Exact and Effective Pair-Wise Potential for Protein-Ligand Interactions Obtained from a Semiempirical Energy Partition

In this work, the partition method introduced by Carvalho and Melo was used to study the complex between Cucurbita maxima trypsin inhibitor (CMTI-I) and glycerol at the AM1 level. An effective potential, combining non-bonding and polarization plus charge transfer (PLCT) terms, was introduced to evaluate the magnitude of the interaction between each amino acid and the ligand. In this case study, the nonbonding–PLCT non-compensation characterizes the stabilization energy of the association process in study. The main residues (Gly29, Cys3 and Arg5) with net attractive effects and Arg1 (with a net repulsive effect), responsible by the stability of protein-ligand complex, are associated with large nonbonding energies non-compensated by PLCT effects. The results obtained enable us to conclude that the present decomposition scheme can be used for understanding the cohesive phenomena in proteins

Review of Computational approaches for predicting the physicochemical and biological properties of nanoparticles

In the growing field of nanotechnology there is a need to determine the physicochemical and potential toxicological properties of nanomaterials since many industrial, medical and consumer applications are based on an understanding of these properties and on a controlled exposure to the materials. This document provides a literature review on the current status of computational studies aimed at predicting the physicochemical properties and biological effects (including toxicity) of nanomaterials, with an emphasis on medical applications. Although a number of models have been published for physicochemical property prediction, very few models have been published for predicting biological effects, toxicity or the underlying mechanisms of action. This is due to two main reasons: a) nanomaterials form a colloidal phase when in contact with biological systems making the definition and calculation of properties (descriptors) suitable for the prediction of toxicity a new and challenging task, and b) nanomaterials form a very heterogeneous class of materials, not only in terms of their chemical composition, but also in terms of size, shape, agglomeration state, and surface reactivity. There is thus an urgent need to extend the traditional structure-activity paradigm to develop methods for predicting the toxicity of nanomaterials, and to make the resulting models readily available. This document concludes by proposing some lines of research to fill the gap in knowledge and predictive methodologyJRC.I.6-Systems toxicolog

Review of Data Sources, QSARs and Integrated Testing Strategies for Aquatic Toxicity

This review collects information on sources of aquatic toxicity data and computational tools for estimation of chemical toxicity aquatic to aquatic organisms, such as expert systems and quantitative structure-activity relationship (QSAR) models. The review also captures current thinking of what constitutes an integrated testing strategy (ITS) for this endpoint. The emphasis of the review is on the usefulness of the models and for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), which entered into force on 1 June 2007. Effects on organisms from three trophic levels (fish, Daphnia and algae) were subject of this review. In addition to traditional data sources such as databases, papers publishing experimental data are also identified. Models for narcoses, general (global) models as well as models for specific chemical classes and mechanisms of action are summarised. Where possible, models were included in a form allowing reproduction without consultation with the original paper. This review builds on work carried out in the framework of the REACH Implementation Projects, and was prepared as a contribution to the EU funded Integrated Project, OSIRIS.JRC.I.3-Toxicology and chemical substance

Evaluation of pesticide toxicity: a hierarchical QSAR approach to model the acute aquatic toxicity and avian oral toxicity of pesticides

The thesis aimed to extract information relevant to the hazard and risk assessment of pesticides. In particular, quantitative structure-activity relationship (QSAR) approaches have been used to build up a mathematical model able to predict the aquatic acute toxicity, LC50, and the avian oral toxicity, LD50, for pesticides. Ecotoxicological values were collected from several databases, and screened according to quality criteria. A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures were encoded into molecular descriptors by an automated, seamless procedure available within the OpenMolGRID system. Different linear and non-linear regression techniques were used to obtain reliable and thoroughly validated QSARs. The final model was developed by a counter-propagation neural network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with McFarland's principle for biological activity and makes use of seven molecular descriptors. The model was assessed thoroughly in test (R2 = 0.8) and validation sets (R2 = 0.72), the y-scrambling test and a sensitivity/stability test. The second endpoint considered in this thesis was avian oral toxicity. As previously, the chemical description of chemicals was generated automatically by the OpenMolGRID system. The best classification model was chosen on the basis of the performances on a validation set of 19 data points, and was obtained from a support vector machine using 94 data points and nine variables selected by genetic algorithms (Error Ratetraining = 0.021, Error Ratevalidation = 0.158). The model allowed for a mechanistic estimation of the toxicological action. In fact, several descriptors selected for the final classification model encode for the interaction of the pesticides with other molecules. The presence of hetero-atoms, e.g. sulphur atoms, is correlated with the toxicity, and the pool of descriptor selected is generally dependent from the 3D conformation of the structures. These suggest that, in the case of avian oral toxicity, pesticides probably exert their toxic action through the interaction with some macromolecule and/or protein of the biological system

The impact of algal toxicity on life-cycle impact assessment of plastic additives and the potential of using QSAR predictions to fill the algae data gap

