Comparison of NOEC values to EC10/EC20 values ...

EXTERNAL SCIENTIFIC REPORT APPROVED: 26 November 2015

PUBLISHED: 03 December 2015

Comparison of NOEC values to EC10/EC20 values, including confidence intervals, in aquatic and terrestrial ecotoxicological risk assessment. AZIENDA OSPEDALIERA L. SACCO – POLO UNIVERSITARIO / ICPS – INTERNATIONAL CENTRE FOR PESTICIDES AND HEALTH RISK PREVENTION WAGENINGEN UNIVERSITY AND RESEARCH CENTRE, STICHTING DIENST LANDBOUWKUNDIG ONDERZOEK, PRAKTIJKONDERZOEK PLANT & OMGEVING/ PLANT RESEARCH INTERNATIONAL, BIOMETRIS Abstract Ecotoxicological studies performed for the authorization of plant protection products (PPP) usually result in the reporting of endpoint values in terms of effect concentration (EC) affecting a percentage x of test organisms or where a x percentage of an effect is observed (EC x). The new Regulation (EC) No. 1107/2009 for the authorization of PPPs and the related data requirements provide that ecotoxicological endpoint data from chronic or long-term studies submitted by the Applicant are reported as EC10 or EC20 values together with the NOEC. NOEC values have been criticized since their values strongly depends on the experimental study design, whereas EC x values take into account the whole concentration-response curve and are therefore considered more appropriate. The aim of the project is to investigate the comparability of the EC x approach to the current NOEC approach on a larger data sets in view of the new Regulation requirements. Ecotoxicological data gathered from 70 active substances’ approval dossiers were collected and stored into a MS Access database. All the extracted ecotoxicological data were analyzed in order to derive NOEC and calculate EC10, EC20, EC50 with confidence intervals, using statistical models from the exponential and Hill families for continuous data, and logistic, log-logistic and complementary log-log models for quantal data. The optimal model was selected based on likelihood ratio tests and the Akaike Information Criterion. ECx/NOEC ratio distributions were calculated considering the whole set of data and model outputs; data were grouped in different categories to remark any differences in the ECx/NOEC ratio distributions. © ICPS and Wagenigen UR 2015

Key words: ecotoxicological endpoints; pesticide; dose-response curve; statistical analysis

Question number: EFSA-Q-2013-00428 Correspondence: [email protected]

www.efsa.europa.eu/publications

EFSA Supporting publication 2015:EN-906

COMPARISON OF NOEC VALUES TO EC10/EC20 VALUES

General Information

CONTRIBUTORS: The following contributed to this report in the capacities indicated: Name

Title

Affiliation

Giovanna Azimonti

Study Director

ICPS

Francesco Galimberti

Study Coordinator, Report Author

ICPS

Flavio Marchetto

Report Author

ICPS

Luca Menaballi

Data Entrya

ICPS

Sonia Ullucci

Data Entrya

ICPS

Federica Pellicioli

Data Entrya

ICPS

Caffi Alessandra

Data Entryb

ICPS

Ceriani Lidia

Data Entryc

ICPS

Ippolito Alessio

Data Modeld

ICPS

Moretto Angelo

Advisory Role

ICPS

Waldo de Boer

Report Author

PRI

Hilko van der Voet

Report Author

PRI

Disclaimer: The present document has been produced and adopted by the bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. Suggested citation: Azimonti et al., 2015. Comparison of NOEC values to EC10/EC20 values, including confidence intervals, in aquatic and terrestrial ecotoxicological risk assessment. EFSA supporting publication 2015:EN-906. 274 pp.


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Table of contents Abstract......................................................................................................................................... 1 General Information ....................................................................................................................... 2 1. Introduction ....................................................................................................................... 5 1.1. Background and Terms of Reference as provided by the requestor ........................................ 5 1.2. Objectives and Concepts ..................................................................................................... 5 2. Context and scientific background of the project ................................................................... 5 2.1. State of the art: brief literature review ................................................................................. 5 2.2. Aims of the project ............................................................................................................. 6 2.2.1. Overall objective: ................................................................................................................ 6 2.2.2. Specific Objectives: ............................................................................................................. 6 2.2.3. Overall notes ...................................................................................................................... 7 3. Methodology ...................................................................................................................... 7 3.1. The Pesticide List ................................................................................................................ 7 3.2. Data acquisition ................................................................................................................ 10 3.3. Data Extraction ................................................................................................................. 12 3.3.1. Inclusion/Exclusion criteria of ecotoxicological and toxicological studies ............................... 12 3.4. Data Model description ...................................................................................................... 14 3.4.1. Main Table ....................................................................................................................... 14 3.4.2. Dataset Table ................................................................................................................... 15 3.4.3. Endpoint Table ................................................................................................................. 15 3.4.4. Global output Table .......................................................................................................... 16 3.4.5. Modelling Results Table ..................................................................................................... 16 3.5. Criteria for the selection of the active ingredients and final substance list ............................. 16 3.6. SOP for data extraction, data entry and quality check ......................................................... 16 3.6.1. Data extraction ................................................................................................................. 16 3.6.2. Data entry ........................................................................................................................ 28 3.6.3. Data check and quality control ........................................................................................... 28 3.7. Statistical analysis ............................................................................................................. 31 3.7.1. Choice of software ............................................................................................................ 31 3.7.2. Data organization and operating procedure ........................................................................ 31 3.7.3. NOEC calculation .............................................................................................................. 32 3.7.4. Dose response models ...................................................................................................... 33 3.7.5. Model comparison and selection of model .......................................................................... 35 3.7.6. Final analysis comparing ECx to NOEC ................................................................................ 36 4. Results ............................................................................................................................. 37 4.1. Description of database ..................................................................................................... 37 4.2. Comparison of calculated NOECs and reported NOECs in dossiers ........................................ 44 4.3. Dose response models ...................................................................................................... 45 4.3.1. Example ........................................................................................................................... 45 4.3.2. Results of all dose-reponse models .................................................................................... 48 4.3.3. Overview of selected models ............................................................................................. 49 4.3.4. Estimated ECx values compared to dose ranges .................................................................. 49 4.4. Comparison of NOECs and ECxs ......................................................................................... 50 4.5. Ratio EC10/NOEC in relation to steepness of dose response relation ..................................... 58 4.6. Summary of Results .......................................................................................................... 58 5. Conclusions ...................................................................................................................... 60 6. Recommendations ............................................................................................................ 61


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The present document has been produced and adopted by the bodies identified above as authors. In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the authors in the context of a grant agreement between the European Food Safety Authority and the authors. The present document is published complying with the transparency principle to which the Authority is subject. It cannot be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.


7. References ....................................................................................................................... 63 Appendix A – Comparisons EC to NOEC .................................................................................... 64 Appendix B – XSD ................................................................................................................. 104 Appendix C – Register of Amendments ................................................................................... 136 Appendix D – SOP – The data entry procedure ....................................................................... 172 Appendix E – Comparison GenStat vs Proast .......................................................................... 177 Appendix F – Phase 1, Phase 2 and data Submission .............................................................. 179 Appendix G – Output Results ................................................................................................. 220


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1.

Introduction

1.1.

Background and Terms of Reference as provided by the requestor

Grant number: GP/EFSA/PRAS/2013/01 The new Regulation (EC) No. 1107/2009 for the authorization of PPPs and the related data requirements (Commission Regulation (EU) No. 283/2013 and 284/2013) provide that ecotoxicological endpoint data from chronic or long-term studies submitted by the Applicant are reported as EC 10 or EC20 values together with the NOEC. However, NOEC values have been criticized since their values strongly depends on the experimental study design, whereas ECx values take into account the whole concentration-response curve and are therefore considered more appropriate. However, there is no systematic comparison available to compare NOEC values to EC10 and EC20 values as derived from the same study. The aim of the project is to investigate the comparability of the EC x approach to the current NOEC approach on a larger data sets in view of the new data requirements in relation to Regulation (EC) No 1107/2009.

1.2.

Objectives and Concepts

The objectives of the project are to analyze all the available ecotoxicological data gathered from a certain number of active substances’ approval dossiers in order to calculate NOEC, EC10, EC20, and EC50 values with the respective confidence intervals using appropriate statistical approaches

2.

Context and scientific background of the project

2.1.

State of the art: brief literature review

Ecotoxicological studies performed for the authorization of Pesticides usually result in the reporting of endpoint values in terms of effect concentrations (EC). This endpoint may be expressed as the percentage x of the test affected organisms or as the percentage change x of an effect observed (EC x). For acute tests usually the EC50 is reported, while for long term and chronic studies, the common practice so far is reporting of NOEC values (no observed effect concentration). The new Regulation (EC) No. 1107/2009 for the authorization of PPPs and the related data requirements (Commission Regulation (EU) No. 283/2013 and 284/2013) provide that ecotoxicological endpoint data from chronic or long-term studies submitted by the Applicant are reported as EC10 or EC20 values together with the NOEC. However, NOEC values have been criticized since their values strongly depends on the experimental study design, whereas EC x values take into account the whole concentration-response curve and are therefore considered more appropriate. When taking into account the confidence interval around the ECx value, the quality of the whole dataset can therefore be considered. So far, there is no systematic comparison available to confront NOEC values to EC 10 and EC20 values derived from the same study. Therefore, not knowing in detail how the EC10 and EC20 values relate to the NOEC values, a change to the EC10 or EC20 may result in a change of the level of protection for the relevant organisms. In order to give appropriate guidance on how to use the new approach based on EC 10 and EC20 values in the ongoing revision of the Guidance Documents on Aquatic and Terrestrial Ecotoxicology and a potential future revision of the Guidance Document for risk assessment for Birds and Mammals, it is therefore crucial to perform a systematic comparison, which can then be used to select the appropriate EC x or adjust, if needed, the assessment factor in order to maintain the level of protection.


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According to two Scientific Opinions (20071, 20092), the PPR Panel recommended to replace the commonly used NOEC by the scientifically more sound EC10 or EC20 values considering also their respective confidence intervals; this allows taking into account the full data set of a concentration‐ response study and rewards better experimental study design. Similar investigations were performed by EFSA’s Scientific Committee comparing the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) to the benchmark dose (BMD) approach derived from dose‐response studies in animal testing for human risk assessment3. EFSA’s Scientific Committee recommended the BMD approach as the method of choice for the determination of reference points for RA of chemicals for human health and encouraged EFSA’s Scientific Panels and Units to adopt this approach.

2.2.

Aims of the project

The aim of the project is to investigate the comparability of the EC x approach to the current NOEC approach in the Environmental Risk Assessment of Plant Protection Products on a wide data set in view of the new data requirements in relation to Regulation (EC) No 1107/2009. Therefore, a collection of data from the ecotoxicological section of 70 active substance approval dossiers, was performed. A large number of data sets from the same study were used to derive the NOEC, EC 10 or EC20 values together with their confidence interval, in order to identify the best endpoint to be used for Risk Assessment.

2.2.1.

Overall objective:

To judge the level of protection that can be reached by using the NOEC, EC 10, EC20, EC50 values or their lower limit confidence intervals, a detailed re‐evaluation and comparison of original data submitted in active substance approval dossiers is needed.

2.2.2.

Specific Objectives:

The specific objectives of this activity are:  To prepare a database of concentration response data extracted from long‐term/chronic studies for aquatic and terrestrial organisms including birds and mammals from active substance approval dossiers used in the authorization process.  Re‐evaluation of extracted data to calculate NOEC, EC 10, EC20, EC50 values with the respective confidence intervals using appropriate statistical approaches.  Comparison of the derived NOEC, EC10, EC20, EC50 values and their lower limit confidence intervals and drawing conclusions and recommendations.

1

Scientific Opinion of the Panel on Plant protection products and their Residues on a request from the Commission related to the revision of Annexes II and III to Council Directive 91/414/EEC concerning the placing of plant protection products on the market ‐ Ecotoxicological studies. The EFSA Journal (2007) 461, 1‐44 2

Scientific Opinion of the Panel on Plant Protection Products and their Residues on a request from EFSA updating the opinion related to Annex II & III: Ecotoxicological studies. The EFSA Journal (2009) 1165, 1‐25. 3 Guidance of the Scientific Committee on a request from EFSA on the use of the benchmark dose approach in risk assessment. The EFSA Journal (2009) 1150, 1‐72


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2.2.3.

Overall notes

In the present work the term “concentration” is used among different organisms even if, usually, the usual terminology would have been: • “Level” for birds and mammals • “Application rate” for plants and non-target arthropods • “Dose” for mammals • “Concentration” for aquatic organisms

3.

Methodology

3.1.

The Pesticide List

EFSA provided a list of 70 substances, which was amended twice due to the lack of ecotoxicological information in the identified dossiers. The final list of substances is reported in Table 1: each pesticide is presented with the acronym, category and EFSA coding. The field action reports the occurred amendment. In Figure 1 is presented the distribution of the substance categories. Table 1 – List of the 70 active substances considered in the project. ID

Substance Name

Acronym

Category

Code

61

2,4D

2,4

IN

RF-0010-003-PPP

37

Aclonifen

Acl

HB

RF-0017-001-PPP

38

Amitrole (aminotriazole)

Ami

HB

RF-0025-001-PPP

18

Azimsulfuron

Azi

HB

RF-0031-001-PPP

7

Azoxystrobin

Azo

IN

RF-0035-001-PPP

20

Benalaxyl

Ben

FU

RF-0038-002-PPP

78

Carbosulfan

Cab

IN

RF-0068-001-PPP

11

Carbendazim

Car

FU

RF-0041-002-PPP

2

Chlorothalonil

Chr

FU

RF-0084-001-PPP

40

Chlorsulfuron

Chs

HB

RF-0089-001-PPP

21

Clothianidin

Clo

IN

RF-0101-001-PPP

39

Carfentrazone-ethyl

Crf

HB

RF-0070-003-PPP

41

Cyazofamid

Cya

FU

RF-0104-001-PPP

79

Cyhalofop-butyl

Cyb

HB

RF-0109-001-PPP

22

Cyprodinil

Cyp

FU

RF-0114-001-PPP

75

Dazomet

Daz

NE

RF-0118-001-PPP

52

Dodemorph

Dde

FU

RF-0645-001-PPP

51

Dinocap

Din

FU, AC

RF-0143-002-PPP

25

Diuron

Diu

HB

RF-0152-002-PPP

23

Dimethachlor

Dme

HB

RF-0136-001-PPP


7

Action

1st Substitution

2nd Substitution

1st Substitution




ID

Substance Name

Acronym

Category

Code

24

Dimoxystrobin

Dmo

FU

RF-0141-001-PPP

69

Dodine

Dod

IN

RF-0154-001-PPP

63

Emamectin

Ema

IN

RF-0648-001-PPP

3

Epoxiconazole

Epo

FU

RF-0157-001-PPP

42

Ethofumesate

Eth

HB

RF-0163-002-PPP

26

Ethoxysulfuron

Etx

HB

RF-0166-001-PPP

54

Famoxadone

Fam

FU

RF-0171-001-PPP

55

Fenamiphos phenamiphos)

