Probir Kumar Ojha

Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: Emphasis on scaling of response data

Quantitative structure–activity relationship (QSAR) techniques have found wide application in the fields of drug design, property modeling, and toxicity prediction of untested chemicals. A rigorous validation of the developed models plays the key role for their successful application in prediction for new compounds. The rm2 metrics introduced by Roy et al. have been extensively used by different research groups for validation of regression‐based QSAR models. This concept has been further advanced here with introduction of scaling of response data prior to computation of rm2. Further, a web application (accessible from http://aptsoftware.co.in/rmsquare/ and http://203.200.173.43:8080/rmsquare/) for calculation of the rm2 metrics has been introduced here. The present study reports that the web application can be easily used for computation of rm2 metrics provided observed and QSAR‐predicted data for a set of compounds are available. Further, scaling of response data is recommended prior to rm2 calculation.

Chemometric modeling, docking and in silico design of triazolopyrimidine-based dihydroorotate dehydrogenase inhibitors as antimalarials

In the present work, QSAR and molecular docking studies have been performed on triazolopyrimidine-based dihydroorotate dehydrogenase (DHODH) inhibitors as antimalarial agents. The QSAR studies have been carried out using classical QSAR (physicochemical) approach using linear free energy related (LFER) model and molecular shape analysis using shape, spatial, electronic, thermodynamic and structural descriptors. Docking studies suggest that the 2-methyltriazolopyrimidine ring interacts with some polar and some nonpolar amino acids whereas the substituted phenyl ring binds with a hydrophobic pocket of the enzyme formed by some nonpolar amino acid residues. According to QSAR and molecular docking studies, we have designed some new compounds which showed good in silico predicted activity as per the developed QSAR models.

Chemometric modelling of antimalarial activity of aryltriazolylhydroxamates

We have performed quantitative structure–activity relationship (QSAR) and quantitative activity–activity relationship (QAAR) studies for aryltriazolylhydroxamates having antimalarial activity data against both chloroquine-sensitive (D6 clone) and chloroquine-resistant (W2 clone) strains of Plasmodium falciparum to understand the relationships between the biological activity and molecular properties for the design of new compounds. The QSAR studies were performed using 35 compounds among which 26 molecules were taken using k-means clustering technique in the training set for the derivation of the QSAR models and nine molecules were kept as the test-set compounds to evaluate the predictive ability of the derived models. The chemometric tool used for the analysis was the genetic function approximation. The developed models were analysed in terms of their predictive ability, and comparable results were obtained for cross-validated predictive variance (Q 2) and externally predicted variance (R 2 pred) values (0.761 and 0.829, respectively, for the D6 model, 0.708 and 0.748, respectively, for the W2 model and 0.984 and 0.982, respectively for the QAAR model). The QSAR models suggest that the number of methylene groups (between the triazolyl and hydroxamate moieties) and partially negatively charged surface areas of the molecules are important parameters for the antimalarial activity.

Diacerein-mediated inhibition of IL-6/IL-6R signaling induces apoptotic effects on breast cancer

Interleukin-6 (IL-6) signaling network has been implicated in oncogenic transformations making it attractive target for the discovery of novel cancer therapeutics. In this study, potent antiproliferative and apoptotic effect of diacerein were observed against breast cancer. In vitro apoptosis was induced by this drug in breast cancer cells as verified by increased sub-G1 population, LIVE/DEAD assay, cell cytotoxicity and presence of terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL)-positive cells, as well as downregulation of antiapoptotic proteins Bcl-2 and Bcl-xL and upregulation of apoptotic protein Bax. In addition, apoptosis induction was found to be caspase dependent. Further molecular investigations indicated that diacerein instigated apoptosis was associated with inhibition of IL-6/IL-6R autocrine signaling axis. Suppression of STAT3, MAPK and Akt pathways were also observed as a consequence of diacerein-mediated upstream inhibition of IL-6/IL-6R. Fluorescence study and western blot analysis revealed cytosolic accumulation of STAT3 in diacerein-treated cells. The docking study showed diacerein/IL-6R interaction that was further validated by competitive binding assay and isothermal titration calorimetry. Most interestingly, it was found that diacerein considerably suppressed tumor growth in MDA-MB-231 xenograft model. The in vivo antitumor effect was correlated with decreased proliferation (Ki-67), increased apoptosis (TUNEL) and inhibition of IL-6/IL-6R-mediated STAT3, MAPK and Akt pathway in tumor remnants. Taken together, diacerein offered a novel blueprint for cancer therapy by hampering IL-6/IL-6R/STAT3/MAPK/Akt network.