Purpose There is a need to find a quick way to assess the impacts of the growing amount of globally manufactured and emitted chemical substances. This paper evaluates the use of Quantitative Structure Activity Relationships (QSAR) for predicting environmental effects of plastic additives in Life Cycle Impact Assessment (LCIA). It also evaluates the impact on so called Characterization Factors (CF) when including toxicity on algae as opposed to only chordate and arthropod. Method A review concluded that few (39) toxicity data for algae (experimental and QSAR predicted) were available for the 159 plastic additives of concern. To fill the data gap, a QSAR for algal toxicity was constructed that was able to predict toxicity for 54 substances. CFs were calculated and assessed based on; 1. QSAR predicted data for arthropod and chordate, 2. QSAR predicted data for arthropod, chordate and algae and 3. Experimental data for all three phyla. Results and discussion CFs could be calculated considering algal toxicity for totally 97 out of the 159 substances. Algae were overall less sensitive to the substances leading to lower CFs when it was included. The correlation between the effect data of algae and the other two phyla was very small resulting in an altered internal rank when algal data was included. Conclusions & recommendations - The sensitivity of the species varied both between phyla and between substances. - The inclusion of algal effect data did alter the internal rank of the resulting CFs although not extensively. - Algae generally exhibited lower sensitivity to the additives. Not including algae in LCIA studies might therefore result in more conservative CFs.Användning av QSAR vid screening av kemikalier - besparar oss tid, pengar och djurs lidande Spridning av kemikalier i miljön är ett växande globalt problem. För att snabbt kunna minska eller byta ut de kemikalier som utgör störst hot mot människor och miljö måste man först identifiera de mest skadliga. Detta är dock ingen enkel uppgift eftersom modellerna man använder kräver stora mängder data som ofta inte finns tillgänglig. QSAR-metodik kan vara en lösning på problemet. Karaktäriseringsmodeller som används för att ge ett mått på kemikaliers ”giftighet” kräver en mängd fysiokemisk data som t.ex. vattenlöslighet, molekylvikt, nedbrytningshastiget m.m. ”USEtox” som är den modell som använts i den här studien kräver dessutom uppgifter på toxicitet för arter inom minst tre olika fyla (stammar). För akvatisk toxicitet vill man, baserat på EU-direktiv, helst använda fisk, djurplankton och alger. Det har dock visat sig vara ont om akvatisk toxicitetsdata, speciellt på alger. Det är här användandet av QSAR-modeller kommer in. QSAR står för Quantitative stucture-activity relationship och bygger på antagandet att kemikalier med liknande struktur har liknande aktivitet. Inom toxikologin använder man sig av QSAR genom att ”träna” modeller med värden på kemikalier med kända toxiska effekter och sedan låta modellerna förutspå effekten hos andra kemikalier. Metoden kan spara både tid och pengar samt minska behoven av att testa på djur. Studien som gjorts hade följaktligen två syften; det ena var att se hur väl QSAR prediktioner kan ersätta testade toxicitetsvärden, det andra var att se hur viktigt det egentligen är att inkludera algdata eftersom den är så otillräcklig. Studien En jämförande studie gjordes på 159 kemikalier som används som tillsatser i plast. Med hjälp av tre olika dataset togs karaktäriserings faktorier (KF), som alltså är ett sammanlagt mått på ”giftighet”, fram i USEtox modellen. Dataset (DS) 1 och 2 innehöll toxicitetsdata framtagen med QSAR; DS 1; utan alg och DS 2; med alg. DS 3 innehöll experimentellt framtagen data. Först jämfördes KF som baserat på DS 1 och 2 (med och utan algdata) och sedan jämfördes dessa med KF som baserats på experimentell data (DS 3). Resultaten visade att då algdata var inkluderad blev KF generellt lägre, alltså kemikalien fick en lägre prioritering jämfört vad den fick utan algdata. Man kunde också se att grunden till detta var att alg generellt klarade högre doser av de flesta av kemikalierna än de andra arterna. När KF som baserats på experimentella värden jämfördes med KF baserade på QSAR visade det sig att det fanns positiva samband samt att QSAR-baserade värden där alg var inkluderad var mer lika de KF som baserats på experimentella värden. Sammanfattningsvis visar studien att känsligheten mellan arter och fyla kan skilja sig åt och belyser vikten av att inkludera arter med olika biologi för att kunna göra korrekta bedömningar och prioriteringar för kemikalier. Den visar också att QSAR tycks vara ett bra alternativ då experimentell data inte finns tillgänglig. Det krävs däremot mer omfattande studier för att kunna dra några säkra slutsatser. Hur bra prediktioner en QSAR-modell gör beror också på om det är en generell modell eller mer specifik. De mer specifika modellerna gör oftast mer säkra bedömningar men kan å andra sidan inte användas för så varierande ämnen

Environmental Photoinduced Toxicity of Polycyclic Aromatic Hydrocarbons: Occurrence and Toxicity of Photomodified PAHs and Predictive Modeling of Photoinduced Toxicity

