Inroads to Predict in Vivo Toxicology—An Introduction to the eTOX Project

There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison (“read-across”), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX (“electronic toxicity”) consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables

The activities of drug inactive ingredients on biological targets

Excipients, considered "inactive ingredients," are a major component of formulated drugs and play key roles in their pharmacokinetics. Despite their pervasiveness, whether they are active on any targets has not been systematically explored. We computed the likelihood that approved excipients would bind to molecular targets. Testing in vitro revealed 25 excipient activities, ranging from low-nanomolar to high-micromolar concentration. Another 109 activities were identified by testing against clinical safety targets. In cellular models, five excipients had fingerprints predictive of system-level toxicity. Exposures of seven excipients were investigated, and in certain populations, two of these may reach levels of in vitro target potency, including brain and gut exposure of thimerosal and its major metabolite, which had dopamine D3 receptor dissociation constant Kd values of 320 and 210 nM, respectively. Although most excipients deserve their status as inert, many approved excipients may directly modulate physiologically relevant targets

Physico-Chemical Property prediction of emerging pollutants:PFCs and (B)TAZs for environmental distribution

Perfluorinated compounds (PFCs) and (Benzo)triazoles (B/TAZs) are considered as “emerging pollutants” as they are broadly distributed in the environment, because of their extensive use and are considered to be hazardous, as they cause adverse effects to humans and other non-target species. The lack of physico-chemical properties of these pollutants urges the use of available limited data to predict such properties for other existing or novel chemicals, as suggested by REACH. Internally robust and externally validated QSPR models were developed for these compounds. For PFCs, QSPR models on Water Solubility (WS), Vapor Pressure (VP) and Critical Micelle Concentration (CMC) were developed and structural applicability domain (AD) was verified. 79% of PFCs in ECHA list were found within the AD of all three models. In addition, the relationships between the modeled end-points and Bioconcentration Factor (BCF) were studied. The increasing trend of BCFs is in opposite direction to that of WS and CMC and it is found different, by Principal Component Analysis (PCA), for carboxylates and sulfonates. For B/TAZs, four QSPR models on WS, VP, KOW (Octanol/Water partition) and Melting Point (MP) were developed. 66 of 351 studied compounds were found within the structural AD of all four models. These compounds were studied in multivariate plot by PCA to understand their leaching and volatility behavior. Comparison with soil sorption partition coefficient (KOC) was performed by using predictions from earlier published models. More soluble, volatile and sorbed chemicals are highlighted. The 1H-B/TAZs were found to be among the more soluble and less sorbed compounds

Modelling physico-chemical properties of (benzo)triazoles, and screening for environmental partitioning

(Benzo)triazoles are distributed throughout the environment, mainly in water compartments, because of their wide use in industry where they are employed in pharmaceutical, agricultural and deicing products. They are hazardous chemicals that adversely affect humans and other non-target species, and are on the list of substances of very high concern (SVHC) in the new European regulation of chemicals - REACH (Registration, Evaluation, Authorization and Restriction of Chemical substances). Thus there is a vital need for further investigations to understand the behavior of these compounds in biota and the environment. In such a scenario, physico-chemical properties like aqueous solubility, hydrophobicity, vapor pressure and melting point can be useful. However, the limited availability and the high cost of lab testing prevents the acquisition of necessary experimental data that industry must submit for the registration of these chemicals. In such cases a preliminary analysis can be made using Quantitative Structure-Property Relationships (QSPR) models. For such an analysis, we propose Multiple Linear Regression (MLR) models based on theoretical molecular descriptors selected by Genetic Algorithm (GA). Training and prediction sets were prepared a priori by splitting the available experimental data, which were then used to derive statistically robust and predictive (both internally and externally) models. These models, after verification of their structural applicability domain (AD), were used to predict the properties of a total of 351 compounds, including those in the REACH preregistration list. Finally, Principal Component Analysis was applied to the predictions to rank the environmental partitioning properties (relevant for leaching and volatility) of new and untested (benzo)triazoles within the AD of each model. Our study using this approach highlighted compounds dangerous for the aquatic compartment. Similar analyses using predictions obtained by the EPI Suite and VCCLAB tools are also compared and discussed in this paper

Physico-Chemical Property prediction of emerging pollutants:PFCs and (B)TAZs for environmental distribution

Perfluorinated compounds (PFCs) and (Benzo)triazoles (B/TAZs) are considered as \u201cemerging pollutants\u201d as they are broadly distributed in the environment, because of their extensive use and are considered to be hazardous, as they cause adverse effects to humans and other non-target species. The lack of physico-chemical properties of these pollutants urges the use of available limited data to predict such properties for other existing or novel chemicals, as suggested by REACH. Internally robust and externally validated QSPR models were developed for these compounds. For PFCs, QSPR models on Water Solubility (WS), Vapor Pressure (VP) and Critical Micelle Concentration (CMC) were developed and structural applicability domain (AD) was verified. 79% of PFCs in ECHA list were found within the AD of all three models. In addition, the relationships between the modeled end-points and Bioconcentration Factor (BCF) were studied. The increasing trend of BCFs is in opposite direction to that of WS and CMC and it is found different, by Principal Component Analysis (PCA), for carboxylates and sulfonates. For B/TAZs, four QSPR models on WS, VP, KOW (Octanol/Water partition) and Melting Point (MP) were developed. 66 of 351 studied compounds were found within the structural AD of all four models. These compounds were studied in multivariate plot by PCA to understand their leaching and volatility behavior. Comparison with soil sorption partition coefficient (KOC) was performed by using predictions from earlier published models. More soluble, volatile and sorbed chemicals are highlighted. The 1H-B/TAZs were found to be among the more soluble and less sorbed compounds