(1391-D) Using computational simulation to distill best practices for applying the Morrison equation to biochemical pharmacology data
Tuesday, February 6, 2024
2:00 PM – 3:00 PM EST
Location: Exhibit Halls AB
Abstract: Small molecule pharmaceutical research projects aim to develop selective, potent and safe small molecules that can be administered to patients; the preferred administration method is a pill that can be orally consumed. To satisfy that aim, lead molecules need to be orally bioavailable, possess low clearance and good potency for their target. Improving potency of lead compounds is a primary objective of SAR optimization and is a critical approach to reducing the effective dose to an amount small enough that the tablet or capsule can be swallowed by the patient.
For projects that deploy biochemical pharmacology assays in the front-line position of their testing funnel, advanced leads often achieve potencies that exceed what is distinguishable via a typical IC50 determination with the 4-parameter Hill equation. When this happens, the compounds can no longer be rank-ordered for potency. We often switch our analysis to a tight-binding Ki analysis (TBKi) which uses the Morrison Equation that allows for accurate curve fitting of titrated compounds to lower potency than a classical IC50. The question then follows- at what potency (relative to assay conditions) does analysis with the Morrison Equation lose its ability to rank order compounds? To address this, we simulated and analyzed pharmacology data over a range of Ki values using various titration schemes. We performed TBKi analysis of this dataset with different permutations of fit parameters and compared the calculated vs. actual Ki values to discern the boundaries of the analysis method. This poster shares our findings in the form of best-practices for applying the Morrison Equation via our TBKi approach.