Fem

NE

RF-0173-004-PPP

43

Fenamidone

Fen

FU

RF-0172-001-PPP

56

Fenpropimorph

Fep

FU

RF-0185-001-PPP

9

Flazasulfuron

Fla

HB

RF-0193-001-PPP

45

Flufenacet fluthiamide)

Flf

HB

RF-0203-002-PPP

44

Florasulam

Flo

HB

RF-0195-001-PPP

46

Flurtamone

Flr

HB

RF-0217-001-PPP

12

Flumioxazin

Flu

HB

RF-0206-001-PPP

13

Formetanate

For

IN, AC

RF-0223-002-PPP

48

Fuberidazole

Fub

FU

RF-0227-001-PPP

57

Imazalil enilconazole)

Ima

FU

RF-0246-001-PPP

10

Imidacloprid

Imi

IN

RF-0250-001-PPP

8

Imazamox

Imz

HB

RF-0247-001-PPP

15

Iodosulfuron

Iod

HB

RF-0252-001-PPP

27

Kresoxim-methyl

Kre

FU

RF-0260-001-PPP

49

lambda-Cyhalothrin

Lam

IN

RF-0261-001-PPP

77

Lenacil

Len

HB

RF-0262-001-PPP

50

Lufenuron

Luf

IN

RF-0265-001-PPP

28

Metconazole

Met

FU, PG

RF-0286-001-PPP

29

Methomyl

Mto

IN

RF-0293-003-PPP

30

Metribuzin

Mtr

HB

RF-0300-001-PPP

4

Oxadiargyl

Oxa

HB

RF-0317-001-PPP

5

Oxadiazon

Oxd

HB

RF-0318-001-PPP

60

Oxamyl

Oxm

IN

RF-0320-001-PPP

32

Pendimethalin

Pen

HB

RF-0331-001-PPP

33

Phenmedipham

Phe

HB

RF-0334-001-PPP

72

Pyridaben

Pyd

IN, AC

RF-0375-001-PPP

1st Substitution

73

Pyriproxyfen

Pyp

IN

RF-0378-001-PPP

1st Substitution

82

Pyrethrins

Pyr

IN

RF-0374-001-PPP

2nd Substitution

58

Quinoxyfen

Qui

FU

RF-0382-001-PPP

6

Spiroxamine

Spi

FU

RF-0397-001-PPP

(aka

(formerly


(aka

8

Action

1st Substitution




ID

Substance Name

Acronym

Category

Code

Action

76

Spiromesifen

Spr

IN, AC

RF-0395-001-PPP

1st Substitution

74

Sulfoxaflor

Sul

IN

missing code

1st Substitution

1

Tebuconazole

Teb

FU

RF-0403-001-PPP

67

Tefluthrin

Tef

IN

RF-0408-001-PPP

81

Tetraconazole

Tet

FU

RF-0414-001-PPP

64

Thiamethoxam

Tha

IN

RF-0418-001-PPP

68

Thiacloprid

Thc

IN

RF-0417-001-PPP

34

Thiabendazole

Thi

FU

RF-0416-001-PPP

80

Tricyclazole

Trc

FU

RF-0437-001-PPP

59

Trifloxystrobin

Trf

FU

RF-0439-001-PPP

36

Tri-allate

Trl

HB

RF-0430-001-PPP

17

Triticonazole

Trt

FU

RF-0447-001-PPP

14

Fosthiazate

Fos

NE

RF-0226-001-PPP

Substituted

16

Milbemectin

Mil

IN

RF-0303-004-PPP

Substituted

19

Beflubutamid

Bef

HB

RF-0037-001-PPP

Substituted

31

Oxasulfuron

Oxs

HB

RF-0321-001-PPP

Substituted

35

Thiophanate-methyl

Tho

FU

RF-0422-001-PPP

Substituted

47

Flusilazole

Fls

FU

RF-0218-001-PPP

Substituted

53

Etoxazole

Eto

IN

RF-0169-001-PPP

Substituted

62

Furathiocarb

Fur

IN

RF-0228-001-PPP

Substituted

65

Parathion

Par

IN

RF-0327-001-PPP

Substituted

66

Aldicarb

Ald

IN

RF-0020-002-PPP

Substituted

70

Acetamiprid

Ace

IN

RF-0014-001-PPP

Substituted

2nd Substitution

2nd Substitution

Figure 1 – Distribution of the 70 selected substances within their categories


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3.2.

Data acquisition

ICPS, on behalf of the Italian Ministry of Health, actively participates as representative Member State for the Pesticide Authorization Process of active ingredients for European Authorization. ICPS has granted the access to the active substance approval dossiers. Consequently, as stated in the application form, the data extraction was partially performed in EFSA (22-24 April 2014 and 25-26 September 2014) and in ICPS premises. At the beginning of the project, also the pesticide Companies provided on ICPS’ request, all the Ecotoxicological studies (K Documents, mainly pdf extracted from Caddy.xml), of their active substances belonging to the interest list. The global number of collected studies is reported in Table 2. In several cases, the dossiers available in EFSA were provided as scanned copies with no possibilities of using OCR, images (TIFF), and generic pdfs. Table 2 – number of pdf gathered from EFSA and from pesticide Companies (only for Ecotoxicological studies)

Substances

Database

2,4D

EFSA

Companies

6

44

15

Aclonifen

17

26

15

Amitrole

8

14

Azimsulfuron

9

32

14

Azoxystrobin

9

23

42

Benalaxyl

7

76

Carbosulfan

9

DAR

Carbendazim

5

41+DAR

Chlorothalonil

9

DAR

43

Chlorsulfuron

11

21

80

Clothianidin

1

DAR

Carfentrazoneethyl Cyazofamid

8

DAR

1

19

Cyhalofop-butyl

9

11

14

28+CADDY

Dazomet

9

22

Dodemorph

6

CADDY

7

Dinocap

1

DAR

40

Diuron

18

21

Dimethachlor

12

37

55

Dimoxystrobin

12

12

20

Dodine

8

20

Emamectin

8

19

47

Epoxiconazole

9

21

23

Ethofumesate

8

40

17

Ethoxysulfuron

4

Cyprodinil


89

6

10




Substances

Database

EFSA

Companies

Famoxadone

8

DAR

Fenamiphos

9

21

Fenamidone

7

62

25

Fenpropimorph

8

34+CADDY

12

10

DAR

Flufenacet

9

7+DAR

14

Florasulam

10

40+DAR

109

Flurtamone

5

DAR

12

Flumioxazin

10

16+CADDY

Formetanate

6

11

Fuberidazole

5

22

Imazalil

4

7+CADDY

Imidacloprid

15

48+CADDY

48

Imazamox

15

CADDY

12

Iodosulfuron

7

DAR

18

Kresoxim-methyl

8

CADDY

24

lambdaCyhalothrin Lenacil

10

65

9

76

Lufenuron

11

54

72

Metconazole

8

15+CADDY

12

Methomyl

7

CADDY

56

Metribuzin

10

DAR

36

Oxadiargyl

4

DAR

8

Oxadiazon

15

CADDY

21

7

CADDY

54

Pendimethalin

14

57

19

Phenmedipham

2

DAR

13

Pyridaben

7

24

Pyriproxyfen

9

27

Pyrethrins

4

10

Quinoxyfen

6

DAR

35

Spiroxamine

20

26

63

Spiromesifen

10

CADDY

Sulfoxaflor

11

91

Tebuconazole

11

4+CADDY

50

Tefluthrin

5

48

48

Tetraconazole

9

27

Flazasulfuron

Oxamyl


71

13

11




Substances

Database

Thiamethoxam

2

CADDY

38

Thiacloprid

11

31

21

Thiabendazole

11

23

11

Tricyclazole

12

11

Trifloxystrobin

14

DAR

Tri-allate

15

26

7

CADDY

Triticonazole

3.3.

EFSA

Companies

11

Data Extraction

All the collected K documents of the 70 active substances’ approval dossiers were stored into a dedicated workstation at ICPS premises. Each study was archived by substances and separated into appropriate folders. All documents were reviewed according to the established inclusion/exclusion criteria (presented below), then were separated into two categories: accepted and rejected. Accepted studies were enumerated and classified as described in the following paragraphs. Rejected studies were just stored into folders and archived by substances. No further work on them was performed.

3.3.1. 



   

Inclusion/Exclusion criteria of ecotoxicological and toxicological studies Since the project is focused on endpoints for long term risk assessment, only chronic or long term studies were taken into account for the following organisms: birds (BIR), mammals (MAM), aquatic organisms [fish (FIS), daphnids (DAP), aquatic invertebrates (AIN), algae (ALG), aquatic macrophytes (MAC)], non-target arthropods (NTA), earthworms (EAR), other soil macro-organisms (SOA), non-target terrestrial plants (NTP). For mammals, only multi-generation reproduction toxicity studies and prenatal developmental toxicity studies were considered from the toxicological section. Effects reported in a study must be suitable to extrapolate a dose/response curve: treatment-related statistically significant effects for at least one tested concentration has to be found. Studies with effects lower than 10% but statistically significant at the highest tested concentration were considered acceptable. At least three tested concentrations were considered necessary to extrapolate a curve. Effects must be suitable to extrapolate a NOEC; no studies with a reported NOEC < lowest tested concentration or = highest tested concentration were considered. Only studies with active substances were considered. Studies with formulated products are generally accepted only for non-target arthropods and non-target terrestrial plants since studies with active substances are not available. Metabolites are not part of this project. Only definitive studies were considered, pilot studies were not taken into account since these are performed to identify a range of concentrations for the final studies and not to determine endpoints. Effects with a complete recovery during the exposure period for all the tested concentrations were not considered acceptable.


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Some studies had the purpose to determine the percentage of effects at every tested concentration rather than to extrapolate a dose–response curve or to fix a NOEC (for example NTA) . In this case, it was not possible to establish the acceptability of the study according to the inclusion criteria. The final number of valid, accepted and inserted studies is 615. When a study was designed for more than one species/generation, a replicate of the study has been performed. As a consequence, the final number, including the replicates, of inserted studies in the database is 843. To clarify, each study was coded with a the following specific criteria:     

Identifier of the Substance: string (3 characters, ex “FEN” which stands for Fenamidone). Identifier of the tested organism: string (3 characters, ex. “ALG” which stands for Algae). The reference to the Tested organisms is reported in Table 3. Unique identifier of the Author: string (Numeric identifier of the Author; Name and Surname of the First Author, year of publication, ex. “309_HoR99” which stands for (309) Hoberg J. R. 1999). Unique identifier of the Study: numeric. Replicates of the Study: string (“r” followed by the number of replicates, ex. r1).

With these criteria it is easy to recognize and organize quickly the tested substance of the study, the organism involved, the author and the year of publication. The whole code is called ID_TOX. The data extraction was divided into two phases: phase 1 and phase 2. Phase 1: all the required data were extracted from the studies and inserted into the database. Phase 2: all the inserted data were checked and quality controlled and submitted to PRI to be statistically analyzed. The scheme of the data submission to PRI is reported in Table 4. The phase 1 started in March 2014 and ended in April 2015. At the end of phase 1, the global number of accepted extracted studies was 668 (901 considering replicates). The phase 2 started in January 2015 and ended in May 2015. At the end of phase 2, the total number of accepted and inserted studies was 615 (843 considering replicates). Due to the revision process 52 studies (58 considering replicates) were excluded from the final database. For more details, please refer to Appendix F –. Table 3 – Tested organisms in the selected studies

ID

Organism Name

Acronym

1

Daphnia

DAP

Number studies 65

2

Fish

FIS

88

3

Algae

ALG

112

4

Mammals

MAM

140

5

Macrophytes

MAC

47

6

Birds

BIR

33

7

Non-target plants

NTP

40

9

Earthworms

EAR

6

13

Aquatic Invertebrates

AIN

38

14

Non-target arthropods

NTA

25


13

of




16

Soil arthropods

SOA

21

Table 4 - Submission date and number of studies submitted

Submission Date 13/02/2015

Number of studies with replicates submitted 18

27/02/2015

14

31/03/2015

72

13/04/2015

109

30/04/2015

122

20/05/2015

142

06/06/2015

366

Total

843

A detailed description of the standard operating procedure for data extraction can be found in paragraph 3.6.1.

3.4.

Data Model description

Microsoft ACCESS software (of MS OFFICE 2003 and 2010 versions) was used to organize and store the whole set of data. The choice was made due to the characteristics of MS Access:   

Support a limited number of records: the predicted amount of tables and contained records do not cause software crashes. Easy to be used (also by not expert users): the use of forms and dropdown menus, in addition to specific SOPs allow simple and understandable data insertion. Data portability: it is possible to easily export data into other formats (xml, txt …)

During the the kickoff meeting (31/01/2014), it was decided that the main conceptual scheme of the data model consists into 5 related tables largely based on OECD harmonized templates and containing:     

Main Table: description of the study and metadata Dataset Table: Experimental data Endpoint Table: Endpoint resulting from the data extraction Global Output Table: Output of all the statistical analysis. Modelling Result Table: Collection of PRI modelling results and selection of the most suitable model.

For completeness, the structure of all the database tables are included.

3.4.1.

Main Table

This table contains all the descriptive data of the ecotoxicological and toxicological studies and a collection of data based on the OECD harmonized templates, and in particular:     

ID_TOX: unique study identifier Study remarks: sort of abstract of the study according to an internal SOP (paragraph 0) Limit test (yes/no): test made with a unique concentration/level compared to control Compartment (Table): the main compartment of the tested organisms Test Type: (cascade field depending on Compartment - Table): Typology of the test, related to the compartment and organism


14




         

Guidelines (cascade field depending on Test Type - Table): Guideline name, Guideline qualifier, Guideline code, Guideline note, Guideline deviation: A series of fields describing which guideline the study followed (or not). GLP compliance (Table): field containing information whether the study is performed according to Good Laboratory Practices or not. Species (cascade field depending on Test Type - Table): identifier of the organisms’ specie Strain (Table): identifier of the species’ strain Sex (Table): identifier of the gender of the tested organisms Exposure Type (cascade field depending on Test Type - Table): Typology of the Exposure depending on the tested organism Exposure Duration: numerical, total duration of the designed study, expressed in days Positive/Negative Control: typology of the control Location (Table): Location where the study was performed Toxicity (Table): kind of toxicity

Where “Table” is reported, a contextual dropdown menu gives the possibility specifically select the appropriate field value.

3.4.2.

Dataset Table

This table contains all the quantitative core data of the extracted studies, and in particular:              

3.4.3.

