Mutation Research, 266 (1992) 7-25 © 1992 Elsevier Science Publishers B.V. All rights reserved 0027-5107/92/$05.00

MUTREV 07311

INTERNATIONAL COMHISSION FOR PROTECTION AGAINST ENVIRONHENTAL MUTAGENS AND CARCINOGENS

A method for comparing and combining short-term genotoxicity test data: The basic system P.H.M. L o h m a n a, M.L. M e n d e l s o h n b, D.H. M o o r e II b, M.D. W a t e r s c, D.J. Brusick d, j . A s h b y e a n d W.J.A. L o h m a n a a MGC - - Laboratory o f Radiation Genetics and Chemical Mutagenesis, Unit'ersity of Leiden (The Netherlands), b Biomedical Sciences, Lawrence Lirermore National Laboratory, P.O. Box 5507, LiL'ermore, CA 94550 (U.S.A.), c U.S. Em~ronmental Protection Agency, Research Triangle Park, NC 27711 (U.S.A.), d Hazleton Laboratories" America, Inc., Vienna, VA 22180 (U.S.A.) and e ICI, Central Toxicology Laboratory, AIderley Park, Macclesfield (Great Britain)

(Received 9 September 1991) (Accepted 7 October 1991)

Keywords: Short-term genotoxicity test data; Weight-of-evidence analysis; Testing parameters, comparing; Brusick scoring system

The goal of this effort is to reorganize and compress genotoxicity test data in order to provide weight-of-evidence analysis, sensible approaches to the field's enormous data-bases, and sound analytical methods for comparing chemicals, tests or combinations of the two. The driving principle is to combine the major parameters of testing (dose, metabolic activation, sign of response) into a single score which could be pooled by test (e.g., Salmonella reverse mutation), by class of tests (e.g., prokaryote gene mutation), and by family of classes (e.g., in vitro tests) hierarchically into a composite score for a chemical. The system (a) would cope with redun-

Correspondence: Dr. P.H.M. Lohman, MGC - - Laboratory of Radiation Genetics and Chemical Mutagenesis, University of Leiden, Wassenaarseweg 72, 2333 AL Leiden (The Netherlands). ICPEMC is affiliated with the International Association of Environmental Mutagen Societies (IAEMS) and the Institut de la Vie.

dant data, disagreement, and sporadically filled matrices, (b) would supply statistical properties by chemical and by test, and (c) would have features of self-learning to improve predictive performance and internal consistency for any one of several types of genetic hazard, including genotoxicity per se, carcinogenicity (Nesnow, 1990) and reproductive toxicity (Bridges and Mendelsohn, 1986). The method chosen began with a scoring system developed by Brusick (1981). It has passed through a half-dozen evolutionary stages, and is now sufficiently structured, stabilized and functional to be published in detail. Two brief descriptions have appeared as progress reports over the past five years (Brusick et al., 1986; Lohman et al., 1990). General scheme

A unit of data in this system is a literature entry involving a genotoxicity test, a chemical, a series of doses, and an outcome. We use the

8 TABLE 1 T H E C U R R E N T CLASSES A N D F A M I L I E S

in t'itm family A1 A2 A3 A4 A5 A6 A7 A8 A9 AI0

Primary D N A damage - prokaryotes Primary D N A damage - lower eukalVotes Primary D N A damage - mammalian cells G e n e mutation - prokaryotes G e n e mutation - lower eukaryotes G e n e mutation - mammalian cells Aneuploidy - lower eukaryotes Sister chromatid exchange - mammalian cells Chromosome aberration - mammalian cells Transformation - mammalian cells

In vivo family Somatic cells Bl B2 B3 134 135 B6

D N A repair, somatic - mammal G e n e mutation, somatic - insect, Drosophila Spot test, somatic - mammal Sister chromatid exchange, somatic - mammal Micronuclei, somatic - mammal Chromosome aberration, somatic - mammal

Germ cells B7

B8 B9 B10 BI ! BI2

Heritable damage - insect. Drosophila Heritable specific locus test - mammal Dominant lethal - mammal Heritable translocation - mammal Chromosomal aberrations, germ cells - mammal Sperm morphology - mammal

same concept of dose that is used by Waters et al. (1988) in their Genetic Activity Profiles. That is, for a given literature entry, test, chemical, and dose series, if the result has been scored as a positive then the defining dose is taken to be the logarithm of the lowest dose with a positive out. come. If the result has been scored as a negative then the defining dose is the logarithm of the highest dose with a negative outcome. Logarithms were used to cope with the greater than a million-fold range of doses in genotoxicity data. Further refinements leading to a standard dose will be described in the next paper in this series (Moore et al., 1992). The standard (defining) dose is multiplied by modifiers representing the presence or absence of metabolic activation for in vitro tests, evidence for target localization for in rive tests, and any a priori weighting that is to be given to the particular test.

All relevant examples in the database using the same test and chemical are then averaged together with appropriate weighting, including a replication modifier, to give a composite test score.

