Critical effect sizes in toxicological risk assessment: a comprehensive and critical evaluation

Environmental Toxicology and Pharmacology 10 (2001) 33 – 52

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Critical effect sizes in toxicological risk assessment: a comprehensive and critical evaluation Susan Dekkers *, Cees de Heer, Monique A.J. Rennen Department of Toxicological Risk Assessment, TNO Nutrition and Food Research, P.O. Box 360, NL-3700 AJ Zeist, The Netherlands Received 22 December 2000; received in revised form 31 January 2001; accepted 9 February 2001

Abstract A key issue in toxicological risk assessment is determining the effect level below which there is no reason for concern. In the Benchmark approach, this breaking point between adverse and non-adverse is called the critical effect size (CES). This study aimed to investigate the possibilities to determine CESs for toxicological effect parameters commonly used in human risk assessment and includes a literature review and an opinion analysis among European toxicologists. The results indicate that the current knowledge is insufficient to define CESs for all individual parameters. Furthermore, the use of a single universal CES seems no option. It is concluded that it is not yet possible to reach international consensus on CESs for most toxicological parameters. However, every parameter for which consensus on the CES is reached is a step forward, because this can facilitate discussions on the adversity and relevance of certain changes in that parameter, irrespective of the method applied in risk assessment. © 2001 Elsevier Science B.V. All rights reserved. Keywords: Critical effect size; Benchmark modelling; Risk assessment

1. Introduction Hardly a week goes by without a story about a chemical exposure potentially threatening human health. The process of estimating the probability that a chemical causes adverse health effects can be considered to be part of a process called risk assessment. Human risk assessment may also involve the determination of exposure limits or human limit values (HLVs) below which no adverse health effects in humans are expected to occur (Vermeire et al., 1999). In the absence of adequate human data to establish HLVs, generally animal studies are used (Gaylor, 1992). Presently, the most frequently applied method to derive HLVs is the No-Observed-Adverse-Effect Level/ Safety Factor (NOAEL/SF) method. This method usually involves determining the highest experimental dose at which no statistically significant changes in toxicologically relevant endpoints are observed in animals and dividing that value by assessment factors to ac* Corresponding author. Tel.: + 31-30-6944565; fax: +31-306944926. E-mail address: [email protected] (S. Dekkers).

count for the lack of data and inherent uncertainty in the extrapolations (Allen et al., 1998; Fung et al., 1998; Vermeire et al., 1999). Although HLVs derived by the NOAEL/SF method are intended to represent a no-effect level, adverse effects at these dose levels cannot always be completely ruled out (Gaylor, 1992). The recognition of the deficiencies of the NOAEL/SF method (see Section 2.3), has stimulated the development of alternative methodologies, including the Benchmark approach (Crump, 1984; US-EPA, 1995). Various organisations and regulatory agencies have considered adopting the Benchmark approach, which addresses several quantitative and statistical criticisms of the NOAEL/SF method (Murrell et al., 1998). Although there is an increasing interest in the Benchmark approach within the OECD, it has not found its way to the ‘regulatory toxicology’ yet. One of the major points of discussion, essential in the Benchmark approach, is the determination of the effect level below which there is no reason for concern. For dichotomous effect parameters, where an animal is classified as either normal or diseased, this level is expressed as a certain increase in the incidence of adverse outcomes, called the Benchmark Risk (BMR). For continuous effect

