Quantitative Structure Activity Relationships (QSAR) - Covance

Quantitative Structure Activity Relationships (QSAR)In Toxicity Testing

Oncology

ContentsIn Silico Approaches for Toxicity Testing

The Value and Use of In Silico Approaches

QSARs: What Are They and How Are They Used?

Types of QSARs

Toxicity Endpoints QSARs Are Used to Predict

QSAR Modeling Systems

Validation of QSAR Models

QSAR Reporting

QSARs at Labcorp

The Future of QSARs

Glossary

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2Quantitative Structure Activity Relationships (QSAR) in Toxicity TestingOncology

In Silico Approaches for Toxicity TestingFigure 1. Factors Driving the Use of In Silico Techniques and Other Non-Animal Technology (NAT)

The 3Rs Principle:

The need to Replace, Reduce and Refine (3Rs) the use of animals in chemical testing, which was driven

by societal pressure and enacted in many regulations, including Registration, Evaluation, Authorization

and Restriction of Chemicals (REACH)

Advances in Scientific Knowledge:

New molecular- and cellular-based methods, combined with the application of adverse outcome

pathways, have helped to identify key cellular events or targets that can be assessed with NATs

Increases in Computational Power:

Advances in computing and the rise of machine learning have expanded the

possibilities from a modeling and computational perspective

Political and Societal Pressure:

The need to assess and catalog the chemicals in everyday use has driven governments to heighten

regulation and instigate more comprehensive testing of chemicals

In silico approaches are an important type of Non-Animal Technology (NAT) being utilized across the globe to predict chemical toxicity and address endpoints specified by regulators.

In silico approaches, along with other NATs such as in vitro and ex vivo techniques, have been widely used in toxicology for several decades, but with a number of key drivers for change (Figure 1), their regulatory acceptance is now increasing.


Use of In Silico Approaches Benefits of In Silico Approaches

• • Predict toxicity• • Prioritize a chemical in development

through screening• • Guide additional toxicity testing• • Minimize later-stage chemical failures• • Supplement other experimental data

in weight-of-evidence arguments

• • Reduction and refinement in animal use

• • Faster data generation compared with in vivo testing

• • Increased accuracy and efficiency in testing

In silico approaches essentially use computers to mimic what happens in vivo. In silico toxicological analysis uses computational approaches such as algorithms, software and mathematic models to analyze and predict the toxicity of chemicals.

Table 1. Uses and Benefits of In Silico Approaches

The Value and Use of In Silico ApproachesIn silico approaches have a range of uses and benefits, as listed in Table 1. In a regulatory context, in silico approaches are used alone or in combination with data from in vitro studies for the toxicity of a test chemical against a specific endpoint to support a weight-of-evidence argument.

In silico approaches can also be used to predict the outcomes of some in vitro tests. A good example of this is the prediction of Ames mutagenicity which is an in vitro test. There are many in silico approaches known as QSAR models available which have high accuracy in predicting Ames mutagenicity.


Substructurepossibly leading

to a structural alert

Molecular descriptorsuch as: Atomic charge 3D shape

(Q)SARs: What Are They and How Are They Used?QSARs and SARs are used to predict the biological activity of a test chemical for which no data exists by comparing its structure to that of similar chemicals for which experimental data associated with the specific activity in question are available.

While QSARs provide a quantitative description of the relationship between a biological activity and the chemistry of a substance, SARs provide qualitative information; that is, they predict if there is an association between the chemical structure and the activity.

Figure 2. QSARs and SARs

SARA qualitative relationship (that is, an association) between a molecular structure or substructure and the presence or absence of a biological activity, or the capacity to modulate a biological activity imparted by another substructure. A substructure – which may be a single atom or a group of adjacently connected atoms in the molecule – that is associated with the presence of a biological activity is sometimes called a structural alert.

QSARA quantitative relationship between a biological activity (for example, toxicity) and one or more molecular descriptors that are used to predict the activity. A molecular descriptor is a structural or physicochemical property of a molecule or part of a molecule that is used as an independent variable in a QSAR. Identifying appropriate molecular descriptors is key to generating an effective QSAR model. Examples of molecular descriptors include, for example, the atomic charge and topological descriptors for the shape and size of the molecule.


