Organisation for Economic Co-operation and Development

ENV/CBC/MONO(2023)32/ANN2
Unclassified

English - Or. English
21 November 2023

ENVIRONMENT DIRECTORATECHEMICALS AND BIOTECHNOLOGY COMMITTEE

Cancels & replaces the same document of 17 August 2023

Annex II – (Q)SAR prediction reporting format (QPRF) v.2.0
Series on Testing and Assessment
No. 386

The (Q)SAR Assessment Framework document is available under the cote ENV/CBC/MONO(2023)32.
An editable format is available athttps://www.oecd.org/chemicalsafety/risk-assessment/qsar-assessment-framework-annex-2-qsarprediction-reporting-format.docx

JT03532231
OFDEThis document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to thedelimitation of international frontiers and boundaries and to the name of any territory, city or area.

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ANNEX II – (Q)SAR PREDICTION REPORTING FORMAT (QPRF) V.2.0
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ENV/CBC/MONO(2023)32/ANN2  3OECD Environment, Health and Safety Publications
SERIES ON TESTING AND ASSESSMENTNO. 386

Annex II – (Q)SAR prediction reporting format (QPRF) v.2.0

Environment Directorate
ORGANISATION FOR ECONOMIC COOPERATION AND DEVELOPMENTParis 2023

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About the OECDThe Organisation for Economic Co-operation and Development (OECD) is an intergovernmentalorganisation in which representatives of 38 industrialised countries in North and South America, Europe
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Organisations, jointly or separately, to achieve the sound management ofchemicals in relation to human health and the environment.

ANNEX II – (Q)SAR PREDICTION REPORTING FORMAT (QPRF) V.2.0
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ENV/CBC/MONO(2023)32/ANN2  5This publication is available electronically, at no charge.
-Also published in the Series on Testing and Assessment: link
For this and many other Environment,
Health and Safety publications, consult the OECD’s
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Applications for permission to reproduce or translate all or part of this material should be made to: Head of Publications
Service, RIGHTS@oecd.org, OECD, 2 rue André-Pascal, 75775 Paris Cedex 16, FranceOECD Environment, Health and Safety Publications

ANNEX II – (Q)SAR PREDICTION REPORTING FORMAT (QPRF) V.2.0
Unclassified6  ENV/CBC/MONO(2023)32/ANN2Annex II – (Q)SAR prediction
reporting format (QPRF) v.2.0

1.

ElementExplanation

General information

Information about the compilation of the current QPRF is provided in this section.

1.1. Date of QPRF

Report the date of compilation of the QPRF. Example: 30th January 2023.

1.2. QPRF author and contact details Report the contact details of the author of the QPRF.
2.

Substance

2.1. CAS number

Information about the substance under analysis. Some substances might beassociated to more than one structure. The information on the structure(s) used as
input is expected in Section 5.1.
Report the CAS number.

2.2. EC number

Report the EC number.

2.3. Other regulatory numerical
identifiers
2.4. Chemical name

Report other numerical identifiers, e.g. METI number

2.5. Structural formula

Report the structural formula.

Report the chemical name (e.g., IUPAC and/or CAS names)

2.6. Structural and composition
informationa. SMILES
b. InChI

Report the InChI code of the substance, if available.

c. Other structuralrepresentation

Indicate if another structural representation was used to generate the prediction.
Indicate whether this information is included as supporting information. Example:“mol file used and included in the supporting information”.

d. Stereochemical features

Indicate whether the substance is a stereoisomer and consequently may haveproperties that depend on the orientation of its atoms in space.

e. Composition information

Comment on whether there is more than one constituent, impurity or additive in thecomposition of the substance that should be considered for the assessment.

Comments on substanceinformation

Add any other information, as relevant

Model and software

Information about the model and software used to make the predictionf.

3.

Report the SMILES of the substance, if available.