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental contaminants known for their photoinduced toxicity. There are two mechanisms through which this may occur: photosensitization and photomodification. Photosensitization generally leads to the production of singlet oxygen, a reactive oxygen species (ROS), which is highly damaging to biological molecules. Photomodification of PAHs, usually via oxygenation, results in the formation of new compounds (oxyPAHs), and can occur under environmentally relevant levels of actinic radiation. PAHs and oxyPAHs readily adsorb to the organic phase of particulate matter in the environment such as sediments. It is logical to conclude that sediment transport will also facilitate the transport of these contaminants, and it has been shown that in the course of transport, degradative processes evoke a change in the profile of the PAHs present. Sediment samples taken along a transect from Hamilton Harbour were fractionated, and analyzed using a 2D HPLC method. All sediments contained intact and modified PAHs, although a marked change was noted in the profile of compounds present in the samples, which differ in distance from shore. Fractions of sediment extract were tested for toxicity using a bacterial respiration assay. Toxicity was observed in fractions containing modified PAHs, and was similar to that of intact PAH-containing fractions. Subsequently, the toxicities of 16 intact PAHs were assessed to Daphnia magna under two ultraviolet radiation (UV) conditions. The toxicity of intact PAHs generally increased in the presence of full spectrum simulated solar radiation (SSR), relative to visible light plus UVA only. To expand the existing data on the effects of PAH photoproducts to animals, fourteen oxyPAHs were also assayed with D. magna, most of which were highly toxic without further photomodification. The data presented highlight the effects of UV radiation on mediating PAH toxicity. The importance of the role of photomodification is also stressed, as several oxyPAHs were highly toxic to D. magna, a key bioindicator species in aquatic ecosystems. A QSAR model previously developed for Lemna gibba showed that a photosensitization factor (PSF) and a photomodification factor (PMF) could be combined to describe toxicity. To determine whether it was predictive for D. magna, toxicity was assessed as both EC50 and ET50. As with L. gibba and Vibrio fischeri, neither the PSF nor the PMF alone correlated to D. magna toxicity. However, a PSF modified for D. magna did in fact exhibit correlation with toxicity, which was further improved when summed with a modified PMF. The greatest correlation was observed with EC50 toxicity data. This research provides further evidence that models that include factors for photosensitization and photomodification will likely be applicable across a broad range of species. To gain further knowledge of the roles that the variables contributing to the photosensitization and photomodification, a structural equation model was constructed based on the D. magna QSAR. This model accounted for a high amount of variance in six sets of toxicity data, as well as insight into the mechanisms of phototoxicity affecting different aquatic organisms

The prediction of mutagenicity and pKa for pharmaceutically relevant compounds using 'quantum chemical topology' descriptors

Quantum Chemical Topology (QCT) descriptors, calculated from ab initio wave functions, have been utilised to model pKa and mutagenicity for data sets of pharmaceutically relevant compounds. The pKa of a compound is a pivotal property in both life science and chemistry since the propensity of a compound to donate or accept a proton is fundamental to understanding chemical and biological processes. The prediction of mutagenicity, specifically as determined by the Ames test, is important to aid medicinal chemists select compounds avoiding this potential pitfall in drug design. Carbocyclic and heterocyclic aromatic amines were chosen because this compounds class is synthetically very useful but also prone to positive outcomes in the battery of genotoxicity assays.The importance of pKa and genotoxic characteristics cannot be overestimated in drug design, where the multivariate optimisations of properties that influence the Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) profiles now features very early on in the drug discovery process.Models were constructed using carboxylic acids in conjunction with the Quantum Topological Molecular Similarity (QTMS) method. The models produced Root Mean Square Error of Prediction (RMSEP) values of less than 0.5 pKa units and compared favourably to other pKa prediction methods. The ortho-substituted benzoic acids had the largest RMSEP which was significantly improved by splitting the compounds into high-correlation subsets. For these subsets, single-term equations containing one ab initio bond length were able to accurately predict pKa. The pKa prediction equations were extended to phenols and anilines.Quantitative Structure Activity Relationship (QSAR) models of acceptable quality were built based on literature data to predict the mutagenic potency (LogMP) of carbo- and heterocyclic aromatic amines using QTMS. However, these models failed to predict Ames test values for compounds screened at GSK. Contradictory internal and external data for several compounds motivated us to determine the fidelity of the Ames test for this compound class. The systematic investigation involved recrystallisation to purify compounds, analytical methods to measure the purity and finally comparative Ames testing. Unexpectedly, the Ames test results were very reproducible when 14 representative repurified molecules were tested as the freebase and the hydrochloride salt in two different solvents (water and DMSO). This work formed the basis for the analysis of Ames data at GSK and a systematic Ames testing programme for aromatic amines. So far, an unprecedentedly large list of 400 compounds has been made available to guide medicinal chemists. We constructed a model for the subset of 100 meta-/para-substituted anilines that could predict 70% of the Ames classifications. The experimental values of several of the model outliers appeared questionable after closer inspection and three of these have been retested so far. The retests lead to the reclassification of two of them and thereby to improved model accuracy of 78%. This demonstrates the power of the iterative process of model building, critical analysis of experimental data, retesting outliers and rebuilding the model.EThOS - Electronic Theses Online ServiceEPSRCGlaxoSmithKlineGBUnited Kingdo