A research to develop a predicting system of mammalian subacute toxicity (1) prediction of subacute toxicity using the biological parameters of acute toxicities

Chemosphere, Vol. 32, No. 5, pp. 979-998, 1996

Pergamon

0045-6535(95)00363-8

Copyright © 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0045-6535/96 $15.00+0.00

A RESEARCH TO DEVELOP A PREDICTING SYSTEM OF M A M M A L I A N SUBACUTE TOXICITY (1) PREDICTION OF SUBACUTE TOXICITY USING THE BIOLOGICAL PARAMETERS OF ACUTE TOXICITIES

Takaaki Yamaguchi* t, Hiroshi Nishimura i, Tomoyuld Watanabe ' p Shoji Saito t

I

l~sasi~ Yabuld ~, Kunio Shiba t , Naol~ko I~be t , Fumio Kishida ~, Miehiko Kumano ~ , Fumiaki Shono ~ Haruhiko Adachi s Masatoshi Matsuo t

1,*: Environmental Health Science Laboratory, Sumitomo Chemical Co., Ltd., 3 - 1 - 98, Kasugade- Naka, Konohana- Ku, Osaka 554, Japan 2: Environment and Safety Department, Sumitomo Chemical Co., Ltd., 2 - 27-1, Shinkawa, Chuo- Ku, Tokyo 104, Japan 3: Sumika Technos Corporation, 4 - 2 - 1 , Takatsukasa, Takarazuka 665, Japan

(Received in Japan 30 September 1995; accepted 21 November 1995)

ABSTRACT Predicting equations of subacute toxicity were developed by analyzing rat acute and subacute toxicity data of 56 chemicals of various structures. estimated as a 'biological parameter".

Minimum or 10% effect level in acute or subacute toxicity was Good regression equations were established between the

geometrical means ("combined parameters") of an]/two of the parameters of acute and subacute toxic'ides and introduction of log P to the equations improved the correlations with a statistically significant multiple regression coefficient.

The lowest predicted effect level of the subacute toxicity, which is selected from

the data calculated by the above several correlations, can predict the upper limit of the no observed effect level. 979

980 INTRODUCTION

In recent years, for research and development of new chemical substances, it becomes one of the important factors that these have a lower environmental load in nature. Eventually, it becomes essential to evaluate not only their acute effects on human or environments, but also their chronic influences when they are to be exposed for a long period of time, and the cost for such verification is becoming a breaking factor for research and development. Thus, the development of a new technique which estimates the environmental load of a chemical substance including toxic effects on human with lower cost is now being attempted. For example, the development of bJ vitro new alternative methods using cultured cells, the utilization of a data base or software which relates mammalian and environmental toxicities and so on are internationally carried forwards " . As for the latter, quantitative structure-activity relationship (QSAR) techniques have been applied practically to decide appropriate toxicity tests needed for regulatory purposes by EPA/V3C.A, ITC/TSCA and FDANDAA in the USA. In addition, it was concluded recently in a joint meeting of EU and US-EPA that the approach by QSAR techniques was useful to specify the new chemical substances which are to be required toxicological examinations ~ . In these QSAR techniques, however, while there are some considerations about common mechanisms of toxicity among chemicals with a similar structure (Congeneric chemicals, Congeners) for establishing of correlation formulas, for chemicals with various structures (Non-congeneric" chemicals, Non-congeners) there often lack such common considerations. In addition, biological or physiological factors which are basic toxic indices are often ignored. In a previous study s~ , we researched acute and subacute oral toxicities of industrial common chemicals in rats and reported the followings; 1)their subacute toxicological spectrum in target organs/tissues and morphologic changes was very limited and specific, 2)the important targets were liver, kidneys, blood (spleen) and stomach and these are considered the sites of dominant exposure due to the kinetics of chemical substances, 3)the morphological changes were hepatoceilular

hypertrophy, deposition of

substance in renal tubules, extrameduUary hematopoiesis in spleen and mucous lesion in stomach, and these are implying adaptation such as induction of drug-metabolizing enzymes, overload to renal function, anemia from erythrocyte destruction, and direct reaction, respectively, 4)there seems to occur a series of direct and adaptive reactions to exclude the "foreign compounds" which do not show any specific biological activities,

5)it is also considered that there is a possibility to establish a correlation between toxicological

findings or target organs/tissues of both acute and subacute toxicitiea by their continuity.

Therefore, in

the present study, a predicting equation of subacute (28-day repeated dose) toxicity is attempted to develop from acute (single dose) toxicity data by considering both common mechanisms and biological factors for non- congeneric industrial chemical substances.

981

MATERIALS AND M E T H O D S

1. CHEMICAL SUBSTANCES AND THE TOXICOLOGICAL DATA Data of 56 industrial chemical substances were collected from the member companies of Japan Chemical Industry Association (JCIA) under confidential contract 4). And their toxicological data (acute and subacute oral toxicities in rats) were subjected to the analysis and those from additional 3 chemical substances were used for the verification (See Table 1). All the toxicity data were from the studies performed under Good Laboratory Practice (GLP) or the equivalent according to "Law Concerning the Examination and Regulation of Manufacture, etc., of Chemical Substances; Testing Methods for New Chemical Substances (Chemical Substances Control Law)" 5~ by the Ministry of International Trade anti Industry (MITI) in Japan. 2. CONSTRUCTION OF A DATA BASE Toxicological data for male and female rats were utilized separately. distinguished.

