Lipinski’s rule. Compound 2f was the most potent compound against Hep3B and Hep-G2 cancer cell lines with IC50 values of 5.76 and 34.64 µg/mL, respectively. Moreover, compounds 2a–2c and 2e showed potent inhibitory activity against Hep3B with an IC50 value range of 7.66–11.60 µg/mL. Hep3B secretions of α-fetoprotein (α-FP) results showed that compound 2f reduced the secretion of Hep3B to 168.33 ng/mL and compound 2d reduced the secretion to value approximately 598.33 ng/mL, in comparison with untreated cells’ value of 1116.67 ng/mL. Furthermore, cell cycle analysis showed that the 2f compound induced arrest in the G2-M phase in 6.73% of the total cells and that was lower than the activity of the positive control doxorubicin (7.4%). Moreover, 2b and 2f compounds reduced the necrosis rate of Hep3B to 4-folds and shifted the cells to apoptosis.

The rapid dispersion of new psychoactive substances (NPS) presents challenges to customs services and analytical laboratories, which are involved in their detection and characterization. When the seized material is limited in quantity or of a complex nature, or when the target substance is present in very small amounts, the need to use advanced analytical techniques, efficient workflows and chemo-informatics tools is essential for the complete identification and elucidation of these substances. The current work describes the application of such a workflow in the analysis of a single blotter paper, seized by Swedish customs, that led to the identification of a lysergic acid diethylamide (LSD) derivative, 1-butyl-lysergic acid diethylamide (1B-LSD). Such blotter paper generally contains an amount in the range of 30–100 ug. This substance, which is closely related to 1-propionyl-lysergic acid diethylamide (1P-LSD), seems to have only recently reached the drug street market. Its identification was made possible by comprehensively combining gas chromatography with mass spectrometry detection (GC–MS), liquid chromatography coupled with high-resolution tandem MS (LC–HR-MS/MS), Orbitrap-MS and both 1D and 2D nuclear-magnetic-resonance (NMR) spectroscopy. All the obtained data have been managed, assessed, processed and evaluated using a chemo-informatics platform to produce the effective chemical and structural identification of 1B-LSD in the seized material.

Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.

315 Background: While most primary care practices have informatics infrastructures to support population management, such infrastructures are not commonplace in oncology. Yet, value-based care requires that oncology practices be able to identify patients in real time, use risk stratification to target care efficiently, and monitor care quality to identify improvement opportunities. We describe an oncology informatics infrastructure development initiative in a large academic medical center. Methods: We convened a quality improvement team of administrators, analysts, clinicians, health services researchers and performance improvement staff. The team was sponsored by a senior leadership committee convened for a strategic planning initiative. We used PDSA cycles to develop and test ways to leverage data from an electronic health record (EHR) and billing system for oncology patient identification, risk stratification, and routine quality monitoring. We used clinician engagement, medical record review, and tumor registry comparisons to validate query strategies. Results: After considering different query strategies, we opted to identify patients via a new cancer treatment episode (as defined by a cancer diagnosis combined with evidence of pharmaceutical, radiation, and/or surgical treatment for cancer with no evidence of such treatment in the prior six months). This was done using diagnostic and procedural codes for chemo/immunotherapy and radiation treatment, and pathology reports and procedural codes for surgery. Using this approach, we identified over 7800 cancer treatment episodes within the health system in 2018. These episodes corresponded to 4178 chemo/immunotherapy, 1437 radiation, and 3440 surgical treatments. Quality monitoring has identified opportunities to enhance data capture, harmonize documentation processes across practitioners and practices, and initiate quality improvement efforts. Conclusions: Using data from the EHR and billing systems we are able to identify oncology patients as they initiate a cancer treatment episode. In so doing, we are able to track the quality of care delivered to oncology patients as they move across the care continuum from treatment to survivorship.

