Slide 1: QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP
( QSAR ) Presented by : Latika Nagpal Slide 2: Correlate chemical structure with activity using statistical approach QSAR and Drug Design Slide 3: QSAR
Binding Slide 4: What is QSAR? A QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics.
QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new compounds. Slide 5: The biological effects of a chemical compound can often be predicted by its molecular structure based on the assumption that similar compounds have similar physical and biological properties. This principle, referred to as Structure Activity Relationships (SAR), is a powerful tool for predicting biological effects of a compound without the cost and time associated with testing by traditional whole-animal laboratory toxicology studies and/or in vitro / in vivo assays Slide 6: The number of compounds required for synthesis in order to place 10 different groups in 4 positions of benzene ring is 104
Solution: Synthesize a small number of compounds and from their data derive rules to predict the biological activity of other compounds. Why QSAR ? Slide 7: A QSAR generally takes the form of a linear equation
Biological Activity = Const + (C1 P1) + (C2 P2) + (C3 P3) + ...
where the parameters P1 through Pn are computed for each molecule in the series and the coefficients C1 through Cn are calculated by fitting variations in the parameters and the biological activity BASIC PRINCIPLE Slide 8: The Sandoz Institute for Medical Research on the development of novel analgesic agents can be used as an example of a simple QSAR. In this study, vanillylamides and vanillylthioureas related to capsaicin were prepared and their activity was tested in an in vitro assay which measured 45Ca2+ influx into dorsal root ganglia neurons. The data, which was reported as the EC50 (μM), is shown in Table (note that compound 6f is the most active of the series) QSAR STUDY : AN EXAMPLE Slide 9: In the absence of additional information, the only way to derive a best "guess" for the activity of 6i is to calculate the average of the values for the current compounds in the series. The average, 7.24, provides a
guess for the value of compound 8 but, how good is this guess? The graphical presentation of the data points is shown in Graph 1. Slide 10: The standard deviation of the data, s, shows how far the activity values are spread about their average.
This value provides an indication of the quality of the guess by showing the amount of variability inherent in the data. The standard deviation is calculated as shown below 7.24 s = √ (11.8-7.24)2 + (1.24 – 7.24)2 + (…)2 s = √ (539.41 / 6) = 9.48 Instead of relying on this limited analysis, to develop an understanding of the factors that influence activity within this series and use Slide 11: Binding data measured with sufficient precision to distinguish between compounds
A set of parameters which can be easily obtained and which are likely to be related
to receptor affinity;
A method for detecting a relationship between the parameters and binding data
A method for validating the QSAR
The QSAR equation is a linear model which relates variations in biological activity to variations in the values of computed (or measured) properties for a series of molecules. For the method to work efficiently, the compounds selected to describe the "chemical space" of the experiments (the training set) should be diverse. In many synthesis campaigns, compounds are prepared which are structurally similar to the lead structure. Not surprisingly, the activity values for this series of compounds will frequently span a limited range as well. In these cases, additional compounds must be made and tested to fill out the training set.