JNTU Pharm. D III Year MEDICINAL CHEMISTRY Previous Question Paper (October/November - 2019 - R08)

Hansch analysis is a quantitative structure-activity relationship (QSAR) approach that correlates the biological activity of a series of compounds with their physicochemical properties. It uses a linear free energy relationship (LFER) model to express biological activity as a function of hydrophobic, electronic, and steric parameters.

The general equation is:

Where π represents the hydrophobic substituent constant, σ represents the Hammett electronic constant, and Es represents the Taft steric factor.

Merits of Hansch analysis:

• It provides a mathematical model to predict the activity of new, unsynthesized derivatives within a chemical series.
• It allows for the simultaneous evaluation of multiple physicochemical properties, providing a holistic view of structural requirements for activity.
• It helps in identifying the optimal range of substituent properties (e.g., lipophilicity) required for maximum potency.
• It reduces the number of compounds that need to be synthesized and tested, saving time and resources.
• It enables the identification of outliers, which can lead to the discovery of novel binding mechanisms or unexpected structural interactions.

Computer-Aided Drug Design (CADD) is a specialized discipline that utilizes computational methods, software, and algorithms to facilitate the discovery and development of new therapeutic agents. It involves the use of molecular modeling, simulation, and informatics to understand the interaction between a drug candidate and its biological target.

Importance of CADD:

• Accelerated Discovery: Significantly reduces the time required for identifying lead compounds by screening large virtual libraries rather than physical ones.
• Cost Efficiency: Lowers the overall cost of drug development by minimizing the number of compounds that require laboratory synthesis and biological evaluation.
• Rational Design: Enables structure-based drug design (SBDD), where the 3D structure of a target protein is used to design ligands that fit precisely into the binding site.
• Optimized Pharmacokinetics: Assists in predicting ADME (Absorption, Distribution, Metabolism, and Excretion) properties early in the design phase, reducing the risk of late-stage clinical failure.
• Target Specificity: Improves the selectivity of drug candidates, thereby reducing potential off-target side effects and improving the therapeutic index.