Generic counter ID_TOX: unique study identifier Concentrations: numerical value of the concentration of the active substance Concentration units (Table): measurement unit of the concentration value Concentration note: additional notes Replicates: replicates of the experiment for each tested concentrations Time: duration of the exposure Time look up: last day of exposure or range/days of effect observation Value 1: value belonging from the experiment (with SD and N where data are averaged) Effect (Table): list of possible effects depending on the study design Value/Data Type (Table): list of parameters to be monitored in order to assess the effect Data Type Measurement Unit: measurement unit of the data type Note: generic notes Value 2: other values belonging to the experiment design for quantal and for some particular count data

Endpoint Table

This table contains the list of resulting Endpoints coming from the studies.       

Generic counter ID_TOX: unique study identifier Endpoint: type of Endpoint Effect (Table): effect(s) as reported in the Dataset Table related to the Endpoint(s) of the study Data/Value Type (Table): Data Type as reported in the Dataset Table related to the Endpoint(s) of the study Value: Value of the endpoint with its measurement unit and measurement qualifiers (=, >, < ..) Model: model used to calculate the Endpoint in the study


15




3.4.4.

Global output Table

This table contains the list of all model results and the selection of the best fitting model. The record identifier is the ID_Tox (and an internal automatic counter).        

3.4.5.

Effect (Table): effect(s) as reported in the Dataset Table Data/Value Type (Table): Data Type as reported in the Dataset Table related to the Endpoint Time: duration of the exposure Model: name of the model applied Log-Likelihood: log-Likelihood statistical value ECx, ECx up and low: list of calculated values for EC10, EC20 and EC50 and their upper and lower bounds Selected: Yes/no, the best solution model output Link: hypertext, a link to the graphical model dose/response curve, residuals and normal scores.

Modelling Results Table

This table contains the list of the best fitting data applied models from the PRI statistical analysis. The record identifier is the ID_Tox (and an internal automatic counter).      

Effect (Table): effect(s) as reported in the Dataset Table Data/Value Type (Table): Data Type as reported in the Dataset Table related to the Endpoint Family/Quantal: model family hill/exponential for count and continuous data and the logistic/loglogistic /complementary log-log for quantal data. Log-Likelihood: log-Likelihood statistical value AIC: Akaike’s Information Criterium Significance: if the model applied to the dataset is significant or not

All the data structures (xsd files and data models) of all the database tables can be found in Appendix B –.

3.5.

Criteria for the selection of the active ingredients and final substance list

A set of (initially 70) substances was chosen by EFSA to represent a balanced sample of the main groups of mode of action (insecticides, fungicides, herbicides) and chemical classes of pesticides in order to avoid biased results. Another criteria for selecting the substances was a good representation of tests with all different groups of organisms.

3.6.

SOP for data extraction, data entry and quality check

3.6.1.

Data extraction

Since pesticide dossiers are not formatted with a single style, nor level of information is homogeneously reported, automatic processes for data extraction resulted unfeasible; therefore, data extraction was performed directly by staff members during the review of all the studies considered acceptable. A unique procedure to describe the data extraction operations from all the selected studies is not possible due to the high heterogeneity among the study report formats, therefore, three examples of data extractions are reported accordingly to the three main compartments aquatic, soil and terrestrial.


16




The description of the data models reports the conceptual scheme behind the construction of the whole database and the way to manage the data, but it also provide a full list of fields of data to be gathered during the data extraction. The full list is presented in Table 5.

SOP – data extraction on aquatic organisms: algae The following figures (Figure 2 to Figure 10) show the relevant parts of a study report of a growth inhibition test on algae exposed to Tri-allate. The highlighted parts, represent the information needed to be gathered to feed the database (red: generic information, blue: information for Study remarks – paragraph 0). The study of Palmer S.J., Kendall T.Z. and Krueger H.O. of 2002, was coded with the ID_TOX: Trl.ALG.344_PaJ02.485.r0.

Figure 2 – Data extraction: GLP

Figure 3 – Data extraction: Guidelines and Guideline Deviations


17




Table 5 - List of fields to be extracted from the studies

NAME

Type

Length

Mandatory

Description

Controlled Terminology

Table

Subs_Acro

Text

3

Yes

Unique acronym Substance identifier

Substance Table

Main

Orgs_Acro

Text

3

Yes

Unique acronym Organism identifier

Organism Table

Main

Authors_Acro

Text

10

Yes

Unique string Author identifier

Author Table

Main

ID_TOX

Text

50

Yes

Unique string for main record identifier

auto generated record

Main

Study_Remarks

Memo

2000

Preferably

General remarks of the study (SOP)

Main

Limit_Test

Numeric

integer

Yes

Limit test

Main

ID_Compartment

Numeric

integer

Yes

Unique id for Compartments

Compartments Table

Main

ID_TestType

Numeric

integer

Yes

Unique id for Test Type

Test Type Table

Main

ID_Guidelines

Numeric

integer

Yes

Unique id for Guidelines

Guidelines Table and then Guidelines Table

Guidelines

Guideline_Qualifier

Numeric

integer

Yes

Unique id for qualifier

Guideline Qualifier Table

Guidelines

glp_compl

Numeric

integer

Yes

Unique id for GLP compliance

GLP Table

Main

Guideline_deviation

Memo

2000

Yes

Remarks on deviation from Guidelines

Guidelines

Guideline_note

Memo

2000

Yes

Remarks on Guidelines

Guidelines

ID_Species

Numeric

integer

Yes

Unique id for Species

Species Table

Main

ID_Strain

Numeric

integer

Yes

Unique id for Strain

Strain Table

Main

Sex

Numeric

integer

Yes

Unique id for Sex

Sex Table

Main

ID_ExposureType

Numeric

integer

Yes

Unique id for Exposure Type

Exposure Type Table

Main

ID_ExposureDuration

Numeric

float

Yes

Duration of exposure expressed in days

ID_ESFATT

Numeric

integer

ID_positive_cntr

Flag Y/N

Y/N

Yes

Whether or not there is a positive control

Main

ID_negative_cntr

Flag Y/N

Y/N

Yes

Whether or not there is a negative control

Main

ID_EfsaToxicity

Numeric

integer

Yes

Unique id for EFSA toxicity

EFSA Toxicity Table

Main

ID_EFSATargetTissue

Numeric

integer

Unique id for EFSA Target Tissues

EFSA Target Tissues

Main


Unique id for EFSA Test Type

Main EFSA Test Type Table

18

Main




NAME

Type

Length

Mandatory

Description

Controlled Terminology

Table

ID_Location

Numeric

integer

Yes

Unique id for the Location Table

Location Table

Main

ExposureRegime

Numeric

integer

Yes

Unique id for the Exposure Regime Table

Exposure Regime Table

Main

Rep

Text

50

Yes

Replicates of the experiment for each concentration

Dataset

Conc

Text

50

Yes

Concentrations (text because of the nominal-measured conc.)

Dataset

ConcUnit

Numeric

Yes

Unique Id for the measurement units

Dataset

Conc_note

Text

50

Yes

Remarks on concentrations

Dataset

Time

Text

50

Yes

Last day of the exposure

Dataset

TimeLookup

Text

50

Yes

Remarks on last day of the exposure

Dataset

Value1

Numeric

Float

Yes

Value belonging from the experiment

Dataset

note

Text

50

Yes

Eventual additional notes

Dataset

Valuetype1

Numeric

Integer

Yes

Unique id for the Data Type

Data Type Table

Dataset

Effect

Numeric

Integer

Yes

Unique id for the Effects

Effect Table

Dataset

Value2

Numeric

Float

um_datatype

Text

50

Value1N

Numeric

Value1SD

Measurement Unit Table

Other Value belonging from the experiment mainly Quantal data

Dataset

Measurement Unit of the data type

Dataset

Float

Number of individuals (value1)

Dataset

Numeric

Float

Standard Deviation of value1

Dataset

Value2N

Numeric

Float


Dataset

Value2SD

Numeric

Float


Dataset

EndPoint

text

50

Yes

Type of Endpoint

Endpoint

Effect

Text

50

Yes

Main effect

EpQual

Numeric

integer

Yes

Sign before numbers =,, =, ca

EP_Value

Numeric

float

Yes

Value of the Endpoint

Units

Numeric

integer

Yes

Unique id for Measurement Units

Model

Text

150


Yes

Endpoint Qualifier Table

Endpoint Endpoint

Model used in the Study to Derive NOEC

19


Endpoint Endpoint




Figure 4 - Data extraction: Exposure type, Time step, Study duration, Species, Study Remarks (Nominal concentrations, measured concentrations)

Figure 5 - Data extraction: Exposure regime, Study remarks (Solvent control, Replicates, Initial cell density)


20




Figure 6 – Concentrations, ValueType, Endpoint, Endpoint Values

Figure 7 – Data extraction: Measurement Unit (concentration), Measurement Unit (Datatype), Replicates, Time Lookup, Value1 www.efsa.europa.eu/publications

21




Figure 8 – Data extraction: Study Remarks (type of control)

Figure 9 – Data extraction: Effect

Figure 10 – Data extraction: Model

SOP – data extraction on soil organisms: earthworms

The following figures (Figure 11 to Figure 14) show the relevant parts of a Laboratory test on eisenia foetida exposed to Sulfoxaflor. The highlighted parts, represent the information needed to be gathered to feed the database (red: generic information, blue: information for Study remarks – paragraph 0). The study of McCormac A . (2009), was coded with the ID_TOX: Sul.EAR.487_McA09.673.r0.


22




Figure 11 – Data extraction: EFSA test type, Guidelines, Test Type, GLP, Location, Species, Negative Control, Exposure Type, Study duration, Study remarks (Number of individuals, Number of replicates, Concentrations)

Figure 12 – Data extraction: Effect, Measurement Unit, Data type, Concentrations, Model www.efsa.europa.eu/publications

23




Figure 13 – Data extraction: Time lookup, Measurement Unit for Data type, Replicates, Value1

Figure 14 – Data extraction: Endpoint Value, Endpoint units, Endpoint, Model

SOP – data extraction on terrestrial organisms: mammals

The following figures (Figure 15 to Figure 22) show the relevant parts of a Developmental teratology toxicity study in rats with Spiromesifen. The highlighted parts, represent the information needed to be


24




gathered to feed the database (red: generic information, blue: information for Study remarks – paragraph 0). The study of Klaus A. (2001), was coded with the ID_TOX: Spr.MAM.543_KlA01.730.r0.

Figure 15 – Data extraction: GLP

Figure 16 – Data extraction: Exposure Type, Sex, Concentrations, Measurement Unit, Study duration, Time Step, Guidelines, Study Remarks (number of replicates)


25




Figure 17 – Data extraction: Species, Strain

Figure 18 – Data extraction: Deviations

Figure 19 – Data extraction: Test type, Effects, EFSA toxicity


26




Figure 20 – Data extraction: Value Type, Measurement unit of data type, Value1, Note, Time Lookup

Figure 21 – Data extraction: Model

Figure 22 – Data extraction: Test Sub type, Endpoint Value, Endpoint Measurement Unit


27




SOP – Study Remarks The “Study Remarks” field represent a synthetic resume of the relevant parts of the study. The main information to be collected and inserted in this field are reported below:    

 

3.6.2.

Type of the test, if reported (i.e. “Early life stage, reproduction test”) Concentrations, reported as nominal and measured with the corresponding measurement unit Control: type of control (“Solvent, Water..”) and if the effects related to the endpoint refer to the blank control or for example to a specific control or to a pooled one (i.e. "A solvent/surf control was performed with .......(substance); results refer to pooled/solvent controls ") Specify which is the chosen endpoint(s) of the study. Only one endpoint for study (e.g. the most sensitive endpoint, with the appropriate confidence interval – no lower limit ≤ 0). When more effects have the same endpoint, all the effects were reported. For toxicological studies on mammals, the endpoint has to be also ecotoxicologically relevant. (“Endpoint is based on..”) Treated groups: specify the number of replicates for each concentration and the number of individuals per replicate (" .... replicates of ..... individuals were tested for each concentration and control."). Other information: o Renewal o Additional info due to deviations to the guidelines o Anomalous results (outliers, blight, mass kill..) o Anomalous trends

Data entry

The MS Access database was created with a series of distinct input forms in order to make the data entry process easier and faster and in order to reduce typing/entry errors. The influence due to data manipulation by the staff operator was kept as low as possible. Where feasible, data were copied and pasted directly from the dossier into the Access data entry form. The Access DB was formatted in order to decrease the possibility of errors and missing entries. For constrained fields, for which only pre-defined set of values can be used, drop-down menu were compiled. A system of metadata was implemented to keep track of the data flow history. Each study was associated with the name of the operator who performed the data entry (phase 1) and the data quality check (phase 2) and the corresponding date in which the data entry/control was performed. In order to ensure data protection from informatics failures, an automatic backup system was implemented. In addition, a raid 1 system was configured on the dedicated database machine. Access to the computer where data were stored was protected by a password and for avoiding any kind of PC intrusion, the computer was not connected either to internet or to ICPS’ network.

SOP – The data entry procedure

3.6.3.

Data check and quality control

All procedures for data extraction and data entry were associated with Quality Assurance (QA) and Quality Control (QC) methods. Several protocols were applied in order to enhance a high quality of the data flow. As stated above, the process of data extraction and data entry and data quality control was practically divided into three successive steps. During a first step, data were extracted from the dossiers and structured www.efsa.europa.eu/publications

28




within a predefined Access database template. The second step (phase 2) consisted on the automatic flow of data from the Access db to the final database. The use of this intermediate phase (the Access db template) significantly helped the staff in transforming the unstructured information present in the dossier to a structured database. QA and QC procedures were implied before, during and after data extraction and entry in the database.

QA/QC before data extraction and data entry An internal kickoff meeting was organized to allow all the ICPS operators recruited for the present project to test the data extraction on the same study in order to face directly their grade of comprehension of the information to be gathered. A register (hard copy) was maintained for the whole duration of the project close to the computer were the database was stored. Any doubt, question and request was reported into this register. All the information gathered into the register were shared and a solution was found. All the information gathered and solved brought to the above described SOPs. A demonstration on how to use the database was performed at the beginning of the project and any time there were the need to further explanations. Any DB updates and upgrades were shared together with the group.

QA/QC during data extraction and data entry (phase 1) Two people operated simultaneously onto the same Access DB. One performed the data extraction while the other one inserted the data into the DB. At the end of the data insertion an immediate check on the origin of the data was performed to check any inconsistencies. Wrong entries were corrected immediately.

