Toxic. in Vitro Vol. 9, No. 6, pp. 871-929, 1995 

08&V-2333(%)00092-5 

Copyright 0 1995 Elsevier Science Ltd 

Printed in Great Britain. All rights reserved 

0887-2333/95 $9.50 + 0.00 

The EC/HO International Validation Study 

on Alternatives to the Draize Eye 

Irritation Test 

M. BALLS*, P. A. BOTHAM?, L. H. BRUNERS and H. SPIELMANN 

*ECVAM, JRC Environment Institute, 21020 Ispra (VA), Italy, tCentra1 Toxicology Laboratory, Zeneca, 

Macclesfield, Cheshire SKlO4TJ, UK, IProcter & Gamble Health and Beauty Care (Europe), Egham, 

Surrey TW209NW, UK and GZEBET, BgVV, 12277 Berlin, Germany 

Abstract-This is the final report of the Management Team for a European Commission/British Home 

Office (EC/HO) validation study on alternatives to the Draize eye irritation test. The principal goal of 

the study was to establish whether one or more of nine non-animal tests could be used to replace the Draize 

test for all severely irritating materials (or those belonging to specific classes) or the animal test completely 

for chemicals with or without regard to chemical class. Sixty chemicals were independently selected, coded 

and supplied, then the data obtained in 37 laboratories were analysed independently. The results of 

comparisons between 27 alternative test index scores and the Modified Maximum Average Scores 

(MMASs) obtained in the Draize eye test were compared. Tables of results showing Pearson’s product 

moment correlation coefficients and Spearman’s rank coefficients for each laboratory are provided, and 

correlation matrices of alternative test index scores among the different groups of laboratories are shown 

for each endpoint. Scatter-plots are provided, in which the alternative test scores obtained by the lead 

laboratories for the nine tests are plotted against the MMAS for the full set of chemicals and 12 

surfactants. It is concluded that, with the possible exception of predicting the irritancy of surfactants, none 

of the nine tests met any of the four performance targets. Possible reasons for this outcome are discussed. 

Introduction 

For some considerable time, a number of governmental authorities, including the British Government and 

the Commission of the European Union (EU), as well 

as various industrial companies and industry associations, scientists and animal welfare groups, have 

been concerned that relevant and reliable in vitro 

alternatives methods should replace the Draize eye 

irritation test in rabbits as soon as possible. An 

extensive literature now exists. 

Abbreviations : AMA = alkaline membrane assay; 

BCOP = bovine comeal opacity/permeability; 

CEET = enucleated chicken eye test; DFU = directions 

for use; EC = European Commission; ECETOC = 

European Centre for Ecotoxicology and Toxicology of 

Chemicals; ECVAM = European Centre for the Validation of Alternative Methods; EDE = EYTEX Draize 

equivalent; EU = European Union; FL = fluorescein 

leakage; GLP = Good Laboratory Practice; HETCAM= hen’s egg-2-chorioallantoic membrane test; 

HO = British Home Office: ICE = isolated chicken eve: 

IRAG = US Interagency’ Regulatory Alternatiies 

Group; IRE = isolated rabbit eye; MDCK = MadinDarby canine kidney; MMAS = modified maximum average score; NRU = neutral red uptake; OD = optical 

density; OECD = Organisation for Economic Cooperation and Development; QC = quality control; 

RBC = red blood cell haemolysis; RMA = rapid membrane assay; SM = silicon microphysiometer; SOP = 

Standard Operating Procedure; UMA = upright membrane assay. 

A meeting to consider the matter was held in 

London in November 1991, under the auspices of the 

British Home Office (HO) and Directorate General 

XI of the European Commission (EC), with the 

participation of representatives of British industrial 

companies and academia, and the European Centre 

for Ecotoxicology and Toxicology of Chemicals 

(ECETOC). Recent progress in the development of 

alternative methods and steps towards their validation [including the results of an EC pilot study 

(Commission of the European Communities, 1991)] 

were considered, and it was concluded that the 

methods now available were of sufficient promise for 

a formal validation study to be undertaken. It was 

also recognized that the setting up of an ECETOC 

task force to identify a set of chemicals of known and 

different degrees of ocular irritation, specifically for 

use in validation studies (Bagley et al., 1992; 

ECETOC, 1992), afforded a suitable opportunity for 

the selection of an appropriate set of chemicals 

suitable for use in such a validation study on alternatives to the Draize eye irritation test. As a result of 

this meeting, what was later to become known as the 

EC/HO international validation study on alternatives 

to the Draize eye irritation test was established. 

A Steering Group was formed and a series of 

meetings was held to make detailed proposals about 

the methods, laboratories, test chemicals and management procedures which could be included in such 

a study. It was agreed that the first priority should be 

871 
872 M. Balls et al 

to evaluate the performance of non-animal tests as 

screens for the detection of chemicals likely to be 

severely irritant in the eye, while also taking all 

possible steps to evaluate the usefulness of tests and 

test batteries for predicting lesser degrees of ocular 

irritancy. It was also agreed that the proposed study 

should be conducted according to the principles of 

validation of non-animal tests as formulated at an 

international workshop held in Amden, Switzerland, 

in 1990 (Balls et al., 1990a), with the aim of gaining 

acceptance of new testing strategies, according to a 

further set of principles established at another international workshop, held later in the same year in 

Vouliagmeni, Greece (Balls et al., 1990b). 

In June 1992, a further meeting was held at the 

Home Office, London, at which proposals put forward by the Steering Group were discussed with an 

international group of experts on regulatory testing 

and on alternatives, from academia and industry, and 

from the British Department of Health, the former 

German Federal Health Authority (BGA) and the 

Organisation for Economic Cooperation and Development (OECD). 

A Management Team representative of academia, 

industry, the British Government and ECETOC, and 

composed of members experienced in the conduct 

and management of validation studies was appointed 

with the following membership: Professor Michael 

Balls (University of Nottingham and HO Animal 

Procedures Committee); Dr Philip Botham (Zeneca 

and ECETOC); Dr Leon Bruner (The Procter & 

Gamble Company); and Professor Horst Spielmann 

(BGA and Free University of Berlin). 

The Management Team was charged with drawing 

up a plan for the study, preparing a budget and 

seeking financial support for the study from the 

British Government, the EC and industrial and nonindustrial laboratories which would be invited to 

participate in the study. 

A Chemicals Selection Committee was appointed, 

under the chairmanship of Dr Arthur Walker (Apojay Consultancy, Whitley Bay, UK), to make suggestions for the list of chemicals to be used in the study 

and to investigate how they might be coded and 

supplied to the participating laboratories. 

Following discussions among members of the 

Management Team and with the Home Office, the 

University of Nottingham was invited to be the 

contractor for a grant application to the EC, on 

condition that coding and supply of the chemicals, 

and data receipt and analysis should be the responsibility of institutions independent of the university or 

the Management Team in order to guarantee lack of 

bias in the study. 

At the suggestion of the Steering Group, a further 

international meeting was held at the Home Office, 

London, on 12-13 October 1992, at which the participants at the June 1992 meeting were joined by other 

experts from the UK, Europe and the USA, including 

members of the US Interagency Regulatory Altematives Group (IRAG) and representatives of many of 

the laboratories which were to be invited to take part 

in the study (see Appendix 1). At this meeting, the 

goal of the study, tests to be evaluated and procedures for the selection of laboratories and chemicals, and for the submission and analysis of data were 

discussed and accepted. 

The goal of the study 

It was agreed by the Steering Group that the 

principal goal of the study would be to provide 

scientiJically credible data for proposing to regulatory 

authorities that one or more non-whole-animal 

methods should be adopted as a replacement for the 

Draize rabbit eye irritation test, and that four target 

steps related to this goal would be to determine 

whether or not the data obtained in the study indicated that it would be possible: 

1. to replace the Draize eye test for identifying all 

severely irritating materials; or 

2. to replace the Draize eye test for identifying 

severely irritating materials belonging to specific 

chemical classes; or 

3. to replace the Draize eye test completely (i.e. to 

identify all levels of irritancy of materials without regard to chemical class); or 

4. to replace the Draize eye test for identifying all 

levels of irritancy of materials belonging to 

specific chemical classes. 

The tests to be evaluated 

The Steering Group was able to benefit from a 

progress report on a survey of potential alternatives 

to the Draize eye test, being prepared for the EC [then 

in draft form, but now published (Atkinson et al., 

1994)j. As a result of this report, and following 

further discussion with the wider groups of experts in 

June and October 1992, it was agreed that the 

following nine tests should be included in the study: 

1. A physicochemical test, based on the interaction 

between test chemicals and a purpose-designed 

protein matrix (Gordon and Bergman, 1987). 

2. A red blood cell haemolysis test, which is an 

indicator of cell membrane and protein damage 

(Pape et al., 1987). 

3. A test designed to indicate damage to a cellular 

barrier, by measuring leakage of fluorescein 

across a confluent monolayer of epithelial cells 

(Shaw et al., 1990). 

4. A cell viability test, namely the neutral red 

uptake assay (Borenfreund and Puerner, 1985). 

5. A test involving the evaluation of effects on 

cellular metabolism in a silicon microphysiometer (Bruner et al., 1991). 

6. The hen’s egg-chorioallantoic membrane test 

(HET-CAM; Luepke, 1985). 
EC/HO validation study on alternatives to the Drake test 873 

7. The isolated, enucleated rabbit eye test (Burton 

et al., 1981). 

8. A similar test involving the use of isolated, 

enucleated chicken eyes (Prinsen and Koeter, 

1992). 

9. The isolated bovine cornea test (Gautheron 

et al., 1992). 

These tests were selected because they were representative of the kinds of approaches available at the 

time, and because they met some or all of the 

following criteria: 

The specific purposes for which the test had 

been developed were well defined and likely to 

complement, but not duplicate, those of other 

tests in the set. 

The test had been adequately developed, standardized and documented, and a need for it in 

relation to other candidate tests had been 

demonstrated. 

The test had shown promise in one or more 

interlaboratory evaluation studies (e.g. those of 

the EC, the US Soap & Detergent Association, 

the US Cosmetic, Toiletry & Fragrance Association, or ZEBET). 

The test was judged potentially to be capable of 

routine use in laboratories worldwide. 

Further details of the nine tests and how they were 

conducted are given in Appendix 2. 

Management of the study 

The four members of the Management Team 

jointly took responsibility for the detailed design and 

execution of the project and its day-to-day management, including: 

8. 

Selection of laboratories and of a lead laboratory for each test. 

Issuing instructions to laboratories and receiving and answering queries from them. 

Issuing requests to and receiving instructions 

from the Chemicals Selection Committee. 

Agreeing a contract with BIBRA International 

with regard to chemicals purchase, distribution 

and supply. 

Agreeing a contract with BIBRA International 

with respect to data collection and analysis. 

Reporting to the sponsors of the study, the EC 

and HO. 

Advising the EC contractor (the University of 

Nottingham) on the finances of the study, 

including arranging for contracts with participating laboratories and BIBRA International. 

Monitoring progress in relation to the goals of 

the study. 

9. Dealing with safety issues. 

10. 

11. 

12. 

Protecting the integrity of the study and, in 

particular, the secrecy of the identity of the test 

materials. 

Arranging for meetings with the participating 

laboratories to discuss the outcome of the 

study. Two meetings were held: at Egham 

(Surrey, UK) in March 1994 and at Ittingen 

(Switzerland) in September 1994. 

Producing this report on the study. 

The Management Team maintained contact by 

letter, fax, conference telephone calls and regular 

meetings. 

The participating laboratories 

Following discussion at the Expert Meeting in June 

1992, it was agreed that, in line with the Amden 

proposals on validation (Balls et al., 1990a), four or 

five laboratories should be invited to conduct each 

test. Suggestions were made concerning the laboratories to be selected, taking into account the need for 

appropriate experience, adherence to the principles of 

Good Laboratory Practice (GLP) and for international participation. After further discussions by 

the Management Team, a lead laboratory for each 

test was identified, on the basis of demonstrated 

experience with the test concerned. 

The lead laboratories were responsible for maintaining contact with the Management Team on behalf 

of the nine groups of laboratories, with respect to: 

Logistical problems. 

Questions related to test protocols and GLP. 

Assuring consistent handling of test materials 

and application of protocols. 

Overseeing data reporting, but without having 

access to the actual data from other laboratories. 

The full set of laboratories and contact persons was 

as follows: 

1. The Red Blood Cell Haemolysis (RBC) Test 

Zeneca CTL, Macclesfield, Cheshire, UK (Dr 

Richard Lewis)-lead laboratory. 

Beiersdorf AG, Hamburg, Germany (Dr Wolfgang Pape). 

Shell Research Ltd, Sittingbourne, Kent, UK 

(Dr John Gardner). 

Rhone-Poulenc, Valbonne, France (Dr David 

Esdaile). 

2. The EYTEA? Method 

1. Atochem, La Defense, Paris, France (Dr JeanFran9ois Regnierplead laboratory. 
814 M. Balls et al. 

2. 

3. 

4. 

Albright & Wilson Ltd, Whitehaven, Cumbria, 

UK (Dr Cliff Martin). 

Avon Products Inc.. Suffern, NY, USA (Dr 

Kevin Renskers). 

Sanofi Recherche, Montpellier. France (Dr 

Catherine Robles). 

3. The Bovine Cornea1 Opacity/Permeability (BCOP) 

Test 

1. Laboratories MSD-Chibret, Riom, France (Dr 

Pierre Gautheron+lead laboratory. 

CIDA. Barcelona, Spain (Dr Lourdes Mayordomo). 

Janssen Pharmaceutics, Beerse. Belgium (Dr 

Philippe Vanparys). 

ZEBET, BgVV, Berlin (Dr Joachim Heuer). 

RBM, Ivrea, Italy (Dr Raffaella Pirovano). 

4. The HET-CAM Test 

Henkel KGaA, Dusseldorf, Germany (Dr Winfried Steiling)--lead laboratory. 

Beiersdorf AC, Hamburg, Germany (Dr Wolfgang Pape). 

L’Oreal, Aulnay-sous-Bois, France (Dr Odile 

de Silva). 

Parfums Christian Dior, Saint Jean de Braye, 

France (M Pascal Courtellemont). 

5. The Fluorescein Leakage (FL) Test 

Department of Human Morphology, University of Nottingham, Nottingham, UK (MS 

Karen Atkinson)--lead laboratory. 

L’Orial, Aulnay-sous-Bois, France (Dr Martine Cottin). 

State University of New York, Binghampton, 

NY, USA (Dr Robert Van Buskirk). 

Philadelphia College of Pharmacy & Science, 

Philadelphia, PA, USA (Dr Ruy Tchao). 

6. The Isolated Chicken Eye (ICE) Test 

TN0 Toxicology & Nutrition Institute, Zeist, 

The Netherlands (Mr Menk Prinsen)-lead 

laboratory. 

Shell Research Ltd, Sittingbourne, Kent, UK 

(Dr John Gardner). 

Rhone-Poulenc. Valbonne, France (Dr David 

Esdaile). 

ESL, Unilever Research, Sharnbrook, Bedford, 

UK (Dr Lesley Earl). 

7. The Isolated Rabbit Eye (IRE) Test 

1. ESL, Unilever Research, Sharnbrook, Bedford, 

UK (Dr Lesley Earltlead laboratory. 

2. Shell Research Ltd, Sittingbourne, Kent, UK 

(Dr John Gardner). 

3. 

4. 

Zeneca CTL, Macclesfield, Cheshire, UK (Dr 

Richard Lewis). 

IHE, Ministry of Public Health of Environment, Brussels, Belgium (Dr GA Jacobs). 

8. The Silicon Microphysiometer (SM) Test 

The Procter & Gamble Company, Cincinnati, 

OH, USA (Dr Rosemarie Osborne)-lead laboratory. 

Microbiological Associates Inc., Rockville, 

MD, USA (Dr Rodger Curren). 

L’Oreal. Aulnay-sous-Bois, France (Dr Martine Cottin). 

Department of Chemistry, Stanford University, 

Stanford, CA, USA (Dr Harden McConnell). 

9. The Neutral Red Uptake (NRU) Test 

Microbiological Associates Inc., Rockville, 

MD, USA (Dr Rodger Currentlead laboratory. 

Istituto Superiore di Sanita, Rome, Italy (Dr 

Annalaura Stammati). 

Hatano Research Institute, Kanagawa, Japan 

(Dr Hiroshi Ono). 

Kurabo Industries Ltd, Osaka, Japan (Dr Ryohei Yamamoto). 

Selection of chemicals 

In 1992, ECETOC published a Reference Data 

Bank for ocular irritation (Bagley et al., 1992; 

ECETOC, 1992) which presented the eye irritation 

data on individual rabbits obtained in Draize eye 

tests involving readily available chemicals of high 

purity. One object of the provision of in uivo eye 

irritation data in the publication was to make it 

unnecessary for experimenters to carry out in uivo 

studies simply to generate data to be used in the 

assessment of alternative eye irritation tests. The 

following stringent criteria were used in the selection 

of chemicals and data, with the result that the 

data bank comprised 60 chemicals assessed in 72 

tests: 

1. The reference chemicals should be single chemical entities available at known high consistency 

and purity and expected to be stable in storage. 

2. The in uivo rabbit eye irritation data should have 

been generated since 198 1, in studies carried out 

according to OECD Test Guideline 405 (OECD, 

1987) and following the principles of Good 

Laboratory Practice (GLP) and should have 

been obtained in tests: 

-normally using at least three New Zealand 

White rabbits, tested at the same time; 

-involving instillation of 0.1 ml or the equiv- 
EC/HO validation study on alternatives to the Draize test 875 

alent weight into the conjunctival sac; 

-in which topical intraocular anaesthesia was 

not used; 

-in which observations were made (at least) 24, 

48 and 72 hr following instillation, and: 

-included individual scores (using the Draize 

scale) for each tissue measured in each 

rabbit; 

--enabled reversibility/irreversibility to be 

assessed; 

--in which chemicals were tested undiluted (except that data from studies using dilutions of 

the chemical were acceptable, when higher 

concentrations of the chemical could have 

been expected to cause severe effects). 

The ECETOC Task Force ranked the irritancy 

potential of the chemicals in terms of MMAS. This 

index was ‘modified’ from that originally proposed by 

Draize, but only in that it was developed from Draize 

scores calculated 24 hr or more after instillation of 

the chemical (because not all the chemicals in the data 

bank had been assessed 1 hr after instillation in the 

rabbit eye). 

The ECETOC Task Force that developed the Data 

Bank became the Chemicals Selection Committee for 

the EC/HO validation study, although individuals 

who would be practically involved in the study were 

excluded in order to maintain strict confidentiality. A 

first selection of 60 test materials from the ECETOC 

data bank was presented to the meeting held in 

London in June 1992. This selection was criticized in 

that the number of chemicals tested in solid form was 

inadequate, there were insufficient chemicals of moderate to severe irritancy, and no pesticides were 

included. At the meeting, the Chemicals Selection 

Committee was made aware of other studies, not then 

published, and of other sources of unpublished data, 

which widened the range of both chemical types and 

irritant properties. Consequently, additional data 

were obtained, and those chemicals and data which 

met the ECETOC selection criteria have been deposited in the ECETOC Data Bank. A revised proposal for a set of 60 test materials was then made and 

accepted by the Management Team for the EC/HO 

validation study. The final selection consisted of 52 

chemicals assessed in 60 different tests (four chemicals 

were tested at two, and two at three concentrations). 

26 of the test materials were liquids. The remaining 

34 were solids, 20 of which were administered as such 

in the in vivo tests, while the remaining 14 were 

administered as diluted solutions (Table 1). 

After the in vitro testing had begun, further checking of the in vivo data revealed that 14 of the materials 

had been tested by a protocol which involved rinsing 

after 1 hr or removal of solids after 1 hr. These data 

therefore did not meet the ECETOC criteria. It was 

decided, with great reluctance, that these 14 materials 

would have to be tested in vivo because the in vitro 

testing had already begun. This work was commissioned by those who had given the original data to 

ECETOC, at no cost to ECETOC or the EC/HO 

study. One of these 14 chemicals, thiourea, killed all 

three rabbits used by a neurotoxic effect. This chemical was therefore excluded from further testing and 

from the in viva/in vitro comparisons in the EC/HO 

study. 

Table 1 shows the 60 test materials in order of 

decreasing MMAS. The source, purity and solubility 

of the chemicals, the concentration and form in which 

they were tested in vivo, the number of animals used 

in each study, and the form tested in vitro, are also 

included in Table 1. In Table 2, the test materials are 

presented, together with the MMAS, in their 14 

different chemical groups. The standard deviations of 

the mean MMASs are given in Table 1 and illustrated 

in Fig. 1. 

Supply, coding and distribution of samples 

The responsibility for these three functions was 

assigned to BIBRA International, Carshalton, Surrey, UK (Brantom et al., 1995). 

SUPPlY 

Chemicals with the same source and specification 

as those tested in vivo were obtained, wherever feasible. Where this was not possible, a specification as 

close as possible to that used in the in vivo testing was 

chosen. 

Dispensing 

To avoid any possible confusion of one test substance with another: 

-only one substance was dispensed at any one 

time; 

-all samples were sealed and labelled with unique 

coded labels before the next substance was dispensed. 

After dispensing, samples were returned to appropriate storage conditions until they were packed for 

despatch. 

Coding and labelling 

Each substance was first allocated a numeric code. 

Each participating laboratory was also allocated a 

unique code number so that subsequent analysis of 

in vitro data would be possible without knowledge of 

the identity of the laboratory or test substance. 

The number of aliquots of each substance required 

for the full study was defined (requiring precise 

definition of the number of laboratories participating, 

and the total number of assays). Random codes were 
NO. Chemical’ 

Table 1. 60 test materials used in the EC/HO study, listed in order of decreasing MMAS 

Chemicals information In viuo t

Form Concn 

CAS no. Source’ Cataloaue no. Puritv Solubilitv’ tested* tested 

I 

2 

3 

4 

5 

6 

7 

8 

9 

IO 

II 

I2 

13 

14 

15 

I6 

17 

I8 

19 

20 

21 

22 

23 

24 

25 

26 

27 

28 

29 

30 

31 

32 

Sodium hydroxide 1310-73-2 Fisher 

Benzalkonium chloride X001-54-5 Sigma 

Trichloroacetic acid 76-03-9 Fisher 

Cetylpyridinium bromide 140-72-7 Sigma 

Cetylpyridinium bromide 140-72-7 Sigma 

Benzalkonium chloride’ X001-54-5 Sigma 

Captan 90 concentrate* 133-06-2 EPA 

Chlorhexidine’ 50-56-I Degussa 

Cyclohexanol 10X-93-0 Fisher 

Quniacrine* 69-05-6 Sigma 

Promethazine HCI* 58-33-3 Aldrich 

Parafluoraniline 371-40-4 Aldrich 

Triton X-100 9002-93-I Sigma 

Acetone+ 67-64-I Fisher 

n-Hexanol I I l-27-3 E-Kodak 

I-Naphthalene acetic acid* 61-31-4 EPA 

Sodium oxalate’ 62-76-O Aldrich 

Isobutanol x 78-83-l Fisher 

Imidazole* 288-32-4 Aldrich 

Sodium lauryl sulfate+ 151-21-3 Sigma 

2-Ethyl-I -hexanol 104-76-7 Fisher 

4Carboxybenzaldehyde 619-66-9 Sigma 

Methyl ethyl ketone’ 78-93-3 Fisher 

Pyridine’ 11 O-86- I Aldrich 

I-Naphthalene acetic acid* 86-87-3 EPA 

Benzalkonium chloride (I]/[21 8001-54-5 Sigma 

2,2-Dimethylbutanoic acid 595-37-9 Aldrich 

Gammabutyrolactone’ 96-48-O Aldrich 

Thiourea’ 62-56-6 Aldrich 

n-Gctanol* 11 l-87-5 Aldrich 

Methyl acetate + 79-20-9 Fisher 

t,-Aspartic acid* 70-47-3 Degussa 

Reagent grade 

B-1383 

Reagent grade 

C-588 I 

C-588 I 

B-1383 

98 

98 

98 

98 

90 

855757 97 

Q 3251 

2X,41 I-4 98 

F380-0 99 

T6878 98 

Al8 99 

BIHC 98 

95 

22,343.3 >99 

851962 99.9 

I-20-2 99 

L-4509 98 

85535 99 

Cl904 95 

M-209 99 

27,04C-7 > 99.9 

96 

B-1383 98 

D I5,260-9 96 

810,360-X > 99 

24,025-7 > 99 

29,324-5 > 99 

M-203 98 

100 

S SU 10 

Sf SU IO 

S SU 30 

Sf SU IO 

Sf SU 6 

Sf SU 5 

S SO 100 

I* SO 100 

S L 100 

S SO loo 

s* SO 100 

I L 100 

Sf SU 10 

S L 100 

I* L 100 

S* SO 100 

S SO 100 

I* L 100 

S SO 100 

Sf SU I5 

S L 100 

I* SO 100 

S L 100 

S L 100 

I’ SO 100 

Sf SU I 

I L 100 

S L 100 

S SO 100 

I* L 100 

S L 100 

S SO 100 
33 

34 

35 

36 

37 

38 

39 

40 

41 

42 

43 

44 

45 

46 

41 

48 

49 

50 

51 

52 

53 

54 

55 

56 

51 

58 

59 

60 

Benzoyl-~-tar&c acid* 2143-38-6 Aldrich 34,584-9 

Tli1on x-100 [I]/[21 9002-93-I Sigma T-6878 

Potassium cyanate* 590-28-3 Degussa 

1sopropano1+ 67-63-O Fisher 854888 

Sodium perborate, 4H,O 10486-00-7 Dupont 004666 

Dibenzyl phosphate* 1623-08-l Aldrich D3,655-0 

2,5-Dimethylohexanediol* 110-03-2 BASF 

Methyl cyanoacetate* 105-34-o Aldrich 10,842-I 

Sodium hydroxide l 1310-73-2 Fisher Reagent grade 

Ethanol’ 64-17-5 Local vendor - 

2,bDichlorotenzoyl chloride 4659-45-4 Aldrich 18,751-B 

Ammonium nitrate 6484-52-2 Aldrich 25,606-4 

Ethyl-2-methylacetoacetate m-14-3 Fluka 65440 

Sodium lauryl sulfate 151-21-3 Sigma L-4509 

Ethyl acetate’ 141-78-6 fisher E-145 

Maneb* 12427-38-2 EPA 

- 

Fomesafen* 72128-02-o EPA 

- 

Tetmaminopyrimidine sulfate* 5392-28-9 Aldrich T380-7 

Toluene 108-88-3 Fisher T-324 

n-Butyl acetate 123-86-4 Fisher B-396 

Trichloroacetic acid+ 76-03-9 Fisher Reagent grade 

Methyl isobutyl ketone 108-10-I Fisher M-213 

Tween 20 9005-64-5 Sigma P-1379 

Ethyl trimethyl acetate 3938-95-2 Aldrich 23,455-9 

Methylcyclopentane 96-31-1 Fluka 66495 

Cetylpyridinium bromide 140-72-7 Sigma C-5881 

_. __ ^. _ __ . . . . 

98 

98 

91 

99.9 

98.6 

99 

99.5 

99 

99 

> 99.9 

c.91 

98 

99 

c.90 

91.5 

91 

99 

99 

- 

98 

98 

99 

> 99 

98 

> 99.5 

I 

Sf 

S 

s 

S 

I’ 

I 

s* 

S 

s 

I* 

S 

S* 

Sf 

S 

s 

S 

I* 

I* 

If 

S 

I* 

Sf 

I* 

I* 

Sf 

S 

Sf 

SO loo 

SU 5 

SO 100 

L 100 

SO 100 

SO 100 

so 100 

L 100 

SU 1 

L 100 

L 100 

so 100 

L 100 

SU 3 

L 100 

so 100 

so 100 

so 100 

L 100 

L 100 

SU 3 

L 100 

L 100 

L 100 

L 100 

SU 0.1 

L 100 

L 100 

tilycerol 56-x1-5 Matlmkrcdt 

Polyethylene glycol 400 25322-68-3 Aldrich 20.239-B 

‘Chemicals: l = 22 chemicals not from the ECETOC Reference Chemicals Data Bank; ’ = 10 chemicals used in the preliminary stud

in the same laboratory. 

2Source: EPA = US Environmental Protection Agency. 

?Golubility: S = soluble in water; I = insoluble in water; Sf = surfactant; l = solubility uncertain. 

‘Physical form: So = solid; Su = solution; L = liquid. 

‘MMAS = modified maximum average score (Bagley ef al., 1992; ECETOC, 1992). 

6sD = standard deviation. 

