The structure–activity relationship (SAR) is the relationship between the chemical structure of a molecule and its biological activity. This idea was first presented by Crum-Brown and Fraser in 1865.[1]
The analysis of SAR enables the determination of the chemical group responsible for evoking a target biological effect in the organism. This allows modification of the effect or the potency of a bioactive compound (typically a drug) by changing its chemical structure. Medicinal chemists use the techniques of chemical synthesis to insert new chemical groups into the biomedical compound and test the modifications for their biological effects.
This method was refined to build mathematical relationships between the chemical structure and the biological activity, known as quantitative structure–activity relationships (QSAR). A related term is structure affinity relationship (SAFIR).
The large number of synthetic organic chemicals currently in production presents a major challenge for timely collection of detailed environmental data on each compound. The concept of structure biodegradability relationships (SBR) has been applied to explain variability in persistence among organic chemicals in the environment. Early attempts generally consisted of examining the degradation of a homologous series of structurally related compounds under identical conditions with a complex "universal" inoculum, typically derived from numerous sources.[2] This approach revealed that the nature and positions of substituents affected the apparent biodegradability of several chemical classes, with resulting general themes, such as halogens generally conferring persistence under aerobic conditions.[3] Subsequently, more quantitative approaches have been developed using principles of QSAR and often accounting for the role of sorption (bioavailability) in chemical fate.[4]
Approach Structure-Activity Relationship (SAR) is an approach designed to find relationships between chemical structure (or structural-related properties) and biological activity (or target property) of studied compounds. As such it is the concept of linking chemical structure to a chemical property (e.g., water solubility) or biological activity including toxicity (e.g., fish acute mortality). Qualitative SARs and quantitative SARs, collectively are referred to as (Q)SARs. Qualitative relationships are derived from non-continuous data (e.g., yes or no data), while quantitative relationships are derived for continuous data (e.g., toxic potency data). The approach is not new as A.F.A. Cros in 1863 noted in “Action de l’alcool amylique sur l’organisme”, the relationship between the toxicity of primary aliphatic alcohols and their water solubility. Assumption Objectives Models Activity = f (physiochemical or structural properties) The development of a (Q)SARs model requires three components: 1) A data set that provides activity (usually measured experimentally) for a group of chemicals (i.e., the dependent variable). This group of chemicals is typically defined by some selection criteria. 2) A structural criteria or structure-related property data set for the same group of chemicals (i.e., the independent variables).
Uses of (Q)SAR to fill data gaps
For this group of chemicals a qualitative relationship is observed between Structure X and Activity Z. Using this relationship, measured values of Activity Z for compounds A, B and D can be use to fill the data gap of Activity Z for the untested compound E. This is done by reading-across from compound A, B, and D to compound E (predicting Activity Z to be positive for Compound E). For this same group of similar chemicals the relationship between Property Y and Activity T is quantitative and modeled as [Activity T = 5.0 (Property Y) + 5.0]. Using this (Q)SAR model the potency of Activity T for compound D is predicted to be 25. Further readings on (Q)SAR QSAR models: • Selassie CD. 2003. History of Quantitative Structure-Activity Relationships In: Abraham, DJ (ed.) Burger’s Medicinal Chemistry and Drug Discovery Sixth Edition, Volume 1: Drug Discovery. John Wiley&Sons, Inc, pp. 1-48. • Cronin MTD, Walker JD, Jaworska JS, Comber MHI, Watts CD, and Worth AP. 2003. Use of QSARs in international decision-making frameworks to predict ecological effects and environmental fate of chemical substances. Environ. Health Perspect. 111:1376–1390. • Cronin MTD, Jaworska JS, Walker JD, Comber MHI, Watts CD and Worth AP. 2003. Use of QSARs in international decision-making frameworks to predict health effects of chemical substances. Environ. Health Perspect. 111: 1391-1401. • OECD Guidance Document on the Validation of (Q)SAR Models • Web site of AltTox.org Grouping of chemicals: • OECD Guidance on Grouping of Chemicals • Web site of the former European Chemicals Bureau • Environment, health and safety brief on (Q)SARs |