QC after data entry (phase 2) As stated in the previous paragraphs, the quality control and consistency check of the inserted data started in January 2015 and ended in May 2015. During this activity a register of amendments was kept. SOP – Data quality control and consistency check The list of activities the reviewer had to perform is reported below:  





Consistency check between MAIN, DATASET and EndPoint Table (ID_Tox) MAIN table: o Review and update of the new added fields:  Additional guidelines  Exposure regime  Location  EFSA Toxicity  Review the Study Remarks field o Add the pdf file to the ID_Tox specific folder o Flag as checked the combobox o Save Record EndPoint Study table: o Check the end point of the Study o Check the measurement unit o Add the effect and datatype in the appropriate new columns o Flag as checked the combobox Dataset table: o Replicate field has to be a number o Concentration field has to be a number


29




Check consistency of the Concentration note according to the study remarks Time field reports the duration of the exposure TimeLookup field reports the last day of effect or the delta between starting and ending days of effect o Check value1 and value2 with the study row data o Check data field (particular attention has to be paid to old entries) o Clear the measurement unit for datatype and consistency check has to be performed o Add additional field as Value1N. Value2N, Value1SD, Value2SD. o Save Record register of amendments (RoAs): o Add all the modified information in the RoAs with the corresponding ID_TOX o o o



Register of the amendments This register kept track of all the amendments made during the data quality control. Each record of this register identifies:    

The The The The

relevant study; part in which the error was found; wrong entry and the specific correction; data of correction.

The register can be found in the Appendix C –. All the 901 extracted studies were quality controlled and checked. 58 studies were deleted according to the inclusion/exclusion criteria. The number of studies controlled and checked (Phase 2) by the ICPS team in average were reported in Figure 23. It can be seen from the tendency line (black) that the number of controlled studies increased from January to June 2015. This was mainly due to the typology of the studies, to the increased skills of the operators in performing the quality control and mainly to the reduction of the number and type of errors to be corrected.

Figure 23 – number of studies controlled and checked by ICPS team (in average).


30




3.7.

Statistical analysis

3.7.1.

Choice of software

Dose-response modelling requires the use of statistical software, for which many options exist. In this project dose response modelling was performed using the MODEL, FIT and FITNONLINEAR directives available within the statistical software package GenStat (2014) 4. GenStat was the obvious choice for its ability to do data manipulation, statistical modelling in loops over all datasets and graphical features. Another well-known stand-alone program for dose-response modelling is PROAST (see http://www.rivm.nl/en/Documents_and_publications/Scientific/Models/PROAST). This is a program suited for analysing datasets one by one using an interactive menu of choices. For this reason it was not used as the primary program in this project, where looping over many datasets in a batch-wise approach was essential. The correspondence between the results produced by GenStat and by PROAST was verified by running a number of analyses in parallel, and checking the equality of the resulting parameters and EC x estimates. An example can be found in Appendix E –.

3.7.2.

Data organization and operating procedure

The summarized dossier data from ICPS were submitted in various batches to WUR PRI Biometris for statistical analysis. In Table 6 the submission dates are listed together with the count of submitted data sets per date. In total, 958 data sets were submitted. For 6 datasets, dose response modelling failed due to lack of data or because data quality was insufficient. Note that the number of data sets differs from the submitted number of studies, identified by id_TOX. In some studies, more than one effect and/or datatype is investigated. To identify a data set uniquely, the id_TOX has to be combined with Effect and DataType. For example on the first submission date, for the study identified with id_TOX Cya.FIS.453_BoL00.637.r0 two DataTypes, length and weight, are available, resulting in two different sets of data.

The data of the first submission date were used to explore the available statistical models for continuous and quantal data and for developing an algorithm for analysing the data in a semi-automated way. The relevant data were exported to Excel files. A batch command file was written that for all selected datasets called for a GenStat analysis of the dose-response data of this data set. In the GenStat program, data were imported from the relevant Excel file, then a sequence of models (see next section) was fitted to the data, the EC 10, EC20 and EC50 were estimated, the preferred model was determined and results were written to an output file. The batch file was run overnight. After finishing all runs of a submission date, analysis results were examined. Non-linear models may fail if the requested model does not match the data very well and if wrong starting values for the parameters to be estimated are used. For the data sets where dose response modelling initially failed, the starting values were adapted, and the process was repeated until convergence was reached.

4

VSN International (2014). GenStat for Windows 17th Edition. VSN International, Hemel Hempstead, UK. Web page: GenStat.co.uk www.efsa.europa.eu/publications

31




To summarize all results a hierarchical scheme was developped. At the highest level the NOEC and EC x estimates for the selected model was tabulated for all 952 datasets. At the second level all model comparisons between the dose-response models for a specific dataset can be inspected. At the third level the detailed results (parameters estimates, EC x estimates, graphs) for each combination of dataset and model is available.

Table 6 - Submission dates Submission date

Number of data sets 21 14 85 131

Analysed

30/04/2015 20/05/2015

147

147

142

140

6/06/2015

418

416

Total

958

952

13/02/2015 27/02/2015 31/03/2015 13/04/2015

3.7.3.

21 14 85 129

Failed id_TOX (id, reason)

1: Car.DAP.157_BaN92.266.r0 (141, most responses are zero) 2: Dmo.DAP.105_JaJ00.199.r0 (166, most responses are zero)

1: Flr.MAM.691_HoM89.952.r0 (471, not enough data) 2: Thi.MAM.696_hoM89.959.r0 (537, errors in data) 1: Spr.BIR.538_MaJ01.724.r0 (889, errors in data) 2: Spi.MAM.601_HoB95.825.r0 (failed submission)

NOEC calculation

For each dose-response data set, the NOEC (i.c. the highest concentration not significantly different from the control) is calculated and reported. For continuous data, significance is calculated using Dunnett’s procedure for multiple comparisons, and using a one-sided test with confidence probability 95%. Differences between the mean of the control

x c and

the mean of each dose group

x j are calculated and tested against the

critical value:

1 1 td MS error *    n n  j   c Where:

MSerror is the estimated variance, nc and nj, the number of measurements in the control and dose group, www.efsa.europa.eu/publications

32




td is evaluated at k (number of comparisons) dferror degrees of freedom5 (Dunnet 1955).

If all dose levels differ significantly from the control, the lowest dose used is set as the endpoint value. For quantal data, Fisher’s exact test, in particular useful when dealing with small counts, for 2 x 2 contingency tables based on the hypergeometric distribution is applied6 (Kendall and Stuart, 1979).

Calculated NOECs have been compared to already available NOEC values by investigating the logratios.

3.7.4.

Dose response models

For continuous dose-response data, a sequence of models from the exponential and the Hill model family are applied. This sequence of models, indicated as M1 (constant response), E2...E5 for the exponential family and H2...H5 for the Hill family, are nested and differ in complexity and number of parameters 7 8 (Slob, 2002; Slob and Setzer, 2014).

Table 7 - Exponential and Hill models Exponential family Hill family M1 y = a M1 y = a b = 0, c = 0, d = 1 E2 y = a*exp(bx) H2 y = a*[1 - x/(x+b)] c = 0, d = 1 E3 y = a*exp(bx^d) H3 y = a*[1 - x^d / (b^d + x^d)] c=0 E4 y = a*[c - (c - 1)*exp(-bx)] H4 y = a*[1 + (c - 1) x / (b + x)] d=1 E5 y= a*[c - (c - 1)*exp(-bx^d)] H5 y = a*[1 + (c - 1) x^d / (b^d + x^d)] In all models, y is an continuous endpoint, x represents the dose. Parameter a denotes the endpoint level at dose x = 0, parameter b the sensitivity of the study population, parameter c the maximum relative change resulting in level ac at high doses, and parameter d reflects the rate by with the response changes with dose. All responses are assumed to be log-normally distributed, that is, with homogeneous variance on logscale. All models are fitted after log-transforming the data. Occasionally, dose response data are reported as summary statistics per dose group, e.g. as means and standard deviations. Then model fitting proceeds as follows:

5

Dunnett, C.W. (1955). A multiple comparison procedure for comparing several treatments with a treatments with a control. JASA 1955: 50, p10961121 6 Kendall, M. and Stuart, A. (1979). The advanced theory of statistics, Volume 2, 4th edition. Griffins. London 7

Slob, W. (2002). Dose-response modelling of continuous endpoints. Toxicol Science 2002: 66, pp298-312

8

Slob, W. and R.W. Setzer (2014). Shape and steepness of toxicological dose-response relationships of continuous endpoints. Crit Rev Toxicol 2014: 44(3), pp270-297 www.efsa.europa.eu/publications

33




CV y 

sy y

  y  z  ln   (1  CV y2 )   

sz2  ln(CVy2  1)

where z = ln(y)

For individual observations, the contribution of any observation y to the log-likelihood function is:

 y 2 ] /var } 0.5  {-ln(var) - [ln  f(x) 

where var is the variance of ln(y).

For data reported as means per dose group the contribution to the log-likelihood function is:

 n ln( V ( z ) ) 

n( z  E ( z )) 2  (n  1) s z2 2V ( z )

where

n denotes the group size, E(z) the value of the dose response model at log-scale, and V(z) the residual variance on log-scale.


34




3.7.5.

Model comparison and selection of model

To compare the fit of two models, the deviance, which is minus twice the difference of the log-likelihood value between two models, is compared to a critical value to decide whether the null model is rejected in favour of the alternative model. Under the null hypothesis of equally good fit the likelihood ratio test statistic follows a Chi-squared distribution with number of degrees equal to the difference in number of parameters of the two models. The log-likelihood ratio test is used to determine the optimal model(s) within each family of models. Note that within each family of models, model 1 is nested within model 2, model 2 is nested within models 3 and 4, and models 4 and 3 are nested within model 5 9 (EFSA Guidance, 2009). The critical difference in log-likelihood values is 1.92 for 1 df at =0.05 . In most cases, the selected models from both families have the same number of parameters. The choice of the final model between non-nested alternative models is based on Akaike information criterion:

AIC = 2 × k – 2 × ll

where

k is the number of parameters in the model and ll is the value of the log-likelihood function.

The preferred model is the model with the minimum value for AIC among the models that are significantly better than the next simpler model. The EC10, EC20 and EC50 are reported together with a 95% confidence interval using a bootstrap method with 100 uncertainty runs. Specifically, the bootstrap method resamples 100 times the collection of residuals to the fitted model, adds these resampled residuals to the fitted values and refits the model. In this way 100 bootstrap versions of EC10, EC20 and EC50 are obtained, from which the empirical 2.5th and 97.5th percentiles are calculated. For continuous variables with a zero responses, a value 1 has been added. For quantal data, the logistic, log-logistic and complementary log-log are applied. Here the EC10, EC20 and EC50 are reported together with a 95% confidence interval based on Fieller’s theorem that allows the calculation of uncertainty bounds. Note that the EC 10, EC20 and EC50 endpoints are defined as extra risk, taking natural mortality into consideration.

9

EFSA (2009). Guidance of the Scientific Committee on a request from EFSA on the use of the benchmark dose approach in risk assessment. The EFSA Journal 2009: 1150, pp1-72 www.efsa.europa.eu/publications

35




Table 8 - quantal models Logistic

y = 1 / (1 + exp(-b(x - e)))

Log-Logistic

y = 1 / 1 + exp(b(log(x) - log(e))

Complementary log-log

y = exp(-exp(a + bx))

Diagnostic plots, residuals vs fitted values and QQ-plots, have been used for a visual inspection of the model fit and outlier detection. Eventually outliers were removed and fitting of the models was repeated.

3.7.6.

Final analysis comparing ECx to NOEC

In the final analysis estimates of NOEC, EC10, EC20 and EC50 was made for all available studies which met the criteria for valid application of statistical models. The final analysis included the comparison of the calculated endpoints. For each study, simple ratios ECx /NOEC were calculated. Whereas in the full set of results all calculated EC x values are reported, it was clear that in some case the estimates were very far below the minimum positive dose or very far above the maximum dose used in the study. Such estimates, while mathematically correct, demand a very high confidence in the correctness of the model. Therefore, when comparing ECx and NOEC, we restricted the data to ECx values that were not lower than 0.1 times the minimum positive dose of the study design and not higher than 10 times the highest dose in the study design (‘valid’ studies). A main output of the project is a comparison of NOEC with EC 10, EC20 and EC50 values. For this the ratios ECx/NOEC were summarized and displayed at a logarithmic scale using boxplots produced by the R command boxplot. The boxplots indicate the median and represent the range between the first and third quartile as a box, with whiskers extending to the minimum and maximum, but never farther out than +/1.58 times the inter-quartile range (IQR) divided by the square root of the number of observations, in which case the data points outside are shown separately. It is interesting to compare also the lower level confidence interval values of the EC X to NOEC. In this manner the uncertainty of the individual ECx estimates has been estimated from the ecotoxicologal data can be integrated into the analysis. Moreover, the NOEC is also a lower bound of sorts. However, it should be noted that the ratio ECxLB/NOEC does conflate variability between species and uncertainty due to limited sampling. An optimal approach in risk assessment is to keep variability and uncertainty separated as much as possible. ECxLB values should never be presented without the corresponding EC x estimates themselves, because they would provide a biased estimate of the true ECx value. The ’slope’ of a dose-response curve is not uniquely defined when different models are selected for different datasets. Therefore we characterised the steepness of the dose-response curve by the ratio EC50/EC10. The relationship between the EC50/EC10 ratios representing inverse steepness of the dose-response curve and the EC10/NOEC ratios was evaluated. In addition, ratios calculated for each study were aggregated with different criteria:  

All studies pooled together per taxon (class of organisms)


36




  

per response typology (i.e. continuous/quantal) per environmental compound (water/sediment/soil) aggregated over all datasets where the final EC x estimate was in the range between the lowest and the highest positive dose (so no extrapolation), or lower than the lowest positive dose, or higher than the highest dose.

For each one of these groups, descriptors of central tendency (mean, median, etc) as well as first and third quantiles were calculated.

4.

Results

The full database and results of statistical analysis can be found at this link: www.icps.it/ftp/EFSA/GP_EFSA_PRAS_2013_01/GP_EFSA_PRAS_2013_01.rar

4.1.

Description of database

The final database was developed with MS Access 2010. Once opened, in the main form there are a series of options to direct the user to the different project results (Figure 24): The four buttons on the left refer to the data extracted from the ecotoxicological and toxicological studies of the selected 70 pesticides (ICPS). The functionality of these buttons is described below in this paragraph. In the central part of the homepage form of the database are presented three buttons which refer to the statistical analysis performed by PRI (described in paragraph 4.3.2 and 4.3.3). In the right part of the homepage, the Full Analysis Report of each study can be inspected. This part of the database contains all data exctrated from the studies and all the statistical analysis, together with graphics and direct links to the pdfs.

