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Quantitative structure-activity relationships
Toxic'. in Vitro Vol. 3, No. 4, pp. 351-353, 1989 Printed in Great Britain. All rights reserved
0887-2333/89 $3.00 + 0.00 Copyright ~ 1989 Pergamon Press plc
Meeting Report QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS D. A. BASKETTER Environmental Safety Laboratory, Unilever Research, Colworth House, Sharnbrook, Beds. MK44 ILQ, UK (Received 29 April 1989)
As the quantity of toxicological data on a myriad of chemicals continues to amass, and in the face of an ever-increasing desire to reduce to an absolute minimum the numbers of animals used in the processes of safety evaluation, it is apposite to consider whether there is a set of rules relating the physicochemical properties of chemicals to their biological effects. From such structure-activity relationships it might be possible to predict whether a new chemical would be likely to possess a particular biological or toxicological activity. However, to estimate the extent to which it possessed a predicted function would require the development of quantitative structure-activity relationships (QSARs). This theme formed the topic for discussion at the most recent meeting of the Industrial In Vitro Toxicology Society (Milton Keynes, March 1989). The presentations fell naturally into two parts. The morning session dealt largely with the theory of QSAR development, whilst in the afternoon the application of QSARs in specific areas of toxicology was discussed. It fell to J. Dearden (School of Health Sciences, Liverpool Polytechnic) to open the meeting. Although QSARs can be considered to have originated in toxicology with the work of Overton and Meyer at the turn of the century, most studies to date have concerned drugs and pesticides. However, in recent years a great deal of effort has gone into QSAR studies of toxicity, and particularly ecotoxicity. Bioaccumulation, biodegradability and acute toxicity itself can all be modelled, thus aiding the prediction of toxicity and the selection of compounds for further testing. The value of QSARs in this respect is that, by testing a relatively small number of compounds, predictions can be made for a large number of related compounds. A QSAR is a mathematical equation relating biological response to one or more parameters representing molecular structure and/or physicochemical properties. Such properties may be broadly classified into three groups--hydrophobic, electronic and steric. Of these, hydrophobicity (usually modelled by log P, where P is the octanol-water partition coefficient) tends to dominate, at least in in vivo studies, since it is a major determinant of movement into and within an organism. QSAR studies are generally carried out on congeneric series of compounds, to ensure a common mechanism of action.
In practice, biological response is often observed to rise and then fall again as log P increases across a series of compounds. This leads to the general form of a QSAR being expressed as" log(l/C) = a log P + b (log p)z + cE + dS + e where C is the concentration (or dose) of compound required to produce a given response, E is an electronic parameter, S is a steric parameter and a, b, c, d and e are constants. Recent advances in computing power and software have made it possible to use parameters obtained from computational chemistry, including so-called 3-D parameters such as interatomic distances, torsion angles and electrostatic potentials, in QSAR studies. A wealth of software for personal computers has recently become available, enabling graphics, computational chemistry and QSAR analysis to be carried out at the bench. Perhaps the most encouraging aspect to emerge from this introductory presentation was that, despite the many apparent difficulties, it is possible to develop QSARs for some toxicological effects of chemicals. R. Hyde (Wellcome Research Foundation, Beckenham) delved deeply into aspects of the development of QSARs by describing the 'QSAR/Computer Chemistry Interface'. In a talk that betrayed his great enthusiasm for the subject, Dr Hyde showed the importance of an integrated approach between traditional QSAR techniques and computer chemistry. The QSAR approach has traditionally aimed at optimizing bioavailability, using parameters such as pK and log P. Its use in refining the activity of compounds at their site of action, be it receptor or enzyme, was a later development. The potential of this refinement was initially restricted by limited availability of parameters. However, computational chemistry, increasingly popular as a molecular-design tool, and best known through its graphical output, provides a large number of molecular parameters that are 'grist to the mill' of QSARs. The use of computer chemistry as a source of parameters for QSARs offers advantages in terms of enhanced ability to describe molecular properties for diverse sets of molecules, but can pose problems in terms of conformation selection, orientation, and statistical interpretation. In the final presentation of this session, which explored largely theoretical aspects, D. Livingstone 351
