Partha Pratim Roy

QSAR Modeling is not “Push a Button and Find a Correlation”: A Case Study of Toxicity of (Benzo-)triazoles on Algae

A case study of toxicity of (benzo)triazoles ((B)TAZs) to the algae Pseudokirchneriella subcapitata is used to discuss some problems and solutions in QSAR modeling, particularly in the environmental context. The relevance of data curation (not only of experimental data, but also of chemical structures and input formats for the calculation of molecular descriptors), the crucial points of QSAR model validation and the potential application for new chemicals (internal robustness, exclusion of chance correlation, external predictivity, applicability domain) are described, while developing MLR‐OLS models based on molecular descriptors, calculated by various QSAR software tools (commercial DRAGON, free PaDEL‐Descriptor and QSPR‐THESAURUS). Additionally, the utility of consensus models is highlighted. This work summarizes a methodology for a rigorous statistical approach to obtain reliable QSAR predictions, also for a large number of (B)TAZs in the ECHA preregistration list of REACH (even if starting from limited experimental data availability), and has evidenced some ambiguities and discrepancies related to SMILES notations from different databases; furthermore it highlighted some general problems related to QSAR model generation and was useful in the implementation of the PaDEL‐Descriptor software.

Daphnia and fish toxicity of (benzo)triazoles: Validated QSAR models, and interspecies quantitative activity–activity modelling

Due to their chemical properties synthetic triazoles and benzo-triazoles ((B)TAZs) are mainly distributed to the water compartments in the environment, and because of their wide use the potential effects on aquatic organisms are cause of concern. Non testing approaches like those based on quantitative structure-activity relationships (QSARs) are valuable tools to maximize the information contained in existing experimental data and predict missing information while minimizing animal testing. In the present study, externally validated QSAR models for the prediction of acute (B)TAZs toxicity in Daphnia magna and Oncorhynchus mykiss have been developed according to the principles for the validation of QSARs and their acceptability for regulatory purposes, proposed by the Organization for Economic Co-operation and Development (OECD). These models are based on theoretical molecular descriptors, and are statistically robust, externally predictive and characterized by a verifiable structural applicability domain. They have been applied to predict acute toxicity for over 300 (B)TAZs without experimental data, many of which are in the pre-registration list of the REACH regulation. Additionally, a model based on quantitative activity-activity relationships (QAAR) has been developed, which allows for interspecies extrapolation from daphnids to fish. The importance of QSAR/QAAR, especially when dealing with specific chemical classes like (B)TAZs, for screening and prioritization of pollutants under REACH, has been highlighted.

QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo‐)triazoles

The crucial importance of the three central OECD principles for quantitative structure‐activity relationship (QSAR) model validation is highlighted in a case study of tropospheric degradation of volatile organic compounds (VOCs) by OH, applied to two CADASTER chemical classes (PBDEs and (benzo‐)triazoles). The application of any QSAR model to chemicals without experimental data largely depends on model reproducibility by the user. The reproducibility of an unambiguous algorithm (OECD Principle 2) is guaranteed by redeveloping MLR models based on both updated version of DRAGON software for molecular descriptors calculation and some freely available online descriptors. The Genetic Algorithm has confirmed its ability to always select the most informative descriptors independently on the input pool of variables. The ability of the GA‐selected descriptors to model chemicals not used in model development is verified by three different splittings (random by response, K‐ANN and K‐means clustering), thus ensuring the external predictivity of the new models, independently of the training/prediction set composition (OECD Principle 5). The relevance of checking the structural applicability domain becomes very evident on comparing the predictions for CADASTER chemicals, using the new models proposed herein, with those obtained by EPI Suite.

1,3,4-Oxadiazoles: An emerging scaffold to target growth factors, enzymes and kinases as anticancer agents.

Five member heterocyclic 1,3,4-oxadiazole nucleus find unique place in medicinal chemistry and plays significant role in producing anticancer activity. The small and simple 1,3,4-oxadiazole nucleus is present in various compounds involved in research aimed at evaluating new products that posses interesting pharmacological properties such as antitumour activity. Mono and 2,5-di-substituted-1,3,4-oxadiazole derivatives have attracted considerable attention owing to their effective biological activity and extensive use. The important mechanism involved during its tumour suppression is related with the inhibition of different growth factors, enzymes and kinases including telomerase enzyme, histone deacetylase (HDAC), methionine aminopeptidase (MetAP), thymidylate synthase (TS), glycogen synthase kinase-3 (GSK), epidermal growth factor (EGF), vascular endothelial growth factor (VEGF) and focal adhesion kinase (FAK). The focused criteria of this review is to highlights the targeted inhibitory activity of 1,3,4-oxadiazole derivatives and their structure activity relationship to generate potential anticancer agents.

Synthesis, thymidine phosphorylase inhibitory and computational study of novel 1,3,4-oxadiazole-2-thione derivatives as potential anticancer agents

A series of novel 1,3,4-oxadiazole-2-thione derivatives were designed, synthesized and evaluated for in vitro anticancer activity against breast cancer (MCF-7) cell line and thymidine phosphorylase. The synthesis of target compounds was performed by cyclization reaction using aromatic amines and carbon disulphide to get mannich bases. The synthesized compound 2j exhibited the most potent anticancer activity against MCF-7 cell line. Compounds 2d, 2j, 2o and 2h showed potent thymidine phosphorylase inhibitory activity. The SAR study revealed that the substitution of phenyl ring with electron withdrawing group at R1 position and less bulky amines group at R2 position of 1,3,4-oxadiazole-2-thione ring showed significant growth inhibitory activity. Further in silico ADMET properties of synthesized compounds were calculated along with molecular docking to study the binding mode of the compounds in the active site of thymidine phosphorylase (TP). The molecular docking studies showed that amines group have good binding interaction on active site residues of TP such as compounds 2j and 2o exhibited hydrogen bond interaction with amino acid residues GLY152, THR151 and HIS116 of thymidine phosphorylase (PDB ID: 1UOU). The result of biological activity and docking study revealed that amines group at R2 point of 1,3,4-oxadiazole-2-thione moiety is essential for anticancer activity.