Two main types of QSAR models are in common use: statistical-based models and expert rule-based models. Hybrid systems combining both approaches are also in use; for example, some of the VEGA* models.

Statistical-Based ModelsThese models are derived by compiling endpoint data for a variety of representative chemicals and using the molecular descriptors for these chemicals as variables or predictors on which the statistical analysis is based. The database of endpoints is known as the training set.

With increasing computational power, the quantity of molecular descriptors used as input to QSAR models is also increasing, with hundreds of descriptors possible. An example of a statistical-based QSAR model is OPERA*.

Expert Rule-Based ModelsThese QSAR models are built by initially classifying chemicals into groups, according to a known property of the chemical or part of that chemical, based on expert knowledge. An example of an expert rule-based QSAR model is Derek Nexus.

Types of QSARs


Toxicity Endpoints QSARs Are Used to Predict

Physicochemical Endpoints Environmental Fate and Pathway Endpoints

• • Melting point/freezing point• • Boiling point• • Relative density• • Vapor pressure• • Surface tension• • Water solubility• • Partition coefficient (log Kow)• • Dissociation constant• • Viscosity

• • Ready biodegradability• • Hydrolysis• • Bioaccumulation in aquatic species• • Adsorption/desorption screening

Ecotoxicological Endpoints Human Toxicological Endpoints

• • Short-term toxicity to fish• • Long-term toxicity to fish• • Short-term toxicity to aquatic invertebrates (Daphnia)• • Long-term toxicity to aquatic invertebrates (Daphnia)• • Toxicity to aquatic plants (algae)• • Short-term toxicity to terrestrial invertebrates

• • Acute toxicity• • Skin irritation or corrosion• • Eye irritation• • Skin sensitization• • Repeated dose toxicity• • In vitro gene mutation in bacteria (Ames test)• • Mutagenicity (other endpoints than in vitro

gene mutation in bacteria)• • Reproductive toxicity

Table 2. Example Regulatory Endpoints Addressed by QSAR Models Under REACHAnother key aspect of building an effective QSAR model is the activity or endpoint that the model needs to predict. These endpoints are set by regulators such as the Environmental Protection Agency (EPA) in the U.S. or European Chemicals Agency (ECHA) in Europe. Example endpoints for REACH are shown in Table 2.

Regulators do not accept the predictions of a single QSAR model, as this usually provides insufficient information to fulfill all regulatory requirements. Moreover, the explanatory power of only one prediction may be questioned. It is widely accepted that the predictive capability for a specific endpoint can be significantly increased by applying two or more models. In regulatory submissions, QSARs are, therefore, most often used as part of a weight-of-evidence argument either by applying several models for a specific endpoint or in combination with in vitro data, if applicable.


Modeling Systems

Freely Available? Characterization

CASE Ultra (Multicase) No Rule-based expert and statistical-based models

Derek Nexus (Lhasa) No Expert knowledge system where knowledge base is composed of alerts, example compounds and rules which each contribute to the predictions made by the system, provides mechanistic explanation of alert

EPI Suite (EPA) Yes SAR system based on fragments and regression equations

OECD QSAR toolbox (OECD)

Yes Read-across/trend analysis based on grouping of chemicals according to the presence or modulation of a particular effect for all members of the category based on the presumption of a common chemical or toxicological mechanism or mode of action

VEGA Yes Data-driven and knowledge discovery approaches: rule-based expert systems; data miners that extract relevant fragments from the analysis of their correlation with the endpoint; regression models that use molecular descriptors and nonlinear methods; ensemble methods; hybrid models that mix the above methods

Toxicity Estimation Software Tool (TEST) (EPA)

Yes Data-driven and knowledge discovery approaches: rule-based expert systems; data miners that extract relevant fragments from the analysis of their correlation with the endpoint; regression models that use molecular descriptors and nonlinear methods; ensemble methods; hybrid models that mix the above methods

OPERA OPEn (q)saR App

No Statistical-based QSAR models using 2D descriptors, classification and continuous models fitted with a weighted k-nearest neighbor (kNN) approach

Toxtree (EURL ECVAM) Yes Expert rules-based (structural alerts), decision tree approach

ToxRead Yes Expert rules-based. Read-across based on structural alerts and similar structures

Table 3. Some Commonly Used QSAR Modeling Systems

QSAR Modeling SystemsQSAR modeling systems can provide either a single model or bring together multiple models in one platform. Many different QSAR modeling systems are available, many of which are free of charge, while others are commercial.