>Magister Scientiae - MSc
Leonotis leonurus (L. Leonurus) has relatively abundant diterpenes and has been used as a traditional herbal medicine for treating several ailments including influenza, muscular cramps, skin related diseases, menstrual, antilipidemic, hyperglycaemia and hypertension. In this study, diterpenoid compounds such as; Dubiin, SaponifiedDubiin, Hispanol, Marrubiin and DC9 were isolated from L. Leonurus plant. The cardiovascular effects of these isolated compounds were investigated in order to determine the response of anaesthetised normotensive Wistar rats (in-vivo) to the compounds. Also, the druglikeness of the isolated diterpenoid compounds and their binding interaction with β1 adrenoceptor (PDB: 2Y04), angiotensin II receptor (Ang II) (PDB: 3R8A), Angiotensin converting enzyme (ACE) (PDB: 4XX3), and renin receptor (PDB: 2X8Z) by using molecular docking methods and Chemoinformatics analysis was performed (in-silico). Important molecular descriptors and molecular docking were used in our Chemoinformatics (in-silico) analysis to study the druglikeness and the binding affinity for of each molecule (Dubiin, SaponifiedDubiin, Hispanol, Marrubiin and DC9). The molecular descriptors and the binding energy were calculated by using the molecular operating environment software (MOE 2013). The lowest energy and highest cluster conformations of the molecules were further analysed. All the five (5) diterpenoids were predicted to have good oral bioavailability after oral administration and passed the BloodBrain Barrier (BBB) rules. Also, the compounds were predicted to have high probability of being good Druglike candidates, except for DC9, which is predicted to have lower possibilities of being Druglike candidate than the other diterpenoids. Furthermore, these compounds (Dubiin, SaponifiedDubiin, Hispanol, Marrubiin and DC9) were shown to interact with β1 adrenoceptors in-silico, an interaction that was confirmed in-vivo by increases in Blood pressure (SP, DP and MAP) and Heart rate (HR). In anaesthetized normotensive male Wistar rats (in-vivo), Dubiin (0.5 40mg/kg; IV), SaponifiedDubiin (0.5 60mg/kg; IV) Hispanol (0.5 40mg/kg; IV), DC9 (0.5 40mg/kg; IV) and Marrubiin (0.5 40mg/kg; IV) produced dose dependent increase in Systolic pressure (SP), Diastolic pressure (DP), and Mean arterial pressure (MAP) at all doses. Also, the compounds produced dose dependent increase in Heart rate (HR). From the in-vivo and in-silico studies it can be concluded that all the five (5) isolated diterpenoid compounds showed cardiovascular effects on Blood pressure (BP) and Heart rate (HR) by acting as β1 adrenoceptor agonists. Also, these diterpenoids compounds could be responsible for the cardiovascular effect observed in the methanol extracts from previous studies. These cardioactive compounds are prototype or ''lead compounds'' for designing and developing new nontoxic and effective drugs for cardiovascular disease (CVD) treatment.

Un plan de synthèse est, pour une molécule donnée, une séquence de réactions permettant de la produire à partir de molécules commercialisées ou facilement synthétisables. En chémoinformatique, prédire ou aider à la conception de plans de synthèse pour de nouvelles molécules est un défi. Cela consiste à analyser les très grandes bases de données de réactions moléculaires existantes pour construire de nouveaux plans de synthèse à partir de plans existants pour des molécules similaires. Dans ce contexte, la similarité entre molécules repose sur la topologie.Nous introduisons une représentation structurelle des molécules appelée graphe de cycles. Cette représentation est basée sur les cycles du graphe moléculaire et leurs interconnexions.Cette représentation canonique permet de définir une mesure de similarité entre les structures de molécules. Elle nécessite un temps de calcul raisonnable. Nos études montrent qu’elle est plus adaptée pour les travaux sur les plans de synthèse que les autres mesures de similarité existantes.À partir des graphes des cycles, nous proposons une classification des réactions en fonction des effets sur la structure des molécules. Il s'agit de la première étape pour la prédiction des plans de synthèse
A synthesis pathway is, for a given molecule, a sequence of reactions making possible to obtain it from purchasable molecules or easily synthesizable. In chemoinformatics, predicting or assisting the conception of synthesis pathways for new molecules is a challenge. It consists in analyzing the very large databases of existing molecular reactions to build new synthesis pathways from existing plans of similar molecules. In this context, the similarity between molecules relies on their topology.We introduce a structural representation of molecules called the graph of cycles. This representation is based on the cycles in the molecular graph and their interconnections.This representation is canonical and allows us to define a similarity measure between structures of molecules and is computable in a reasonable amount of time. Our studies show that it is more adapted for works on synthesis pathways than the other existing similarity measures.Based on the graph of cycles, we proposed a classification of reactions according to the effects on the structure of molecules. This is the first step for the prediction of synthesis pathways