The next level of aggregation combines tests into classes by the averaging of weighted data and the application of a multiplicity modifier. The classification scheme for classes is based on and is almost identical to that of Gene-Tox (Waters, 1979; Waters and Auletta, 1981). In all, 22 classes ak'e used, 10 in vitro and 12 in vivo. The classes are averaged into families, one for in rive and one for in vitro, and finally all classes are averaged into a single composite agent score. Table 1 outlines the class and family structure, and Table 2 summarizes the currently active tests in the system. Data structures

The minimal criteria at present are (1) that a test must meet conventional design standards and be represented in the data base by at least 5 chemicals, and (2) that a chemical must have been tested by the accepted tests in at least three in vitro and two in rive classes. These criteria and

Lowest Effective Dose (pg/ml, mglkg bwldat

Log bose

I)'qJo ]

.....................

001 OI

........................

I I0 I00 1000 10000 109000

I'olitive regults

Units 8 ?

I': C

~sr cab

S

A

Slndy + actlmtloll

.........................

4

~L

.............

0

!

Study. aca'm~bn

I I0

tO0

....... . . . . . .........

lOgO . . . . . . . . . IOOO0 . . . . . . I@O000 . . . . .

l

...........

',. . . . . . . . . . . . . . .

l

.1 4 $ Negative results

iilghest Ineffective Dose (pglml. mglkg bwlda)

Fig. 1. The scoring convention used by Waters et al. (1988). The units and logarithmic scaling of lowest effective and highest ineffective dose are shown on the left. On the right are some of the plotting conventions that are used.

TABLE 2 THE CURRENT TEST STRUCTURES AND CODES

(A) In vitro family Class AI: Primary DNA damage - prokaryotes Bacillus subtilis rec strains, differential toxicity. Escherichia coli rec strains, differential toxicity. Escherichia coli pol A/W3110-P3478, diff. toxicity (spot test). Escherichia coli pol A/W3110-P3478, diff. toxicity (liq. susp. test). Other DNA repair-deficient bacteria, differential toxicity.

BSD ERD ECD ECL BRD

Class A2: Primary DNA damage - lower eukaryotes SCG Saccharomyces cerevisiae, gene conversion. SCH Saccharomyces cerevisiae, homozygosity by recombination or gene conversion. Class A3: Primary DNA damage - mammalian cells UHF Unscheduled DNA synthesis, human fibroblasts in vitro UHL Unscheduled DNA synthesis, human lymphocytes in vitro UHT Unscheduled DNA synthesis, transformed human cells in vitro UIH Unscheduled DNA synthesis, other human cells in vitro. URP Unscheduled DNA synthesis, rat primary hepatocytes UIA Unscheduled DNA synthesis, other animal cells in vitro Class A4: Gene mutation - prokaryotes SAL Salmonella typhimurium, strains TA 1535, 1537, 1538, 98, 100 EC2 Escherichia coli WP2, reverse mutation. ECW Escherichia coli WP2 uvrA, reverse mutation. ECR Escherichia coil (other misc. strains), reverse mutation ECK Escherichia coli K! 2, forward or reverse mutation ECF Escherichia coli (excluding strain KI2), forward mutation BSM Bacillus subtilis multi-gene test. Class AS: Gene mutation - lower eukaryotes SCF Saccharomyces cere~'isiae, forward mutation. SCR Saccharomyces cere~,isiae, reverse mutation. SZF Schizosaccharomyces pombe, forward mutation. NCF Neurospora crassa, forward mutation. NCR Neurospora crassa, reverse mutation. Class A6: G e n e GCO Ggo G9H C5T C51 GIA

mutation -

mammalian cells Gene mutation, Chinese hamster ovary cells in vitro Gene mutation, Chinese hamster lung V-79 cells in vitro, ouabain Gene mutation, Chinese hamster lung V-79 cells in vitro, HPRT Gene mutation, mouse L5178Y cells in vitro, TK locus Gene mutation, mouse L5178Y cells in vitro, all other loci Gene mutation, other animal cells in vitro.

Class A7: Aneuploidy- lower eukaryotes SCN Saccharomyces cerecisiae, aneuploidy. Class,48: Sister-chromatid exchange - mammalian cells SHF Sister-chromatid exchange, human fibroblasts in vitro SHL Sister-chromatid exchange, human lymphocytes in vitro SIH Sister-chromatid exchange, other human cells in vitro SIC Sister-chromatid exchange, Chinese hamster cells in vitro SIS Sister.chromatid exchange, Syrian hamster cells in vitro SIM Sister-chromatid exchange, mouse cells in vitro. SIR Sister-chromatid exchange, rat cells in vitro. SIT Sister-chromatid exchange, transformed cells in vitro SIA Sister-chromatid exchange, other animal cells in vitro

TABLE 2 (continued) Chs.r ,49: Chromosome aberration - mammalian cells Chromosomal aberrations, human fibroblasts in vitro CHF Chromosomal aberrations, human lymphocytes in vitro CHL Chromosomal aberrations, other human cells in vitro. CIH Chromosomal aberrations, Chinese hamster cells in vitro CIC Chromosomal aberrations, Syrian hamster cells in vitro CIS Chromosomal aberrations, rat cells in vitro. CIR Chromosomal aberrations, transformed cells in vitro CIT Chromosomal aberrations, other animal cells in vitro CIA Class AR?: Transfonuation TCS T-i!5 TBM TCM TRR TCL

- mammalian Cell Cell Cell Cell Cell Cell

cells transformation, transformation, transformation, transformation, transformation, transformation,