‘n = number of animals used in Draize eye tests. 
878 M. Balls er al 

Table 2. Chemical grouping of the 60 test materials 

MMAS 

Acids (four chemicals, five tests) 

Trichloroacetic acid (3%) 

Dibenzoyl-L-tartaric acid*t 

L-Aspartic acid’t 

2,2-Dimethylbutanoic acid 

Trichloroacetic acid (30%)$ 

Acyl halide (one chemical) 

2,6-Dichlorobenzoyl chloride 

Alcohols (nine chemicals) 

Glycerol 

Ethanol’ 

2,5-Dimethylhexanediol’t 

lsopropanolf 

n-Octanol 

2-Ethyl-hexanol 

Isobutanol$ 

n-Hexanol 

Cyclohexanol 

Aldebyde (one chemical) 

4-Carboxybenzaldehydet 

Alkalis (one chemical, two tests) 

Sodium hydroxide (I %)I 

Sodium hydroxide (10%) 

Esters (six chemicals) 

Ethyl trimethylacetate 

n-Butyl acetate 

Ethyl acetate1 

Ethyl-2-methylaetoactate 

Methyl cyanoacetate 

Methyl acetatei 

Heterocyclics (three chemicals) 

Tetraaminopyrimidine sulfate*t 

Pyridine’ 

Imidazole’t 

Hydmcuhons (two chemicals) 

Methylcyclopentane 

Toluene 

Inorganic dwmicals (four chemicals) 

Ammonium nitrate7 

Sodium perborate 4H,Ot 

Potassium cyanate*t 

Sodium oxa1ate.t 

Ketones (three chemicals) 

Methylisobutylketone 

Methylethylketonef 

Acetone: 

Organqlwphate (one chemical) 

Dibenzyl phosphate*? 

P&kidea (five chemicals) 

Fomesafen (acid form)*? 

Maneb*t 

I-Nephthalene acetic acid*? 

1-Naphthalene acetic acid, Na salt* 

Captan 90 concentrate’t 

Surfactanta (six chemicals, I2 tests) 

Anionics 

Sodium lauryl sulfate (3%) 16.0 

Sodium lauryl sulfate (lS%)f 59.2 

CatiOOieS 

Benzalkonium chloride [I]/[21 (1%) 

Benzalkonium chloride (So/.)$ 

Benzalkonium chloride (IO%) 

Cetylpyridinium bromide (0. I %) 

Cetylpyridinium bromide (6%) 

Cetyipyridinium bromide (10%) 

NOlliODiea 

Polyethylene glycol 400 

Tween 20 

Triton X-100 [11/[2] (5%) 

Triton X-100 (10%) 

Miscellaneous (six chemicals) 

Gamma-butyrolactone* 

Thiourea*t 

Parafluoroaniline 

Promethazinc HCI*t 

Quinacrine*t 

Chlorhexidine*t 

6.1 

36.1 

37.3 

44.1 

106 

23.8 

1.7 

24.0 

28.3 

30.5 

41 .o 

51.3 

60.3 

64.8 

79.8 

50.3 

25.8 

108 

3.8 

1.5 

15.0 

la.0 

21.1 

39.5 

10.3 

48.0 

59.3 

3.7 

9.0 

18.3 

30.5 

31.3 

61.3 

4.8 

50.0 

65.8 

30.0 

13.5 

14.3 

46.1 

64.5 

83.0 

34.3156.3 

83.8 

ioa.0 

2.1 

85.8 

89.7 

0.0 

4.0 

32.3133.8 

68.1 

43.0 

6F8 

71.7 

82.0 

82.3 

*22 chemicals not from the ECETOC reference chemicals data bank; 

t20 solids; $10 chemicals used in the preliminary study; 

[I]/[21 = two tests on the same chemical in the same laboratory. 

generated by computer software for the total number 

of samples, so that each sample had a unique number. 

Distribution 

Shipping chemical samples worldwide was carried 

out in compliance with various regulations governing 

the transport of dangerous substances and health and 

safety. These regulations generally require that the 

UN hazard and packing class are known for all 

substances to be transported. Despite the small quantities involved in this study, there are significant 

limitations on the substances which can be transported in the same container, as well as prohibitions 

imposed by IATA on carrying certain chemicals as 

air freight. The procedure for handling many of these 

issues was developed successfully by working with a 

number of the major shipping companies. 

Samples were shipped under conditions designed to 

minimize damage during transit. As most substances 

are expected to be most stable when stored at low 

temperatures, recipients were advised to store all the 

samples in a refrigerator. 

The US and Japan require that any shipment of 

chemicals must be accompanied by relevant health 

and safety data. Most recipients were able to identify 

a safety officer to receive the samples and retain the 

health and safety data before passing the samples to 

the experimental laboratory. Therefore, disclosure of 

the codes of samples was unnecessary, so the blind 

nature of the study was not jeopardized. 

Health and safety data 

An emergency procedure was established to allow 

any participant to obtain the necessary health and 

safety data in the event of an accident. The procedure 

involved lodging a sealed envelope containing a full 

set of the coding information for each laboratory 

with the West Midlands Poison Centre (Dudley, West 

Midlands, UK). All participating laboratories were 

given the telephone number of the Poison Centre so 

that if an emergency occurred, the code could be 

broken, in which case the Poison Centre would 

inform the Management Team. This happened on 

one occasion, without any serious consequences for 

the technician concerned or for the integrity of the 

study. 

Data collection and analysis 

Data collection and analysis were performed by 

BIBRA International (Carshalton, Surrey, UK). The 

data analysis was conducted by a group that was 

completely independent from those who coded and 

distributed the test substances. This independence 

was maintained to ensure that the analysis was 

conducted in a blind fashion. 

Data collection and quality control review 

A data matrix containing the identity of each 

chemical, the in vivo eye irritation data and each 

alternative method endpoint was established, based 
EC/HO validation study on alternatives to the Draize test 879 

80 

50 60 

Compound No. 

Fig. I. MMAS, mean f standard deviation, for 59 chemicals, as supplied to the management team by the 

Chemicals Selection Committee. The MMAS score used in the analysis for two chemicals (nos 26 and 34) 

was a mean based on two separate studies. The values from each study are shown separately in the graph. 

on information provided by the chemicals subgroup 

and participating laboratories. The data from each 

alternative method were provided on report forms 

submitted to BIBRA by each laboratory. The data 

contained in each report form were then entered into 

a computer spreadsheet to create the data matrix. The 

integrity of the data entry process was checked in an 

internal quality control review by BIBRA In addition, BIBRA confirmed that the data entry was 

correct by asking each of the participating laboratories to review the tabulated results against the 

results they had submitted. The Management Team 

reviewed each of the proposed changes that followed 

this check, to confirm that any alterations in data 

were justified, because of errors in data transcription. 

Statistical analysis 

Statistical analyses of the data contained in the 

data matrix were carried out using the Minitab 

package (Minitab, 1992; Ryan et al., 1985). The data 

matrix contained 60 rows (for each test substance in 

the test set) by 83 columns (for each of the alternative 

method and in vivo endpoints). The data analysis was 

carried out on the full set and six subsets of the 

reference test substances. The sets of test substances 

evaluated are shown in Table 3. The allocation of 

each test material into the subsets was determined by 

the Chemicals Selection Committee. Some of the 

materials were of uncertain solubility. These chemicals are indicated in Table 1. No attempt was made 

to incorporate this uncertainty into the analysis. 

The statistical procedures used in the data analysis 

were designed to assess (1) the between-laboratories 

reproducibility of the alternative method data and (2) 

the relationship between the in vivo eye irritation 

potential of each test substance versus each endpoint 

obtained from each alternative method. 

Evaluation of interlaboratory variability 

An analysis of the degree of interlaboratory variability in alternative method scores was carried out, 

first by determining the correlation between the 

Table 3. Subsets of test substances evaluated in the data analysis 

process. This table indicates the seven subsets of reference test 

substances which were evaluated in the data analysis process. The 

number of test substances placed into each subset is also indicated. 

The full sat was subdivided in two ways: first into non-surfactant 

chemicals soluble or insoluble in water, and surfactant; and secondly, 

into solids, solutions made from solids and liquids (see Table 1) 

Set evaluated No. of substances 

A. Full test set 60 

B. Chemicals soluble in water 30 

C. Chemicals insoluble in water 18 

D. Surfactants 12 

E. Solids 20 

F. Solutions 14 

G. Liquids 26 
880 M. Balls et al. 

in vitro test results generated for each alternative 

method score in each laboratory. This a!lowed the 

construction of matrices containing Pearson’s correlation coefficients for each possible interlaboratory 

comparison. These matrices are shown for the entire 

test set, water-soluble materials, water-insoluble materials, surfactants, solids, solutions and liquids in 

Table 4A-G. In addition, an analysis of variance was 

carried out to quantify the degree of variability in the 

results among the laboratories and chemicals. The 

residuals obtained from this analysis were plotted for 

each of the test substances evaluated in the alternative 

methods. The data from the analysis of variance are 

not shown in this report. The interested reader will 

find these data in the full report of the study, which 

is available from ECVAM. 

Evaluation of the relationship between the in viva and 

alternative method data 

A statistical analysis was conducted to assess the 

relationship between the in vivo and alternative 

method data. This was done to determine whether the 

alternative methods could predict the eye irritation 

potential of the reference test substances. 59 rather 

than 60 test substances were included in this analysis 

since in vivo data were unavailable for thiourea (see 

above for explanation). 

The in vivo endpoint used to define the irritancy 

potential of the test substances was the MMAS, as 

defined in the ECETOC Technical Report on Eye 

Irritation (Bagley et al., 1992; ECETOC, 1992). The 

MMAS is based on the eye irritation scoring scheme 

developed by Draize et al. (1944). The MMAS was 

used as the in vivo eye irritation endpoint because the 

working group which met at the Home Office in 

October 1992 concluded that this endpoint would be 

most appropriate for use in this study. 

Initially, scatterplots were prepared so that the 

relationship between the MMAS versus each alternative method endpoint could be visualized. These 

scatterplots are shown in Figs 2 and 3. Figure 2 shows 

the results obtained from the lead laboratories for the 

full set of test substances. Figure 3 shows the results 

from the lead laboratories for the subset of surfactant 

test substances. Each symbol shown in the plots 

represents a result from a single test substance. 

Censored values (i.e. results given with values < or 

> for certain cut-off scores) are shown in the figures 

as upward-pointing or downward-pointing triangles, 

respectively. The study statistician included these 

censored values in the calculations of linear regression, 95% confidence intervals and correlation 

coefficients. For the HET-CAM and BCOP assays, 

the protocols indicated that scores >2 and >200, 

respectively, should be reported as either 2 or 200, 

respectively. As laboratories reported uncensored 

scores greater than 2 and 200, data analysis was 

completed on two sets of data. One analysis included 

the uncensored data and a second used cut-off values. 

The linear regression line to be used for predicting the 

MMAS from an alternative method index and the 

95% confidence interval for the linear regression line 

are shown on each scatterplot. The 95% confidence 

interval for an MMAS prediction from a given 

alternative method index is also shown. 

In some cases, laboratories reported that some 

materials could not be tested because of technical 

factors such as limited water solubility. These results 

were reported as missing values (see Table 4) and 

were not included in the scatterplots or statistical 

analysis. Quantitative statements provided by the 

laboratories (such as ‘non irritant at solubility’ for the 

SM test) were not included in the quantitative analysis. Rather, these results were reported in tabular 

form. Logarithmic transformation was performed on 

the RBC test DMax, D,,, and H,, scores, and on the 

NRU assay NRU,, scores. 

Statistics were calculated in the quantitative data 

analyses process for the entire test set, water-soluble 

materials, water-insoluble materials, surfactants, 

solids, solutions and liquids. The results from this 

analysis are shown in Table 4A-G, respectively. This 

summary table indicates (starting from left to right) 

the alternative methods index analysed, the number 

of substances tested (n), the number of chemicals for 

which no data were provided (missing), the Pearson 

product moment correlation coefficient (r), Spearman’s rank correlation coefficient (rank r), the linear 

regression equation to be used for predicting an 

MMAS from an alternative method index score and 

an estimate of the sample standard deviation from the 

regression(s). This latter value is used to estimate the 

95% confidence interval for the prediction of an 

MMAS from an alternative method score. 

Finally, a Mann-Whitney test was conducted to 

determine whether the rankings of the alternative 

methods scores between two sets of chemicals is 

different. The rankings of the alternative method 

index scores for chemicals with MMAS values >59 

were compared against those from chemicals having 

MMAS scores < 59 in the first test. A second test was 

performed to compare the rankings of the alternative 

method scores for chemicals with MMAS ~25 versus 

those having MMAS>25. Although the MannWhitney test sometimes provided statistically significant W statistics, inspection of the scatterplots 

indicated these values are not biologically significant. 

Accordingly, the results of Mann-Whitney tests are 

not shown in this report. Interested readers will find 

the results from this analysis contained in the full 

study report, which is available from ECVAM. 

Results 

A summary of the data analysis for the seven sets 

of comparisons between the in vivo and in vitro data 

is shown in Figs 2 and 3 and Table 4. Figure 1 shows 

the eye irritation scores given as the MMAS for the 

59 materials included in the reference set of test 

substances. The error bars show the variation of the 
Table 4. Summary of statistical analysis of EC/HO study 

In uilro testing In viva 0. in vitro correlation Interla

No. of No. of Spearman’s 

Alternative samples samples Pearson’s rank 

method tested not tested correlation correlation 

index in vitro in vitro coefficient coefficient Linear regression equation Lab Lab 

A. The full set of 60 chemicals 

RBCbLI 

RBCDLZ 

RBCDL3 

RBCDU 

56 

60 

58 

59 

IogDL I 56 

logDL2 60 

logDL3 58 

logDL4 59 

RBCDM 1 56 

RBCDMZ 60 

RBCDM3 58 

RBCDM4 60 

IogDM I 56 

logDM2 60 

logDM3 58 

logDM4 60 

RBCHI 56 

RBCHZ 60 

RBCH3 58 

RBCH4 59 

IogHl 56 

logH2 60 

logH3 58 

logH4 59 

EYTEX 5 

EYTEX 6 

EYTEX 7 

EYTEX 8 

BCOPW 

BCOPPIO 

BCOPPI I 

BCOPPIZ 

BCOPPI 3 

BCOP09 

BCOPOIO 

BCOPOl 1 

BCOPOl2 

BCOPOI 3 

49 

49 

57 

51 

60 

60 

60 

60 

60 

60 

60 

60 

60 

60 

4 

0 

2 

1 

4 

0 

2 

1 

4 

0 

2 

0 

4 

0 

2 

0 

4 

0 

2 

I 

4 

0 

2 

I 

II 

II 

3 

9 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

- 0.206 

- 0.254 

- 0.196 

- 0.238 

- 0.012 

- 0.108 

- 0.055 

- 0.107 

0.433 

0.365 

0.400 

0.277 

0.400 

0.321 

0.420 

0.286 

- 0.309 

- 0.268 

- 0.236 

- 0.306 

- 0.310 

~ 0.31 I 

~ 0.200 

~ 0.401 

0.376 

0.294 

0.383 

0.298 

0.338 

0.319 

0.392 

0.433 

0.503 

0.391 

0.293 

0.326 

0.342 

0.373 

- 0.058 

- 0.128 

- 0.015 

-0.119 

- 0.058 

- 0.128 

- 0.015 

-0.119 

0.351 

0.310 

0.385 

0.229 

0.351 

0.310 

0.385 

0.229 

- 0.298 

- 0.305 

- 0.229 

- 0.391 

- 0.298 

- 0.305 

- 0.229 

- 0.391 

0.415 

0.338 

0.459 

0.350 

0.363 

0.355 

0.375 

0.465 

0.449 

0.465 

0.555 

0.529 

0.48 I 

0.527 

MMAS = 48.485 - 1.35E-04 RBCDLl 

MMAS = 49.302 - 1.69E-04 RBCDLZ 

MMAS = 48.180 - I .27E-04 RBCDL3 

MMAS = 48.309 - 1.52E-04 RBCDL4 

MMAS = 43.811 - 0.371 IogDLI 

MMAS = 56.683 - 3.501 logDL2 

MMAS = 48.037 - 1.265 logDL3 

MMAS = 57.294 - 3.761 logDL4 

MMAS = 34.718 + 0.562 RBCDMl 

MMAS = 33.873 + 0.323 RBCDMZ 

MMAS = 36.294 + 0.461 RBCDM3 

MMAS = 38.051 + 0.284 RBCDM4 

MMAS = 34.953 + 15.997 1ogDMl 

MMAS = 33.616 + 11.016 logDM2 

MMAS = 35.024 + 16.179 logDM3 

MMAS = 37.588 + 10.759 logDM4 

MMAS = 49.279 - 2.27E-04 RBCHI 

MMAS = 48.298 - 2.26E-04 RBCHZ 

MMAS = 48.226 - 1.738-04 RBCH3 

MMAS = 47.896 - 2.28E-04 RBCH4 

MMAS = 75.519 - 8.999 logHI 

MMAS = 76.352 - 9.266 logH2 

MMAS = 60.362 - 4.903 logH3 

MMAS = 88.200 - 12.714 logH4 

MMAS = 13.452 + 0.826 EYTEX 5 

MMAS = 21.258 + 0.571 EYTEX 6 

MMAS = 14.382 + 0.783 EYTEX 7 

MMAS = 19.376 + 0.636 EYTEX 8 

MMAS = 33.009 + 5.305 BCOPP9 

MMAS = 34.162 + 4.163 BCOPPIO 

MMAS = 30.824 + 6.118 BCOPPI I 

MMAS = 30.384 + 7.351 BCOPP12 

MMAS = 27.646 + 12.035 BCOPPI 3 

MMAS = 33.939 + 0.185 BCOP09 

MMAS = 37.142 +0.127 BCOPOIO 

MMAS = 37.122 + 0.143 BCOPOll 

MMAS = 36.034 + 0.127 BCOP012 

MMAS = 34.858 + 0.197 BCOP013 

1.oo

0.92

0.84

0.82

1.00

0.85

0.66

0.74

I .oO

0.70

0.67

0.64

1.00

0.69

0.73

0.68

1.00

0.91

0.82

0.94

1.00

0.90

0.76

0.85

1.00

0.75

0.91

0.92

1.00

0.77

0.88

0.79

0.85

1.00

0.92

0.93

0.94

0.97
In uirro testing In uivo 0. in uitro correlation 

No. of No. of Spearman’s 

Alternative samples samples Pearson’s rank 

method tested not tested correlation correlation 

BCOP19 

BCOPI IO 

BCOPI I I 

BCOPI I 2 

BCOPI13 

index in vitro 

60 

60 

60 

60 

60 

in vim coefficient coefficient Linear regression equation 

MMAS = 26.881 + 0.21 I BCOP19 

MMAS = 30.601 +0.162 BCOPIlO 

MMAS = 30.482 + 0.179 BCOPI I I 

BCOPI9b 60 

BCOPIlOb 60 

BCOPI I I b 60 

BCOPI I 2b 60 

BCOPI I 3b 60 

0 0.492 

0 0.411 

0 0.448 

0 0.448 

0 0.490 

0 0.508 

0 0.524 

0 0.553 

0 0.536 

0 0.549 

II 0.310 

20 0.368 

13 0.41 I 

19 0.517 

49 0.186 

47 0.108 

47 0.332 

43 0.060 

II 0.416 

20 0.527 

13 0.465 

I9 0.451 

49 0.308 

47 0.089 

47 0.320 

I9 *** 

1 -0.555 

0 -0.565 

0 -0.151 

I -0.370 

0 0.522 

0 0.525 

I 0.567 

2 0.433 

0 0.461 

0 0.346 

0 0.491 

0 0.529 

0 0.445 

0 0.380 

0.520 

0.529 

0.555 

0.571 

0.568 

0.522 

0.533 

0.557 

0.573 

0.570 

HETQ14 

HETQl5 

HETQ16 

HETQl I 

49 

40 

41 

41 

0.489 

0.441 

0.480 

0.596 

HETS14 

HETSIS 

HETS16 

HETSl7 

II 

13 

13 

17 

0.29 I 

0.051 

0.340 

0.018 

HETQ14b 

HETQl5b 

HETQ16b 

HETQI 7b 

49 

40 

47 

41 

0.48 I 

0.588 

0.462 

0.581 

HETS14b 

HETSISb 

HETS16b 

HETS17b 

11 

13 

13 

41 

0.318 

0.069 

0.329 

tt. 

MMAS = 30.739 + 0.155 BCOPI12 

MMAS = 28.926 + 0.235 BCOPI13 

MMAS = 24.100 + 0.264 BCOPI9b 

MMAS = 24.438 + 0.264 BCOPI IOb 

MMAS = 23.478 + 0.310 BCOPII lb 

MMAS = 23.571 + 0.280 BCOPII2b 

MMAS = 25.558 + 0.306 BCOPII3b 

MMAS = 32.356 + 1.799 HETQ14 

MMAS = 26.833 + 3.473 HETQl5 

MMAS = 32.721 + 2.919 HETQ16 

MMAS = 18.271 + 5.105 HETQl7 

MMAS = 45.789 + 2.554 HETSl4 

MMAS = 37.824 + 0.352 HETSIS 

MMAS = 37.068 + 2.473 HETSl6 

MMAS = 45.455 + 0.262 HETSl7 

MMAS = 6.665 + 20.347 HETQI4b 

MMAS = - 6.835 + 29.423 HETQI 5h 

MMAS = 0.675 + 27.262 HETQ16b 

MMAS = - 22.371 + 34.706 HETQl7h 

MMAS =44.184+ 10.158 HETS14b 

MMAS = 31.449 + 5.881 HETSISb 

MMAS = 35.805 + 8.061 HETSl6b 

*** 

FL20 18 

FL20 19 

FL20 20 

FL20 21 

59 

60 

60 

59 

-0.579 

- 0.598 

-0.176 

-0.405 

ICES 22 60 

ICES 27 60 

ICES 24 59 

ICES 25 58 

0.510 

0.508 

0.495 

0.372 

ICE0 22 60 

ICE0 27 60 

ICE0 24 60 

ICE0 25 60 

0.353 

0.341 

0.432 

0.493 

ICEF 22 60 

ICEF 27 60 

0.450 

0.357 

MMAS = 59.062 - 0.044 FL20 18 

MMAS = 61.695 - 0.042 FL20 19 

MMAS = 50.939 - 0.014 FL20 20 

MMAS = 55.218 - 0.027 FL20 21 

MMAS = 22.869 + I I38 ICES 22 

MMAS = 21.394 + 0.349 ICES 27 

MMAS = 19.780 + 1.124 ICES 24 

MMAS = 28.239 + 0.449 ICES 25 

MMAS = 23.828 + 12.809 ICE0 22 

MMAS = 27.806 + 8.784 ICE0 27 

MMAS = 14.040 + 17.484 ICE0 24 

MMAS = 16.237 + 15.481 ICE0 25 

MMAS = 16.782 + 14.612 ICEF 22 

MMAS = 26.381 + 10.892 ICEF 27 

- 

Interla

Lab 

1.00

0.89

0.92

0.92

0.95

1.00

0.89

0.91

0.90

0.93

1.00

0.79

0.47

0.55

I.00

0.17

-0.171 

-0.103 

l.ooO 

0.62

0.70

0.44

l.OO

-o&3 

* 

I.OO

0.84

0.49

0.37

1.00

0.72

0.75

0.62

1.00

0.70

0.75

0.75

I .oo

0.69
ICEF 24 59 I 0.532 0.576 MMAS = 10.901 + 16.137 ICEF 24 

ICEF 25 60 0 0.568 0.544 MMAS = 4.544 + 20.030 ICEF 25 

ICEC 22 60 0 0.491 0.416 

ICEC 27 60 0 0.490 0.449 

ICEC 24 60 0 0.594 0.542 

ICEC 25 60 0 0.599 0.552 

MMAS = 18.576 + 0.294 ICEC 22 

MMAS = 20.087 + 0. I81 ICEC 27 

MMAS = 8.342 + 0.376 ICEC 24 

MMAS = II.869 + 0.300 ICEC 25 

IREA 26 60 0 0.461 0.439 

IREA 23 60 0 0.443 0.364 

IREA 28 60 0 0.407 0.316 

IREA 29 60 0 0.502 0.510 

IREB 26 60 0 0.498 0.467 

IREB 23 60 0 0.485 0.451 

IREB 28 60 0 0.568 0.544 

IREB 29 60 0 0.606 0.606 

IREC 26 58 2 0.247 0.166 

IREC 23 60 0 0.420 0.289 

IREC 28 58 2 0.410 0.515 

IREC 29 60 0 0.528 0.512 

IRED 26 58 2 0.447 0.364 

IRED 23 60 0 0.542 0.435 

IRED 28 58 2 0.485 0.582 

IRED 29 60 0 0.61 I 0.624 

IRESUM 26 60 0 0.451 0.438 

IRESUM 23 59 I 0.483 0.473 

IRESUM 28 60 0 0.399 0.603 

IREBUM 29 54 6 0.416 0.551 

SM u) 38 

SM 31 32 

SM 32 35 

SM 33 31 

- 0.360 - 0.341 

- 0.616 - 0.609 

- 0.357 - 0.352 

- 0.578 - 0.587 

logNRU38 

logNRU35 

logNRU37 

logNRU38 

60 

a 

60 

60 

22 

28 

25 

29 

0 

0 

0 

0 

3 

0 

I 

0 

3 

0 

I 

0 

3 

0 

I 

0 

- 0.401 - 0.355 

- 0.449 - 0.399 

- 0.360 - 0.335 

- 0.409 - 0.358 

MMAS = 27.985 + 12.800 IREA 26 

MMAS = 35.539 + 14.158 IREA 23 

MMAS = 36.042 + 13.038 IREA 28 

MMAS = 28.676 + 18.440 IREA 29 

MMAS = 21.781 + Il.935 IREB 26 

MMAS = 22.029 + 12.050 IREB 23 

MMAS = 24.894 + 14.304 IREB 28 

MMAS = 21.818 + 16.829 IREB 29 

MMAS = 32.71 I + 0.413 IREC 26 

MMAS = 28.792 + 0.694 IREC 23 

MMAS = 26.888 + 0.858 IREC 28 

MMAS = 28.1 IO + 0.733 IREC 29 

MMAS = 25.703 + 0.457 IRED 26 

MMAS = 21.063 + 0.532 IRED 23 

MMAS = 25.770 + 0.428 IRED 28 

MMAS = 25.926 + 0.442 IRED 29 

MMAS = 32.725 + O.OI6IRESUM 26 

MMAS = 34.431 + 0.022IRESUM 23 

MMAS = 37.621 + 0.02OIRESUM 28 

MMAS = 38.701 + 0.026IRESUM 29 

MMAS = 93.415 - 12.234 SM 30 

MMAS = 139.832 - 22.643 SM 31 

MMAS = 96.516 - 13.352 SM 32 

MMAS = 130.366 - 20.081 SM 33 

MMAS = 43.758 - 10.170 logNRU34 

MMAS = 45.036 - I I.806 logNRU35 

MMAS = 43.377 - 8.925 logNRU37 

MMAS = 44.662 - II.094 logNRU38 

B. Chemicals soluble in wawr 

RBCDLI 27 

RBCDL2 30 

RBCDL3 29 

RBCDL4 30 

IogDLl 27 

logDL2 30 

logDL3 29 

logDL4 30 

RBCDM 1 27 

RBCDMZ 30 

RBCDM3 29 

RBCDM4 30 

- 0.391 - 0.223 

- 0.353 - 0.281 

- 0.332 -0.151 

- 0.361 - 0.336 

- 0.020 - 0.223 

- 0.233 - 0.281 

-0.136 -0.151 

- 0.338 - 0.336 

0.344 0.197 

0.226 0.154 

0.291 0.214 

0.028 - 0.090 

MMAS = 58.922 - 2.54E-04 RBCDLl 

MMAS = 56.316 - 2.22E-04 RBCDLZ 

MMAS = 55.1 I8 - 2.OOE-04 RBCDL3 

MMAS = 56.971 - 2.178-04 RBCDL4 

MMAS = 53.876 - 2.317 IogDLl 

MMAS = 77.070 - 7.515 logDL2 

MMAS = 56.077 - 2.777 logDL3 

MMAS = 97.161 - 12.080 logDL4 

MMAS = 38.063 + 0.433 RBCDMI 

MMAS = 40.181 + 0.205 RBCDMZ 

MMAS = 40.382 + 0.324 RBCDM3 

MMAS = 44.585 + 0.023 RBCDM4 

0.76

0.71

1.00

0.82

0.84

0.84

1.00

0.44

0.58

0.61

1.00

0.72

0.71

0.68

l.OW 

0.52

0.48

0.62

1.00

0.62

0.70

0.81

1.00

0.50

0.57

0.68

l.M)