Figure 24 – Database homepage form


37




By clicking on the “Open Main Table”, it is possible to access to the descriptive data extracted from the Studies and stored according to the agreed data models (par. 3.4). The table contains 843 records (615 studies as reported in the Author’s Table plus replicates), each coded with the appropriate ID_TOX as reported above. The number of records of Aquatic compartment is 357 (42%), 27 (3%) from Soil compartment and 459 (54%) from Terrestrial compartment. The Soil compartment is slightly underrepresented in comparison to Aquatic and terrestrial compartments. In particular within the Soil compartment, only 6 accepted studies, according to inclusion/exclusion criteria, refer to Earthworms and 21 to Other Soil Organisms. In Table 9, for each compartment was reported the number of records grouped by Taxa. For each Taxa were also enumerated the relative study test types according to the study design selected. Considering a subset of data based on taxa, the classes less represented were:  Earthworms - the majority of the documents provided belong to directive 91/414 data requirement, where tests on earthworms depended on the exposure pattern to the active substance (91/414 EEC).  Non Target Arthropods – due to the study design purposes (50% effect) and due to the suggested limited number of doses.  SOA – tests were required when additional data for soil organisms contributing to organic matter breakdown, depending on active substance degradation rate and on available information with regard to effects to various organisms (91/414 EEC). Instead the most represented were:  Non Target Plants – due to the high number of species within each study  Mammals – due to differences between ecotoxicological study design and mammalian toxicological study design and due to the multigeneration studies. Table 10 describes the distribution among taxa, compartments and species of the selected studies. The distribution among studies of the type and regime of exposure and the location of the test are presented in Table 11 and Table 12. For 807 (96%) records there were a GLP compliance reported in the studies. The “Open Dataset Table” in the homepage form of the database reports all the collected raw data values from the studies, concentrations, replicates, effects, datatypes, measurement units, type of data, the duration of exposure and the interval of the measurement of the effect (from Figure 39 to Figure 48 and Table 13). The “Open End Point Table” and “Open Guidelines” show the data gathered during the data extraction of the Endpoints and of the Guidelines which drive the studies. Table 9 – Distribution of Taxa among Studies (ID_TOX) with their Test Type by Compartments Taxa

Test Type Name

Test Type % tot 21%

Compartment

aquatic invertebrates long-term tox

N. Records 8

AIN AIN

sediment long-term tox

31

79%

AIN Sum ALG

39 algae tox

ALG Sum www.efsa.europa.eu/publications

Aquatic

Compartment %tot 2%

Global %tot 1%

Aquatic

9%

4%

Aquatic

116

100%

116

Aquatic

Aquatic 38

5% 32%

14%

14%




Taxa

Test Type Name

DAP

aquatic invertebrates long-term tox

DAP Sum FIS

fish long-term tox

Aquatic Sum

357 earthworm long-term tox

NTA long-term tox

Aquatic

25%

11%

11%

Aquatic

13%

6%

6% 42%

100%

Soil

22%

1%

Soil

21

100%

21

Soil Sum

1%

Soil

78%

2%

Soil

2%

27 bird repro tox

3%

33

BIR Sum

Global %tot 8%

8%

Aquatic

6

SOA Sum BIR

100%

6

EAR Sum

Aquatic

Compartment %tot 18%

Aquatic

47

47

SOA

100%

89 aquatic macrophyte tox

Compartment

Aquatic

89

MAC Sum EAR

Test Type % tot 100%

66

FIS Sum MAC

N. Records 66

100%

33

Terrestrial

7%

4%

Terrestrial

4%

MAM

developmental studies with mammals

91

47%

Terrestrial

20%

11%

MAM

repro tox mammals

101

53%

Terrestrial

22%

12%

MAM Sum NTA

192 other terr organism long-term tox

NTA Sum NTP

Terrestrial

25

100%

25 terr. Plant tox

209

Terrestrial Sum

459

Global Sum

843

Terrestrial

5%

3%

Terrestrial

209

NTP Sum

23%

100%

3%

Terrestrial

46%

25%

Terrestrial

25% 54%

Table 10 – Distribution of species among Taxa and Compartment Compartment

Taxa

Species

Sum

Aquatic

AIN

Americamysis bahia

7

18%

Chironomus riparius

29

74%

Chironomus tentans

1

3%

Gammarus pulex

1

3%

1

3%

Hyalella azteca AIN Sum ALG


% Tot per Taxa

39 Anabaena flosaquae

13

11%

Ankistrodesmus bibraianus

3

3%

Ankistrodesmus falcatus

1

1%

Chlamydomonas reinhardtii

1

1%

Closterium cornu

1

1%

Gymnodinium impatiens

1

1%

Nannochloropsis limnetica

1

1%

39




Compartment

Taxa

Species

Sum

Navicula pelliculosa

19

16%

Pseudokirchneriella subcapitata

12

10%

Scenedesmus subspicatus

17

15%

Selenastrum capricornutum

36

31%

Skeletonema costatum

8

7%

Synechoccus leopoliensis

2

2%

Xanthonema debile

1

1%

ALG Sum DAP

116 Daphnia magna

DAP Sum FIS

5

6%

Danio rerio

6

7%

Oncorhynchus mykiss

46

52%

Pimephales promelas

32

36%

89 Ceratophyllum demersum

1

2%

Elodea canadensis

1

2%

Glyceria maxima

1

2%

Lemna gibba L.

40

85%

Lemna minor L.

2

4%

Myriophyllum spicatum

1

2%

Spirodela polyrhiza

1

2%

47

Aquatic Sum

357 EAR

Eisenia foetida

6

EAR Sum SOA

100%

6 Aleochara bilineata

3

14%

Folsomia candida

9

43%

Hypoaspis aculeifer

5

24%

Poecilus cupreus

4

19%

SOA Sum

21

Soil Sum Terrestrial

100%

Cyprinodon variegatus

MAC Sum Soil

66

66

FIS Sum MAC

% Tot per Taxa

27 BIR

Anas platyrhynchos

18

55%

Colinus virginianus

14

42%

1

3%

Coturnix japonica BIR Sum MAM

33 Mus musculus Oryctolagus cuniculus Rattus norvegicus

MAM Sum www.efsa.europa.eu/publications

2

1%

41

21%

149

78%

192 40




Compartment

Taxa

Species

Sum

NTA

Aphidius rhopalosiphi

4

16%

Chrysoperla carnea

4

16%

Coccinella septempunctata

6

24%

Orius laevigatus

2

8%

Typhlodromus pyri

9

36%

NTA Sum NTP

% Tot per Taxa

25 Abutilon theophrasti

1

0.5%

Allium cepa

16

8%

Avena sativa

14

7%

Beta vulgaris

14

7%

Brassica napus

18

9%

Brassica oleracea

8

4%

Brassica rapa

1

0.5%

Capsicum annuum

2

1%

Coriander sativum

2

1%

12

6%

Cyperus esculentus

2

1%

Cyperus rotundus

1

0.5%

Daucus carota

7

3%

Digitaria sanguinalis

1

0.5%

Echinochloa crus-galli

2

1%

15

7%

Gossypium hirsutum

3

1%

Helianthus annuus

1

0.5%

Hordeum vulgare

3

1%

10

5%

Lepidium sativum

1

0.5%

Linum usitatissimum

1

0.5%

Lolium perenne

10

5%

Lycopersicum esculentum

20

10%

Pisum sativum

7

3%

Raphanus sativus

9

4%

Setaria faberi

1

0.5%

Sorghum bicolor

5

2%

Sorghum vulgare

2

1%

Triticum aestivum

7

3%

13

6%

Cucumis sativus

Glycine max

Lactuca sativa

Zea mays NTP Sum

209

Terrestrial Sum

459

Global Sum

843


41




Table 11 – Distribution of Exposure Type by Taxa and Compartment

Compartment

Taxa

Exposure Type

Aquatic

AIN

FRESHWATER

Sum

SALTWATER

AIN Sum ALG

FRESHWATER

ALG Sum FRESHWATER FRESHWATER

FRESHWATER

ARITIFICIAL SOIL SAND

SOA Sum ORAL: FEED

96%

4

4%

6

100%

1

5%

20

95%

100%

Dermal route

2

1%

ORAL: DRINKING WATER ORAL: FEED

1

1%

104

54%

85

44%

MAM Sum

192 GLASS SURFACE

16

64%

PLANT TISSUE

8

32%

PLASTIC DISHES

1

4%

NTA Sum

25 SOIL INCORPORATION SPRAY APPLICATION

NTP Sum

12

6%

197

94%

Terrestrial Sum

209 459

Global Sum

843


100%

33

ORAL: GAVAGE

NTP

85

33

BIR Sum

NTA

100%

21 27

Soil Sum

MAM

6%

6 STANDARD SOIL (LUFA)

BIR

7

47 357

EAR Sum

Terrestrial

94%

47

Aquatic Sum

SOA

109

89

MAC Sum EAR

15%

66

FIS Sum

Soil

6

66

SALTWATER MAC

85%

116

DAP Sum FIS

33

39 SALTWATER

DAP

%tot

42




Table 12 – Distibution of Exposure Regime and Study Location Grouped by Taxa and Compartment

Compartment

Taxa

Exposure Regime

Location

Sum

Aquatic

AIN

Flow-through

Laboratory Study

6

15%

Semistatic

Laboratory Study

1

3%

Static

Laboratory Study

32

82%

AIN Sum ALG

39 Semistatic

Laboratory Study

1

1%

Static

Laboratory Study

115

99%

Flow-through

Laboratory Study

26

39%

Semistatic

Laboratory Study

39

59%

Static

Laboratory Study

1

2%

ALG Sum DAP

116

DAP Sum FIS

66 Flow-through

Laboratory Study

79

89%

pulse-exposure

Laboratory Study

1

1%

Semistatic

Laboratory Study

5

6%

Static

Laboratory Study

4

4%

FIS Sum MAC

89 pulse-exposure

Laboratory Study

2

4%

Semistatic

Laboratory Study

17

36%

Static

Laboratory Study

28

60%

MAC Sum

47 357

Aquatic Sum Soil

EAR

repeated exposure

Laboratory Study

4

67%

single exposure

Laboratory Study

2

33%

EAR Sum SOA

6 repeated exposure single exposure

Extended Laboratory Study

3

14%

Laboratory Study

1

5%


3

14%

14

67%

Laboratory Study

SOA Sum

21 27

Soil Sum Terrestrial

BIR

repeated exposure

Laboratory Study

33

BIR Sum MAM

repeated exposure

Laboratory Study

192


100%

192 single exposure


13

52%

Laboratory Study

12

48%

NTA Sum NTP

100%

33

MAM Sum NTA

% tot

25 single exposure

Greenhouses

43

156

75%




Compartment

Taxa

Exposure Regime

Location

Sum

Laboratory Study

% tot

53

NTP Sum Terrestrial Sum

209 459

Global Sum

843

25%

Table 13 – Overview type of response for taxa

4.2.

Taxon

Continuous response

Quantal response

total

AIN ALG BIR DAP EAR FIS MAC MAM NTA NTP SOA

15 118 42 75 6 86 50 179 12 202 14

26 0 31 10 0 43 0 11 13 10 9

41 118 73 85 6 129 50 190 25 212 23

total

799

153

952

Comparison of calculated NOECs and reported NOECs in dossiers

In 17 cases the calculation of the NOEC was not possible because data were reported as mean values and corresponding standard deviations were missing. In 64 cases there was no reported NOEC. Figure 25 plots the calculated NOEC against the reported NOEC. In 645 out of the 872 cases where both NOECs were available (74%), the calculated NOEC is equal to the reported NOEC. In 121 cases (14%) calculated NOECs were lower, in 110 cases (12%) the reported NOECs were lower. This can be explained by the use of various testing methods as reported in dossiers e.g. Bonferroni, Jonckheere, one-least significance difference, Shapiro Wilks, Bartlett’s, Williams, Wilcoxon’s rank, Kruskall Wallis, using different significance levels of the use of two-sided significance tests.


44




Figure 25 - Comparison of calculated and reported NOEC. Points labelled with dataset id (see database).

4.3.

Dose response models

4.3.1.

Example

In Figure 26, an example of the results of fitting a sequence of dose response models is shown. Models are fitted for: id_TOX = Fep.ALG.438_Kuj00.621.r0; Effect =Growth and; DataType = Cell count. Starting at the upper left, results for the constant model are shown, at the upper right results for the Exponential model 2, etc., and ending in the bottom row with the Hill model 5. For each model, the fitted dose response model is shown together with the data points. The green vertical line segments show the EC 10, EC20 and EC50 values. The dose response model is displayed on the original and natural logarithmic scale. Diagnostic plots of the residuals vs. fitted values and Normal-scores are shown at the right. In Table 14 and in Figure 26 for each family the likelihood ratio test statistics are displayed. For the exponential family, models 4 and 5 are selected; for the Hill family, models 3 and 4 fit significantly better than model 2. The overall test based on Akaike’s Information Criterium selects Hill model 3 as the best


45




model of both families (lowest AIC = -112.9). Note that Hill 3 and 5 are very similar, but Hill 3 is selected for reasons of parsimony.

Constant

exponential model 2

exponential model 3

exponential model 4


46




exponential model 5

Hill model 2

Hill model 4 Hill model 3


47




Hill model 5 Figure 26 - Sequence of fitted models

Table 14 – Example of comparison among selected Models

Model Exponential E2-E1 E3-E2 E4-E2 E5-E4 E5-E3

Log-Likelihood

2 * diff

significant

10.28 27.39 35.22 58.55 58.55

70.8 34.23 49.88 81.88 62.3

Yes Yes Yes Yes Yes

Model Hill H2-H1 H3-H2 H4-H2 H5-H4 H5-H3

Log-Likelihood

2 * diff

significant

21.11 59.46 47.78 59.61 59.61

92.46 76.7 53.35 71.44 0.3024

Yes Yes Yes Yes No

4.3.2.

Results of all dose-reponse models

EC10

AIC

0.09043 0.02164 0.01929 0.001391 0.001391

-16.55 -48.79 -64.44 -109.1 -109.1

EC10

AIC

0.04437 0.001496 0.01014 0.001781 0.001781

-38.21 -112.9 -89.57 -111.2 -111.2

The full results of fitting all 9 dose response models to all 952 datasets are available in the Access database under the button “Full Analysis Report” for a specific study.


48




4.3.3.

Overview of selected models

In Appendix D the analysis results of the selected models for all datasets are displayed. In this example, the most important variables are shown. The overview of selected models is also available via the MS Access database under the button “Output Overview”. For completion, also the whole model results are reported in the “Output All Models” option in the database. The complexity of the selected models was varying between models with just 1 parameter (7 continuous and 6 quantal datasets) to models with 4 parameters (140 continuous datasets). For continuous data exponential models were selected 438 times and Hill models 348 times. In general these two model types produced similar results. By clicking on “Model Comparisons” button in the homepage form of the database it is possible to access to all the model comparison data. Table 15 - Overview fitted models

model m1(constant response) Exp_m2 Exp_m3 Exp_m4 Exp_m5 Hill_m2 Hill_m3 Hill_m4 Hill_m5 q1 (constant response) QComplloglog QLogistic QLoglogistic total

4.3.4.

number of data sets 7 136 149 74 79 103 166 18 61 6 55 75 23 952

Estimated ECx values compared to dose ranges

Dose response modelling allows to estimate ECx values even if these are outside the range of positive concentrations used in the study. Table 16 summarises how often this was the case for the three endpoints and their lower confidence limits. EC10 estimates needed the least extrapolation.