08) In ciLa family Class BI: DNA repair, somatic - mammal Unscheduled UVM Unscheduled UVR Unscheduled UPR Unscheduled UVA

DNA DNA DNA DNA

Syrian hamster embryo cells, clonal assay SA7/Syrian hamster embryo cells. BALB/C3T3 mouse cells. C3HlOT1/2 mouse cells. RLV/Fischer rat embryo cells other established cell lines

synthesis, synthesis, synthesis, synthesis,

mouse cells in vivo. rat cells (other than hepatocytes) rat hepatocytes in vivo other animal cells in vivo

Class 82: Ciene mutation, somatic - insect, Drosophila Drosophila melanogaster, somatic mutation (and recombination). DMM C./ass B3: Spot test, somatic - mammal Mouse spot test. MST CIuos $4: Sister~chrsmatid SLH SVH SVA Clu$$ I35: Micronuclei, MVC MVM MVR

exchange, somatic - mammal Sister.chromatid exchange, human lymphocytes in vivo. Sister-chromatid exchange, other human cells in vivo Sister~chromutid exchsnge, enimnl cells in viva,

somatic - mammal Micronucleus Micronucleus Micronucleus

Class 136: Chromosome GRH CLH CBA CLA CVA

aberration,

test, hamster in vive. test, mouse in viva, test, rut in vivo.

somatic - mttmmrrl Chromosomal aberrations, Chromosomal aberrations, Chromosomal aberrations, Chromosomal aberrations, Chromoaomal aberrations,

human bone-marrow cells in vivo. human lymphocytes in vivo animal bone-marrow cells in vivo animal leukocytes in vivo other animal cells treated in vivo

Ciusr E)7: Heritable DMH DML DMX

damage - insect, Drosophila Drosophila melunogaster, heritable translocation test. Drosophila melanogaster, dominant lethal test. Drosophila melanoguster, sex-linked recessive lethal mutation

C1es.r &?: Heritable SLD

specific-locus test - mammal Mouse specific-locus test, stages other than postspermatogonia.

Class B9: Dominant DLM DLR

lethal - mammal Dominant Dominant

lethal test, mice. lethal test, rats.

in vivo

TABLE

2 (continued)

Class BIO: Heritable

translocation

MHT

- mammal

Mouse heritable translocation test.

CIass BII: Chromosomal aberrations, germ cells - mammal COE

Chromosomal aberrations, ooqtes or embryos treated ia vivo

CGG

Chromosomal aberrations, spermatogonia treated in vivo, gonia observed.

CGC

Chromosomal aberrations, spermatogonia treated in vivo, cytes observed.

CCC

Chromosomal aberrations, spermatocytes treated in viva, cytes observed.

Cluss BI2: Sperm morphology - mammal SPM

Sperm morphology, mouse.

SPR

Sperm morphology, rat.

the supporting class structure can be readily adjusted to meet future developments and needs. The data for this report have been reviewed by IARC expert committees (Waters et al., 1989; IARC, 19871, and have been collected by EPA for the IARC Monograph Vols. 39 and 41, and for the updating of IARC Supplement No. 4 to create IARC Supplement 6 (IARC, 1987). This database involves 85 tests and 113 chemicals, aggregating to 4452 units of test, chemical and literature entry. Selection of chemicals is based on the criteria used by IARC for their monographs, and is primarily driven by the chemical

ETHYLENE

8

-I-

DOS

75-21-8

OXIDE

SAN

RM

being a suspected or possible carcinogen. The 113 chemicals are listed alphabetically with their CAS numbers in Table 3. The data recorded for each entry are: the chemical, the test name or three-letter code (see Table 21, the doses tested, whether the responses were positive or negative, whether or not metabolic activation was used for in vitro tests, whether or not there was evidence for target site localization for in vivo tests, and the reference citation. These are entered into a specially designed computer system which files the data and does all of the processing.

ZNC PPR

HP Sl. ml

IITDDD SSMMM

CCMXII

000 CYY

OH0

TUSS :;‘I!;

c :’

SMMCC VVVBI. AA

AMR

uss

MC

VLVVL HHH

II II

Fig. 2. Ethylene oxide, as displayed by the Waters method. See text for details.

12 TABLE 3 LIST OF CHEMICALS (IN ALPHABETICAL ORDER) No.

Chemical

CAS No.

1, 2, 3. 4, 5,

2,4-D Acetaldehyde AcwIonitrile Actinomycin D Adriamycin

94-75-7 75-07-0 107-13-1 50-76-0 23214-92-8

6, 7, 8, 9.

Aflatoxin BI AIdrin Amitrole Aniline

10.

Arsenic + 3

1162-65-8 309-00-2 61-82-5 62-53-3 7440-38-2 + 3

1!, i 2. ! 3. 14. 15,

Asbestos Auramine Azathioprine BCNU Benz[a]anthracene

1332-21-4 492-80-8 446-86-6 154-93-8 56-55-3

16.

Benzene Benzidine Benzol a]pyrene Benzyl chloride Bleomycin

71-43-2 92-87-5 50-32-8 100.44.7 11056-06.7

Ci. acid red 14 Cadmium Caffeine Caprolactam Carbon tetrachloride

3567-69.9 7440-43-9

17.

18. 19. 20. 21. 22. 23. 24,

25,

58-08-2

105-60-2 56-23-5

26, 27, 28, 29, 30.

