536 research outputs found

    Predicting Proteome-Early Drug Induced Cardiac Toxicity Relationships (Pro-EDICToRs) with Node Overlapping Parameters (NOPs) of a new class of Blood Mass-Spectra graphs

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    The 11th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryBlood Serum Proteome-Mass Spectra (SP-MS) may allow detecting Proteome-Early Drug Induced Cardiac Toxicity Relationships (called here Pro-EDICToRs). However, due to the thousands of proteins in the SP identifying general Pro-EDICToRs patterns instead of a single protein marker may represents a more realistic alternative. In this sense, first we introduced a novel Cartesian 2D spectrum graph for SP-MS. Next, we introduced the graph node-overlapping parameters (nopk) to numerically characterize SP-MS using them as inputs to seek a Quantitative Proteome-Toxicity Relationship (QPTR) classifier for Pro-EDICToRs with accuracy higher than 80%. Principal Component Analysis (PCA) on the nopk values present in the QPTR model explains with one factor (F1) the 82.7% of variance. Next, these nopk values were used to construct by the first time a Pro-EDICToRs Complex Network having nodes (samples) linked by edges (similarity between two samples). We compared the topology of two sub-networks (cardiac toxicity and control samples); finding extreme relative differences for the re-linking (P) and Zagreb (M2) indices (9.5 and 54.2 % respectively) out of 11 parameters. We also compared subnetworks with well known ideal random networks including Barabasi-Albert, Kleinberg Small World, Erdos-Renyi, and Epsstein Power Law models. Finally, we proposed Partial Order (PO) schemes of the 115 samples based on LDA-probabilities, F1-scores and/or network node degrees. PCA-CN and LDA-PCA based POs with Tanimoto’s coefficients equal or higher than 0.75 are promising for the study of Pro-EDICToRs. These results shows that simple QPTRs models based on MS graph numerical parameters are an interesting tool for proteome researchThe authors thank projects funded by the Xunta de Galicia (PXIB20304PR and BTF20302PR) and the Ministerio de Sanidad y Consumo (PI061457). González-Díaz H. acknowledges tenure track research position funded by the Program Isidro Parga Pondal, Xunta de Galici

    Using chemical structure and inocula characteristics to predictively model biodegradation rate

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    PhD ThesisPredictive biodegradation models [i.e. Quantitative Structure Biodegradation Relationship (QSBR) models] might be used as an alternative to current regulatory biodegradation tests to predict chemical persistence. Current models are mostly based on the results derived from regulatory Ready Biodegradability Tests (RBTs), which are highly variable and were not designed to provide half-life data and therefore fundamentally undermines efforts to reliably predict chemical persistence. Improvement to existing approaches for developing and verifying predictive models and their reliability, respectively, have been proposed, and the use of functional gene and 16S rRNA amplicon sequencing techniques towards identifying and quantifying the putative chemical degraders have been studied. Several QSBR models for aromatic chemicals were developed according to OECD principles. Models for mono-aromatic chemicals were verified and calibrated with experimentally determined rates (both from pure culture and natural mixed communities). Traditional test methods were combined with functional genes and 16S amplicon sequence analyses to develop a relationship between rate, chemical concentration and competent putative chemical degrader abundance. QSBR models for mono-aromatic chemicals were stable (R2 = 0.8924), robust (Q2LOO = 0.8718) and had good predictive ability (Q2F1 = 0.8829, Q2F2 = 0.8835, and Q2F3 = 0.9178). In these models, biodegradation rates were associated with electronic, lipophilic and steric descriptors, and thus provided information on the mechanisms of different rate-limiting steps associated with the biodegradation process. However, all the variation in biodegradation rates cannot be explained by the structure alone, the prevailing environmental conditions have a significant role in determining the extent of chemical degradation. Biodegradation rates (k) of chemicals in natural mixed communities were significantly correlated with the ratio of abundance of initial putative degrader abundances (X0) and the starting chemical concentration (C0) (Pearson correlation coefficient (r) > 0.9 and p-value < 0.05). Predictive models developed by relating k with X0 and C0 reliably predicted the rate of studied chemicals. Experimentally determined rates further formed the basis towards calibrating the developed QSBR models. The molecular analysis revealed that majority of identified putative chemical degraders were rare taxa, and their enrichment did not necessarily influence the overall biomass count of the microbial community, and therefore biodegradation models that only consider the overall biomass would not account for the kind of relationships found in this study. Application of 16S amplicon sequencing and functional gene analyses techniques in biodegradation studies will help in depth screening of diversity and function of microbial community in an inoculum and enables better understanding of biodegradation outcomes.funded by the Engineering and Physical Sciences Research Counci