L’objectif de cette thèse est d’analyser et prédire les conformations d’un système moléculaire en combinant la théorie des graphes et la chimie computationnelle.Dans le cadre des simulations de dynamique moléculaire, une molécule peut avoir une ou plusieurs conformations au cours du temps. Dans les trajectoires de simulation de dynamique moléculaire, on peut avoir des trajectoires n’explorant qu’une seule conformation ou des trajectoires explorant plusieurs conformations, donc plusieurs transitions entre conformations sont observées. L’exploration de ces conformations dépend du temps de la simulation et de l'énergie (température) fixée dans le système. Pour avoir une bonne exploration des conformations d’un système moléculaire, il faut générer et analyser plusieurs trajectoires à différentes énergies. Notre objectif est de proposer un algorithme universel qui permet d’analyser la dynamique conformationnelle de ces trajectoires d’une façon rapide et automatique. Les trajectoires fournissent les positions cartésiennes des atomes du système moléculaire à des intervalles de temps réguliers. Chaque intervalle contenant un ensemble de positions est appelé image. L’algorithme utilise des règles de géométrie (distances, angles, etc.) sur les positions pour trouver les liaisons (liaisons covalentes, liaisons hydrogène et interactions électrostatiques) créées entre les atomes, permettant par la suite d’obtenir le graphe mixte qui modélise une conformation. Nous ne considérons un changement conformationnel que s’il y a un changement dans les liaisons calculées à partir des positions données. L’algorithme permet de donner l’ensemble des conformations explorées sur une ou plusieurs trajectoires, la durée d’exploration de chaque conformation, ainsi que le graphe de transitions qui contient tous les changements conformationnels observés.Les conformations se caractérisent par une énergie appelée énergie potentielle. Cette énergie est représentée par une courbe appelée surface d’énergie potentielle. En chimie théorique et computationnelle, certains s’intéressent à trouver des points particuliers sur cette surface. Il s'agit des minima qui représentent les conformations les plus stables et des maxima ou états de transition qui représentent les points de passage d'une conformation à une autre. En effet, d'une part, la conformation la plus stable est celle de plus basse énergie. D'autres part, pour aller d’une conformation à une autre il faut une énergie supplémentaire, le point maximum représente l'état de transition. Les méthodes développées pour calculer ces points nécessitent une connaissance de l’énergie potentielle ce qui est coûteux en temps et en calculs. Notre objectif est de proposer une méthode alternative en utilisant des mesures ah doc basées sur des propriétés des graphes qu’on a utilisées dans le premier algorithme et sans faire appel à la géométrie ni aux calculs moléculaires. Ces mesures permettent de générer des conformations avec un classement énergétique ainsi de définir le coût énergétique de chaque transition permise. Les conformations possibles avec les transitions représentent respectivement les sommets et les arcs de ce qu’on appelle le “graphe des possibles”. Les hypothèses utilisées dans le modèle proposé est que seules les liaisons hydrogène peuvent changer entre les conformations et que le nombre de liaisons hydrogène présentes dans le système permet de déterminer son coût énergétique.L’algorithme d'analyser des trajectoires a été testé sur trois types de systèmes moléculaires en phase gazeuse de taille et de complexité croissantes. Bien que la complexité théorique de l’algorithme est exponentielle (tests d’isomorphisme) les résultats ont montré que l’algorithme est rapide (quelques secondes). De plus, cet algorithme peut être facilement adapté et appliqué à d’autres systèmes. Pour la prédiction conformationnelle, le modèle proposé a été testé sur des peptides isolés
This PhD is part of transdisciplinary works, combining graph theory and computational chemistry.In molecular dynamics simulations, a molecular system can adopt different conformations over time. Along a trajectory, one conformation or more can thus be explored. This depends on the simulation time and energy within the system. To get a good exploration of the molecular conformations, one must generate and analyse several trajectories (this can amount to thousands of trajectories). Our objective is to propose an automatic method that provides rapid and efficient analysis of the conformational dynamics explored over these trajectories. The trajectories of interest here are in cartesian coordinates of the atoms that constitute the molecular system, recorded at regular time intervals (time-steps). Each interval containing a set of positions is called a snapshot. At each snapshot, our developed algorithm uses geometric rules (distances, angles, etc.) to compute bonds (covalent bonds, hydrogen bonds and any other kind of intermolecular criterium) formed between atoms in order to get the mixed graph modelling one given conformation. Within our current definitions, a conformational change is characterized by either a change in the hydrogen bonds or in the covalent bonds. One choice or the other depends on the underlying physics and chemistry of interest. The proposed algorithm provides all conformations explored along one or several trajectories, the period of time for the existence of each one of these conformations, and also provides the graph of transitions that shows all conformational changes that have been observed during the trajectories. A user-friendly interface has been developed, that can de distributed freely.Our proposed algorithm for analysing the trajectories of molecular dynamics simulations has been tested on three kinds of gas phase molecular systems (peptides, ionic clusters). This model can be easily adapted and applied to any other molecular systems as well as to condensed matter systems, with little effort. Although the theoretical complexity of the algorithm is exponential (isomorphism tests), results have shown that the algorithm is rapid.We have also worked on computationally low cost graph methods that can be applied in order to pre-characterize specific conformations/points on a potential energy surface (it describes the energy of a system in terms of positions of the atoms). These points are the minima on the surface, representing the most stable conformations of a molecular system, and the maxima on that surface, representing transition states between two conformers. Our developed methods and algorithms aim at getting these specific points, without the prerequisite knowledge/calculation of the potential energy surface by quantum chemistry methods (or even by classical representations). By avoiding an explicit calculation of the potential energy surface by quantum chemistry methods, one saves computational time and effort. We have proposed an alternative method using ad doc measures based on properties of the graphs (already used in the first part of the PhD), without any knowledge of energy and/or molecular calculations. These measures allow getting the possible conformations with a realistic energy classification, as well as transition states, at very low computational cost. The algorithm has been tested on gas phase peptides