CCNU Chlorambucil Chlorodlfluoromethane Chloroform

13010.47-4 305-03-3 56-75-7 75-45-6 67-66-3

31, 32. 33. 34. 35.

Chloroi~rene Chromium + 6 Chrysene Cisplatin Cyclohexylamine

126-99-8 7440.47-3 + 6 218-01-9 15663-27-1 108-91-8

36. 37. 38. 39. 40.

Cyclophosphamide DDT Diazepam Dibromochloropmpane Dichloromethane

50-18-0 8017-34-3 439-14-5 96-12-8 75-09-2

41. 43. 42. 44. 45.

Dieldrin Diethyl sulphate r)iet hylhexylphthalate Diethylstilboestrol Dimethoate

60-57-1 64.67-5 117-81-7 56-53-1 60-51-5

46. 47. 48. 49. 50.

Dimethyl sulphate Dimethylcarbamoyl chloride Endrin Epichlorohydrin Ethanol

77-78-1 79.44-7 72-20.8 106-89-8 64.17-5

Chloramphenicol

13 TABLE 3 (continued) No.

Chemical

CAS No.

51. 52. 53. 54. 55.

Ethylene dibromide Ethylene oxide Ethylenethiourea Fluoride, sodium FluorouracU, 5-

106-93-4 75-21-8 96-45-7 7681-49-4 51-21-8

56. 57. 58. 59. 60.

Formaldehyde Halothane Heptachlor Hexachlorocyclohexane Hycanthone methanesuifonate

50-00-0 151-67-7 76-44-8 58-89-9 23255-93-8

61. 62. 63. 64. 65.

Hydrazine lsoniazid Lead Malathion Maleic hydrazide

302-01-2 54.85-3 7439-92-1 121-75-5 123-33-1

66. 67. 68. 69. 70.

Malonaldehyde MCPA Melamine Melphalan Mercaptopurine, 6-

542-78-9 94-74-6 108-78-1 148-82-3 50-44-2

71. 72. 73. 74. 75.

Mestranol Methotrexate Metho~chlor Methoxypsoralen, 8- ( + uvr) Methyl bromide

72.33-3 59-05-2 72-43-5 298-81-7 74.83-9

76. 77. 78. 79. 80.

Methyl parathion Metronidazole MNNG Myleran Nitrosodiphenylamine, N-

298-00-0 443-48-1 70-25-7 55-98-1 86-30-6

81. 82. 83. 84.

Naphthylamine, 1Naphthylamine, 2Nickel Nitrosen mustard

134.32-7 91-59-8 7440-02-0 51-75-2

85. 86. 87. 88. 89. 90.

Nitro.o.phenylenediamine, pPentachloronitrobenzene Pentachlorophenoi Phenobarbital Phenyibutazone Phenytoin

99.56-9 82-68-8 87-86-5 50-06-6 50.33-9 57.41-0

91. 92. 93. 94. 95.

Polybrominated biphenyls Polychlorinated biphenyls Procarhazine HCI Progesterone Propylene oxide

67774-32-7 1336-36-3 366-70-1 57-83-0 75-56-9

96. 97. 98. 99. 100.

Saccharin Saccharin, sodium Styrene Styrene oxide TCDD, 2,3,7,8-

81-07-2 128-44-9 100-42-5 96-09-3 1746-01-6

14 TABLE 3 (continued) No.

Chemical

CAS No.

101. 102. 103. 104. 105.

Tetrachioroethylene Tetraethylthiuram disulfide Thiotepa Toluidine. oTriaziquone

127-18-4 97-77-8 52-24-4

106. !07. 108. 109. 110.

Trichloroethane, 1,1,1Trichloroethylene Tris(2,3-dibromopropyl)PO4 Uracil mustard Vinblastine sulphate

71-55-6 79-01-6 126-72-7 66-75-1 143-67-9

I I i. 112. I 13.

Vincrisline sulphate Vinyl chloride Vinylidene chloride

2068-78-2 75-01-4 75-35-4

95-53-4

68-76-8

TABLE 4 SUMMARY OF MATHEMATICAL OPERATIONS AND FORMULAE Level

Entry

Weight

Score

R: replicate: primary data entry: Ds is tr:msformed dose score: Ma is activation modifier: Mj is judgemental modifier.

Er = Ds * Ma * Mj

Wr = Ma * Mj

Sr = 100. Er / Wr

T: test: weighted average of replicates: replication modifier is Mn: Nr is number of replicates for a test,

EI - Mn * SUM( Er )/ Nr

Wt - Mn * Wr

St - IlHl* E t / W t

C: class: weighted average of tests: test multiplicity modifier is Mm; Nt is number of tests for a class.

E c - ~/m * S U M ( E t ) / M

Wc = Mm * SUM(Wt )/bit

Sc = IO0*Ec / Wc

F: in vitro family or in vivo family, simple average of classes: Nc is number of classes for a family.

A: agent: numerical average of all class scores for an agent, fl and t2 refer to in vitro and in vivo.

s f = S U M ( E c ) / N¢

Sa=

SUM(Ec)rl + SrU M(Ec)f: Ncft+ Ncf2

'~~

In Vitro 1C0

15

In Vivo

,9

~9 q

0

"gr~

f

k

-100 A6

B 2

0

7

6'4~

//\

1oo

o~

%

o Q2

ETHYLENE OXIDE

I O0

tn v/Iro

(R)

rn ~ m q t _ _ '~'_ _ %

n x _..