    Marine Natural Products with Antifouling Activity

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    Marine fouling affects most man-made surfaces temporarily or permanently immersed in the sea, causing important economic costs. Intense research is aimed at methods for preventing or reducing fouling development. The most widespread solution to inhibit fouling is to make surfaces unsuitable for settlers by coating them with antifouling paints containing toxic compounds. Most such antifouling agents give undesirable effects on nontarget species, including commercially important ones. The search for new nontoxic antifouling technologies has become a necessity, particularly after the ban of organotin compounds such as tributyltin (TBT), once the most widespread and used antifouling agent. Alternative organic and metal-based biocides are now used in antifouling paints, but their possible toxic effects on the aquatic environment are not yet fully understood. A nontoxic alternative for antifouling protection comes from the possibility of adopting natural antifouling compounds that are and may be found in marine sessile invertebrates like sponges, bryozoans, corals, and tunicates and in marine microorganisms. Such metabolites can prevent their producers from being fouled on by other organisms or be responsible for specific metabolic functions that may interfere with biofouling species adhesion. As natural marine compounds, they may inhibit settlement through a nontoxic mechanism without adverse effects to the environment. Such compounds could be developed into active ingredients of new antifouling coatings. So far, a rather limited number of natural products antifoulants (NPAs) has been isolated from marine organisms, but a huge reservoir of compounds with potential antifouling activity is hidden in marine organisms. The Special Issue on Marine Natural Products with Antifouling Activity aims at the discovery of such compounds their activity, toxicity and potential application in environmentally friendly antifouling coatings

    QSAR model development for early stage screening of monoclonal antibody therapeutics to facilitate rapid developability

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    PhD ThesisMonoclonal antibodies (mAbs) and related therapeutics are highly desirable from a biopharmaceutical perspective as they are highly target specific and well tolerated within the human system. Nevertheless, several mAbs have been discontinued or withdrawn based either on their inability to demonstrate efficacy and/or due to adverse effects. With nearly 80% of drugs failing in clinical development mainly due to lack of efficacy and safety there arises an urgent need for better understanding of biological activity, affinity, pharmacology, toxicity, immunogenicity etc. thus leading to early prediction of success/failure. In this study a hybrid modelling framework was developed that enabled early stage screening of mAbs. The applicability of the experimental methods was first tested on chemical compounds to assess the assay quality following which they were used to assess potential off target adverse effects of mAbs. Furthermore, hypersensitivity reactions were assessed using Skimune™, a non-artificial human skin explants based assay for safety and efficacy assessment of novel compounds and drugs, developed by Alcyomics Ltd. The suitability of Skimune™ for assessing the immune related adverse effects of aggregated mAbs was studied where aggregation was induced using a heat stress protocol. The aggregates were characterised by protein analysis techniques such as analytical ultra-centrifugation following which the immunogenicity tested using Skimune™ assay. Numerical features (descriptors) of mAbs were identified and generated using ProtDCal, EMBOSS Pepstat software as well as amino acid scales for different. Five independent and novel X block datasets consisting of these descriptors were generated based on the physicochemical, electronic, thermodynamic, electronic and topological properties of amino acids: Domain, Window, Substructure, Single Amino Acid, and Running Sum. This study describes the development of a hybrid QSAR based model with a structured workflow and clear evaluation metrics, with several optimisation steps, that could be beneficial for broader and more generic PLS modelling. Based on the results and observation from this study, it was demonstrated incremental improvement via selection of datasets and variables help in further optimisation of these hybrid models. Furthermore, using hypersensitivity and cross reactivity as responses and physicochemical characteristics of mAbs as descriptors, the QSAR models generated for different applicability domains allow for rapid early stage screening and developability. These models were validated with external test set comprising of proprietary compounds from industrial partners, thus paving way for enhanced developability that tackles manufacturing failures as well as attrition rates.European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie actions grant agreemen

