Project description:The environmental fate of many functional molecules that are produced on a large scale as precursors or as additives to specialty goods (plastics, fibers, construction materials, etc.), let alone those synthesized by the pharmaceutical industry, is generally unknown. Assessing their environmental fate is crucial when taking decisions on the manufacturing, handling, usage, and release of these substances, as is the evaluation of their toxicity in humans and other higher organisms. While this data are often hard to come by, the experimental data already available on the biodegradability and toxicity of many unusual compounds (including genuinely xenobiotic molecules) make it possible to develop machine learning systems to predict these features. As such, we have created a predictor of the "risk" associated with the use and release of any chemical. This new system merges computational methods to predict biodegradability with others that assess biological toxicity. The combined platform, named BiodegPred (https://sysbiol.cnb.csic.es/BiodegPred/), provides an informed prognosis of the chance a given molecule can eventually be catabolized in the biosphere, as well as of its eventual toxicity, all available through a simple web interface. While the platform described does not give much information about specific degradation kinetics or particular biodegradation pathways, BiodegPred has been instrumental in anticipating the probable behavior of a large number of new molecules (e.g. antiviral compounds) for which no biodegradation data previously existed.
Project description:To better understand the mechanism of in vivo toxicity of N-nitroso compounds (NNCs), the toxicity data of 80 NNCs related to their rat acute oral toxicity data (50% lethal dose concentration, LD50) were used to establish quantitative structure-activity relationship (QSAR) and classification models. Quantum chemistry methods calculated descriptors and Dragon descriptors were combined to describe the molecular information of all compounds. Genetic algorithm (GA) and multiple linear regression (MLR) analyses were combined to develop QSAR models. Fingerprints and machine learning methods were used to establish classification models. The quality and predictive performance of all established models were evaluated by internal and external validation techniques. The best GA-MLR-based QSAR model containing eight molecular descriptors was obtained with Q²loo = 0.7533, R² = 0.8071, Q²ext = 0.7041 and R²ext = 0.7195. The results derived from QSAR studies showed that the acute oral toxicity of NNCs mainly depends on three factors, namely, the polarizability, the ionization potential (IP) and the presence/absence and frequency of C?O bond. For classification studies, the best model was obtained using the MACCS keys fingerprint combined with artificial neural network (ANN) algorithm. The classification models suggested that several representative substructures, including nitrile, hetero N nonbasic, alkylchloride and amine-containing fragments are main contributors for the high toxicity of NNCs. Overall, the developed QSAR and classification models of the rat acute oral toxicity of NNCs showed satisfying predictive abilities. The results provide an insight into the understanding of the toxicity mechanism of NNCs in vivo, which might be used for a preliminary assessment of NNCs toxicity to mammals.
Project description:Over the last few years more and more organ and idiosyncratic toxicities were linked to mitochondrial toxicity. Despite well-established assays, such as the seahorse and Glucose/Galactose assay, an in silico approach to mitochondrial toxicity is still feasible, particularly when it comes to the assessment of large compound libraries. Therefore, in silico approaches could be very beneficial to indicate hazards early in the drug development pipeline. By combining multiple endpoints, we derived the largest so far published dataset on mitochondrial toxicity. A thorough data analysis shows that molecules causing mitochondrial toxicity can be distinguished by physicochemical properties. Finally, the combination of machine learning and structural alerts highlights the suitability for in silico risk assessment of mitochondrial toxicity.
Project description:We used microarrays to detail the global programme of gene expression underlying CS1-regulated biological processes including increased cell adhesion and cell proliferation.
Project description:Toxicity evaluation is an extremely important process during drug development. It is usually initiated by experiments on animals, which is time-consuming and costly. To speed up such a process, a quantitative structure-activity relationship (QSAR) study was performed to develop a computational model for correlating the structures of 581 aromatic compounds with their aquatic toxicity to tetrahymena pyriformis. A set of 68 molecular descriptors derived solely from the structures of the aromatic compounds were calculated based on Gaussian 03, HyperChem 7.5, and TSAR V3.3. A comprehensive feature selection method, minimum Redundancy Maximum Relevance (mRMR)-genetic algorithm (GA)-support vector regression (SVR) method, was applied to select the best descriptor subset in QSAR analysis. The SVR method was employed to model the toxicity potency from a training set of 500 compounds. Five-fold cross-validation method was used to optimize the parameters of SVR model. The new SVR model was tested on an independent dataset of 81 compounds. Both high internal consistent and external predictive rates were obtained, indicating the SVR model is very promising to become an effective tool for fast detecting the toxicity.
Project description:Alkylglycerone phosphate synthase (AGPS) is an oncogene and can be considered as an antitumor drug target. The aim of the present study was to design novel nitrogenous heterocyclic compound improving targetability by computer-aided drug design technology targeting AGPS. A total of 12 nitrogenous heterocyclic compounds were designed and predicted the absorption, distribution, metabolism and excretion parameters/toxicity. Their activity in terms of proliferation inhibition, cell cycle arrest and apoptosis induction was then measured using an MTS assay and a high-content screening system in U251 cells. The results showed that anti-glioma activity was present in compounds N4, N5, N6, N7, N8 and N12, which was in accordance with the computer prediction. Therefore, these compounds may be suitable for the development of a novel glioma therapeutic drug.
Project description:Generation and extensive characterization of a gender-balanced, racially/ethnically diverse library of iPSCs from forty clinically healthy human individuals who range in age from 22-61. Specifically, here we provide mRNAseq data of duplicate samples of one clone from each of these forty iPSC lines.
Project description:Baseline transcriptomic signatures of cardiomyocytes differentiated from hiPSC lines generated from clinically well-characterized, diverse healthy human individuals. We provide mRNAseq data of various replicate samples of cardiomyocytes differentiated from 6 hiPSC lines.
Project description:Based on the wide use of cobalt substances in a range of important technologies, it has become important to predict the toxicological properties of new or lesser-studied substances as accurately as possible. We studied a group of 6 cobalt substances with inorganic ligands, which were tested for their bioaccessibility (surrogate measure of bioavailability) through in vitro bioelution in simulated gastric and intestinal fluids. Representatives of the group also underwent in vivo blood kinetics and mass balance tests, and both oral acute and repeated dose toxicity (RDT) testing. We were able to show a good correlation between high in vitro bioaccessibility with high in vivo bioavailability and subsequent high in vivo toxicity; consequently, low in vitro bioaccessibility correlated well with low in vivo bioavailability and low in vivo toxicity. In vitro bioelution in simulated gastric fluid was the most precise predictor of the difference in the oral RDT lowest observed adverse effect levels of 2 compounds representing the highly and poorly bioaccessible subset of substances. The 2 compounds cobalt dichloride hexahydrate and tricobalt tetraoxide differed by a factor of 440 in their in vitro bioaccessibility and by a factor of 310 in their RDT lowest observed adverse effect level. In summary, this set of studies shows that solubility, specifically in vitro bioelution in simulated gastric fluid, is a good, yet conservative, predictor of in vivo bioavailability and oral systemic toxicity of inorganic cobalt substances. Bioelution data are therefore an invaluable tool for grouping and read across of cobalt substances for hazard and risk assessment.