The strain of rats was not

For the feeding studies, a mean chemical concentration in diet (ppm) was converted to an

administration quantity by garage (mg/kg) by factor of 1/20 in accordance with the calculation method in oncogenicity study of US- EPA e~ Important toxicological data were then extracted by considering the following 5 items; 1) the observation commonly performed in both acute and subacute toxicity studies, 2) the findings or values with dose-relationships, 3) the findings or values participated in the decision of no observed effect level (NOEL), 4) the findings or values with toxicological meanings, and 5) the items observed in many chemical substances. The "minimum or 10%" effect levels were calculated for the changes represented by frequency (e.g., clinical signs and pathological findings and so on) as "administration quantity (mg/kg) which showed a minimum frequency (1/number of animals in group) (Minimum Effect Level, ELmin.)" (Fig. 1); and for the changes represented by value (e.g., body weight gains, food consumptions, organ weights, blood biochemistry and so on) as "administration quantity (mg/kg) which showed a 10% change from the control group (10% Effect Level, 10%EL)" (Fig. 2). These levels were handled as a specific quantity in each change. Molecular weight and n-octanol/water partition coefficient (P) were utilized as a physico-chemical property. As for the partition coefficient, log P was calculated from the chemical structure using IBM 4381 computer system with CLOGP program (Ver~.41)r)

The data including classification in

accordance with section reference number in "The Gazetted List" of Chemical Substances Control Law 8~ ,

982 chemical structures, physico-chemical properties and quantified toxicity data (hereinafter "biological parameters") were taken into a data processing system, KAMELO (Fujitsu, Tokyo) to construct a data base. 7

~6 ~ID5 t,n

d

z oc

~.a

,j

soo

,ooo

,soo 2000 Dose level (mg/kg)

The estimation of the minimum effect level where the minimum No. of animal will show the clinical sign.

Fig. 1

Dose levels (mg/kg)

~=~c ~,

8/~0 8~,°o t~°o l~Po ~l°o°

Dose = 800+(1600-800)/5 -- 960 g

500(

iiii

g 450

o

Fig. 2

500 '

,o'oo

,5oo " 2000 Dose level (mg/kg)

The estimation of the 10% effect level where the deviation wiU occur at ± 10% of the control value. Dose levels (~lkg)

Body we~htt00 ~88 ~81 188 t~8~ Dose ~ 800+[(~00- 800) X (500- 4~0)]/(500- 4.~0) = 146.7 KemarKs; I ne m e ~ w~s appued-only to me case ot statistical slgruncance.

983 3. EXTRACTION OF THE DOMINANT BIOLOGICAL PARAMETERS AND CONSTRUCTION OF THE COMBINED PARAMETERS In order to make widely and generally acceptable regressio n equations, 5 dominant (lst to 5th) biological parameters were extracted. Any two parameters were chosen from the 5 dominant biological parameters and their geometrical means were calculated to yield "combined parameters". 4. ESTABLISHMENT AND VERIFICATION OF THE PREDICTING EQUATIONS FOR SUBACUTE TOXICITY BY USING THE BIOLOGICAL AND COMBINED PARAMETERS The correlations between the biological parameters or combined parameters of both acute and subacute toxicities were examined using a single or multiple regression program of KAMELO and predicting equations for subacute toxicity were established.

In addition, an improvement was made for the

established correlations consisting of the combined parameters by introducing log P. Among the calculated subacute toxicity parameters, the lowest value was defined as a "lowest predicted effect level (pred EL(lowest))".

It was compared with ELmin or 10%EL of the NOEL-reflecting changes

in subacute toxicity study. Pied EL(lowest) was also compared with the measured NOEL of subacute toxicity.

Pred EL's(lowest) were calculated for 3 chemical substances by using their acute toxicity data

which were not used for establishing correlation equations, and these were used for verification of the correlation equations together with the measured values of their subacute toxicitiea.

RESULTS

1. CONSTRUCTION OF A DATA BASE Chemical substances and the toxicological data filed in KAMELO are shown in Table 1. 2. EXTRACTION OF THE DOMINANT BIOLOGICAL PARAMETERS Five (1st to 5th) dominant biological parameters are shown in Table 2. The combined parameters composed of any two of these dominant parameters are shown in Table 3.

2 C,~H3oO~N~ 3 C6HsNF,

3 CsHsNF,

3 3 3 3 3

3 C6HsCIFNO,

3 C,H60CI, 3 C,H60CI,

6F

7[ 7F 8I 8F 91

9F

10W IOF

C8HIoO~ C,HIoO~ C,HleO, C,H~o0z CeH~C1FN02

? ? 2.7 2.7 2.7

1.7

1.6 1.7

177 3.0 177 3.0

176 2.7

154 154 156 156 176

129

318 129

1.8 0.32 0.32 0.075 0.075 1.6

5F 6M

174 192 192 204 204 318

2 2 2 2 2 2

2F 3M 3F 4M 4F 5M

C,H~,O~ CxoH~6N, C~oHtsN4 C~oH~o04 C~oH2oO, C~4H~oO~N,

142 2.0 142 2.0 174 1.8

2 CaHz40, 2 C,H,~02 2 C,H, s03

IM IF 2M

Acute Toxicity DSA ATA IRE PER

OTHERS

430 200

1200 37 33 000 000 590

130

3800< 3800< ? ? 140 66

750 750 5000< 1000 12

3500 4200 200 3400 4700 200

350

1500 1300 5000< 5000< 150

1500 1600< 200

740 ? 1500 500

2000< 2000< 270 190 250 .? 2000< 2000< 2000< 2000< 520 ?