0.94

0.56

0.92

1.00

0.95

0.96

0.94

I.00 

0.91

0.83

0.73

1.00

0.82

0.73

0.59

1.00

0.80

0.75

0.57
In vitro testing b vim 0. in vitro correlation Interl

No. of No. of Spearman’s 

Ahnative SampIeS SamplcS Pearson’s rank 

method tested not tested correlation correlation 

index in virro in vitro wetiicicnt cocllicient Linear regression equation Lab Lab

IogDM 1 27 3 0.297 0.197 

logDM2 30 0 0.186 0.154 

logDM3 29 I 0.284 0.214 

logDM4 30 0 0.020 - 0.090 

RBCHI 27 3 - 0.443 - 0.329 

RBCHZ 30 0 - 0.388 - 0.322 

RBCH3 29 I - 0.255 -0.154 

RBCH4 30 0 - 0.401 - 0.430 

IogH I 27 3 - 0.226 - 0.329 

logH2 30 0 - 0.310 - 0.322 

logH3 29 I - 0.092 - 0.154 

logH4 30 0 - 0.445 - 0.430 

EYTEX 5 26 4 0.225 0.219 

EYTEX 6 24 6 0.161 0.177 

EYTEX 7 29 I 0.125 0.241 

EYTEX 8 25 5 0.037 0.101 

BCOPP9 

BCOPPIO 

BCoPPl I 

BCOPPIZ 

BCOPP13 

30 

30 

30 

30 

30 

0.200 0.188 

0.086 0.198 

0.228 0.228 

0.244 0.250 

0.381 0.327 

BCOP09 

BCOPOIO 

BCOPOI I 

BCOPol2 

BCOPOI 3 

30 

30 

30 

30 

30 

0.580 0.351 

0.614 0.473 

0.614 0.376 

0.651 0.43 I 

0.561 0.320 

BCOP19 

BCOPIIO 

BCOPI 1 I 

BCOPI 12 

BCOPI I3 

30 

30 

30 

30 

30 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

5 

13 

7 

12 

0.558 0.388 

0.477 0.326 

0.582 0.368 

0.625 0.448 

0.575 0.396 

BCOPI9b 

BCOPIlOb 

BCOPII 1 b 

BCOPIIZb 

BCOPI I3b 

30 

30 

30 

30 

30 

0.482 0.388 

0.446 0.326 

0.523 0.368 

0.531 0.448 

0.554 0.395 

HETQ14 25 

HETQ15 17 

HETQ16 23 

HETQI7 18 

0.314 0.327 

0.380 0.326 

0.598 0.517 

0.758 0.681 

MMAS = 38.538 + 1 I.509 IogDM 1 

MMAS = 40.176 + 6.291 logDM2 

MMAS = 39.859 + 10.593 logDM3 

MMAS = 44.720 + 0.654 logDM4 

MMAS = 58.413 - 3.028-04 RBCHI 

MMAS = 56.657 - 2.928-04 RBCHZ 

MMAS = 52.563 - 1.66E-04 RBCH3 

MMAS = 55.291 - 2.61E-04 RBCH4 

MMAS = 73.725 - 7.105 logHI 

MMAS = 86.275 - 9.945 logH2 

MMAS = 53.878 - 2.184 logH3 

MMAS = 106.656 - 15.139 logH4 

MMAS = 28.178 + 0.484 EYTEX 5 

MMAS = 36.002 + 0.283 EYTEX 6 

MMAS = 36.985 + 0.254 EYTEX 7 

MMAS = 41. I57 + 0.070 EYTEX 8 

MMAS = 39.694 + 2.652 BCOPP9 

MMAS ~43.157 + 1.030 BCOPPlO 

MMAS = 38.655 + 3.278 BCOPPll 

MMAS = 38.179 + 3.817 BCOPP12 

MMAS = 34.672 + 8.377 BCOPP13 

MMAS = 31.123 +0.276 BCOP09 

MMAS = 29.565 + 0.382 BCOPOIO 

MMAS = 32.196 + 0.301 BCOPOI I 

MMAS = 29.024 + 0.259 BCOPOIZ 

MMAS = 31.321 + 0.310 BCOPO13 

MMAS = 27.607 + 0.215 BCOP19 

MMAS = 30.780 + 0.208 BCOPIIO 

MMAS = 28.646 + 0.227 BCOPII I 

MMAS = 26.463 + 0.209 BCOPIIZ 

MMAS = 28.727 + 0.259 BCOPI13 

MMAS = 27.943 + 0.226 BCOPI9b 

MMAS= 31.191 +0.207 BCOPIlOb 

MMAS = 28.189 + 0.252 BCOPII lb 

MMAS = 25.439 + 0.246 BCOPIIZb 

MMAS = 28.539 + 0.270 BCOPI 13b 

MMAS = 34.226 + 1.371 HETQ14 

MMAS = 24.989 + 3.201 HETQl5 

MMAS = 30.640 + 3.150 HETQ16 

MMAS = 2.232 + 7.294 HETQI7 

1.oo

0.73

0.73

0.50

1.00

0.93

0.69

0.94

I .oo

0.87

0.76

0.76

1.0

0.79

0.92

0.86

1.0

0.73

0.86

0.76

0.88

1.0

0.94

0.97

0.96

0.95

1.0

0.90

0.95

0.92

0.95

1.0

0.88

0.93

0.89

0.93

1.0

0.7

0.35

0.45
HBTS14 5 25 l .* l ** 

HETslS 9 21 0.309 0.357 

HETSdl5 I 23 0.137 0.082 

HBTSl7 II I9 0.137 0.131 

HETQl4b 

HETQl5b 

HETQlLb 

HETQI7b 

25 

I7 

23 

I8 

FL20 18 29 

FL20 I9 30 

FL20 20 30 

FLU, 21 30 

ICES 22 30 

ICES 27 30 

ICES 24 29 

ICES 25 29 

ICE0 22 

ICE0 27 

ICE0 24 

ICE0 25 

30 

30 

30 

30 

ICEF 22 30 

ICEF 27 30 

ICEF 24 29 

ICEF 25 30 

ICEC 22 

ICEC 27 

ICEC 24 

ICEC 25 

30 

30 

30 

30 

IREA 26 

IREA 23 

IREA 28 

IREA 29 

30 

30 

30 

30 

IREB 26 

IREB 23 

IREB 28 

IREB 29 

30 

30 

30 

30 

IREC 26 29 

IREC 23 30 

IREC 28 2s 

IREC 29 30 

IRED 26 29 

IRED 23 30 

IRED 28 28 

IRED 29 30 

5 

I3 

7 

I2 

I 

0 

0 

0 

0 

0 

I 

I 

0 

0 

0 

0 

0 

0 

1 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

I 

0 

2 

0 

I 

0 

2 

0 

0.3% 0.366 

0.185 0.309 

0.451 0.480 

0.364 0.384 

- 0.380 - 0.334 

- 0.513 - 0.512 

- 0.354 - 0.420 

- 0.343 - 0.383 

0.417 0.397 

0.572 0.509 

0.438 0.294 

0.507 0.315 

0.478 0.311 

0.379 0.362 

0.508 0.3% 

0.501 0.401 

0.380 0.377 

0.408 0.373 

0.384 0.453 

0.329 0.291 

0.451 0.334 

0.523 0.450 

0.520 0.429 

0.558 0.386 

0.470 0.377 

0.438 0.259 

0.514 0.238 

0.422 0.308 

0.379 0.304 MMAS = 29.813 + 8.207 IREB 26 

0.341 0.226 MMAS = 31.548 + 7.688 IREB 23 

0.516 0.426 MMAS = 30.358 + Il.276 IREB 28 

0.514 0.440 MMAS = 30.019 + 12.374 IREB 29 

0.305 0.162 

0.492 0.253 

0.246 0.253 

0.429 0.233 

0.405 0.307 

0.552 0.365 

0.329 0.293 

0.528 0.511 

*** 

MMAS = 29.2% + 1.098 HETSIS 

IUMAS = 42.749 + 0.989 HETS16 

MMAS = 45.942 + 0.603 HETSI7 

MMAS = 17.411 + 15.294 HETA14b 

MMAS = 17.337 + 14.415 HETQISb 

MMAS = 1.690 + 26.615 HETQl6b 

MMAS = - 71.906 + 59.035 HETQl7b 

MMAS = 57.493 - 0.033 FL20 18 

MMAS = 64.096 - 0.041 FL20 19 

MMAS = 63.736 - 0.035 FL20 20 

MMAS = 57.514 - 0.024 FL20 21 

MMAS = 30.327 + 0.849 ICES 22 

MMAS = 24.341+ 0.299 ICES 27 

MMAS = 26.559 + 0.753 ICES 24 

MMAS = 30.692 + 0.406 ICES 25 

MMAS = 26.348 + I I .975 ICE0 22 

MMAS = 28.193 + 9.230 ICE0 27 

MMAS = 14.906 + 16.836 ICE0 24 

MMAS = 22.642 + 12.893 ICE0 25 

MMAS = 26.565 + 1 I. I95 ICEF 22 

MMAS = 29.592 + 10.479 ICEF 21 

MMAS = 23.797 + IO.198 ICEF 24 

MMAS = 23.281 + I I .040 ICEF 25 

MMAS = 25.236 + 0.242 ICEC 22 

MMAS = 23.514 + 0.159 ICEC 21 

MMAS = 16.500 + 0.294 ICEC 24 

MMAS = 19.442 + 0.240 ICEC 25 

MMAS = 31.303 + Il.526 IREA 26 

MMAS = 36.917 + 12.481 IREA 23 

MMAS = 36.212 + 13.346 IREA 28 

MMAS = 34.001 + 12.532 IREA 29 

MMAS = 35.016 + 0.380 IREC 26 

MMAS = 32.589 + 0.585 IREC 23 

MMAS = 33.443 + 0.379 IREC 28 

MMAS = 34.822 + 0.463 IREC 29 

MMAS = 31.031 + 0.322 IRED 26 

MMAS = 26.628 + 0.437 IRED 23 

MMAS = 31.923 + 0.232 IRED 28 

MMAS = 31.973 + 0.326 IRED 29 

1.0

0.7

0.6

0.9

1.0

0.8

0.4

0.3

1.0

0.8

0.7

0.6

1.0

0.77

0.77

0.84

1.0

0.80

0.84

0.67

1.c

0.89

0.88

0.88

1.0

0.50

0.62

0.60

1.0

0.75

0.69

0.69

1.0

0.60

0.65

0.11

I .oo

0.66

0.79

0.85
Table kontd 

In vitro testing In Vito 0. in vitro correlation Interlab

No. of No. of Speamm’s 

Alternative samples samples PeatWo’s rank 

method tested not tested correlation con-elation 

index in vitro in vitro coefficient coefficient Linear regression equation Lab Laba 

IRESUM 26 30 

IRESUM 23 29 

IRESUM 28 30 

IRESUM 29 2s 

SM 30 22 

SM 31 19 

SM 32 20 

SM 33 17 

log NRU34 30 

log NRU3S 30 

log NRU37 30 

log NRU38 30 

C. Chemicals ittsoluble in water 

RBCDLI 17 

RBCDLZ 18 

RBCDW 17 

RBCDL4 17 

log DLl 17 

log DL2 18 

log DL3 17 

log DLA 17 

RBCDMI 17 

RBCDMZ 18 

RBCDM3 17 

RBCDM4 18 

log DM 1 17 

log DM2 18 

log DM3 17 

log DM4 18 

RBCHI 17 

RBCHZ I8 

RBCH3 17 

RBCH4 17 

logHI 17 

log H2 18 

log H3 17 

log H4 17 

EYTEX S 16 

EYTEX 6 16 

EYTEX 7 18 

EYTEX 8 18 

8 

II 

IO 

13 

0 

0 

0 

0 

I 

0 

I 

I 

I 

0 

I 

I 

I 

0 

I 

0 

1 

0 

1 

0 

I 

0 

1 

I 

I 

0 

I 

I 

2 

2 

0 

0 

0.471 0.34s MMAS = 34.720 + 0.015 IRESUM 26 

0.515 0.311 MMAS = 36.818 + 0.020 IRESUM 23 

0.560 0.410 MMAS = 37.393 + 0.023 IRESUM 28 

0.530 0.426 MMAS = 39.355 + 0.024 IRESUM 29 

- 0.357 - 0.346 MMAS = 96.463 - 12.074 SM 30 

- 0.591 - 0.451 MMAS = 148.542 ~ 23.757 SM 31 

- 0.328 - 0.231 MMAS = 84.252 - 9.925 SM 32 

- 0.577 - 0.413 MMAS = 152.142 - 23.601 SM 33 

- 0.298 -0.312 MMAS = 46.753 - 5.945 log NRU34 

- 0.376 - 0.384 MMAS = 48.541 - 8.459 log NRU35 

- 0.283 - 0.337 MMAS = 46.931 - 5.774 log NRU37 

- 0.374 - 0.363 MMAS = 48.896 - 8.649 log NRU38 

0.102 - 0.046 

0.046 0.173 

0.141 0.230 

-0.018 0.073 

- O.OSl - 0.046 

0.196 0.173 

0.234 0.230 

0.051 0.073 

- 0.050 - 0.031 

0.306 0.330 

0.071 0.248 

l ** l ** 

- 0.050 - 0.031 

0.277 0.330 

0.201 0.248 

*** *** 

0.100 -0.062 

- 0.003 0.069 

- 0.001 0.053 

- 0.017 0.181 

- 0.053 - 0.062 

0.058 0.069 

- 0.063 0.053 

0.078 0.181 

0.604 0.630 

0.699 0.679 

0.748 0.798 

0.570 0.650 

MMAS = 33.790 + 9.138-05 RBCDLI 

MMAS = 33.848 + 4.768-05 RBCDLZ 

MMAS = 34.227 + 1.08E-04 RBCDL3 

MMAS = 31.641 - 1.748-05 RBCDL4 

MMAS = 39.972 - 1.533 log DLI 

MMAS = II.259 + 6.964 log DL2 

MMAS = 15.454 + 6.304 log DL3 

MMAS = 25.027 + 1.963 log DL4 

MMAS = 35.244 - 0.076 RBCDM I 

MMAS = 28.955 + 0.198 RBCDMZ 

MMAS = 35.230 + 0.076 RBCDM3 

l ** 

MMAS = 35.244 - 2.846 log DMdl 

MMAS = 29.063 + 7.551 log DM2 

MMAS = 34.003 + 9.103 log DM3 

*** 

MMAS = 33.840 + 8.9OE-OS RBCHI 

MMAS = 34.348 - 3.34E-06 RBCHZ 

MMAS = 35.744 - I .47E-06 RBCH3 

MMAS = 31.631 - 1.638-05 RBCH4 

MMAS=40.190-1.613 logHI 

MMAS = 27.824 + 2.019 log H2 

MMAS = 39.752 - 1.348 log H3 

MMAS = 22.048 + 2.894 log H4 

MMAS= -4.270+ I.103 EYTEX 5 

MMAS = - 19.016 + 1.345 EYTEX 6 

MMAS = - 8.947 + 1.180 EYTEX 7 

MMAS = - 2.281 + I.013 EYTEX 8 

l.ooo 

0.56

0.59

0.83

1.00

0.90

0.42

0.87

1.00

0.96

0.98

0.93

1.00

0.95

0.98

0.82

1.00

0.77

0.65

0.60

1.00

0.40

- 0.07

I 

1.00

0.37

- 0.09

* 

1.00

0.90

0.91

0.82

1.00

0.82

0.42

0.64

1.00

0.79

0.88

0.99
BCOPP9 I8 0 0.167 0.152 

BCOPPIO I8 0 0.207 0.005 

BCOPPI I I8 0 0.250 0.104 

BCOPPIZ I8 0 0.153 0.212 

BCoPP13 I8 0 0.236 0.123 

MMAS = 30.592 + 3.469 BCOPP9 

MMAS = 29.854 + 3.181 BCOPPIO 

MMAS = 29.205 + 4.201 BCOPPI I 

MMAS = 31.475 + 3.638 BCOPPIZ 

MMAS = 28.929 + 7.328 BCOPP13 

BCOP09 I8 0 0.277 0.659 MMAS = 9.384 + 0.085 BCOP09 

BCOPQIO I8 0 0.180 0.742 MMAS = 31.666 +0.042 BCOPQIO 

BCOPOI I I8 0 0.135 0.706 MMAS = 32.648 + 0.035 BCOPOl I 

BCoPo12 I8 0 0.138 0.623 MMAS = 32.424 + 0.031 BCOPOl2 

BCoPo13 I8 0 0.213 0.729 MMAS=31.190+0.070 BCOPO13 

BCOPB I8 0 0.336 0.614 

BCOPIIO I8 0 0.240 0.581 

BCOPIII I8 0 0.198 0.690 

BCoPIl2 I8 0 0.160 0.657 

BCOPI13 I8 0 0.257 0.635 

MMAS = 26.159 + 0. I IO BCOP19 

MMAS = 29.334 + 0.059 BCOPIIO 

MMAS = 30.838 + 0.052 BCOPII I 

MMAS = 31.650 + 0.037 BCOPIIZ 

MMAS = 29.478 + 0.087 BCOPII 3 

BCOPI9b I8 0 0.446 0.614 

BCOPIlOb 18 0 0.430 0.582 

BCOPIllb I8 0 0.416 0.690 

BCOPI I2b I8 0 0.359 0.657 

BCOPI I3b 18 0 0.372 0.635 

MMAS = 21.560+0.187 BCOPI9b 

MMAS = 22.048 + 0.178 BCOPIlOb 

MMAS = 23.091 + 0.206 BCOPII I b 

MMAS=25.192+0.160 BCOPll2b 

MMAS = 25.791 +0.171 BCOP113b 

HETQ14 I2 6 0.445 0.688 

HETQl5 II 7 0.341 0.426 

HETQ16 I2 6 0.232 0.41 I 

HETQ17 II 7 0.242 0.345 

HETS14 6 12 0.472 0.613 

HETSIS 4 I4 - 0.922 - 0.971 

HETS16 6 I2 0.716 0.738 

HETSI7 6 I2 -0.111 - 0.353 

HETQI4b I2 6 0.541 0.651 

HETQlSb II 7 0.609 0.615 

HETQl6b I2 6 0.41 I 0.396 

HETQI7b II 7 0.370 0.441 

MMAS = 18.634 + 2.728 HETQ14 

MMAS = 20.228 + 2.633 HETQI5 

MMAS = 25.651 + 1.928 HETQ16 

MMAS = 23.326 + 1.591 HETQ17 

MMAS = 35.935 + 4.524 HETS14 

MMAS = 64.010 - 1.831 HETSIS 

MMAS = 27.481 + 5.715 HETS16 

MMAS = 45.785 - 0.461 HETS17 

MMAS = - 1.402 + 19.074 HETOl4b 

MMAS = - 9.337 + 26.126 HETGI 5b 

MMAS = 7.441 + 15.821 HETQI6b 

MMAS = - 32.097 + 33.340 HETQl7b 

FL20 I8 

FL20 I9 

FL20 20 

FL20 21 

I8 

I8 

18 

17 

0 

0 

0 

I 

0 

0 

0 

I 

0 

0 

0 

0 

- 0.613 - 0.593 

- 0.41 I - 0.471 

- 0.130 -0.199 

- 0.212 - 0.247 

ICES 22 

ICES 27 

ICES 24 

ICES 25 

I8 

I8 

I8 

I7 

0.560 0.515 

0.751 0.680 

0.655 0.501 

0.539 0.604 

ICE0 22 18 

ICE0 27 I8 

ICE0 24 18 

ICE0 25 18 

0.583 0.521 

0.353 0.255 

0.501 0.441 

0.584 0.549 

MMAS = 50.08 I - 0.036 FL20 I8 

MMAS = 47.WO - 0.025 FL20 I9 

MMAS = 40. I52 - 0.009 FL20 20 

MMAS = 38.261 - 0.012 FL20 21 

MMAS = 16.149 + 0.977 ICES 22 

MMAS = 2.279 + 0.522 ICES 27 

MMAS = 14.047 + 1.130 ICES 24 

MMAS = 15.847 + 0.528 ICES 25 

MMAS = 9.755 + 14.739 ICE0 22 

MMAS = 17.117 + 8.346 ICE0 27 

MMAS = 8.325 + 15.422 ICE0 24 

MMAS = 8.157 + 15.289 ICE0 25 

l.O

0.9

0.93

0.78

0.90

I .oo

0.95

0.9

0.94

0.98

1.0

0.94

0.89

0.93

0.98

1.0

0.94

0.86

0.94

0.97

1.0

0.94

0.80

0.62

1.0

0.09

-0.159 

- 0.2

1.0

0.69

0.8

0.62

1.0

0.77

0.60

0.35

l.O

0.67

0.75

0.5

1.0

0.49

0.70

0.78
Table 4-conrd 

In viva 0. in vitro correlation Interla

Alternative 

method 

No. of No. of Spearman’s 

samples samples Pearson’s rank 

tested not tested comlation correlation 

index in vitro in vitro coetlicient coei%cient Linear regression equation Lab Laba 

