Simulation Modeling in Toxicology - ACS Symposium Series (ACS

Chapter 5

Simulation Modeling in Toxicology

Downloaded by UNIV OF MASSACHUSETTS AMHERST on May 25, 2018 | https://pubs.acs.org Publication Date: March 12, 1987 | doi: 10.1021/bk-1987-0336.ch005

J. T. Stevens and D. D. Sumner Ciba-Geigy Corporation, P.O. Box 18300, Greensboro, NC 27419

Attempts to understand and manage toxicological manifestations are generally a reactive rather than a predictive endeavor. Although we have responded by addressing untoward reactions with no effect levels and safety factors and oncogenic responses with quantitative and qualitative risk modeling, little has been established as a foundation for prediction of responses. The purpose of this paper will be to present a summary of the state-of-the-art on structure activity modeling; this process may assist in the evolution of predictive approaches to toxicology.

The performance of mechanistic studies to determine how xenobiotics produce their toxic responses is a promising approach for the understanding of risks (I). This technique would appear equally appropriate for a proactive, as well as reactive, examination of the factors involved. Understanding is based on experience; this commodity is as invaluable in proactive reasoning as reactive scrutiny. As we explore approaches to Structure-ActivityRelationship and biotransformation kinetics, it will become apparent that the foundation for the future is firmly anchored to the past. The link between similarity of structure and similarity of biological response is the key to making predictions on biological and/or toxicological properties. Our ability to simulate in models is only as good as our ability to accurately establish that link. For this consideration of simulation modeling, some of the currently available approaches for prediction of toxicity through chemistry will be examined—looking at both integrated knowledge and empirical evaluation. Structure Activity Relationships The information base between toxicological response and chemical structures has been growing exponentially. The integration of these data into a comprehensive and reliable structure activity 0097-6156/87/0336-0043$06.00/0 © 1987 American Chemical Society

Ragsdale and Kuhr; Pesticides ACS Symposium Series; American Chemical Society: Washington, DC, 1987.

44

PESTICIDES: MINIMIZING THE RISKS

r e l a t i o n s h i p system (SAR) h a s , g e n e r a l l y , been l i n e a r . Although computers have a s s i s t e d g r e a t l y i n t h i s e v o l u t i o n , development has r e q u i r e d t h a t c e r t a i n b a s i c c r i t e r i a f o r e f f e c t i v e assessment be met. These r e q u i r e m e n t s a r e i n c l u d e d i n T a b l e I . Table I .

Criteria

for Reliable Structure A c t i v i t y

Modeling

Broad d a t a base (>60 compounds)


- Congeneric

c h e m i s t r y (good

p r e d i c t i o n o n l y w i t h i n the

same c l a s s o f c h e m i s t r y ) - Same mode/mechanism o f a c t i o n Relevant

test/organism

The i n c o r p o r a t i o n o f these c r i t e r i a i n t o a s t r u c t u r e a c t i v i t y model i s no s i m p l e t a s k . A l t h o u g h i t i s not n e c e s s a r y t o have e i t h e r c o n g e n e r i c c h e m i s t r y o r t h e same mode o f a c t i o n f o r p r a c t i c a l SAR models, t h e s e d a t a p r o v i d e a f o u n d a t i o n f o r g r e a t e r a c c u r a c y . A wide v a r i e t y o f b i o l o g i c a l r e a c t i o n s must be c o n s i d e r e d . Q u a n t i t a t i v e s t r u c t u r e a c t i v i t y r e l a t i o n s h i p s (QSAR) have been used i n d e s i g n i n g s t r u c t u r e s f o r e f f i c a c y f o r both p h a r m a c e u t i c a l s and a g r i c u l t u r a l c h e m i c a l s . Hansch (2) was one o f the f i r s t t o attempt t o i n t e g r a t e t h e c o n g e n e r i c c h e m i s t r y , m a t h e m a t i c a l l y , w i t h b i o l o g i c a l a c t i v i t y ; a g e n e r a l i z e d Hansch r e g r e s s i o n e q u a t i o n resemb l e s the f o l l o w i n g O):

T 8

1 _ Constant + ( c o e f f i c i e n t ! x parameter^) + E f f e c t i v e Dose ( c o e f f . 2 x par.2) + ...+ ( c o e f f . x p a r . ) n

n

Hansch i n c o r p o r a t e d s e v e r a l c h e m i c a l / p h y s i c a l c h e m i c a l c h a r a c t e r i s t i c s i n t o t h i s approach (4^). He found Log P v a l u e s ( l o g o c t a n o l / w a t e r p o r t i o n c o e f f i c i e n t ) were u s u a l l y a p p l i c a b l e w i t h o t h e r parameters, such as Hammet l i n e a r f r e e - e n e r g y r e l a t i o n s h i p s and Van d e r Waals r a d i i s e l e c t i v e l y a p p l i c a b l e . Continued work i n t h i s a r e a by Hansch and o t h e r workers (5_) has expanded t h e number o f relevant c h a r a c t e r i s t i c s to include molecular o r b i t a l c a l c u l a t i o n s and d i f f u s i o n parameters. S t i l l , t h i s q u a n t i t a t i v e approach embodi e s c o n t i n u o u s parameters as an e n d p o i n t , a p a r a m e t r i c p h i l o s o p h y . On t h e o t h e r hand, s e v e r a l i n v e s t i g a t o r s (6^, 7) have taken another approach, based on p a t t e r n r e c o g n i t i o n . These dichotomous models s e a r c h f o r agreement between dependent v a r i a b l e s ; i . e . , whether a c h e m i c a l e n t i t y o r s u b s t r u c t u r e can be a s s o c i a t e d w i t h a p a r t i c u l a r t o x i c p r o p e r t y . F o r example, c e r t a i n N - n i t r o s a m i n e groups a r e a s s o c i a t e d w i t h tumors i n a n i m a l s . Since t h i s considerat i o n i s n o t dependent on a r e l a t i o n s h i p between t h e endpoint and the dose, t h e q u a n t i t a t i v e term i s dropped from QSAR and the e f f o r t s i m p l y named SAR. T h i s approach i s b e s t e x p r e s s e d by t h e dependent equation: T o x i c Response = f (Chemical

Structure)



5.

STEVENS A N D SUMNER


T h i s i s an overwhelming concept when one e n v i s i o n s the number o f permutations p o s s i b l e . The Food and Drug A d m i n i s t r a t i o n (FDA) has t a c k l e d t h i s f o r m i d a b l e t a s k w i t h j u s t 33 q u e s t i o n s t o d e f i n e t h r e e presumptive t o x i c i t y c a t e g o r i e s ( 8 ) . The c a t e g o r i e s a r e used t o determine the degree o f t e s t i n g r e q u i r e d by the FDA. This scheme, though b a s i c and v e r y p r a g m a t i c , r e p r e s e n t s a p o t e n t i a l l y u s e f u l t o o l f o r t o x i c o l o g i s t s , b i o l o g i s t s and s y n t h e s i s c h e m i s t s . I t o f f e r s a mechanism t o r e c o g n i z e the p o t e n t i a l h a z a r d o f a compound o r to change the m o l e c u l e to a v o i d u n n e c e s s a r y t o x i c i t y . The 33 q u e s t i o n s on s t r u c t u r e are answered by a yes o r no (Appendix 1 ) . Each answer l e a d s e i t h e r t o a f u r t h e r q u e s t i o n o r to the c l a s s i f i c a t i o n i n t o one o f t h r e e c l a s s e s o f presumptive t o x i c i t y (Table I I ) . Table

II.

FDA

Presumptive

Toxicity

Classification

Description Relatively

I

innocuous

Intermediately

Classifications

II

toxic

Presumptively mostly

toxic

III

The r e q u i r e m e n t s f o r t h i s approach are s i m p l e . The s t r u c t u r a l f o r m u l a , as w e l l as a knowledge o f c h e m i s t r y and b i o l o g y a r e used to make judgements on m e t a b o l i s m . The FDA has e v a l u a t e d the r e l i a b i l i t y o f the scheme, u s i n g l i t e r a t u r e d a t a and FDA's i n v e n t o r y o f over 1,500 s u b s t a n c e s and the no o b s e r v a b l e e f f e c t l e v e l s d e r i v e d from s u b c h r o n i c and chronic studies. Thus f a r , i n a r e t r o s p e c t i v e and p r o s p e c t i v e r e v i e w , based upon the a v a i l a b l e i n f o r m a t i o n at the Agency, the FDA has no i n d i c a t i o n t h a t t h e s e 33 q u e s t i o n s do not a d e q u a t e l y c l a s s i f y compounds ( c r o s s - c o m p a r i s o n o f s t r u c t u r e t o f i n d i n g s ) i n t o the t h r e e presumptive c l a s s e s . However, i t i s o b v i o u s t h a t the FDA approach i s a g e n e r a l i z e d t o x i c i t y c l a s s i f i c a t i o n and cannot s u p p l y the answers t o q u e s t i o n s such a s , what are the m e t a b o l i t e s and which compounds w i l l be t e r a t o g e n s , mutagens o r oncogens. A l t h o u g h the FDA approach has b u i l t i t s f o u n d a t i o n on a broad d a t a base, i t does not narrow i t s spectrum t o a p r e c i s e t o x i c o l o g i c a l r e s p o n s e o r mode o f a c t i o n . I t has been c l e a r f o r some time t h a t p a t t e r n r e c o g n i t i o n approaches would not go f a r w i t h o u t the computer (9.). U t i l i z i n g t e c h n i q u e s , such as r e g r e s s i o n , d i s c r i m i n a n t , and f a c t o r a n a l y s e s p r o g r e s s i n SAR (10) has been f u r t h e r enhanced. T h i s e v o l u t i o n has l e d t o such u s e f u l t o o l s as d e s c r i b e d i n T a b l e I I I .


45

46


Table I I I .

Computer Assisted Research Models

Function

Model


• Computer Assisted • Can predict precursor's need for Synthesis Planning (CASP) synthesis as well as most l i k e l y metabolites • Constrained Structure Generation (CONGEN)

• Can generate a l l possible isomers i f the substructure elements and the molecular formula are provided

• ADAPT (11)

• Affords an opportunity to predict the a c t i v i t y and properties of unstudied structures through application of pattern recognition and s t a t i s t i c s

Although to the synthesis chemist, CASP and CONGEN may seem highly i n t r i g u i n g , to the b i o l o g i s t , a system such as ADAPT opens the door to the design of new and efficacious molecules for a myriad of uses. In fact, many of the major chemical industries have begun to incorporate such computer assisted systems into their research programs as a component of informed design, rather than the formerly predominant serendipitous discovery. These SAR techniques have not surplanted standard b i o l o g i c a l e f f i c a c y models; however, the information gained helps to establish the foundation for enhanced pattern recognition. In pattern recognition modeling, such as ADAPT, i t i s d i f f i cult to e f f e c t i v e l y v i s u a l i z e and manipulate chemical structure. Instead, there has been an e f f o r t to translate abstract structure into quantities and/or numerical e n t i t i e s (10), referred to as molecular descriptors. Such descriptors have been c l a s s i f i e d as presented i n Table IV. Table IV.

Molecular Descriptors Used i n SAR

Classes of Descriptors

Examples

• Geometric/Biophysical Rotation axes, molecular volume and surface area • Physiochemical

Log P, atomic charges, linear-free energy relationships

• Structural

Molecular weight, atomic numbers, types of bonding, molecular o r b i t a l c a l c u l a tions, ring structures

• Substructural/ topological

Topological and physiochemical propert i e s of substructural arrangements, molecular symmetry and/or bonding


5.


47


I t i s o b v i o u s l y not p o s s i b l e t o u n r a v e l the e n t i r e c o m p l e x i t y o f the p h y s i c a l , c h e m i c a l and b i o l o g i c a l p r o p e r t i e s o f even the simplest of molecules. However, f o c u s i n g on the a p p a r e n t l y pertinent d e s c r i p t o r s f o r s t r u c t u r e s , one can, v i a p a t t e r n r e c o g n i t i o n , b e g i n to equate t o x i c o l o g i c a l r e s p o n s e w i t h s t r u c t u r e s :


Toxicological

response =

f(structure) = f(molecular

descriptors)

J u r s et_ a l (10) q u a l i t a t i v e l y and q u a n t i t a t i v e l y examined the c o r r e l a t i o n of a v a r i e t y of molecular d e s c r i p t o r s f o r p o l y c y c l i c a r o m a t i c and n i t r o a m i n e compounds w i t h c a r c i n o g e n e s i s . The r e s u l t o f t h e s e e f f o r t s has been the e v o l u t i o n o f p r e d i c t i v e e q u a t i o n s which c a p t u r e the o n c o g e n i c r e s p o n s e f o r these c l a s s e s o f compounds as a f u n c t i o n o f the m o l e c u l a r d e s c r i p t o r s . E n s l e i n and coworkers (12, 13, 14, 15) have u t i l i z e d t h i s approach to d e v e l o p p r e d i c t i v e models f o r c a r c i n o g e n i c i t y , t e r a t o g e n i c i t y and m u t a g e n i c i t y , as w e l l as f o r a c u t e t o x i c i t y endpoints . T h i s approach i n p r e d i c t i v e t o x i c o l o g y has m a n i f e s t i t s e l f by the i n c o r p o r a t i o n o f c e r t a i n key p r i n c i p l e s . These i n c l u d e : • Marker compounds, compounds w i t h a known b i o l o g i c a l e n d p o i n t , used t o produce p r e d i c t i v e e q u a t i o n s . • E q u a t i o n s are used f o r comparison o f unknown compounds and to t e s t the system. • F i n a l l y , a s t a t i s t i c a l approach, such as s t e p w i s e r e g r e s s i o n ( i f e n d p o i n t i s c o n t i n u o u s ) or d i s c r i m i n a n t a n a l y s e s ( i f the endp o i n t i s c a t e g o r i c a l ) to v e r i f y the q u a l i t y o f f i t . D e s p i t e the g r e a t s u c c e s s t h a t has been a c h i e v e d w i t h the approach t a k e n by E n s l e i n and c o w o r k e r s , the u t i l i t y o f the system i s l i m i t e d by the depth o f the d a t a b a s e a v a i l a b l e i n the open literature. I f p r o p r i e t a r y d a t a were a v a i l a b l e from the f i l e s o f p h a r m a c e u t i c a l and a g r i c u l t u r a l companies, a new d i m e n s i o n to the r e l i a b i l i t y might be added. The p o s s i b i l i t y t h a t new compounds can be examined f o r t o x i c i t y b e f o r e they are s y n t h e s i z e d i s intriguing. However, the r e l e a s e o f p r o p r i e t a r y i n f o r m a t i o n from the bulwark o f i n h e r e n t l y c o m p e t i t i v e o r g a n i z a t i o n s i s not l i k e l y i n the near f u t u r e . T h e r e f o r e , Dr. E n s l e i n p l a n s to make h i s s o f t w a r e a v a i l a b l e by mid-1987. Then, p e r h a p s , the c r i t e r i a f o r r e l i a b l e s t r u c t u r e a c t i v i t y m o d e l i n g i n the a r e a o f t o x i c o l o g y may be b e t t e r s e r v e d . However, u n t i l t h e s e c r i t e r i a are a c h i e v e d , i t w i l l be e s s e n t i a l to r e l y on the more pragmatic approaches t o s i m u l a t i o n modeling; that i s , bioassays. Predictive Empirical

Systems

With p r e s s u r e s from the animal r i g h t s movement, an impetus has been g e n e r a t e d f o r the development o f i n v i t r o and/or computer models to reduce the l e v e l o f i n v i v o t e s t i n g . In the s e v e n t i e s , the hope o f the f u t u r e was p l a c e d i n what was then c o n s i d e r e d a p o t e n t i a l replacement t e c h n i q u e f o r the l i f e t i m e rodent b i o a s s a y for cancer a s s e s s m e n t — t h e short-term mutagenicity t e s t s , p a r t i c u l a r l y the Ames E v a l u a t i o n ( 1 6 ) . B r u s i c k (17) has shown t h a t the c o r r e l a t i o n between a p o s i t i v e mouse b i o a s s a y and a p o s i t i v e r a t b i o a s s a y f o r a s e l e c t e d group o f m a t e r i a l s i s no b e t t e r t h a n the

American Chemical Society Library 1155 16th St.,

N.w.

Ragsdale and Kuhr; Pesticides D.C. Society: 20036 ACS Symposium Series; Washington, American Chemical Washington, DC, 1987.

PESTICIDES: M I N I M I Z I N G T H E RISKS

48

match f o r a p o s i t i v e Ames and/or a p o s i t i v e r a t o r mouse b i o a s s a y ( T a b l e V ) . I n a d d i t i o n , a comparison o f the E n s l e i n ' s SAR C a r c i n o gen model (18) f o r these same human c a r c i n o g e n s i s p r o v i d e d . T a b l e V.

Chemicals E v a l u a t e d as P o s i t i v e f o r C a r c i n o g e n i c i t y i n Humans Compared t o t h e Response i n Rodent and B a c t e r i a l P r e d i c t i v e A s s a y s , D. B r u s i c k (17)


1

Chemical 4-Aminobiphenyl Arsenic Asbestos Benzene Benzidine Bis(chloromethyl)ether Chromium and some chromium compounds Cyclophosphamide Diethylstilbestrol Melphalan Mustard Gas 2-Naphthylamine Soot, t a r s Vinyl chloride Percent

p r e d i c t a b i l i t y o f humans

Ames E n s l e i n s Mouse Rat Model Bioassay Bioassay Test +

+

+

+

-+

-+

-

-+

-+ + +

0 0 0 + 0 0

+

+

-+

0 + + + 0 +

+ + + + + + No D a t a

+

-

-+

+ + + + + + +

69

79

+ + + + 71

100

+ = positive - = negative 0 = cannot be e v a l u a t e d

The d a t a from E n s l e i n ' s Model show t h a t a good match w i t h the rodent b i o a s s a y s i s p o s s i b l e f o r o r g a n i c s upon which the SAR model i s based. The SAR model i s i n e f f e c t i v e f o r m e t a l s , s i m p l e h y d r o carbons l i k e benzene, m i x t u r e s and hormones. W i t h i n these l i m i t s , the p r e d i c t i o n i s 100%. C o n s i d e r i n g the r e l i a b i l i t y o f rodent and i n v i t r o b i o a s s a y s t o p r e d i c t t h e human r e s p o n s e , i t i s p o s s i b l e , w i t h c o n t i n u e d development, t h a t i n t h e f u t u r e , SAR may become a p o w e r f u l t o o l t o supplement our o t h e r s o u r c e s o f t o x i c o l o g i c a l information.


5.


49


Appendix 1 If 'No'

If 'Yes

1


.. .Proceed to... 1.

Normal body constituent?

2

2.

Certain nitrogen FG's?

3

3.

"Non-physiological" elements?

5

4.

Innocuous salt of above?

5.

Simple

6.

Certain p-alkoxy benzenes?

7.

Heterocyclic?

8.

Lactone or c y c l i c diester?

9.

Certain lactones?

23

—— III

10.

Three-member heterocycle?

11

III

11.

Hetero ring; strange FG's ...?

12

12.

Heteroaromatic?

22

13.

Any substituents?

14.

More than one aromatic

15.

Readily hydrolyzed ...?

33

16.

Common terpene?

17

I

17.


19

18

18.

Is i t one of

I

II

19.

Open chain?

23

20.

Linear or simply branched aliphatic ...?

22

HC or common CHO?

?

4

III

7

6

1

7

III

16

ring?

III

_

8 9

10, 20,10

33

_

13

III

14

22

15 22(16)

—— —

20 21


50


If 'No'

If 'Yes'


...Proceed to... 21.

Three or more types of FG's?

18

Ill

22.

Common component of food or structurally closely related ...?

33

II

23.

Aromatic?

24

27

24.

Monocarbocyclic; certain FG's ...?

25

18

25.

Cyclopropane or cyclobutane ...?

26

II

26.

No unusual FG's; certain ketones?

22

II

27.

Any ring substituents?

III

28

28.

More than one aromatic ring?

30

29

29.


33

30,18

30.

Other than certain substituents?

18(19)

31

31.

Acyclic acetal, -ketal, -ester ...?

32

18,19

32.

Only certain FG's, plus ...?

22

II

33.

Enough sulfonate/sulfamate?

III

I

Literature Cited 1. 2. 3. 4. 5. 6. 7. 8. 9.

Stevens, J. T.; Sumner, D. D. J . Toxicol.-Clin. Toxicol. 1983, 19,781. Hansch, C. Chem. Res. 1969, 2, 232. Enslein, K. Pharmacol. Rev. 1984, 36, 131S. Leo, A; Jow, P.Y.C.; Silipo, C.; Hansch, C. J. Med. Chem. 1975, 18, 865. "QSAR and Strategies in the Design of Bioactive Compounds," Seydel, J. K., ed. Verlagsgesellschaft, Weinheim, FRG. 1985. Jurs, P. C.; Chou, J. T.; Yuan, M. J. Med. Chem. 1979, 22, 476. Tinker, J . F. J . Computational Chemistry. 1981, 2, 231. Rulis, A. The Toxicology Forum; 1982 Annual Summer Mtg. 1982, p. 352. Jurs, P. C.; Ham, C. L . ; Brugger, W. E. In "Odor Quality and Chemical Structure"; Moskowitz, H. R.; Warren, C. B; Eds.; ACS Symposium Series No. 148, American Chemical Society: Washington, D.C., 1981; pp. 143-160.


5.

STEVENS AND SUMNER

10. 11. 12. 13. Downloaded by UNIV OF MASSACHUSETTS AMHERST on May 25, 2018 | https://pubs.acs.org Publication Date: March 12, 1987 | doi: 10.1021/bk-1987-0336.ch005

14. 15. 16. 17. 18.


51

Jurs, P. C ; Hasan, M. N.; Henry, D. R.; Stouch, T. R.; Whalen-Pederson, E. K. Fund. Appl. Toxicol. 1983, 3, 343. Stuper, A. J.; Brugger, W. E . ; Jurs, P. C. In "Computer Assisted Studies of Chemical Structure and Biological Function;" Wiley-Interscience, New York, 1979. Enslein, K.; Craig, P. N. J . Toxicol. Environ. Health 1982, 10, 521. Enslein, K.; Lander, T. R.; Strange, J. R. Teratog. Carcinog. Mutagen. 1983, 3, 289. Enslein, K.; Lander, T. R.; Tomb, M. E . ; Landis, W. G. Teratog. Carcinog. Mutagen. 1984, 6 Enslein, K.; Lander, T. R.; Tomb, M. E . ; Craig, P. N. Benchmark Papers Toxicol. 1983, 1, 1. Wolff, G. L . ; J . Environ. Pathol. Toxicol. 1977,1,79 Brusick, D. In "Application of Biological Markers to Carcinogenicity Testing"; Milman, H. A.; Sell, S.; Eds. Plenum Press, N.Y., 1983; pp. 153-163. Enslein, K., personal communication.

RECEIVED October 14, 1986


Simulation Modeling in Toxicology - ACS Symposium Series (ACS

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