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parameters, where effect measure is expressed on a continuous scale, this effect level is called the critical effect size (CES). A CES reflects the breaking point between non-adverse and adverse changes in toxicological effect parameters. In other words, a CES is the maximum (change in the) magnitude of a specific (combination of) toxicological effect parameter(s) which is assumed to be non-adverse. In current risk assessment methods, the severity or adversity of certain changes in effect parameters is usually not quantified. In the NOAEL/SF method, the adversity of the (statistically significant) changes in effect parameters is usually evaluated by expert judgement. Some of the aspects, which are taken into consideration in determining a NOAEL are the toxicological relevance of the effect, the reversibility or the ability of the organism to repair or compensate for the effect, and the degree to which the effect is a stage in the development of disabling illness and death. In order to establish CESs, the same aspects have to be considered. CESs are not only suitable in the Benchmark approach. They may also be helpful in other methods to derive HLVs. Accepted CESs can facilitate the discussions on the adversity and relevance of certain changes in effect parameters during the risk assessment process (both using the NOAEL and the Benchmark approach) (Crump, 1984). This will make the final decisions on adversity more transparent and less ad hoc. The study is aimed at the investigation of the possibilities to determine CESs for toxicological effect parameters commonly used in human risk assessment. The following aspects related to this possibility are addressed: the need to determine CESs, the background information on which CESs can be based, the effect level on which CESs can be defined, the dependency of CESs on experimental conditions, the expressions used to define CESs, the magnitude of CESs, the objectivity and subjectivity in estimating CESs, and the possibility to reach consensus on CESs. The purpose of this study is two-fold: (a) to determine whether CESs for common toxicological effect parameters can be derived from literature; and (b) to collect opinions of (inter)national toxicologists.

2. Background

2.1. Literature The following paragraphs present an overview of the Benchmark approach, which initiated the determination of CESs and which variations and extensions demonstrate several possible ways of using CESs in human risk assessment. This overview is mainly based on five specific documents (Crump, 1984; ILSI, 1994; Kramer et al., 1995; US-EPA, 1995, 1998).

2.2. History and de6elopment of the Benchmark approach Exposure to chemical substances may cause adverse health effects in humans. The probability of causing an adverse health effect depends on several factors, such as the concentration of the substance, the duration and route of exposure, the sensitivity of the individual, etc. The estimation of this probability in human risk assessment, is characterised by a 4 -step procedure, including (1) hazard identification; (2) dose–response assessment; (3) exposure assessment; and (4) risk characterisation. Using the first two steps in the human risk assessment a safe exposure level for the tested species can be estimated and extrapolated to the human situation, to estimate an exposure level, which is expected to be without appreciable health risk for humans. The earliest practice to predict safe exposure levels for chemical substances was the Acceptable Daily Intake (ADI) approach. This method attempted to predict a dose that could be tolerated over lifetime without producing harm, by deriving a NOAEL in animal experiments and dividing that value by a 100-fold safety factor. Since the choice of the value 100 was more or less arbitrary, some attempts have been made to underpin this factor, for example by separating it into various assessment factors, including factors accounting for inter- and intraspecies. In the EU Technical Guidance Documents (TGD), another method to predict safe exposure levels is described, which involves the assessment of the Margin of Safety (MOS) or the NOAEL/ exposure ratio (Vermeire et al., 1999). In 1992, Gaylor criticised the NOAEL as being ill defined. Based on published data on 93 developmental toxicants, he showed that the risk of dead or resorbed foetuses and malformations at the NOAEL (based on developmental effects) exceeds 1% in about one-fourth of the cases (Gaylor, 1992). In response to the limitations of the use of the NOAEL in deriving HLVs (see Section 2.3), new risk assessment methods have been developed, which use a more quantitative approach. One of the methods that has been developed as alternative for the NOAEL/SF method, is the Benchmark approach. In the first description by Crump, a mathematical dose–response curve is fitted to the response data of each relevant toxicological effect parameter. For each of these parameters a CES should be chosen, reflecting the degree of change in effect assumed to distinguish an adverse from a non-adverse effect. With the use of the dose–response curve, the critical effect dose (CED) can be estimated as the dose at which the average animal shows the CES defined for a particular effect parameter. The lower limit of the statistical confidence interval (CI) of the CED is called the Benchmark Dose (BMD). Assessment factors can be applied to the BMD in a similar way as to NOAELs

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to determine the HLV. Fig. 1 gives an illustration of both the NOAEL/SF method and Crumps Benchmark approach for a continuous toxicological effect parameter. After the first description of the Benchmark approach in 1984, the method has been modified and extended by many others (see Section 2.4). The Benchmark approach has most widely been studied in the context of developmental toxicity endpoints (Allen et al., 1998). Foster and Auton discussed the issues concerning the use and adoption of the Benchmark method in developmental toxicity studies and concluded that the method offers significant advantages over the NOAEL/SF method (Foster and Auton, 1995). Many authors in the literature consider the methodology a significant contribution to risk assessment, and worth serious consideration (Murrell et al., 1998). The Health Council’s Committee on Health-based recommended exposure limits felt the Benchmark methodology to be particularly promising (Health Council of The Netherlands, 1996). In the international arena, the Benchmark approach is considered to be one of the efforts to develop a standard approach (Murrell et al., 1998). The US-EPA already used the Benchmark approach to derive a HLV for methyl mercury and several other compounds (US-EPA, 1997). As described in the introduction, within the OECD, the Benchmark approach is often a point of discussion, but not yet implemented.