    QSAR models for the (eco-)toxicological characterization and prioritization of emerging pollutants: case studies and potential applications within REACH.

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    Under the European REACH regulation (Registration, Evaluation, Authorisation and Restriction of Chemical substances - (EC) No 1907/2006), there is an urgent need to acquire a large amount of information necessary to assess and manage the potential risk of thousands of industrial chemicals. Meanwhile, REACH aims at reducing animal testing by promoting the intelligent and integrated use of alternative methods, such as in vitro testing and in silico techniques. Among these methods, models based on quantitative structure-activity relationships (QSAR) are useful tools to fill data gaps and to support the hazard and risk assessment of chemicals. The present thesis was performed in the context of the CADASTER Project (CAse studies on the Development and Application of in-Silico Techniques for Environmental hazard and Risk assessment), which aims to integrate in-silico models (e.g. QSARs) in risk assessment procedures, by showing how to increase the use of non-testing information for regulatory decision-making under REACH. The aim of this thesis was the development of QSAR/QSPR models for the characterization of the (eco-)toxicological profile and environmental behaviour of chemical substances of emerging concern. The attention was focused on four classes of compounds studied within the CADASTER project, i.e. brominated flame retardants (BFRs), fragrances, prefluorinated compounds (PFCs) and (benzo)-triazoles (B-TAZs), for which limited amount of experimental data is currently available, especially for the basic endpoints required in regulation for the hazard and risk assessment. Through several case-studies, the present thesis showed how QSAR models can be applied for the optimization of experimental testing as well as to provide useful information for the safety assessment of chemicals and support decision-making. In the first case-study, simple multiple linear regression (MLR) and classification models were developed ad hoc for BFRs and PFCs to predict specific endpoints related to endocrine disrupting (ED) potential (e.g. dioxin-like activity, estrogenic and androgenic receptor binding, interference with thyroxin transport and estradiol metabolism). The analysis of modelling molecular descriptors allowed to highlight some structural features and important structural alerts responsible for increasing specific ED activities. The developed models were applied to screen over 200 BFRs and 33 PFCs without experimental data, and to prioritize the most hazardous chemicals (on the basis of ED potency profile), which have been then suggested to other CADASTER partners in order to focus the experimental testing. In the second case-study, MLR models have been developed, specifically for B-TAZs, for the prediction of three key endpoints required in regulation to assess aquatic toxicity, i.e. acute toxicity in algae (EC50 72h Pseudokirchneriella subcapitata), daphnids (EC50 48h Daphnia magna) and fish (LC50 96h Onchorynchus mykiss). Also in this case, the developed QSARs were applied for screening purposes. Among over 350 B-TAZs lacking experimental data, 20 compounds, which were predicted as toxic (EC(LC)50 64 10 mg/L) or very toxic (EC(LC)50 64 1 mg/L) to the three aquatic species, were prioritized for further experimental testing. Finally, in the third case-study, classification QSPR models were developed for the prediction of ready biodegradability of fragrance materials. Ready biodegradation is among the basic endpoints required for the assessment of environmental persistence of chemicals. When compared with some existing models commonly used for predicting biodegradation, the here proposed QSPRs showed higher classification accuracy toward fragrance materials. This comparison highlighted the importance of using local models when dealing with specific classes of chemicals. All the proposed QSARs have been developed on the basis of the OECD principles for QSAR acceptability for regulatory purposes, paying particular attention to the external validation procedure and to the statistical definition of the applicability domain of the models. QSAR models based on molecular descriptors generated by both commercial (DRAGON) and freely-available (PaDELDescriptor, QSPR-Thesaurus) software have been proposed. The use of free tool allows for a wider applicability of the here proposed QSAR models. Concluding, the QSAR models developed within this thesis are useful tools to support hazard and risk assessment of specific classes of emerging pollutants, and show how non-testing information can be used for regulatory decisions, thus minimizing costs, time and saving animal lives. Beyond their use for regulatory purposes, the here proposed QSARs can find application in the rational design of new safer compounds that are potentially less hazardous for human health and environment