1300 I000< 500< 600 870 590<

200

200

C5:1100, CI0:200

200

3800 200 3800 200

200

900< C5:410, CI0:520, C12:72 200 200

1000<1000< 030 1000<100 670 1000<1000< 630

Subacute Toxicity DSA LIV KID CHO

OTI~RS

1000<1000< C2:100,L3:470 560 1000
63 63 100

NOEL BWD

of affected animts or lOt deviation from the controi)

1400 II00 II00 C5:2000 1600 1600 1600 C5:2700, CI0:1700 1300 2500 1000 1000 1000 1000 900< 60 900< C10:79, C12:58

200

CI0:650 C10:200

2000 I000< C10:210 500< C10:100 2000< 2000< 430 C10:430

1300< 1300< 430 200 200 200

2000< I000< 500< 600 000 590<

8000< ? "3000 8000< 3600 8000< 8800 ? 3600 3600 3600 640 C5:3000 2000< 2000<'030 2000< 670 2000<

CLOGPLDSO BWD

MW

No. Sex Se~ MF

Table I. Physico-chemical property and acute and suhacute toxicity (doses of minimumN~ of the chemicals used in the study.

~0 oo

3 C,,H2,N,03Cl

4 4 4 4 4

4 C,=H, rNO

4 C,.H, eO 4 C,,H,60

4 CffioH,e03 4 C, oH, zOs

16W 16F 17M 17F 18M

18F

19M 19F

20M 20F

C,,H,o02 C,2H, oO, CL,H,a C,,H,s C,2E,,NO

3.8 3.8 4.8 4.8 3.0

306 3.7 306 3.7

260 6.6 260 6.6

191 3.0

186 186 186 186 191

PER

430 88

2000< C5:600

5000 5000< 5000,< 5000 5000 5000< 5000< 5000< 2600< 2600< 2600< 370 C12:1900 560 2600<2600< 100 C5:1300,C12:840 1700 1700 5000< 5000< C12:2000

70

OTHERS

5000< ? 5000< ?

5000< 5000< 5000< 1000 5000< 5000< 5000< 1000

2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000<

5000< 5000< 3000 2000 3000 5000< C12:5000

5000< 4300 5000< ? 670 ? 1100 ? 5000< 6100

?

2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 5000< 6600 1800 5000< 2200 5000< 5000< 5400 1800 5000< 2200 5000< 2000< ? 2000< 2000< 2000< 2000< 2000< ? 2000< 2000< 2000< 2000< 1000 1600 100 600 570 2000<

1700 1600 2000< 2000 2000 2000<

Acute Toxicity ~ ATA IRE

2000< 2000< 28~ 2000< 2000< 280

264 0.41 780

4.0 4.0 3.7 3.7 2.5 2.5 0.41

15F

228 228 235 235 250 250 264

3 3 3 3 3 3 3

12M 12F 13M 13F 1411 14F 15M

C,,H2,0, C,,H,,O, C.H,O.C12 CgHaO~C12 C,,H,,O, C,,H, eO, C,,B~,N,03CI

208 2.6 208 2.6

3 C,,H,,SO, 3 C,,H,,SO,

llM IIF

CLOGP LD50 BID

~If

(Continued)

No. Sex Sec~

Table 1.

950 950

360 220

480 750

400 840

Subacute Toxicity DSA LIV KID CHO OTHERS

ZOO0< C2:430,012:87 I000< C2:220. L2:250 L16:37 20 I000< I000< 340 130 I000< {{33:40, L31:80 20 I000< I000< 480 i000< 350 L31:360 <40 1000< 120 43 29 960 K33:360, L2:480 200 1000< 120 490 1000< 130 K2:380, K33:360 300 1000<1000< 860 1000<1000< 1000 I000< I000< I000< i000< I000< K3:>1000 8 360 53 190 170 200< C12:120, B31:67 011:100 8 280 40 24 200< 200< B21:93, B31:13 C3:40, C5:40. K3:72 011:22 I0 600 700< 300 700< 700< LT:290, L31:220 I0 700< 700< 290 700< 700< L16:7, L31:220 7 600 380< 600 350 380< K33:11 4 330 380< 190 380< 380< 10 1170 1000< 250 1000< 1000< B31:50, C12:400 011:410, 016:35 10 I000
20 20

NOEL BID

oo

5000<1500 5000< 5000< 5000< 5000< 5000<5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 3300< ? 3300< 3300< 3300< 3300< 3300< ? 3300< 3300< 3300< 3300< 3600< ? 3600< 3600< 3600< 3600< 3600< ? 3600< 3600< 3600< 3600<

348 5.3

348 5.3

369 5. 9 369 5. 9 387 ?

387 416 416 478 478

4 C=.H=.Os

4 C,=H=40a

241

24F

25M 4 C==H=,NO, 25F 4 C==H=,NO, 26M 4 C==H,,O=S,

26F 2711 27F 28M 28F

4 4 4 4 4

C=oHI,0=S3 C,=H=,NOzS C==Hs,NO=S C~=H,oO, C,,H, oO=

? ? ? ? ?