1,3,4-Oxadiazoles as Telomerase Inhibitor: Potential Anticancer Agents

Cancer is a rapidly growing disease of current era which poses a major life threaten situation to human beings. Continuous research is going on in the direction to develop effective molecules for the treatment of the cancer. These efforts include searching of more active heterocyclic compounds possessing potential anticancer activity. The 1,3,4-Oxadiazole scaffold is a five member heterocyclic ring having versatile activities and created interest for synthetic organic and medicinal chemists for the designing of novel compounds having anticancer activity. The important mechanism behind tumor suppression by 1,3,4-Oxadiazole is related with the inhibition of different growth factors, enzymes and kinases etc. The current literature surveys revealed that 1,3,4-Oxadiazole is a promising lead for anti-cancer agents by the inhibition of telomerase activity. In cancerous cells telomerase enzyme is activated which maintains and restores the telomere which leads to cell proliferation. The telomerase inhibitors with enhanced specificity and improved pharmacokinetics have been considered for design and development of novel anti-cancer agents. This review focuses primarily on telomerase enzyme its function and mechanism of action. It also describes the interaction of telomerase enzyme with 1,3,4-Oxadiazole inhibitors including their structure activity relationships (SARs). With the knowledge of this molecular target, structural insights and SARs, this review may be helpful for (medicinal) chemists to design more potent, safe, selective and cost effective anti-cancer agents.

Application of GFA-MLR and G/PLS Techniques in QSAR/QSPR Studies with Application in Medicinal Chemistry and Predictive Toxicology

Quantitative structure–activity/property/toxicity relationship (QSAR/QSPR/QSTR) models enable predictions of activity/property/toxicities to be made directly from the chemical structure. Feature selection is one of the integral parts in the development of QSAR/QSPR models which is also included in the Organization of Economic Co-operation and Development (OECD) principle of “an unambiguous algorithm” for QSAR model development and validation. Genetic algorithm (GA) based on the principle of Darwin’s theory of natural selection and evolutions are being widely used in recent times for the selection of descriptors in the development of predictive models for toxicity assessment and virtual screening of hazardous chemicals and design of drug compounds with therapeutic activity. The GA algorithm can handle a huge number of descriptors and generate a population of models competitive with or superior to the results of standard regression analysis. Genetic function approximation (GFA) involves the combination of multivariate adaptive regression splines (MARS) algorithm of Friedman with genetic algorithm of Holland to evolve population of equations. GFA calculations are based on three operators: selection, crossover and mutation. Using spline based terms in the model construction, GFA can either remove the outlier compounds or identify a range of effect. GFA followed by multiple linear regression (GFA-MLR) or partial least squares (G/PLS) regression is frequently used by different research groups for the development of predictive QSAR/QSPR models. This chapter presents examples of some case studies of the use of GFA-MLR and G/PLS techniques in developing predictive models in medicinal chemistry and predictive toxicology applications.

Combined experimental and in silico approaches for exploring antiperoxidative potential of structurally diverse classes of antioxidants on docetaxel-induced lipid peroxidation using 4-HNE as the model marker

The objective of the present work was tantamount to explain the antiperoxidative potential and structural requirements of twenty-eight structurally diverse classes of antioxidants on docetaxel-induced lipid peroxidation. Both experimental and computational approaches were taken to the work. The experiments were performed in vitro and goat liver was used as a source of lipid. 4-hydroxy-2-nonenal was used as model marker for estimation of docetaxel-lipid interaction. The computational portion of the work was limited to QSAR analysis of those antioxidants for better understanding of the structural requirements of antioxidants on docetaxel-lipid interaction. The study was done with freely online available 2D descriptors available on PaDEL (open source). Stepwise regression analysis was used as chemometric tool. The experimental study showed the lipid peroxidation induction capacity of docetaxel. It was also noted that all twenty-eight antioxidants had the ability to suppress the lipid peroxidation. But among them butylated hydroxyl toluene showed the highest potential (-20.5%) and flavone showing lowest potential (-0.8%) to suppress the docetaxel-induced lipid peroxidation. The computational study indicates the importance of topology of the whole molecules, topological distances among atoms within a molecule and specific fragment pattern present in a molecule required for inhibition of lipid peroxidation.

Reply to the comment of S. Rayne on "QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles".

We appreciate the interest of Dr. Rayne on our article and we completely agree that the dataset of (benzo‐)triazoles, which were screened by the hydroxyl radical reaction quantitative structure‐activity relationship (QSAR) model, was not only composed of benzo‐triazoles but also included some simpler triazoles (without the condensed benzene ring), such as the chemicals listed by Dr. Rayne, as well as some related heterocycles (also few not aromatic). We want to clarify that in this article (as well as in other articles in which the same dataset was screened), for conciseness, the abbreviations (B)TAZs and BTAZs were used as general (and certainly too simplified) notations meaning an extended dataset of benzo‐triazoles, triazoles, and related compounds.