The study aims to develop machine-learning models to predict cardiac adverse events in female breast cancer patients who receive adjuvant therapy. We selected breast cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2004 and December 2020. Patients were monitored at the date of prescribed chemo- and/or -target therapies until cardiac adverse events occurred during a year. Variables were used, including demographics, comorbidities, medications, and lab values. Logistics regression (LR) and artificial neural network (ANN) were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 1321 patients (an equal 15039 visits) were included. The best performance of the artificial neural network (ANN) model was achieved with the AUC, precision, recall, and F1-score of 0.89, 0.14, 0.82, and 0.2, respectively. The most important features were a pre-existing cardiac disease, tumor size, estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), cancer stage, and age at index date. Further research is necessary to determine the feasibility of applying the algorithm in the clinical setting and explore whether this tool could improve care and outcomes.

For the last two decades, breakthrough research has been going on in all aspects of materials science at accelerated pace. New materials of unprecedented functionality and performance are being developed and characterized. Moreover, the new materials with improved functionality are in high demand in the marketplace and this need increases in an exponential way for the new materials of desired functionality and performance. Here we show the application of combined computational and cheminformatics methods in various materials properties prediction, including organometallic materials, polymeric materials and nanomaterials. Since most of the materials are complex entities from a chemical point of view, the investigation of them requires an interdisciplinary approach, involving multiple aspects ranging from physics and chemistry to biology and informatics. In this report we show how the combination of computational chemistry, available experimental data, machine learning and cheminformatics approaches can help in materials research and properties assessment, such as physico-chemical properties, toxicity, and biological activity. We discuss here a few case studies where data-driven models developed to reveal the relationships between the physicochemical properties, biological activity and structural characteristics, by application quantum chemical, protein-ligand docking, cheminformatics approaches and developed nanodescriptors.