3.1 Modela. Model or submodel nameIdentify the model used to make the prediction
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ENV/CBC/MONO(2023)32/ANN2  7b. Model version

Identify, where relevant, the version number and/or date of the model andsubmodel.

c. Reference to QMRF

Provide relevant information about the QMRF that stores information about themodel used to make the prediction.

Comments on model

add any other information, as relevant

3.2 Software
a. Software name4.

Identify the software used to make the prediction

b. Software version

Identify the version number of the software.

c. Software reference

Provide relevant information about the software

d. Software availability
Comments on software

Provide information on the availability of the software (e.g. whether it is available fordownload, if it is commercial or free to use.)
Add any other information, as relevant

Prediction

Information about the prediction of the model

4.1 a. Predicted Propertya.

Define the property for which the model provides predictions (this information
should correspond to the information provided in the QMRF under fields 3.2 and3.3). Information such as species, duration, should be included in the description ofthe property, where possible. Examples: 28 days repeated dose oral toxicity to rat.
96-hour toxicity to fish (fresh water). Provide information about the specifics of thedata curation procedure, e.g. inclusion/exclusion rules, for binary endpoints thedefinitions of the positives and negatives etc.
b.

b. Test guideline coveredc.

indicate if the data in the training set covers one or more specific test guidelines.
d.

c. Dependent variable

Report the dependent variable for which the model provides predictions including
any transformations introduced for modelling purposes (note that this information
should correspond to the information provided in the QMRF under field 3.5).
Example: NOEL, LOAEL, LC50e.
f.
g.
h. Comments on the predicted i.
property

Add any other information, as relevant

4.2
a. a. Predicted valueb.

Report the predicted value (including units) obtained from the application of themodel to the input structure. For alert-based expert models, report the alert
triggered together with the reasoning. Example: “aromatic amine - mutagenicity,plausible”.

c.

j.

k.
l.
b. Predicted value (comments)m. The predicted value is categorical (e.g. yes/no or low/medium/high), explain thecut-off values that were used as the basis for classification.
n.

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d. c. Unite.

o. Indicate the unit in which the predicted value is expressedp.

q. Comments on the predicted r.
values.

Add any other information, as relevant

5Information about the input used to generate the prediction. It should be detailed
enough to allow reproducibility of the prediction by others when using the samemodel and software.

Input

5.1 a.

Input structure

Specify what kind of input was used to represent the input structure (e.g.,SMILES, CAS RN, mol file) and associated valueb.

Stereochemical featuresc.

Tautomerism

indicate whether the substance is a stereoisomer and consequently may haveproperties that depend on the orientation of its atoms in space. Identify thestereochemical features that may affect the reliability of predictions for thesubstance, e.g. cis-trans isomerism, chiral centers. Are these features encoded in
the structural representations mentioned above?
Indicate whether the substance is known to undergo tautomerism and what impact
it may have on the prediction.

Comments on the input
structure
5.2 Descriptors

Comments on descriptors

5.3 Model and/or software settings

6

Add any other information, as relevant

Report, for each descriptor ((at least for descriptors manually input)
a. The value and unit of the descriptor
b. If the descriptor is measured experimentally or calculatedc. Reference to the descriptor source
d. A justification if different types of descriptors are used for the prediction
compared to those that were used for model development and validation

Add any other information, as relevant

For models or software that allow customization of their algorithm, indicate thecustom settings used to generate the prediction

Comments on settings

Add any other information, as relevant

Applicability domain (AD) andlimitations

Information about how the substance relates to the AD as defined by the model
developers and any other documented limitations. Any other reliability
considerations can be reported in Section 7.

6.1 Applicability domain (AD)and limitations

In this section, the assessment of the AD is limited to the AD definition asintended by model developers. In the Reliability section, other AD
considerations can be added.

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ENV/CBC/MONO(2023)32/ANN2  97a.

AD assessmentb.

AD assessment justification Describe why the substance is within or outside the AD assessmentc.

Any other limitation

Indicate if the prediction is within or outside the defined AD of the model.
Specify if the assessment was done manually by the user or automatically by
the software. If the AD is not defined, indicate in this field “Not applicable”.