T.LV. 3 / ~
352
D. A. BASKETTER
(Wellcome Research Foundation, Beckenham) complemented his colleague's talk with a discussion of analytical, particularly statistical, techniques in QSARs. Multivariate statistical methods provide a useful alternative to the 'traditional' technique of multiple linear regression that is commonly employed in QSAR studies. Indeed, for certain types of response data, multiple regression is not an appropriate method and other techniques must be employed. The properties of different data types influence the methods that may be used to analyse them. The increasing use of computer chemistry to generate molecular descriptors for QSARs has resulted in very large data matrices. Once again, multiple regression is not a suitable technique for the analysis of such data sets, and multivariate methods are required. Pattern recognition techniques, both 'supervised' and 'unsupervised', can be used in the analysis of multiple descriptor, multiple response, and multiple descriptor and response data, for example in drug-design studies. These can reduce to a manageable size the data matrix from which a QSAR can be derived. Commencing the move towards a more detailed examination of the application of QSARs to specific aspects of toxicology, D. Roberts (Unilever Research, Port Sunlight) explored QSAR interpretations of aquatic toxicity and biodegradability data for linear alkyl benzene sulphonates (LAS). Commercial LAS consist of mixtures of isomeric and homologous (4-sulphophenyl)-substituted linear alkanes. It is well established that both acute lethal toxicity to aquatic organisms and initial biodegradation rates of the individual components of LAS vary according to the 'distance principle', tending to increase with increasing carbon number and with increasing proximity of the 4-sulphophenyl group to the end of the alkyl chain. For acute lethal toxicity, the distance principle can be put on a quantitative basis by QSAR equations expressing log (I/LCs0) as linear functions of log P (the octanol-water partition coefficient calculated by the method of Leo and Hansch, modified by the incorporation of a position-dependent branching factor). The QSAR equations obtained are very similar to published QSARs for chemically unreactive nonelectrolytes, implying a non-specific baseline toxicity mechanism for LAS. In contrast, initial biodegradation rates are not well correlated by log P. By a process of testing various structure descriptors on a trial-and-error basis, followed by mechanistic hypothesis and simple mathematical modelling to refine the choice of descriptor, a QSAR can be obtained correlating relative rate constants for biodegradation with a composite descriptor based on the carbon numbers of the two branches of the alkyl chain. In contrast to these presenters who described the relatively straightforward way in which QSARs had been developed for aquatic and ecotoxicology, the final two speakers reviewed more comolex areas of mammalian toxicology. D. Basketter (Unilever Research, Colworth) examined the application of QSARs to contact allergy. The first QSAR to be reported in this area of toxicology was the relative alkylation index (RAI)
model described by D. Roberts and D. Williams (Unilever Research, Port Sunlight). This showed that the rate of sensitization could be described by an expression that related three p a r a m e t e r s ~ o s e , lipophilicity and chemical reactivity--all of which were to be measured in vitro. However, this simple expression failed to take into account several factors - - t h e multiple dosing employed in test methods used to generate in vivo data, the intrinsic antigenicity (foreignness) of the chemical hapten and the metabolism in skin. Data for a family of potent sensitizers, alkyl transfer agents, were used to show that multiple dosing regimens could be accommodated by a modified RAI expression that used separate calculations of RAI for each dose. In addition, the data indicated that the intrinsic antigenicity of a small hapten might be a less important variable than anticipated, since there was apparently no contribution by this factor for a range of alkyl haptens from C~ to C18, Data from a series of 1,4-substituted benzene derivatives were used to show that metabolism of topically applied chemicals did occur in skin, and that this might be expected to produce 'families' of reactive intermediates. The problems of developing simple QSAR models in the face of such events were obvious. A further difficulty in developing QSARs for contact allergens lay in the quality of the data derived from predictive models in the guinea-pig. Whilst this species is an excellent predictor of materials that might present a sensitization hazard to man (and therefore this model might meet with reluctance in terms