Optimization for Affinity and Selectivity

This section will provide an overview on ADME models from our group to illustrate our approach for building predictive models on structurally diverse training sets. Datasets for intestinal human absorption and human serum albumin binding are discussed, while models for other relevant ADME properties have also been obtained. Those models, however, do not stand alone but are used in combination with those models tailored for affinity and selectivity in the frame of multidimensional lead optimization. 

Adverse effects may result from the action of the drug on its principal therapeutic target. In that case, there is nothing the chemist can do to optimize the safety of the drug, since any modification of the molecule to increase its affinity and selectivity for the target will increase both its therapeutic and its undesired effects. 

While the methods described in this chapter have been optimized for affinity selection-MS using continuous SEC, they are readily adaptable to spin-column, gel permeation, or other well validated and highly accessible two-stage AS-MS designs. The use of AS-MS for studying protein-ligand interactions, especially for the discovery of ligands from pools of compounds, has been reported by a number of experts in the pharmaceutical industry and academia over the past decade. 

The resulting semi-quantitative model was used in conjunction with structure-based docking and scoring, 3D-QS AR based affinity and selectivity predictions and in silico ADME models to estimate membrane permeability, solubility, and other key properties for the optimization process in this series. Hence, in this as well as in other series, multiple models can be collectively applied for ranking and prioritizing synthesis candidates and focused virtual libraries during advanced stages of multidimensional compound optimization. 

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