The development of a number of these systems has been driven by regulators themselves; for example, EPI (Estimation Programs Interface) Suite™ from the EPA or the Organization for Economic Co-operation and Development (OECD) QSAR toolbox. Table 3 provides a summary of some of the QSAR modeling systems that we commonly use.


Validation of QSAR ModelsThe OECD has published a guidance document on the validation of (Q)SAR models, which establishes five principles as a framework for regulatory decision-making on the reliability of a prediction (Figure 3). Addressing and adhering to these five principles helps ensure the QSAR model fits with regulatory requirements.

A Defined Endpoint

This is a specific and recognized endpoint; for example, if you want to predict the mutagenicity of your test substance, you would need a test with a recognized endpoint that defines mutagenicity in categorical term such as the Ames test.

The Ames test is a bacterial reverse mutation assay that can predict the mutagenicity of a test substance as ‘positive’, ‘negative’ or ‘ambiguous’.

An Unambiguous Algorithm

When we talk about the algorithm behind a QSAR, we are taking about the specific relationship between the molecular descriptors of the chemical structure and the ‘activity’ endpoint for that QSAR model. There are two types of algorithms behind QSARs, which we have already discussed: statistical-based models and expert knowledge rule-based models.

The model used to produce the QSAR must be transparent to allow for checking, enable its reproduction by others and explain how the endpoint estimates were produced.

A Defined Domain of Applicability

The applicability domain (AD) essentially outlines the scope and limitations of a QSAR model; the reliability of predictions from a model depends on the similarity between the test chemical and the chemicals used to build the model. Knowledge of the AD, therefore, helps users to judge if a prediction for a test chemical is likely to be reliable or not.

The AD of a QSAR can be thought of as ‘a theoretical region in multidimensional space in which the model is expected to make reliable predictions’.

Appropriate Measures of Goodness-of-Fit, Robustness and Predictivity

This essentially outlines the performance of a QSAR model. Goodness-of-fit describes how well a model fits the data and takes into account the variance in the data, while the robustness refers to how stable a model is when any of the chemicals in the model are removed. Goodness-of-fit and robustness are ways of establishing the internal performance of a model, while the predictivity provides a measure of external performance. Predictivity, in many ways, is the most important measure, as it describes how well the model can predict outcomes for a new chemical.

A Mechanistic Interpretation, if Possible

A mechanistic interpretation explains the prediction in the context of existing chemical and biological knowledge and provides a supportive argument for why a specific prediction is rational.

01 02 03 04 05Figure 3:

It is difficult to compare the different tools, as they all perform differently for different regulatory endpoints and different classes of chemicals. No single QSAR can, therefore, be relied on by itself, and so multiple models are employed for regulatory decision-making. The validity of each QSAR tool in making a prediction is an important factor considered by regulators when assessing the contribution of QSARs to regulatory submissions.


QSAR Reporting

(Q)SAR Prediction Reporting Format (QPRF):

This describes how a prediction for a specific test chemical has been obtained and includes information on the model prediction(s), the endpoint, a precise identification of the substance modeled, the relationship between the modeled substance and the defined applicability domain, and the identities of close analogues.

(Q)SAR Model Reporting Format (QMRF):

This describes the model behind any prediction and is a harmonized template containing information on the model algorithm, molecular descriptors, the structures and data used for internal and external validation, and the result of the validation. Developers of models are encouraged to share this information by submitting models to the QSAR Model Database developed by the JRC*; this helps drive further innovation in the development of QSARs.