    Technical report : the NIOSH occupational exposure banding process for chemical risk management

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    "Occupational exposure limits (OELs) play a critical role in protecting workers and emergency response personnel from exposure to dangerous concentrations of hazardous materials [Cook 1987; Deveau et al. 2015; Paustenbach 1998; Nikfar and Malekirad 2014; Schulte et al. 2010; Skowro\uf1 and Czerczak 2015]. In the absence of an OEL, determining the appropriate controls needed to protect workers from chemical exposures can be challenging. According to the U.S. Environmental Protection Agency (US EPA), the Toxic Substances Control Act (TSCA) Chemical Substance Inventory currently contains over 85,000 chemicals that are commercially available [US EPA 2015], yet only about 1,000 of these have been assigned an authoritative (government, consensus, or peer reviewed) OEL. Furthermore, the rate at which new chemical substances are being introduced into commerce significantly outpaces OEL development, creating a need for guidance on thousands of chemical substances that lack reliable exposure limits [OSHA 2014]. To protect worker health in the absence of an OEL, occupational hygienists and safety professionals use a variety of tools such as safety data sheets, exposure monitoring, medical surveillance, and toxicity testing to make risk management decisions. However, one of the challenges faced by occupational hygienists and safety professionals is that despite the myriad sources of data on chemical substances, they have no decision-making framework to screen and discriminate the most relevant data when assessing chemical substances and developing exposure control guidance. Occupational exposure banding, also known as hazard banding or health hazard banding, is a systematic process that uses qualitative and quantitative hazard information on selected health-effect endpoints to identify potential exposure ranges or categories. The National Institute for Occupational Safety and Health (NIOSH) occupational exposure banding process seeks to create a consistent and documented process with a decision logic to characterize chemical hazards so that timely, well-informed risk management decisions can be made for chemical substances that lack OELs. Users can band a chemical manually or by using the occupational exposure banding e-Tool. Overall, this document provides the background, rationale, and instructions for the occupational exposure banding process and gives guidance for risk managers to identify control levels for chemicals without authoritative OELs. Using hazard-based categories to communicate potential health concerns serves to signal workers and employers of the need for risk management. This concept is not new. Numerous hazard classification and category-based systems have seen extensive use in the occupational setting. Such systems are deeply embedded in occupational hygiene practice, particularly in the pharmaceutical industry [NIOSH 2009c; Naumann et al. 1996], and are also elements of well-developed, modern hazard communication programs such as the United Nations 2013 Globally Harmonized System of Classification and Labelling of Chemicals (GHS). The NIOSH occupational exposure banding process is distinguished from other hazard classification and category-based systems in several ways. The unique attributes of the NIOSH process include: (1) a three-tiered system that allows users of varying expertise to use the process; (2) determination of potential health impacts based on nine health endpoints separately; (3) hazard-based categories linked to quantitative exposure ranges; and (4) assessment of the process via extensive evaluation exercises to determine consistency of the occupational exposure banding process with OELs. Each tier of the process has different requirements for data sufficiency, which allows a variety of stakeholders to use the process in many different situations. The most appropriate tier for banding depends on the availability and quality of the data, how it will be used, and the training and expertise of the user. Whereas Tier 1 requires relatively little information and modest specialized training, each successive tier requires more chemical-specific data and more user expertise to successfully assign an occupational exposure band (OEB). A primary goal of Tier 1 is to give the user a quick summary of the most important health effects associated with exposure to the chemical substance of interest and to quickly identify toxic chemical substances that should be considered for substitution or elimination. Tier 1 would likely be most appropriate when banding a large number of chemical substances and deciding which ones to prioritize for elimination or substitution. In general, Tier 1 can be used as a quick screening method and should be completed first, prior to progressing to Tier 2. NIOSH recommends always progressing to Tier 2 if user expertise and data are available, even when Tier 1 banding has been completed. Tier 2 requires the user to examine a number of publicly available databases and extract relevant toxicological and weight-of-evidence data to be used in the NIOSH banding algorithm. Tier 3 employs a critical assessment to evaluate experimental data and discern toxicological outcomes. A general overview of the entire process is in the next section, Occupational Exposure Banding at a Glance. The NIOSH occupational exposure banding process considers the totality of the information across all of the nine standard toxicological health endpoints: (1) carcinogenicity; (2) reproductive toxicity; (3) specific target organ toxicity; (4) genotoxicity; (5) respiratory