Development of a robust and validated 2D-QSPR model for sweetness potency of diverse functional organic molecules

In the present report, we have developed a predictive QSPR model using only easily computable two-dimensional (2D) descriptors from diverse classes of sweetening agents to find out the key structural properties which regulate their sweet potency. The available data set was curated to remove salts, mixtures and compounds without having a definite structure. A k-fold double cross validation technique was employed for variable selection prior to development of the final model. The final model was developed using partial least squares (PLS) regression analysis and selected based on a mean absolute error (MAE) based criteria for the validation sets. The model was validated extensively using different internal and external validation strategies in accordance with the Organization for Economic Co-operation and Development (OECD) guidelines. This work presented development of a validated quantitative structure-property relationship (QSPR) model obtained from k-fold double cross-validation technique in order to find out the key structural information required to enhance the sweet potency of the molecules. Finally, we have designed and proposed 13 new molecules based on the insights obtained from the QSPR model. The designed compounds showed good in silico predicted sweetness potency with acceptable ADMET profile.

Advances in quantitative structure–activity relationship models of antimalarials

Importance of the field: Malaria still remains one of the deadliest infectious diseases having a tremendous morbidity and mortality impact in the developing world. Computational tools such as quantitative structure–activity relationship (QSAR) studies help medicinal chemists to understand the consistent relationship between antimalarial activity and molecular properties, and design new potent and selective ligands that may act on different classes of antimalarial drug targets so that these compounds may eventually be synthesized and assayed. Area covered in this review: In the present review, we focus on the current knowledge of QSARs and pharmacophore models of different classes of antimalarial drugs. In this context, we also review the reported docking studies of antimalarial compounds acting on different targets to explore the interaction pattern at the molecular level. What the reader will gain: The reader will gain an overview of advances of QSAR and related theoretical models of antimalarial drug compounds. Take home message: This review infers that most of the reported QSAR models are analog based QSARs with a limited applicability domain, but QSAR models based on diverse chemical structures acting on a particular target have been reported in very few cases.

Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models?: Quality of predictions from an “intelligent” use of multiple models

Quantitative structure‐activity/property/toxicity relationship (QSAR/QSPR/QSTR) models are effectively employed to fill data gaps by predicting a given response from known structural features or physicochemical properties of new query compounds. The performance of a model should be assessed based on the quality of predictions checked through diverse validation metrics, which confirm the reliability of the developed QSAR models along with the acceptability of their prediction quality for untested compounds. There is an ongoing effort by QSAR modelers to improve the quality of predictions by lowering the predicted residuals for query compounds. In this endeavor, consensus models integrating all validated individual models were found to be more externally predictive than individual models in many previous studies. The objective of this work has been to explore whether the quality of predictions of external compounds can be enhanced through an “intelligent” selection of multiple models. The consensus predictions used in this study are not simple average of predictions from multiple models. It has been considered in the present study that a particular QSAR model may not be equally effective for prediction of all query compounds in the list. Our approach is different from the previous ones in that none of the previously reported methods considered selection of predictive models in a query compound specific way while at the same time using all or most of the valid models for the total set of query chemicals. We have implemented our approach in a software tool that is freely available via the web http://teqip.jdvu.ac.in/QSAR_Tools/ and http://dtclab.webs.com/software‐tools.

Multilayered Variable Selection in QSPR: A Case Study of Modeling Melting Point of Bromide Ionic Liquids

Ionic liquids (ILs) are widely used in industry as green solvent alternatives because of their exceptional solvating ability and extremely low vapor pressure. For many applications of ILs, a low melting point temperature is desirable. Several simple ILs do not exhibit a clear melting point in the accessible temperature range of the DSC apparatus. Therefore, a computational approach is required to understand the relationship between the melting point of ILs and their structural characteristics. In the present study, the authors have developed predictive quantitative structure-property relationship (QSPR) models for melting point of ILs. A pool of 376 bromide ILs having quantitative melting point data were used to develop predictive models. A multilayered variable selection strategy has been adopted for development of final QSPR models. The models would provide an important guidance for the chemists to predict melting point of bromide ILs theoretically thereby saving the time and resources involved in the experimental determination. KeywoRdS Ionic Liquids, Melting Point, Multilayered Variable Selection, PLS, QSPR

Exploring QSAR, Pharmacophore Mapping and Docking Studies and Virtual Library Generation for Cycloguanil Derivatives as PfDHFR-TS Inhibitors