Table 16 - Number of datasets where ECx was or was not based on extrapolation beyond the data range in the study.

EC10 EC20 EC50 EC10 lower bound EC20 lower bound EC50 lower bound www.efsa.europa.eu/publications

extrapolation below lowest positive dose 119 46 12 231 120 66

no extrapolation 708 639 489 634 629 515 49

extrapolation above highest dose 95 222 380 49 156 307 EFSA Supporting publication 2015:EN-906



In the inclusion criteria the consortium agreed to consider studies with overall effects lower than 10% as valid studies to be collected. In Figure 27 is reported a pie graph showing the number of these studies over the total amount and the respective contribution of each taxa. The group of Mammals and Birds represents almost the 70% of the inner pie.

Figure 27 – Distribution of analysed data with overall effects lower than 10% by taxa.

4.4.

Comparison of NOECs and ECxs

The full set of all reported and calculated NOECs as well as the calculated ECx values and their lower and upper bounds are reported in the database. In the comparisons shown here between the NOEC and EC x, the calculated NOECs are used. Figure 28 and Figure 29 show boxplots (on a log scale) for the ratios ECx / NOEC and the correponding lower bounds. The boxplots display the variability of the data: the box indicates the lower (25%) and upper (75%) quartile. The horizontal line in each box represents the median. The vertical line segments (whiskers) indicate the 5% and 95% percentile points. Outliers are plotted as individual dots. The width of each box is proportional to the square root of the sample sizes. Individual estimates can be very different. Obviously the ratios are higher in the order EC 50 > EC20 > EC10. For EC10 and its lower bound they are on average more similar to the NOEC than for EC 20 and EC50.


50




Figure 28 - Overview of ECx/NOEC ratios.


51




Figure 29 - Overview of ECx lower bound/NOEC ratios.

Further comparisons in this section are restricted to EC 10, the other graphs can be found in Error! Reference source not found.. In this Annex there are also tables with summary statistic for each ratio: NVAL = number of values NOBS = number of observations (excluding missing values) NMV = number of missing values MEAN = mean MEDIAN = median Q1 = first quartile Q3 = third quartile

For the remainder of the results the comparison are restricted to the case where the estimated EC x was in the interval [0.1*min(dose), 10*max(dose)].


52




If the ratios are grouped by the animal taxon (Figure 31), then birds and mammals stand out with relatively high ratios. This result may be due to the fact that studies with birds and mammals have relatively few dose levels, often only 3 or 4 excluding the control (Table 17). The same pattern of relatively high EC x/NOEC ratios is found in a grouping of all datasets based on the number of dose levels in the study (Figure 32). A partial explanation for this phenomenon may be that in studies with few dose levels the relative distances between the doses tend to be large. This is shown in Figure 33. The NOEC is the highest dose with no significant difference to the control. If one would have had a dose group in between this dose and the next higher dose, it could also have been a non-significant difference, and in that case the NOEC would have been estimated higher. Therefore the NOEC has a higher probability to be low when the distance to the next higher dose is large. Therefore, putting these results together, the relatively high EC x/NOEC ratios for birds and mammals may be due to relatively low NOECs, as a result of the wide inter-dose intervals. This pattern is not influenced by the studies with overall effects lower than 10% as presented in Figure 30.

Figure 30 - EC10/NOEC ratios by taxa: in pink are presented set of data without studies with overall effects lower than 10%


53




Figure 31 - ECx/NOEC ratios grouped by animal taxon. Restricted to datasets with EC 10 estimates within [0.1*min(dose), 10*max(dose)].

Table 17. Taxa vs number of dose levels: number of studies

# dose levels taxon AIN ALG BIR DAP EAR FIS MAC MAM NTA NTP SOA

4

5

6

7

8

9

10

11

12

14

2 0 64 4 0 6 0 141 10 9 1

0 3 3 11 0 22 1 25 2 8 1

18 52 0 43 5 69 28 1 9 79 12

12 24 0 16 1 21 15 0 3 29 2

4 17 0 6 0 1 3 0 0 22 1

1 6 0 3 0 0 1 0 0 8 2

0 4 0 1 0 2 0 0 0 17 1

0 1 0 0 0 0 0 0 0 23 0

0 0 0 0 0 0 0 0 0 9 0

0 0 0 0 0 0 0 0 0 0 1


54




Figure 32 - ECx/NOEC ratios grouped by number of dose levels. Restricted to datasets with EC 10 estimates within [0.1*min(dose), 10*max(dose)].

Figure 33 - Larger inter-dose distances in studies with few dose levels.

Grouping the ratios by the compartment shows relatively high values for the terrestrial compartment. Again, this result seems mainly caused by the bird and mammal studies with few dose levels.


55




Figure 34 - ECx/NOEC ratios grouped by compartment. Restricted to datasets with EC 10 estimates within [0.1*min(dose), 10*max(dose)].

Grouping the ratios by the type of data (continuous or quantal) does not show large differences.


56




Figure - 35 ECx/NOEC ratios grouped by data type. Restricted to datasets with EC10 estimates within [0.1*min(dose), 10*max(dose)].


57




4.5.

Ratio EC10/NOEC in relation to steepness of dose response relation

No relation was observed between the ratio EC 10 / NOEC with the ratio EC 50 / EC10 as a measure of the inverted steepness of dose response curve. log10(EC10 / NOEC) = 0.16 + 0.026 log10(EC50 / EC10), with a p value p=0.55 for the regression coeffcient. In this analysis the data were restricted to the cases where EC 10 was inside the interval [0.1*min(dose), 10*max(dose)].

Figure 36 - Relation of ratio EC10 / NOEC to inverted steepness of dose response curve.

4.6.

Summary of Results

A database has been prepared to summarize available dose response data regarding long-term chronic studies for ecotoxicological risk assessment in aquatic and terrestrial organisms, including birds and mammals. The final database includes 952 datasets from 843 (615 plus replicates) studies regarding 70 pesticides. NOECs have been gathered from the dossiers, and have also been calculated from the data themselves to obtain standardized estimates. Reported and calculated NOECs were the same in 74% of cases, but could be very different in some cases. EC10, EC20, EC50 have been calculated with confidence intervals, using statistical models from the exponential and Hill families for continuous data, and logistic, log-logistic and complementary log-log models for quantal data. For each dataset an optimal model was selected based on likelihood ratio tests and the Akaike


58




Information Criterion. For some data (Table 18) the selected models ECx calculation was not possible, because there was no indication of a dose-effect relation, or the fitted model was visually unacceptable. Table 18 – successful selected models ECx calculation

EC10 97%

EC20 95%

EC50 92%

EC10l

EC20l

EC50l

96%

95%

93%

Some of the estimated ECx were out of the dose range in the study (< than the minimum dose or > than the maximum dose). This involved extrapolation in a percentage of cases as reported in Table 19. In such cases ECx cannot be equal to NOEC, and will be larger if EC x is above the maximum dose, or lower if EC x is below the lowest tested dose. This situation may affect the ratio EC x /NOEC and therefore these studies have to be considered case-by-case in the global evaluation of the ECx/NOEC ratio. Table 19 – percentage of extrapolation where estimated ECx were out of the dose range in the study

EC10

EC10l

EC20

EC20l

EC50

EC50l

23%

30%

44%

31%

30%

42%

To better clarify, Table 20 reports two subpopulations of the out of dose range estimated EC x which consist in 2 categories: < 0.1 times the minimum dose and > 10 times the maximum dose. These values are too far out of the dose range in the study to be toxicologically interpretable. Table 20 – Subpopulation of not toxicologically interpretable values

EC10

EC20

EC50

EC10l

EC20l

EC50l

Successful ECx calculation < Min Dose /10

96.6%

95.3%

92.3%

95.9%

95.0%

93.1%

0.8%

1.7%

6.9%

0.0%

0.4%

3.2%

> Max Dose *10

1.0%

0.4%

0.5%

6.2%

3.4%

3.4%

Considering all “valid” studies (i.e. ECx values included within- or not too far out of- the tested range), among all analysed ECx, EC10 shows the median value of ratio ECx/NOEC distribution closest to 1 (1.31, see Error! Reference source not found.). In individual cases larger differences were observed, with EC10 /NOEC median ratios up to 8 times lower or 16 times higher (95% confidence interval) than 1.

Considering EC20, the median value for EC20/NOEC distribution in the “valid” studies is higher than 2 (2.08, Error! Reference source not found.), suggesting a high difference, in a study, between the two values . The median EC50/NOEC ratio is obviously still even higher. The dispersion of values around the median value, described by the interquartile range, increases from EC 10 (2.21) to EC50 (8.4). The ratio between the ECx lower bound (ECxLB) and NOEC traces a pattern very similar to the ECx/NOEC ratio: the median value for EC10LB is close to 1 (0.9, Error! Reference source not found.) with a lower interquartile range (1.92) with respect to EC10, while the median value for EC20LB is 1.55 (interquartile range of 3.47) and the median value for EC50LB is 3.33 (interquartile range of 8.97).


59




NOECs appear to be low in studies with few dose levels (e.g. 3 or 4 as for many bird and mammal studies), leading to relatively high ECx/NOEC ratios compared to studies with more dose levels. Grouping of data in different categories (taxa, compartment, type of data, number of doses) does not show remarkable differences, except for birds and mammals (terrestrial compartment) possibly because of the number of tested doses (see above). This variation can be easily determined in: o o o

the number of dose level grouping, in the grouping by taxa (bird and mammal boxplots) by compartments (terrestrial boxplot which includes birds and mammals).

