QSAR prediction of toxicity for a new 1,2,4-triazole derivatives with 2-bromo-5-methoxyphenyl fragment

1,2,4-Triazole derivatives are of researchers’ significant interest due to their diverse biological properties, such as antimicrobial, anti-inflammatory, anticancer, and antioxidant activities. The integration of a 2-bromo-4-fluorophenyl fragment into the triazole structure can significantly enhance these activities. However, the evaluation of the toxicity of such compounds remains a critically important aspect for their practical application. To reduce the time and cost of experimental studies, QSAR (Quantitative Structure-Activity Relationship) methods are actively used, allowing the prediction of toxicity based on the molecular structure of compounds. Aim of the study. To assess the toxicity of new S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-R-1,2,4-triazol-3-thiols using the QSAR method, specifically to predict acute toxicity parameters (LD50), and to determine the influence of different (length) radicals on the toxicity of these compounds. Materials and methods. The objects of the virtual study were derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols. They were evaluated at the Department of Toxicological and Inorganic Chemistry of the Zaporizhzhia State Medical and Pharmaceutical University. The toxicity assessment was conducted using the nearest neighbor method via the Toxicity Estimation Software Tool (TEST). The prediction of the lethal dose (LD50) for rats was based on the structural similarity of the studied compounds with known substances, for which experimental toxicity data are available. Results. The conducted QSAR analysis demonstrated that structural changes in S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols significantly affect the predicted toxicity. The primary factor influencing the changes in LD50 values is the variation of radicals at the 5th position of the triazole ring. Conclusions. The results of the study showed, that the toxicity of new S-alkyl derivatives of triazol-3-thiols depends on the type of alkyl substituent. Compounds with propyl to heptyl fragments exhibit increased toxicity, while derivatives with thiol, octyl, nonyl, and decyl residues are characterized by lower toxicity.

Painkiller drugs or analgesics are potent pain reliever chemical agents, which are commonly used in pain therapy. Mathematical modeling by QSAR (quantitative structure activity relationship) methods are well known practices to determine predictive toxicity in biota. Now-a-days, an easy screening of chemicals, QSAR can be done by using several recommended softwares. The present study was carried out by using software namely T.E.S.T. (Toxicity estimation software tool) for rat oral LD50 (median lethal dose) predictive toxicity for common painkiller drugs. These painkiller drugs were selected as 35 compounds and tabulated on the basis characteristics of one non-narcotic viz. acetaminophen, twenty non-steroidal anti-inflammatory such as bromofenac, diclofenac, diflunsial, etodolac, fenoprofen, flurbiprofen, ibuprofen, indomethacin, ketoprofen, ketorolac, maclofenamate sodium, mefenamic acid, meloxicam, nabumetone, naproxen, oxaprozin, phenylbutazone, piroxicam, sulindac and tolmetin as well as fourteen narcotic viz. buprenorphine, butorphanol, codeine, hydrocodone, hydromorphone, levorphanol, meperidine, methadone, morphine, nalbuphine, oxycodone, pentazocine, dextropropoxyphene and tapentadol. The data were tabulated on experimental (bioassay) from ChemIDPlus and predictive toxicity of 30 compounds out of 35 compounds by using T.E.S.T. The predictive data were found by T.E.S.T. that 20 and 10 compounds were very toxic and moderately toxic respectively but not extremely, super toxic and non-toxic in rat model after acute oral exposure. It is suggested to evaluate the predicted data further with other available recommended softwares with different test models like daphnia, fish etc. to know aquatic toxicity when these compounds may discharge into waterbodies.

Painkiller drugs or analgesics are potent pain reliever chemical agents, which are commonly used in pain therapy. Mathematical modeling by QSAR (quantitative structure activity relationship) methods are well known practices to determine predictive toxicity in biota. Now-a-days, an easy screening of chemicals, QSAR can be done by using several recommended softwares. The present study was carried out by using software namely T.E.S.T. (Toxicity estimation software tool) for rat oral LD50 (median lethal dose) predictive toxicity for common painkiller drugs. These painkiller drugs were selected as 35 compounds and tabulated on the basis characteristics of one non-narcotic viz. acetaminophen, twenty non-steroidal anti-inflammatory such as bromofenac, diclofenac, diflunsial, etodolac, fenoprofen, flurbiprofen, ibuprofen, indomethacin, ketoprofen, ketorolac, maclofenamate sodium, mefenamic acid, meloxicam, nabumetone, naproxen, oxaprozin, phenylbutazone, piroxicam, sulindac and tolmetin as well as fourteen narcotic viz. buprenorphine, butorphanol, codeine, hydrocodone, hydromorphone, levorphanol, meperidine, methadone, morphine, nalbuphine, oxycodone, pentazocine, dextropropoxyphene and tapentadol. The data were tabulated on experimental (bioassay) from ChemIDPlus and predictive toxicity of 30 compounds out of 35 compounds by using T.E.S.T. The predictive data were found by T.E.S.T. that 20 and 10 compounds were very toxic and moderately toxic respectively but not extremely, super toxic and non-toxic in rat model after acute oral exposure. It is suggested to evaluate the predicted data further with other available recommended softwares with different test models like daphnia, fish etc. to know aquatic toxicity when these compounds may discharge into waterbodies.