2.3. The Benchmark approach 6ersus the NOAEL/SF method The use of an experimentally derived NOAEL in setting human exposure limits for chemical substances has often been criticised within the scientific community (Allen et al., 1998). The limitations of the NOAEL/SF method approach have been discussed and reviewed extensively by many authors. Some of the aspects in

Fig. 1. Illustration of the NOAEL/SF method and Crumps Benchmark approach. (It is noted that the BMD can also turn out to be higher than the NOAEL).

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which the Benchmark approach differs from the NOAEL/SF method are how it deals with variability, uncertainty, precision and dose– response information, its dependency on the experimental design, and its ability to estimate risks at dose levels exceeding the HLV.

2.3.1. Variability, uncertainty and precision The NOAEL is based on one single experimental dose, which implies that the variability of the data is not taken into account and a threshold of toxicity is presumed. The induction of toxic effects, however, results from a gradual process obeying distinct dose–response characteristics (Zeilmaker et al., 1995; Fung et al., 1998). Although the NOAEL is considered to reflect a worst-case value of the ‘true’ no-adverse-effect level (NAEL), which is assumed to lay somewhere between the NOAEL and lowest-observed-adverse-effect level (LOAEL), adverse effects at these dose levels cannot be completely ruled out. Even when the differences between test and control groups are not statistically significant, the presence of real effect cannot be excluded (Gaylor, 1992). In Crumps Benchmark approach, information about the variability within the data set and the uncertainty around the BMD, is accounted for by the use of the lower confidence limit of the CED. Some variations of Crumps Benchmark approach incorporate the variability in the data set in a different way. Some Benchmark approaches even make it possible to use the entire uncertainty distribution of the CED in the calculation of the BMD, which should make it possible to quantify, to a certain extent, the degree of conservatism of any particular exposure limit (see Section 2.4.3) (Slob and Pieters, 1998). 2.3.2. Dose–response information The shape and slope of the dose–response curve are only semi-quantitatively considered, and ignored once the NOAEL is identified (Beck et al., 1993). In the Benchmark approach, quantitative dose–response information is taken into account by fitting a dose–response model to all of the dose–response data, so that all doses and the slope of the curve influence the calculations. 2.3.3. Dependency on experimental design Since the NOAEL depends on the statistical power of the study, the number of animals per dose group, the homogeneousness of the population of animals used, and the quality of the measurements influence the determination of the NOAEL. Poor experimental designs with a small sample size and large measurement errors tend to give statistical significant effects at higher dose levels, which may result in an underestimation of the possible toxicological risks, if no correction is made for the experimental error. In Crumps Benchmark ap-

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proach, the experimental error is accounted for by using the lower confidence limit, which gives a more appropriate reflection of the quality of the experiment. Smaller studies with poor spacing between dose levels tend to result in wider confidence limits and lower BMDs. In some variations of Crumps Benchmark approach, the quality of experiment is incorporated in another way (see Section 2.4.3). Since the NOAEL has to be one of the experimental doses, its value also heavily depends on the number and spacing of experimental doses. The definition of the BMD, on the other hand, is more flexible and not restricted to one of the experimental dose levels. Therefore, the BMDs is less dependent on dose selection and spacing and can sometimes be determined even when the NOAEL cannot be identified.

