02397nas a2200301 4500008004100000022001400041245011400055210006900169260000800238300001400246490000700260520143100267653002001698653002901718653001901747653001901766653000901785653002701794100002001821700001901841700002101860700001801881700002001899700001901919700002001938700001901958856011801977 2019 eng d a1570-180800aRecursive partitioning analysis and anti-tubercular screening of 3-aminopyrazine-2-carbohydrazide derivatives0 aRecursive partitioning analysis and antitubercular screening of  cAPR a1264-12750 v163 a

Background: Treating tuberculosis is a challenge due to the development of drug resistance. Hence, it is imperative to develop novel leads having high potency and efficacy to curb drug resistance. Methods: The present research work is focused on microwave-assisted synthesis of novel twenty-six 3-amino-N'-benzylidenepyrazine-2-carbohydrazide derivatives (3a-z), where, lyophilization technique was used for isolation of the major intermediate, 3-aminopyrazin-2-carbohydrazide. All synthesized compounds were subjected for anti-tubercular screening against Mycobacterium tuberculosis H37Ra by using XTT Reduction Menadione Assay (XRMA) protocol. Results: Out of 26 synthesized compounds, four N'-substitutedbenzaldehyde-3-amino-pyrazine-2-carbohydrazide derivatives viz. 3i, 3j 3v and 3z showed significant activity against M. tuberculosis H37Ra. The compounds 3i, 3j, 3v and 3z showed 99, 98, 92 and 87 % inhibition respectively as compared to 94% inhibition shown by the standard drug rifampicin. The MIC and IC50 values were in the range of 24.3-110 and 5.9-20.8 mu g/ml respectively. Conclusion: A classification model called Recursive Partitioning (RP) based on binary Quantitative Structure-Activity Relationship (QSAR) was derived for the establishment of structure-activity relationship (SAR). The predictions derived on the basis of RP model were found to be in agreement with anti-tubercular screening data.

10a3-aminopyrazine10aanti-tubercular activity10aCarbohydrazide10alyophilization10aQSAR10arecursive partitioning1 aMiniyar, Pankaj1 aMahajan, Anand1 aAnuse, Dattatray1 aKumar, Ashish1 aBarmade, Mahesh1 aSarkar, Dhiman1 aArkile, Manisha1 aKhedkar, Vijay uhttp://library.ncl.res.in/content/recursive-partitioning-analysis-and-anti-tubercular-screening-3-aminopyrazine-202829nas a2200241 4500008004100000022001400041245016200055210006900217260000800286300001400294490000700308520192300315653002602238653001502264653003902279653003702318653002302355653000902378653002302387100002702410700001902437856013102456 2021 eng d a1381-199100a2D-QSAR modeling and two-fold classification of 1,2,4-triazole derivatives for antitubercular potency against the dormant stage of Mycobacterium tuberculosis0 a2DQSAR modeling and twofold classification of 124triazole deriva cAPR a1227-12420 v263 a

The dormant or latent form of Mycobacterium tuberculosis (MTB) is not killed by the conventional antitubercular drugs. The treatment of latent TB is essential to reduce the period of treatment as well as incidences of drug resistance. In this background, we have made an attempt to develop the quantitative structure-activity relationship models (QSAR: regression and classification based) against the dormant form of MTB and later used the developed classifier models (linear discriminant analysis (LDA) and random forest (RF)) for the two-fold classifications. The logic of applying this concept of two-fold classification for the MTB modeling is to increase the confidence of correct classification. The 2D-QSAR modeling suggested the contribution of burden eigen, edge adjacency, van der Waals (vdW) surface area, topological charge, and pharmacophoric indices in predicting the antitubercular activity against the dormant MTB. The prediction qualities of the training and test sets were found to be moderate and good, according to the mean absolute error (MAE)-based criteria's. The LDA and RF models unveiled the importance of burden eigen, edge adjacency, Geary autocorrelation, and drug-like indices as discriminating features to differentiate the antitubercular compounds into higher and lower active groups. The LDA model showed the classification accuracies of 85.14% and 87.10% for the training and test sets, while the RF model exhibited the accuracies of 100.00% and 80.65% for both the sets. The descriptors selected in the final models are only two-dimensional (2D), which are easy to compute and does not require computationally expensive steps of structure conversion, optimization, and energy minimization mandatorily needed before the computation of 3D descriptors. These models could be used for identifying and selection of higher active compounds against the dormant form of the MTB.

10aClassification models10aDormant TB10aLinear discriminant analysis (LDA)10aMycobacterium tuberculosis (MTB)10aNonlinear modeling10aQSAR10aRandom forest (RF)1 aAher, Rahul, Balasaheb1 aSarkar, Dhiman uhttp://library.ncl.res.in/content/2d-qsar-modeling-and-two-fold-classification-124-triazole-derivatives-antitubercular-potency02400nas a2200301 4500008004100000022001400041245011100055210006900166260000800235300001200243490000700255520143200262653001301694653002201707653002501729653000901754653001401763100002301777700002701800700002101827700002201848700002201870700002301892700001901915700002301934700001701957856012401974 2021 eng d a1573-409900aIn silico studies, synthesis and antitubercular activity of some novel quinoline - azitidinone derivatives0 aIn silico studies synthesis and antitubercular activity of some  cJAN a134-1430 v173 a

Background: Diarylquinolines like Bedaquiline have shown promising antitubercular activity by their action of Mycobacterial ATPase. Objective: The structural features necessary for a good antitubercular activity for a series of quinoline derivatives were explored through computational chemistry tools like QSAR and combinatorial library generation. In the current study, 3-Chloro-4-(2-mercaptoquinoline-3-yl)-1-substitutedphenylazitidin-2-one derivatives have been designed and synthesized based on molecular modeling studies as anti-tubercular agents. Methods: 2D and 3D QSAR analyses were used to designed compounds having a quinoline scaffold. The synthesized compounds were evaluated against active and dormant strains of Mycobacterium tuberculosis (MTB) H37 Ra and Mycobacterium bovis BCG. The compounds were also tested for cytotoxicity against MCF-7, A549 and Panc-1 cell lines using MTT assay. The binding affinity of designed compounds was gauged by molecular docking studies. Results: Statistically significant QSAR models generated by the SA-MLR method for 2D QSAR exhibited r(2) = 0.852, q(2) = 0.811, whereas 3D QSAR with SA-kNN showed q(2) = 0.77. The synthesized compounds exhibited MIC in the range of 1.38-14.59(mu g/ml). These compounds showed some crucial interaction with MTB ATPase. Conclusion: The present study has shown some promising results which can be further explored for lead generation.

10acombilib10aMolecular docking10amycobacterial ATPase10aQSAR10aQuinoline1 aChitre, Trupti, S.1 aAsgaonkar, Kalyani, D.1 aVikhe, Amrut, B.1 aPatil, Shital, M.1 aGarud, Dinesh, R.1 aKhedkar, Vijay, M.1 aSarkar, Dhiman1 aNawale, Laxman, U.1 aYeware, Amar uhttp://library.ncl.res.in/content/silico-studies-synthesis-and-antitubercular-activity-some-novel-quinoline-azitidinone