ICEF 22 18 0 0.566 0.586 

ICEF 27 18 0 0.233 0.197 

ICEF 24 18 0 0.732 0.712 

ICEF 25 18 0 0.779 0.736 

ICEC 22 18 0 0.618 0.580 

ICEC 21 18 0 0.603 0.510 

ICEC 24 18 0 0.671 0.565 

ICEC 25 18 0 0.748 0.664 

IREA 26 18 0 0.475 0.480 

IRBA 23 I8 0 0.104 0.117 

IREA 28 18 0 0.407 0.434 

IREA 29 18 0 0.706 0.770 

IRE9 26 18 0 0.549 0.463 

IRE9 23 18 0 0.422 0.346 

IREB 28 18 0 0.677 0.599 

IRE9 29 18 6 0.730 0.795 

IREC 26 17 I 0.177 0.159 

IREC 23 I8 0 0.243 0.223 

IREC 28 18 0 0.661 0.629 

IREC 29 I8 0 0.762 0.692 

IRED 26 I7 I 0.501 0.434 

IRED 23 I8 0 0.342 0.381 

IRED 28 I8 0 0.738 0.620 

IRED 29 18 0 0.763 0.656 

IRESUM 26 I8 0 0.502 0.458 

IRESUM 23 I8 0 0.156 0.467 

IRESUM 28 I8 0 0.312 0.626 

IRESUM 29 I8 0 0.315 0.625 

SM 30 5 I3 0.434 0.141 

SM 31 2 I6 - l.ooo - l.Olxl 

SM 32 4 I4 0.662 0.541 

SM 33 2 I6 - 1.000 - l.Olxl 

IoaNRU34 18 

IoaNRU35 18 

IoaNRU37 18 

logNRU38 18 

- 0.398 - 0.181 

- 0.336 - 0.127 

-0.187 - 0.077 

-0.177 - 0.059 

D. Surfactants 

RBCDLI 

RBCDLZ 

RBCDL3 

RBCDL4 

12 

12 

12 

I2 

- 0.509 - 0.563 MMAS = 75.917 - 4.20E-04 RBCLI 

- 0.664 - 0.672 MMAS = 77.594 - 0.001 RBCDL2 

- 0.422 - 0.322 MMAS = 64.706 - 3.338-04 RBCDL3 

- 0.657 - 0.676 MMAS = 79.068 - 0.001 RBCDL4 

MMAS = - 0.588 + 16.982 ICEF 22 

MMAS = 23.787 + 5.869 ICEF 27 

MMAS = 1.724 + 18.433 ICEF 24 

MMAS = 9.534 + 23.353 ICEF 25 

MMAS = 3.543 + 0.331 ICEC 22 

MMAS = 2.1 I7 + 0.233 ICEC 27 

MMAS = 3.824 + 0.351 ICEC 24 

MMAS = - 0.117 + 0.327 ICEC 25 

MMAS = 21.557 + 10.785 IREA 26 

MMAS = 33.710 + 3.653 IREA 23 

MMAS = 28.448 + Il.741 IREA 28 

MMAS = 13.609 + 27.290 IREA 29 

MMAS = 16.675 + II.507 IREB 26 

MMAS = 20.533 + 8.991 IREB 23 

MMAS = 15.088 + 14.985 IRB 28 

MMAS = II ,978 + 18.696 IRE9 29 

MMAS = 26.552 + 0.279 IREC 26 

MMAS = 24.025 + 0.629 IREC 23 

MMAS = 16.758 + 1.520 IREC 28 

MMAS = 12.631 + 1.525 IREC 29 

MMAS = 17.393 + 0.502 IRED 26 

MMAS = 19.025 + 0.454 IRED 23 

MMAS = 14.454 + 0.693 IRED 28 

MMAS = 17.213 + 0.555 IRED 29 

MMAS = 25.763 + 0.014 IRESUM 26 

MMAS = 32.596 + 0.008 IRESUM 23 

MMAS = 31.042 + 0.014 IRESUM 28 

MMAS = 30.601 + 0.029 IRESUM 29 

MMAS = 3.676 + 8.1 I4 SM 30 

MMAS = 253.548 - 43.370 SM 31 

MMAS = - 79.463 + 32.957 SM 32 

MMAS = 267.224 - 45.994 SM 33 

MMAS = 35.506 - 12.853 logNRU34 

MMAS = 36.367 - 8.963 logNRU35 

MMAS = 34.368 - 5.31 I loaNRU37 

MMAS = 34.858 - 5.412 loaNRU38 

_ 

1.oo

0.43

0.84

0.74

1.00

0.70

0.84

0.76

1.00

0.41

0.59

0.65

l.Of

0.62

0.68

0.60

1.00

0.16

0.27

0.21

1.00

0.49

0.70

0.79

1.00

0.48

0.55

0.62

l.oo

1.00

1.00

1.00

I.@

0.93

0.88

0.92

1.00

0.85

0.71

0.84
IOgDLl 

logDL2 

logDL3 

lOgDL4 

RBCDM I 

RBCDMZ 

RBCDM3 

RBCDM4 

logDM 1 

logDIU2 

logDM3 

logDM4 

RBCHl 

RBCI-IZ 

RBCH3 

RBCH4 

logH1 

lo&I2 

logH3 

logH4 

EYTEX 5 

EYTEX 6 

EYTBX 7 

EYTEX 8 

BCOPP9 

BCOPPIO 

BCOPPI I 

BCOPPl2 

BCOPPI 3 

BCOP09 

BCOPOIO 

BCOPOII 

BCOFol2 

BCoPo13 

BCOP19 

BCOPIIO 

BCOPII 1 

BCOPIIZ 

BCOPIl3 

BCOP19b 

BCOPIlOb 

BCOPIl lb 

BCOPIlZb 

BCOPII 3b 

12 

12 

12 

12 

12 

12 

12 

12 

I2 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

7 

9 

10 

8 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

5 

3 

2 

4 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

- 0.509 - 0.563 

- 0.647 - 0.672 

- 0.226 - 0.322 

- 0.657 - 0.676 

0.784 0.827 

0.737 0.752 

0.789 0.790 

0.813 0.830 

0.693 0.827 

0.577 0.752 

0.697 0.790 

0.716 0.830 

- 0.612 - 0.897 

- 0.562 - 0.930 

- 0.596 - 0.904 

- 0.611 - 0.953 

- 0.879 

- 0.873 

- 0.836 

- 0.894 

- 0.897 

- 0.930 

- 0.904 

- 0.953 

0.543 0.465 

0.346 0.186 

0.539 0.534 

0.533 0.628 

0.642 0.566 

0.728 0.615 

0.718 0.531 

0.837 0.769 

0.835 0.797 

0.663 0.678 

0.789 0.853 

0.758 0.846 

0.706 0.715 

0.762 0.810 

0.772 0.713 

0.861 0.685 

0.859 0.790 

0.876 0.825 

0.895 0.818 

0.772 0.713 

0.861 0.685 

0.859 0.790 

0.876 0.825 

0.895 0.818 

MIUAS = 222.899 - 37.795 logDL1 

MMAS = 187.564 - 31.821 logDL2 

MMAS = 72.647 - 5.955 logDL3 

MMAS = 266.143 -48.105 logDL4 

MMAS= 17.569+ 1.151 RBCDMI 

MMAS = 17.897 + 0.872 RBCDMZ 

MMAS= 17.967 + 1.133 RBCDh43 

MMAS= 17.875 + 1.132 RBCDM4 

MMAS = 19.779 + 30.562 1ogDMl 

MMAS = 20.371 + 25.751 logDM2 

MMAS = 19.637 + 30.754 logDM3 

MMAS = 18.953 + 31.614 logDM4 

MMAS = 60.581- 0.001 RBCHl 

MMAS = 58.647 - 0.001 RBCH2 

MMAS = 60.020 - 0.001 RBCH3 

MMAS = 60.462 - 0.001 RBCH4 

MMAS = 151.533 - 32.322 logHI 

MMAS = 146.781 - 32.314 logH2 

MMAS = 148.156- 31.628 logH3 

MMAS = 148.335 - 31.922 logH4 

MMAS = - 4.891 + 1.564 EYTEX 5 

MMAS = 15.797 + 1.049 EYTEX 6 

MMAS = - 0.530 + 1.478 EYTEX 7 

MMAS = - I .653 + 1.634 EYTEX 8 

MMAS = 16.884 + 13.200 BCOPP9 

MMAS = 10.503 + 11.264 BCOPPIO 

MMAS = 6.526 + 14.373 BCOPPI 1 

MMAS = 3.748 + 16.928 BCOPPl2 

MMAS = 0.105 + 24.116 BCOPP13 

MMAS = 28.041+ 0.864 BCOP09 

MMAS = 23.986 + 1.122 BCOPOIO 

MMAS = 27.164 + 1.325 PCOPOI l 

MMAS = 2$.656+ 1.169 BCOPOIZ 

MMAS = 24.321 + 1.143 BCOPO13 

MMAS = Il.685 + 0.610 BCOPI9 

MMAS = 5.582 + 0.587 BCOPIIO 

MMAS = 2.572 + 0.759 BCOPIl I 

MMAS = 6.448 + 0.742 BCOPIIZ 

MMAS = 4.630 + 0.850 BCOPIl3 

MMAS = 11.685 + 0.610 BCOPI9b 

MMAS = 5.582 + 0.587 BCOPIlOb 

MMAS = 2.572 + 0.759 BCOPII lb 

MMAS = 6.448 + 0.742 BCOPIl2b 

MMAS = 4.630 + 0.850 BCOPIl3b 

1.00

0.83

0.64

0.84

1.00

0.96

0.99

0.99

l.wO 

0.90

l.Om 

0.99

1.00

0.73

0.99

1.00

1.00

0.94

0.98

0.99

l.Oc

0.71

0.97

0.92

1.00

0.83

0.93

0.84

0.76

1.00

0.95

0.97

0.94

0.96

1.00

0.91

0.95

0.91

0.91

1.00

0.91

0.95

0.91

0.91
Table bontd 

In vitro testing In viva 0. in vitro correlation Interla

No. of No. of Spearman’s 

Alternative SampIeS samples Pearson’s rank 

method tested not tested correlation correlation 

index in vitro in vitro cc&icient coefficient Linear regression equation Lab Lab 

MMAS = 3.558 + 14.740 HETQ14 

MMAS = 13.625 + 13.908 HETQIS 

HETQ14 

HBTQl5 

HETQl6 

HETQI7 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

12 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.688 

0.742 

0.448 

0.847 

0.643 

0.776 

0.596 

0.839 

HETQ14b 

HETQl Sb 

HETQ16b 

HETQ16b 

0.476 0.570 

0.774 0.780 

0.622 0.619 

0.701 0.753 

FL20 18 

FL20 19 

FL20 20 

FL24l21 

- 0.745 - 0.671 

- 0.769 - 0.634 

0.099 0.081 

- 0.586 - 0.434 

ICES 22 

ICES 27 

ICES 24 

ICES 25 

0.851 0.841 

0.676 0.762 

0.889 1.811 

0.428 0.350 

MMAS = 18.981 + 18.142 HETCj16 

MMAS = - 0.033 + 15.659 HETQl7 

MMAS = - 21.392 + 38.797 HETQ14b 

MMAS = - 12.443 + 40.443 HETQI5b 

MMAS = - 23.624 + 50.840 HETQ16b 

MMAS = - 17.076 + 40.330 HETQ17b 

MMAS = 68.875 - 0.070 FL20 18 

MMAS = 69.287 - 0.064 FL20 19 

MMAS = 42.385 + 0.010 FL20 20 

MMAS = 69.808 - 0.058 FL20 21 

MMAS = 9.026 + 2.551 ICES 22 

MMAS = - 6.095 + I.363 ICES 27 

MMAS = 5.439 + 2.692 ICES 24 

MMAS = 30.031 + 0.889 ICES 25 

MMAS = 25.81 I + 24.472 ICE0 22 

MMAS = 26.171 + 27.920 ICE0 27 

MMAS = 2.807 + 38.784 ICE0 24 

MMAS = 8.167 + 25.282 ICE0 25 

MMAS = - 7.856 + 34.865 ICEF 22 

MMAS = 7.354 + 37.895 ICEF 27 

MMAS = - 18.941 + 31.449 ICEF 24 

MMAS = - 16.600 + 37.161 ICEF 25 

MMAS = 9.143 + 0.606 ICEC 22 

MMAS = - I.395 + 0.638 ICEC 27 

MMAS = - 19.331 + 0.818 ICEC 24 

MMAS = - 5.939 + 0.614 ICEC 25 

MMAS = 23.213 + 26.458 IREA 26 

MMAS = 33.667 + 24.005 IREA 23 

MMAS = 40.836 + 53.010 IREA 28 

MMAS = 23.614 + 55.245 IREA 29 

MMAS = 8.758 + 22.316 IREB 26 

MMAS = - 4.376 f 28.571 IREB 23 

MMAS = 19.706 + 28.538 IREB 28 

MMAS = 9.146 + 29.472 IREB 29 

MMAS = 29.574 + 0.963 IREC 26 

MMAS = - 9.127 + 2.602 IREC 23 

MMAS = 10.553 + 2.447 IREC 28 

MMAS = 15.834 + 1.461 IREC 29 

1.o

0.79

0.43

0.81

I .w

0.58

0.67

0.42

1.00

0.95

0.40

0.35

1.00

0.74

0.92

0.64

ICE0 22 

ICE0 27 

ICE0 24 

ICE0 25 

0.601 0.526 

0.730 0.808 

0.727 0.792 

0.63 I 0.677 

l.oo

0.61

0.71

0.43

ICEF 22 

ICEF 27 

ICEF 24 

ICEF 25 

0.758 0.693 

0.838 0.823 

0.638 0.640 

0.879 0.873 

1.00

0.66

0.63

0.95

ICEC 22 

ICEC 27 

ICEC 24 

ICEC 25 

0.724 0.657 

0.833 0.872 

0.820 0.795 

0.759 0.797 

1.00

0.82

0.85

0.87

IREA 26 

IREA 23 

IREA 28 

IREA 29 

0.590 0.532 

0.618 0.615 

0.466 0.486 

0.833 0.855 

1.00

0.43

0.31

0.67

IREB 26 

IREB 23 

IREB 28 

IREB 29 

0.696 0.623 

0.853 0.828 

0.750 0.739 

0.762 0.682 

1.00

0.78

0.89

0.81

IREC 26 

IREC 23 

IREC 28 

IREC 29 

0.204 0.007 

0.532 0.487 

0.625 0.699 

0.690 0.720 

1.00

0.09

-0.148 

-0.010 
IRED 26 

IRBD 23 

IRED 28 

IRBD 29 

12 

12 

12 

12 

IRBSUM 26 

IRBSUM 23 

1RIwJM 28 

IRBSUM 29 

12 

12 

12 

II 

SM 30 

SM 31 

SM 32 

SM 33 

11 

11 

II 

I2 

logNRU34 

loSNRU35 

logNRU37 

loSNRU38 

I2 

I2 

I2 

12 

E. Solids 

RBCDLl 

RBCDLZ 

RBCDL3 

RBCDLA 

18 

20 

18 

19 

1ogDLl 18 

lo.gDL2 20 

logDL3 18 

IOgDLA 19 

RBCDMI 

RBCDMZ 

RBCDM3 

RBCDM4 

18 

20 

18 

20 

1ogDM I 18 

logDM2 20 

logDM3 I8 

logDM4 20 

RBCH 1 18 

RBCHZ 20 

RBCH3 18 

RBCH4 19 

IogH 1 18 

logH2 20 

logH3 18 

loSH4 19 

EYTEX 5 16 

EYTEX 6 17 

EYTEX 7 20 

EYTEX 8 17 

0 

0 

0 

0 

0 

0 

0 

I 

1 

I 

I 

0 

0 

0 

0 

0 

2 

0 

2 

1 

2 

0 

2 

1 

2 

0 

2 

0 

2 

0 

2 

0 

2 

0 

2 

I 

2 

0 

2 

I 

4 

3 

0 

3 

0.621 0.504 MMAS=11.704+1.175 IRED 26 

0.653 0.713 MMAS = 8.581 + 0.860 IRED 23 

0.532 0.734 MMAS = 27.536 + 0.597 IRED 28 

0.677 0.746 MMAS = 20.806 + 0.661 IRED 29 

0.513 0.613 MMAS = 33.972 + 0.034 IRESUM 26 

0.666 0.839 MMAS = 29.ooO + 0.043 IRESUM 23 

0.555 0.776 MMAS = 33.987 + 0.228 IRESUM 28 

0.554 0.597 MMAS = 33.813 + 0.202 IRESUM 29 

- 0.746 - 0.800 MMAS = 201.566 - 45.800 SM 30 

- 0.791 - 0.864 MMAS = 207.530 - 45.109 SM 31 

-0.818 - 0.836 MMAS = 259.055 - 62.599 SM 32 

- 0.706 - 0.755 MMAS = 152.848 - 29.258 SM 33 

- 0.837 - 0.874 MMAS = 38.485 - 35.029 loSNRU34 

- 0.848 - 0.862 MMAS = 36.639 - 35.195 logNRU35 

- 0.831 - 0.867 MMAS = 35.880 - 32.070 loSNRU37 

-0.815 - 0.874 MMAS = 38.052 - 32.396 loSNRU38 

- 0.162 0.068 

- 0.192 - 0.097 

- 0.286 - 0.192 

- 0.165 - 0.127 

0.067 0.068 

- 0.120 - 0.097 

- 0.177 -0.192 

- 0.089 -0.127 

0.058 0.085 

0.067 0.070 

0.070 0.228 

- 0.350 - 0.332 

0.107 0.085 

0.053 0.070 

0.191 0.228 

- 0.337 - 0.332 

- 0.280 - 0.062 

- 0.250 - 0.228 

- 0.403 - 0.268 

-0.313 - 0.281 

MMAS = 45.402 - 9.09E-05 RBCDLl 

MMAS = 47.759 - 1.16E-04 RBCDL2 

MMAS = 5 1.418 - 1.62E-04 RBCDL3 

MMAS = 45.698 - 8.39E-05 RBCDL4 

MMAS = 38.152 + 1.410 loSDLl 

MMAS = 56.361 - 3.147 loSDL2 

MMAS = 56.305 - 2.836 logDL3 

MMAS = 51.641 - 2.286 loSDL4 

MMAS = 42.547 + 0.071 RBCDMI 

MMAS = 43.413 + 0.043 RBCDMZ 

MMAS = 46.564 + 0.059 RBCDM3 

MMAS = 48.975 - 0.293 RBCDM4 

MMAS = 42.122 + 4.379 IoSDM I 

MMAS = 43.537 + 1.420 loSDM2 

MMAS = 44.880 + 6.289 loSDM3 

MMAS = 48.949 - 10.730 logDM4 

MMAS = 46.682 - 1.75E-04 RBCHl 

MMAS = 48.556 - 1.70E-04 RBCHZ 

MMAS = 52.399 - 2.54E-04 RBCH3 

MMAS = 46.882 - 1.988-04 RBCH4 

- 0.022 - 0.062 

- 0.186 - 0.228 

- 0.217 - 0.268 

- 0.249 - 0.281 

-0.134 0.061 

- 0.026 0.228 

-0.100 0.231 

- 0.189 0.161 

MMAS = 44.654 - 0.488 logHI 

MMAS = 61.520 - 4.677 logH2 

MMAS = 61.271 - 4.311 loBH3 

MMAS = 67.937 - 6.949 logH4 

MMAS = 54.282 - 0.210 EYTEX 5 

MMAS = 47.913 - 0.038 EYTEX 6 

MMAS = 5 1.778 - 0.170 EYTEX 7 

MMAS = 56.432 - 0.309 EYTEX 8 

1.00

0.64

0.40

0.68

1.00

0.36

0.76

0.66

1.0

0.95

0.78

0.96

l.KJ

0.99

0.98

0.99

1.00

0.99

0.86

0.77

1.00

0.83

0.60

0.67

I.00

0.42

- 0.03

- 0.17

1.00

0.40

0.15

- 0.18

1.00

0.98

0.87

0.99

1.00

0.86

0.76

0.72

l.Oc

0.89

0.92

0.98
Table 4-conrd 

In vitro testing In viva 0. in vim correlation 

._ 

No. of No. of Spearman’s 

Alternative Zllllples samples Pearson’s rank 

method tested not tested correlation correlation 

BCOPP9 

BCOPP IO 

BCOPPI 1 

BCOPPI 2 

BCOPPI 3 

index in vitro 

20 

20 

20 

20 

20 

20 

20 

20 

20 

20 

20 

20 

20 

20 

20 

20 

20 

20 

20 

20 

9 

0 

7 

1 

II 

13 

13 

17 

9 

0 

7 

1 

20 

20 

20 

20 

20 

20 

20 

19 

in vitro coefficient coefficient Linear regression equation 

MMAS = 45.904 - 0.977 BCOPP9 

MMAS = 47.977 - 2.339 BCOPPIO 

MMAS = 45.768 - 0.890 BCOPPI I 

BCOP09 

BCOPOIO 

BCOPOI I 

BCoPo12 

BCOPol3 

BCOPB 

BCOPllO 

BCOPI I I 

BCoP112 

BCOPII 3 

BCOPI9b 

BCOPIlOb 

BCOPII lb 

BCOPI12b 

BCOPII 3b 

HETQ14 

HETQl5 

HETQl6 

HE-I-Q17 

HETS14 

HETSlS 

HETS16 

HETSl7 

HETQl4b 

HETQl5b 

HETQI6b 

HETQl7b 

FL20 18 

FL20 19 

FL20 20 

FL20 21 

ICES 22 

ICES 27 

ICES 24 

ICES 25 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

II 

20 

13 

19 

9 

7 

7 

3 

II 

20 

I3 

I9 

0 

0 

0 

0 

0 

0 

0 

1 

- 0.091 - 0.036 

- 0.265 - 0.216 

- 0.072 0.003 

- 0.028 -0.116 

0.025 - 0.266 

0.096 0.174 

0.044 0.267 

0.056 0.25 I 

0.082 0.348 

0.139 0.216 

0.056 0.061 

- 0.061 0.020 

0.033 0.148 

0.078 0.328 

0.142 0.201 

0.121 0.061 

0.025 0.022 

0.182 0.148 

0.297 0.328 

0.237 0.201 

0.578 0.694 

l ** l ** 

0.808 0.875 

*** l ** 

0.186 0.289 

0.108 - 0.002 

0.332 0.326 

0.060 - 0.009 

0.458 0.512 

l ** *** 

0.694 0.816 

*** *** 

- 0.042 - 0.003 

- 0.367 - 0.357 

- 0.412 - 0.395 

- 0.356 - 0.343 

0.428 0.160 

0.427 0.316 

0.545 0.464 

0.331 0.418 

MMAS = 45.267 - 0.635 BCOPPIZ 

MMAS = 44.484 + 0.551 BCOPP13 

MMAS = 42.877 + 0.030 BCOP09 

MMAS=44.106+0.010 BCOPOlO 

MMAS = 44.024 + 0.014 BCOPOI I 

MMAS = 43.359 + 0.019 BCOPOIZ 

MMAS = 42.328 + 0.044 BCOPOI 3 

MMAS = 43.415 + 0.017 BCOP19 

MMAS = 46.121 - 0.014 BCOPIIO 

MMAS = 44.227 + 0.008 BCOPI I I 

MMAS = 43.230 + 0.018 BCOPIIZ 

MMAS = 41.896 + 0.044 BCOPI13 

MMAS = 41.384 + 0.045 BCOPI9b 

MMAS = 44.167 + 0.009 BCOPIlOb 

MMAS = 40.173 + 0.076 BCOPI I 1 b 

MMAS = 35.586 + 0.125 BCOPIIZb 

MMAS = 39.187 + 0.092 BCOPIl3b 

MMAS = 29.913 + 3.537 HETQ14 

l ** 

MMAS = 13.547 + 12.013 HETQl6 

*** 

MMAS = 45.789 + 2.554 HETSl4 

MMAS = 37.824 + 0.352 HETS15 

MMAS = 37.068 + 2.473 HETSl6 

MMAS = 45.455 + 0.262 HETSI 7 

MMAS = 24.537 + 10.920 HETQl4b 

*** 

MMAS = - 4.827 + 29.946 HETQl6b 

*** 

MMAS = 45.651 - 0.004 FL20 I8 

MMAS = 58.179 - 0.047 FL20 I9 

MMAS = 61.378 - 0.045 FL20 20 

MMAS = 54.240 - 0.037 FL20 21 

MMAS = 34.980 + 0.753 ICES 22 

MMAS = 33.570 + 0.179 ICES 27 

MMAS = 30.517 +0.918 ICES 24 

MMAS = 35.838 + 0.304 ICES 25 

Inter

Lab Lab 

1.0

0.7

0.9

0.8

0.9

l.O

0.9

0.9

0.9

0.9

1.0

0.9

0.8

0.8

0.9

1.0

0.8

0.8

0.8

0.9

1.0

O.& 

l 

1.0

0.1

-0.17

- 0.1

1.0

0.5

* 

1.0

0.72

0.16

0.82

1.0

0.86

0.84

0.77

~ . ,_^ ~ . ^. _. .- ._.-_- ,... . . . . . _ _.^._. _-. _.. 
ICE0 22 m 0 0.307 0.026 MMAS = 35.837 + 6.283 ICE0 22 

ICE0 27 20 0 0.220 0.216 MMAS = 37.840 + 4.178 ICE0 27 

ICE0 24 m 0 0.516 0.429 MMAS = 19.720 + 17.243 ICE0 24 

ICE0 25 20 0 0.398 0.250 MMAS = 31.451 + 9.016 ICE0 25 

ICEF 22 m 0 0.268 0.195 MMAS = 35.086 + 6.407 ICEF 22 

ICEF 27 m 0 0.223 0.193 MMAS = 38.244 + 4.759 ICEF 27 

ICEF 24 m 0 0.345 0.364 MMAS = 30.352 + 8.829 ICEF 24 

ICEF 25 m 0 0.323 0.257 MMAS = 27.545 + 10.494 ICEF 25 

ICEC 22 m 0 0.335 0.060 MMAS = 34.183 + 0.147 ICEC 22 

ICEC 27 20 0 0.356 0.269 MMAS = 34.099 + 0.086 ICEC 27 

ICEC 24 m 0 0.492 0.424 MMAS = 24.001 + 0.269 ICEC 24 

ICEC 25 20 0 0.445 0.347 MMAS = 29.204 + 0.175 ICEC 25 

IRW 26 20 0 0.001 - 0.056 MMAS = 44.809 + 0.018 IREiA 26 

IREA 23 20 0 0.225 0.277 MMAS = 43.215 + 7.680 IREA 23 

IREA 28 20 0 0.235 0.237 MMAS = 42.416 + 7.652 IREA 28 

IREA 20 0 0.403 0.373 MMAS = 34.070 + 13.948 IREA 29 

IREB 26 m 0 0.231 0.130 MMAS = 39.341 + 4.123 IREB 26 

IREB 23 m 0 0.298 0.301 MMAS = 37.694 + 5.975 IREB 23 

IREB 28 m 0 0.564 0.534 MMAS = 36.028 + 12.211 IREB 28 

IREB 29 20 0 0.284 0.302 MMAS = 38.013 + 7.934 IREB 29 

IREC 26 19 1 - 0.0% -0.182 MMAS = 44.090 - 0.064 IREC 26 

IREC 23 20 0 0.340 0.295 MMAS = 38.830 + 0.431 IREC 23 

IREC 28 20 0 0.422 0.504 MMAS = 35.475 + 0.674 IREC 28 

IREC 29 20 0 0.487 0.390 MMAS=36.115+0.516 IREC 29 

IRED 26 19 I 0.112 -0.085 MMAS = 40.198 + 0.089 IRED 26 

IRED 23 20 0 0.384 0.299 MMAS = 36.571 + 0.319 IRED 23 

IRED 28 20 0 0.492 0.565 MMAS = 34.311 + 0.375 IRED 28 

IRED 20 0 0.566 0.612 MMAS = 36.312 + 0.290 IRED 29 

IRESUM 26 20 0 0.033 0.045 MMAS = 44.143 + 0.001 IRESUM 26 

IRESUM 23 19 1 0.182 0.286 MMAS = 43.579 + 0.009 IRESUM 23 

IRESUM 28 20 0 0.216 0.545 MMAS = 43.168 + 0.010 IRESUM 28 

IRESUM 29 15 5 0.293 0.177 MMAS = 49.195 + 0.014 IRESUM 29 

SM 30 I2 8 0.058 0.027 MMAS = 33.719 + 1.204 SM 30 

SM 31 7 13 - 0.655 - 0.795 MMAS= 101.344-14.179 SM 31 

SM 32 6 14 - 0.810 - 0.771 MMAS = 137.020 - 26.029 SM 32 

SM 33 6 14 - 0.492 - 0.647 MMAS = 92.257 - 11.293 SM 33 

logNRU34 

logNRU35 

logNRU37 

logNRU38 

- 0.565 - 0.442 MMAS = 39.306 - 10.245 logNRU34 

- 0.487 - 0.365 MMAS = 41.869 - 9.238 logNRU35 

-0.433 - 0.352 MMAS = 39.995 - 8.148 logNRU37 

- 0.667 - 0.478 MMAS = 38.720 - 14.847 logNRU38 

F. Sohuiom 

RBCDLI 

RBCDLZ 

RBCDL3 

RBCDL4 

- 0.593 - 0.621 MMAS = 86.666 - 4.87E-04 RBCDLI 

- 0.692 - 0.651 MMAS = 86.319 - 0.001 RBCDLZ 

- 0.449 - 0.251 MMAS = 74.755 - 3.7OE-04 RBCDL3 

- 0.693 - 0.681 MMAS = 87.784 - 0.001 RBCDL4 

IogDL I 

logDL2 

logDL3 

lOgDL4 

20 

20 

20 

20 

14 

14 

14 

14 

14 

14 

14 

14 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

- 0.593 - 0.621 MMAS = 257.016 - 43.804 IogDLl 

- 0.621 - 0.651 MMAS = 200.238 - 32.746 logDL2 

- 0.139 - 0.251 MMAS = 75.317 - 3.976 logDL3 

- 0.693 - 0.681 MMAS = 288.739 - 51.674 logDL4 

l.O

0.59

0.75

0.86

1.00

0.72

0.86

0.73

l.w

0.80

0.87

0.88

1.00

0.19

0.39

0.40

1.0

0.78

0.62

0.56

l.Ow 

0.33

0.67

0.55

1.0

0.54

0.79

0.79

1.0

0.19

0.19

0.43

1.0

0.97

0.48

0.88

1.0

0.97

0.97

0.90

1.0

0.87

0.73

0.86

1.0

0.8

0.57

0.86
Table &confd 

In uirro testing In viva 0. in vitro correlation Interla

No. of No. of Spearman’s 

Alternative samples samples Pearson’s rank 

method tested not tested correlation correlation 

index in vitro in vitro coefficient coefficient Linear regression equation Lab Lab 