2.3.4. Estimating the risk at dose le6els exceeding the HLV The NOAEL/SF method is not suitable to estimate the toxicological risks at dose levels exceeding the NOAEL. The size of the population at risk can be estimated with the NOAEL/SF method, but the magnitude of the risk cannot be determined (Allen et al., 1998; Zeilmaker et al., 1995). With the Benchmark approach, however, the toxicity of an exposure exceeding the BMD can be estimated, provided that the exceeding exposure is within the range of dose levels on which the dose–response curve is based. 2.4. Variations and extensions of the Benchmark approach The Benchmark approach was first described by Crump in 1984. Crump defined the BMD as ‘the lower statistical confidence limit of the dose corresponding to a specified increase in level of health effect over the background level’ (Crump, 1984). Different approaches and definitions have been proposed afterwards. In the literature on the different approaches, different terminologies are used. Since no common nomenclature exists, an overview of the different terms used in this study is given in Appendix A. In 1994, the ILSI Risk Science Institute organised a workshop on Benchmark dose methodology at the request of the US-EPA (ILSI, 1994; Barnes et al., 1995). Another workshop was held in 1996 organised by EPA’s Risk Assessment Forum (RAF) (US-EPA, 1996a). The purpose of this workshop was to solicit the views of experts on the draft of EPA’s Benchmark Dose Technical Guidance Document (US-EPA, 1996b). Although there was a lot of discussion in these workshops, no consensus was reached on which variation and extension of the Benchmark approach is most appropriate for the use in human health risk assessment. Some of the variations and extensions concerning

Fig. 2. The Benchmark approach for a dichotomous effect parameter.

the definition or use of CESs are discussed in the following paragraphs.

2.4.1. Form of data used for modelling Most quantitative risk assessments using the Benchmark approach have focused on dichotomous effect parameters where an animal is classified as either normal or diseased. For this type of effect parameters, like the presence or absence of a tumour, birth defects, and other developmental toxicity parameters, a considerable amount of research is performed on the use of the Benchmark approach. Less research is performed on using the Benchmark approach for continuous effect parameters, like organ weights, haematology/clinical chemistry, etc. (Allen et al., 1998). The application of the Benchmark approach to dichotomous data is straight forward, since the data are expressed as the number (or percentage) of subjects exhibiting a defined response at a given dose. The CED is then defined as the dose at which a certain percentage of the animals switches from non-responding to responding, i.e. the CED for dichotomous effects can be defined as the dose producing a certain increase in the incidence of adverse outcomes. This increase in incidence (or probability) is usually called the Benchmark risk (BMR). After defining a dose–response model for the percentage of responding animals, a CED can be estimated using a BMR as cut-off point (see Fig. 2). The application of the Benchmark approach to continuous data, like changes in organ weights, is more complex. A dose–response model can be fitted to the (continuous) data, or one can convert the continuous data into dichotomous data (by dividing the continuous response into two categories). When the continuous data are dichotomised, the percentage of responding animals can be modelled and a BMD can be derived in the same way as described for dichotomous data (using an accepted BMR). A disadvantage of such a conversion is that it leads to the loss of information about the magnitude of the effect. Furthermore, BMDs for di-

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chotomous data can be estimated less precise than the BMDs estimated using continuous data. When a dose– response model is fitted to the continuous data and a CES is defined, the CED and the BMD can be estimated. Professional judgement is needed to determine the CES. Besides dichotomous and continuous data also ordinal data (where the effect level is divided in more than two categories) can be used in the Benchmark approach. However, the application of the Benchmark approach to ordinal data will not further be discussed.

2.4.2. Definition of CESs (or BMR) As described in Section 1, the definition of a CES (or BMR) is essential in the Benchmark approach. The expression used to distinguish a non-adverse effect from an adverse effect for continuous effect parameters is different from the expression for dichotomous effect parameters. To apply the Benchmark approach to a continuous effect parameter the maximum magnitude of the effect, which is assumed to be non-adverse (or CES) should first be determined. For dichotomous effect parameters, the value of the CES is already defined with the definition of the dichotomous effect. An example of this is mortality, for which the CES in the experimental animal is usually determined by nature. For other dichotomous effects the CES is often hidden in the mind of the experimental observer when deciding if a particular observation should be classified as a response or not (Slob and Pieters, 1998). To apply the Benchmark approach to dichotomous effect parameters, a tolerable risk level, called the Benchmark risk (BMR) needs to be determined. The determination of BMRs is different from the determination of CESs, in such a way that it does not consider the effect size (or the magnitude of the effect), but the effect level (or risk of observing the effect). Additional considerations, like the tolerability of the risk, are usually more important in establishing BMRs. 2.4.3. Accounting for uncertainity Most variations and extensions of the Benchmark approach try to make a better characterisation of the uncertainties surrounding the HLV (ILSI, 1994). Depending on the quality of the data, the CED can be estimated with a certain degree of precision. To account for this uncertainty Crump suggested the use of the lower statistical confidence limit (LCL) of the estimated CED. In most of the literature, the BMD is defined as this LCL of the dose. However, defining the BMD in terms of a confidence bound on a point estimate has some disadvantages similar to the disadvantages of the NOAEL/SF method. For example, it results in a characteristic dose that varies with the statistical precision and depends on the study design (Murrell et al., 1998;

