Qsar and Modeling Studies on Antimalarial and Antitubercular Agents

Abstract

In drug research, the chemical structure and its biological response, activity, toxicity, physiological concentrations (accumulation / retention / excretion) etc. are conjoined systems. A study of these systems with respect to each other will come to aid in promoting the insight and in tuning them for optimum response. In this, optimization of biological response of the chemical structure is a cherished goal. Here, understanding of the structure-activity relation comes to aid this process. In QSAR studies, transformations of chemical structure into numerical descriptors (variables) play a pivotal role. A meaningful inter- and intra-variables communications result in the evolution of a variety of models with different predictive and diagnostic values. In this each model may address different sub-structural regions and attributes in explaining the chosen phenomenon. This Provides scope to understand the predictive and diagnostic aspects of different sub-structural regions and in averaging them beyond the individual models. For this, it is necessary to characterize the molecules and their structural fragments from different perspectives for the generations of large number of diverse descriptors. Moreover, When dealing with a large number of descriptors, for the optimum utilization of information content of the generated datasets. it is necessary to adopt typical protocol(s) to identify the best models as well as information rich descriptor corresponding to the phenomenon under investigation. In this the high dimensional QSAR studies involving multiple models provide scope to understand the predictive and diagnostic aspects of different sub structural regions and in averaging and extrapolating them beyond the individual models. The work presented in this thesis is focused on the development of protocol(s) for the identification of information rich descriptors from diverse descriptor classes and construction of robust QSAR models for the phenomenon under study i.e. biological activity. Among the infectious disease the malaria and tuberculosis (TB) are leading cause of morbidity and mortality in the tropical and subtropical countries. According to WHO they collectively causes approximately 3 million deaths every year. In both cases the development of drug resistant strains curtails the life span of chemotherapeutic agents and complicates the treatment strategies. In this background we carried out high dimensional QSAR and modeling studies on some antimalarial and antitubercular agents using various physicochemical, structural, topological and/ or topographical descriptors to rationalize their structure-activity profile. The work embodies in this thesis is arranged in to seven chapters. The Chapter 1 describes the detailed review of QSAR and modeling approaches in in silico drug design. In this various kind of chemical structure indices and modeling techniques are reviewed. The Chapter 2 deals with an overview on malaria, present therapy, novel targets and leads for development of new antimalarial drug. The Chapter 3 describes the QSAR study on the 4-(3’,5’-disustituted aniline)quinolines as antimalarial agents using combinatorial protocol in multiple linear regression (CP-MLR) procedure. In this we have developed an efficient algorithm to identify information rich descriptors from a large datasets of descriptors. The Chapter 4 describes the QSAR study Plasmodium falciparum and rat protein farnesyltransferase (PFT) inhibitory activities of 6-cyano-1-(3-methyl-3H-imidazoly-4-ylmethyl)-3-substituted-1,2,3,4-tetrahydroquinoline (THQ) analogues in order to explore the similarities/ deviations between the two enzymes for these analogues. The study has been carried out using the CP-MLR with several 2D- and 3D – descriptors from molecular operating environment (MOE). The Chapter 5 gives the overview of tuberculosis, present therapy, various targets and leads for the development of new antitubercular drugs. The Chapter 6 deals with QSAR studies two different leads associated with antitubercular activity i.e. functionalized alkenols (functionalized heptenol and octenol derivatives, acyclic sugars) and C-3 alkyl/arylalkyl 2,3-dideoxy hexenopyranosides ( cyclic sugars ). The studies on functionalized alkenols and 2,3-dideoxy hexenopyranosides have been carried out with different classes of topological and topographical descriptors respectively. The Chapter 7 describes the PLS based modeling study on substituted nitrofuranyl amide (NFA) analogues for their antitubercular activity with various topological and topographical descriptors. In each chapter the detailed computational procedure for the model development and the selected QSAR models are discussed in connection with the phenomenon under study i.e. biological activity.