sensitization; (6) skin sensitization; (7) acute toxicity; (8) skin corrosion and irritation; and (9) eye damage/irritation. The process looks at each health endpoint separately for each chemical substance, and the endpoint bands allow the user to make judgements about which health effects are the primary concerns for workers who are exposed. This type of specificity allows users to customize their control strategies on the basis of potency of the chemical substance and the target organ/health effect. In addition, the banding process considers multiple routes of exposure (e.g., inhalation, dermal, eye, and oral) to determine the overall OEB. Another important component of the NIOSH occupational exposure banding process is the five exposure bands. Occupational exposure banding uses limited chemical toxicity data to group chemical substances into one of five bands, ranging from A through E. These bands, or OEBs, define the range of air concentrations expected to protect worker health. Band E represents the lowest exposure concentration range recommendation, whereas band A represents the highest exposure concentration range [McKernan et al. 2016]. Users should note that throughout this document, bands that represent lower exposure ranges are assigned to more potent/toxic chemical substances than bands that represent higher exposure ranges. One major benefit of occupational exposure banding is that the amount of time and data required to categorize a chemical substance into an OEB is far less than that required to develop an OEL. An OEB is not meant to replace an OEL; rather, it serves as a starting point to inform risk management decisions when an OEL is not available. An OEB can also assist with prioritizing chemical substances for which an OEL should be developed and can guide users, including enterprises of all sizes, in setting internal OEBs or ranges for controlling exposures to specific chemical substances. The NIOSH occupational exposure banding process is one approach or tool for assessing chemical hazards and prioritizing control efforts. Occupational hygienists have several tools in their toolbox to protect and improve occupational health in the workplace. Likewise, there are several components in a comprehensive occupational safety and health program. For example, exposure monitoring, medical surveillance, engineering controls, OELs, quantitative risk assessments, and personal protective equipment are all tools routinely used. Occupational exposure banding is an additional tool for professionals to consider. Although occupational exposure banding will not solve every problem or address every need, it will be a helpful addition to the occupational hygiene toolbox because it provides a blueprint for making risk management decisions. NIOSH has performed evaluation exercises to assess consistency of the occupational exposure banding process with OELs. To evaluate the Tier 1 process, NIOSH compared the OELs of 606 chemical substances to the derived Tier 1 band for those chemical substances. This evaluation found that the NIOSH Tier 1 banding process resulted in a band that included the OEL or was more stringent than the OEL for 91% of chemical substances. Five iterative phases of Tier 2 reliability testing were performed to assess Tier 2 as the process evolved. These assessments involved over 130 chemical substances with OELs. Results of these evaluations show that Tier 2 OEBs are highly likely to be at least as stringent as OELs. Tier 2 OEBs include the OEL or are more stringent than the OEL for 98% of chemical substances tested. Comparing OEBs with OELs is not an appropriate comparison, given several considerations. OEBs are completely health-based concentration ranges derived from the totality of the toxicity information available for a specific chemical substance. OELs, by contrast, are derived with additional considerations, including possible adjustments for analytical feasibility, engineering control achievability, and in some cases economic factors. Consequently, given these additional adjustments for OELs, the OEBs and OELs will not always align perfectly. Overall, however, the results of the evaluation exercises demonstrate that the occupational exposure banding process is accurate and reproducible and can be a useful tool for evaluating chemical substances that do not have OELs. Although the occupational exposure banding process was developed for all chemical substances that lack OELs, it should not be applied to some, such as pharmaceutical drugs and radioisotopes. This document details other situations that warrant special consideration, such as banding nanomaterials or mixtures of two or more chemical substances. A substantial number of chemical substances lack authoritative OELs, and risk management guidance is needed for these. Occupational exposure banding is one additional tool that can provide such guidance. An OEB provides a range of air concentrations that is expected to be protective of worker health. The process and adherence to the resultant OEB are voluntary and are not required or tied to any legal obligations. This document details the use and application of the occupational exposure banding process and provides a summary of efforts taken to evaluate its comparability to OELs and usability." - NIOSHTIC-2NIOSHTC no. 20056369Suggested citation: NIOSH [2019]. Technical report: The NIOSH occupational exposure banding process for chemical risk management. By Lentz TJ, Seaton M, Rane P, Gilbert SJ, McKernan LT, Whittaker C. Cincinnati, OH: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, DHHS (NIOSH) Publication No. 2019-132, https://doi.org/10.26616/NIOSHPUB2019132201910.26616/NIOSHPUB2019132645