5000< ? 5000< ?

322 3.4 322 3.4

4 C, oH,sO=S= 4 C, aH,,O,S=

5000< 5000< 5000< 1000 5000< 5000< 5000< 1000

1000< I000< C10:510, C12:100

23M 23F

510

1200 2000< 1200 1200 2000< 2000<

22F

41

313 3.5

4 C,.H,sNO=

22M

510

4 C,sH,.NOffi

1000< 1000< C10:440, C12:210

1300 1400 1200 1200 2000< 2000< C12:1500

440

OTHERS

313 3.5

4 C=oE,sO.

21F

41

PER

600

4 C=oH,,O,

21M

530

Acute Toxicity CLOGPLD50 BWD DSA ATA IRE

322 4.5

¢1 570

(Continued)

322 4.5

NO.

Table 1.

380

800< 350

840

B31:800,C12:36 131:800 800 770 800< 270 960 800< B31:800, C12:800 131:800, Ol1:410 K33:60, Ll:IT, L2:31 6 90 68 20 77 62 L3:18, O12:70 Ll:68, L2:62 6 120< 120 20 84 70 012:86 <50 780 I000<570 1000<1000< KT:480. L2:390 200 880 I000< 460 I000< I000< C12:360, K3:230 Ll:490. L2:610 011:270.022:200 10 79 1000< 34 47 10001000 1000 I000< 1000< I000< I000< I000< 200 1000< 1000< 1000< 1000< 1000< L7:180 36

480

Subacute Toxicity NOEL BWD DSA LIV KID CHO OTHERS

",D Oo 0~

4 Cs,B, oO, 4 C,,H, oO, 4 CsxH,,N,O, xSzNa2

4 4 4 4 4 4 4 5 5 5 5 5 5

30M 30F 31M

31F 32M 32F 3311 33F 341 34F 351 35F 361 36F 3711 37F

C,:B,,N.O, xS,Na, C,,H.oO,S,Ca C,,H.oO,S,Ca C, JH.N,O,,S, CssH,eN,01,S, C.Hs,NIzS6 C.Hs,NltSe C6HeN,O, C,B6N,O, C,H,S, C,HaS= C,H, zN~O, C,H,,N=O=

4 C,,B, oN,06S,

29F

2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 380 82 77 II00<200 Ii00< 320 110 71 1100<230 1100< 2500< ? 500 2500<2500< 500 C12:500 2500< ? 500 2500<2500< 500 C12:500 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 5000< 7700 3600 640 8000< 640 C12:540 5000< ? 040 040 8000< 640 C5:640,C12:640 1400 2000< 2000< I00 I00 2000< 1400 2000< 2000< I00 I00 2000< CIO:I300 52 70 32 32 92< 69 CI0:31 39 55 92< 5" 60 69 C10:27 650 420 60 60 300 3000
593 5.4 593 5.4 810 1.8

810 1.8 916 ? 916 ? 1175 ? 1175 ? 1710 3.2 1710 3. 2 138 ? 138 ? 152 1.6 152 1.6 169 1.4 169 1.4

C12:250

C12:250

531 0.51 2200 3200< 1480 3200< 1600 250

4 C,,H, oN,O,S,

29M

531 0.51 2800 3200< 1500 3200< 2000 250

OTRERS

Acute Toxicity DSA ATA IRE PER

CLOGPLD50 .BID

MI

(Continued)

Sex Sec. IIF

NO.

Table I.

?

1000< 1000< 730

94

Suhacute Toxicity DSA LIV KID CHO OTHERS

B21:I30,B31:55 C:12:190,~:040 K33:450, L23:140 L31:130, 011:94 100 ? 1000< 1000< 1000< 1000< B21:133. B31:55 C:12:190, K33:175 L3:67, L23:250 L31:130, 011:104 1000 I000< 1000< I000< 1000< 1000< 1000 1000
100

NOEL BID

C~ .,.j

5 C,sH,,N,O,CI,Na 5 CtsH,,NzO,C1zNa 5 C,,B,40,o 5 C4sH,,O,o 5 C~,H,aN40~oS, 5 C,,B,,N,O, oSz 5 C,,B,,N,O,,S, CINa=. sLi,., 42F 5 C,,H,+NgO,,S, CINa,. ,L£,., 43M 5 C,,H,,N,O,~S,Na3 43F 5 C,,H,,NsO,,S,Nas 441 5 C,,],,NoO,,SsNa, 44F 5 C~,H,,NoO,,SsNa4 45M 5 C,,Ez,N,O,,S,CINas 45F 5 CszHz,N,O,,S,CI~, 461 5 C,,HzsN, oOzzS,CINa, 46F 5 C,,B,~N,oO,,S,CINa, 471 5 C~oH,,N,O,,S,Na, 47F 5 C, oB, sN,O,,S,Na. 48M 5 Cs,H,zN,,O~,S,o Cl,Na, 48F 5 Cs,H4,N~03,S~o ChNa,

5 C,,H,sNs02

38F

301 39F 40M 40F 41M 41F 42M

5 C,,H,sN30,

38N

Sex See.. Iff

NO.

Table I. (Continued) 01~RS

I000< I000< I000< I000< i000< B31:550

1000 1000< I000< 1000< 1000< I000< i000 I000< I000< 1000< 1000< 1000<

5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000<

2027 ?

2027 ?