RBCDM I 

RBCDMZ 

RBCDM3 

RBCDM4 

logDM 1 

logDM2 

logDM3 

logDM4 

RBCH I 

RBCHZ 

RBCH3 

RBCH4 

logHI 

logH2 

logH3 

logH4 

EYTEX 5 

EYTEX 6 

EYTEX I 

EYTEX 8 

BCOPP9 

BCOPPIO 

BCOPPI I 

BCOPPI 2 

BCOPPI 3 

BCOP09 

BCOPOIO 

BCOPDII 

BCoPo12 

BCoFJo13 

BCOP19 

BCOPIlO 

BCOPI I I 

BCOPIl2 

BCOPI 13 

BCOPI9b 

BCOPIlOb 

BCOPIllb 

BCOPIIZb 

BCOPI l3b 

14 

14 

14 

14 

14 

14 

14 

14 

14 

14 

14 

14 

14 

14 

14 

14 

10 

II 

13 

10 

14 

14 

I4 

14 

14 

14 

I4 

I4 

14 

14 

14 

14 

14 

14 

14 

I4 

14 

14 

14 

I4 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

4 

3 

I 

4 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.546 0.526 

0.497 0.442 

0.474 0.504 

0.432 0.483 

0.435 0.526 

0.438 0.442 

0.403 0.504 

0.413 0.483 

- 0.484 ~ 0.609 

- 0.628 - 0.546 

- 0.180 ~ 0.363 

- 0.462 ~ 0.595 

- 0.640 - 0.609 

- 0.598 - 0.546 

- 0.326 - 0.363 

- 0.616 - 0.596 

0.455 0.671 

0.319 0.335 

0.490 0.673 

0.370 0.372 

0.426 0.319 

0.575 0.451 

0.548 0.293 

0.701 0.629 

0.745 0.770 

0.502 0.365 

0.599 0.594 

0.494 0.497 

0.467 0.403 

0.509 0.497 

0.586 0.546 

0.771 0.689 

0.628 0.640 

0.598 0.563 

0.643 0.568 

0.558 0.543 

0.775 0.693 

0.641 0.644 

0.581 0.561 

0.649 0.565 

MMAS = 26.657 + 0.857 RBCDMI 

MMAS = 32.009 + 0.575 BCDM2 

MMAS = 30.204 + 0.712 RBCDM3 

MMAS = 35.956 + 0.530 RBCDM4 

MMAS = 29.636 + 22.889 IogDMl 

MMAS = 29.694 + 21.794 logDM2 

MMAS = 31.708 + 20.880 logDM3 

MMAS = 31.332 + 20.949 logDM4 

MMAS = 67.254 - 0.001 RBCHI 

MMAS = 72.859 - 0.002 RBCHZ 

MMAS = 63.519 - 2.27E-04 RBCH3 

MMAS = 66.618 - 0.001 RBCH4 

MMAS = 146.096 - 27.346 logHI 

MMAS = 134.127 -24.148 logH2 

MMAS = 100.394 - 12.383 logH3 

MMAS = 139.708 - 25.704 logH4 

MMAS = - 4.250 f 1.569 EYTEX 5 

MMAS = 22.594 + 0.813 EYTEX 6 

MMAS = 2.479 + 1.419 EYTEX I 

MMAS= l2.540+ I.150 EYTEX 8 

MMAS = 30.307 + 10.132 BCOPP9 

MMAS = 18.942 + 10.743 BCOPPlO 

MMAS = 13.654 + 13.707 BCOPPI 1 

MMAS = 7.437 + 16.837 BCOPPIZ 

MMAS = - I .786 + 25.384 BCOPPI 3 

MMAS = 42.995 + 0.252 BCOP09 

MMAS = 38.031 + 0.419 BCOPOIO 

MMAS = 45.345 + 0.251 BCOPOl I 

MMAS = 46.834 + 0.186 BCOPOIZ 

MMAS = 41.647 + 0.326 PCOPO13 

MMAS = 29.938 + 0.270 BCOP19 

MMAS = 10.545 + 0.451 BCOPIlO 

MMAS = 26.938 + 0.303 BCOPIl I 

MMAS = 33.872 + 0.223 BCOPIIZ 

MMAS = 25.803 + 0.368 BCOPI13 

MMAS = 24.666 + 0.353 BCOPI9b 

MMAS = 6.71 I + 0.502 BCOPIlOb 

MMAS = 16.633 + 0.442 BCOPII lb 

MMAS = 24.757 f 0.362 BCOPIIZb 

MMAS = 22.206 + 0.424 BCOP113b 

1.0

0.9

0.9

0.8

I .o

0.9

0.9

0.9

l.oo

0.9

0.4

0.9

1.0

0.9

0.9

0.9

1.0

0.7

0.9

0.7

I.0

0.7

0.9

0.7

0.6

1.0

0.9

0.9

0.9

0.9

1.0

0.9

0.9

0.9

0.9

1.0

0.8

0.9

0.9

0.9
HETQ14 

HETQIS 

HETQ16 

HETQI7 

14 

14 

14 

14 

HETQ14b 

HETQl5b 

HETQl6b 

HETQI 7b 

14 

14 

14 

14 

FL20 18 

FL20 19 

FL20 20 

FL20 21 

14 

14 

14 

14 

ICES 22 

ICES 27 

ICES 24 

ICES 25 

14 

14 

13 

13 

ICE0 22 14 

ICE0 27 14 

ICE0 24 14 

ICE0 25 14 

ICEF 22 

ICEF 27 

ICEF 24 

ICEF 25 

ICEC 22 

ICEC 27 

ICEC 24 

ICEC 25 

IREA 26 

IREA 23 

IREA 28 

IREA 29 

IREB 26 

IREB 23 

IREB 28 

IREB 29 

IREC 26 

IREC 23 

IREC 28 

IREC 29 

IRED 26 

IRED 23 

IRED 28 

IRED 29 

14 

14 

13 

14 

14 

14 

14 

14 

14 

14 

14 

14 

14 

14 

14 

14 

13 

14 

12 

14 

13 

14 

12 

14 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

1 

1 

0 

0 

0 

0 

0 

0 

1 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

I 

0 

2 

0 

I 

0 

2 

0 

0.125 0.268 

0.340 0.436 

0.341 0.315 

0.678 0.658 

**t l ** 

0.483 0.493 

0.238 0.292 

0.430 0.448 

- 0.654 - 0.616 

- 0.612 - 0.477 

0.116 0.104 

- 0.634 -0.832 

0.853 0.823 

0.640 0.678 

0.657 0.608 

0.471 0.342 

0.751 0.725 

0.559 0.600 

0.549 0.503 

0.593 0.561 

0.713 0.734 

0.672 0.705 

0.799 0.810 

0.833 0.824 

0.777 0.761 

0.680 0.617 

0.743 0.722 

0.692 0.726 

0.718 0.702 

0.672 0.665 

0.502 0.425 

0.603 0.624 

0.733 0.722 

0.763 0.761 

0.660 0.673 

0.657 0.598 

0.463 0.308 

0.564 0.726 

0.157 0.436 

0.407 0.367 

0.553 0.591 

0.686 0.664 

0.240 0.495 

0.529 0.623 

MMAS = 54.195 + 0.905 HETQ14 

MMAS = 40.839 + 4.228 HETQl5 

MMAS = 49.585 + 2.071 HETQ16 

MMAS = 19.294 + 8.415 HETQl7 

*** 

MMAS = - 2.657 + 34.574 HETQl5b 

MMAS = 14.869 + 26.376 HETQ16b 

MMAS = 2.670 + 30.814 HETQ17b 

MMAS = 75.327 - 0.086 FL20 18 

MMAS = 73.927 - 0.069 FL20 19 

MMAS = 53.194 + 0.011 FL20 20 

MMAS = 79.839 - 0.077 FL20 21 

MMAS = 9.018 + 2.647 ICES 22 

MMAS = 29.664 + 0.484 ICES 27 

MMAS = 23.299 + 1.374 ICES 24 

MMAS = 40.525 + 0.452 ICES 25 

MMAS = 31.431 + 20.437 ICE0 22 

MMAS = 34.426 + 16.433 ICE0 27 

MMAS = 26.751 + 18.810 ICE0 24 

MMAS = 12.823 + 21.467 ICE0 25 

MMAS = 2.430 + 30.020 ICEF 22 

MMAS = 17.736 + 26.829 ICEF 27 

MMAS = - 25.773 + 37.446 ICEF 24 

MMAS = - 17.319 + 37.148 ICEF 25 

MMAS = 15.501 + 0.529 ICEC 22 

MMAS = 22.987 + 0.296 ICEC 27 

MMAS = 2.019 + 0.541 ICEC 24 

MMAS = 13.495 + 0.371 ICEC 25 

MMAS = 28.604 + 22.699 IREA 26 

MMAS = 37.044 + 21.329 IREA 23 

MMAS = 48.073 + 14.913 IREA 28 

MMAS = 35.352 + 25.778 IREA 29 

MMAS = 6.570 + 23.330 IREB 26 

MMAS = 1.915 + 24.087 IREB 23 

MMAS = 29.086 + 18.513 IREB 28 

MMAS = 18.633 + 21.799 IREB 29 

MMAS = 34.226 + 0.845 IREC 26 

MMAS = 33.188 + 0.874 IREC 23 

MMAS = 44.360 + 0.378 IREC 28 

MMAS = 43.136 + 0.520 IREC 29 

MMAS = 29.714 + 0.638 IRED 26 

MMAS = 16.878 + 0.698 IRED 23 

MMAS = 42.224 + 0.211 IRED 28 

MMAS = 33.191 + 0.441 IRED 29 

1.00

0.88

0.77

0.71

1.00

0.99

0.36

0.58

1.00

0.61

0.75

0.53

1.00

0.79

0.79

0.79

1.00

0.78

0.60

0.90

1.00

0.87

0.87

0.90

1.00

0.64

0.73

0.78

1.00

0.80

0.86

0.78

1.00

0.91

0.46

0.80

1.00

0.77

0.61

0.86
Table 4-contd 

In vitro testing In ho 0. in virro correlation Interla

No. of No. of Spearman’s 

Alternative samples samples PeWsoIl’s rank 

method teated not tested comlation correlation 

index in vitro in vitro co&c&t coefficient Linear regression equation Lab Lab 

IRESUM 26 0.651 0.122 

IRESUM 23 

IRESUM 28 

IRESUM 29 

14 

I4 

14 

14 

0.743 0.770 

0.548 0.703 

0.539 0.631 

1.00

0.77

0.86

0.88

SM 30 

Shl 31 

SM 32 

SM 33 

14 

14 

14 

I4 

- 0.765 - 0.724 

- 0.775 - 0.770 

- 0.754 - 0.752 

- 0.755 - 0.783 

1.00

0.96

0.88

0.97

logNRU34 

logNRU35 

logNRU37 

logNRU38 

14 

14 

I4 

14 

- 0.473 - 0.466 

- 0.609 - 0.565 

~ 0.484 - 0.455 

~ 0.495 - 0.475 

MMAS = 40.064 + 0.024 IRESUM 26 

MMAS = 34.283 + 0.033 IRESUM 23 

MMAS = 49.242 + 0.027 IRESUM 28 

MMAS = 48.404 + 0.032 IRESUM 29 

MMAS = 211.546 - 44.800 SM 30 

MMAS = 223.200 - 47.046 SM 31 

MMAS = 225.846 - 49.054 SM 32 

MMAS = 204.210 - 41.085 SM 33 

MMAS = 61.273 - 15.387 logNRU34 

MMAS = 58.658 - 20.474 logNRU35 

MMAS = 59.469 - 15.257 logNRU37 

MMAS = 60.047 ~ 16.707 logNRU38 

G. Liquti 

RBCDLl 

RBCDLZ 

RBCDL3 

RBCDL4 

24 

26 

26 

26 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

2 

0 

0 

0 

2 

0 

0 

0 

2 

0 

0 

0 

2 

0 

0 

0 

2 

0 

0 

0 

2 

0 

0 

0 

3 

5 

2 

2 

1.00

0.97

0.99

0.99

0.040 0.079 

0.001 0.019 

0.067 0.124 

- 0.055 -0.113 

MMAS = 30.323 + 2.17E-05 RBCDLl 

MMAS = 31.948 + 4.48E-07 RBCDLZ 

MMAS = 30.484 + 3.35E-05 RBCDL3 

MMAS = 33.124 - 2.838-05 RBCDL4 

MMAS = 13.615 +4.158 IogDLl 

MMAS = 21.357 + 2.617 logDL2 

MMAS = 17.991 + 3.609 logDL3 

MMAS = 43.438 - 2.895 logDL4 

MMAS = 30.709 + 0.227 RBCDMI 

MMAS = 29.693 + 0. I87 RBCDMZ 

MMAS = 30.587 + 0.444 RBCDM3 

*** 

1.00

0.90

0.88

0.88 

logDL1 24 

logDL2 26 

logDL3 26 

logDL4 26 

0.139 0.079 

0.104 0.019 

0.168 0.124 

~ 0.109 -0.113 

1.00

0;.94

0.81

0.88

RBCDM 1 24 

RBCDMZ 26 

RBCDM3 26 

RBCDM4 26 

0.114 0.112 

0.177 0.171 

0.198 0.227 

*** *** 

l.oo

0.58

0.83

0.90

logDM I 24 

logDM2 26 

logDM3 26 

logDM4 26 

0.089 0.112 MMAS = 30.845 + 4.925 IogDM 1 

0.128 0.171 MMAS = 29.975 + 4.274 logDM2 

0.216 0.227 MMAS=30.138+11.711 logDM3 

*** t** l ** 

l.lX

0.59

0.51

0.76

RBCHI 24 

RBCH2 26 

RBCH3 26 

RBCH4 26 

- 0.020 - 0.004 

- 0.031 - 0.009 

- 0.033 - 0.016 

-0.111 - 0.125 

MMAS = 32.085 - l.l8E-05 RBCHl 

MMAS = 32.643 - I .9OE-05 RBCHZ 

MMAS = 32.660 - I .76E-O5 RBCH3 

MMAS = 34.200 - 5.98E-05 RBCH4 

MMAS = 18.340 + 3.089 IogHl 

MMAS = 22.624 + 2.354 logH2 

MMAS = 33.497 - 0.413 logH3 

MMAS = 44.106 - 3.100 logH4 

MMAS= -4.215+1.211 EYTEX 5 

MMAS = - 6.505 + 1.191 EYTEX 6 

MMAS= -1.500+1.094 EYTEX 7 

MMAS = 0.875 + 1.094 EYTEX 8 

1.00

0.87

0.85

0.91

logHI 24 

logH2 26 

logH3 26 

logH4 26 

0.101 - 0.004 

0.091 ~ 0.009 

- 0.023 - 0.016 

-0.117 - 0.125 

1.00

0.95

0.77

0.90

EYTEX 5 23 

EYTEX 6 21 

EYTEX 7 24 

EYTEX 8 24 

0.580 0.681 

0.600 0.625 

0.643 0.666 

0.529 0.624 

1.00

0.57

0.87

0.83
BCOPP9 26 

BCOPPIO 26 

BCOPPI I 26 

BCOPPIZ 26 

BCOPPI 3 26 

BCoPo9 26 

BCOPOIO 26 

BCOPOII 26 

BCoPol2 26 

BCoPo13 26 

BCoPI9 26 

BCOPIIO 26 

BCOPII I 26 

BCOPIIZ 26 

BCOPI I3 26 

BCOPbb 26 

BCOPIlOb 26 

BCOPIllb 26 

BCOPIlZb 26 

BCOPIl3b 26 

HETQ14 26 

HETQl5 26 

HETQIL 26 

HETQI7 26 

HETQl4b 26 

HETQl5b 26 

HETQl6b 26 

HETQI 7b 26 

FL20 18 25 

FL20 19 26 

FL20 20 26 

FL20 21 25 

ICES 22 26 

ICES 27 26 

ICES 24 26 

ICES 25 26 

ICE0 22 26 

ICE0 27 26 

ICE0 24 26 

ICE0 25 26 

ICEF 22 26 

ICEF 27 26 

ICEF 24 26 

ICEF 25 26 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

I 

0 

0 

I 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.676 0.692 

0.541 0.560 

0.603 0.634 

0.429 0.612 

0.589 0.639 

0.528 0.71 I 

0.491 0.687 

0.537 0.728 

0.503 0.706 

0.489 0.687 

0.690 0.770 

0.612 0.664 

0.638 0.722 

0.521 0.717 

0.602 0.747 

MMAS = 12.458 + 10.910 BCOPP9 

MMAS = 17.489 + 8.829 BCOPPIO 

MMAS = 15.607 + 8.919 BCOPPI 1 

MMAS = 22.292 + 5.915 BCOPPIZ 

MMAS = 16.315 + 14.554 BCOPP13 

MMAS = 20.005 + 0.509 BCOP09 

MMAS = 21.680 +0.504 BCOPOIO 

MMAS = 20.528 + 0.698 BCOPOI I 

MMAS=21.120+0.438 BCOPOIZ 

MMAS = 21.324 + 0.567 BCOPO13 

MMAS = I I .622 + 0.404 BCOP19 

MMAS = 14.722 + 0.383 BCOPIIO 

MMAS = 14.536 + 0.397 BCOPII I 

MMAS = 19.174 + 0.261 BCOPIIZ 

MMAS = 15.739 + 0.465 BCOP113 

0.690 0.770 MMAS = I 1.622 + 0.404 BCOP19b 

0.612 0.664 MMAS = 14.722 +0.383 BCOPIlOb 

0.638 0.722 MMAS = 14.536 + 0.397 BCOPII lb 

0.521 0.717 MMAS = 19.174+0.261 BCOPIIZb 

0.602 0.747 MMAS = 15.739 + 0.465 BCOPIl3b 

0.406 0.596 MMAS = 23.053 + I.741 HETQ14 

0.444 0.489 MMAS = 19.61 I + 2.957 HETQl5 

0.328 0.549 MMAS = 25.853 + 2.592 HETQ16 

0.48 I 0.616 MMAS = 17.094 + 3.426 HETQ17 

0.541 0.576 MMAS = - 0.723 + 19.990 HETQl4b 

0.540 0.587 MMAS = - 2.650 + 22.109 HETQl5b 

0.502 0.546 MMAS = 3.125 + 20.751 HETQ16b 

0.550 0.625 MMAS = - 36.545 + 37.550 HETQ17b 

- 0.658 - 0.691 MMAS = 56.201 - 0.041 FL20 I8 

- 0.491 - 0.489 MMAS = 52.564 - 0.030 FL20 I9 

-0.180 - 0.274 MMAS = 45.617 - 0.018 FL20 20 

0.134 0.008 MMAS = 23.995 + 0.008 FL20 21 

0.577 0.608 MMAS = 12.182 + 1.004 ICES 22 

0.703 0.725 MMAS = - 1.407 + 0.564 ICES 27 

0.573 0.550 MMAS=9.816+ 1.108 ICES 24 

0.484 0.51 I MMAS = 16.845 + 0.526 ICES 25 

0.449 0.471 

0.442 0.379 

0.528 0.577 

0.548 0.606 

0.535 0.595 

0.401 0.421 

0.619 0.646 

0.676 0.657 

MMAS = 10.418 + 13.869 PCEO 22 

MMAS = 13.755 + 10.1 I9 ICE0 27 

MMAS = 2.540 + 17.194 ICE0 24 

MMAS = 8.183 + 14.689 ICE0 25 

MMAS = 1.912 + 15.916 ICEF 22 

MMAS = 17.422 + 9.385 ICEF 27 

MMAS = 2.733 + 14.951 ICEF 24 

MMAS = - 4.941 + 18.419 ICEF 25 

1.o

0.7

0.8

0.7

0.8

1.0

0.9

0.9

0.9

0.9

l.O

0.8

0.8

0.8

0.9

1.0

0.8

0.8

0.8

0.9

1.0

0.7

0.2

0.4

1.0

0.7

0.7

0.6

1.0

0.7

0.5

- 0.3

1.0

0.77

0.69

0.62

1.0

0.75

0.77

0.7

1.0

0.60

0.74

0.59
Table Lcontd 

In vitro testing In viva 0. in vitro correlation Interla

No. of No. of Spearman’s 

Alternative samples samples Pearson’s rank 

method tested not tested correlation correlation 

index in vitro in vitro coefficient coefficient Linear rearession eauation Lab Lab 

ICEC 22 26 

ICEC 27 26 

ICEC 24 26 

ICEC 25 26 

IREA 26 26 

IREA 23 26 

IREA 28 26 

IREA 29 26 

IREB 26 26 

IREB 23 26 

IREB 28 26 

IREB 29 26 

IREC 26 26 

IREC 23 26 

IREC 28 26 

IREC 29 26 

IRED 26 26 

IRED 23 26 

IRED 28 26 

IRED 29 26 

IRESUM 26 26 

IRESUM 23 26 

IRESUM 28 26 

IRESUM 29 25 

SM 30 

SM 31 

SM 32 

SM 33 

12 

II 

15 

11 

logNRU34 

logNRU35 

26 

26 

26 

26 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

I 

14 

15 

II 

15 

0 

0 

0 

0 

0.557 0.583 

0.620 0.640 

0.616 0.613 

0.666 0.676 

1.00

0.85

0.83

0.77

0.595 0.617 

0.197 0.261 

0.393 0.413 

0.459 0.522 

MMAS = 3.836 + 0.317 ICEC 22 

MMAS = 1.698 + 0.240 ICEC 27 

MMAS = 0.701 + 0.335 ICEC 24 

MMAS = - I .I 51 + 0.327 ICEC 25 

MMAS = 15.963 + 16.847 IREA 26 

MMAS = 29.455 + 5.941 IREA 23 

MMAS = 26.042 + II.673 IREA 28 

MMAS = 23.402 + 13.101 IREA 29 

MMAS = 8.952 + 12.925 IREB 26 

MMAS = 15.515 + 9.506 IREB 23 

MMAS = 10.189 + 15.472 IREB 28 

MMAS = Ii.213 + 17.687 IREB 29 

MMAS = 28.182 + 0.236 IREC 26 

MMAS = 24.805 + 0.377 IREC 23 

MMAS = 10.775 + 1.392 IREC 28 

MMAS = 7.258 + I .586 IREC 29 

MMAS = 13.322 + 0.578 IRED 26 

MMAS = 5.559 + 0.653 IRED 23 

MMAS = 8.512 + 0.702 IRED 28 

MMAS = 12.267 + 0.610 IRED 29 

MMAS = 23.816 + 0.021 IRESUM 26 

MMAS = 29.217 + 0.010 IRESUM 23 

MMAS = 28.214 + 0.016 IRESUM 28 

MMAS = 29.974 + 0.017 IRESUM 29 

MMAS = 31.281 + 1.819 SM 3 

MMAS = - 1.948 + 7.668 SM 31 

MMAS = 22.989 + 3.812 SM 32 

MMAS = 21.559 + 2.431 SM 33 

MMAS = 33.679 - 2.982 logNRU34 

MMAS = 33.867 - 2.679 logNRU35 

MMAS = 32.373 - 0.662 logNRU37 

1.0

0.49

0.68

0.70

0.588 0.580 

0.402 0.384 

0.651 0.661 

0.759 0.764 

I .o

0.52

0.62

0.66

0.115 0.192 

0.143 0.139 

0.624 0.61 I 

0.709 0.774 

l.oo

0.13

0.24

0.34

0.591 0.577 

0.527 0.524 

0.743 0.699 

0.736 0.782 

l.oo

0.53

0.7

0.79

0.514 0.627 

0.203 0.524 

0.336 0.660 

0.259 0.743 

1 .o

0.52

0.69

0.62

0.053 - 0.131 

0.153 - 0.121 

0.158 0.177 

0.072 - 0.364 

1.0

0.87

0.26

0.83

- 0.099 

- 0.087 

- 0.024 

0.047 

- 0.089 

-0.135 

- 0.064 

I.00

0.9

0.9

0.94

logNRU37 

logNRU38 - 0.032 MMAS = 31.048 + 1.334 logNRU38 d 

The seven parts of this table (AG) correspond to the seven sets of comparisons listed in Table 3. 

Further details of each of the nine tests are given in Appendix 2. The 27 endpoints are numbered in sequence in this legend (El to E27). 

Table refer to the laboratories which conducted each particular test. Laboratory 36 left the study without submitting any results. 

RBC = the Red Blood Cell Haemo!vsis Test 

El BRCDL = D Low 

E2 LogDL = D Low. log transformed 

E3 RBCDM = D Max 

E4 Log DM = D Max. log transformed 
Legend to Fig. 4. contd 

E5 RBCH = H50 

E6 LogH = H50, log transfomxd 

EYTEX = the EYTEX Reagent Turbidity Test 

El EYTEX = EYTEX Draize Equivalent 

BCOP = the Bovine Comeal OpacitylPermeobility Test 

E8 BCOPP = Permeability 

E9 BCOPO 

El0 BCOPI 

I= 

El1 BCOPIb = Index, with cut-off at 200 

HET = The Hen’s Egg Test (Chorioollontoic Membrane Assay) 

El2 HETQ =QScore 

El3 HETS =sSco?e 

El4 HETQB = Q Score, with cut-off at 2 

El5 HETSB = S Score, with cut-off at 2 

FL = The Fluorescein Leakage Test 

El6 FL, = 20% Fluorescein leakage 

ICE = the Isolated, Emcleated Chicken Eye Test 

El7 ICES = Swelling 

El8 ICE0 = Opacity 

El9 ICEF = Fluorescein retention 

E20 ICEC = Classification 

IRE = the Isolated, Emcleated Rabbit Eye Test 

E21 IREA = Opacity (1 hour) 

E22 IREB = Opacity (4 hour) 

E23 IREC = Swelling (I hour) 

E24 IRED = Swelling (4 hours) 

E25 IRESUM = Summary score 

SM = the Silicon Microphysiometer Test 

E26 SM = log MRDSO 

NRU = the Neutral Red Uptake Test 

E21 NRU = log NRUSO 

The subsequent columns show the following: 

Number of samples tested in vitro 

Number of samples not tested in vitro 

Pearson’s product moment correlation coefficient 

Spearman’s rank correlation coefficient 

The linear regression equation 

The interlaboratory correlation matrix, for which the letters a-d (or e) refer to the four or five laboratories which conducted each test

MMAS = Modified Maximum Average Score (Bagley et al., 1992; ECETOC, 1992). 
M. Balls et al. 

Y=48.485-0.00014X Y=43.811 -0.371X 

r= -0.206 r = -0.012 

- .Y_ * ---.---- 

- -.. _ 

100 --_ 

- -_ 

. 

80 

1 

0 25000 50000 75000 100000 0 1 2 3 4 I 

RBCDLI IogDLl 

Y q 34.718 + 0.562X 

r= 0.433 

100 

80 

20 

0 

0 10 20 30 40 50 60 70 60 90 100 

RBCDMI 

I . 

80 : 

. 

. 

20 

; . 

z 

0 

L I’, 

; 1’ 

0 1 2 

IogDMl 

Fig. 2(a). 
EC/HO validation study on alternatives to the Drake test 901 

Y = 49.279 - 0.00023X Y = 75.519 -8.999X 

r = -0.309 r = -0.310 

120 

100 

60 

3 60 

I 

40 

20 

0 

J 

* i- . 

-. 

-’ ‘- -_ ,__ 

--__ 

- - -_ ,_ 

- .- _. 

I . 

. 

. 

. l 

. . 

. 

l . . 

I 

.;--::1:---.-_ 

. ._ 

’ 

: 

1 

. . 

1 I I I 

0 25000 50000 75000 100000 0 1 2 3 4 5 

RBCHI IogHl 

120 - 

Y = 13.452 + 0.826X 

r = 0.376 

, ‘. 

, * 

100 - 

I~ ” / 

/’ /’ 

,’ /’ 

80 - _ ..*/’ l , 

3 60 

5 

40 

20 

0 

0 10 20 30 40 50 

EYTEX 5 

120 

100 

80 

3 60 

I 

40 

20 

0 

Y = 33.009 + 5.305): 

r= 0.338 

/ ’ / / 

/’ 

,c . 

.’ 

/ 

/ 

I : 

, 

, 

/ 

. 

. 

,’ 

: :: 

. . 

. . l 

. 

. 

. l 

. 

., ‘. 

. _, ” 

.:: . 

l . /’ 

: 

,_ ” 

, 

I I I I I I I I I I I’ 

0 12 3 4 5 6 7 8 9 10 

BCOPPS 

Fig. 2(b). 
902 M. Balls et al. 

Y = 33.939 +0.185x 

r = 0.391 

Y = 26.881 +0.211x 

I- = 0.492 

120 

100 

80 

2 

2 6o 

2 

40 

20 

0 

? 

I 

I 

1 i 

: 

120 

100 

80 

2 

z- 6o 

=i 

. . I 

.’ - / 

. 

: . 

/ 

, 

p 

;----- ” 

I I -- 

0 100 200 300 400 

BCOPOS 

Y = 24.100 + 0.264): Y = 32.356 + 1.799X 

r= 0.508 r= 0.310 

,’ 

/ 

. 

.a . 

./ 

. .’ 

/ 

. 

. .- 

. 

40 ~( . * 

‘A 

., 

*. * . 

I’. 

20 -' . . * 

. * 

. * 

. 

-. . 

,’ ’ 

o i i’: ’ / ,, ” ” 

0 100 

BCOPISb 

Cut-off point at 200 

. 

. 

. 

-7’ 

200 

80 

2 

2 6o 

z 

40 

20 

0 

2 60 

1’ /* _. / . 

/ 

/’ 

/ 

/ . 

‘. ) *a.,’ 

. .*, 

. .i ,.’ 

. 

: 

.* l 

_’ 

. .‘, : 

,. l ’ 

* . 

.:: 

.’ 

. 

.‘. 

. . ‘. 

, / 

._ . * / 

0 100 200 300 400 500 

BCOPIS 

/ ./ . / . 

i 

. 

1 

0 5 IO 15 20 25 

HETQ14 

Fig. 2(c). 
EC/HO validation study on alternatives to the Drake test 903 

Y = 45.789 + 2.554): Y = 6.665 +20.347X 

r= 0.186 r= 0.416 

Ok 

0 5 10 15 

HETS14 

a0 

52 

z 6o 

2 

40 

Y=44.184 + 10.158x Y = 59.062 -0.044X 

r= 0.308 r= -0.555 

I--‘__---- .-..___ 

120 

100 

80 

2 

2 6o 

Is 

40 

20 

0 

0 1 

HETS14b 

2 

Cut-off point at S = 2.0 

I* 

; 

/’ 

/’ 

/’ 

/’ . 

/’ I 

/’ . 

/’ I 

, ** 

,’ 

__c. t 

t 

t 

1 

HETQ14b 

Cut-off point at Q = 2.0 

120 

100 

80 

40 

20 

0 

. \ \ 

* 1 

\ 

\ . 

. \ 

\. 

‘*. \ .\ 

‘. 

. 

\ 

., . . \ 

. . \ 

\ 

\ 

\ 

.\ 

I 

1 

0 500 1000 1500 

FL20 18 

Fig. 2(d). 
904 M. Balls et al. 

. . i . 

100 

80 

40 

i. 

,’ . 

l i .*. 

/ . *., * : .* . 1 .,’ * 

_. 

*, . 

,’ . 

* 

. . . 

. 

. 

80 

2 60 

200 

ICES 22 

0 1 2 3 4 

ICE0 22 

Y = 16.782 + 14.612X Y = 18.576 +0.294X 

r = 0.445 r = 0.491 

II 

I 

/’ / 

,’ 

, : 

,, ‘. ’ / 

/’ . 

. 

,,” . 

: 

I ’ . 

. 

. 

. 

. 

. . 

3 1 2 3 

ICEF 22 

100 

80 

, / / 

,’ 

, 

,’ . 

: 

Y = 23.828 + 12.809X 

r= 0.461 

1 

120 

100 

80 

2 

Is 6o 

Is 

40 

20 

0 

i 

i 

/ 

,, *: 

/ . 

.I . 

. 4 * . 

/ . 

,’ 

. 

* .’ 

l .:. 

. 

. ’ . 

,’ 

. ._’ 

,- 

/ 

* . 

.; * 

.d ’ ,G . 

_’ . 

. I’ 

. 

. . ** 

., 

. . 

50 150 250 350 

ICEC 22 

Fig. 2(e) 
EC/HO validation study on alternatives to the Drake test 905 

80 

40 

20 

0 

80 

Y= 27.985 + 12.800X 

r= 0.461 

/' 

0 

: 

1 

. 

. 

2 3 

/ -’ 

I 

4 

/ 

. 

. 

; : 

, ,-?’ ’ 

IREA 

_x 

26 

, 

, 

1 I I I I 

Y = 32.711 +0.413X Y q 25.703 +0.457X 

r= 0.247 r= 0.447 

l *. . . . 

l . l . 

. . . * _’ 

. 

-- 

. 

. 

0 50 100 

IREC 26 

120 

100 

80 

40 

20 

0 

80 

Y=21.781 + 11.935X 

r = 0.498 

6’ 

, 

, 

, x : 

: 

.X 

/ 

/ 

, 

/’ / . 

, . 

. 

. ,/’ l , ,,,.,* 

, .’ 

. 

.’ 

.’ : 

I 

. 

0 

,_ I’ 

1 

I 

. 

2 

. 

3 

,/’ 

4 

I . 

IRE6 

. 

: 

26 

/’ 

I I I I I 

, / / / / l 

. 

I I I 

50 100 

IRED 26 

150 

Fig. 2(f). 
906 M. Balls et al. 

Y = 32.725+0.016X Y = 93.415 - 12.234X 

r = 0.451 r= -0.360 

120 

, 

,*’ . 

,’ 

100 

, 

2 60 

0 1000 2000 

IRESUM 

3000 

Y = 43.758 - 10.170x 

I-= -0.401 

120 i \ \ 

80 

-3 -2 -1 0 1 2 

logNRU34 

80 

2 60 

40 

20 

0 

. 

. . 

. 

‘. 

.* 

. . 

\- 

. 

.._ * 

. 

. 

. * ‘. 

. 

. 

. . . J-l 

. * 

’ 

. 

. 

. 

. 

. 

‘-. 

. * . 

I I I I I I I I 

2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 

SM 30 

Fig. 2(g). 

