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70 Chemoinformatics: Directions Toward Combating Neglected Diseases Shaikh et al. of new drug discovery, finding new drug molecules has been an extremely challenging, time consuming and uneconomical process. In order to address these issues, molecular modelling tool viz. Quantitative Structure– Activity Relationships (QSAR) has been successfully implemented for decades in the development of new drug candidates. Although it does not completely eliminate the trial and error factor involved in the development of a new drug, it certainly decreases the number of compounds synthesized by facilitating the selection of the most promising candidates. The success of QSAR has attracted scientific community in the pharmaceutical arena to investigate relationships of molecular parameters with properties other than activity. Originally based on the idea that compounds with similar physico-chemical properties trigger similar biological effects, Quantitative Structure–Activity Relationships (QSAR) builds virtual statistical models to establish a correlation between structural and electronic properties of potential drug candidates and their binding affinity towards a common macromolecular target. It includes all statistical methods, by which biological activities are related with structural elements (Free Wilson analysis), physico-chemical properties (Hansch analysis), or fields (3D-QSAR). QSAR studies have meant that, once a correlation between structure and property is found it is possible to predict quantities such as the binding affinity or the toxic potential of existing or hypothetical molecules. The origin of QSAR dates back to 19 th Century, when Crum-Brown and Fraser [1,2] published the first quantitative relationship between ‘pharmacological activity’ and ‘chemical structure’ in 1868 followed by the seminal contribution of Meyer and Overton in 1890s who noted that the toxicity of organic components depended on their lipophilicity [3,4]. True progress in the ‘calculus’ of Structure–Activity Relationships (SAR) was made in the 1960s, when Hansch and Fujita published a LFER related model considered to be the formal beginning of QSAR [5]. The first approach to building quantitative relationships which described activity as a function of chemical structure was based on the principles of thermodynamics, wherein the free-energy terms ΔE, ΔH and ΔS were represented by a set of parameters which could be derived for a molecule. Electronic effects viz. electron donating and withdrawing abilities, partial atomic charges and electrostatic field densities were represented by Hammett sigma (σ) values, resonance parameters (R values), inductive parameters (F values) and Taft substituent values (ρ*, σ*,) while steric effects (Es) and molecular volume and surface area were represented by values calculated for Molar Refractivity and the Taft steric parameter. The steric effects were calculated using partition coefficient (log P) or the hydrophobic parameter (π), derived from the partition coefficient. The linear mathematical models (equations 1 and 2) which described the relationship between this physico-chemical properties and the observed Biological Response (BR) was the typical Hansch equation as shown below (see equations 1 and 2). BR f ( electronic steric hydrophobic ) … (equation 1) log 1/[C] = A(logP) – B(logP) 2 + C(ES) + D() + E + … … (equation 2) For developing QSAR models the BR (biological response) is usually written as log 1/C (where C is the molar concentration of drug producing a standard response) since it only defines the quantitative measure of potency in our system (degree of perturbation). With the developments in biological assay techniques, Minimum Inhibitory Concentrations (MICs), IC50 values, Ki values and their ratios are used as values for C. The use and development of QSAR methodology have then never looked back, Hansch and his group alone have performed almost 2000 biomedical QSAR studies. Though the Hansch approach was not free from limitations, it permitted complex biological systems to be modelled successfully using simple parameters. An alternative approach to compound design was suggested by Free and Wilson [6] where they used a series of substituent constants which related biological activity to the presence of a specific functional group at a specific location on the parent molecule. The mathematical equation which explained the relationship between biological activity and the presence or absence of a substituent was expressed as described in equation 3:
Antitubercular Agents & QSAR Chemoinformatics: Directions Toward Combating Neglected Diseases 71 Activity = A + G X i j ij ij … (equation 3) where A is the average biological response for the series; Gij the contribution to activity of a functional group i in the j th position and Xij the presence (1) or absence (0) of the functional group i in the j th position. This approach involved use of the above equation to build a matrix for the series and represented this matrix as a series of equations. Subsequently, substituent constants were derived for every functional group at every position. The importance of the constants was estimated using the statistical tests. As a standard practice the models were tested for their validity and used to predict activity values for yet to be synthesized molecules. The early QSAR studies typically relied on a single physico-chemical property, such as the solubility or the pKa value, to explain the variance in biological effect of a molecule (1D QSAR). Later on, Hansch, Fujita, Free and Wilson extended the technique to include the connectivity of a compound by considering physicochemical properties of single atoms and functional groups and their contribution to biological activity and this method was then referred to as 2D-QSAR [7-18]. Subsequently, realizing the limitation in the Hansch formulation, QSAR was further taken in for development by a number of scientists who added new dimensions leading to the nD-QSAR methods. The word dimensions referred to here are the properties (independent variables) added to develop the QSAR models. With the introduction of added dimensions, the original Hansch QSAR was referred to as 2D-QSAR. The third dimension came with the consideration of the 3D molecular properties of molecules. A number of 3D-QSAR techniques are now known. CoMFA [19,20], AFMoC [21,22], COMMA [23-25], CoMSIA [26,27], CoMSA [28-35], CoMPASS [36,37], CoRIA [38-40], CoRSA, SoMFA [41] and many more make the list of 3D-QSAR techniques. 3D-QSAR can be further divided into grid based and non-grid based methods. The introduction of Comparative Molecular Field Analysis (CoMFA) [19,20] in 1988 by Cramer marked another milestone in the arena of QSAR paradigm as, for the first time, structure–activity relationship models were developed on the three-dimensional structural properties of the molecules (3D-QSAR). The widely used CoMFA method is based on molecular field analysis and represents real 3D-QSAR methods wherein the ligands’ interaction with chemical probes is mapped onto a surface or grid surrounding a series of compounds (aligned in 3D space). This grid mimics a surrogate of the active site of the true biological receptor. 3D-QSAR methods are an extension of the traditional 2D-QSAR approach, wherein the physico-chemical descriptors like molecular volume, molecular shape, HOMO and LUMO energies, and ionization potential calculated from the 3D structures of the chemicals are correlated with the observed biological activity. A similar approach was adopted in developing other known 3D-QSAR formalisms like CoMSIA (Comparative Molecular Similarity Index Analysis) [26,27], SOMFA (self - organizing molecular field analysis) [41], and COMMA (Comparative Molecular Moment Analysis) [23-25]. The main problems encountered in building 3D-QSAR models are related to improper superposition of the ligands, greater flexibility of the ligands, identification of the bioactive conformation, and more than one binding mode of ligands. While these problems have long been recognized, only recently, 4D-QSAR and 5D-QSAR have been developed allowing for the representation of multiple conformations, orientation and protonation states. As an example, Vedani et al. have developed QSAR techniques beyond the third dimension by accounting for the effect of multiple conformations as the 4 th dimension, the induced-fit mechanism as the 5 th dimension, and evaluation of solvation models as the 6 th dimension [42]. A latest extension of the QSAR concept to 6 th dimension (6D QSAR) allows for the simultaneous consideration of different solvation models [43-45]. In a nutshell, QSAR tries to correlate the physicochemical properties to the biological response, thus indirectly these physicochemical properties represent not only the action of the drug at the receptor site but its Absorption, Distribution, Metabolism Excretion and Toxicity (ADMET). ADMET properties of drug candidates were most times not considered implicitly. More recently, the technique has been extended to predict Absorption, Distribution, Metabolism, Elimination, Toxicity (ADMET) properties of compounds. Interestingly, prediction of the toxic potential of a drug or environmental chemical using QSAR has attracted much attention and various readymade tools (software or web based tools) are even available which work on the same principles. This outgrowth of QSAR which deals with pharmacokinetic issues is referred as Quantitative Structure Property Relationship (QSPR).
Page 2 and 3: Chemoinformatics: Directions Toward
Page 4 and 5: CONTENTS Foreword i Preface ii List
Page 6 and 7: ii PREFACE Low-income populations i
Page 10 and 11: New Approaches to the Development C
Page 12 and 13: 34 Chemoinformatics: Directions Tow
Page 16 and 17: Antimalarial Agents Chemoinformatic
Page 22 and 23: Antileishmaniasis Agents Chemoinfor
Page 24 and 25: 146 Chemoinformatics: Directions To
Page 30 and 31: Molecular Modeling of the Toxoplasm

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