C-3 Alkyl/Arylalkyl-2,3-dideoxy Hex-2-enopyranosides as Antitubercular Agents:  Synthesis, Biological Evaluation, and QSAR Study

Abstract

A series of C-3 alkyl and arylalkyl 2,3-dideoxy hex-2-enopyranoside derivatives were synthesized by Morita-Baylis-Hillman reaction using enulosides 4, 5, and 6 and various aliphatic and aromatic aldehydes. The compounds were evaluated in vitro for the complete inhibition of growth of Mycobacterium tuberculosis H37Rv. They exhibited moderate to good activity in the range of 25-1.56 mug/mL. Among these, 4d, 4h, 5c, and 4hr showed activity at minimum inhibitory concentrations, 3.12, 6.25, 1.56, and 1.56 mug/mL, respectively. These compounds were safe against cytotoxicity in VERO cell line and mouse macrophage cell line J 744A.1. A QSAR analysis by CP-MLR with alignment-free 3D-descriptors indicated the relevance of structure space comparable to the minimum energy conformation (from conformational analysis) of 5c to the activity. The study indicates that the compounds attaining the conformational space of 5c and reflecting some symmetry, minimum eccentricity, and closely placed geometric and electronegativity centers therein are favorable for activity.

Activity of 7-methyljuglone derivatives against Mycobacterium tuberculosis and as subversive substrates for mycothiol disulfide reductase

“…The low activities in the tetra-acetate series (17)(18)(19) are likely due to the lack of the quinone motif. The alkoxy derivatives (11)(12)(13)(14)(15)(16) were found to be less active than their hydroxyl precursors 3, 5 and 6. …”

Consensus features of CP-MLR and GA in modeling HIV-1 RT inhibitory activity of 4-benzyl/benzoylpyridin-2-one analogues

“…With this in view, two feature selection approaches namely combinatorial protocol in multiple linear regression (CP-MLR) and GA have been used to identify the descriptors for modeling the activity of 4-benzyl/benzoyl-3-dimethylaminopyridin-2(1H)-ones. In this, CP-MLR is a filter-based feature selection procedure [20][21][22] . It involves a systematic search for the identification of influential features to model the activity.…”

Chemical Structure Indices in In Silico Molecular Design

“…The information content of these and many other graph theoretical descriptors have been validated through correlation studies involving different additive-constitutive properties of hydrocarbons [70][71][72] and biological properties of several classes of compounds [73][74][75][76]. An interesting application of graph theory is the explanation and prediction of 13 C-NMR profile of organic compounds [77].…”