SAR data

Table 4 SAR data with molecular structures of 5-HTia receptor ligands reported by Palu-chowska et al. (2002) [63]...

We have discussed now a number of important molecular properties which are used to profile lead and drug molecules. In many cases, certain combinations of these properhes are correlated to some extent within a series of compounds. In particular, the size-related properties of MW, PSA, and log P show this tendency. One should be aware of this phenomenon and it should be taken into account when interpreting the underlying SAR data. However, there is no strong correlation between these three properties in general. When looking at a random subset of 10000 compounds from GVKBIO [9], we find that MW and log P are correlated with r=0.32, log P and PSA with r=0.35, and MW and PSA with r=0.61. 

It should be noted that much of the early SAR data on opioids was generated using whole-animal analgesic assays rather than with isolated tissue preparations rich in receptor subtypes. 

Once interesting hits have been identified, additional analogs can be quickly identified for biological testing to provide preliminary Structure-Activity Relationship (SAR) data. 

The approach discussed to use VolSurf derived in silica models to understand structure-PK relationships for pharmacokinetic properties was also applied to one series of selective cardiac KATP channel blockers [160]. It was found that compounds fulfilling the predefined selectivity profile exhibit only less-optimal pharmacokinetic properties because of a short plasma mean residential time (MRT). Consequently, the MRT for 28 compounds from rabbit iv studies for one series was used as dependent variable to derive a VolSurf PLS model in addition to ligand affinity SAR data. The chemical... 

This chapter discusses the application of popular cheminfor-matics approaches, such as rigorously built QSAR models and shape pharmacophore models, to the problem of targeted library design. QSAR models offer unique ability to rationalize existing experimental SAR data in the form of robust quantitative... 

Effective structure-activity relationship (SAR) generation is at the centre of any medicinal chemistry campaign. Much work has been done to devise effective methods to explain and explore SAR data for medicinal chemistry teams to drive the design cycles within drug discovery projects (1). Recent work on SAR generation highlights the commonly observed discontinuity of SAR and bioactivity data, the so-called activity cliffs (2). This also emphasises the need to empirically determine SAR for each lead... 

Developing a system capable of collecting multivariate SAR data and exploiting the data to produce predictive SAR models is a major systems integration task. However, recent advances in computers, operating systems, and computational chemical tools now enables the implementation of a system that can track compounds, store chemical property data in a comprehensive relational database, and operate on virtual libraries in an iterative fashion to develop SAR models and refine chemical properties [28]. 

The remainder of this section discusses how SAR data can be used by chemists as a powerful tool for designing safer chemical substances. Unless indicated otherwise, the word activity refers to toxicity. Chapter 13 provides more detailed discussions on the use of SARs, and demonstrates the usefulness of this approach with respect to the design of safer aromatic amines. 

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. 

A few recommendations regarding the set of molecules to be used to exploit the ROC approach fully can be given as follows. First, as the objective is to account for all available SAR data, the more molecules are included, the better... 

As we have seen, pharmacophore models are compilations of SAR data. Consequently, they can be used by both medicinal and computational chemists to guide them in their research. 

Regarding the statistics reporting the SAR data, we were rather lenient since accurate activity prediction was not our objective. Hence, models exhibiting a... 

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