To facilitate practical application of (Q)SAR approaches in regulatory contexts by governments and industry and to improve their regulatory acceptance, the OECD (Q)SAR project has developed various outcomes such as the principles for the validation of (Q)SAR models, guidance documents as well as the QSAR Toolbox. The OECD (Q)SAR Project is carried out with the financial assistance of the European Union.
Quantitative Structure-Activity Relationships Project
(Quantitative) Structure-Activity Relationships [(Q)SARs] are methods for estimating properties of a chemical from its molecular structure and have the potential to provide information on hazards of chemicals, while reducing time, monetary cost and animal testing currently needed.
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About
Introduction to (Quantitative) Structure Activity Relationships
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.
The central axiom of SAR is that the activity of molecules is reflected in their structure. Hence, similar molecules have similar activities. The SAR approach therefore assumes that the structure of a molecule (e.g., its geometric , electronic properties etc.) contains the features responsible for its physical, chemical, and biological properties. It relies on the ability to represent the chemical by one or more descriptors of which 2-dimension structure is one. The underlying problem is how to define differences at the molecular level, since each kind of activity might depend on different molecular similarities.
Biological activity (e.g., toxicity) of substances is governed by their properties, which in turn are determined by their chemical structure.The objectives of SAR are two-fold. First, to determine as accurately as possible the limits of variation in the structure of a chemical that are consistent with the production of a specific effect (e.g., can a chemical elicit a specific toxic endpoint). Second, to define the ways, which alterations in structure and thereby the overall properties of a compound influence endpoint potency.
(Q)SARs are also models or mathematical relationship (often a statistical correlation), which relates a structure-related property to the presence or absence, or potency of another property or activity of interest. (Q)SAR's most basic mathematical form is:
Activity = f (physiochemical or structural properties)
The development of a (Q)SARs model requires three components:
- 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.
- A structural criteria or structure-related property data set for the same group of chemicals (i.e., the independent variables).
- A means of relating (usually a statistical analysis method) these two data arrays. Methods for relating structure to activity range from the simple, linear regression, through more complex aproaches such as partical least squares analyisis to the most complex, machine learning techniques such as neural networks.
(Q)SAR may be used to predict properties and activities for untested compounds, which are in the same group of chemicals.
Compound A | Compound B | Compound C | Compound D | Compound E | |
Structure X | + | + | - | + | + |
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Property Y | 1 | 2 | 3 | 4 | 5 |
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Activity Z | + | + | - | + | ? |
Activity T | 10 | 15 | 5 | ? | 30 |
Using the data in the table above demonstrates how the (Q)SAR approaches are used. An examination of the data in the table, in particular for Structure X reveals chemicals A, B, D, and E form a group of similar chemical as Structure X are common to all four compound (but not to chemical C).
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.
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
History of the OECD (Q)SAR Project
International co-operation among OECD member countries on (Q)SARs started in the early 1990s. One of the recommendations of the OECD workshop on notification schemes for new chemicals held in 1989 focused upon the need to evaluate the predictive power of the (Q)SAR models used by the United States by comparing the results of the (Q)SAR assessment with those obtained from the base-set testing of new chemicals required by the European Commission.
In accordance with the recommendation, the "Structure Activity Relationship/Minimum Premarketing dataset" (SAR/MPD) study was undertaken from 1991 to 1993, which compared (Q)SAR predictions done with base-set test data for 175 chemicals. The results of the study were jointly published in 1994 by the US EPA and the OECD. ENV Monograph No. 88: pages 1-81, pages 82-181, pages 182-296, pages 297-366.
In the early to mid-1990’s, the OECD undertook several additional efforts to assess a variety of (Q)SAR methodologies such as the OECD Workshop on QSARs in Aquatic Effects Assessment held in 1990. The outcomes of the workshop (ENV Monograph No. 58) was used in the development and publication of the OECD Guidance Document for Aquatic Effects Assessment (ENV Monograph No. 92).
The OECD also published two other documents related to (Q)SARs that were based upon member country-led projects. The first was on the Application of Structure Activity Relationships to the Estimation of Properties Important in Exposure Assessment (ENV Monograph No. 67), and the second was on Structure Activity Relationships for Biodegradation (ENV Monograph No. 68).
In March 2002, a workshop organized by CEFIC and ICCA on “Regulatory Use of (Q)SARs for Human Health and Environmental Endpoints” was held in Setubal, Portugal, and the recommendations from the workshop were then submitted to the OECD. In November 2002, the OECD 34th Joint Meeting of the Chemical Committee and the Working Party on Chemicals, Pesticides and Biotechnology held a Special Session on (Q)SARs to review and discuss the workshop’s recommendations as well as information submitted from the OECD member countries and other organizations.