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Mantovani et al., 1998). Although it is important to account for statistical uncertainty, risk assessments based on the lower confidence limit of a point estimate risk do not fully convey the range of information considered and used in developing the assessment (Copeland, et al., 1993). Therefore, Murrell et al. (1998) recommend to use both the point estimate and the statistical confidence bounds. This makes it possible to use the information in a rational method for taking the statistical uncertainty of the measurement into account. Slob and Pieters (1998) proposed to find the complete probabilistic uncertainty distribution of the CED estimate. Once a dose–response model has been fitted to the continuous data, Monte Carlo sampling is used to generate a large number of new data sets from this model, each time with the same number of data points per dose group as in the real experiment. For each generated data set the CED is re-estimated. Taking all these CEDs together results in the required distribution from which any desired confidence interval can be derived. 3. Methods

3.1. Study design To investigate the possibility of reaching consensus on CESs of common toxicological effect parameters, the following study design was used (see Fig. 3). First of all, literature was reviewed for earlier studies that have used or suggested CESs. The CESs found in literature, were evaluated as to the way they were defined and determined. Following this literature review, a pilot study was carried out to determine the best way to collect expert opinions on the determination of CESs. Based on the outcome of the pilot study and because of the desire for international harmonisation, it was decided to send a written questionnaire to a group of toxicologists within the European Union. The problems and suggestions of determining CESs addressed in the literature and pilot study were taken into account with the development of the questionnaire for the opinion analysis.

Fig. 3. Study design.

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3.1.1. Data collection

4. Results and discussion

3.1.1.1. Literature. A literature search was performed in the databases Medline (1966– 1999/05), Serline on SilverPlatter (1998), and Toxline (pre 1981– 1998/12) using free text searches and thesaurus terms. Different terms and combinations of terms where used, including toxico*, benchmark, risk assessment, endpoint, adverse (health) effect, clinical chemistry, and critical effect size. From the searches all records about the Benchmark approach in general, and all records in which a benchmark dose for a particular chemical was calculated were selected by reading the abstracts. Also, records comparing different methods in the human risk assessment and records discussing the determination of adverse effects where selected by reading the abstracts. All selected records were requested through the NCC (Nederlandse Centrale Catalogus) and the British library. All literature received within 8 weeks after their application, was selected for further review. In addition, a search was carried out in the TNO archives from which several reports from TNO, RIVM and the Health Council of the Netherlands where selected and obtained for further review.

4.1. Selected literature

3.1.1.2. Expert opinions. For the selection of the experts, a few Dutch toxicologists and the scientific secretary of the Committee of the Health Council of the Netherlands on Health-based exposure limits involved with the Benchmark approach were asked to provide a list of European toxicologists which they assumed to have ideas about CESs. Additional experts where added on recommendation of the toxicologists approached. Based on the results of the pilot study, the questionnaire was partly adjusted to collect opinions from European experts. The questions were restricted to obtain opinions on CESs for animal data and continuous toxicological effect parameters. Since for the majority of substances, no sufficient human data are available to derive HLVs and because a considerable amount of research has already been done for dichotomous effect parameters and the determination of BMRs. 3.1.2. Data analysis In the literature review and the expert opinion analysis the following aspects were evaluated “ the need to determine CESs; “ background information on which CESs can be based; “ effect level, on which CESs can be defined; “ dependency of CESs on experimental conditions; “ expressions used to define CESs; “ magnitude for CESs; “ objectivity and subjectivity in estimating CESs; “ reaching consensus on CESs.