Several quantitative structure-activity relationship (QSAR) methods were applied to 29 chemically diverse D(1) dopamine antagonists. In addition to conventional 3D comparative molecular field analysis (CoMFA), cross-validated R(2) guided region selection (q(2)-GRS) CoMFA (see ref 1) was employed, as were two novel variable selection QSAR methods recently developed in one of our laboratories. These latter methods included genetic algorithm-partial least squares (GA-PLS) and K nearest neighbor (KNN) procedures (see refs 2-4), which utilize 2D topological descriptors of chemical structures. Each QSAR approach resulted in a highly predictive model, with cross-validated R(2) (q(2)) values of 0.57 for CoMFA, 0.54 for q(2)-GRS, 0.73 for GA-PLS, and 0.79 for KNN. The success of all of the QSAR methods indicates the presence of an intrinsic structure-activity relationship in this group of compounds and affords more robust design and prediction of biological activities of novel D(1) ligands.

Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR), has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance’s database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (the LD50) for determining relative toxicity of a number of substances. In general, the smaller the LD50 value, the more toxic the chemical, and the larger the LD50 value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s) of interest, during emergency responses, LD50 values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD50 models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD) are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD50 values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field.

Ewing's sarcoma family of tumors are characterized by the EWS-FLI1 oncogenic fusion protein. Our previous studies show that RNA Helicase A (RHA) enhances EWS-FLI1 driven oncogenesis, and interruption of this protein-protein complex by small molecule inhibitors validate this interaction as a unique therapeutic target (NatMed 2009 Jul;15(7):750-6). EWS-FLI1 is a significantly hydrophobic disordered protein with unknown three-dimensional structure, which therefore precludes standard structure-based small molecule design. Given the challenges of drug design targeted to EWS-FLI1, we hypothesize that the optimization of a peptide displacement assay using small molecules can predict and direct the design of more potent analogs. Fluorescence polarization (FP) was originally used to measure RHA peptide displacement from EWS-FLI1, but is time consuming and reagent limiting. To identify new strategies for higher-throughput screening, a TECAN ULTRA 384 was used for assay detection in a 96-well plate format. However, this resulted in high background and low signal. A second approach, AlphaScreen (amplified luminescent proximity homogenous assay, PerkinElmer), uses a bead-based interaction system based on energy conversion and generation of chemiluminescence when acceptor and donor beads are brought together during binding. Results show a 6-fold dynamic range between bound and quenched beads, in comparison to just 2.5-fold for FP and less than two-fold for TECAN. Our small molecule, YK-4-279, is able to reduce the signal down to baseline, indicating that YK-4-279 dissociates the RHA peptide and EWS-FLI1. Key negative controls support specific binding between EWS-FLI1 and the RHA peptide. In addition, YK-4-279 was unable to quench the signal from a positive control using immunoglobulin, supporting the conclusion that YK-4-279 disrupts the specific protein-peptide binding rather than directly extinguishing the chemiluminescence. Data from the AlphaScreen will be used as input to a quantitative-structure activity relationship (QSAR) method. This QSAR method will predict biological activity from chemical structure and the knowledge gained from AlphaScreen will aid in the optimization and design of future analogs. In our initial work, we used the Free-Wilson QSAR method, converting the structure of the small molecules into a numerical format allowing for a mathematical comparison between structures, to populate a table with 32 small molecule analogs of YK-4-279. The AlphaScreen data may also be used with other QSAR methods, to which we may transition in later work. AlphaScreen technology provides a method to quantitatively compare binding of small molecules in a high-throughput screening system, yields a greater dynamic range with more specificity, and provides data that will help to predict the binding of other analogs with similar chemical structures. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2681.