Resistance of available antimalarial drugs against Plasmodium species is one of the major problems of malaria control in the developing world. In the present study, we have performed QSAR, pharmacophore mapping and molecular docking studies of cycloguanil derivatives as Plasmodium falciparum dihydrofolate reductase thymidylate synthase (PfDHFR-TS) inhibitors to explore essential features required for the antimalarial activity and important interaction patterns between the enzyme and ligands for the design of new potent PfDHFR-TS inhibitors. The QSAR studies have been carried out using topological parameters along with thermodynamic and structural descriptors. Acceptable values of internal and external validation parameters for the developed QSAR models confirm acceptability of the models. Pharmacophore mapping revealed that two hydrogen bond donor (HBD) features and a hydrophobic feature (HYD) are important parameters for PfDHFR-TS inhibitory activity. The docking studies suggest that the PfDHFR-TS inhibitors interact with Asp54, Ile14, Ile164, ser108, Ser111, Tyr170, Met55, Ala16, Thr185, Leu46, Cys15, Phe58, Ile112, Trp48, Tyr57 and Leu119 amino acid residues. The QSAR, pharmacophore and docking studies inferred that i) branching of the substituents at R1 and R2 positions should be less (small alkyl chain substituents are favored); ii) the electronegativity of the molecules should be high but within some limit; iii) the size and volume of the molecules should be high; iv) molecules should be flexible enough; v) R configuration at C6 position of the triazine ring favors the inhibitory binding affinity; vi) the substituents of the phenyl ring at 3, 4 and 5 position of the phenyl ring should be small hydrophobic groups. Based on these studies, we have designed a library of cycloguanil derivatives with good in silico predicted PfDHFR-TS inhibitory activity.

Lead Hopping for PfDHODH Inhibitors as Antimalarials Based on Pharmacophore Mapping, Molecular Docking and Comparative Binding Energy Analysis (COMBINE): A Three-Layered Virtual Screening Approach

Malaria is a major worldwide public health threat securing the fifth position among the top ten causes of worldwide death with worrying social and economic burdens due to the rapid emergence of resistance to the currently available antimalarial drugs like chloroquine, sulfadoxine-pyrimethamine including Artemisinin. The causative agent for the disease is a parasite belonging to the Plasmodium species transmitted to human by the aid of female Anopheles mosquito. Although several antimalarial drugs have been reported till date, the efficacy of these drugs has been severely limited by widespread drug resistance necessitating new target based therapy. The enzymes governing the pyrimidine biosynthesis within the malarial parasite constitute essential targets for a number of clinically effective therapies since the pyrimidine metabolism pathway proves to be a vulnerable component of the parasite’s biology. Amongst the chemically validated targets, P. falciparum dihydroorotate dehydrogenase (PfDHODH), present in the mitochondria, is one of the essential druggable targets identified against P. falciparum that catalyses fourth reaction (formation of dihydroorotate to orotate which represents the rate limiting step in de novo pyrimidine biosynthesis) of pyrimidine de novo biosynthesis. Inhibition of the enzyme results in the shutdown of the mitochondrial electron transport chain thereby arresting crucial metabolic pathways within the microorganism leading to the inhibition of pyrimidine biosynthesis and consequent parasite death, rendering this enzyme a valid and attractive drug target against P. falciparum. Subsequently, the PfDHODH enzyme has been reviewed by several authors 7] as a promising drug target for novel antimalarial chemotherapy. The human and parasitic DHODH enzymes differ extensively in their amino acid sequence at the inhibitor binding-pocket thereby providing the structural basis for the identification of species-specific inhibitors. Several attempts have been made by different authors’ groups [8–10] to identify potent PfDHODH inhibitors based on in silico methodologies. Quantitative structure-activity relationship (QSAR) techniques constitute an essential in silico tool aiming to develop statistically valid models that may be utilized for database screening and activity prediction of untested molecules. The in silico virtual screening and computer-aided drug design methodologies enable an initial screening of large databases based on molecular properties, thereby saving both time and money involved in synthesizing and analyzing each of the molecules available in the database. The in silico screening techniques thus reduce the number of molecules to be synthesized and analyzed by identifying the hit compounds only. In the present work, 3D pharmacophore models have been developed based on a series of triazolopyrimidine derivatives exhibiting PfDHODH inhibitory activity. 13] Statistical validation of the model was performed based on a test set with already reported PfDHODH inhibitory activity and the essential pharmacophoric features were identified. Thus, the best pharmacophore model was used as 3D search queries for screening the NCI database (http://cactus.nci.nih.gov/download/nci) to identify new hits having potent PfDHODH inhibitory activity. The Lipinski filter and the ADMET filter were employed for the selection of the drug like molecules with the requisite pharmacokinetic properties. Finally, the lead compounds, selected based on the best fit values of the molecules, were subjected to molecular docking studies to refine the retrieved hits. The virtual screening approach in combination with pharmacophore modeling, molecular docking and COMBINE (comparative binding energy) based QSAR has been utilized in this work for identifying the requisite pharmacophoric features and screening the lead compounds with potential PfDHODH inhibitory activity. Ten pharmacophore hypotheses (Table 1) thus developed yielded acceptable results in terms of cost functions and