of replacement), the methods and subjective results are not suited to the development of QSARs. The value of data from models such as the murine local lymph node assay (I. Kimber, ICI, Central Toxicology Laboratory Alderley Park), which uses a single dose with an objective endpoint, was noted. The last presentation came from J. Ashby (ICI, Central Toxicology Laboratory Alderley Park), who reviewed the difficulties in establishing QSAR for genotoxicity. Cancer is plural, that is, there are many types, with many possible causes. Development of a direct SAR for the prediction of cancer is often not feasible since the carcinogenic molecule is often a metabolite. Possible routes of metabolism may be predicted, but it is difficult to take account of variations due to species, age, sex and target organs. There are examples of materials which have structures that are not characteristic of carcinogenic materials and are Ames-test negative, but which give positive results in an in vivo bioassay such as isophorone. There are only 23 known human carcinogens, so it is not possible to develop a QSAR from such a small database. Many more materials have been tested in animal models, but, for example, the rat is only 70% predictive for cancers in the mouse, let alone man. Which model should be used for the prediction of cancer in man? QSAR publications up to 1974 on cancer are flawed since the bioassay data on which they are based is inadequate. When contemplating QSARs in the context of genotoxicity it is important to use high quality bioassay data, such as those available from the National Toxicology Program. The carcinogenicity prediction and battery selec-
Quantitative structure-activity relationships tion (CPBS) computer system was discussed, but was felt to be unacceptable because it did not take account of the structure of the chemical, but only the performance of the test. Another computer system, computer-assisted structure evaluation (CASE), uses molecular structures to predict mutagenicity and carcinogenicity. It is anticipated that in some way
353
CPBS and CASE will be integrated, the value of which remains to be seen. To conclude, the tone of the meeting indicated that for a defined system, a QSAR is possible, but for complex biological systems a comprehensive QSAR is unlikely in the near future.
0887-2333/89 $3.00 + 0.00 Copyright ~ 1989 Pergamon Press plc
Meeting Report QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS D. A. BASKETTER Environmental Safety Laboratory, Unilever Research, Colworth House, Sharnbrook, Beds. MK44 ILQ, UK (Received 29 April 1989)
As the quantity of toxicological data on a myriad of chemicals continues to amass, and in the face of an ever-increasing desire to reduce to an absolute minimum the numbers of animals used in the processes of safety evaluation, it is apposite to consider whether there is a set of rules relating the physicochemical properties of chemicals to their biological effects. From such structure-activity relationships it might be possible to predict whether a new chemical would be likely to possess a particular biological or toxicological activity. However, to estimate the extent to which it possessed a predicted function would require the development of quantitative structure-activity relationships (QSARs). This theme formed the topic for discussion at the most recent meeting of the Industrial In Vitro Toxicology Society (Milton Keynes, March 1989). The presentations fell naturally into two parts. The morning session dealt largely with the theory of QSAR development, whilst in the afternoon the application of QSARs in specific areas of toxicology was discussed. It fell to J. Dearden (School of Health Sciences, Liverpool Polytechnic) to open the meeting. Although QSARs can be considered to have originated in toxicology with the work of Overton and Meyer at the turn of the century, most studies to date have concerned drugs and pesticides. However, in recent years a great deal of effort has gone into QSAR studies of toxicity, and particularly ecotoxicity. Bioaccumulation, biodegradability and acute toxicity itself can all be modelled, thus aiding the prediction of toxicity and the selection of compounds for further testing. The value of QSARs in this respect is that, by testing a relatively small number of compounds, predictions can be made for a large number of related compounds. A QSAR is a mathematical equation relating biological response to one or more parameters representing molecular structure and/or physicochemical properties. Such properties may be broadly classified into three groups--hydrophobic, electronic and steric. Of these, hydrophobicity (usually modelled by log P, where P is the octanol-water partition coefficient) tends to dominate, at least in in vivo studies, since it is a major determinant of movement into and within an organism. QSAR studies are generally carried out on congeneric series of compounds, to ensure a common mechanism of action.