Ewing's sarcoma family of tumors (ESFT) are characterized by the EWS-FLI1 oncogenic fusion protein which results from a t(11;22) chromosomal translocation. Our previous studies show that RNA Helicase A (RHA) enhances EWS-FLI1 driven oncogenesis, and interruption of this protein-protein complex by small molecule inhibitors validate this interaction as a unique therapeutic target (NatMed 2009 Jul;15(7):750-6). EWS-FLI1 is a significantly hydrophobic disordered protein with unknown three-dimensional structure, which therefore precludes standard structure-based small molecule design. Fortunately, disordered proteins have a greater potential for binding with small molecule inhibitors due to structural flexibility. Given the challenges of drug design targeted to EWS-FLI1, we hypothesize that the optimization of a peptide displacement assay using small molecules can predict and direct the design of more potent analogs. Our methods originally used fluorescence polarization (FP) to measure RHA peptide displacement from EWS-FLI1. FP is a time consuming and reagent-limiting assay, so we evaluated new strategies using a 96-well plate format with the goal of high-throughput data collection. The first approach used a TECAN ULTRA 384 for assay detection and resulted in readings with high background noise and low signal. A second approach, AlphaScreen (amplified luminescent proximity homogenous assay, PerkinElmer), uses a bead-based interaction system based on energy conversion and generation of chemiluminescence when acceptor and donor beads are brought together during binding. The AlphaScreen assay uses approximately 3 ug of EWS-FLI1 protein per assay well and binds the 6-histadine tagged protein to the nickel-chelated donor beads while anti-FITC acceptor beads detect the flouresceintagged peptide. Our results show a 6-fold dynamic range between bound and quenched beads, in comparison to just 2.5-fold for FP and less than two-fold for the TECAN platform. Our small molecule YK-4-279 is able to reduce the signal back down to baseline, indicating that YK-4-279 dissociates the RHA peptide and EWS-FLI1. Key negative controls support specific binding between EWS-FLI1 and the RHA peptide. In addition, YK-4-279 was unable to quench the signal from a positive control using immunoglobulin, supporting the conclusion that YK-4-279 does indeed disrupt the specific protein-peptide binding rather than directly extinguishing the chemiluminescence. Data from the AlphaScreen will be used as input to a quantitative-structure activity relationship (QSAR) method. This QSAR method will predict biological activity from chemical structure and the knowledge gained from AlphaScreen will aid in the optimization and design of future analogs. In our initial work, we used the Free-Wilson QSAR method, converting the structure of the small molecules into a numerical format which allows for a mathematical comparison between structures, to populate a table with 32 small molecule analogs of YK-4-279. The AlphaScreen data may also be used with other QSAR methods, to which we may transition in later work. The AlphaScreen technology provides a method to quantitatively compare binding of small molecules in a high-throughput screening system, yields a greater dynamic range with more specificity, and provides data that will help to predict the binding of other analogs with similar chemical structures. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):B181.

There are several Quantitative Structure-Activity Relationship (QSAR) methods to assist in the design of compounds for medicinal use. Owing to the different QSAR methodologies, deciding which QSAR method to use depends on the composition of system of interest and the desired results. The relationship between a compound's binding affinity/activity to its structural properties was first noted in the 1930s by Hammett and later refined by Hansch and Fujita in the mid-1960s. In 1988 Cramer and coworkers created Comparative Molecular Field Analysis (CoMFA) incorporating the three-dimensional (3D) aspects of the compounds, specifically the electrostatic fields of the compound, into the QSAR model. Hopfinger and coworkers included an additional dimension to 3D-QSAR methodology in 1997 that eliminated the question of "Which conformation to use in a QSAR study?", creating 4D-QSAR. In 1999 Chemical Computing Group Inc. (CCG) developed the Binary-QSAR methodology and added novel 3D-QSAR descriptors to the traditional QSAR model allowing the 3D properties of compounds to be incorporated into the traditional QSAR model. Recently CCG released Probabilistic Receptor Potentials to calculate the substrate's atomic preferences in the active site. These potentials are constructed by fitting analytical functions to experimental properties of the substrates using knowledge-based methods. An overview of these and other QSAR methods will be discussed along with an in-depth examination of the methodologies used to construct QSAR models. Also, included in this chapter is a case study of molecules used to create QSAR models utilizing different methodologies and QSAR programs.