Indicate if any other known limitation beyond those defined in the applicability
domain are applicable to the prediction. Example: if the input structure is aperfluorinated substance and it has been documented that the model cannot
predict these types of substances, the use of the model for this particular
substance needs additional justification.

Comments on AD

Add any other information, as relevant

Reliability assessment

Information about reliability of the prediction beyond the AD as defined by themodel developers.
Indicate if it is possible to reproduce the prediction. This requires e.g., publicavailability of the software implementing the model and/or a clear description of themodel.

7.1 Reproducibility

Comments on reproducibility

Add any other information, as relevant

7.2 Overall performance of themodelWith a reference to the predictive performance of the model reported in the QMRF,indicate why the performance is considered acceptable for the intended regulatory
purpose

7.3 Additional reliability aspects
based on the training set

Discuss whether the input structure is covered by the training set in terms of:a.

Descriptor spaceb.

Structural fragment space

Discuss if the chemical contains fragments that are represented in the modeltraining set, including stereoisomerism when relevantc.

Response space

Discuss if the predicted value of the chemical falls within the response (i.e.,predicted property) space of the model training set

d.

Mechanism considerations

Discuss if the mechanism of action of the chemical, if possible (where relevant), iscovered by the model, i.e. that there are substances in the training set following (orexpected to follow) the same mechanism as the input structure.

e.

Metabolic considerations

Discuss if the metabolism of the chemical, where relevant, is covered by model, i.e.
if the training set of the model includes substances which have (or are expected tohave) a similar metabolism compared to the input structure

Comments on additionalreliability aspects

Discuss if the values of the descriptors used in the model of the chemical are within
the limits (i.e. highest and lowest values) of the descriptors associated with training
set chemicals.

Add any other information, as relevant

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7.4 Analogues

List the structural and/or mechanistic analogues with associated experimental
data that can be used to support the reliability of the prediction. For eachanalogue indicate:a.

Identifiers

E.g. CAS number, the structural formula, the SMILES codeb.

Source of the analogue

From the training or test sets, or accessible from other sources (in this case it
should be explained how the structural analogue was retrieved)c.

Experimental value for theproperty of interest

Self-explanatory field named.

Reference for experimentalvalue

To verify the validity of the experimental datae.

Predicted value for theproperty of interest

Self-explanatory field namef.

Accuracy of the prediction

Comparison between experimental and predicted valueg.

Comments on similarity

Justification about the reasons that qualify a structural analogue as relevant

Considerations on analogues

Discuss how predicted and experimental data for structural analogues support theprediction of the chemical under consideration.

Comments on analogues

Add any other information, as relevant

7.5

Other reliable information on
the property

Indicate if any other information other than the prediction is available for theproperty. Additional information can be obtained from additional models ordifferent type of information. Provide considerations on how the other information
fit with the prediction.

7.6

Conclusion on reliability

Summary of reliability assessment

8

Purpose of use (for regulatoryapplications)This information aims to facilitate considerations about the adequacy of the (Q)SAR
prediction or the result derived from multiple predictions for a specific regulatory
use
Explain the regulatory purpose for which the prediction is being used.

8.1. Regulatory purpose
8.2. Approach for regulatoryinterpretation of the prediction
or result derived from multiple
predictions:8.3. Regulatory interpretation of theresult

Describe how the result is going to be interpreted in light of the specific regulatory
purpose (e.g. by applying an algorithm or regulatory criteria). This may involve theneed to convert the units of the dependent variable (e.g. from log molar units tomg/l). It may also involve the application of another algorithm, an assessment
factor, or regulatory criteria, and the use or consideration of additional information
in a weight-of-evidence assessment. For a result derived from multiple predictions,explain how the individual predictions were integrated to obtain the final result.
Report the interpretation of the model result in relation to the defined regulatory
purpose.

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8.4

Uncertainty

8.5. Conclusion

1.