We have a wealth of experience and expertise in developing and conducting QSARs as part of broader NAT testing for regulatory submissions.

Assess the Chemical and the Question Being Asked

01 02 03 04

Establish a Plan for Testing Conduct QSAR Models and Evaluate the Predictions

Report and Recommendations

At the start of any analysis, we bring in experts from dierent disciplines to

look at the test chemical from a myriad of angles within the bigger picture of the

regulatory environment in which the chemical is being reviewed. We gather

as much data as possible about the test chemical, such as its structure, and any existing data for its analogues. We also look at the regulatory question being

investigated and the endpoints of interest.

From the information gathered in step 1, we can develop a plan for testing the chemical. In some

situations – for example, when the regulatory question is a simple one, such as if an impurity

in a substance is of relevance to its toxicity – the testing plan can be straightforward and a weight-of-evidence argument from QSAR

predictions may suice. However, in the majority of cases, the endpoints requested require additional

in vitro testing, as well as in silico QSAR analysis. An example of this is skin sensitization, where

integrated approaches to testing and assessment (IATA) combine a number of in silico and in vitro tests to predict outcomes. Based on our knowledge and

insight from running hundreds of such studies, we can design a step-by-step testing strategy that aligns with the regulatory goals and employs in

silico and in vitro techniques cost-eectively.

We will likely use all the QSAR modelsavailable to analyze the test chemical. Theresults and prediction statistics from each

model are tabulated to allow us to easily reviewand evaluate them to see how reliable eachprediction is and how valid it is based on thecriteria previously presented. If there is good

agreement between the models, on the basis ofwhich a weight-of-evidence argument can be

created, we will move to reporting at this point.If the results are mixed or unclear, additionalprocedures may be undertaken. These may

involve the use of additional models or read-across.

We will follow best-practice reporting approaches to write up the results.

If the results are ambiguous, we will make recommendations about

appropriate next steps such as in vitro tests to consider. Very complex

endpoints, such as those related to developmental toxicity, can be

diicult to predict from QSARs, especially where mechanisms are not yet fully

understood. In these situations, in vitro tests are o�en necessary and we would

flag the possibility of needing these as part of step 2.

An example of an expert rule-based QSAR model is Derek Nexus.

Figure 4. Our Approach to QSAR Modeling for Regulatory Submissions

QSARs at Labcorp


The Future of QSARsNanomaterialsNanomaterials are tiny particles – about 10−9 m in size. With particles at this size, the physical and chemical properties of a compound are changed, and this can affect its toxicity. In addition, surface coatings of nanomaterials can affect toxicity, making QSAR modeling even more complex.

The field of QSAR modeling is likely to expand as more and more data are generated for new chemicals. With this comes greater scientific understanding of the cellular events in adverse outcome pathways, which will further expand the field. In addition, there are some new questions on the horizon regarding organometals and nanomaterials.

SummaryQSARs are valuable in silico tools that are

used in combination with other NATs to answer regulatory questions about the likely toxicity

of a new chemical.

Many different QSAR systems exist, each with different strengths and weaknesses. To use QSARs to maximum

effect, you need to understand the chemical being studied and the endpoints being investigated so that you can interpret results from different models based

on each model’s strengths, scope, applicability and predictive performance. In many situations, QSARs are not an answer in themselves, but rather an important facet of the argument for a chemical’s toxicity profile.


GlossaryAbbreviation

AD Applicability Domain

EPA Environmental Protection Agency

EPI SuiteTM Estimations Programs Interface

JRC Joint Research Centre

NAT Non-Animal Technology

OECD Organisation for Economic Co-operation and Development

QPRF Q(SAR) Prediction Report Format

QSAR Qualitative Structure Activity Relationship

REACH Registration, Evaluation, Authorisation and Restriction of Chemicals

SAR Structure Activity Relationships

TEST (EPA) Toxicity Estimation Software


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Originally authored in 2021. © 2021 Laboratory Corporation of America® Holdings All rights reserved. EBKCPC010-0720Oncology

Quantitative Structure Activity Relationships (QSAR) - Covance

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