Lead optimization QSAR

Lewis, R. A. A general method for exploiting QSAR models in lead optimization. /. Med. Chem. 2005, 48, 1638-1648. 

QSAR retain, however, a certain significance in late stages of lead optimization where they can serve to... 

The benefit of QSAR is a more efficient lead optimization process. If a good QSAR formula can be derived, the activity of leads can be approximated by calculation without... 

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. 

Brown N, Lewis RA (2006) Exploiting QSAR methods in lead optimization. Curr Opin Drug Discov Devel 9(4) 419—424... 

There are a number of QSAR approaches useful for predicting receptor binding affinity. These range from simple rejection filters for drug-like chemical identification to more sophisticated QSAR models used in lead optimization. We have constructed various types of QSAR models for ER binding. 

In search for potent and systemically available inhibitors of the matrix metalloproteinase MMP-8 (Matter et al. 1999 Matter et al. 2002) following oral administration, a local ADME model was derived to support lead optimization. For an internal series of inhibitors on the tetrahydroisoquinoline scaffold, hydroxamic acids for zinc ion binding in 3-position are essential for MMP affinity in first generation inhibitors. However, those compounds are characterized by insufficient pharmacokinetic properties and low systemic exposure following oral administration. Driven by X-ray and 3D-QSAR studies (CoMFA), alternative Zn2+ binding groups like carboxylates were... 

Moreover, a final 3D-QSAR model vahdation was done using a prospective study with an external test set. The 82 compounds from the data set were used in a lead optimization project. A CoMFA model gave an (cross validated) value of 0.698 for four relevant PLS components and a conventional of 0.938 were obtained for those 82 compounds. The steric descriptors contributed 54% to the total variance, whereas the electrostatic field explained 46%. The CoMSIA model led to an (cross vahdated) value of 0.660 for five PLS components and a conventional of 0.933. The contributions for steric, electrostatic, and hydrophobic fields were 25, 44, and 31%. As a result, it was proved that the basic S4-directed substituents should be replaced against more hydrophobic building blocks to improve pharmacokinetic properties. The structural and chemical interpretation of CoMFA and CoMSIA contour maps directly pointed to those regions in the Factor Xa binding site, where steric, electronic, or hydrophobic effects play a dominant role in ligand-receptor interactions. 

On the other hand, several superstructural approaches were designed specifically for the QSAR analysis and lead optimization for organic compounds. Let us consider them in more detail. 

In addition, substituent properties can be used systematically to find quantitative structure-activity relationships (QSAR) during lead optimization in structures of pharmaceutical interest [10, 11]. 

Many predictive, validated models have been developed using these QSAR techniques and have often assisted in the selection of structures for lead optimization. Often the QSAR results are not available until after the process of lead optimization has already progressed, and these models represent retrospective analysis of the lead optimization process rather than a direct influence on the design of the lead optimization compounds. 

Quantitative structure-activity relationships QSAR. The QSAR approach pioneered by Hansch and co-workers relates biological data of congeneric structures to physical properties such as hydrophobicity, electronic, and steric effects using linear regression techniques to estimate the relative importance of each of those effects contributing to the biological effect. The molecular descriptors used can be 1-D or 3-D (3D-QSAR). A statistically sound QSAR regression equation can be used for lead optimization. 

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