Estimate and comment on the uncertainty of the prediction for the input structure.
The methodology proposed in the OECD (Q)SAR Assessment Framework can beused for this purpose.
Provide an assessment of whether the final result is considered adequate for aregulatory conclusion, or whether additional information is required (if this is thecase the content of additional information should be specified).

The MS word version is available at XXXXXX

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2.
Further, a mapping with between field numbers in QPRF v.2.0 and QPRF format v.1.1 in ECHA
Guidance on information requirements and chemical safety assessment Chapter R61 is provided.
QPRF mapping
Administrative information
Date of QPRF
QPRF author and contact details

QPRF v2.011.1
1.2

QPRF v.1.1
22.1
2.2

Substance
CAS number
EC number
Other numerical identifiers
Chemical name
Structural formula
Structural and composition information
SMILES
InChIOther structural representation
Stereochemical features
Composition information
Comments on substance information

2
2.1
2.2
2.3
2.4
2.5
2.6
2.6.a
2.6.b
2.6.c
2.6.d
2.6.e
-1
1.1
1.2
NA
1.3
1.4
1.5
1.6.a
1.6.b
1.6.c
1.6.d
NA
NA

Model and software
Model nameVersionReference to QMRF
Comments on model
Software implementation
Software nameSoftware version
Software reference
Software availability
Comments on model and software

33.1.a3.1.b
3.1.c
3.2.a
3.2.b
3.2.c
3.2.d
3.2.e
-

3.2.a
3.2.b
3.2.cNANANANANANANAPrediction
Property
Test guideline(s) covered
Dependent variable
Comments on predicted property
Predicted value
Predicted value (comments)
Unit

4
4.1.a
4.1.b
4.1.c
4.2.a
4.2.b
4.2.c

3.1aNA
3.1bNA
3.2.d
3.2.eNA1

https://echa.europa.eu/documents/10162/13632/information_requirements_r6_en.pdf/77f49f81-b76d-40ab-85134f3a533b6ac9

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Comments on predicted value-NAInput
Input structure
Input structure
Stereochemical features
Tautomerism
Comments on the input structure
Descriptor value and unit
Descriptor measured or calculated
Descriptor reference
Comments on descriptors
User defined settings
Comments on settings

55.1
5.1.a
5.1.b
5.1.c
5.2.a
5.2.b
5.2.c
5.3
-

3.2.f
1.5.dNA
NA
3.2.g
3.2.g
3.2.g
NANANA

Applicability domain and limitations
AD assessment
AD assessment methodology
Any other limitations
Comments on AD

6
6.1.a
6.1.b
6.1.c-

3.3a
3.3a
3.3a
3.3a

Reliability assessment
Reproducibility
Comments on reproducibility
Overall performance of the model
Descriptor space
Structural space
Response space
Mechanistic considerations
Metabolic considerations
Comments on additional reliability
Analogues: identifiers
Analogues: source
Analogues: Experimental value for the property of interest
Analogues: Reference for experimental value
Analogues: predicted value for the property of interest
Analogues: Accuracy of the prediction
Analogues: comments on similarity
Considerations on structural analogues
Comments on analogues
Other reliable information on the property
Conclusion on reliability

7
7.1
7.2
7.3.a
7.3.b7.3.c
7.3.d
7.3.e
7.4.a
7.4.b
7.4.c
7.4.d
7.4.e
7.4.f
7.4.g
7.4
7.5
7.6

NANA
NA
3.3.a.i
3.3.a.ii
NA
3.3.a.iii - 3.5
3.3.a.iv
NA
3.3.b - c
3.3.b - c
3.3.b - c
3.3.b - c
3.3.b - c
3.3.b - c
3.3.b - c
3.3.b - c
3.3.b - c
NANA

Purpose of use (for regulatory applications)

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Regulatory purpose
Approach for regulatory interpretation of the prediction or result
derived from multiple predictions
Regulatory outcome
Uncertainty
Conclusion

8.1
8.2

4.1
4.2

8.3
8.4
8.5

4.3
3.4
4.4

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