Lead optimization Hansch analysis

The simplicity of Hansch analysis also means that experienced medicinal chemists may be able to identify trends in activity without the assistance of a QSAR equation. Making individual new lead analogues is generally a slow process, and a medicinal chemist has ample time to examine SAR data. While a chemist will not be able to quantify a structure-activity relationship, just knowing the approximate trend of the relationship is usually adequate for lead optimization. Hansch analysis is valuable only if it can reveal something that is not already known about the compounds being tested. 

Objectives Optimize biological activity of drugs Find new active lead compounds Characteristics Response in isolated systems Effects are specific and well defined Specific mechanism of action Receptor is known in most cases Techniques Hansch Approach Multivariate Analysis Computerized molecular modeling Estimate rates of fate processes Analyze Processes Whole organism response Net effects (mortality growth, etc.) Specific nonspecific mechanisms Receptor unknown in most cases Hansch Approach Multivariate Analysis Molecular modeling not applied... 

The most appropriate and widely used method for extracting information from large data sets is QSAR and its relatives, quantitative structure-property relationships (QSPR) for property modeling, and quantitative structure-toxicity relationships (QSTR) for toxicity modeling. QSAR is a simple, well validated, computationally efficient method of modeling first developed by Hansch and Fujita several decades ago (30). QSAR has proven to be very effective for discovery and optimization of drug leads as well as prediction of physical properties, toxicity, and several other important parameters. QSAR is capable of accounting for some transport and metabolic (ADMET) processes and is suitable for analysis of in vivo data. 

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