Fig. 2. Scatterplots showing the relationships between the MMAS for all 59 test chemicals and the scores 

from the lead laboratories for the nine tests, with regression lines and 95% confidence intervals. The 

ordinate is labelled with the name of the test, endpoint and laboratory number (e.g. 

RBC + DL + 1 = RBCDLl and HET + Q + 14 = HETQ14). The order of the scatterplots and descriptions of the endpoints are as in Table 4. Further details of the tests are given in Appendix 2. 
EC/HO validation study on alternatives to the Drake test 907 

120 

100 

80 

; 

1 

i 

I 

I 

‘9 

40 

20 

a 

120 

100 

80 

$ 60 

5 

Y= 75.917 -0.00042x Y = 222.899 - 37.795X 

r = -0.509 r- -0.509 

. ‘. 

. 

‘. 

. 

. . 

. . 

‘. 

~~...,_ 

. 

\ 

. 

/I 

120 

100 

80 

g 60 

z 

40 

20 

0 

0 25000 50000 75000 100000 

RBCDLI 

Y = 17.569+ 1.151X Y = 19.779 +30.562X 

r= 0.784 r= 0.693 

III I II I II I 

0 10 20 30 40 50 60 70 80 90100 0 1 2 

RBCDMI IogDMl 

120 

100 

80 

3 60 

.zi 

40 

20 

a 

_ 

I I ! I I 

0 1 2 3 4 5 

IogDLl 

I 

I /. / / ,’ 

/ 

/ 

. 

. 

. 

,’ 

.’ 

.’ / 

. 

. 

.I_ 

.’ 

j__/ 

.: 

_’ 

. .’ 

_’ 

.’ 

.’ . 

: / 

. 

/ 

I I I 

Fig. 3(a). 
908 M. Balls et al. 

120 120 

80 80 

Y = 60.581-0.00061X 

r= -0.612 

80 

2 60 

zi 

0 25000 50000 75000 100000 

RBCHI IogHl 

Y = -4.891 + 1.564X Y = 16.884 + 13.200X 

r = 0.543 r = 0.642 

1 

. 

_/ 

/ . 

e--1 

20 30 40 50 

EYTEX 5 

Y = 151.533-32.322X 

r= -0.879 

\ 

\ 

120 

100 

80 

2 60 

1 

i 

4 

l . / 

,’ 

. 

I I I I I I II II 

012345678910 

BCOPPS 

Fig. 3(b). 
EC/HO validation study on alternatives to the Drake test 909 

80 

Y = 28.041 + 0.864X Y = 11.685 +0.610X 

r= 0.663 r = 0.772 

20 -( 

o]; j:;, , , , 1 

0 100 200 300 400 

BCOPOS 

120 

100 

80 

40 

20 

0 

i 

Y = 11.685 +0.610X Y = 3.558 + 14.740): 

r= 0.772 r = 0.688 

I 

/ / 

i 

. 

/ / / / / ’ 

/ . ;’ 

/ . 

/ / 

:I’ ),,,, ,’ 

. 

: 

.I’ ,’ 

,’ 

,’ 

_’ 

,’ 

.’ 

,’ . 

,’ 

.’ 

.;/ 

: 

.’ . 

/ 

/ 

/ 

/ 

*. / 

/ 

I 

/ 

* . / 

. 

I I I 

0 100 200 

BCOPISb 

Cut-off point at 200 

80 

40 

20 

OC-;, I I I $ 

0 100 200 300 400 500 

100 

80 

2 

Is 6o 

5 

40 

20 

0 

BCOPIS 

T I I I I 

0 5 10 15 

HETQ14 

20 25 

Fig. 3(c). 
910 M. Balls et al. 

120 

100 

2 60 

40 

20 

0 

0 

Y = -21 392 + 38.797X 

r=O476 

. 

Cu:-off mnta! 0 = 7 0 

1 

HETQ14b 

2 

Y = 9.026 + 2.551X 

r = 0.85; 

120 

40' ; 

I : 

1 

20 -1 1 : 

j //: 

.’ 

o- .’ 

L--- ---p----- 

0 100 200 0 1 2 3 4 

ICES 22 ICE0 22 

Y = 68.875 -0.070X 

r = -0.745 

120 :- 

0 500 1000 1500 

FL20 18 

Y = 25.811 + 24.472X 

r = 0.601 

120 -I--- I 

/ ,/ I, , 

80 ~1 

40 -i 

’ 

. . :7 . .’ 

. 

/ 

;_;= ./.- 

. 

,.’ 

_’ 

. I’ 

.’ 

20 

i . 

I / / 

Fig. 3(d) 
EC/HO validation study on alternatives to the Draize test 911 

120 

100 

80 

T 

Y = -7.856 + 34.865); Y = 9.143 +0.606x 

r= 0.758 r = 0.724 

.’ 

.’ 

20 ~' /,’ / 

.’ 

/ 

. 

/ / / 

0 1 2 3 

ICEF 22 

Y = 23.213 +26.458X Y = 8.758 +22.316X 

r = 0.590 r= 0.696 

80 

ul 

5 60 

2 

/ * 

1 2 3 

IREA 26 

80 

100 

80 

z 60 

40 

20 

0 

*, . ,; I 

250 350 

ICEC 22 

/ . / 

/ / . 

/ l .’ 

/ .’ 

. 

0 1 2 3 4 

IREB 26 

Fig. 3(e). 
912 M. Balls et al. 

120 

100 

80 

2 60 

Y = 29.574 + 0.963X Y= 11.704 + 1.175x 

r = 0.204 r= 0.621 

. 

*, 

. 

. 

. 

‘/ 

. 

40 4 ~:-I . 

20 

. 

Y = 33.972 +0.034x 

r = 0.513 

120 j , 

100 -1 

. 

l 

80 i 

. 

0 1000 2000 3000 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 

IRESUM SM 30 

120 

80 i 

i 

20 

0 ~. 

120 

80 

I 

i 

i 

. 

I 

. 

* ,’ 

. 

. ,’ 

,j 

i 

_’ 

. 

/ *’ 

* / 

.’ 

,/’ 

’ . 

. 

0 50 100 150 

IRED 26 

IY=201.566-45.800X 

r= -0.746 

\ \ 

. \ \ 

\ 

Fig. 3(f). 
EC/HO validation study on alternatives to the Drake test 913 

Y = 38.485 -35.029x 

r = -0.837 

\ 

logNRU34 

Fig. 3(g). 

Fig. 3. Scatterplots showing the relationships between the MMAS for 12 surfactant test materials and 

the scores from the lead laboratories for the nine tests, with regression lines and 95% confidence intervals. 

The order of the scatterplots and descriptions of the endpoints are as in Table 4. Further details of the 

tests are given in Appendix 2. 

eye irritation scores within each eye irritation test as 

the standard deviation. The eye irritation potential of 

the test substances covered nearly the entire range 

that may be encountered in a rabbit eye irritation 

study (on the basis of MMAS =0-108) and the 

irritancy of the test substances was uniformly distributed across this entire range. 

The capacity of the in vitro methods to predict eye 

irritation potential was assessed by preparing plots 

showing the relationship between the in vivo MMAS 

and the in vitro endpoints tested. Although comparisons were assessed for all the subsets of test substances shown in Table 3, plots for only the entire set 

of test substances (Fig. 2) and the surfactant subset 

(Fig. 3) are shown. The in vivo MMAS are plotted on 

the ordinate, and the corresponding in vitro endpoint 

scores are plotted on the abscissa. Simple regression 

analysis was conducted for each in vitro endpoint 

reported in this paper (see legend for Table 4 and 

Appendix 2). The results of this analysis are shown on 

each of the plots, and summary statistics are presented in Table 4A-G. The best-fit linear regression 

line is shown as a solid line, and the standard 

deviation for the regression line is shown as dotted 

lines (narrow lines, narrow spaces) closest to the 

regression line. The 95% confidence interval for the 

prediction of an in vivo MMAS (95% CI,& from a 

given in vitro score is shown as dotted lines (broad 

lines, narrow spaces) furthest away from the regression lines. The equation for each regression line 

and the Pearson’s correlation coefficient is shown at 

the top of each figure. The results from only one 

laboratory (the lead laboratory) are shown in the two 

figures, because of space restrictions. Nevertheless, as 

is clear from Table 4, the results from all the laboratories in each group were highly correlated with each 

other. Thus, the figures presented provide a reasonable representation of the results from all the laboratories. 

The plots of results from the entire test set (Fig. 2) 

show that there is generally a broad scattering of the 

data points around the regression line. The correlation coefficients for each of the endpoints vary, 

depending on the endpoint measured, ranging from a 

high of 0.616 (SM31, Table 4A) to a low of 0.012 (log 

DLl, Table 4A). Additionally, the 95% CI,, are 

relatively wide, frequently greater than f40. The 

plots showing the results from the various chemical 

subsets had similar relationships between the in vivo 

and alternative method data (figures not shown) with 

confidence intervals of similar width. The results for 

the surfactants were somewhat more encouraging, in 

that the correlation coefficients tended to be higher 

(Table 4D) and the 95% CIpd tended to be narrower 

(Fig. 3) than was observed with the more diverse 

chemical subsets. 

The summary statistics from the regression analysis 

are shown in Table 4A-G. Each section of the table 

lists the alternative method index measured, the 

number of samples tested in the in vitro assay, the 
914 M. Balls et al. 

number of test substances for which no results were 

reported, the Pearson’s correlation coefficient, Spearman’s rank correlation coefficient, and the equation 

of the best-fit linear regression line for each subset of 

chemicals that underwent statistical analysis. The 

interlaboratory reproducibility of the in z?itro 

methods was assessed by calculating the correlations 

of the in vitro scores among the participating laboratories for each test. The correlation between laboratories is shown in a matrix for each endpoint in the 

extreme right-hand columns of Table 4. In most 

cases, the results obtained in the different laboratories 

were highly correlated with each other (RBCDL, log 

NRU, BCOPI; Table 4A). There were, however, 

endpoints where the between-laboratories reproducibility was not as high, which suggests poorer interlaboratory reproducibility (HETS, Table 4A). 

As noted earlier, a Mann-Whitney test was also 

conducted, to assess whether the rankings of the 

in vitro scores for chemicals with MMAS values > 59 

were significantly different from those with scores 

~59. The same test was performed for the sets of 

substances having MMAS values < 25 and > 25. The 

Mann-Whitney method tests the hypothesis that 

there are directional differences in response between 

the data sets. A significant difference only indicates 

that the scores of the two groups are different. It does 

not provide estimates of how large the differences are. 

Although the analysis indicated that there were a few 

statistically significant separations in scores, inspection of the scatterplots showed that there was no clear 

association of in vitro scores with either high or low 

in vivo irritation potential (Figs 2 and 3). The results 

of this analysis are therefore not presented in this 

report. 

Discussion 

The purpose of this study was to determine 

whether one or more non-whole-animal methods may 

be useful as a replacement for the Draize eye irritation test for identifying either severely irritating test 

materials or for assessing the irritancy potential of 

chemicals across the entire measurable range. Nine 

promising alternative methods were evaluated using 

a reference set of 60 test substances in order to answer 

this question. The results from the alternative 

methods for 59 of these substances were compared 

directly with the in vivo eye irritation potential of the 

test substances as measured in the OECD Test Guideline 405 procedure. Examination of the plots comparing the relationships between the in vivo and in vitro 

data indicate that there is considerable scatter of the 

data points around the regression line. Additionally, 

the data analysis shows that the 95% C& are wide, 

in many cases greater than f40. Thus, while the 

results of this study show that some alternative 

method endpoints may be capable of predicting the 

in vivo eye irritation potential (MMAS) of test substances, the precision of the predictions is so low that 

the practical utility of such predictions is questionable. 

There are several factors associated with the study 

design and the state of the art that may help to 

explain why the irritancy predictions are not very 

precise. These factors include the choice of materials 

included in the test set, the protocols that were 

assessed in the study, variability in the in vivo data, 

the in vivo endpoint used to compare with the alternative method data, and the data analysis procedures 

used to assist in quantification of method performance. The contribution of each of these factors to the 

overall outcome of the study is discussed in the 

following paragraphs. 

One of the most important factors contributing to 

the results obtained may be the diverse nature of the 

chemicals included in the reference set of test substances. One of the goals of the study was to determine whether the methods are useful for predicting 

irritancy potential of test substances representing 

many chemical classes and physical forms, and to 

determine the ability of the methods to assess irritancy potential without reference to benchmark standards. The consensus at the International Expert 

Group meeting held in London in October 1992 was 

that the test set needed to be as broad as possible, in 

order to demonstrate the general applicability of the 

alternative methods for use in the regulatory setting. 

Accordingly, the reference set of test substances was 

assembled to maximize the diversity of the test chemicals. While it is clear that it would be most useful for 

regulators to have alternative methods that can test 

all substances without reference to benchmark controls, experience with currently available alternative 

methods in the industrial toxicology setting suggests 

the procedures work best when their use is limited to 

specific classes of test substances that act through 

similar mechanisms of toxicity (Chamberlain and 

Barratt, 1995). Furthermore, the results from in vitro 

tests are usually interpreted with reference to data 

from benchmark test materials (see, for example, 

O’Brien et al., 1994). Since the principal objective of 

the study was to determine whether currently-available alternative methods could replace the in vivo test 

for regulatory purposes, it is not surprising that the 

accuracy and precision of the predictions observed 

was low. Most in vitro methods currently in use have 

not been developed to provide answers in the regulatory context. Rather, they have been developed to 

make assessments within limited classes, relative to 

benchmark controls. In effect, then, the methods 

tested in this study were evaluated in a manner which 

is not consistent with their use by many industry 

toxicologists during the in-house safety assessment 

process. 

The next issue to consider is the individual protocols tested. In some cases, the protocols evaluated 

had not been used before. This is because significant 

changes were made in the test protocols in order to 

establish a common procedure that could be agreed 
EC/HO validation study on alternatives to the Drake test 915 

by each of the participating laboratories. It is not yet 

known whether the changes in procedure altered the 

predictive capacity of the methods. If these changes 

did alter the performance of the methods, this may 

have affected the outcome of the study. In order to 

ensure that only well developed methods are assessed 

in future studies, it will be important to insist that the 

protocols be optimized, agreed, and tested, and that 

a Prediction Model be established before an alternative method is accepted into future validation studies 

(Balls et al., 1995; Bruner et al., 1995; Curren et al., 

1995). 

The quality of the in vivo eye irritation data that 

were used to assess the performance of the methods 

is another important issue. The in vivo data used in 

this study were judged against a set of criteria formulated by an ECETOC Task Force (Bagley et nl., 1992; 

ECETOC, 1992). Thus, the quality of the data used 

was as high as could be reasonably expected without 

conducting large amounts of additional eye irritation 

testing. One of the quality factors that it is not 

possible to assess under the conditions of the present 

study is the accuracy and precision of the in vivo eye 

irritation scores used for comparison with the alternative method results. This is because there were no 

data on the interlaboratory reproducibility of the eye 

irritation scores on the majority of materials in the 

test set. This is important, because it is well known 

that the inter-laboratory variability in the Draize 

rabbit eye irritation test can be high (Weil and Scala, 

1971). High variability in the in vivo data can have 

considerable influence on the alternative method performance evaluated in a validation study. Computer 

simulations show that the excellent performance of 

highly predictive alternative methods can be obscured 

to the point where they appear to perform at levels 

equivalent to or even below those observed in this 

study (Bruner et al., 1995; Bruner and Carr; see 

Loprieno et al., 1995). Given that the accuracy and 

precision of the in vivo data are not known, it may not 

be possible to clearly assess the influence of data 

variability on the predictive capacity of the methods 

for this study unless additional in vivo testing is 

conducted. 

Next, it is important to consider whether the 

MMAS is the most appropriate in vivo eye irritation 

parameter to use for comparison with the alternative 

method scores obtained in validation studies. The use 

of the MMAS has been criticized as being inappropriate, because (1) it represents a composite score of 

responses from several tissues that does not accurately reflect the responses of the individual tissues, 

and (2) some alternative methods have been designed 

to predict only broad eye irritation classifications 

rather than irritancy according to a supposedly morerefined scheme such as the llO-point Draize scale. 

Despite these concerns, there is considerable support 

for the use of the MMAS as the most appropriate 

endpoint to use for assessing alternative method 

performance in the present study. This question has 

been addressed in the US CTFA evaluation of 

alternative methods for cosmetic eye safety testing. 

Feder et al. (1991) examined the extent of association 

between the Kay-Calandra Irritation Score (Kay and 

Calandra, 1962) the maximum average (total) scores 

for the cornea, or iris, or conjunctiva (Williams et al., 

1982) the FHSA classification scheme (US Code of 

Federal Regulations, 1988) and days to clear (US 

EPA, 1984). Their analysis resulted in correlation 

coefficients ranging between 0.9 and 0.98 for comparisons between the MAS versus the Kay-Calandra 

score, cornea score, conjunctival score and iris score. 

These results led to the conclusion that any of these 

measurements could be used as a comparable indication of in vivo eye irritation potential of test 

materials (Feder et al., 1991; Gettings et al., 1990). 

Thus, while the analysis of alternative method performance in the present study could have been carried 

out using any of the available scoring schemes, the 

high correlation between all of the endpoints suggests 

that such an analysis is unlikely to markedly change 

the overall conclusions of the study. This conclusion 

should not, however, preclude further analysis of the 

results once the data are made generally available. 

Such post hoc analysis may provide leads that could 

be tested in future validation studies. 

The initial statistical analysis presented in this 

report has involved the use of linear regression 

techniques and calculation of 95% CI,,. No attempt 

has been made to fit the data to more complex 

models. The analysis did not use other models for two 

reasons. First, the simple plots of the in vivo irritancy 

versus the alternative methods provide a clear view of 

the relationship between the in vivo and in vitro data 

obtained from the study. This clear view allows users 

of methods to identify materials for which the in vivo 

and alternative method results may be usefully related, and allows the identification of outlier data 

points. Exploration of the data presented in these 

plots may allow the development of models and 

hypotheses that can be tested in subsequent validation programmes. Secondly, the developers of the 

protocols tested in the study did not define models to 

be used for assessing the performance of the alternative methods at any time before the start of the data 

analysis phase of the programme. In order to use 

more complex data analysis techniques, it would have 

been necessary for participants to identify other 

models that could have been evaluated during the 

data analysis process. The data analyst could have 

explored the data in an attempt to identify potential 

useful models, but this is a subjective process that 

may not have yielded models that better fit the data. 

Additionally, even if the analyst had identified such 

models, they would have to be assessed in a subsequent validation study in order to ensure the proposed model was useful generally, and not specific to 

the current data set. Once the data from the present 

study are made available, it is expected that the data 

set will be explored fully by the participants in the 
916 M. Balls el al. 

study and by other interested parties in an effort to 

identify models that could be used for predicting 

in uivo responses from alternative method results. If 

such models are identified, they can be assessed in 

future validation studies. 

Finally, the analysis presented in this report only 

considered whether single endpoints obtained from 

the alternative methods could be used to predict the 

eye irritation potential of test substances. It is possible that multiple alternative method endpoints may 

be combined in a manner that provides improved 

predictions of eye irritancy compared with the use of 

only single endpoints. To address this question, a 

multivariate analysis of the data from this study has 

been initiated. A summary of the results from this 

analysis will appear as soon as the study is completed. 

The utility of such approaches is not yet clear, but 

early work suggests multivariate data analysis may be 

promising. The results of the ongoing multivariate 

analysis may provide useful models that also could be 

tested in future validation programmes. 

conclusions 

In conclusion, the results of this study suggest that 

some of the endpoints of the in vitro methods may 

have some capacity for predicting the eye irritation 

potential of test substances. However, the confidence 

intervals associated with these predictions are so wide 

that the use of these methods may not be practical. 

The lack of precision in these predictions leads to 

obvious questions about how the methods can be 

used in the safety assessment process by industrial 

toxicologists. The most likely explanation for this 

dichotomy is that this study was not designed to 

answer the questions posed by many of the industrial 

toxicologists who already use the methods on a daily 

basis. As noted in the Discussion section, currentlyavailable alternative methods are rarely used in isolation by toxicologists, but rather in conjunction with 

a great deal of supplementary information. Most 

importantly, testing with alternative methods is 

usually conducted with relatively restricted subsets of 

test substances, which presumably act through similar toxic mechanisms, and the results from alternative 

methods are interpreted in relation to experience with 

well characterized benchmark controls. 

This study was principally designed to answer 

questions asked by regulatory toxicologists, who 

must assess a broad range of chemicals that do not 

necessarily act through similar toxic mechanisms. 

Most often, regulatory assessments must also be 

made without reference to benchmark controls. Since 

most of the alternative methods have been developed 

to meet the needs of the industrial toxicologist, it is 

perhaps not surprising that the performance of the 

methods in this validation study was, in a regulatory 

sense, disappointing. 

It will be important to deal with this dichotomy as 

a matter of urgency if the new procedures are to be 

accepted by regulatory authorities. There are two 

possible solutions. The first would be for regulatory 

toxicologists to adopt a more flexible approach to the 

use of alternative methods, as has been suggested by, 

for example, Balls et al. (1990b) and Koiter (1994). 

This would require the acceptance of methods that 

have been shown to be useful for predicting the 

irritancy of only limited classes of test substances that 

act through common toxic mechanisms, and for 

assessing the toxicity of materials relative to appropriate benchmark controls. A precedent for this 

approach has been set by the US Department of 

Transportation, which recently accepted two in vitro 

tests for assessing skin corrosion for limited classes of 

test substances. 

If this approach were not deemed acceptable, the 

second solution would be to continue and accelerate 

the research and development process, in order to 

provide alternative methods that gave acceptably 

precise predictions for materials of all classes and 

without reference to benchmark controls. This would 

be the more demanding of the two options, since it 

would require the development of universal methods 

that were both capable of assessing the major mechanisms of eye toxicity and suitable for testing all types 

of test substances. It is not likely that such research 

and development could be completed in the short 

term. 

If it could be agreed that the first solution were 

acceptable, then it should be possible in the relatively 

short term to identify the most important classes of 

test substances and benchmark controls to be evaluated and used in future validation studies. Studies 

focused on more-limited sets of test substances are 

likely to be more fruitful, since they will assess current 

methods in a manner consistent with their presentday use by industrial toxicologists. With co-operation 

between industrial and regulatory toxicologists, it 

should be possible to identify approaches and procedures that will minimize animal use in the shortest 

possible time frame. In the meantime, the basic 

research needed to produce more-generally applicable 

batteries of alternative methods must continue. 

As new, more highly predictive, alternative methods 

are developed, based on better mechanistic understanding, they can gradually be incorporated into the 

eye safety assessment process. Of crucial importance 

to these developments would be the willingness on the 

part of toxicologists, and especially industrial toxicologists, to publish their data (including the identities 

of the materials tested) in the peer-review literature. 

The issue of the precision and accuracy of data 

obtained from the in uiuo eye irritation test must also 

be addressed. If the in vivo data used to assess the 

performance of an alternative method are not precise, 

then it is not reasonable to expect high levels of 

correlation between the in oiuo and alternative 

method data, even if the alternative methods are good 

predictors of eye irritancy. Noisy data will obscure 

the adequate performance of the alternative methods 
EC/HO validation study on alternatives to the Draize test 917 

being evaluated. Future studies must therefore determine the quality of the in vivo data used in the 

assessment process, so that it is possible to put a 

proper perspective on the results obtained from a 

validation study (Balls et al., 1995; Bruner et al., 

1995). 

Even if it is possible to accurately assess the quality 

of the in vivo data used in the studies, there will still 

be problems if it is found that the in vivo data are 

highly variable. The only solution to this variability 

problem will be to conduct more basic research, in 

order to develop procedures that improve the scientist’s ability to assess the true eye irritancy of test 

chemicals. While the Draize test and its variants have 

served acceptably well up to now as predictors of eye 

irritation potential for safety and regulatory assessments, the rabbit eye irritation test may ultimately 

prove to be an inadequate tool for the development 

of useful alternative methods. In order to properly 

address these deficiencies, it will be necessary to 

conduct basic research into how to better measure eye 

irritation potential, to make use of developments in 

QSAR analysis (Chamberlain and Barratt, 1993, as 

well as in in vitro technology, and to assess the 

availability and utility of human workplace and 

marketplace experience data. 

The chemical and physical properties of the test 

materials used in this study were not given to the 

participating laboratories during the course of the 

practical work. This caused difficulties for some of the 

laboratories, since the testing of materials with 

alternative methods is not normally conducted in the 

complete absence of such information. For example, 

the solubility of a chemical in aqueous tissue culture 

media or its pH can dictate whether a chemical is or 

is not compatible with a particular test method. In 

addition, it is known that the irritancy of materials 

with certain chemical properties may be poorly predicted by a given method. Thus, there is a danger that 

poor results with materials for which a method is not 

suited will mask good results for materials for which 

it is suitable. It will therefore be necessary in future 

studies to consider the degree of blindness which is 

appropriate (so that the tests being evaluated can be 

conducted properly and thus fairly assessed) and 

necessary (so that the materials cannot be identified 

before the completion of the study, and confidence in 

the integrity of the evaluation can be assured). These 

problems are more easily solved when the range of 

toxicity required for the validation study is represented by members of series of substances which are 

chemically related and which have similar physical 

properties. 

A note of caution must be recorded with regard to 

the use of certain statistical methods in attempts to 

solve some of the difficulties associated with developing alternative methods. Statistical methods should 

only be seen as tools for assisting in the proper 

assessment of the results produced in validation 

studies. There is a growing tendency to use increasingly complex statistical methods as a means of trying 

to identify associations between information provided by in vivo and alternative methods by detecting 

relationships which are hidden within very noisy sets 

of data. The utility of models developed on a post hoc 

basis must be verified by using rigorous procedures 

that provide convincing confirmation that they are 

themselves valid and generally useful, rather than 

being merely convenient for use with the limited data 

set used to develop them. 

Finally, although the results of this study were not 

as positive as had been hoped and expected, the 

outcome does highlight the important problems 

that must be faced and overcome before nonanimal alternative methods can replace the in vivo 

eye irritation test. Some of the many lessons learned 

are already being put into practice. For example, 

the current COLIPA eye irritation test validation 

study has been designed to test only methods which 

incorporate clearly designed prediction models. 

In addition, the reference set of test substances 

is focused on ingredients and formulations with 

a relatively narrow use, for example in cosmetic 

products. 

Thus, in the short term, the fastest progress will 

be made by evaluating alternative methods for 

more-circumscribed sets of test substances and by 

making use of testing strategies which involve the 

use of benchmark controls. In the longer term, 

basic research must continue, in order to provide 

methods that will more accurately and more 

universally be able to predict eye irritation in 

humans. 

Acknowledgements-The Management Team for the 

EC/HO study would like to acknowledge the major contributions made by many individuals and institutions to this 

complex endeavour, and, in particular, to thank: E Division 

of the British Home Office, for encouraging the setting up 

of the study and for providing facilities for meetings of the 

Steering droup and the International Expert -Group; 

DGXIICI6 of the Eurouean Commission. for orovidine 

essential financial suppdrt; ECVAM for ‘suppl$ng sup: 

plementary financial support; the University of Nottingham 

for the financial management of the project, as EC Contractor; Dr Paul Brantom and his colleagues at BIBRA International, for the coding and supply of the test materials; Dr 

David Love11 and his colleagues at BIBRA, for the data 

collection and statistical analysis; Dr Arthur Walker and the 

other members of the ECETOC Task Force and the Chemicals Selection Committee, for all the effort they put into 

finding the 60 test materials; the members of the Steering 

Group and the International Expert Group, for their advice; 

all concerned in the 37 participating laboratories, for their 

conduct of the practical work, and to their institutions, for 

their financial contributions; Dr Krys Ungar of INVITOX, 

for assistance with the test protocols; the Molecular Devices 

Corporation, for financial support for one of the SM 

laboratories; the Millipore Corporation, for assistance with 

the supply of filter inserts for -the FL assay, and In Vitro 

International. for assistance with the SUDD~V of EYTEX 

kits; Dr Karen Atkinson and Dr Donald-Siraughan, for 

their helpful advice; and AMA Services and Dr Christoph 

Reinhardt, for assistance with the organization of meetings 

of the participating laboratories. 
918 M. Ball .s el al. 

Gautheron P.. Dukic M., Alix D. and Sina J. F. (1992) 

Marenus K. D., Pape W. J. W., Roddy J. Schnetzinger R.. 

Silber P. M., Teal J. J., Weise S. L. and Gettings S. D. 

(199 1) The CTFA evaluation of alternatives program: an 

Bovine cornea1 opacity and permeability test: an in vitro 

evaluation of in vitro alternatives to the Draize primary 

assay of ocular irritancy. Fundamental and Applied Toxieye irritation test (Phase I) hydroalcoholic formulations; 

(Part 1) statistical methods. In Vitro Toxicology 4, 

231 246. 

i,ology 18, 4422449. 