In practice, biological response is often observed to rise and then fall again as log P increases across a series of compounds. This leads to the general form of a QSAR being expressed as" log(l/C) = a log P + b (log p)z + cE + dS + e where C is the concentration (or dose) of compound required to produce a given response, E is an electronic parameter, S is a steric parameter and a, b, c, d and e are constants. Recent advances in computing power and software have made it possible to use parameters obtained from computational chemistry, including so-called 3-D parameters such as interatomic distances, torsion angles and electrostatic potentials, in QSAR studies. A wealth of software for personal computers has recently become available, enabling graphics, computational chemistry and QSAR analysis to be carried out at the bench. Perhaps the most encouraging aspect to emerge from this introductory presentation was that, despite the many apparent difficulties, it is possible to develop QSARs for some toxicological effects of chemicals. R. Hyde (Wellcome Research Foundation, Beckenham) delved deeply into aspects of the development of QSARs by describing the 'QSAR/Computer Chemistry Interface'. In a talk that betrayed his great enthusiasm for the subject, Dr Hyde showed the importance of an integrated approach between traditional QSAR techniques and computer chemistry. The QSAR approach has traditionally aimed at optimizing bioavailability, using parameters such as pK and log P. Its use in refining the activity of compounds at their site of action, be it receptor or enzyme, was a later development. The potential of this refinement was initially restricted by limited availability of parameters. However, computational chemistry, increasingly popular as a molecular-design tool, and best known through its graphical output, provides a large number of molecular parameters that are 'grist to the mill' of QSARs. The use of computer chemistry as a source of parameters for QSARs offers advantages in terms of enhanced ability to describe molecular properties for diverse sets of molecules, but can pose problems in terms of conformation selection, orientation, and statistical interpretation. In the final presentation of this session, which explored largely theoretical aspects, D. Livingstone 351
T.LV. 3 / ~
352
D. A. BASKETTER
(Wellcome Research Foundation, Beckenham) complemented his colleague's talk with a discussion of analytical, particularly statistical, techniques in QSARs. Multivariate statistical methods provide a useful alternative to the 'traditional' technique of multiple linear regression that is commonly employed in QSAR studies. Indeed, for certain types of response data, multiple regression is not an appropriate method and other techniques must be employed. The properties of different data types influence the methods that may be used to analyse them. The increasing use of computer chemistry to generate molecular descriptors for QSARs has resulted in very large data matrices. Once again, multiple regression is not a suitable technique for the analysis of such data sets, and multivariate methods are required. Pattern recognition techniques, both 'supervised' and 'unsupervised', can be used in the analysis of multiple descriptor, multiple response, and multiple descriptor and response data, for example in drug-design studies. These can reduce to a manageable size the data matrix from which a QSAR can be derived. Commencing the move towards a more detailed examination of the application of QSARs to specific aspects of toxicology, D. Roberts (Unilever Research, Port Sunlight) explored QSAR interpretations of aquatic toxicity and biodegradability data for linear alkyl benzene sulphonates (LAS). Commercial LAS consist of mixtures of isomeric and homologous (4-sulphophenyl)-substituted linear alkanes. It is well established that both acute lethal toxicity to aquatic organisms and initial biodegradation rates of the individual components of LAS vary according to the 'distance principle', tending to increase with increasing carbon number and with increasing proximity of the 4-sulphophenyl group to the end of the alkyl chain. For acute lethal toxicity, the distance principle can be put on a quantitative basis by QSAR equations expressing log (I/LCs0) as linear functions of log P (the octanol-water partition coefficient calculated by the method of Leo and Hansch, modified by the incorporation of a position-dependent branching factor). The QSAR equations obtained are very similar to published QSARs for chemically unreactive nonelectrolytes, implying a non-specific baseline toxicity mechanism for LAS. In contrast, initial biodegradation rates are not well correlated by