The percentages of studies with a EC10/NOEC ratio No extrapolation

max dose

NVAL

115

708

95

NOBS

113

693

95

NMV

2

15

0

MEAN

0.32

1.54

9.74

MEDIAN

0.40

1.38

5.78

Q1

0.19

0.84

3.24

Q3

0.69

2.81

13.75


67




EC10/NOEC

EC10LowerBound/NOEC

NVAL

902

902

NOBS

886

874

NMV

16

28

MEAN

1.51

0.40

MEDIAN

1.31

0.90

Q1

0.74

0.36

Q3

2.95

2.28


68




EC20/NOEC

EC20LowerBound/NOEC

NVAL

886

886

NOBS

869

854

NMV

17

32

MEAN

2.60

1.26

MEDIAN

2.08

1.52

Q1

1.18

0.77

Q3

5.00

4.06


69




EC50/NOEC

EC50LowerBound/NOEC

NVAL

813

813

NOBS

796

780

NMV

17

33

MEAN

5.45

2.55

MEDIAN

4.20

3.07

Q1

2.28

1.56

Q3

10.68

8.10


70




AIN

ALG

BIR

DAP

EAR

FIS

MAC

MAM

NTA

NTP

SOA

NVAL

37

116

70

84

6

121

49

168

25

205

21

NOBS

37

107

67

84

6

121

48

167

24

204

21

NMV

0

9

3

0

0

0

1

1

1

1

0

MEAN

1.08

1.13

2.65

1.52

0.83

1.29

0.81

3.60

1.61

0.98

1.42

MEDIA N

0.85

1.08

2.22

1.26

0.85

1.22

0.80

3.46

1.17

1.02

1.17

Q1

0.52

0.64

0.99

0.93

0.68

0.74

0.53

2.02

0.58

0.49

0.80

Q2

1.62

1.92

4.95

2.54

0.94

2.45

1.26

6.23

2.37

1.73

2.12


71




AIN

ALG

BIR

DAP

EAR

FIS

MAC

MAM

NTA

NTP

SOA

NVAL

37

116

70

84

6

121

49

168

25

205

21

NOBS

36

107

67

84

6

121

48

157

24

203

21

NMV

1

9

3

0

0

0

1

11

1

2

0

MEA N

0.70

0.81

0.30

0.29

0.56

0.09

0.69

1.29

0.07

0.33

0.21

MEDI AN

0.59

0.83

1.69

0.83

0.62

0.69

0.74

2.50

0.43

0.58

0.81

Q1

0.32

0.42

0.50

0.46

0.37

0.21

0.37

0.95

0.14

0.24

0.44

Q2

1.33

1.79

4.08

1.97

0.76

1.63

1.38

5

1.13

1.51

1.46


72




AIN

ALG

BIR

DAP

EAR

FIS

MAC

MAM

NTA

NTP

SOA

NVAL

37

118

70

83

6

115

49

161

25

201

21

NOBS

37

108

67

83

6

115

48

160

24

200

21

NMV

0

10

3

0

0

0

1

1

1

1

0

MEA N

1.91

1.87

5.40

2.45

1.44

2.22

1.36

5.63

2.96

1.86

2.23

MEDI AN

1.59

1.55

4.37

1.83

1.46

1.91

1.25

5.20

1.85

1.73

1.65

Q1

0.83

1.03

2.25

1.20

1.01

1.11

1.04

2.99

1.07

1.01

1.32

Q2

2.51

3.40

11.02

3.78

2.05

4.62

1.68

11.39

4.53

3.06

3.04


73




AIN

ALG

BIR

DAP

EAR

FIS

MAC

MAM

NTA

NTP

SOA

NVAL

37

118

70

83

6

115

49

161

25

201

21

NOBS

36

108

67

83

6

115

48

147

24

199

21

NMV

1

10

3

0

0

0

1

14

1

2

0

MEA N

1.48

1.37

0.91

1.24

1.04

0.97

1.12

2.41

1.62

0.93

1.49

MEDI AN

1.25

1.28

3.27

1.31

0.93

1.37

1.13

4.42

1.22

1.22

1.16

Q1

0.69

0.74

1.02

0.89

0.73

0.59

0.78

1.66

0.58

0.55

0.90

Q2

2.39

2.59

8.85

3.12

1.71

3.61

1.64

9.03

2.36

2.32

2.12


74




AIN

ALG

BIR

DAP

EAR

FIS

MAC

MAM

NTA

NTP

SOA

NVAL

36

114

62

79

6

105

46

135

24

185

21

NOBS

36

104

59

79

6

105

45

134

23

184

21

NMV

0

10

3

0

0

0

1

1

1

1

0

MEA N

3.37

4.30

10.67

4.90

3.68

4.54

3.14

9.51

6.32

5.01

4.32

MEDI AN

2.75

3.49

8.02

3.41

3.72

3.62

2.69

9.20

3.90

3.92

4.00

Q1

1.61

2.06

4.08

2.09

1.96

2.14

1.80

4.28

2.28

2.64

2.27

Q2

4.63

7.47

20.39

9.88

8.16

9.33

5.18

25.12

11.83

8.58

7.52


75




AIN

ALG

BIR

DAP

EAR

FIS

MAC

MAM

NTA

NTP

SOA

NVAL

36

114

62

79

6

105

46

135

24

185

21

NOBS

35

104

59

79

6

105

45

120

23

183

21

NMV

1

10

3

0

0

0

1

15

1

2

0

MEA N

2.81

3.12

1.46

2.62

2.91

2.64

2.30

3.53

4.47

1.95

3.04

MEDI AN

2.50

2.62

6.14

2.82

2.55

2.64

1.99

7.19

3.56

2.67

2.27

Q1

1.24

1.59

2.62

1.60

1.58

1.46

1.30

2.76

1.57

1.47

1.56

Q2

4.09

5.87

15.46

7.45

6.83

7.17

3.52

21.51

9.39

6.02

5.56


76




4

5

6

7

8

9

10

11

12

14

NVAL

241

79

319

126

57

21

25

24

9

1

NOBS

237

76

316

123

54

21

25

24

9

1

NMV

4

3

3

3

3

0

0

0

0

0

MEAN

2.82

2.17

1.10

1.19

1.24

1.19

0.91

0.90

0.84

0.75

MEDI AN

2.88

2.02

1.11

1.05

1.06

1.10

1.00

0.77

1.21

0.75

Q1

1.30

0.97

0.64

0.61

0.64

0.85

0.59

0.44

0.80

0.75

Q2

5.56

3.04

1.79

2.39

2.15

1.55

1.63

1.43

2.42

0.75


77




4

5

6

7

8

9

10

11

12

14

NVAL

241

79

319

126

57

21

25

24

9

1

NOBS

229

74

314

123

54

21

25

24

9

1

NMV

12

5

5

3

3

0

0

0

0

0

MEAN

0.80

0.32

0.25

0.23

0.92

0.77

0.49

0.51

0.18

0.62

MEDI AN

2.08

1.31

0.68

0.69

0.65

0.82

0.61

0.53

0.75

0.62

Q1

0.64

0.40

0.32

0.28

0.42

0.58

0.35

0.22

0.17

0.62

Q2

4.11

2.30

1.34

1.45

1.99

2.11

2.44

1.05

2.52

0.62


78




4

5

6

7

8

9

10

11

12

14

NVAL

235

72

319

125

56

22

25

22

9

1

NOBS

231

69

315

122

53

22

25

22

9

1

NMV

4

3

4

3

3

0

0

0

0

0

MEAN

4.95

3.43

1.89

2.08

2.34

1.74

1.68

1.65

2.02

0.89

MEDI AN

4.61

3.09

1.69

1.64

1.53

1.52

1.85

1.61

2.09

0.89

Q1

1.95

1.58

1.06

1.04

1.08

1.12

1.12

1.05

1.00

0.89

Q2

10.83

5.98

2.87

3.12

5.04

2.76

2.63

3.42

4.54

0.89


79




4

5

6

7

8

9

10

11

12

14

NVAL

235

72

319

125

56

22

25

22

9

1

NOBS

221

66

313

122

53

22

25

22

9

1

NMV

14

6

6

3

3

0

0

0

0

0

MEAN

1.91

1.15

1.10

1.08

1.83

1.06

1.08

0.28

0.71

0.76

MEDI AN

3.77

2.35

1.23

1.22

1.33

1.19

1.22

1.08

1.91

0.76

Q1

1.19

0.88

0.71

0.64

0.75

0.84

0.86

0.65

0.39

0.76

Q2

8.39

4.35

2.17

2.73

3.95

2.09

2.44

1.39

3.30

0.76


80




4

5

6

7

8

9

10

11

12

14

NVAL

202

67

301

118

53

22

21

21

7

1

NOBS

198

64

297

115

50

22

21

21

7

1

NMV

4

3

4

3

3

0

0

0

0

0

MEAN

8.85

7.05

4.16

4.81

5.66

4.08

3.49

4.54

6.69

1.15

MEDI AN

7.47

6.53

3.31

3.23

3.67

2.68

3.41

4.34

3.98

1.15

Q1

3.47

2.88

2.14

2.01

2.36

1.95

2.27

2.85

1.82

1.15

Q2

22.69

17.41

7.36

7.68

11.74

4.23

5.36

7.22

29.53

1.15


81




4

5

6

7

8

9

10

11

12

14

NVAL

202

67

301

118

53

22

21

21

7

1

NOBS

187

61

295

115

50

22

21

21

7

1

NMV

15

6

6

3

3

0

0

0

0

0

MEAN

3.06

2.38

1.95

3.25

3.98

2.25

2.18

3.14

3.32

1.05

MEDI AN

6.02

4.63

2.48

2.46

2.89

1.75

2.00

3.46

2.06

1.05

Q1

2.22

1.75

1.49

1.38

1.84

1.28

1.55

2.12

1.34

1.05

Q2

18.60

13.18

5.23

6.33

9.32

3.33

3.46

5.60

7.80

1.05


82




Aquatic

Soil

Terrestrial

NVAL

407

27

468

NOBS

397

27

462

NMV

10

0

6

MEAN

1.20

1.26

1.86

MEDIAN

1.11

1.07

1.73

Q1

0.68

0.78

0.82

Q2

1.98

1.75

3.86


83




Aquatic

Soil

Terrestrial

NVAL

407

27

468

NOBS

396

27

451

NMV

11

0

17

MEAN

0.32

0.27

0.49

MEDIAN

0.76

0.76

1.10

Q1

0.37

0.39

0.34

Q2

1.63

1.18

3.03


84




Aquatic

Soil

Terrestrial

NVAL

402

27

457

NOBS

391

27

451

NMV

11

0

6

MEAN

2.01

2.02

3.31

MEDIAN

1.67

1.65

3.00

Q1

1.08

1.23

1.38

Q2

3.29

2.24

7.32


85




Aquatic

Soil

Terrestrial

NVAL

402

27

457

NOBS

390

27

437

NMV

12

0

20

MEAN

1.19

1.38

1.31

MEDIAN

1.29

1.11

2.06

Q1

0.74

0.83

0.82

Q2

2.53

1.90

5.62


86




Aquatic

Soil

Terrestrial

NVAL

380

27

406

NOBS

369

27

400

NMV

11

0

6

MEAN

4.22

4.17

7.04

MEDIAN

3.16

4.00

5.58

Q1

2.01

2.06

2.96

Q2

7.75

7.71

16.45


87




Aquatic

Soil

Terrestrial

NVAL

380

27

406

NOBS

368

27

385

NMV

12

0

21

MEAN

2.73

3.01

2.36

MEDIAN

2.52

2.27

3.99

Q1

1.48

1.58

1.64

Q2

5.90

5.87

12.31


88




continuous

quantal

NVAL

902

79

NOBS

886

76

NMV

16

3

MEAN

1.51

2.17

MEDIAN

1.31

2.02

Q1

0.74

0.97

Q2

2.95

3.04


89




continuous

quantal

NVAL

902

79

NOBS

874

74

NMV

28

5

MEAN

0.40

0.32

MEDIAN

0.90

1.31

Q1

0.36

0.40

Q2

2.28

2.30


90




continuous

quantal

NVAL

886

72

NOBS

869

69

NMV

17

3

MEAN

2.60

3.43

MEDIAN

2.08

3.09

Q1

1.18

1.58

Q2

5.00

5.98


91




continuous

quantal

NVAL

886

72

NOBS

854

66

NMV

32

6

MEAN

1.26

1.15

MEDIAN

1.52

2.35

Q1

0.77

0.88

Q2

4.06

4.35


92




continuous

quantal

NVAL

813

67

NOBS

796

64

NMV

17

3

MEAN

5.45

7.05

MEDIAN

4.20

6.53

Q1

2.28

2.88

Q2

10.68

17.41


93




continuous

quantal

NVAL

813

67

NOBS

780

61

NMV

33

6

MEAN

2.55

2.38

MEDIAN

3.07

4.63

Q1

1.56

1.75

Q2

8.10

13.18


94




Comparison between ECx/NOEC ratios with and without (right boxplots) overall effects lower than 10% data.


95





96




In the following figures, a graphic presentation of the distribution of raw data by Taxa, Effects and Datatypes is presented. Values are reported as percentages within each taxa. For aquatic invertebrates (AIN) the combination Effect-Development and Datatype-Emergence is the most frequent (56%) among the data extracted from the studies. For Algae, the combination Growth and Cell Count is the most frequent (almost 70%). There are no meaningful differences in the distribution among the combination Effect (only Reproduction) and Datatype in studies on Birds, with the exception of Datatype “Eggs laid” which represents almost the 20% of the total on birds. For Daphnids, the main combination Effect/Datatype is represented by Reproduction/Young Produced (32%); Growth/Length (25%) and Reproduction/Living young produced (14%). No figure is presented for Earthworms because there is only one Effect/Datatype combination: Reproduction/Young produced. The main analysed effect in studies on fishes is Growth with the datatype Weight (27%) and Length (33%). Extracted studies on aquatic macrophytes were designed for one effect (Growth) and mainly on three datatypes: Frond number (30%), Frond yield (26%) and Dry weight (20%). For Mammals, the most relevant Effect/Datatype combination is resulted to be Male body weight (20%). The number of deaths (26% - mortality effect) and Young produced (37% - reproduction) are the most frequent datatype in the studies on NTA. For Other Soil macro organisms the most frequent datatype is Young produced (48% - Reproduction).


97




Figure 39 - AIN - Effects and data Type (%)

Figure 40 - ALG - Effects and data Type (%)


98




Figure 41 - BIR - Effects and data Type (%)

Figure 42 - DAP - Effects and data Type (%)


99




Figure 43 - FIS - Effects and data Type (%)

Figure 44 - MAC - Effects and data Type (%)


100




Figure 45 - MAM - Effects and data Type (%)

Figure 46 - NTA - Effects and data Type (%)


101




Figure 47 - NTP - Effects and data Type (%)


102




Figure 48 - SOA - Effects and data Type (%)


103




Appendix B – XSD

As stated in paragraph 3.4, below are presented the data models. Heading

Name

Definition

Data type

Terminology

Mandatory

AllOutputs

ID

Unique record Identifier

Counter


Yes

id_TOX

ICPS record identifier

Text

Main Table

Yes

id_PRI

PRI record identifier

Numeric

ID_Effects

Unique identifier for Effects

Numeric

Effect Table

Yes

Effects

Effect

Text

Effect Table

Yes

id_datatype

Unique identifier for Datatype

Numeric

DataType Table

Yes

DataType

Datatype

Text

DataType Table

Yes

Time

Duration of the exposure

Text

Yes

Model

Model

Text

Yes

Log-Likelihood

Likelihood ratio test results

Text

numeric value or *

Yes

EC10

EC10 calculated value

Text

numeric value or *

Yes

EC10low

EC10 lower bound calculated value

Text

numeric value or *

Yes

EC10up

EC10 upper bound calculated value

Text

numeric value or *

Yes

EC20


Text

numeric value or *

Yes

EC20low


Text

numeric value or *

Yes

EC20up


Text

numeric value or *

Yes

EC50


Text

numeric value or *

Yes

EC50low


Text

numeric value or *

Yes

EC50up


Text

numeric value or *

Yes


104

Yes




Heading

Authors

Compartments

DATA

Dataset

Name

Definition

Data type

Terminology

Mandatory

SelectedModel

Optimal model selection

Text

"Yes/No"

Yes

Graphs

Hyperlink to graphics

Hyperlink

id_Auth

Unique Author identifier

Counter

first_Auth

Surname and first letter of Author's name

Text

Authors_acro

Author acronym

Text

Other_Auts

Other Authors

Text

No

year

Year

Text

Yes

ID_Compartment

Unique Compartment Identifier

Counter


Yes

Comp_name

Compartment name

Text

Aquatic/Soil/Terrestrial

Yes

ID_DATA

Unique data Identifier

Counter


Yes

DATA

Type of Data

Text

"Quantal/Continuous/Count/Not set"

Yes

Counter

Unique Dataset Identifier

Counter


Yes

id_TOX

Main ICPS record identifier

Text

Main Table

Yes

Conc

Concentrations

Text

Yes

Rep

Replicates of the experiment for each concentration

Text

Yes

ConcUnit

Measurement Unit - Concentration

Numeric

Conc_note

Remarks on Concentration field

Text

No

Time

duration of the exposure

Text

Yes

TimeLookup

last day of exposure or range/days of effect observation

Text

Yes

Value1

Value belonging from the experiment

Numeric

Yes

note

Remarks on value 1

Text

No

Valuetype1


Numeric

DataType Table

Yes

Effect


Numeric

Effect Table

Yes


105

Yes auto generated record

Yes Yes



Yes

Yes




Heading

DataType

Effects

EFSA_Strain

EFSA_TargetTissue

EFSA_Toxicity

EndPoint_Studies

Name

Definition

Data type

Value2

Other Value belonging from the experiment mainly quantal data

Numeric

Data

Unique data Identifier

Numeric

um_datatype

Measurement Unit - Datatype

Text

Yes

Value1N


Numeric

No

Value1SD


Numeric

No

Value2N


Numeric

No

Value2SD


Numeric

No

ID_DataType


Counter

DataType

Datatype

Text

ID_Effects


Counter

Effects

Effect

Text

ID_Strain

Unique Identifier for Strain (Table provided by EFSA)

Counter

Strain_name

Strain name

Text

Counter_EFSA_TargetTissue

Unique Identifier for Target Tissue (Table provided by EFSA)

Counter

EFSA_TargetTissue

Target Tissue name

Text

Yes

ID_EFSATargetTissue

Field to sort values

Numeric

Yes

ID_EFSAtoxicity

Unique Identifier EFSA Toxicity (Table provided by EFSA)

Counter

Tox_EFSACODE

Code EFSA

Text

Yes

Name_EFSAToxicity

Toxicity name

Text

Yes

Identifier

Unique Identifier for Endpoint

Counter


Yes

ID_Tox


Text

Main Table

Yes

EndPoint

EndPoint

Text

Effect New


Counter

Effect Table

Yes

DataType New


Counter

DataType Table

Yes


106

Terminology

Mandatory No

Data Table


Yes

Yes Yes


Yes Yes


Yes Yes



Yes

Yes

Yes




Heading

EFSA_TEST_TYPE

ExposureRegime

ExposureType

GLP_Compliance

GuidelineQualifier

Guidelines

Name

Definition

Data type

Terminology

Mandatory

EpQual

Sign before numbers

Text

"=// =/ ca"

Yes

EP_Value

Value of the EndPoint

Numeric

Units

Unique id for Measurement Units

Numeric

Model

Model used in the Study

Text

counterEFSA_TestType

Unique Identifier for EFSA Test Type (Table provided by EFSA)