    Computational Approaches to Understanding the Structure, Dynamics, Functions, and Mechanisms of Various Bacterial Proteins

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    The 3D structure of a protein can be fundamentally useful for understanding protein function. In the absence of an experimentally determined structure, the most common way to obtain protein structures is to use homology modeling, or the mapping of the target sequence onto a closely related homolog with an available structure. However, despite recent efforts in structural biology, the 3D structures of many proteins remain unknown. Recent advances in genomic and metagenomic sequencing coupled with coevolution analysis and protein structure prediction have allowed for highly accurate models of proteins that were previously considered intractable to model due to the lack of suitable templates. Structural models obtained from homology modeling, coevolution-based modeling, or crystallography can then be used with other computational tools such as small molecule docking or molecular dynamics (MD) simulations to help understand protein function, dynamics, and mechanism.Here coevolution-based modeling was used to build a structural model of the HgcAB complex involved in mercury methylation (Chapter I). Based on the model it was proposed that conserved cysteines in HgcB are involved in shuttling mercury, methylmercury, or both. MD simulations and docking to a homology model of E. coli inosine monophosphate dehydrogenase (IMPDH) provided insights into how a single amino acid mutation could relieve inhibition by altering protein structure and dynamics (Chapter II). Coevolution-based structure prediction was also combined with docking, and experimental activity data to generate machine learning models that predict enzyme substrate scope for a series of bacterial nitrilases (Chapter III). Machine learning was also used to identify physicochemical properties that describe outer membrane permeability and efflux in E. coli and P. aeruginosa and new efflux pump inhibitors for the E. coli AcrAB-TolC efflux pump were identified using existing physicochemical guidelines in combination with small molecule docking to a homology model of AcrA (Chapter IV). Lastly, quantum mechanical/molecular mechanical simulations were used to study the mechanism of a key proton transfer step in Toho-1 beta-lactamase using experimentally determined structures of both the apo and cefotaxime-bound forms. These simulations revealed that substrate binding promotes catalysis by enhancing the favorability of this initial proton transfer step (Chapter V)
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