100 1000<1000< 1000< 1000< 1000< ]{3:>I000 1000< 1000< I000< 1000< 1000< 1000< I000< 1000< I000< 1000< 1000< 1000< 1000 1000< 1000< I000< 1000< 1000< I000 I000< 1000< 1000< 1000< 1000< 100 1000<1000< 1000< 1100 1000< 100 1000<1000< 1000< 1000 1000< I000 1400 I000< 10004 I000< 410 LI:570 1000 1000< I000< I000< 1000< I000< 017:240 I00 I000< I000< I000< 780 1000<011:91 I00 I000< I000< i000< 980 i000<

100

240< 240< C2:75, C3:150 L31:llO 15 240< 60 120 240< 240< C2:60, C3:96 1.31:96 C4:18, L8:0. 34 <2 50< 18 9 50< 4 C4:18, KI:O. 83 <2 50< 18 50< 14 5 <50 1500< 1500< 43 1500<18 <50 1500< 1500< 350 1500< 14 I000 I000< I000< i000< I000< I000< I000 I000< I000< I000< I000< I000<

190

60

15

420

Subacute Toxicity DSA LI¥ KID CBO

NOEL BID

5000< ? 1000 5000<5000< 5000< 5000<5000< 5000< 5000< 5000< 5000< 5000<5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< C12:1300 5000< 5000< 2500 5000< 5000< 5000< 5000< 5000< 3300 5000< 5000< 5000< 5000< II000 130 5000 5000 5000< 5000< 5000< 1400 5000 5000 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000<

1000 5000<5000< 5000<

1400<

OTIIERS

843 -1.7 965 2.1 965 2.1 1034 0.44 1034 0.44 1199 ? I190 ? 1357 ? 1357 ? 1367 ? 1367 ?

843 -1.7 5000< ?

350

200 300< 5 210 180 5 180 300< 5 210 100 5 5000< 5000< 5000< 5000< 5000< 5000< 5000<5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000<

4.5 4.5 6.5 6.5 ? ?

120

321 321 741 741 765 765

7Q

1400<

750

430

840

70

233 ?

70

750

PER

810

Acute Toxicity DSA ATA IRE

233 ?

CLOGP LD50 BID

oo oo

(Continued)

0 6 6 6 7

7 polymer

7 polymer 7 ~lymer 4 C~eH~oN2 4 CI,R, oN, 3 CI2R=~O~ 3 C,zHz,Oz 3 C~H,O2Ch

531/ 53F 54M 54F 551

55F

56M 56F 57M* 57~ 58W* 58~ 591.

59F* 3 C,H,02CI,

polymer polymer polymer pol~er polymer

6 polymer

52F

polymer polymer polymer polymer polymer pol~er polymer

5 5 8 6 0 6 6

4911 40F 50W 50F 51W 51F 52M

Sex Sea XF

No.

Table I.

? ? ? ? ? ? ?

? ? ? ? ?

? 2000< ? ? 2000< ? 2.6 2000> ? 2.6 2000> ? 4.3 2000< ? 4.3 2000< ? 0.84 2000< ? 500

2000< 2000< 1300 670 400 400 500

OT~RS

500

2000< 2000<

2000< 2000< 2000< 2000< 2000< 2000< 1300 1300 2000< C10:1300 1300 2000 2000< CI0:870 2 0 0 0 2000< 2000< 500 2000< 2000< 500 2000< 2000<

5000< 5000< 5000< 5000< 5000< 5000<

5000< 5000< 5000< 5000< 5000<

PER

5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 400 2000< 2000< 2000< C12:400 400 2000< 2000< 2000< C12:400 2000< 2000< 2000< 2000< 2000< 2000< 2000< 2000< 580 2100< 570 1100 C5:980,C10:980 C12:980 ? 570 2100< 570 980 C5:1500,C10:940, C12:1100 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5000< 5~00< 5000<

Acute Toxicity DSA ATA IRE

5000< 5000< 500 ? 2000< 2000< ?

191 0.84 2000< ?

ave1267 ave1267 240 240 198 198 191

460~2320 ?

434~6754 434~6755 980~2000 980~2001 460~2320

1200

5000< 5000< 2000< 2000< 2000< 2000< 1400

CLOGPLD50 BID

<4000 ?

avel800 ave1800 ca2000 ca2000 4000~7500 4000~7500 <4000

~rI

260

300< 230

00

190

L31:200,012:180 032:100

1000< 1000< 1000< 1000< 40 1000< 1000< 340 1000<40 500< 500< 500< 500< 500< 500< 500< 500< 500< 500< 1000< I000< I000< I000< 1000< 1000< I000< 1000< I000< 1000< 300 300< 140 230 300< L31:170

Subacute Toxicity ~ LIV KID CgO OTHERS

1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000< 1000<400 1000<1000< 23 C3:1000.C4:400 012:830.032:440 50 1000<250 1000<1000< 1000< C3:1000,CA:250 032:440 1000 1000< 1000< 1000< 1000< 1000< 1000 1000< 1000< 1000< 1000< 1000< 10 240 120 300< 300< 300< C10:150 30 370 170 100 300< 300< C2:120.L31:300 15 1000< 1000< 83 150 1000< 15 1000< 1000< 200 1000< 1000< L2:350 20 610 1000< 230 1000< 1000< K3:140.L31:310 L2:230,032:30 <20 1000<1000< 280 1100 1000
1000 1000 1000 1000 50

30

200 200 500 500 I000< I000< 30

NOEL BID

OO

- Abbreviations: MF: Molecular formula,

kq[: ~olecular weight,

LI: Increased aspartate aminotransferase (blood biochemistry) L2: Increased alanine uminotransferase (blood biochemistry) L3: Increased alkaline phosphatase (blood biochemistry) L5: Increased ~m-glutumyl transpeptidase (blood biochemistry) L6: Increased total protein (blood biochemistry) LS: Increased triglyceride (blood chemistry) L11: Decreased glucose (blood biochemistry) L15: Decreased alkaline phosphatase (blood biochemistry) L16': Decreased total cholesterol (blood biochemistry) L17: Increased glucose (blood biochemistry) L23: Liver-Pale (gross pathology) L31 : Hepatocellular hypertrophy (histopathology) 010: Decreased platelet count (hematology) 011 : Increased spleen weight 012: Increased adrenal weight 015: Increased ovary weight 016: Decreased adrenal weight 017: Decreased thyroid weight 022: Cecum-Enlarge (gross pathology) 032: Stomach-Damage (histopathology) 047: Testis-Atrophy (histopathology)

CLIO: Increased total cholesterol

OTHERS: BI: Decreased erytbrocyte count (hematology) B3: Decreased hemoglobin concentration (hematology) B6: Increased erytl£rocyte count (hematology) B21: Spleen-Dark (gross pathology) B31: Spleen-Anemia (histopathology) C2: htaxic gait C3: Irregular respiratloo/Bradypnea/Abnormal respiration Ci: Piloerection C5: Hypotbemia CI0: Convulsion/Tremor/IIypersensi t ivity C12: Diarrhea/Soft stool El: Increased creatinine (blood biochemistry) K2: Increased BUN (blood biochemistry) 13: Increased or decreased sodium (blood biochemistry) K7: Increased inorganic phosphate level (blood biochemistry) K24: Kidney-Pale (gross pathology) ~3: Kidney-Damage (bistopathology)

KID: Increased kidney weight (relative),

B~D: Suppression (I0~) of body weight gain, DSA: Decrease of spontaneous activity, ATA: Ataxic gait/Abnormal gait, IRE: Irregular respiration/Bradypnea/Abnormal respiration, PER: Piloerection, LIV: Increased liver weight (relative)

I: Eale, F: Female, Sec. Section-reference No. in "The Gazetted List', ?: No data or no calculation available,

Table I. (Continued)

0

991 3. ESTABLISHMENT OF THE PREDICTING EQUATIONS FOR SUBACUTE TOXICITY BY USING THE BIOLOGICAL AND COMBINED PARAMETERS (1) The correlations between the biological parameters (single regression analysis) Between the biological parameters of both acute and subacute toxicities, there were found some combinations which show a good correlation in a linear regression (Ex. BWD-BWD; r=0.89, n=13., ATA-LIV; r=0.82, n=23., IRE-LIV; r=0.79, n=24., BWD-CHO; r=0.75, n=11., IRE-CHO; r=0.75, n=11).

However, the numbers of chemical substances which constitute the correlations were rather few.

Table 2.

Most frequently (first to fL~h) observed changes extracted from each toxicity study.

Study/Changes

cases

Acute toxicity study 1. Decrease of spontaneous activity (DSA) 2.. Irregular respiration (IRE) 3. Ataxic gait (ATA) 4. Piloerection (PER) 5. Suppression of body weight gain (BWD)

58 39 36 31 26

Subacute toxicity study 1. Increased liver weight (LIV) 2. Increased kidney weight (KID)" 3. Suppression of body weight gain (BWD) 4. Increased cholesterol (CHO) 5. Decrease of spontaneous activity (DSA)

Table 3.

Combined parameters examined for correlations.

Acute toxicity study

Suhacute toxicity study

A B C D E

a b c d e

= = = = =

(BWD x DSA) ' / * (DSA x IRE) ' / * (ATA x IRE) , / t (BWD x IRE) ' / * (BWD x ATA) ' / *

= (BWD x DSA) ,/2 (LIV x KID) ' / ' = (BWD x LIV) ' / z = (BWD x CHO) ,/2 = (LIV x CHO) l / ,

Remarks; In the case that one of the data in the parenthesis was not available, the other value was used.

992 (2) The correlations between the combined parameters The regression equations between the combined parameters of both acute and subacute taxicities were as follows: log a = 0.52 log A - 0A6

(n=32, s=0.54, r=0.64)

log b = 0.73 log B - 0.36

(n=45 s=0.45, r=0.78)

log c = 0.93 log C - 0.18

(n=34 s=0.55, r=0.73)

log b = 0.92 log C - 0.52

(n=36 s=0.52, r=0.74)

log d = 0.89 log D - 0.61

(n=30 s=0.62, r=0.71)

log

(n=40 s=0.56, r=0.75)

e =

0.71 log

A -

0.40

log c = 0.81 log E - 0.56

(n=34 s=0.56, r=0.68)

log c = 0.83 log D - 0.58

(n=37 s=0.54, r=0.78)

log e = 0.74 log E - 0.62

(n=34, s=0.50, r=0.67)

[1] [2] [3] [4] [5] [6] [7] [8] [9]

where "n" is the number of data, "s" a standard error and "r" the single regression coefficient. (3) Improvement of the correlations by introducing partition coefficient (multiple regression analysis) The multiple regression equations which include the combined parameters of both acute and subacute toxicities and log P were as follows. For the comparison, single regression equations of the same data were shown.

logb = 1.1 log C - 0.66

(n=35, (n=35, (n=25, (n=25, (n=28, (n=28,

R=0.82, F=33.7") r=0.80) R=0.87, F=353") r=0.82) R=035, F=31.3") r=0.80)

[10] [11] [12] [13] [14] (Fig. 3) [15] [16] [17]

Iogd = 1.2 log D - 0.15 log P - 0.45

(n=24, s=0.50, R=0.85, F=26.9")

[18] (Fig. 4)

logd = 1.1 log D - 0.81

(n=24, s=0.53, r=0.81)

[19]

logc

(n=33, s=0.51, R=0.76, F=21.1")

[20]

log a = 0.51 log A - 0.018 log P - 0.42

(n=27, s=0.58, R=0.63, F=7.86)

ioga

(n=27, s=0.57, r=0.63)

=

0.51 log A - 0.47

l o g b = 0.75 log B - 0.098 log P - 0.15 log b = 0.74 log B - 0.39 logc = 1,3 log C - 0.22 log P - 0.24 logc = 1.2 log C - 0.75 l o g b = 1.1 log C - 0.16 log P - 026

= 0.74 log A - 0.13 log P - 0.11

s=0.41, s=0.44, s=0.43, s=0.49, s=0.44, s=0.48,

logc = 0.69 log A - 0.42

(n=33, s=0.53, r=0.73)

logc = 1.1 log E - 0.20 log P - 0.27

(n=27, s=0.43, R=0.86,

logc = 0.98 log E - 0.75

(n=27, s=0.50, r=0.80)

logc = 1.1 log D - 0.22 log P - 026

(n=28, s=0.44, R=0.86, F=35.4")

[24]

loge = 0.99 log D - 0.76

(n=28, s=0.51, r=0.79)

[25]

[21] F=33.0")

[22]

[23]

Ioge = 0.97 log E - 0.18 log P - 031

(n=27, s=0.41, R=0.83, F=27.5")

[26]

l o g e = 0.89 log E - 0.78

(n=27, s=0.47, r=0.77)

[27]

where "n" is the number of data, "s" a standard error, "R" the multiple regression coefficient, "F" F - v a l u e of regression equations and "r" the single regression coefficient.

993 t.0

I

I

t

...'

I ,..'

R_-0 87

O~ 0.5 •

.0

/

0.0 -0.5 -1.0 -1.5

-2.0 -2.5 -2.0

I

I

I

I

I

I

-1.5

-1.0

-0.5

0.O

0.5

1.0

1.3 l o g C Fig.

.

0.22 I.,og P -

.5

0.24

Correlation between the calculated and observed biological parameters. ...... : 95% confidence limit

1.0

I

I

I •

I .@0'"'

n=24, R=0.85

.'"'"'

,.~O~0.5 •

Q ,'"'"

.'"'

0.0 -0.5

•

.

:

-1.0

-

- I .5 -2.0 -2.5 -2.0

I

I

l

I

I

-1.5

-1.0

-0.5

0.0

0.5

1.2 l o g D -

Fig.

1.0

0.15 L o g P -

.5

0.45

Correlation between the calculated and observed biological parameters. ...... : 95% confidence limit

994 4. VERIFICATIONOF THE PREDICTING EQUATIONS FOR SUBACUTE TOXICITY When pred EUs(Iowest) calculated in section 3(3) were compared with ELmin or 10%EL of the NOELreflecting changes in subacute toxicity studies, no significant differences were observed between both values (p<0.05, T-test) (Fig. 5). Six chemicals deviated from the regression line; these were 2 high molecular compounds ( 0 , not absorbed) and 4 phenol derivatives ( l l more cumulative). However, when excluding these substances, the single regression coefficient became a higher value of 0.80. In addition, 3 chemicals for the verification ( • ) showed a good agreement.

(.--39, ~'--o.8o) 5'IF •

(fJ

'

'o . " /

51M • ,J

;~...

0

59M~"~..'"'""" °

o

.""

lall 0 -2

....'~F

-3 •'" ~ ' ...-. /

-4 -4

..d

..{

,

-3

-2

i IOM ,

-I

,

0

pred log EL(lowest) Fig.

Comparison of the lowest predicted effect levels with observed effect levels (lowest). ...... : 95% confidence limit

995 In comparison of pred EL's(lowest) with measured NOEL, there was no significant difference between both values (p<0.05, T-test) (Fig. 6). The regression coefficient was 0.67. Pred EL's(lowes0 of 3 chemicals were plotted on the regression line against the measured values. They fitted successfully in the line and verified the validity of the regression.

1.0

I

I

I

I

I

I

(n=45, r--O.6V)

0 Z 0.5

I

0

• "...~

0

° C~o°

o

..'" .'"

.o;o..." :

0.0 -0.5

"ii..................."

-1.0 -1.5 -2.0 -2.5

(...'~!o6"' "'"'"~';'"

-3.0 -3.0

Y"

,

-2.5-2.0-1.5-1.0-0.5

i

,

0.0

0.5

,0

pred log EL(lowest) Fig.

Comparison of the lowest predicted effect levels with observed NOEL. ...... : 95% confidence limit . . . . : pred logEL = obs log NOEL

996 DISCUSSION

In a QSAR approach to mammalian toxicities, the LDS0 values of acute toxicity are often used. This is because these values are considered a specific quantity of.a certain chemical structure to cause biological reaction.

However, more useful parameters for toxicological endpoints are available, for instance;

1)frequency data in each dose level (e.g. clinical signs, pathological findings and so on), and 2)quantitative values (e.g. body weight gains, food consumptions, organ weights, blood biochemistry and so on). Thus, in this study such parameters were fully utilized. Firstly to use these parameters in a quantitative sense, each toxicological endpoint was converted into a quantitative value which specifically relates the chemical structure.

Secondarily, for constructing a generally acceptable regression equation the combined

parameters were developed by combination of any two of the dominant parameters. Actually, equations [1] ~ [9] were available from the combined parameters of both acute and subacute toxicities.

Higher

multiple regression coefficients were also available for the above relating equations by introducing log P (equations [10], [12], [14], [16], [18], [20], [22], [24] and [26]). Log P is one of the important determinative factors- for dynamics (e.g. membrane penetration) of a chemical substance in living body. In oral administration studies, the actual exposure level of test substance absorbed via gut is important to estimate the toxicity. Log P is also a basic parameter for accumulation of a chemical substance in living body. Improvement of the correlations in the present study seems to imply that log P has something to do with absorption and/or accumulation. All the coefficients of log P in multiple regression equations are negative in value and it suggests that the difference in severity between acute and subacute toxicities depends on the difference ( i.e. acceleration) of absorption and/or accumulation.

In biological parameters

and thus, relating equations, there were found no significant differences over chemical classifications. In this study, several relating equations were established using a variety of findings which were observed with higher incidences among other toxic endpoints.

This is because a pred EL(lowest) could be selected

from these equations and utilized for the decision of NOEL which is needed in the guidelines. As in fact, pred EL's(lowest) could give a fairly good accordance with measured NOEL (r=0.67) in the correlating equation (See Fig. 6). The correlation between predicted and measured NOEL was, however, not sufficiently good due to the following reasons; 1) not non- effect, but effect levels such as ELmin or 10%EL were used for non- effect level estimation, while they could describe the observed effect levels (lowest) successfully (See Fig. 5), and 2) the determinative parameters used in measured NOEL was not always contained in those used for prediction of NOEL because the prediction equations dealt only with the dominant endpoints of subacute toxicities.

Thus, 40 measured NOEL's out of 45 chemicals' resided below the predicted values. However,

39 values out of 45 chemicals' are within an error range of 3 to 10-fold to the measured values, which figures were usually used as a ratio for dose selection (See Fig. 6). Thus, it was concluded that the upper levels of NOEL could be predicted from pred EL's(lowest) of suhacute toxicities.

997 CONCLUSION

The important toxicological findings were extracted from acute and subacute toxicity studies. The ELmin or 10%EL of these toxicological findings were calculated and utilized as the biological parameters. Geometrical means of these parameters were also used as the combined parameters to establish the correlations between acute and suhacute toxicities.

As a result, good correlations were established

between the biological parameters or combined parameters of both acute and subacute toxicities and in addition, higher multiple regression coefficients were available for the above established equations of the combined parameters by introducing log P. The upper limits of NOEL could be predicted by selecting the lowest among several effect levels calculated by these multiple regression equations.

Three new

chemicals verified this approach with a good fit to the regression equations. However, in the future, the prediction could be improved and widened by using detailed data of acute or "single dose detailed toxicity" study.

Acknowledgements This research was performed as a part of the project entrusted by JCIA. Our special thanks are due to Dr.lkuo Moriguchi,

honorary professor of Department of Pharmacology, Kitasato University and

Dr.Tsutomu Nishihara, professor of Department of Pharmacology, Osaka University for their helpful suggestions and comments in conducting this research.

REFERENCES

1) Phillips JC, Gibson WB, Yam J e t a/, Survey of the QSAR and in vitro approaches for developing non-animal methods to supersede the in vitro LD50 test. Fd Chem Toxic. 1990; 28, 375-394. 2) EPA Bulletin 1994; 15(2), 37 3) Yamaguchi T, Watanabe T, Isobe N e t

a/, A basic research of toxicicology of industrial common

chemical substances for developing a predicting system of mammalian subacute toxicity. J Occup Health. (In Press) 4) Unpublished and closed data from the member companies of Japan Chemical Industry Association.

998 5) The Ministry of International Trade & Industry, The guideline of "Law Concerning the Examination and Regulation of Manufacture, etc., of Chemical Substances; Testing Methods for New Chemical Substances (amended December 5, 1986), EHWD No. 5 (Director-general of Planning and Coodination Bureau, the Ministry of Environment Agency, Japan), PAB No. 615 (Director-general of Pharmaceutical Affairs Bureau, the Ministry of Health & welfare, Japan) and BIB No. 392 (Director-general of Basic Industries Bureau, the Ministry of International Trade & Industry, Japan)" 6) Father T.

Selection of a Maximum Tolerated Dose (MTD) in oncogenicity studies.

Pesticide

Assessment Guidelines, Subdivision F, Position document: EPA 540/09-88-003, NTIS PB88-116736/ AS 1987. 7) Pomona College. Leg P Database and Med. Chem. Software (Program CLOGP-3). Medical Chemistry Project, Claremont, California.