Counter

ORD_EFSATT


Numeric

Yes

EFSATestType

Test Type name

Text

Yes

ID_Eregime

Unique identifier for Exposure Regime

Counter


Yes

ID_Ttype

Test Type identifier

Numeric

Test Type Table

Yes

ExposureRegime

Exposure regime definition

Text

ID_exposureType

Unique Identifier for Exposure Type

Counter


Yes

ID_TestType


Numeric

Test Type Table

Yes

EXP_Type_Name

Exposure Type definition

Text

Yes

Route_EFSACODE

EFSA CODE

Text

Yes

ID_GLPCompl

Unique Identifier for GLP

Counter


Yes

GLP_COMP

GLP compliance

Text

No/No Data/Not Reported/Yes

Yes

ID_GuiQual

Unique Identifier for Guideline Qualifier

Counter


Yes

Gui_Qualifier

Qualifier

Text

"According to/Equivalent or similar to/No guideline available/No guideline followed/Not reported"

Yes

id_TestType


Numeric

Test Type Table

Yes

ID_SubTT

Unique Identifier for Guidelines (Table provided by EFSA)

Counter


Yes

Study_num

Study number

Text

Yes

Test_Name

Guideline Name

Text

Yes


107

Yes Measurement Unit Table

Yes No


Yes

Yes




Heading

GuidelineTable

Location

Main

Name

Definition

Data type

Gui_EFSACODE

EFSA CODE

Text

Id_GL_table

Unique Identifier for Guideline Table

Counter


Yes

ID_Tox


Text

Main Table

Yes

GuidelinesQual

Identifier for Guideline Qualifier

Numeric

GuidelineQualifier Table

Yes

Guidelines

Identifier for Guidelines

Numeric

Guidelines

Yes

GuidelinesNote

Remarks

Text

ID_Location

Unique Identifier for Location

Counter

EFSA_CODE_Location

EFSA CODE

Text

No

Location

Location definition

Text

Yes

ID_Main

Unique Identifier for studies with replicates

Counter


Yes

Subs_Acro

Acronym of Substance tested

Text

Substance Table

Yes

Orgs_Acro

Acronym of Organism tested

Text

Organism Table

Yes

Authors_Acro

Author acronym

Text

Authors Table

Yes

ID_TOX

Unique Identifier for studies with replicates - string

Text


Yes

Study_Remarks

Remarks

Memo

Limit_Test

Identifier for Limit Test

Numeric

Yes_Mo Table

Yes

ID_Compartment

Identifier for Compartment

Numeric

Compartment Table

Yes

ID_TestType

Identifier for Test Type

Numeric

Test Type Table

Yes

Guideline_deviation

Deviation from Guidelines

Text

glp_compl

Identifier for GLP

Numeric

GLP_Compliance Table

Yes

ID_Species

Identifier for Species

Numeric

Species Table

Yes

ID_Strain

Identifier for Strain

Numeric

EFSA_Strain Table

Yes

Sex

Identifier for Gender

Numeric

Sex Table

Yes

ID_ExposureType

Identifier for Exposure Type

Numeric

ExposureType Table

Yes


108

Terminology

Mandatory Yes

No auto generated record

Yes

No

Yes




Heading

MeasurementUnit

ModelComparisons

Name

Definition

Data type

ID_ExposureDuration

Duration of the exposure in days

Numeric

ID_ESFATT

Identifier for EFSA Test Type

Numeric

EFSA_TEST_TYPE Table

Yes

ID_positive_cntr

Positive control

Yes/No

"Yes/No"

Yes

ID_negative_cntr

Negative control

Yes/No

"Yes/No"

Yes

ID_EfsaToxicity

Identifier for EFSA Toxicity

Numeric

EFSA_Toxicity Table

Yes

ID_EFSATargetTissue

Identifier for Target Tissue

Numeric

EFSA_TargetTissue Table

Yes

Replicate

eventual replicates

Text

ID_Location

Identifier for Location

Numeric

Location Table

Yes

ExposureRegime

Identifier for Exposure Regime

Numeric

ExposureRegime Table

Yes

UnitMeasurement

Measurement Unit definition

Text

ID_UM

Unique Identifier for Measurement Unit

Counter

UM_EFSACODE

EFSA CODE

Text

Yes

ORD_EFSATT


Numeric

Yes

Acronym_UM

simple names

Text

No

Counter

Unique Identifier for Model Comparisons

Counter


Yes

ID_Effect


Numeric

Effect Table

Yes

id_PRI


Numeric

id_TOX


Text

Main Table

Yes

DataType

Datatype

Text

DataType Table

Yes

Effects

Effect

Text

Effect Table

Yes

ID_Datatype


Numeric

DataType Table

Yes

Family/Quantal

equation family / quantal

Text

"quantal/exponential/hill"

Yes

ModelExponential

Delta Models

Text

Yes

Log-Likelihood


Text

Yes


109

Terminology

Mandatory Yes

Yes

Yes auto generated record

Yes

Yes




Heading

Organisms

OverviewTable

Name

Definition

Data type

2 * diff

value 2*Delta Model result

Text

Yes

significant

Significant test

Text

Yes

AIC

Akaike’s Information Criterium

Text

Yes

Id_Orgs

Unique Identifier for Organisms

Counter

Orgs_Name

Organism name

Text

Yes

Orgs_acr

acronym of Organisms

Text

Yes

Counter

Unique Identifier for OverviewTable

Counter

id_PRI


Numeric

id_TOX


Text

Main Table

Yes

ID_Effect


Numeric

Effect Table

Yes

Effect

Effect

Text

Effect Table

Yes

ID_Datatype


Numeric

DataType Table

Yes

DataType

Datatype

Text

DataType Table

Yes

Time

Duration of the exposure

Text

Yes

Model

Model

Text

Yes

NOEC

Calculated NOEC value

Text

Yes

No_Concentrations

Number of Tested levels

Text

Yes

Log-Likelihood


Text

numeric value or *

Yes

EC10


Text

numeric value or *

Yes

EC10low


Text

numeric value or *

Yes

EC10up


Text

numeric value or *

Yes

EC20


Text

numeric value or *

Yes

EC20low


Text

numeric value or *

Yes

EC20up


Text

numeric value or *

Yes


110

Terminology



Mandatory

Yes

Yes Yes




Heading

Qualifier

Sex

Species

Substances

Test_Type

Name

Definition

Data type

Terminology

Mandatory

EC50


Text

numeric value or *

Yes

EC50low


Text

numeric value or *

Yes

EC50up


Text

numeric value or *

Yes

MaxDose

maximum dose

Text

Yes

EpNoec

Endpoint determined in the studies

Text

Yes

EpModel

Model to determine endpoint in the studies

Text

Yes

Graphs

Hyperlink to graphics

Hyperlink

Yes

Id_qualifier

Unique Identifier for Qualifier

Counter

Definition

definition of the qualifier

Text

Yes

Qualifier

Sign before numbers

Text

Yes

Id_Sex

Unique Identifier for gender

Counter


Yes

SexGender

Gender

Text

Female/Male/Unknown/Not Relevant/Male & Female

Yes

ID_Species

Unique Identifier for species

Counter


Yes

Id_TestType

Identifier for Test Type

Numeric

Test Type Table

Yes

Name_Species

Species Definition

Text

Yes

Organism_Type

Taxa

Text

Yes

Spec_EFSACODE

EFSA CODE

Text

Yes

Id_Subs

Unique Identifier for substances

Counter

Subs_Name

Substance name

Text

Yes

Subs_Acro

acronym of substances

Text

Yes

Subs_Cate

Substance category

Text

Yes

Code

EFSA CODE

Text

Yes

ID_TestType

Unique Identifier for Test Type

Counter


111




Yes

Yes

Yes




Heading

Yes_No

Name

Definition

Data type

Terminology

Mandatory

ID_Compartment

Identifier for Compartment

Numeric

Compartment Table

Yes

TT_Name

Test Type definition

Text

Id_yn

Unique Identifier for Yes and No

Counter


Yes

YN

Yes or No

Text

"Yes/No"

Yes


112

Yes




Authors Compartments www.efsa.europa.eu/publications

113




od:autoUnique="yes"

DATA DATASET www.efsa.europa.eu/publications

114





115

od:autoUnique="yes"




DATA TYPE EFFECTS www.efsa.europa.eu/publications

116




od:autoUnique="yes"

EFSA STRAIN EFSA TARGET TISSUE www.efsa.europa.eu/publications

117




EFSA TOXICITY www.efsa.europa.eu/publications

118




ENDPOINT STUDIES


119




EFSA TEST TYPE EXPOSURE REGIME www.efsa.europa.eu/publications

120




EXPOSURE TYPE GLOBAL OUTPUT www.efsa.europa.eu/publications

121





122




GLP COMPLIANCE www.efsa.europa.eu/publications

123




GUIDELINE QUALIFIER


124




GUIDELINES GUIDELINE TABLE www.efsa.europa.eu/publications

125




LOCATION www.efsa.europa.eu/publications

126




MAIN www.efsa.europa.eu/publications

127




MEASUREMENT UNIT www.efsa.europa.eu/publications

128




od:autoUnique="yes"

MODEL RESULTS www.efsa.europa.eu/publications

129




ORGANISMS www.efsa.europa.eu/publications

130




QUALIFIER SEX www.efsa.europa.eu/publications

131




od:autoUnique="yes"

SPECIES www.efsa.europa.eu/publications

132




SUBSTANCES www.efsa.europa.eu/publications

133




TEST TYPE YES NO www.efsa.europa.eu/publications

134




of the major concentration tested. Standard deviation and n (organism number) were added. Effect changed. Standard deviation and n (organism number) were added. Effect changed. Standard deviation and n (organism number) were added. Effect changed. Standard deviation and n (organism number) were added. Effect changed. Standard deviation and n (organism number) were added. Effect changed. No sensible endpoint. Not available NOEC. In the first part of the study NOEC< of the first concentration tesed; in the second part of the study NOEC was > of the major concentration tested. Standard deviation and n (organism number) were added. Effect 170

27/05/2015




ID_TOX

Amendments

CHECKED

SUBMITTED

Operator

27/05/2015

06/06/2015

FP

DELETED

FM

06/06/2015

FM

changed. Trl.NTP.349_ChS92.495.r6 Trt.ALG.193_HoR98.299.r0 Trt.BIR.198_TaC95.304.r0

Standard deviation and n (organism number) were added. Effect changed. NOEC highest tested concentration

No statistical analysis required

not available NOEC. Study in two parts: in the first NOEC < lowest tested concentration, in the second NOEC > highest tested concentration

No statistical analysis required

NOEC < lowest tested concentration




300

Trt.DAP.194_UrK12.300.r0

Yes

Trt

DAP

13/04/2015

301

Trt.DAP.195_DoT92.301.r0

Yes

Trt

DAP

13/04/2015

302

Trt.FIS.196_SeG96.302.r0

Yes

Trt

FIS

30/04/2015

303

Trt.MAC.197_HoR98.303.r0

Yes

Trt

MAC

13/04/2015

831

Trt.MAM.604_HeM93.831.r0

Yes

Trt

MAM

06/06/2015

832


Yes

Trt

MAM

06/06/2015

833


Yes

Trt

MAM

06/06/2015

834

Trt.MAM.605_BuM91.834.r0

Yes

Trt

MAM

06/06/2015


219




Appendix G – Output Results id_ PR I

id_TOX

Effect

DataType

Ti m e

Model

1

Chr.AIN.380_F oA98.537.r0 Chr.ALG.377_S mV98.534.r0 Chr.ALG.378_S mV98.535.r0 Chr.ALG.379_S mV00.536.r0 Chr.ALG.383_H uS92.540.r0 Chr.BIR.376_S hK88.533.r0

Developm ent Growth

Emergence

28

Area under growth curve Area under growth curve Area under growth curve Cell count

5

1

Chr.BIR.376_S hK88.533.r0 Chr.FIS.382_S hK80.539.r0 Chr.FIS.382_S hK80.539.r0 Chr.MAC.384_ SmV98.541.r0 Chr.NTA.385_V iS00.542.r0

Reproduct ion Reproduct ion Reproduct ion Growth

14-day old survivors/normal hatchlings Laid eggs

QLogis tic Hill_m 5 Exp_m 5 Exp_m 3 Exp_m 3 QLoglo gistic

Eggs laid / Spawn Hatching

28 0 34

Dry weight

14

12

Clo.SOA.452_M aC03.636.r0

Mortality

Average production female Deaths

13

Cya.FIS.453_B oL00.637.r0 Cya.FIS.453_B oL00.637.r0 Din.AIN.22_vaJ 01.74.r0 Etx.AIN.400_S oP02.558.r0

Growth

Length

33

Growth

Weight

33

Developm ent Developm ent

Development rate

28

Emergence

28

2 3 4 5 6 7 8 9 10 11

14 15 16

Growth Growth Growth Reproduct ion

Reproduct ion


egg per

5 1 1

1

1 63

NOE C Calc ulate d 0.125

No Co nc s

LogLikeli h.

EC1 0

EC1 0low

EC1 0up

EC2 0

EC2 0low

EC20 up

EC5 0

EC5 0lo w

EC50 up

Max Dos e

NOEC studie s

6

-140

0.14 1 40.7

0.109 5 38.57

0.175 1 42.97

0.23 33 64.2

0.325 9 68.23

0.125

19.29

0.114 9 32.79

0.5

8

0.049 89 28.74

0.273

*

0.08 34 30.6

320

20

*

8

3.588

6.27

3.937

8.557

8.48

5.851

10.8

13.4

15.35

56

3.5

*

7

1.794

21.7

1.106

46.12

33

1.11

56.34

62.1

77.21

96

48

0.05

6

14.6

0.12 3 254 04

0.103 2 9080

0.148 4 15481 3

3364 176

0.234 7 17986 1512

0.1

-280

0.109 6 4061

0.4

4

0.068 39 875.7

0.212

1000

0.08 61 1730

10.6 2 1.11 6 0.19 23 4122 32

1000 0

1000

QLogis tic Exp_m 2 QLogis tic Hill_m 3 Hill_m 5

1000

4

-1941

1165

918.5

1397

1981

2412

5123

4880

5384

6

-14.62

4.05

0.827 8 3.449

4.81

8.59

10.46

6.5

3

290

8

12.11

377

288.7

487.4

462

382.4

548.3

652

2.57 1 7.36 8 610

4.391

-638.2

0.667 4 2.814

3.32

5

0.390 8 1.678

1.414

3

0.50 5 2.26

1000 0 16

1000

6.5

219 9 1.07

703.6

740

290

*

4

5.246

858

833.1

1072

980

949.8

1168

1240

1206

1367

7700

1500

QCom plloglo g Exp_m 3 Exp_m 3 Exp_m 5 QLoglo gistic

30

5

-49.54

33.6

Comparison of NOEC values to EC10/EC20 values ...

Comparison of NOEC values to EC10/EC20 values ...

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