I"

4

~"

75-21-8

/n v/v'o

(B) 4

50, 0 r,,,)

O.

..~

a

~i~_z

....

x_

r,~

-1001

ICLo.s 5cores-;

;;~

a

+r'loss 5cores i

,QS+

2 ,+s7

g,+

+2.7B

Sf

So

Fig. 3. Ethylene oxide, as displayed by the ICPEMC method. See text for details.

Data modifiers The conversion of the dose and outcome information into a transformed dose score is discussed in detail in the next paper in this series (Moore et

al., 1992). The system begins with this dose score, applies the modifiers and does the weighted averaging and other hierarchical aggregation. The first modifier deals with metabolic activation for in vitro tests. The method has the capac-

16

In Vivo

In Vitro

I O0 i %

.

4~

¢'

R5

B6 -100

,dI,.E,~, t

I

B 7

Y

,,g

%

1162-65-8

AFLATOXIN B1

m

(R)

v//ro

/n wvo

(B)

100D]

50,

0 r,j

O,

r~

-50,

- 100,

~Ctoss 5corn I . . . . . . . . . .

;~i;ii

)CrossScorn * z

; : : ; : ; : ; : ; : ;

;u,0sf ,,

,~,

g

~4,62 i

,,a SF

so

Fig. 4. The display for aflatoxin BI,

ity to modify each class of tests independently, but at present settings, all in vitro classes are being modified by the same set of weights. These are:

Ma+ y Ma+n Ma-y Ma- n

= 0.8, for positive outcomes with metabolic activation, = 1.0, for positive outcomes without metabolic activation. = 1,0, for negative outcomes with metabolic activation. -- 0.2, for negative outcomes without metabolic activation.

17 '

q,

@

In V i t r o

100

In Vivo

," 'b ,.~

@

/,

t R5 -100 I'

o

°I" / S

.w"

o %

I00

~~ "

/

BBNZO(A)PYRENE

100

I

/n *,//to

(8)

j/n

i

50-324 ,,/vo

(B) I

50

In o r~

O,

!)_+_!°:~::. ~ ~o_:_~ _+;. m

I~



-50,

-100, ICtoss Score* I

;Clon Score. *

9.52

Fig, 5. The displayfor benzo[a]pyrene.

In this schema, the lowest weight for a negative outcome without activation reflects the high likulihood that such an outcome would be reversed in the presence of activation. The two maximum weights, Ma + n and M a - y, are for the most stringent and therefore most general

test conditions, and the 0.8 for Ma + y reflects our judgement that a positive with activation is slightly less general than a positive without activation. The second modifier concerns target localization for in vivo tests. It can be set individually by

UHL

SA0 SA0 SA5 SA5 SA7 SA9 ECR ECR BSM

SZF NCR NCR NCR

GCO

G9H Ggo

SHF SHL

T7S

2

3 4 5 6 7 8 9 10 11

12 13 14 15

16 17 18 19

20 21

22

24 25. 26 27 28 29 30 3! 32 33

23

SVA SVA SVA - SLH SLH SLH SLH SLH SLH SLH

DMM

In t:ivo test systems a

I

BRD

1

Cd~O

Test code

No.

In t,itro test systems a

S S S S S S S S S S

G

T

S S

G G G G

G G G G

G G G G G G G G G

D

D

End point

+

+ +

+ + + +

+ + + +

+ + + + + + (+)

+

(+ )

Result no act.

+ + + + + + + +

+

Fig.2

E T H Y L E N E O X I D E CAS No. 75-21-8

TABLE 5

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

Result act.

41566

44

24.04

24.04

4O 4O 4O 9 1 0.1 1.6 0.25 0.2 0.19

500O

2.2

45 398 50362 58253 51009 52513 58 445 66360 50122 48853 63391

7357

11.76 11.76 11.76 5.64 12.73 23.21 11.98 -6.02 19.72 19.97

6.02

5.78 9.86

5.78 9.86 34852 40622

36 20

46732

12.01 22.86 20.08

15.71 8.31 22.86 20.08

52223 41833 46732 46732

12 88 2.2

11.76

6.02

16.85

15.28

15.28 8.09 28.18 14.29

6170 66 1100

48331 5101 5106 10610

11.82 9.58

22.04

21.94

21.94

11.82 11.82 9.58

12.28

St

12.28

Sr

Score b

77

50059 47711

34947 ( * ) 43413 ( * ) 34947 ( * ) 34947(*) 34947 ( * ) 52443

220 ! 220 2285 22fl5 220 220 58O

43413(*)

!

I

34268

EMIC a No.

48O

Dose (HID/LED)

Fig. 3

6.02

24.04

7.82

17.54

16.28

15.02

21.94

12.28

Sc

B2

AI0

A8

A6

A5

A4

A3

AI

I

Class

m Oo

MVM MVM MVM MVR

CBA CBA CBA CLA CLA

36 37 38 39

40 41 42 43 44

DMX DMX DMX DMX DMX DMH DMH DLM DLM DLM DLM DLR DLR

M HT

54 55 56 57 58 59 60 6i 62 63 64 65 66

67

C

G G G G G C C C C C C C C

C C C C C C C C C

C C C C C

M M M M

S S

Fig. 2

Fig. 2

+

+, + + + + + + + + + + +

+ + + + + (+ ) + +

+ + + +

+ + + +

+ +

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

30

5000 4000 3970 441 IOOO00 4000 3970 25 150 350 250 400 5

I

I 0.004 9 0.8 O. I O.004 0.25 0.35 1.9 15

9 12 5 40 40

150 10 150 100

15 2.7

37 526

5044 4535 4946 10 863 39210 4535 4946 33 304 37 526 48 974 63153 24861 23 577

61917 51009 38972 58445 60023 50 ! 22 41566 63 391 67539

! 6 fi40 18448 23 577 50362 58253

34400 34824 30388 30388

67539 34464

[ _ _

13.95

3.18 3.62 3.64 9.61 0.02 4.69 4.70 - 8.26 8.57 6.09 7.02 4.26 19.75

- 0.22 4.49 11.79 20.82 39.82 16.52 15.07 8.79 3.39

]

18.54 17.22 2 !.39 i 1.11 11.83

8.81 19.70 8.81 9.91

4.40 7.90

Fig. 3

! 3.95

12.01

7.81

4.70

4.02

13.39

Fig. 3

0.36

19.05

12.44 9.91

8.46 7.90

13.95

6.95

4.30

1 i .71

11,52

9.55

BI(I

B9

B7

B6

B5

B4

" I A R C (1987), Waters et al. (1988). h Calculations of Scores according to Table 3: Dose modifications according to Moore et al. (1992). * Data on the Salmonella tester strains have b e e n combined in one SAL score per data entry ( E M I C number). Per data entry either the highest dose of a negalivc result or lowest dose of a positive result have b e e n choscn.

I

CLH CLH CLH CLH CLH CLH CLH CLH CLH

45 46 47 48 49 50 5! 52 53

i

SLH SVH

34 35

t?+, (+I + + +

(+I + + + + + + +

-

Result no act. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Result act.

88am 0.18 0.53 12tlO 900 500 lOtJO 13.9 220 035 0.25 0.2 0.9 23

9

14 520 88000

(HID/LED)

10855 20122 39707 5145 10868 45751 41566 50122 17990 55 724 31128

6917

43 686 5 379

10759

11813 43686

EMIC = No. Bacteriophage, forward mutation

Binding (covalent) to RNA or protein, human cells in vivo

Hordeum species, chromosomal aberrations Tradescantia species, chromosomal aberrations Chromosomal aberrations, transformed human cells in vitro Unscheduled DNA synthesis, other human cells in vivo Micronucleus test, human cells in vivo Binding (covalent) to DNA, animal cells in vivo

Plants (other), mutation

Hordeum species, mutation

Aspergillus nidulatts, genetic crossing-over Streptomyces grkeojlaws, reverse mutation Aspergillus nidulans, forward mutation

Endpoint indications D, DNA damage; R, recombination; G, gene mutation; S, SCE; M, micronucleus; C, chromosome aberration; T, cell transformation,

a IARC (19871, Waters et al. (19881. * Tests have to be present for at least 5 chemicals in the ICPEMC exercise. **Depicted in Fig. 2 as * (humans in viva).

75 76 77 78 79 80 81 82 83 84

74

71 72 73

70

BVD ** BVD ** BHP **

G R G G G G G G G C C C D M

BPF ANG SGR ANF HSM HSM HSM PLM PLM HSC

68

69

End point

Test code

No.

Tests excbtded from the ICPEMC erercive, but depicted in Fw 2 *

ETHYLENE OXIDE, CAS No. 75-21-S

TABLE 5 kontinued)

2]

In Vitro

CD

@

In Vivo

100

'9

i

b,

0 B ~

~S

B 6

-100

~6

B 7

.'J

100

CAFFEINE

100-

(R)

In v/Ira

I

58-08-2 I

:

(B) '

In vlvo

50.

m O.

-

r.) r/)

-50.

-100. iGLoss

~corn

4

I C[Oll

SCOPes

I

-6,S7

Fig. 6. The display for caffeine.

class, but is presently the same for all in Hvo classes. Mt+ y l n

= 1.0, for positive outcomes with or without

MI - y

= 1.0, for negative outcomes with target

target localization. localization. MI-

n

0.5, for negative outcomes without target

localization.

These modifiers were designed with the idealistic view that localization data would be available, and that it would add considerable credence to a negative result to know that the agent had reached the relevant target cells or tissues. However, not a single one of the thousand or so in vivo entries in the database have such information. Rather than penalize all in vivo negative

22

In Vitro

In Vivo

e:,. %

/

.

'9 " N.



-100

m ~ m

000

~7

oS 9; /

9

100

I

"

/

¢r

"

ETHANOL m

100-

0 ra

v/tro

64-17-5 (Ft)

m

v/vo

{B)

O.

rlj

-50-100.

[]

DCton .

.

.

I z

.

.

[]

¢:¢arn .

.

.

4S6

.

.

=

1Class Scaess I

8910St

.

.

.

.

.

.

.

.

Z 456;

.

.

.

.