The Special Session pointed out the need for transparency in (Q)SAR models and clear procedures for applicability evaluation and validation for (Q)SARs. Based upon such need, the OECD member countries agreed to initiate an activity to develop an internationally accepted set of criteria, as well as procedures for the evaluation of existing and promising (Q)SAR models. An OECD Expert Group on (Q)SARs was subsequently established in early 2003.
The OECD principles for the validation of (Q)SAR models were agreed upon in 2004, and a relevant guidance document was published in 2007.
The OECD also published a case study report compiling current and prospective regulatory applications in 11 OECD member countries in August 2006.
In November 2004, member countries recognized that the focus of the work should shift to the regulatory use and application of (Q)SARs. The (Q)SAR Project now focuses on facilitating the acceptance of (Q)SAR approaches in the assessment of chemicals. The major work item is the development of the OECD (Q)SAR Toolbox firstly released in March 2008. Version 2.0 was released in October 2010.
Grouping of Chemicals: Chemical Categories and Read-Across
A chemical category is a group of chemicals whose physicochemical and human health and/or ecotoxicological properties and/or environmental fate properties are likely to be similar or follow a regular pattern, usually as a result of structural similarity.
The similarities may be based on the following:
- a common functional group (e.g. aldehyde, epoxide, ester, specific metal ion);
- common constituents or chemical classes, similar carbon range numbers;
- an incremental and constant change across the category (e.g. a chain-length category);
- the likelihood of common precursors and/or breakdown products, via physical or biological processes, which result in structurally similar chemicals (e.g. the metabolic pathway approach of examining related chemicals such as acid/ester/salt).
As a result of these similarities, data gap filling in a chemical category can be carried out by applying one or more of the following procedures: read-across, trend analysis, and (external) (Q)SARs.
A chemical category can be represented graphically as a two-dimensional matrix in which different category members occupy different columns, and the different category endpoints occupy different rows, as illustrated in the following figure.
Graphical illustration of a chemical category
In the read-across approach, endpoint information for one chemical (the source chemical) is used to predict the same endpoint for another chemical (the target chemical), which is considered to be "similar" in some way (usually on the basis of structural similarity or on the basis of the same mode or mechanisms of action). In principle, read-across can be used to assess physicochemical properties, toxicity, environmental fate and ecotoxicity. For any of these endpoints, it may be performed in a qualitative or quantitative manner.
Qualitative read-across is similar to the use of a SAR, and the process involves:
- the identification of a chemical substructure or mode or mechanism of action that is common to two substances (which are considered to be analogues); and
- the assumption that the presence (or absence) of a property/activity for a substance can be inferred from the presence (or absence) of the same property/activity for the analogous substance.
The main application of qualitative read-across is in hazard identification.
Quantitative read-across involves:
- the identification of a chemical substructure or mode or mechanism of action that is common to two substances (which are considered to be analogues); and
- the assumption that the known value of a property for one substance can be used to estimate the unknown value of the same property for another substance.
In both cases, expert judgement is needed and some justification should be provided.
Guidance on chemical categories and read-across
The following guidance documents have been developed by OECD on chemical categories:
Guidance on Grouping of Chemicals, second edition Series on Testing and Assessment No. 194, 2014
Guidance Document for using the OECD (Q)SAR Application Toolbox to develop Chemical Categories according to the OECD Guidance on Grouping of Chemicals, Series on Testing and Assessment No. 102, 2009
Validation of (Q)SAR Models
Although a variety of (Q)SAR models have been developed, and some models have been used in assessment of chemicals in some countries for many years, transparent validation process and objective determination of the reliability of (Q)SAR models are crucial in order to further enhance the regulatory acceptance of (Q)SAR models.
In November 2004, the OECD member countries agreed on the principles for validating (Q)SAR models for their use in regulatory assessment of chemical safety. The agreed principles provide member countries with basis for evaluating regulatory applicability of (Q)SAR models and will contribute to their enhanced use for more efficient assessment of chemical safety.
OECD principles for the Varidation, for Regulatory Purpose, of (Q)SAR Models
A full report from the OECD Expert Group on (Q)SARs was also published in 2004: The report from the Expert Group on (Q)SARs on the validation of (Q)SARs
In February 2007, the OECD published a "Guidance Document on the Validation of (Q)SAR Models" with the aim of providing guidance on how specific (Q)SAR models can be evaluated with respect to the OECD principles. A checklist for the validation, a reporting format for the validation and validation case studies are attached as annexes: