Induction of forward mutations at the thymidine kinase locus of mouse lymphoma cells: evidence for electrophilic and non-electrophilic mechanisms

Mutation Research 397 Ž1998. 313–335

Induction of forward mutations at the thymidine kinase locus of mouse lymphoma cells: evidence for electrophilic and non-electrophilic mechanisms B. Henry a , S.G. Grant a

a,)

, G. Klopman b, H.S. Rosenkranz

a

Department of EnÕironmental and Occupational Health, UniÕersity of Pittsburgh, 260 Kappa DriÕe, Pittsburgh, PA 15238, USA b Department of Chemistry, Case Western ReserÕe UniÕersity, CleÕeland, OH 44106, USA Received 9 June 1997; revised 1 October 1997; accepted 10 October 1997

Abstract A database of 209 chemicals tested for induction of forward mutations at the heterozygous thymidine kinase ŽTK ". locus in L5178Y mouse lymphoma cells was analyzed for structure–activity relationships using the MultiCASE expert system. Consistent with evidence of several contributing biological mechanisms, these studies suggest that such mutations may occur by more than one mechanism. As might be expected, there was evidence for a component involving direct electrophilic attack on the cellular DNA, in a manner previously established as causative in the induction of mutations in Salmonella. In addition, however, there was also strong evidence for another mechanism or mechanisms involving chromosome missegregation, cellular toxicity or an alternate site of action, such as the microtubules. q 1998 Elsevier Science B.V. Keywords: Structure–activity relationships; MultiCASE; Mutation; Segregation; Chromosome missegregation; Recombination; Gene inactivation; Molecular carcinogenesis; Tumor suppressor gene

1. Introduction The mouse lymphoma L5178YTK " assay ŽMLA. is a second-generation mutagenicity assay designed to account for perceived deficiencies in earlier tests at haploid loci. Since the optimal design for a system of mutation detection is the detection of a single event, early tests analyzed either haploid organisms, such as Salmonella, or haploid loci in diploid organisms, such as the X-linked hprt locus in CHO cells.

) Corresponding author. Tel.: q1 412 9676535; Fax: q1 412 6241020; E-mail: [email protected]

The majority of genes in higher eukaryotes, including man, are diploid, however, and it was not clear that tests in single allele systems adequately modeled mutational events at diploid loci. The MLA test detects forward ‘mutational’ events at the autosomal mammalian thymidine kinase ŽTK. locus with single-hit kinetics. This is accomplished by utilizing phenotypically wild-type cells that are actually heterozygous for a preexisting TKy mutation, such that a second mutation at the remaining TKq locus is sufficient for expression of the recessive drug-resistant phenotype. Thus, in concert with the aforementioned Salmonella and hprt assays, MLA became one of the earliest mutagenicity assays applied

0027-5107r98r$19.00 q 1998 Elsevier Science B.V. All rights reserved. PII S 0 0 2 7 - 5 1 0 7 Ž 9 7 . 0 0 2 3 1 - 5

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B. Henry et al.r Mutation Research 397 (1998) 313–335

to the screening of potential carcinogens and mutagens w1x. It soon became clear that ‘mutations’ in the MLA system were somewhat different than those found in earlier systems; they occurred at higher spontaneous frequencies, could involve both complex and subtle chromosomal changes and could be induced by chemicals active in no other mutagenicity detection system w2–7x. It was found that, in addition to the types of mutational events documented in other systems, i.e. point mutations and small deletions, any mechanism that inactivated or eliminated the wildtype TKq allele would allow for the phenotypic expression of the preexisting TKy allele and therefore an increase in the frequency of drug-resistant ‘mutant’ colonies. These additional mechanisms included such diverse events as epigenetic gene inactivation, somatic recombination, chromosome loss or missegregation. An indication of this diversity of etiology was in the recognition of two distinct classes of phenotypically TKy colonies: large Ž l. colonies arising by mechanisms such as specific gene mutation, recombination or gene inactivation, and small Ž s . colonies arising by mechanisms that also led to compromised cell viability, such as chromosome loss, large deletion or chromosome missegregation. Indeed, the MLA system was sensitive to all of the molecular events later proven to contribute to somatic segregation or ‘loss of heterozygosity’ ŽLOH. at recessive tumor suppressor or mutator genes during progressive carcinogenesis w4,5,8–11x. Unfortunately, the vast body of experimental results did not discriminate or even record the nature of the colonies that were scored w12x. Practically, however, despite the mechanistic similarities between MLA induction and certain steps in carcinogenesis, subsequent studies revealed that the assay did not effectively identify carcinogens and non-carcinogens w13x. The routine use of the MLA system for such purposes has therefore decreased substantially w14x, although the inclusion of MLA in batteries of assays for carcinogenicityrmutagenicity testing is still recommended in some regulatory jurisdictions. In the present study, we sought to take advantage of recent MLA results Žderived using a standardized protocol. w15–24x to derive a structure–activity relationship ŽSAR. model relating the induction of mutations in this system to chemical structures. The

CASErMultiCASE system w25x is well suited for this purpose as it has been applied successfully to the study of a number of mutagenic and clastogenic phenomena w26–31x. Moreover, it can handle biological phenomena that arise by multiple mechanisms, as appears to be the case in the MLA system. Such SAR studies could reveal insights not only into the basis of a chemical’s mechanism of action but also its possible relationship to other genotoxic and mutagenic phenomena. The SAR model developed may also be used to predict the potential activity of yet untested chemicals in the MLA system. 2. Materials and methods 2.1. Expert system: CASEr MultiCASE The MultiCASE program w25,32x selects its own descriptors automatically from a learning set composed of active and inactive molecules. These descriptors are readily recognizable single, continuous structural fragments that are embedded in the complete molecule. Each of these fragments is characterized by a confidence level and a probability of activity which is derived from their distribution among active and inactive molecules. Positive descriptors are designated as ‘biophores’, i.e. structural fragments that are significantly associated with activity. Similarly, structures significantly associated with lack of activity are designated as ‘biophobes’. Upon completion of these analyses, MultiCASE ranks biophores by their skewed distribution among the activity classes and selects the most important of these biophores as the functionality that is responsible for the experimentally observed activity of the molecules that contain it. MultiCASE then uses the molecules containing this biophore as a learning set to identify the chemical properties Ži.e. structural fragments. or physical chemical properties Že.g. log P, water solubility, quantum mechanical parameters such as HOMO and LUMO, etc.. that modulate Žeither augment or decrease. the activity of the biophore identified initially. This will result in a QSAR equation for this particular subset of molecules. If the dataset is congeneric, then the single biophore and associated modulators may explain the activity of the entire learning set. This will usually not occur, however, and there will be a residue of molecules

B. Henry et al.r Mutation Research 397 (1998) 313–335

not explained by the single biophore and related modulators. When this happens, the program will remove from consideration the molecules already explained by the previous biophore and will search for the next biophore and associated modulators. The process is continued until the activity of all of the molecules in the learning set has been explained. The resulting list of biophores may then be used to predict the activity of yet untested molecules. Thus, upon submission for evaluation, MultiCASE will determine if an unknown molecule contains a biophore. If it does not, the molecule will be predicted to be inactive unless it contains a group that chemically resembles one of the biophores, in which case it will be flagged. When the molecule contains a biophore, the presence of modulators for that biophore will be investigated. Obviously, while biophores are the determining structures, the modulators may determine whether and to what extent the biological potential of the chemical is expressed. MultiCASE will then make qualitative as well as quantitative predictions of the activity of the unknown molecule. Additionally, MultiCASE incorporates the following rules to identify two-dimensional distance descriptors based upon the presence of lipophilic centers. These two dimensional distances are calculated from the molecular structure. Heteroatoms and lipophilic carbon atoms are designated as ‘special’ atoms. A carbon atom is designated as a lipophilic center if it is at least four bonds away from a heteroatom and is also the furthest carbon away from the heteroatom when its neighbors are considered. After all the ‘special’ atoms are identified, the distances between all possible pairs are calculated. The distribution of these descriptors among active and inactive molecules is analyzed for statistical significance. Various atom groupings are also investigated, i.e. hydrogen bond acceptors and donors as well as halogens. The significance of the concordance between experimental and predicted results is then calculated as described previously w33x wherein a x 2 value of 3.86 indicates a confidence level of 95%. The MultiCASE program is available from MULTICASE Inc., P.O. Box 22517, Beachwood, OH 44122, USA. Similarities between structural determinants associated with different biological activities are taken to

315

indicate similarities in mechanisms of action w34,35x. In order to investigate possible commonalities Žand antagonisms. among the databases studied herein, the structural determinants Žactivating and inactivating. identified by MultiCASE as significantly associated with specific biological activities were compared. An overlap is defined as complete identity or when one fragment is embedded in the other Že.g. CH 2 –NH– is embedded in CH 2 –NH–C– . w36x. The informational content of an SAR model was ascertained by challenging it with a collection of 5000 chemicals representing the universe of chemicals. The percent of predictions accompanied by warnings of the presence of fragments ‘unknown’ to the model is designated as the informational content w36x. 2.2. Physical chemical properties To obtain a quantitative measure of the electronegativity of a molecule, we used the half sum of the ionization potential and the electron affinity w37x as given by ŽHOMOq LUMO.r2. Similarly, the electronic gap Ž E . is defined as ŽHOMO y LUMO.r2 w38x. HOMO is the energy of the highest occupied molecular orbital and LUMO is the energy of the lowest unoccupied molecular orbital. HOMO and LUMO were calculated as described previously w39,40x. In this paper, the electronegativities are expressed in units of b , where b is the resonance integral between two double bonded carbon atoms. Log of the octanol–water partition coefficient Žlog P . and solubility in water were calculated as described previously w41,42x. 2.3. Databases The experimental results ŽTable 1. for the induction of forward mutation at the heterozygous thymidine kinase locus of mouse L5178Y lymphoma cells Žthe standardized MLA mutation detection and quantification system. were generated under the auspices of the U.S. National Toxicology Program ŽNTP. w15–24x. For comparative purposes, we also created several databases of mutagenicity or projected mutagenicity in Salmonella typhimurium. Mutagenic activities

B. Henry et al.r Mutation Research 397 (1998) 313–335

316

Table 1 Chemicals in the NTP mouse lymphoma mutation database used for SAR modeling CAS No.

Structure name

Activity

ReferenceŽs.

298-18-0 151-23-3 630-20-6 2489-77-2 96-12-8 95-50-7 78-87-5 106-88-7 7329-37-8 1212-29-9 106-99-0 142-28-9 10061-02-6 105-55-5 106-47-7 123-91-1 3322-93-8 706-87-6 89-25-8 2432-99-7 2438-88-2 1746-01-6 271-89-6 4460-86-0 88-06-2 95-86-3 120-83-2 54150-69-5 609-20-1 91-08-7 1548-70-6 53-96-3 99-57-0 121-88-0 2185-92-4 149-30-4 5307-14-2 55345-04-5 61-82-5 563-47-3 6959-47-3 56-49-5 101-77-9 101-80-4 80-08-0 136-77-6 99-56-9 92-93-3 56-57-5 320-67-2 65-79-4 3131-60-0 148-24-3

Žq,y .-1,2:3,4-Diepoxybutane ŽSodium. lauryl sulfate 1,1,1,2-Tetrachloroethane 1,1,3-Trimethyl-2-thiourea 1,2-Dibromo-3-chloropropane 1,2-Dichlorobenzene 1,2-Dichloropropane 1,2-Epoxybutane 1,2-Epoxyhexadecane 1,3-BisŽcyclohexyl.thiourea 1,3-Butadiene 1,3-Dichloropropane 1,3-Dichloropropene 1,3-Diethyl-2-thiourea 1,4-Dichlorobenzene 1,4-Dioxane 1-Ž1,2-Dibromoethyl.-3,4-dibromocyclohexane 1-Epoxyethyl-3,4-epoxycyclohexene 1-Phenyl-3-methyl-5-pyrazolone 11-Aminoundecanoic acid 2,3,5,6-Tetrachloro-4-nitroanisole 2,3,7,8-Tetrachlorodibenzo-p-dioxin 2,3-Benzofuran 2,4,5-Trimethoxy benzaldehyde 2,4,6-Trichlorophenol 2,4-Diaminophenol 2,4-Dichlorophenol 2,4-Dimethoxyaniline hydrochloride 2,6-Dichloro-p-phenylenediamine 2,6-Toluene diisocyanate 2,6-Toluenediamine dihydrochloride 2-Acetylaminofluorene 2-Amino-4-nitrophenol 2-Amino-5-nitrophenol 2-Biphenylamine hydrochloride 2-Mercaptobenzothiazole 2-Nitro-p-phenylenediamine 2-Nitrofluorene 3-Aminotriazole 3-Chloro-2-methyl-1-propene 3-Chloromethylpyridine hydrochloride 3-Methylcholanthrene X 4,4 -Methylenedianiline X 4,4 -Oxydianiline X 4,4 -Sulfonyldianiline 4-Hexylresorcinol 4-Nitro-o-phenylenediamine 4-Nitrobiphenyl 4-Nitroquinoline-1-oxide 5-Azacytidine 5-Chloro-o-toluidine 6-Azacytidine 8-Hydroxyquinoline

Active Inactive Active Inactive Active Active Active Active Active Active Inactive Inactive Active Active Marginal Inactive Active Active Active Inactive Active Inactive Active Active Active Active Inactive Active Active Active Active Active Active Active Active Inactive Active Active Inactive Active Active Active Active Active Inactive Active Active Inactive Active Active Inactive Inactive Active

w18x w19x w19,24x w19x w24x w24x w24x w18,21x w19x w17x w22x w24x w24x w19x w19,21x w23x w23x w19x w23x w19x w19x w23x w18x w19x w19x w15x w21x w19x w19x w23x w24x w15x w21x w21x w19x w21x w15x w15x w18x w24x w18x w15,18,19,22–24x w19x w19x w19x w21x w15x w15x w15x w20x w19x w20x w15,18x

B. Henry et al.r Mutation Research 397 (1998) 313–335

317

Table 1 Žcontinued. CAS No.

Structure name

Activity

ReferenceŽs.

58429-99-5 41372-08-1 10127-02-3 8048-52-0 57-06-7 41372-08-1 7177-48-2 62-53-3 100-52-7 71-43-2 92-87-5 140-11-4 100-51-6 100-44-7 563-47-3 108-60-1 80-05-7 75-27-4 75-25-2 85-68-7 128-37-0 3567-69-9 569-61-9 2475-45-8 2832-40-8 842-07-9 133-06-2 12789-03-6 115-28-6 79-11-8 108-90-7 510-15-6 124-48-1 113-92-8 94-20-2 87-29-6 56-72-4 108-94-1 69-74-9 21739-91-3 5160-02-1 5989-27-5 1163-19-5 103-23-1 131-17-9 333-41-5 62-73-7 60-57-1 101-90-6 597-25-1 120-61-6 68-12-2 86-30-6 97-77-8

9,10-Dimethyl-1,2-benzanthracene Acid yellow 73 Acridine orange Acriflavin Allyl isothiocyanate Žmustard gas. Alpha-methylDOPA sesquihydrate Ampicillin trihydrate Aniline Benzaldehyde Benzene Benzidine Benzyl acetate Benzyl alcohol Benzyl chloride Beta,beta-dimethylvinyl chloride BisŽ2-chloro-1-methylethyl.ether Bisphenol A Bromodichloromethane Bromoform Butyl benzyl phthalate Butylated hydroxytoluene ŽBHT. C.I. acid red 14 Žazo rubine. C.I. basic red 9 C.I. disperse blue 1 C.I. disperse yellow 3 C.I. solvent yellow 14 Captan Chlordane Chlorendic acid Chloroacetic acid Chlorobenzene Chlorobenzilate Chlorodibromomethane Chloropheniramine maleate Chlorpropamide Cinnamyl anthranilate Coumaphos Cyclohexanone Cytarabine hydrochloride Cytembena D and C red 9 D-limonene Decabromobiphenyl oxide DiŽ2-ethylhexyl.adipate Diallyl phthalate Diazinon Dichlorvos Dieldrin Diglycidyl resorcinol ether Dimethyl morpholinophosphoramidate Dimethyl terephthalate Dimethylformamide Diphenylnitrosamine Disulfiram

Active Active Active Active Active Active Inactive Active Active Inactive Active Active Marginal Active Inactive Active Inactive Active Active Inactive Active Inactive Inactive Active Active Active Active Active Active Active Active Active Active Inactive Inactive Marginal Inactive Inactive Active Active Inactive Inactive Inactive Inactive Active Active Active Active Active Active Inactive Marginal Inactive Inactive

w15x w24x w15x w15x w19x w21x w21x w23x w23x w15x w15x w19x w19,21x w18x w21x w19x w24x w18,21x w21x w24x w18x w19x w21x w21x w19x w23x w15x w19x w18,21x w15x w19x w19x w23x w21,23x w23x w24x w19x w19x w19x w23x w21,24x w21x w19,21x w19x w24x w19x w21x w23x w21x w24x w24x w18x w15x w23x

318

B. Henry et al.r Mutation Research 397 (1998) 313–335

Table 1 Žcontinued. CAS No. 15356-70-4 9002-92-0 150-38-9 115-29-7 72-20-8 1239-45-8 140-88-5 62-50-0 100-41-4 107-07-3 107-21-1 97-53-0 2783-94-0 216-17-2 86-73-7 110-00-9 98-01-1 54-31-9 105-87-3 111-30-8 2784-94-3 33229-34-4 76-44-8 3105-97-3 58-93-5 123-31-9 54-85-3 78-89-1 50-81-7 434-13-9 1634-78-2 542-78-9 87-78-5 108-78-1 91-80-5 598-55-0 624-83-9 80-62-6 66-27-3 1910-42-5 75-09-2 150-68-5 96-31-1 96-45-7 28322-02-3 109-69-3 70-25-7 135-88-6 366-70-1 67-20-9 59-87-0 51-75-2 95-51-2

Structure name DL-menthol Dodecyl alcohol ethoxylated EDTA trisodium salt Endosulfan Endrin Ethidium bromide Ethyl acrylate Ethyl methanesulfonate Ethyl benzene Ethylene chlorohydrin Ethylene glycol Eugenol FD and C yellow 6 Fluometuron Fluorene Furan Furfural Furosemide Geranyl acetate Glutaraldehyde HC blue no. 1 HC blue no. 2 Heptachlor Hycanthone Hydrochlorothiazide Hydroquinone Isoniazid Isophorone L-ascorbic acid Lithocholic acid Malaoxone Malonaldehyde Mannitol Melamine Methapyrilene Methyl carbamate Methyl isocyanate Methyl methacrylate Methyl methanesulfonate Methyl viologen Methylene chloride Monuron X N,N -dimethylurea X N,N -ethylenethiourea N-4-fluorenylacetamide N-butyl chloride X N-methyl-N -nitro-N-nitrosoguanidine N-phenyl-2-naphthylamine Natulan Nitrofurantoin Nitrofurazone Nitrogen mustard o-chloroaniline

Activity

ReferenceŽs.

Inactive Inactive Inactive Active Inactive Inactive Active Active Active Inactive Inactive Active Inactive Inactive Inactive Active Active Active Active Active Active Active Active Active Active Active Inactive Active Inactive Active Active Active Inactive Inactive Inactive Inactive Active Active Active Active Marginal Inactive Inactive Active Inactive Inactive Active Active Active Active Active Active Active

w24x w24x w19x w19x w23x w15x w19x w15,18,19,22–24x w19x w19x w23x w24x w19x w19x w15x w19x w18x w21x w24x w18x w24x w24x w19x w15x w21x w18x w15x w19x w24x w23x w24x w21x w24x w19x w15,23x w21x w16x w21x w15,18,19,22–24x w19x w21x w19x w19x w19x w23x w21x w15x w21x w15x w21x w21x w15x w23x

B. Henry et al.r Mutation Research 397 (1998) 313–335

319

Table 1 Žcontinued. CAS No. 95-53-4 1936-15-8 2058-46-0 72-55-9 123-30-8 105-11-3 106-40-1 106-47-8 100-02-7 1825-21-4 76-01-7 136-40-3 3456-10-9 108-95-2 59-42-7 88-96-0 51-03-6 36355-01-8 121-79-9 115-07-1 75-56-9 110-86-1 91-22-5 50-55-5 108-46-3 83-79-4 94-59-7 148-18-5 126-92-l 18883-66-4 1596-84-5 57-50-1 127-69-5 75-65-0 127-18-4 1897-45-6 60-54-8 124-64-1 108-88-3 584-84-9 52-68-6 79-01-6 91-81-6 78-42-2 51-79-6 73-35-4 1330-20-7 17924-92-4 137-30-4

Structure name o-toluidine Orange G Oxytetracycline X p,p -DDE p-aminophenol p-benzoquinone dioxime p-bromoaniline p-chloroaniline p-nitrophenol Pentachloroanisole Pentachloroethane Phenazopyridine hydrochloride Phenesterine Phenol Phenylephrine Phthalamide Piperonyl butoxide Polybrominated biphenyl Propyl gallate Propylene Propylene oxide Pyridine Quinoline Reserpine Resorcinol Rotenone Safrole Sodium diethyldithiocarbamate SodiumŽ2-ethylhexyl.alcohol sulfate Streptozotocin Succinic acid 2,2-dimethylhydrazide Sucrose Sulfisoxazole tert-butyl alcohol Tetrachloroethylene Tetrachloroisonaphthonitrile Tetracycline TetrakisŽhydroxymethyl.phosphonium ClrSO4 Toluene Toluene-2,4-diisocyanate Trichlorfon Trichloroethylene Tripelennamine TrisŽ2-ethylhexyl.phosphate Urethane Vinylidene chloride Xylene mixture Zearalenone Ziram

Activity

ReferenceŽs.

Inactive Active Active Active Active Active Inactive Active Inactive Active Active Active Inactive Inactive Active Inactive Active Inactive Active Marginal Active Inactive Active Inactive Active Active Inactive Active Inactive Active Active Inactive Active Inactive Inactive Inactive Marginal Active Active Active Active Active Inactive Inactive Inactive Active Active Marginal Active

w15x w23x w21,23x w19x w15x w19x w15x w21x w15x w15x w19x w23x w19x w19x w21x w19x w19x w24x w19x w22x w22x w18x w19x w19x w18x w19,21x w18x w23x w23x w15x w23x w18x w24x w18x w19,21x w19x w21x w21x w19x w23x w19x w24x w15x w24x w16x w22x w21x w19x w19x

This list does not include the 62 physiological chemicals that were included in the ‘supplemented’ MLA database.

320

B. Henry et al.r Mutation Research 397 (1998) 313–335

were derived from the Salmonella mutagenicity database assembled under the aegis of the NTP w36x. The mutagenicity of the several chemicals not included in the NTP database was predicted by CASErMultiCASE based upon an SAR model derived from that database. A CASE analysis of a subset of that database has been described previously w31x. The total Salmonella mutagenicity database consisted of 1534 chemicals. For the purpose of the present study, several sets of randomly selected chemicals containing different ratios of actives and inactives were used to create SAR models. To study the mechanistic relationship between the induction of MLA mutations and other toxicological phenomena, we determined the structural overlaps w43x between the biophores associated with the MLA SAR model and those of other databases. The nature of these other SAR models have been described previously w44x.

3. Results The MLA database consisted of 209 chemicals for which unequivocal responses were obtained ŽTable 1.. Of these compounds, 131 were active in this system, 8 were marginally active and 70 were inactive. Colony size was not consistently reported for these experiments. Both CASE and MultiCASE SAR models were successfully developed from the MLA database. For the purpose of the present analyses, however, only the MultiCASE SAR model will be considered. The MultiCASE biophores identified in this analysis explained the activity of 99% of the molecules in the learning set. A total of 35 chemical substructures, or ‘biophores’ were identified as associated with activity in the MLA system, including 12 ‘major’ biophores ŽTable 2.. In addition, another 17 ‘extended fragments’ or similar chemical substructures formed by simple substitutions from the primary biophores were identified Žfor example, a nitrogen atom for a carbon atom in Biophore 1, or one halogen, bromine, for another, chlorine, in Biophore 7, Table 2.. The importance of the biophores is determined by their

number of occurrences in the dataset. Biophores associated with four or more active compounds are classified as ‘major’ biophores, whereas those associated with three or less active chemicals are classified as ‘minor’ biophores. Examination of the major MultiCASE biophores ŽTable 2. reveals the presence of electrophilic or potentially electrophilic biophores Že.g. epoxides ŽBiophore 8., aromatic amines ŽBiophore 4., haloalkanes ŽBiophores 2, 7.., indicating that MLAinduction can occur by mechanisms involving direct covalent modification of DNA. However, additional major, apparently non-electrophilic, biophores were also identified Že.g. Biophores 1, 5, 10.. The latter observation suggests that induction of MLA mutations may also occur by non-electrophilic mechanisms. Examination of the predictive performance of the MultiCASE MLA SAR model was performed iteratively ten times using 90% of the database as learning sets and the remaining 10% as testing sets Ži.e. external validation, w45x.. The results showed a concordance between experimental and predicted results of 56.4 " 3.1% Žsensitivity 0.59, specificity 0.56, x 2 s 2.4. ŽTable 3.. We have recently determined the effect of several database criteria on the modeling ability of the CASE and MultiCASE programs. Relevant to these studies, we have determined that an optimal database size consists of 300 or more chemicals, and that the dataset should be roughly equally divided between active and inactive chemicals Ži.e. ArI ratio of 1. w36x. We therefore sought a means of improving the MLA model by developing a larger database with an ArI ratio closer to unity. It had previously been shown that the predictive performance of SAR models is very sensitive to the size of the database, until the number of chemicals reaches ; 450 w36,46,47x. We therefore disregarded the possibility of removing active chemicals until we had a balanced database consisting of 140 chemicals Ž70 actives and 70 inactives.. Since the MLA database is deficient in inactive compounds, we made the assumption that ‘natural’ physiological chemicals Že.g. sugars, amino acids, lipids, etc.., since they are normally present in cells and in the tissue culture media, would be inactive in the assay. Accordingly, we constructed a ‘supplemented’ MLA database consisting of 271 chemicals,

B. Henry et al.r Mutation Research 397 (1998) 313–335

321

Table 2 Major MultiCASE biophores associated with mutagenicity in the unsupplemented MLA database Biophore

Chemical structurea

Occurrences

Active

Marginal

Inactive

1

CH5C–CH5C– q5 expanded fragments

25 6 31

21 6 27

1 0 1

3 0 3

2

˚ ™ wCl– x wCl– x § 2.6A

21

18

1

2

3

O–C5 q2 expanded fragments

18 8 26

15 8 23

0 0 0

3 0 3

4

NH 2 –C5CH–CH5C– q1 expanded fragment

16 6 22

13 4 17

0 1 1

3 1 4

5

CH5CH–C.5CH–

12

10

0

2

6

NH–CH 2 –

9

8

0

1

7

Cl–CH– q1 expanded fragment

6 5 11

6 5 11

0 0 0

0 0 0

8

O ^–CH 2 –

6

6

0

0

9

CO–CH 3 q4 expanded fragments

5 10 15

4 9 13

0 1 1

1 0 1

10

CH5C–CH5 q2 expanded fragments

²2-CH 3 : 4 4 8

4 4 8

0 0 0

0 0 0

11

CO–CH5 q1 expanded fragment

4 3 7

4 3 7

0 0 0

0 0 0

12

Cl–CH 2 –C5 q1 expanded fragment

3 1 4

3 1 4

0 0 0

0 0 0

a

˚ ™ wX– x indicates a two-dimensional distance descriptor; ^ indicates an epoxide; C. indicates a carbon atom shared by two wX– x § X.XA ring systems; ²2-CH 3 : indicates a methyl group substituted on the second carbon atom from the left.

including the 131 actives and 8 marginals found in the original NTP data, and 132 inactive chemicals, the previous 70 inactives and 62 physiological chemicals assumed to be inactive. The MultiCASE model based on this supplemented MLA database generated 37 chemical structures associated with biological activity in this system ŽTable 4, where the biophores are shown embed-

ded in representative chemicals in Fig. 1, and Table 5.. Acomparison of the biophores generated from this model with that of the unsupplemented MLA database indicates that the major biophores in the two models are largely identical, albeit in the supplemented one some of the biophores are refined further, e.g. O–C5 vs. O–C5CH– ŽTables 2 and 4.. Although the supplemented database now generates

B. Henry et al.r Mutation Research 397 (1998) 313–335

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Table 3 Predictivity parameters of the MLA MultiCASE SAR model SAR model

n

ArI

Concordance

x2

Sensitivity

Specificity

Informational content

MLA Žunsupplemented. MLA Žsupplemented. Salmonella mutagenicity a Salmonella mutagenicity b

209 271 209 210 210 210 300

1.9 1.0 0.6 1.0 2.0 0.6 1.0

56.4 " 3.1 64.0 " 3.0 70.3 " 3.2 73.8 " 3.0 66.2 " 3.3 72.9 " 3.1 74.0 " 2.5

2.4 21.7 27.6 39.7 11.3 17.4 66.1

0.59 0.55 0.58 0.63 0.71 0.55 0.68

0.56 0.74 0.78 0.80 0.63 0.82 0.79

42.4%

a b

47.2% 43.7%

Based on actual or predicted activities of chemicals from the MLA database. Based on random subsets of the NTP Salmonella mutagenicity database.

16 major biophores, the additional biophores can, in most cases, be traced back to formerly minor biophores in the unsupplemented model. The MultiCASE program is not designed to identify chemical structures associated with inactivity, but the addition of physiological compounds presumed to be inactive in the MLA system improves the model’s ability to discriminate between chemical structures found only in active chemicals and those found randomly in both the active and inactive chemicals in the database. This is particularly true for the minor biophores. These chemical structures represent the most unique aspects of active chemicals that do not contain a chemical structure in common with other active compounds Žor shared with only a few active compounds. ŽTable 5.. There may not be sufficient statistical power to prove the significance of the minor biophores; they should therefore be considered as hypothetically activating structures that could be evaluated further by laboratory testing of additional chemicals containing them. Still, it is of interest that some of the ‘minor’ biophores are clearly ‘structural alerts’ for DNA reactivity w48x Že.g. Biophores 17, 19, 24, 25, 32 in Table 5., thus indicating further the mechanistic significance of this group of structures. The predictive performance of the supplemented database was improved significantly over that of the unsupplemented database, both statistically and in terms of concordance with experimental results. Indeed, the results were just what would be expected for a correction of a database with an excess of active chemicals: sensitivity is slightly decreased in the model based on the supplemented data, despite the fact that overall concordance is improved. The

improvement, however significant, still results in a model with a relatively low predictivity Ž64%. compared to other SAR models of the same size and containing the same chemicals ŽTable 3.. Since the supplemented database is larger and has an optimal ratio of active to inactive chemicals w36x, we investigated several factors that could affect model predictivity by comparing the performance of the MLA model with that developed from data based on induction of mutations in Salmonella. First, a Salmonella mutagenicity MultiCASE SAR model based on the same chemicals that comprise the MLA database was derived ŽTable 3.. This SAR model had a highly significant concordance of 70.3 " 3.2% Ž x 2 s 27.6. between experimental and predicted results. The fact that such a predictive MultiCASE model can be derived from this data suggests it is the biological activity modeled, and not the nature of the chemicals in the dataset, that is limiting the predictivity of the MLA model. The very different nature of activity in the two datasets is demonstrated by the ArI ratios for the same set of chemicals tested by the two ‘mutagenicity’ systems. While approximately 2r3 of the chemicals were active in the MLA system, the results for the Salmonella mutagenicity assay is almost the inverse: only 1r3 of these same chemicals were active or predicted to be active, a ratio more typical of previous mutagenicity databases w36x. Several Salmonella mutagenicity MultiCASE SAR models were then derived from databases created by taking random subsets from the full database of 1534 chemicals ŽTable 3.. The Salmonella model based on 210 random chemicals with an optimal ArI ratio

B. Henry et al.r Mutation Research 397 (1998) 313–335

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Table 4 Major MultiCASE biophores associated with mutagenicity in the supplemented MLA database Biophore

Chemical structurea

Occurrences

Active

Marginal

Inactive

1

CH5C–CH5C– q3 expanded fragments

25 4 29

21 4 25

1 0 1

3 0 3

2

˚ ™ wCl– x wCl– x § 2.6A

21

18

1

2

3

O–C5CH– q4 expanded fragments

14 7 21

12 7 19

0 0 0

2 0 2

4

CH5CH–C.5CH–

12

10

0

2

5

CH 2 –C5CH–CH5C–

7

6

0

1

6

Cl–CH– q1 expanded fragment

6 5 11

6 5 11

0 0 0

0 0 0

7

O ^–CH 2 –

6

6

0

0

8

NH 2 –C5CH–CH5CH– q1 expanded fragment

6 3 9

4 3 7

1 0 1

1 0 1

9

CH5C–CH5

4

4

0

0

10

˚ ™ wCO– x wN5x § 2.6A

4

4

0

0

11

˚ ™ wOH– x wC– x § 8.4A

4

4

0

0

4

3

1

0

4

3

0

1

²2-CH 3 :

Y

12

C. –CO–C.5

13

CH5C–C5C–

14

NH–CS–NH– q3 expanded fragments

3 4 7

3 4 7

0 0 0

0 0 0

15

Cl–CH 2 –CH5 q2 expanded fragments

3 6 9

3 4 7

0 2 2

0 0 0

16

O–CO–CH 3 q1 expanded fragment

2 2 4

2 2 4

0 0 0

0 0 0

a

²2-CH5:

˚ ™ wX– x indicates a two-dimensional distance descriptor; ^ indicates an epoxide; C. indicates a carbon atom shared by two wX– x § X.XA Y ring systems; C indicates a carbon atom with a double bond in addition to those shown; ²X-Y: indicates the presence of a Y group substituted on the Xth carbon atom from the left.

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Fig. 1. Structure of 13 of the major biophores associated with mutagenicity in the MLA. The chemical structures of the biophores are given in bold within the structure of one representative chemical from the MLA database.

B. Henry et al.r Mutation Research 397 (1998) 313–335 Table 5 Minor MultiCASE biophores associated with mutagenicity in the supplemented MLA database Occurrences Biophore Chemical structurea 2

1

a

17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

CO–N5C–CH5CH– ²3-NH 2 : O–C5C–C5C– ²3-Cl: NO–N–CH 3 CH 2 –C.5CH–C5 P–CH 2 – CH 2 –NH–C.5 CO–NH–N– NO5CH–CH5C–C.5CH– ²4-NO 2 : NH 2 –C5CH5C–CH5CH– ²5-OH: OH–C5CH–CH5C–CH5CH– ²5-OH: NH 2 –C5CH5C–CH5CH– ²5-Cl: O–CO–CH5 COH–CH 2 –COH O–CO–C5CH 2 – Cl–CH 2 –CH–O– Cl–CH 2 –CH 2 –N–CH 3 COH–CH 2 –CH 2 –CH 2 –COH CO–CH 2 –C–CH 2 –C5 Y O–CH 2 –C5CH–C. –O– CH5C–CH5 CH 3 –CH 2 –C5CH– Y

C. indicates a carbon atom shared by two ring systems; C indicates a carbon atom with a double bond in addition to those shown; ²X-Y: indicates the presence of a Y group substituted on the Xth carbon atom from the left.

was statistically significant, and had higher sensitivity, specificity and concordance than either MLA model. Identically sized databases with an ArI ratio of 2.0 Žsimilar to that of the unsupplemented MLA data. or 0.6 Žsimilar to that of the Salmonella model based on the chemicals in the MLA database. had somewhat lower concordances, although the differences were not statistically significant. The sensitivities and specificities of the models varied just as would be expected when looking for non-random distribution of chemical structures in such skewed databases w36x. Finally, we constructed a Salmonella model with an ArI ratio of 1.0 from a subset of 300 chemicals in the full database, to examine the effects of increasing the size of the database from 210 chemicals ŽTable 3.. The sensitivity, specificity and concordance of this model are all similar to the smaller database consisting of 210 chemicals, suggesting that this model has reached optimal predictivity, although the level of statistical significance improves.

325

Thus, the MLA SAR model consistently has lower predictivity than similar Salmonella mutagenicity models, and the lower predictivity does not seem to be a function of the specific chemicals in the database, the size of the database, or the ArI ratio. Another plausible explanation for the observed lower predictivity is the possibility that mutations in mouse lymphoma cells are induced by a multiplicity of mechanisms. This would have the effect of trying to model several different biological activities at once, and could result in Ž1. a requirement for more total chemicals in the database Žto adequately represent each possible mechanism., or Ž2. more significantly, interference between the mechanistic aspects of the model, restricting the ability of the program to predict chemicals associated with certain mechanisms. One way multiple mechanisms might affect the MultiCASE model is to increase the number of biophores associated with ‘activity’. The Salmonella model based on the MLA chemical database, despite its greater predictivity, had only 7 major biophores, versus the 12 of the unsupplemented MLA model and the 16 of the supplemented model. Of particular concern would be a proliferation of minor biophores. Since our estimates of the predictivity of the models are based solely on a validation procedure which randomly breaks the data into separate ‘test’ and ‘learning’ sets, an active chemical with a unique biophore that happens to be in the test set cannot, by definition, be present in the learning set to provide a precedent for predicting its activity. Indeed, an examination of the validation performed on the supplemented MLA model demonstrates that all chemicals with a unique minor biophore ŽTable 5. were wrongly predicted to be inactive. The set of chemicals with minor biophores represented twice in the database were predicted correctly 50% of the time. These results cause our estimation of the predictivity of the MutiCASE model to be artifactually low. It should be noted that this is caused by the limitations of our validation procedure, and is not an inherent limitation of the SAR model. Thus, if we simply disregard the active chemicals containing only minor biophores, the predictivity of the MultiCASE MLA model rises: sensitivity to 0.65, specificity is unchanged since inactive chemicals are not involved, and the overall concordance increases to 70.0 " 3.0%, x 2 s 34.7, p - 0.001. This estimation simply

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assumes that, for the purposes of validation, these chemicals lie outside the chemical universe for application of the model. In reality, however, the entire dataset is available for modeling and prediction. If we assume that the rate of prediction for single biophores found earlier for our two-chemical biophores can be extrapolated to the entire set of single chemical biophores, we can estimate that the real concordance for the model when applied to chemicals from outside the dataset will be 68.0 " 2.9%, x 2 s 32.2, p - 0.001 Žsensitivity 0.62.. This necessarily underestimates the contribution of biophores derived from two chemicals, so it should be regarded as a lower limit of predictivity. These analyses demonstrate that the MultiCASE SAR model for MLA actually has reasonable predictivity, and that both the predictivity and applicability of the model could be significantly improved by examination of additional chemicals, especially compounds containing the minor structural biophores identified in this study ŽTable 5.. In order to gain a further understanding of the mechanism or mechanisms of induction in the MLA system, we compared MLA experimental responses with those of other long-term and short-term genotoxicity and related assays ŽTable 6.. In general, the levels of concordance were low, achieving significance between the MLA results and those of only five other datasets: mutations in Salmonella, induction of sister chromatid exchanges and chromosomal aberrations in cultured CHO cells, the presence of ‘structural alerts’ for DNA reactivity and cellular toxicity. On the other hand, as expected from earlier analyses w13x, the concordance between the results of

the rodent carcinogenicity ŽNTP w49,50x and CPDB w51x assays. and MLA was not significant. One advantage of the structural approach to SAR modeling, as exemplified by the MultiCASE program, is that the significance of each biophore, both as a predictive moiety and as a basis for mechanistic hypotheses, is largely independent of the complete model Žmade up of the sum of statistically significant biophores. and its overall predictivity. Unlike SAR models based on physicochemical parameters, which depend on the association between biological activity and measures of chemical, steric and electronic properties to be generalizable across the complete set of chemicals tested, in structural modeling as performed by CASErMultiCASE there is no requirement for any similarity of structure or mechanism between distinct biophores. It is up to the human expert to develop mechanistic hypotheses linking some or all of the biophores after they have been identified by the SAR analysis. This approach is therefore ideal for application to the MLA system, with its inherent expectation of multiple, perhaps independent mechanisms of action, and it allows for further investigation of the results of the MultiCASE modeling, despite the relatively low predictivity of the total SAR model. In accord with the analysis of MultiCASE biophores, the concordances of the experimental results between MLA and these other short-term tests for mutagenicityrgenotoxicity suggest that the induction of MLA proceeds, in part, by an electrophilic, DNA-reactive mechanism, as evidenced by the concordance of MLA with experimental results for Salmonella mutagenicity or the presence of structural

Table 6 Concordance between experimental results for the induction of mutations in mouse lymphoma and other biological phenomena Biological activity

n

Concordance

p-Valuesa

Sensitivity

Specificity

Mutations in Salmonella Carcinogenicity Žrodent. NTP b Carcinogenicity Žrodent. CPDBb Micronucleus, in vivo Sister chromatid exchanges Chromosomal aberrations Structural alerts for DNA reactivity Cell toxicity

160 199 181 58 106 109 132 78

57.5% 55.8% 49.2% 44.8% 72.6% 59.6% 62.1% 68.0%

0.001 0.5 1.0 1.0 0.0003 0.002 0.0006 0.003

0.48 0.66 0.77 0.39 0.78 0.51 0.54 0.70

0.80 0.39 0.14 0.59 0.59 0.89 0.77 0.64

a b

p-Values were calculated from x 2 values assuming 1 degree of freedom. Rodent carcinogenicity models are based on the NTP w45,46x and CPDB w47x databases.

CAN

.

. X X X . . . X . X X X X X . X X X . X X . . X X . X . . . . .

CPD

SAL . X . X X X X . X X X X . X X X . . . . . . X X . . . . . . . .

ALT X X X X X . X X . X X . . X X . . . . . X . X X X . . . . . . .

SCE . . . . . . X X . . . . . X X . X X X X X . X X X . . . . . . .

ChA . X . . . . X . . . X . . . . X . . X X X . . . X . X . . . . .

TPI . . . . . . . . . . . . . X . . . . X X . . . X . . . . . . . .

ISC . . X X . . . . . . . . . X . X . . . X X . X X X . X . . . . .

Dro . X . . . X X . . . . X . . X X . . . . X . . . X . . X X X X X

MNT . . . . . . . . . . . X . X . X . . X X . . . X X X X . . . . .

CTO . X . . . . X . . X X . . . X . . . . . X . . . X . . X X X X X

2m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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

AHH

CAN, rodent carcinogenicity-NTP; CPD, rodent carcinogenicity-CPDB; SAL, mutagenicity in Salmonella; ALT, ‘‘structural alerts’’ for DNA reactivity; SCE, induction of sister chromatid exchanges in cultured CHO cells; ChA, induction of chromosomal aberrations in CHO cells; TPI, inhibition of tubulin polymerization; ISC, in vivo induction of SCEs in mice; Dro, induction of somatic mutationsrrecombinations in Drosophila melanogaster ; MNT, in vivo induction of micronuclei in bone marrow; CTO, cellular toxicity; 2 m , induction of nephropathy in male rats; AHH, binding to AHH receptor. The fragments listed are major CASE biophores associated with the induction of MLA. The presence of an ‘X’ indicates that an ‘identical’ or ‘embedded’ biophore is present in the other database. The biophores used for these comparisons were identified with the CASE program.

Cl–C5C –C –C –C –Cl

N –CH 3 – . X O ^–CH 2 – Cl–CH– X Br–CH– . . CO5N – Y C. –CO–C.5 . O ^–CH –CH 2 – X O–CH5CH – . Cl–C –Cl . Cl–CH 2 –C5 X Cl–CH 2 –CH– X CO–C.5C – . ² 2-Cl: X Cl–C –C – CH5C –CH5C – X CH5CH –C.5CH – X O –C5CH –CH5 X O –CH5CH –CH5 . Cl–C –C –Cl . ² 2-NH :. CH5C –C5CH – ² 2-O : X CH5C –C5CH – ² 2-NH 2X: N5C –CH5CH – ²3-CH 3X : CH5C –C5CH – ²3-N5 :. CH5CH –C5CH – CH5CH –C5CH –C5 X NH 2 –C5CH –CH5CH – X Cl–C5CH –CH5CH – . CH 3 –O –C5C –CH5 ² 3-CH5X: ²3-Cl: . Cl–C5C –C –C – ² 3-Cl: . Cl–C5C –C –CH – ² 3-Cl: . Cl–C5C –C –Cl ² 4-Cl: . Cl–C5C –C –C –C –

Y

Biophore

Table 7 Overlaps among significant structural determinants associated with MLA induction and other biological phenomena

B. Henry et al.r Mutation Research 397 (1998) 313–335 327

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alerts for DNA reactivity. However, the analyses suggest that MLA also derives from other possible mechanisms, involving cellular toxicity, as evidenced by the concordance between the experimental results for MLA and cytotoxicity, and chromosomal effects Žsister chromatid exchanges and chromosomal aberrations.. To elucidate further the possible multiplicity in the basis of the induction of MLA, we examined the overlap among structural determinants Ži.e. biophores. significantly associated with the induction of MLA and those associated with other phenomena. Previous studies had indicated that the extent of such overlaps provides a measure of mechanistic similarity w34,35x. Thus, in accordance with the presence of electrophilic biophores among those associated with the MLA SAR model ŽTables 2 and 4., there was significant structural overlap between MLA and the induction of mutations in Salmonella Ž34%. and between MLA and the presence of ‘structural alerts’ for DNA reactivity Ž37%. ŽTables 7 and 8.. There was, however, also significant overlap between the biophores associated with MLA and those with cellular toxicity Ž23%., as well as between MLA and inhibition of tubulin polymerization Ž10%.. These findings support the idea that the induction of MLA may proceed by several separate mechanisms: one involving direct DNA reactivity and others involving targets such as the microtubules ŽTable 7..

Table 9 Summary of physical chemical properties of chemicals tested for the ability to induce mutations in mouse lymphoma cells Property

Activea

Inactivea

p-Value

Molecular weight Log P Water solubility HOMO LUMO Electronegativity E

199.5 2.19 0.981 0.777 y0.820 y0.022 0.80

245.2 2.52 0.10 0.922 y0.944 y0.011 0.93

0.03 0.4 0.05 0.02 0.2 0.7 0.03

There were 132 active and 70 inactive chemicals in the database. a Mean values. E: Electronic gaps ŽHOMOyLUMO.r2.

A comparison of the physical chemical and quantum chemical properties of the agents that do and do not induce mutations at the TK locus of LY5178 mouse lymphoma cells indicates that the MLA-inducing molecules appear to be significantly more water soluble and less nucleophilic Ži.e. lower HOMO energy. than MLA non-inducing chemicals ŽTable 9.. The significance of these observations remains to be elucidated. It is noteworthy, however, that while MLA-inducing molecules show a trend towards greater electrophilicity Ži.e. higher LUMO energy., the difference between the mean LUMO energies Žquantitative measures of electrophilicity. of MLAinducing and non-inducing molecules is not statistically significant ŽTable 9.. This is in marked contrast

Table 8 Summary of structural overlaps between the SAR models of MLA and those of other biological phenomena Phenomenon

MLA Ž%.

p-Values

MLA q SAL

MLA q Cytox

MLA q SAL q Cytox

Salmonella mutagenicity Carcinogenicity Žrodent. NTP Carcinogenicity Žrodent. CPDB Structural alerts Sister chromatid exchanges Chromosomal aberrations Inhibition tubulin polymerization SCEs in vivo Mutations in Drosophila Micronuclei, in vivo Cellular toxicity TCDD binding 2 m Nephropathy

34 37 42 37 29 22 10 24 32 22 23 0 0

- 0.0001 - 0.0001 - 0.0001 - 0.0001 - 0.0001 - 0.0001 - 0.0001 - 0.0001 - 0.0001 - 0.0001 - 0.0001

34% 20% 20% 24% 12% 10% 5% 12% 15% 10% 12% 0 0

12% 17% 12% 17% 10% 12% 0 5% 24% 2% 29% 0 0

12% 12% 7% 12% 5% 7% 0 0 7% 0 12% 0 0

B. Henry et al.r Mutation Research 397 (1998) 313–335 Table 10 Mean LUMO energies and genotoxicity Biological property

Active

Inactive

p-Value

Salmonella mutagenicity Structural alerts Unscheduled DNA synthesis SCE, in vitro Chromosomal aberrations Micronuclei, in vivo Mouse lymphoma mutations

y0.617 y0.636 y0.482 y0.741 y0.742 y0.691 y0.820

y0.773 y0.847 y0.612 y0.718 y0.730 y0.778 y0.944

- 0.0001 - 0.00001 0.0006 0.4 0.5 0.03 0.2

to chemicals that are mutagenic in Salmonella, induce micronuclei in vivo or that possess ‘structural alerts’ for DNA reactivity. All of these are significantly more electrophilic than their inactive congeners ŽTable 10.. This observation suggests that the induction of MLA is not based solely upon alterations of the cellular DNA. It is of interest that neither the in vitro induction of SCE nor of chromosomal aberrations, both of which share mechanisms with the induction of MLA, are associated with increased electrophilicity ŽTable 10..

4. Discussion We have applied the MultiCASE SAR methodology to a database consisting of the induction by chemicals of mutations in the mouse lymphoma L5178YTK " system. The data were generated and interpreted under the aegis of the U.S. NTP. Despite our attempts to optimize the dataset and the SAR model in comparison with a previous predictive model based on the NTP Salmonella mutagenicity assay w36x, the performance of the Salmonella model was consistently better Žmore predictive. than the MLA model, allowing for changes in database size and ArI ratio. The informational content of the 209 chemicals tested in the MLA system was not significantly lower than that of the 210 chemicals randomly selected for the Salmonella database ŽTable 3.. Differences based on the ArI ratio were observed, but always affected the results in a predictable manner w36x, and did not explain the difference in predictivity between the Salmonella and MLA models. We found evidence of mechanistic similarities Ži.e. overlaps. between mutation induction in the MLA system and other systems, such as the

329

Salmonella mutagenicity assay. This indicates that electrophilicity can play a role in the induction of mutation in mammalian somatic cells. However, we also found evidence of novel mechanisms that make this system more complex than those derived from haploid organisms. For example, Biophore 1 ŽTable 4. and its related expanded fragments Žassociated with activity in a total of 21 chemicals in the MLA database. all represent conjugated p-systems predominately found in molecules containing an aromatic ring system. The aromatic p-system represents an electron rich moiety that can participate in charge transfer processes andror p – p complexation. Biophores 3, 4, 5, 8, 9, 12, 13, 15 and 16 are all conjugated or isolated p-systems, making this the most common chemical feature associated with MLA activity. Such p-systems can interact directly with DNA, by covalent modification or by intercalation between stacked bases. Similarly, they can interact non-covalently with proteins, including tubulin, especially those with many or functionally important aromatic amino acids. Either or both of these effects could be invoked as mechanisms for the induction of MLA. Significantly, such biophores were recently found to be important in the experimental induction of chromosomal missegregation in a similar MultiCASE SAR study w26x. The structural overlaps between MLA and the induction of micronuclei and between MLA and inhibition of tubulin polymerization suggest that aneuploidy is a component in the induction of mutation in the MLA system w44x. Whether this dichotomy reflects mechanistic differences in the induction of large and small mutant colonies remains to be established Žsee also ref. w12x.. However, it may well explain the lack of high experimental concordance between the experimental results for MLA and for mutagenicity in Salmonella, or for MLA and for carcinogenicity in rodents ŽTable 6.. The recent recognition that somatic loss of heterozygosity, as may occur in the generation of small colony mutants, could result from a number of mechanisms including chromosomal loss and duplication, may provide a basis for the observation recorded herein. This is supported by the overlap between the MLA-related biophores and those associated with the induction of cancers in rodents Ž37–42%.. Mechanistic overlaps such as these serve to discriminate dif-

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ferent mechanisms of action, and show similarities even when the biological activities involved share some mechanisms and not others. Thus, although the experimental overlap between MLA and carcinogenicity is insignificant, the structural comparisons reveal an important connection between the two phenomena at a mechanistic level. Our SAR studies are therefore consistent with the fact that the induction of ‘mutations’ at the thymidine kinase locus of mouse lymphoma cells proceeds by several distinct mechanisms. Despite this complication, we have constructed a MultiCASE SAR model of this system with reasonable predictivity that has already provided mechanistic insights into the processes contributing to this endpoint. Fundamental questions remain however, as to what are the processes contributing to mutation in the MLA system, what is the system sensitive to, and how does it relate to carcinogenesis. From our knowledge of the underlying biology of the system and molecular carcinogenesis, we can attempt to address these questions. The MLA system is known to be affected by agents that act at both the levels of the individual base pairs and the entire chromosome, and this biological observation was reiterated in our analysis of the biophores associated with this assay. If multiple mechanisms are contributing to an endpoint, what information can be gained from overall measures of ‘activity’? Suppose there are five mechanisms contributing equally to an endpoint; that is they occur at equal background frequencies and are equally susceptible to induction by exogenous exposures. If a specific dose of chemical was sufficient to induce a twofold increase in the frequency of one such mechanism, the overall observed increase would only be 6r5 or 120%. Indeed, it would require a sixfold induction of a single mechanism to produce an overall twofold increase in the system. Thus, this system is relatively insensitive to compounds that act through a single mechanism. By contrast, however, the most active chemicals in the system would be those that affected simultaneously multiple mechanisms, such as a DNA base change that could be resolved into a point mutation, or produce a single-strand gap that was recombinogenic. Thus, a system with multiple mechanisms is doubly complicated because it is most sensitive to agents with multiple activities.

On the other hand, we know that although multiple mechanisms theoretically contribute to mutagenicity in the MLA system, they do not contribute equally. Loss of the chromosome carrying the wildtype TK gene, for example, results in a cell hemizygous for the mutant allele that should be fully competent to survive the selective conditions. Such an aneuploid cell, however, has compromised viability, such that, despite its fulfilling the requirements for loss of heterozygosity at the TK gene, it is rarely if ever observed in the assay. Thus, various mechanisms can contribute to an endpoint, but do so with different frequencies. Once again, suppose a system with two major mechanisms, however, in this case the two mechanisms differ 10-fold in background frequency. A twofold induction in the higher frequency mechanism is almost accurately reflected in the overall frequency Ž21r11s 190% induction.. Similar to the previous example, however, it would require an 11-fold increase in the frequency of the second mechanism to produce a twofold increase in overall activity. Thus, a system with multiple mechanisms is most sensitive to chemicals affecting those mechanisms with the highest background frequency. With these considerations in mind, let us now examine what we know of the molecular etiology of carcinogenesis w52x. Two types of genes have been implicated in the oncogenic process, differing in their genetic mode of action: dominant oncogenes and recessive tumor suppressor genes. A very simplified molecular pathway of tumorigenesis involving one each of these two types of genes is shown in Fig. 2. As can be seen, these two genes require three molecular events for carcinogenesis to proceed, activation of the oncogene, initial inactivation of one copy of the tumor suppressor gene, then inactivation of the remaining wild-type allele of the tumor suppressor gene locus. Genetically, the MLA system most closely resembles, and therefore models, the third of these three events. Activation of an oncogene involves the overexpression or unregulated expression of a growth-promoting factor. As such, there activation mechanism must involve a gain of function, or at least a conservation of function. Thus, certain point mutations may activate an oncogene, or induce a very specific translocation that does not disrupt the transcriptional

B. Henry et al.r Mutation Research 397 (1998) 313–335

331

Fig. 2. Molecular pathway of tumorigenesis involving dominant oncogenes and recessive tumor suppressor genes.

and translational processes. Oncogene activation is therefore similar to gain-of-function in vitro phenomena such as ouabain-resistance, in which the NaqrKq-ATPase must remain functional but no longer binds the inhibiting compound. Practically, these limitations mean that the mutagenic events that activate oncogenes are only a very small and specific subset of all the mutagenic events occurring at that locus. The spontaneous in vitro mutation frequency for ouabain-resistance varies from 10y8 to 10y6 w53,54x. Similarly, in vivo mutation in the hemoglobin S assay Žsomatic mutation of one allele of the b-hemoglobin locus to the sickle cell genotype, detected with a monoclonal antibody to HbS. occurs at a frequency of 10y8 w55x. If the background mutational frequency of a mammalian gene is of the order of 10y6 , then oncogene activation is a less frequent event, perhaps of the order of 10y7 or lower. The initial inactivation event in the loss of a tumor suppressor gene can occur by just about any mutational mechanism; point mutations, translocations, deletions all serve to render one copy of the gene ineffective. Other, more esoteric mechanisms of ‘mutation’ such as chromosome loss, chromosome loss and duplication, or mitotic recombination are

either nonviable or make no effective change in the gene dosage at the tumor suppressor gene locus. By analogy with oncogene activation, tumor suppressor gene inactivation proceeds by a less restricted set of mutational mechanisms and can be assumed to occur essentially at the background frequency of mutations, approximately 10y6 . This step in oncogenesis is best modeled by in vitro and in vivo mutational systems such as selection for the functional loss of the X-linked HPRT gene w56,57x. The final step in this mutational pathway involves any mechanism that allows for the expression of the recessive mutation that occurred as the second step. Originally it was supposed that this would consist of a second, de novo, mutational event, but we now know that this loss of heterozygosity may proceed by many mechanisms. In vivo, this step is similar to the ‘mutational’ events reported by the GPA Žglycophorin A. assay w58x, with a ‘spontaneous’ frequency of 10y5 , or the HLA assay, with a spontaneous frequency of 10y4 w59x Žgenetically similar in vitro systems have demonstrated frequencies as high as 10y3 w60x.. The higher 10y5 -frequency of this step appears to be a combination of more contributing mechanisms, as well as intrinsically higher muta-

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tional frequencies associated with these new mechanisms. To distinguish the molecular events contributing to this secondary gene inactivation from the purely mutational events involved in the initial inactivation, we have coined the phrase ‘somatic segregation’ to describe in toto the events contributing to this novel process w52,60x. Integrating these two discussions, the MLA system, although it has the theoretical capacity to detect the types of mutational events responsible for oncogene activation and tumor suppressor gene inactivation, should be most sensitive to the events responsible for somatic segregation. These events, whether induced or uninduced, are the highest frequency events that occur during oncogenesis, i.e. they are the least likely to be rate-limiting. Carcinogenesis, on the other hand, will respond most strongly to chemicals that modify the frequency of the ratelimiting step. Thus, there is a basic disparity between the events recorded by the MLA system and carcinogenicity assays, despite the fact that they are all potentially involved in carcinogenesis. To put it another way, all compounds that test positive in the MLA system are potentially carcinogenic; however, they are most likely to affect the least critical steps in the process. This in turn may explain that while there is a low concordance between MLA and the results of rodent carcinogenicity assays ŽTable 6., there is a significant mechanistic overlap as demonstrated by the commonalities between respective SAR models ŽTables 7 and 8.. If the MLA system then has inherent worth in modeling carcinogenesis, how can the information from the assay best be utilized? The best way to understand the underlying events of MLA induction would be to somehow separate the results by mechanism. One possibility has been the aforementioned l and s colonies, which have been shown to occur by distinct spectra of mutational mechanisms. This distinction is not specific enough, however, to significantly help us in understanding MLA induction at the molecular level. Indeed, the only sure way of analyzing this system mechanistically is to characterize at the molecular level induced ‘mutants’ for changes at the gene and chromosomal level. A surface survey of mechanisms, for example analyzing a set number of colonies per experiment, is unlikely to be useful because induction may occur simultane-

ously by several mechanisms at several different frequencies, and more subtle effects will always be masked by effects on higher frequency phenomena. Ideally, the MLA system could be improved by the addition of marker loci that would easily distinguish between chromosomal and gene-specific events, and between mutational, epigenetic Žmethylation. and recombinational processes. In its current form, the MLA system suffers from the problem that it produces information on too many processes simultaneously, that can only be distinguished through very labor-intensive molecular analyses. It should be remembered, however, that the assay very accurately models an important step in oncogenesis that is probably implicated in all forms of cancer. Recent results from the US Environmental Protection Agency Gene-Tox Program have suggested that the study design used to generate the NTP MLA data utilized in our study is not optimal for the detection of rodent carcinogens w61,62x. The authors point out that the NTP study design was not optimized for detection of small size s colonies, that are thought to be associated with chromosomal mechanisms of variation. Furthermore, the emphasis on reproducibility in the NTP data forced a reduction in dose range per test. These authors contend w61,62x that this led to a lack of detection of chemicals whose mutagenicity and overt cytotoxicity occur over a similar, narrow concentration range. Utilizing data culled from the literature, they have established a database of 602 chemicals tested in the MLA system under conditions they consider to be more appropriate than the NTP protocol for associating the results with carcinogenicity, and in direct comparisons with rodent carcinogenicity test results demonstrated a sensitivity of 100% Žversus 72% for the NTP data. and a concordance of 91% Žversus 59% for NTP.. In a future study we intend to model this much larger MLA database using MultiCASE and to determine whether the changes in data evaluation criteria that result in improved concordance with rodent carcinogenicity significantly alter the internal predictivity or mechanistic hypotheses that arise from our SAR modeling. From the preceding discussion, however, it should be clear that all mechanisms that potentially induce mutations at the heterozygous TK locus are implicated in the carcinogenic process. If, indeed, the NTP data consist of that subset of chemicals that

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induce mutations by mechanisms that do not affect the viability of the cell, i.e. mainly gene-specific point mutations and deletions, but also mitotic recombination, chromosome loss and duplication and, at least hypothetically, gene conversion, then this represents a specific subset of the mechanisms active in the MLA phenomenon and that is what our present SAR model relates to, regardless of its overlap with carcinogenesis. The criteria adopted by the Gene-Tox program attempt to maximize recovery of mutations giving rise to small mutations, thus driving the resulting mutation frequency higher, resulting in a dataset that will model mainly deleterious events such as deletion and chromosome loss, or more generously, deletion and chromosome loss in addition to those mechanisms detectable by the NTP protocol. This database, therefore, represents either a different subset of possible mechanisms or a supplemented set; in either case it would be of interest to compare the results of the two SAR models. Of course, this discussion is predicated on the association of different mechanisms with different colony sizes and the relative ability of the two MLA protocols to recognize chemicals acting by chromosomal mechanisms. It has also been suggested recently that different colony sizes are not so much due to differences in mutation mechanism, but due to chromosomal effects on closely linked mutant loci w63x. In either case, the NTP data and the Gene-Tox data are sufficiently different enough that they both deserve SAR analysis. Ultimately, we intend to carry out this second analysis.

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Acknowledgements This study was supported by the US Department of Defense ŽContract No. DAAA21-93-C-0046.. The authors are grateful to Dr. Orest Macina and Dr. Nancy Sussman for many valuable discussions.

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