I

-~7.?0 I

Q IllZ S[

I

Sa

Fig, 7. The display for ethanol.

results, the system now sets the three localization weights to 1.0. Replication and multiplicity modifiers have the same role in this system as the square root of n does in conventional statistics. The replication

modifier, Mn, responds to the number of entries per chemical per test, and the multiplicity moditier, Mm, to the number of tests per chemical per class. Each tapers according to one-half the square root of n, and plateaus when n reaches 4.

23

The modifiers are as follows: For For For For

I 2 3 4

event, events, events, o r m o r e events,

Mnl z Mn 2 = Mn3 = Mn4 +

0.50, 0.71, 0.87, = 1.00,

M m = 0.50. M m 2 = 0.71. M m 3 = 0.87. M m 4 + = 1.00.

This design recognizes the advantage of multiple, independent sources of data, while avoiding excessive weighting due to over-representation of popular tests and of classes with many variant tests. It is important to realize that the system continues to average the data even when these modifiers have reached their saturation value. Finally, there is a judgmental modifier, Mj, which is meant to reflect a priori or a posteriori insights into the relative importance of tests or classes. These modifiers are currently set to 1.0, however, we anticipate that eventually the system will have sets of weights based on particular applications of the scoring system. Thus one might rank classes in order of their effectiveness in predicting earcinogenicity, only to have differing rank orders for predicting heritable or somatic mutagenicity. Clearly a class like in vitro transformation of mammalian ceils would have more relevance to carcinogenicity than to either of the other two endpoints. Similarly, the heritable in vivo classes in the system will relate most closely to predicting heritable endpoint.~, but may have limited value for other purpose.,,. Another interesting way to use these modifi¢,'rs is to have the system itself measure the performance characteristics of tests or classes and thereby set the modifiers for particular applications as it accumulates experience. We emphasize that such uses are for the future; the judgmental modifiers are now set to 1.0.

Data processing The procedures for entering and combining data are presently carried out with Fortran 77 programs as present on mainframe computers. Raw entries are stored unraodified in data files. The program does the calculations, maintains the files of data and constants, compiles the results and prepares the graphics, l~: also has mechanisms for adjusting the conslants (factors and modifiers) based on experience. An alternate ver-

sion of the program written in Pascal is available for IBM-AT compatible personal computers (requiting 640 kb of memory, a hard disc of 20 mb minimum, and graphics capability), but this currently lacks the mechanisms for automated adjustment and input of new data. The method of coalescing the data preserves the effects of data values, amounts of data and modifiers by using weighted averaging. This is accomplished with two parallel series of values: an ENTRY value which can be summed or averaged and includes weighting by modifiers; and a SCORE which has the weights removed and falls on a consistent scale roughly between + 100 and - 100. Thus each level of analysis (replicate, test, class, family) has its own ENTRY SCORE and weight (e.g. for the replicate level these are Er, Sr and Wr; for the test level, Et, St and Wt). Table 4 summarizes the mathematical manipulations that follow. For the primary datum from a single test on a single agent as published in a single report, the transformed dose score including sign of outcome is Ds. This value is multiplied by the relevant modifiers to become the ENTRY value, Er, for that datum. Thus for an in vitro datum, Er = Ds * Ma * Mj. Its weight, Wr, is Ma*Mj, the product of its activation and judgmental modifiers. Its SCORE, St, is 100. Er/Wr ffi 100. Ds. To combine replicates, ENTRY values for replicates of the same test on the same chemical are summed and an Et value is calculated by multiplying the sum by the replication modifier, Mn, and dividing it by the number of replicates: E t = M n . S u m ( E r ) / N r . The weight, Wt, is Mn* Wr, and the SCORE is 100. Et/Wt. The same process occurs to combine tests into classes, this time introducing the multiplicity modifier. Family and agent SCORES are simple averages of their component class SCORES.

Display of data The detailed display of data in this system is derived from the graphical methods developed by Waters and colleagues for IARC (IARC, 1987; Waters et al., 1988). The scoring convention of the Waters method is shown in Fig. 1, and a Waters profile for ethylene oxide is shown in Fig.

2. The data for each test are summarized by a line whose direction reflects the direction of response ( + or - ) and whose magnitude reflects the means of the lowest effective doses (LEDs) or highest ineffective doses (HIDs). Data from individual published reports are superimposed on the line as (A) for tests conducted in the presence of an exogenous metabolic system, or ( - ) for tests in the absence of an exogenous metabolic system. The lines are solid in direction of the majority outcome, and dashed in the direction of a minority outcome. The codes SA0, SA5, SA7 and SA9 represent results from Ames tester strains. An example of the corresponding ICPEMC approach is detailed, again for ethylene oxide. Table 5 shows the raw data including EMIC reference number, common to both the Waters and the ICPEMC approach. It also shows the ICPEMC summary calculations, including the test, replicate score (Sr), test score (St), class score (Sc), family score (in vitro or in rive) and agent score (Sa). Note that the same convention for SAL entries (SA0, SA5, etc.) is used for data entry, but in the ICPEMC system these are combined into a single SAL test score. The reader can follow the calculations by referring to Table 4. The method of close modification, however, is presented in the next paper in this series (Moore et al., 1991). Note that the last 17 entries have been excluded from the ICPEMC analysis because the tests had not been used on the requisite five chemicals. Fig. 3 demonstrates the two plotting methods used by the ICPEMC system. The primary display is the upper, circular diagram in which replicate ( - ) and test ( ) scores are organized by Family (A for in vitro, and B for in rive), and by class (A1, A2...AI0; BI, B2... BI2). The common center of the circles represents a score of -100, the first (smallest) circle is zero, and the second circle is + 100. Where data are sufficient, the standard deviation of a test score is indicated by a radial line. Coded test names are shown between the outermost circles. A secc~nd method of plotting is given in the lower part of Fig. 3. Here the test detail is eliminated to emphasize an overview of the results. The class scores are shown as boxes superimposed when appropriate with vertical hatched