log P. By a process of testing various structure descriptors on a trial-and-error basis, followed by mechanistic hypothesis and simple mathematical modelling to refine the choice of descriptor, a QSAR can be obtained correlating relative rate constants for biodegradation with a composite descriptor based on the carbon numbers of the two branches of the alkyl chain. In contrast to these presenters who described the relatively straightforward way in which QSARs had been developed for aquatic and ecotoxicology, the final two speakers reviewed more comolex areas of mammalian toxicology. D. Basketter (Unilever Research, Colworth) examined the application of QSARs to contact allergy. The first QSAR to be reported in this area of toxicology was the relative alkylation index (RAI)
model described by D. Roberts and D. Williams (Unilever Research, Port Sunlight). This showed that the rate of sensitization could be described by an expression that related three p a r a m e t e r s ~ o s e , lipophilicity and chemical reactivity--all of which were to be measured in vitro. However, this simple expression failed to take into account several factors - - t h e multiple dosing employed in test methods used to generate in vivo data, the intrinsic antigenicity (foreignness) of the chemical hapten and the metabolism in skin. Data for a family of potent sensitizers, alkyl transfer agents, were used to show that multiple dosing regimens could be accommodated by a modified RAI expression that used separate calculations of RAI for each dose. In addition, the data indicated that the intrinsic antigenicity of a small hapten might be a less important variable than anticipated, since there was apparently no contribution by this factor for a range of alkyl haptens from C~ to C18, Data from a series of 1,4-substituted benzene derivatives were used to show that metabolism of topically applied chemicals did occur in skin, and that this might be expected to produce 'families' of reactive intermediates. The problems of developing simple QSAR models in the face of such events were obvious. A further difficulty in developing QSARs for contact allergens lay in the quality of the data derived from predictive models in the guinea-pig. Whilst this species is an excellent predictor of materials that might present a sensitization hazard to man (and therefore this model might meet with reluctance in terms of replacement), the methods and subjective results are not suited to the development of QSARs. The value of data from models such as the murine local lymph node assay (I. Kimber, ICI, Central Toxicology Laboratory Alderley Park), which uses a single dose with an objective endpoint, was noted. The last presentation came from J. Ashby (ICI, Central Toxicology Laboratory Alderley Park), who reviewed the difficulties in establishing QSAR for genotoxicity. Cancer is plural, that is, there are many types, with many possible causes. Development of a direct SAR for the prediction of cancer is often not feasible since the carcinogenic molecule is often a metabolite. Possible routes of metabolism may be predicted, but it is difficult to take account of variations due to species, age, sex and target organs. There are examples of materials which have structures that are not characteristic of carcinogenic materials and are Ames-test negative, but which give positive results in an in vivo bioassay such as isophorone. There are only 23 known human carcinogens, so it is not possible to develop a QSAR from such a small database. Many more materials have been tested in animal models, but, for example, the rat is only 70% predictive for cancers in the mouse, let alone man. Which model should be used for the prediction of cancer in man? QSAR publications up to 1974 on cancer are flawed since the bioassay data on which they are based is inadequate. When contemplating QSARs in the context of genotoxicity it is important to use high quality bioassay data, such as those available from the National Toxicology Program. The carcinogenicity prediction and battery selec-
Quantitative structure-activity relationships tion (CPBS) computer system was discussed, but was felt to be unacceptable because it did not take account of the structure of the chemical, but only the performance of the test. Another computer system, computer-assisted structure evaluation (CASE), uses molecular structures to predict mutagenicity and carcinogenicity. It is anticipated that in some way
353
CPBS and CASE will be integrated, the value of which remains to be seen. To conclude, the tone of the meeting indicated that for a defined system, a QSAR is possible, but for complex biological systems a comprehensive QSAR is unlikely in the near future.