Gettings S. D.. Dipasquale L. C.. Bagley D. M., Chudkowski M., Demetruhas J. L., Feder P. I., Hintze K. L., 

Marenus K. D., Pape W. J. W.. Roddy M., Schnetzinger 

R.. Silber P., Teal J. J. and Weise S. L. (1990) The CTFA 

evaluation of alternatives program: an evaluation of 

in vitro alternatives to the Drdize primary eye irritation 

test. In Vitro Toxicology 3, 293-302. 

Gordon V. C. and Bergman H. C. (1987) EYTEX, an 

in vitro method for evaluation of ocular irritancy. In 

Alternative Methods in Toxicology. Edited by A. M. 

Goldberg. Vol. 5. pp. 87-90. Mary Ann Liebert, New 

York. 

Kay J. J. and Calandra J. C. (1962) Interpretation of eye 

irritation tests. Journal of the Society of Cosmetic 

Chemists 13, 281 289. 

Koeter H. B. W. M. (1994) Principles for a pragmatic 

approach to the regulatory acceptance of alternative tests. 

Toxicology in Vitro 8, 925930. 

Loprieno N., Bruner L. H., Carr G. J., Chamberlain M., 

Cottin M., De Silva 0. and Kato S. (1995) Alternatives 

in cosmetics testing. Toxicology in Vitro 9, 8277838. 

Luepke N. P. (1985) Hen’s egg chorioallantoic membrane 

test for irritation potential. Food and Chemical Toxicology 

23, 2877291. 

Minitab (1992) Minitab Reference Manual Release 9. 

Minitab Inc., State College, PA. 

O’Brien K. A. F., Basketter D. A., Jones P. A. and Dixit M. 

(1994) An in Gtro study of the eye irritation potential of 

new shampoo formulations. To.xicology in Vitro 8, 

257 -261. 

OECD (1987) Acute Eye Irritation/Corrosion. OECD 

Guideline 405 for Testing of Chemicals, 9 pp. In OECD 

Guidelines for Testing of Chemicals. OECD Publications 

Service, Paris. 

Pape W. J. W., Pfdnnenbecker U. and Hoppe U. (1987) 

Validation of the red blood cell test system as an in vitro 

assay for the rapid screening of irritation potential of 

surfactants. Molecular Toxicology 1, 525-536. 

Prinsen M. K. and Koeter H. B. W. M. (1992) The 

enucleated eye test with eyes of slaughter animals as an 

alternative to the Draize eye irritation test with rabbits. 

Poster presented at 10th Anniversary CAAT Meeting, 

Johns Hopkins University. Baltimore, MD, April 

1992. 

Ryan B. F., Joiner B. L. and Ryan T. A. (1985) Minitab 

Handbook. 2nd Ed. PWS Kent Publishing Company, 

Boston, MA. 

Shaw A. J., Clothier R. H. and Balls M. (1990) Loss of 

transepithelial impermeability of Madin-Darby canine 

kidney (MDCK) cells as a determinant of ocular irritancy 

potential. ATLA 18, 145-151. 

US EPA (1984) Labelling requirements for pesticides and 

devices, proposed rule. Federal Register 49, 37960. 

Weil C. S. and Scala R. A. (1971) Study of intra- and 

inter-laboratory variability in the results of rabbit eye and 

skin irritation tests. Toxicology and Applied Pharmacology 

19, 276-360. 

Williams S. J., Graepel G. J. and Kennedy G. L. (1982) 

Evaluation of ocular irritancy potential: interlaboratory 

variability and effect of dosage volume. Toxicology 

Letters 12, 235-241. 

REFERENCES 

Atkinson K. A., Fentem J. H. and Balls M. (1994) A SurwI, 

Baglky D. M., Botham P. A., G%dner J. R., Holland G , 

of Alternathes to the Draize Eye Irritation Test and of their 

Kreiling R., Lewis R. W.. Stringer D. A. and Walker A. 

Current State of Validation. Report prepared for DG 

XI/C/6 of the European Commission, Contract 

P. (1992) Eye irritation: reference chemicals data bank. 

89/XI/A2-6. FRAME. Nottinaham. 

Toxicology in Vitro 6, 4877491. 

Balls M., Blaauboer B., Brusick D.. Frazier J., Lamb D.. 

Pemberton M., Reinhardt C., Roberfroid M.. 

Rosenkranz H., Schmid B., Spielmann H., Stammati 

A.-L. and Walum E. (1990a) Report and recommendations of the CAAT/ERGATT Workshop on the vahdation of toxicity test procedures. ATLA i8, 313-326. 

Balls M.. Blaauboer B. J.. Fentem J. H.. Bruner L.. Combes 

R. D.; Ekwali B., Fielder R. J., Guillouzo A., Lewis R. 

W., Lovell D. P., Reinhardt C. A., Repetto G.. Sladowski 

D., Spielmann H. and Zucco F. (1995) Practical aspects 

of the validation of toxicity test procedures. The report 

and recommendations of ECVAM workshop 5. ATLA 

23, 1299147. 

Balls M., Botham P., Cordier A., Fumero S., Kayser D.. 

Koeter H., Koundakjian P., Lindquist N. G., Meyer 0.. 

Pioda L., Reinhardt C., Rozemond H., Smyrniotis T.. 

Spielmann H., Van Looy H., van der Venne M.-T. and 

Walum E. (1990b) Report and recommendations of an 

international workshop on the regulatory acceptance of 

validated non-animal toxicity test procedures. ATLA 18, 

3399344. 

Borenfreund E. and Puerner J. A. (1985) Toxicity determined in vitro by morphological alteration and neutral red 

absorption. Toxicology Letters 24, 119.-224. 

Brantom P. G., Aspin P. and Thompson C. (1995) Supply, 

coding and distribution of samples for validation studies. 

ATLA 23, 348-351. 

Bruner L. H., Carr G. J., Chamberlain M. and Curren R. 

D. Validation of alternative methods for toxicity testing. 

Toxicology in Vitro. In press. 

Bruner L. H., Miller K. R., Owicki J. C., Parce J. W. and 

Muir V. C. (1991) Testing ocular irritancy in t,itro with the 

silicon microphysiometer. Toxicology in Vitro 5, 2777284. 

Burton A. B. D.. York M. and Lawrence R. S. (1981) The 

in vitro assessment of severe eye irritants. ‘Food and 

Cosmetics Toxicology 19, 471-480. 

Commission of the European Communities ( I99 I ) Collabo - 

rative Study on the Evaluation of Alternative Methods to 

the Eye Irritation Test, parts I and II. Dot. X1/632/91: 

V/E. I. 13 I .91. Commission of the European Communities, Brussels. 

Chamberlain M. and Barratt M. (1995) Practical apphcations of QSAR to in vitro toxicology illustrated by consideration of eye irritation. Toxicology in Vitro 9, 543--547. 

Code ofFederal Regulations (1988) Title 16 Section 1500.42: 

Federal Hazardous Substances Act Regulations, 

pp. 350-351. 

Curren R. D., Southee J. A., Spielmann H., Liebsch M., 

Fentem J. H. and Balls M. (1995) The role of prevalidation in the development, validation and acceptance of 

alternative methods. ATLA 23, 21 I-217. 

Draize J. H., Woodard G. K. and Calvery H. 0. (1944) 

Methods for the study of irritation and toxicity of substances applied topically to the skin and mucous membranes. Journal of Pharmacology and Experimental 

Therapeutics 82, 377-390. 

ECETOC (1992) Eye Irritation-Reference Chemicals Data 

Bank. Technical Report No. 48. European Centre for 

Ecotoxicology and Toxicology of Chemicals, Brussels. 

Feder P. I., Lord0 R. A., Dipasquale L. C., Bagley D. M., 

Chudkowski M., Demetrulias J. L., Hintze K. L., 
APPENDIX I 

EC/HO Study-Membership of Steering Group, 

Expert Group and International Group 

Dr David Lewis (Central Toxicology Laboratory, 

Zeneca, Macclesfield, UK) 

Dr Hugo van Looy (OECD, Paris, France) 

Dr David Love11 (BIBRA International, Carshalton, 

UK) 

INTERNATIONAL GROUP Dr Wolfgang Pape (Beiersdorf AG, Hamburg, 

Germany) 

Membership of the Steering Group: 

Professor Michael Balls (FRAME and University 

of Nottingham, UK) 

Mr Menk Prinsen (TNO, Zeist, The Netherlands) 

Dr Jean-FranGois Regnier (ATOCHEM, Paris, 

France) 

Dr Philip Botham (Central Toxicology Laboratory, 

Zeneca, Macclesfield, UK) 

Dr Mark Chamberlain (Environmental Safety 

Laboratory, Unilever Research, Sharnbrook, 

UK) 

Dr Odile de Silva (L’Or&al, Aulnay-sous-Bois, 

France) 

Dr Lewis Smith (MRC Toxicology Unit, Leicester, 

UK) 

Dr John Gardner (Shell Research Centre, Sittingbourne, UK) 

Dr Anna-Laura Stammati (ISS, Rome, Italy) 

Dr Winfried Steiling (Henkel KGaA, Diisseldorf, 

Germany) 

Mr Tim Smyrniotis (DGXI/A/2, EC, Brussels, 

Belgium) 

Dr Neil Wilcox (US Food & Drug Administration, 

Rockville, MD, USA) 

Dr Donald Straughan (Home Office, London, UK) 

Dr Arthur Walker (ECETOC, Brussels, Belgium). APPENDIX 2 

Additional participants at the June 1992 Meeting of 

the Expert Group: 

Details of tests involved in the EC/HO Study 

1. The red blood cell haemolysis test 

Mr Ian Archbold (Home Office, London, UK) 

Dr Monique Adolphe (EPHE, Paris, France) 

Dr Leon Bruner (Procter & Gamble, Cincinnati, 

OH, USA) 

a. Principle of the method 

Dr Robin Fielder (Department of Health, London, 

UK) 

Dr Hermann Kogter (OECD, Paris, France) 

Professor Horst Spielmann (ZEBET and Free 

University of Berlin, Germany). 

Additional participants at the October 1992 Meeting of 

the International Expert Group: 

MS Karen Atkinson (FRAME, Nottingham, UK) 

Dr Ih Chu (Bureau of Chemical Hazards, Ottawa, 

Ontario, Canada) 

The method is based on that of Pape et al. (1987) 

and Lewis et al. (1993). Certain classes of chemical 

irritants may cause damage to cell plasma membranes 

and/or denaturation of membrane proteins and other 

cellular proteins. Such events can be correlated with 

the initial inflammatory response in tissue irritation 

and with changes in protein conformation, such as 

those that occur in opacification of the cornea. The 

biological endpoints of cell lysis and protein denaturation has been used singly or in combination 

to predict the ocular irritancy of certain classes of 

chemicals (mainly surfactants). 

b. Basic procedure, endpoints and endpoint measurement 

Dr David Clark (Environmental Safety Laboratory, 

Unilever Research, Sharnbrook, UK) 

Dr Rodger Curren (Microbiological Associates, 

Rockville, MD, USA) 

Professor Anthony Dayan (St Bartholemew’s 

Medical College, London, UK) 

Dr David Esdaile (RhBne-Poulenc, Valbonne, 

France) 

Dr Julia Fentem (FRAME, Nottingham, UK) 

Dr Pierre Gautheron (MSDChibret, Riom, 

France) 

Dr Kailash Gupta (US Consumer Product Safety 

Commission, Bethesda, MD, USA) 

Mr Michael Head (Home Office, London, UK) 

Dr Richard Hill (US Environmental Protection 

Agency, Washington, DC, USA) 

Dr Patricia Koundakjian (Health & Safety 

Membrane damage is assessed by measuring the 

leakage of haemoglobin from red blood cells incubated with test material under standard conditions. 

The haemoglobin is detected spectrophotometrically 

(at 541 nm). Protein denaturation is measured by 

determining the reduction in absorbance at 541 nm 

(oxyhaemoglobin absorption maximum) with increasing concentration of test substance. All determinations 

are made relative to concurrent control sample, 

totally lysed with distilled water. 

Initially, a range-finding assay is conducted over 

a wide range of test substance concentrations. There 

can be various outcomes of this initial study: 

Executive, Bootle, UK) 

i. No denaturation or lysis. 

ii. Lysis in the absence of denaturation. 

iii. Lysis and protein denaturation. 

iv. Denaturation alone. 

EC/HO validation study on alternatives to the Draize test 919 
920 M. Balls et al. 

The degree of any denaturation can be determined 

directly from the range-finding study at the predetermined concentrations used. Lysis is assessed 

accurately at concentration intervals selected from 

the results of the range-finding study. 

c. Expression qf results 

Membrane damage (lysis) is expressed as the H,, 

value (the concentration causing 50% haemolysis 

relative to the totally lysed sample). Denaturation 

is expressed as Dr,,,, (the lowest concentration 

causing denaturation-a 10% decrease in absorbance 

at 541 nm) and Dhlax (the maximum percentage 

denaturation seen at any concentration tested). 

d. Application of results 

H,, and DLow are inversely correlated with MMAS; 

D Max is positively correlated with MMAS. Most 

of the materials used in initial evaluations of this 

method were surfactants. The development and use 

of lysis and denaturation are therefore specific to this 

class of chemistry. In practice, the red blood cell test 

has been used with other non-animal approaches and 

structure activity information to build a profile of the 

test chemical relative to standards of known ocular 

irritancy. 

e. Dijiculties experienced and limitations of the 

method 

As with all cell-based assay systems, protocols 

must be prepared that are able to handle watersoluble and water-insoluble test materials. Very 

limited evaluations have been carried out of suitable 

vehicles for use with the red blood cell assay. 

2. The EYTEX method 

a. Principle of the method 

Small quantities of test sample, when presented to 

EYTEX reagent, produce a turbidity which is directly 

proportional to the irritancy of the sample. This 

turbidity is produced by changes in conformation 

and degree of hydration of the EYTEX reagent and 

mimics the response of the protein in the cornea 

during cornea1 injury or ocular irritation. 

The turbidity, measured in a spectrophotometer, 

is compared with that produced by eye irritant 

standards of known Draize score. The EYTEX 

Draize Equivalent (EDE) score is then determined 

from a calibration curve. 

b. Operation of the method 

Three protocols, the Upright Membrane Assay 

(UMA), Rapid Membrane Assay (RMA) and 

Alkaline Membrane Assay (AMA), were used in this 

study. The UMA protocol is a broad-range screening 

protocol for samples with a 10% dilution pH < 8.0. 

The AMA protocol is used for samples with 10% 

dilution pH > 8.0. The RMA protocol was used for 

surfactants and when a sample did not qualify in the 

UMA protocol. The shorter incubation time, 5 hr cf. 

24 hr for the UMA protocol, minimized inhibition 

and interference effects. 

UMA, RMA and AMA kits were obtained from 

InVitro International (Irvine, CA, USA), care being 

taken to ensure the use only of kits from the same 

batch of EYTEX reagent. 

The EYTEX reagent is a lyophilized powder 

containing a range of proteins and lipids along with 

buffer salts, which is reconstituted by addition of 

distilled water. Calibration and quality control (QC) 

samples, dilution buffer activator and inhibition 

check reagents are also included in the kit. 

Optical density (OD) is measured on a model 60/60 

calorimeter (Jenway. Great Dunmow, Essex, UK) 

with a 400 nm interference filter. 

c. Analysis of unknowns 

If the sample was designated a surfactant, a fivevolume assay was used. 20,30,50 and 100 ~1 of 100% 

sample and 100 ~1 of a 10% dilution were analysed 

in the RMA. If the result did not qualify with the 

RMA protocol, the sample was not qualified for 

analysis. All other samples which were neutral or acid 

unknowns, were run at 20, 30, 50 and 100 ~1 with the 

UMA protocol, and, if not qualified, then run with 

the RMA protocol. If alkaline, the samples were run 

with the AMA protocol. If a sample could not be 

analysed at loo%, then it was not qualified by this 

method for inclusion in the validation programme. 

Generally, non-qualified samples would be run 

at a lower concentration, as determined by a concentration screening assay. This approach was not 

followed in this study, since the in viuo data were only 

available on 100% samples. Each sample was assayed 

in duplicate. The results were only accepted after 

achieving two qualified results in the appropriate 

protocol. 

Detailed instructions for performing each of the 

three protocols are given in the ‘Directions for Use’ 

(DFU) in Gordon (1993). All qualification assessments and calculations of the EDEs were carried out 

using the Data Analyses Quality Control Program 

(DAQC-ES), available from InVitro International, 

with automatic data entry and average range 

updating. 

Qualification of sample data is the foundation 

stone of the EYTEX system. It is the concept which 

ensures the viability of intralaboratory and interlaboratory agreement since it forces the user to discard non-conforming results and follow the standard 

procedures precisely. Qualification is the process 

whereby an assay is considered technically valid if, 

and only if, it adheres to specified performance 

standards. These standards are the calibrators and 

QC samples used in each assay. Performance is 

acceptable only if the scores for these standards lie 

within predefined acceptance ranges initially defined 

in the DFU and subsequently based on the average 
EC/HO validation study on alternatives to the Draize test 921 

of the last 10 qualified assays obtained with the untouched material in the top of the bullet 

system. After every 10 new assays, the acceptance -for example 100 mg added, but only 

ranges for calibrators and QC samples are updated. 50 mg accessible. 

The EYTEX system requires qualification of the 

assays and sample. The sample can qualify only if 

the assay, that is calibrators and QC samples, is 

satisfactory. Often, the assay can qualify, but not 

the sample. This can be due to numerous causes, 

for example physical reasons, such as membrane 

damage, or chemical reasons, such as inhibition or 

interference, where the shape of the dose-response 

curve does not conform to that required by the DFU. 

Inhibition is the apparent reduction in OD with 

increasing dose of sample. It is caused by the solubilizing effect of the sample itself (particularly with 

surfactants) on the coagulating EYTEX reagent, 

resulting in a lower estimation of irritancy. 

The EYTEX protocols are vague in this respect and 

should be tightened up. 

Interference and inhibition (particularly noticeable 

with surfactants) can cause severe underestimation. 

Overcoming this problem requires a concentration 

screen to be carried out before the assay to determine 

the optimum test concentration for a qualified assay. 

This was not allowed for in this study-immediate 

non-qualification and exclusion followed. The concentration response is a function of chemical type 

rather than physical type and thus is difficult to allow 

for when running blind samples. 

Sample qualification problems are normally overcome by repeating the assay at a lower concentration, 

selected from a prior concentration screen. However, 

concentration screening of samples was not allowed 

in this validation study for reasons given earlier. 

The results of an EYTEX assay is the EDE, 

read-off (or calculated by the DAQC-ES program) 

directly from a calibration curve of Draize 24-hr 

maximum score versus OD, plotted from the known 

calibration standard run at the same time as the 

sample. The EDE may then be used to predict the 

irritancy potential of the sample by comparison with 

in vivo data and classifications. 

Inhibition and interference are the most likely 

causes of ‘low’ scores of materials with MMAS > 5 1. 

With the current EYTEX system, the ‘top’ calibration standard has a Draize 24-hr score of 51, and this 

produces a turbidity which is very near to complete 

absorbance for the spectrophotometer. Samples with 

MMAS > 51 would therefore still only give an EDE 

of 2 51, as a result giving a plateau at 51 in the plot 

of EDE versus in vivo MMAS. 

d. D@culties with the method 

The following are known to be problems: 

It is also possible that a sample with an MMAS 

much higher than 51, but which caused interference 

or inhibition and subsequent reduction in OD, 

would be severely underestimated if inhibition was 

not recognized. Thus, it could be argued that the 

incorporation of in vivo data > 5 1 MMAS is invalid 

for the EYTEX system since this must have an effect 

on any correlation studies. 

Some samples can attack the plastic cuvettes 

used in the measurements. Adsorption of the 

turbidity can occur on the cuvette surface. Here, 

use of a special glass cuvette is recommended. 

Coloured samples often need to be measured 

at different wavelengths. Not all laboratories 

recognize when this is necessary; this can lead to 

discrepancies in results. 

pH variation at the borderline region (8.0 + 1.0) 

could lead to one laboratory using the AMA 

(pH 2 8.0) and others using UMA or RMA 

(pH c 8.0) protocols. 

Handling of solids is difficult with the current 

bullet membrane holders. Depending on the 

density of the solid, it is sometimes difficult to 

get the required 100mg into the container. 

Different laboratories may treat this difficulty in 

different ways, for example: 

(4 

@I 

w 

Stop when the bullet is full, for example 

when 75 mg in; 

physically press down the solid to allow 

more sample in-from 75 to 80-90 mg-but 

with the possibility of membrane damage; 

or 

pile material up on top-if soluble, lower 

material can dissolve leaving a bridge of 

Despite all the above difficulties, EYTEX works 

well with many types of materials as long as the 

aforementioned precautions with a concentration 

screen are taken into account. 

e. Limitations of use of the EYTEX System 

Experience has shown that Mg-neutralized surfactants are greatly overestimated, particularly when 

testing on their own. This problem is not as great 

in formulation as it is with other surfactants or 

materials. Carbohydrate materials, particularly multicarboxylated ones, are also overestimated by the 

EYTEX system. 

Recognition of the above problems gives an indication of why the EYTEX test gives the best results 

with samples which are: insoluble in water; solutions; 

or liquids, but relatively poor results with solids, 

surfactants and water-soluble materials. 

3. The bovine corned opacity/permeability (BCOP) 

test 

a. Principle of the method 

In the BCOP test, bovine corneas from eyes freshly 

collected from a local abattoir are used to assess the 

eye irritating properties of test materials. Two end- 
922 M. Balls et ul. 

points are determined, the opacity and permeability 

of isolated corneas. The corneas are freshly prepared 

and mounted in special holders, which consist of an 

anterior and a posterior chamber. Both chambers are 

filled with simple culture medium and test material 

is applied to the anterior chamber of the holder. 

Corneas are exposed for I hr. 

Change in opacity was the only endpoint determined 

when the BCOP assay was originally developed. 

Later, measurement of permeability was added, as 

visualized by the application of a fluorescent dye. 

h. Bask procedure, endpoints and endpoint measurement 

Opacity measurement. Change in light transmission 

passing through the cornea is determined in an 

opacitometer (Electra Design, Riom. France) and 

expressed as a numerical opacity value. The opacity 

values obtained for control and exposed corneas 

represent the absolute opacity for a given cornea. 

Permeability measurement. Determination of this 

endpoint is performed immediately after the opacity 

measurement. The medium was removed from both 

chambers of the holder. It is replaced by fresh 

medium in the posterior compartment and by I ml 

fluorescein solution (0.4% for liquids and surfactants, 

0.5% for solids) in the posterior chamber. Corneas 

are then incubated in a horizontal position for 90 min 

at 37‘C. Medium is removed from the posterior 

chamber and the OD is determined in a spectrophotometer at 490 nm. 

Special protocols for solids and surfactants have 

been developed, in addition to the standard protocol 

for liquids. 

c. Expression qf results 

Opacity values are corrected for basal opacity. 

Each cornea of the experimental group, including 

positive controls, is corrected for the mean value of 

saline-treated corneas (negative control) and at the 

given time point (corrected values). 

In the permeability measurements, the mean OD 

of saline-treated corneas is determined and the individual value measured for each cornea is corrected by 

the mean for saline treatment (corrected value). 

The BCOP score was derived empirically. It is 

calculated for each cornea according to the following 

equation: 

BCOP score = 

opacity + (permeability OD value x 15) 

For each test material, the final in vitro score is the 

mean + SD determined with three corneas. 

d. Application of results 

The lead laboratory suggested the following 

classification scheme in the SOP for the EC/HO 

study, which is based on the Kay and Calandra 

classification scheme for data obtained in vivo in the 

Draize eye irritation test in rabbits: from O-25 = mild 

irritant; from 25.1-55 = moderate irritant; from 

55.1-80 = severe irritant; and r 80.1 = very severe 

irritant. 

The lead laboratory also suggested a cut-off value 

of 200 for BCOP data evaluation in the EC/HO 

validation study. The other Iaboratories agreed to 

this cut-off value since BCOP scores >80 give 

the same level of information as far as severe eye 

irritating properties are concerned. 

In an EU validation study carried out in 12 laboratories with 52 test chemicals, an in vitro/in vivo 

correlation of 0.73 was obtained between BCOP and 

maximum average score (MAS) values (Gautheron 

et al., 1994). A linear correlation between MAS and 

BCOP scores has not been assumed by those using 

the BCOP assay. The BCOP assay is in routine use 

for in-house safety assessment in laboratories in 

France and Belgium. It is also used for regulatory 

purposes in a few companies to classify and label 

chemicals as ‘severely irritating to the eye’ according 

to EU regulations (i.e. as R41). 

e. D@cufties experienced and limitation of the method 

In the EU validation study, all four false negatives 

were solids and three of four false positives were 

liquids (Gautheron et al., 1994). Thus, the methodology may have to be modified to cover all the 

physical forms of test substances. 

Neutralization and buffering of solids by the 

culture medium, which is used to solubilize solids, 

may also have to be taken into account. There is 

a tendency for ‘overestimation’ of the irritating 

properties of alcohols tested in the BCOP assay. 

4. The HET-CAM assay 

a. Principle of the method 

In the HET-CAM assay, test materials are applied 

to the vascularized chorioallantoic membrane (CAM) 

of embryonated chicken eggs. It is assumed that acute 

irritating effects on the small vessels and protein 

membranes of this soft tissue membrane are similar 

to effects induced by the same chemicals in the eye. 

Preliminary results published in the IRAG (1993) 

validation exercise by the CAM-based assay subgroup indicated an acceptable correlation between 

endpoints and/or scores determined on the CAM and 

effects in different tissues of the rabbit eye in the 

Draize eye test, but only for surfactants. 

b. Basic procedure, endpoints and endpoint measurement 

There are several variations of the original 

HET-CAM assay developed by Luepke (1985). In 

most of the protocols, three endpoints are determined 

for assessing the irritating properties of test materials 

on the CAM, namely, haemorrhage, iysis and 

coagulation. 
EC/HO validation study on alternatives to the Draize test 923 

Depending on the test material involved, several 

HETCAM assay protocols have been developed, in 

which either a time point is determined at each of the 

endpoints evaluated after application of undiluted or 

10% diluted test material or the threshold concentration of appearance of the three endpoints is assessed. 

Different scoring systems have been developed in 

each of the HET-CAM assay protocols to take into 

account as much as possible of the information given 

by the three endpoints. 

The Standard Operating Procedure (SOP) used to 

assess irritating potential in the HET-CAM assay in 

the EC/HO validation study is an in-house protocol 

developed by Henkel KGaA (Dusseldorf, Germany). 

In-house validation was based on surfactants and 

their derivatives with low eye irritation potential, 

as shown in the data set for about 100 chemicals 

submitted for the IRAG validation exercise. 

Two protocols developed by Henkel were used in 

the EC/HO study, one for transparent test materials, 

through which reactions on the CAM can be observed. The other is for liquids and solids which are 

not transparent and thus do not permit such a careful 

observation of effects on the CAM. 

Protocol for transparent materials-reaction time 

method. The test chemical is applied to the CAM for 

six eggs on day 9 of embryonation. The reaction times 

are observed for the three endpoints, haemorrhage, 

lysis and coagulation, within a 5-min (300 set) observation period and recorded in a computer file. A computer program automatically calculates an irritation 

index for soluble materials. On each day of testing, 

the same procedure is carried out with a 5% solution 

of the internal reference chemical ‘Texapon ASV’, an 

anionic surfactant, which serves as a control for the 

performance of the HET-CAM assay and as a benchmark chemical for evaluating the irritation potentials 

of test materials. 

Protocol for non-transparent materials-endpoint 

assessment. Test chemicals are applied to the CAM 

of six eggs on day 9 of embryonation. After 3 min, 

the test material is carefully rinsed off the CAM, 

which, after another 30 set, is examined for the three 

endpoints, haemorrhage, lysis and coagulation. 

Each endpoint is scored in the following manner: 

0 = no reaction; 1 = weak reaction; 2 = mild reaction; 

and 3 = strong reaction. 

c. Expression of results 

Evaluation of reaction time method-calculation 

of the Q score. Evaluation of irritation potential is 

determined by comparing the irritation index of a test 

chemical with that of the reference chemical ‘Texapon 

ASV’, and is expressed as the ‘Q score’. If the effect 

of a chemical is identical to that of the benchmark 

Texapon, Q is 1.0. If it is lower, the test material is 

less irritating, and if it is higher, it is more irritating. 

Experience at Henkel suggests the following interpretations for the reaction time method: Q < 1.5 = 

non-irritating material; Q > 1.5 < 2.0 = moderately 

irritating material; and Q > 2.0 = severely irritating 

material. 