The performance of three "spectroscopic" quantitative structure-activity relationship (QSAR) methods (eigenvalue (EVA), electronic eigenvalue (EEVA), and comparative spectra analysis (CoSA)) for relating molecular structure and estrogenic activity are critically evaluated. The methods were tested with respect to the relative binding affinities (RBA) of a diverse set of 36 estrogens previously examined in detail by the comparative molecular field analysis method. The CoSA method with (13)C chemical shifts appears to provide a predictive QSAR model for this data set. EEVA (i.e., molecular orbital energy in this context) is a borderline case, whereas the performances of EVA (i.e., vibrational normal mode) and CoSA with (1)H shifts are substandard and only semiquantitative. The CoSA method with (13)C chemical shifts provides an alternative and supplement to conventional 3D QSAR methods for rationalizing and predicting the estrogenic activity of molecules. If CoSA is to be applied to large data sets, however, it is desirable that the chemical shifts are available from common databases or, alternatively, that they can be estimated with sufficient accuracy using fast prediction schemes. Calculations of NMR chemical shifts by quantum mechanical methods, as in this case study, seem to be too time-consuming at this moment, but the situation is changing rapidly. An inherent shortcoming common to all spectroscopic QSAR methods is that they cannot take the chirality of molecules into account, at least as formulated at present. Moreover, the symmetry of molecules may cause additional problems. There are three pairs of enantiomers and nine symmetric (C(2) or C(2)(v)) molecules present in the data set, so that the predictive ability of full 3D QSAR methods is expected to be better than that of spectroscopic methods. This is demonstrated with SOMFA (self-organizing molecular field analysis). In general, the use of external test sets with randomized data is encouraged as a validation tool in QSAR studies.

Resistance of bacteria to current antibiotics has increased worldwide, being one of the leading unresolved situations in public health. Due to negligence regarding the treatment of community-acquired diseases, even healthcare facilities have been highly impacted by an emerging problem: nosocomial infections. Moreover, infectious diseases, including nosocomial infections, have been found to depend on multiple pathogenicity factors, confirming the need to discover of multi-target antibacterial agents. Drug discovery is a very complex, expensive, and time-consuming process. In this sense, Quantitative Structure-Activity Relationships (QSAR) methods have become complementary tools for medicinal chemistry, permitting the efficient screening of potential drugs, and consequently, rationalizing the organic synthesis as well as the biological evaluation of compounds. In the consolidation of QSAR methods as important components of chemoinformatics, the use of mathematical chemistry, and more specifically, the use of graph-theoretical approaches has played a vital role. Here, we focus our attention on the evolution of QSAR methods, citing the most relevant works devoted to the development of promising graph-theoretical approaches in the last 8 years, and their applications to the prediction of antibacterial activities of chemicals against pathogens causing both community-acquired and nosocomial infections.

Insect molting is regulated by the steroid 20-hydoxyecdysone interacting with the ecdysone receptor (EcR) together with either the retinoid X receptor (RXR) or its homolog, ultraspiracle (USP). Similarly, the non-steroidal diacylhydrazines (DAHs) also bind to EcR, but regulate molting in a dysfunctional manner; they are therefore insecticidal. The four DAHs tebufenozide, methoxyfenozide, chromafenoz- ide and halofenozide have been commercialized to control Lepidoptera and Coleoptera. DAH congeners with various substituents at both benzene rings were synthesized and their ecdysonergic activity in whole body, tissue, cell and protein was quantified. Insecticidal potency (whole body) was measured against three insect species: rice stem borer Chilo suppressalis, beet armyworm Spodoptera exigua, and Colorado potato beetle Leptinotarsa decemlineata. Substituent effects on the activity were analyzed using quantitative structure-activity relationship (QSAR) methods such as the classi- cal (Hansch-Fujita) QSAR and comparative molecular field analysis (CoMFA). These QSAR methods were also applied to analyse in vitro ecdysonergic potency (tissue and cell level) and EcR binding (protein level). Molecular hydrophobicity was extracted as an important physicochemical property to activity at all biosystem levels. CoMFA results for activation of gene expression in the silkworm Bombyx mori are consist- ent with the milieu of the ligand binding pocket homology-modeled from the crystal structure of DAH-bound EcR of the tobacco budworm Heliothis virescens.

Enhanced kinetic performance of peroxymonosulfate/ZVI system with the addition of copper ions: Reactivity, mechanism, and degradation pathways

Amino-modified metal–organic frameworks as peroxymonosulfate catalyst for bisphenol AF decontamination: ROS generation, degradation pathways, and toxicity evaluation

The use of in vitro cytotoxicity measurements in QSAR methods for the prediction of the skin corrosivity potential of acids

Quantitative structure-activity relationships for chemical toxicity to environmental bacteria