bars for the standard deviation of tests within class. The X's indicate the family scores, and the diamond to the fight indicates the agent score, again with its standard deviation. The numbers on the X-axis identify those classes with data. Additional displays are shown in Figs. 4-7 for aflatoxin B1, benzo[a]pyrene, caffeine and ethanol, a group of chemicals arranged by descending agent scores to be used later in this series (Mendelsohn et al., 1992). Results and discussion Presentation of results and interpretation of the performance of the system is deferred for subsequent papers, particularly since the methods of dose adjustment in the system are not described until the next paper by Moore et al. (1992). Acknowledgments The authors are grateful to the International Agency for Research on Cancer, Lyon, France and the Genetic Toxicology Division, Health Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 (U.S.A.), for making the data used in this report available at an early stage of our work. We also gratefully acknowledge the help of Drs. F. Stack and A. Brady, Environmental Health Research and Testing, Inc., Research Triangle Park, NC 27709 (U.S.A.), in providing us with corrected updates of the data during the course of the project. The Medical Biological Laboratory, TNO, P.O. Box 45, 2280 AA Rijswijk (The Netherlands), are thanked for making their computer facilities available (through P.H.M.L.) during the first two years of the project. Some of the work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract W-7405-Eng-48. References Bridges, B.A., and M.L. Mendelsohn (1986) Recommendations for screening for potential human germ cell mutagens: an ICPEMC working paper, ~n: C. Ramel, B. Lambert and J. Magnusson (Eds.), Genetic Toxicology of Envi-

25 ronmental Chemicals, Part B: Genetic Effects and Applied Mutagenesis, Liss, New York, pp. 51-65. Brusick, D. (1981) Unified scoring system and activity definitions for results from in vitro and submammalian mutagenesis test batteries, in: C.R. Richmond, P.J. Walsh and E. Copenhaver (Eds.), Health Risk Analysis, Proc. 3rd Life Sciences Symposium, Gatlinhurg, TN, Franklin Institute, Philadelphia, pp. 273-286. Brusick, D., J. Ashby, F.J. de Serres, P.H.M. Lohman, T. Matsushima, B. Matter, M. Mendelsohn and M. Waters (1986) Weight-of-evidence scheme for evaluation and interpretation of short-term results, in: Genetic Toxicology of Environmental Chemicals, Part B: Genetic Effects and Applied Mutagenesis, Liss, New York, pp. 121-129. International Agency for Research on Cancer (1987) IARC Monographs on the Evaluation of Carcinogenic Risk of Chemicals to Humans, Suppl. 6, Genetic and related effects: An updating of selected IARC Monographs from Vols. 1-42, International Agency for Research on Cancer, Lyon, France. Lohman, P.H.M., M.L. Mendelsohn, D.H. Moore 11, M.D. Waters and D.J. Brusick (1990) The assembly and analysis of sbort-term genotoxicity test data - An ICPEMC Committee 1 working paper, in: M.L. Mendelsohn and R.J. Albertini (Eds.), Mutation and the Environment, Part D, Wiley-Liss, New York, pp. 283-294.

M~ndelsohn, M.L., D.H. Moore 11 and P.H.M. Lohman (1992) A method for comparing and combining short-term genotoxicity test data: results and interpretation, Mutation Res., 266, 43-60. Moore II, D.H., M.L Mendelsohn and P.H.M. Lohman (1992) A method for comparing and combining short-term genotoxicity test data: the optimal use of dose information, Mutation Res., 266, 27-42. Nesnow, S. (1990) A multi-factor carcinogen potency ranking scale for comparing the activity of chemicals, Mutation Res., 239, 83-115. Waters, M.D. (1979) The Gene-Tox program, in: A.W. Hsie, J.P. O'Neill and V.IC McEIheny (Eds.), Banbury Report 2, Cold Spring Harbor Laboratory, Cold Spring Harbor, pp. 449-467. Waters, M.D., and A. Auletta (1981) The Gene-Tox program: Genetic activityevaluation,J. Chem. Inf.Comput. Sci.,21, 35-38. Waters, M.D., H.F. Stack, A.L. Brady, P.H.M. Lohman, L. Haroun and H. Vainio (1988) Use of computerized data listingsand activityprofilesof genetic and related effects in the review of 195 compounds, Mutation Res., 205, 295-312.

International Commission for Protection Against Environmental Mutagens and Carcinogens. A method for comparing and combining short-term genotoxicity test data: the basic system.

Mutation Research, 266 (1992) 7-25 © 1992 Elsevier Science Publishers B.V. All rights reserved 0027-5107/92/$05.00 MUTREV 07311 INTERNATIONAL COMHIS...
934KB Sizes 0 Downloads 0 Views