A cut-off value of 2.5 is suggested by the SOP for 

data evaluation of the HET-CAM assay within the 

EC/HO validation trial. 

Evaluation system for the endpoint assessment 

method--S score (based on experience at Henkel). The 

most sensitive endpoint is used for evaluation and 

this is the one providing the highest scores. The scores 

recorded for this endpoint are summarized for the six 

eggs; the summarized maximum score of six eggs is 

the S score. As the highest single score for one egg is 

3, the maximum score for six eggs is 18. 

Evaluation of the S score for non-transparent 

materials: S < 6 = non-irritating material; S > 6 < 1.5 

= moderately irritating material; and S > 15 = 

severely irritating material. 

No cut-off value is proposed in the SOP. 

d. Application of results 

The HETCAM is an established method in 

many cosmetics companies, particularly in France 

and Germany, for the in-house safety assessment of 

raw materials and formulations. The in vitro/in vivo 

correlation has been very good in the area of mild to 

non-irritating test materials. 

Different protocols are used in various in-house 

testing strategies. The HET-CAM is the most 

important in vitro assay within various batteries used 

to assess eye irritation potential in many cosmetic 

companies. 

Regulatory authorities in Germany have agreed to 

accept data obtained in the HET-CAM assay for 

classifying and labelling chemicals as ‘severely irritating to the eye’ according the EU regulations (i.e. as 

R41). However, the data submitted for that purpose 

were obtained with a different protocol (Spielmann 

et al., 1993). 

e. Difficulties experienced and limitations of the 

method 

The HET-CAM protocol used in the EC/HO 

validation trial for assessing the irritation potential 

of test chemicals relies entirely on a single benchmark chemical, an anionic surfactant, which appears 

to allow discrimination between non-irritant and 

mild irritant organic chemicals. No published 

information is available on how to evaluate the 

irritation potentials of severely irritating or corrosive 

chemicals. 

It is not absolutely clear when to use the reaction 

time method, which generates Q scores, and when the 

endpoint method should be used, which generates S 

scores. Moreover, no information has so far been published on how data produced with the two methods 

should be compared. It is also not clear when and 

how dilutions should be used, or how to compare the 

results obtained with different dilutions with data 
924 M. Balls ef al 

generated in the Draize eye test in uivo with undiluted i. The amount of fluorescein which leaks across the 

materials. cellular barrier 

Sticky test materials cannot be tested in a 

reproducible manner in most CAM-based assays. (%FL) = z x 100 

X 

5. The fluorescein leakage test 

where 

u. Principle qf the method 

This method is based on that of Tchao (1988). The 

loss of trans-epithelial impermeability of a confluent 

monolayer of Madin-Darby canine kidney (MDCK) 

cells following exposure to irritant materials is 

measured by determining the degree of leakage of 

fluorescein across the cell monolayer. In the FL test. 

MDCK cells are grown to confluence on microporous 

filters and exposed to high concentrations of test 

material for I min. The test is based on the ability of 

the chemical to damage tight junctions in the confluent monolayer. The degree of junction disruption 

results in a proportionate increase in permeability, 

measured as the passage of the relatively non-toxic 

dye, fluorescein, across the barrier. This ‘leakage’ can 

be measured spectrophotometrically. The FL test is 

considered to model the disruption of the integrity of 

the cornea1 epithelium by chemicals which may be 

potentially irritant to the eye. 

m = the mean fluorescence leakage intensity 

across the treated cellular barrier, 

.Y = the mean maximum leakage fluorescence 

intensity (through insert with no cells) and 

)‘ = the mean 0% leakage fluorescence intensity 

(across untreated control cellular barrier). 

ii. The concentration of chemical which causes 

20% damage to the cell layer: 

FL 

Xl 

=(20-A)x t”,-MA)+M~ 

(B-A) 

where 

A = %fluorescein leakage < 20%; 

3 = %fluorescein leakage > 20%; 

M, = concentration (mg/ml) of A; and 

MB = concentration (mg/ml) of B. 

b. Basic procedure, endpoint and endpoint measurement 

MDCK cells are maintained in culture and exposed 

for a short time period, for example, I min, to five 

fixed concentrations of test compound. The damage 

caused is determined by the amount of fluorescein 

which leaks through the confluent layer over a set 

period of time. This is compared with two control 

values: the amount of fluorescein that leaks through 

an untreated confluent monolayer, referred to as 0% 

leakage or the blank, and the amount of fluorescein 

which leaks through an insert on which there are no 

cells, referred to as maximum leakage. The percentage of leakage is expressed relative to these controls 

for each of the set concentrations. The fluorescence 

intensity is determined by using a CytoFluorTM 

fluorescence measurement system (Millipore Ltd), at 

excitation and emission wavelengths of 485 and 

530 nm, respectively. Each chemical is tested in 

triplicate, and the test is repeated on at least three 

separate occasions, that is, using freshly-prepared test 

sample dilutions and different starting cell cultures. 

If the FL,, is not reached at a concentration 

greater than lOOmg/ml, inclusive of the undiluted 

material, then the maximum percentage fluorescein 

leakage should be quoted along with the concentration of chemical causing it, for example 100 mg/ml 

(FL 18.5%), or undiluted sample with calculated 

concentration of 955 mg/ml (FL 3.2%). 

Because of the individual characteristics of each 

Cytofluor, it is suggested that a sensitivity is used 

which will give a maximum fluorescein leakage value 

of > 4000 (but < 9999). For an assay to be considered 

qualified, fluorescence values should be: 0% leakage, 

y < 300; x - y = 3500 - 6000. 

d. Application of results 

The FL,, value is used to rank the toxicities of the 

test chemicals. The lower the FL,, value, the more 

toxic the material. If only the initial effect is to be 

used for assessment, as in the EC/HO study, then 

the FL,, value can be used to classify the chemical in 

terms of its irritancy potential, that is, as severely 

irritant (R41), irritant (R36) or non-irritant (no label), 

as suggested below: 

c. Expression of results 

Results are expressed in terms of the concentration 

(mg/ml) of test sample which causes 20% fluorescein 

leakage (FL,,) across the treated cell layer, in comparison with the amounts of fluorescein that leak 

through an untreated confluent cell monolayer (0% 

leakage) and the microporous filter insert alone 

(maximum leakage). The FL,, value for each test 

concentration is calculated as follows: 

6) 

(ii) 

(iii) 

An FL,, of less than lOOmg/ml is likely to 

indicate R36 or R41. 

An FL*,, of greater than 750 mg/ml or undiluted material is likely to indicate no label. 

If the FL,, of the chemical lies between 100 

and 750 mg/ml, the chemical could be R41 if 

there is further deterioration when the FL test 

is performed again after 72 hr, or R36 if 

recovery is noted. 
EC/HO validation study on alternatives to the Draize test 925 

e. D@culties experienced and limitations of the 

method 

One of the greatest difficulties experienced concerns 

the handling of viscous, non-toxic materials, since 

they can be almost impossible to remove after the 

initial I-min exposure. As viscosity increases, there 

can be an initial increase in leakage of fluorescein 

with increasing concentration, followed by a decrease 

with further increases in test chemical concentration. 

This results in a parabolic-shaped curve. Similar curves 

may also result through the binding of chemicals, for 

instance cationic surfactants such as benzalkonium 

chloride, to the microporous membrane, which may 

impede the passage of fluorescein through the filter. 

Such materials may also bind directly to the fluorescein 

solution itself. 

Strong acids or alkalis may also cause damage due 

to pH effects alone rather than to specific chemical 

toxicity. 

Care should be taken when transferring a filter 

insert between 24-well plates to ensure that bubbles 

do not form under the bottom of the insert. Such 

bubbles may impair the passage of nutrients from the 

medium to the cell monolayer, leading to restricted 

growth, or during the fluorescein leakage procedure, 

may impede the passage of fluorescent, thus leading 

to an underestimation of leakage. 

During testing procedures, in particular during 

the washing/aspiration cycles, the microporous membranes of the filter inserts can be damaged if care is 

not taken, leading to false results. This can be determined by examining the inserts before the beginning 

and end of the initial procedures. At the end of the 

assay the filters should be examined closely for 

damage. 

There are certain limitations with the assay, mainly 

concerning the solubility of the compound to be 

tested: 

(9 

(ii) 

(iii) 

(iv) 

(v) 

Only materials which are either in solution or 

suspension can be tested by using the FL test; 

solids cannot be tested since they cannot be 

easily removed from the cell surface after the 

exposure period. 

Non-aqueous materials or materials insoluble 

in mineral oil cannot be tested, unless presented as specific microemulsion formulations. 

Some materials are incompatible with the 

plastic O-ring or microporous membrane of 

the filter insert, for example acetic acid is 

incompatible with the HA microporous 

membrane, but not the Anopore membrane. 

Corrosive materials at the normal set dose 

ranges are likely to kill the cell monolayer; 

since both the profile and rate of recovery aids 

in interpretation of the data, mild materials 

are most suitable for testing with this assay. 

Highly volatile materials need to be kept in 

contact with the cellular layer, for example by 

sealing with mineral oil. 

(vi) Certain materials, for example cationic surfactants, can bind to the microporous membrane 

and directly affect the degree of fluorescein 

leakage. 

6. The isolated, enucleated chicken eye test (CEET) 

a. Principle of the method 

The chicken has been found to be the most suitable 

species of abattoir animal to act as an eye donor for 

the assessment of eye irritation potential of test 

materials. Since 1992, the CEET has been incorporated in standard contract toxicity testing at the TN0 

Nutrition and Food Research Institute as a prescreen 

for the Draize eye test, with very satisfactory results. 

The effects of test materials are assessed following 

application to the corneas of enucleated chicken eyes. 

b. Basic procedure, endpoints and endpoint measurement 

Heads of spring chickens are collected at a local 

abattoir. Within 2 hr of killing, enucleated eyes 

are placed in a superfusion apparatus, which keep 

the eyes in good condition for at least 6 hr. The test 

compound is applied onto the cornea of at least three 

eyes in one single dose of either 30 pl (liquids, gels 

and pastes) or 30 mg (solids) for 10 sec. Before 

dosing, each eye provides its own baseline values for 

the assessment of the cornea1 effects. Control eyes, 

one per test run, are only used for checking the 

experimental conditions. The control and test eyes 

are examined for cornea1 thickness and swelling at 

0, 30, 75, 120, 180 and 240min after treatment, and 

results are recorded according to a fixed scoring 

system. Fluorescein retention by damaged epithelial 

cells is scored at 30 min post-treatment only. All examinations are carried out with a slit-lamp microscope, 

such as is normally used in human ophthalmoscopy. 

c. Expression of results 

The mean percentage of cornea1 swelling and mean 

cornea1 opacity value for all test eyes are calculated 

for the observation points at 30, 75, 120, 180 and 

240 min. The mean fluorescein retention value for all 

test eyes is calculated for the observation point at 

30min only. If desired, or for test substances that 

have adhered to the cornea, fluorescein retention 

can be determined at 240min or whenever the test 

compound is removed. 

Morphological effects include ‘pitting’ of cornea1 

epithehal cells, ‘loosening’ of epithelium, ‘roughening’ 

of the cornea1 surface and ‘sticking’ of the test 

substance to the cornea. These findings can vary 

in severity and they may occur simultaneously. The 

classification of these findings is subjective and 

depends on the interpretation of the investigator. 

On the basis of the severity of the observed findings 

for cornea1 swelling, cornea1 opacity and fluorescein 

retention, the effects are divided into four categories: 
926 M. Balls er al 

I = none; II = slight; III = moderate; and IV = severe. 

Classification into the four categories is conducted 

according to the following method: 

Cornea1 swelling : 

Mean cornea1 swelling (%) 

- 5-5 

6-12 

13-18 (> 75 min after treatment 

(< 75 min after treatment) 

19-26 

27-32 (> 75 min after treatment) 

( < 75 min after treatment) 

>32 

Category 

I 

II 

II 

III 

III 

III 

IV 

IV 

Cornea1 opacity : 

Mean max opacity score 

0.0-0.5 

0.6-1.5 

1.6-2.5 

2.640 

Category 

I 

II 

III 

IV 

Fluorescein retention : 

Mean fluorescein retention score Category 

(30 min after treatment) 

0.0-0.5 I 

0.6-l .5 II 

1.6-2.5 III 

2.6-3.0 IV 

d. Application of the results 

Based on the maximum mean scores defined for 

cornea1 swelling, cornea1 opacity and fluorescein 

retention by damaged epithelial cells, the irritation 

potential of the compound, ranging from not irritating to severely irritating, is assessed according to the 

CEET classification system. 

The ex oivo irritancy classification is assessed by 

reading the irritancy classification that corresponds 

to the combination of the categories obtained for 

cornea1 swelling, cornea1 opacity and fluorescein 

retention, which are presented in the scheme below. 

ClassiJcation 

A. Not irritating 

Combinations of the 

four categories 

3x1 

2 x I, 1 x II 

B. Slightly irritating a. 3 x II 

2 x II, 1 x I 

1 x I, 1 x II, I x III’ 

b. 2 x II, 1 x III 

2 x 1, 1 x IV’ 

C. Moderately irritating 3 x III 

2 x III, 1 x II 

2 x III, 1 x IV 

2 x III, 1 x I’ 

2 x II, 1 x IV’ 

1 x II, 1 x III, 1 x IV1 

D. Severely irritating 3 x IV 

2 x IV, 1 x III 

2 x IV, 1 x II’ 

2 x IV, 1 x I’ 

immediate cornea1 opacity score 3 

cornea1 opacity score 4 

severe loosening of epithelium 

‘Combinations of categories less likely to occur. 

To determine the relevance of the CEET parameters 

in relation to the in viuo tissue scores, statistical 

analyses of the CEET and in viuo data can be 

performed by means of X/I’ plots with Pearson’s 

correlation coefficient. With the results of the CEET, 

an irritation index can be calculated. The genera1 

principle of this index is the summation of the maximum mean scores of the three parameters measured, 

namely cornea1 swelling, cornea1 opacity and fluorescein retention. The opacity and fluorescein scores 

are equally weighted in the index, when compared 

with the maximum % swelling that can be obtained 

in this assay. At TNO, this is about 60%. Therefore, 

the maximum mean score for the cornea1 opacity 

(score 4) and fluorescein retention (score 3) obtained 

with a given compound are multiplied by a factor of 

20. Thus, the irritation index could range from 0 to 

200. In addition, the individual components of the 

index are also compared with the in uiuo data. 

According to Directive 83/467/EEC, Part II (B) of 

Appendix 6, Dangerous Substances Directive, 5th 

Adaptation, the in vivo irritancy grades are divided 

into three classes: non-irritating (NI), irritating (R36) 

and severely irritating (R41). Extrapolation from 

the ex viuo irritancy grade to the EU classification 

is carried out by using scientific judgement, and by 

redividing the original four categories to three. The 

combinations of the four CEET categories that are 

suggested for each of the three EU classes are: 

non-irritating (A + Ba); irritating, R36 (Bb + C); and 

severely irritating, R41 (D). 

e. D@culties experienced and limitations of the 

method 

Although the CEET is very straightforward and 

all kinds of materials can be readily assayed, highly 

hydrophobic compounds and certain solids may need 

a different test strategy because of the variation in 

contact with eye tissue that seems to occur in the 

in oivo rabbit eye test. With solids, modification and 

standardization of the exposure conditions in the 

in viuo rabbit eye test seem more appropriate than 

alteration of the in vitro exposure conditions, since, 

with the present OECD and EC guidelines, exposure 

times in rabbits may range from 1 min up to 24 hr. 

With hydrophobic compounds, alternative test 

strategies have been explored successfully, consisting 

of multiple exposures or removing the aqueous liquid 

from the cornea as much as possible, just before 

application, thus ensuring a more intense contact 

with the compound. 
EC/HO validation study on alternatives to the Draize test 927 

A chicken abbatoir may not be in close proximity 

to the laboratory. Several options are available to 

overcome this problem: (a) moving the experimental 

set-up (which is small and does not require much 

space) to the slaughterhouse; (b) using end-of-lay 

birds instead of spring chickens; and (c) using other 

kinds of birds (e.g. turkeys). 

All data submitted are ‘absolute’ and are submitted 

unmodified for comparison with the in vivo data. 

High numerical values are intended to correlate with 

high in vivo irritancy. 

e. D@culties experienced and limitation of the method 

No technical difficulties are usually experienced 

7. The isolated, enucleated rabbit eye test 

a. Principle of the method 

in the performance of the test. It is ideally suited 

to investigations on undiluted test materials (solids, 

liquids and insoluble materials). 

The effects of test materials are assessed by application to the corneas of enucleated rabbit eyes. The 

eyes are removed from the test animals immediately 

after death and are mounted in moist, temperaturecontrolled chambers. After a period of stabilization, 

the eyes are exposed to the undiluted test material for 

10 set, after which the eyes are thoroughly rinsed with 

saline. The effects are assessed over a 4-hr period after 

treatment. 

The main difficulty is that routine performance 

of the test involves evaluation of all the parameters 

measured during the performance of the test and may 

involve further testing, depending on the results of an 

initial study. The data submitted for evaluation in the 

EC/HO study were single parameters. Such data were 

highly unlikely to reflect the true overall response to 

the test material. 

b. Basic procedure, endpoints and endpoint measurement 

Data submitted for 

Method of comparison with 

What is measured determination in viva results 

Cornea1 opacity’ Draize Scoring System Mean opacity scores 

(subjective) at 1 hr and 4 hr after 

treatment 

In participating in the EC/HO study, the ‘narrative’ 

nature of data and its evaluation became apparent as 

a weakness since the overall study expected a numerical “answer” for comparison against in vivo data. 

Additionally, in-house customization of the procedure 

undermined the use of an all-embracing ‘single 

protocol’. 

Percentage cornea1 Slit-lamp biomicroscope Comeal swelling at 

swelling over pro- fitted with a depth- I hr and 4 hr after 

treatment cornea1 measuring attachment, treatment 

thickness’ or an ultrasonic 

pachymeter 

Assessment of Examination and No data submitted 

comeal epithelial assessment (subjective) 

damage’ of damage using slitlamp biomicroscope 

The protocol was set up to identify severe irritants. 

Most of the test materials applied were not severely 

damaging to the eye. Therefore, the responses in the 

system were minimal and too low to differentiate 

between many/most of the lower irritancy materials 

tested. Therefore, any overall correlation against all 

in vivo data can be predicted to be poor. To evaluate 

lower irritancy materials, a second phase of testing 

would have been required. This was considered by the 

participating laboratories to be beyond the scope of 

the exercise. 

Penetration of 

fluorescein dye 

into cornea2 

One drop of l-2% No data submitted 

fluorescein solution 

applied to cornea for 

10 set, after which 

excess dye rinsed away. 

Nature of comeal 

staining assessed 

subjectively immediately 

after treatment 

8. The silicon microphysiometer test 

a. Principle of the method 

‘Assessments are carried out 30 min after treatment and at 1, 2, 3, 

and 4 hr after treatment. 

2Assessments are carried out 30 min after treatmmt and at 4 hr after 

treatment. 

c. Expression of results 

For each test material, cornea1 opacity, measured 

1 hr and 4 hr after treatment, and cornea1 swelling, 

measured 1 hr and 4 hr after treatment, are used as 

expressions of the irritancy of the test material. The 

four measurements are submitted for analysis individually against the MMAS for each test material. 

The results of each analysis are expressed as a 

correlation coefficient. 

The Silicon Microphysiometer (SM) (Molecular 

Devices Corp., Menlo Park, CA, USA) is based on a 

light-addressable potentiometric sensor that allows 

indirect measurement of real-time changes in cellular 

metabolism. The instrument provides continuous 

monitoring of the rate at which living cells inside a 

flow-through chamber excrete acidic metabolites as a 

normal part of energy metabolism. The microphysiometer detects changes in cell metabolism that 

are likely to play a role in the local response of ocular 

cells to irritants. In contrast to other commonly used 

in vitro tests, the microphysiometer assesses functional 

cell changes occurring at sublytic concentrations of 

test compounds. 

d. Application of results 

b. Basic procedure, endpoint and endpoint measurement 

An untreated control eye is used in each exper- In the SM test, the rate of cellular metabolism 

iment, to ensure that the system operated correctly. (acid metabolite production) is measured in murine 
928 M. Balls et al. 

L929 fibroblasts in a flow-through chamber within 

the microphysiometer instrument. Cells grown on culture inserts are exposed to increasing concentrations 

of test substances, diluted in culture medium, for 

approximately 810 set/dose. The acidification rate is 

measured immediately after washout of each dilution 

of test sample from the chamber. The endpoint of the 

test, the MRD,, value, is determined from doseresponse curves relating test substance concentration 

on the X axis to acidification rate on the Y axis. 

lysosomes of living cells because of the differential pH 

between the inside of the lysosome and the surrounding cytoplasm. The amount of the vital dye taken up 

by the 3T3 cells is directly proportional to the number 

of viable cells in the culture (Barstad et al., 1991). 

c. Expression of results 

MRDso values were expressed in units of log,, pg/ 

ml, where the MRD,, is defined as the dose of test 

substance required to produce a reduction in metabolic (acidification) rate to 50% of baseline control 

levels. 

Test materials are measured over a wide range of 

concentrations and the concentration yielding a 50% 

reduction in NRU is used as the measure for comparison. The test is based on the observation that 

some materials which are damaging to the eye appear 

to be cytotoxic to a number of cell types in vitro. 

Surface active agents would be expected to be cytolytic at different concentrations, depending on their 

irritancy potential. Mouse 3T3 cells were selected for 

this assay because of their ease of maintenance. 

h. Basic procedure, endpoint and endpoint measurement 

d. Application of results 

Most of the materials tested during the development of the test were surfactants or surfactant-based 

products. The SM test has been proposed as a 

screening method for liquid, water-soluble surfactantcontaining materials, such as personal care and 

household cleaning products, to be used as part of 

a tiered testing process. From a practical standpoint, 

the primary use of the data is comparative. So, 

for example, a new shampoo would be tested in the 

in vitro test together with a benchmark shampoo that: 

(1) is similar in chemical composition; (2) is accompanied by historical in vivo data; and (3) has safe 

market experience. In this way, in vitro responses of 

the new product and benchmark are compared to 

predict whether the new product has the same, more 

or less irritancy potential. An important aspect of this 

is that the new product and benchmark need to have 

similar chemical composition. If a new product is 

widely different from the substances evaluated previously in vitro, then a parallel testing approach is used 

to expand the validation database. 

To a lesser extent, the in vitro data are used to 

estimate degrees of irritancy within broad categories, 

that is innocuous-slight (e.g. high MRD,, values), 

slight-moderate-severe (e.g. low MRD,, values). 

The NRU assay has been adapted from the 

protocol of Borenfreund and Puerner (1984). Mouse 

3T3 cells are plated into each well of a 96-well tissue 

culture plate. The plates are incubated, and just 

before treatment depleted medium is removed from 

the wells and 250 p 1 fresh medium is added to control 

‘untreated’ wells whereas 250 ~1 of various concentrations of test agents diluted in medium were added to 

test wells. The plates are incubated for 24 hr at 37°C. 

The spent medium/test agent solutions are removed 

and replaced with fresh medium supplemented with 

neutral red dye in all wells except the blanks. 250 ~1 

medium (without dye) is added to the blanks. The 

plates are then incubated for 3 hr at 37°C and each 

well then received 250 p 1 wash/fix solution [an aqueous 

1% formaldehyde-l % calcium chloride solution, 

for 2 min (room temperature)]. The wash/fix solution 

is decanted and each well receives a solvent solution 

(1% glacial acetic acid-50% ethanol) (100 ~1) for 

20 min at room temperature. Absorbances are measured at 550 nm in a microplate reader (after making 

the appropriate blank correction) and ‘percentage of 

untreated control’ values are calculated for each 

dilution of test agent. The ‘percentage of untreated 

control’ values versus the test agent concentrations 

are then plotted (semi-log plot). 

e. Dijkulties experienced and limitations of the 

method 

c. Expression of results 

The test is limited in its ability to handle aqueousinsoluble substances. 

9. The neutral red uptake test 

The concentration of test material resulting in a 

50% inhibition of NRU (NRU,,) is determined by 

extrapolation from the dose-response curve. At least 

three valid trials are performed for each assay. The 

NRUS, values are given in pg/ml (mean + standard 

deviation) of the neat test material. 

a. Principle of the method d. Application of results 

The Neutral Red Uptake (NRU) assay with mouse 

3T3 cells is a short-term monolayer culture test 

system which uses the cells as the target and cytotoxicity as the endpoint. The procedure is based on 

the work of Borenfreund and her colleagues. Neutral 

red has been shown to be selectively retained by the 

The exact uses of the assay to predict ocular 

irritation depends on the purpose of the testing and 

the experience of the user. For personal care products, 

the assay has been used to select formulations for 

development and quality final products for use 

with/on human panelists (but probably not by direct 
EC/HO validation study on alternatives to the Draize test 929 

instillation into the eye). In some cases, the assay has free medium for normal human anchorage-dependent 

been used as the final safety test. cells. In Vitro Cellular and Developmental Biology 21, 

To predict an in uivo endpoint (such as ocular 160. 

irritation) from the in vivo data, a graph of historical 

Borenfreund E. and Puerner J. A. (1984) A simple quantitatanimal eye irritation data versus the 3T3 in vitro 

ive procedure using monolayer cultures for cytotoxicity 

assays (HTD/NR-90). Journal of Tissue Culture Methods 

cytotoxicity data is usually constructed. The relation- 9, 119-124. 

ship between the variables is generally logarithmic; Gautheron P., Giroux J., Cottin M., Audegond L., Morilla 

therefore, the in vivo data are plotted on a linear scale A., Mayordomo-Blanc0 L., Tortajada A., Haynes G., 

on the y axis, while the cytotoxicity data are plotted 

Vericat J. A., Pirovano R., Gillio Tos E., Hagemann C., 

Vanparys P., Deknudt G., Jacobs G., Prinsen M., Kalweit 

on a log scale on the y axis. Materials whose in vivo S. and Spielmann H. (1994) Interlaboratory assessment of 

ocular %ritation potential is unknown are tested the bovine cornea1 opacity and permeability (BCOP) 

in vitro and the projected in vivo score is extrapolated 

~___.. rn_~ >__I__ 1 ,J._ ” 101 ,,92, 

,- . . 7.. . ,.. 

G; I for EC Validation. 

assay. 1 “xlc”r”gy m Ylrro 0, JO,-2 

from tne grapn. It is tmportant to nave a sumcient 

jrdon V. (1993) EYTEX Protocol 

database for various chemical classifications, since 

InVitro International, Irvine, CA. 

Interagency Regulatory Alternatives Group. Draft Report 

the relationship between cytotoxicity in the 3T3 cells of the IRAG Workshop on Alternatives for Ocular 

to the in uivo response appears to vary with chemical Irritancy, c/o Food and Drug Administration, Washingclass. ton, DC. 

Lewis R. W.. McCall J. C. and Botham P. A. (1993) A 

e. D@culties experienced and limitations of the 

method 

comparison of two cytotoxicity tests for predicting the 

ocular irritancy of surfactants. Toxicology in Vitro 7, 

155-158. 

False negative results can easily be generated if care 

is not taken to ensure that all materials tested are 

water soluble or at least water miscible. In addition, 

materials with appreciably high or low pH values are 

likely to be less toxic in the culture systems because 

of the buffering capacity of the culture medium. In 

particular, those materials with high acid or alkaline 

reserve may cause problems in this type of assay. 

Reactive species, such as hypochlorite, may be underpredicted, because of reaction with culture medium. 

REFERENCES 

Barstad R., Cortesi J. and Janus J. (1991) Use of Clonetics’ 

neutral red bioassay to optimize components of serumLuepke N. P. (1995) Hen’s egg chorioallantoic membrane 

test for irritation potential. Food and Chemical Toxicology 

23, 287-29 1. 

Pape W. J. W., Pfannenbecker U. and Hoppe U. (1987) 

Validation of the red blood cell test system as an in vitro 

assay for the rapid screening of irritation potential of 

surfactants. Molecular Toxicology 1, 525-536. 

Spielmann H., Kalweit S., Liebsch M., Wirnsberger T., 

Gerner I., Bertram-Neis E., Krauser K., Kreiling R., 

Miltenburger H. G., Pape W. and Steiling W. (1993) 

Validation study of alternatives to the Draize eye irritation test in Germany: cytotoxicity testing and 

HETCAM test with 136 industrial chemicals. Toxicology 

in Vitro 7, 505-510. 

Tchao R. (1988) Tram-epithelial permeability of fluorescein 

in vitro as an assay to determine eye irritants. In Alternative Methods in Toxicology. Edited by A. M. Goldberg. 

Vol. 6. pp. 271-283. Mary Ann Liebert, New York.