The searches in the literature databases resulted in a large amount of records. After the removal of the double records, 154 records remained, from which a selection was made by reading the abstracts. 95 records about the Benchmark approach in general or the calculation of a Benchmark dose for a particular chemical and five articles about the definition of adverse effects were selected. The full documents, to which these 100 records referred, were obtained through the NCC (Nederlandse Centrale Catalogus) and the British Library. About 99 of the requested documents were available within 8 weeks after request, while one document was not available. To these 99 documents, three additional articles obtained from references and six reports from TNO, RIVM and the Health Council of the Netherlands were added. Most of these 108 documents only described the different ways to define CESs and/or the problems concerning the establishment of CESs in general. In 36 documents, CESs were suggested and/or used to calculate HLVs.

4.2. Responding experts From the 68 experts approached, 38 did not fill in the questionnaire because of various reasons; nine experts did not have time available, five considered their knowledge about CES too limited, two replied with several comments on the questionnaire without answering the questions, and 22 did not respond at all. Most of the 30 experts who completed the questionnaire, came from UK and The Netherlands, while some others came from Germany, France, Sweden, Norway, Finland, Denmark, Italy, and Spain. The co-operating experts are connected to several universities, research institutes, governmental agencies, and industrial companies. Most of the 30 experts, who returned the questionnaire, had only limited experience with the Benchmark approach. Six of the experts had some practical experience in the application of the benchmark approach. Two experts asked for some additional explanation/information. It is not likely that these explanations influenced the outcome of the opinion analysis. Some experts consulted others to answer the questionnaire. Although these answers reflect the opinion of more individuals, they are regarded as one opinion in the analysis.

4.3. CES described in literature and expert opinions The purpose of the present study was to investigate the possibility to determine CESs for toxicological effect parameters by means of a literature review and an

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expert opinion analysis. In Appendix B, an overview of the CESs described in the literature is given. The results of the both the literature review and the expert opinion analysis are discussed in the following paragraphs.

4.3.1. The need to determine CESs The need to determine CESs is supported by the literature review, as well as by the expert opinion analysis. Many scientists recognise the advantages of using CESs and/or the Benchmark approach. Although in current risk assessments, effect sizes always have to be judged as critical or less than critical, these judgements are seldom made in a clear and transparent way. Getting more insight in these judgements, and knowing which assumptions are merely conventions and which are scientifically based, would aid to a better understanding and harmonisation of various risk assessments. Some experts, however, are not yet convinced that determining CESs could improve the quality of human risk assessment and suggest further evaluation of CESs and/or the Benchmark approach. CESs and/or the Benchmark approach may not be applicable to all toxicological studies and endpoints, and should not be used systematically but as part optional of the assessment, depending on the experimental conditions and data. 4.3.2. Background information CESs can be based on biological and toxicological information (like a plausible mechanism of action), statistical information (like the limit of detection), and/ or other considerations (e.g. consensus on arbitrarily chosen CESs). The results of this study show that in establishing CESs, statistical considerations, as well as biological and toxicological information are important. Some of the biological and toxicological considerations needed in defining a CES concern the toxicological relevance or severity of changes in the effect parameter, the reversibility of these changes, the mechanisms causing the effects, and the relation of the effect parameter under study with other parameters in the sequence of events leading to this toxic effect. Since current toxicological and biological knowledge seldom provides sufficient basis to unequivocally establish the breaking point between a non-adverse and adverse effect size for most toxicological effect parameters (Vermeire et al., 1999), statistical information (like the detectability or measurability of the change in effect size) and arbitrarily chosen values may often become rather important in defining CESs. Although statistical considerations may be necessary in defining CESs, the determination of whether or not an effect is adverse always requires expert (toxicological) judgement. However, the availability of various toxicological or biological information on which a certain CES can be based, does not

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guarantee that consensus will be reached on this CES. Even the CESs for erythrocyte cholinesterase activity and adult body weight, which seemed to be based on biological significance, are often still points of discussion (JMPR, 1998). Without clear scientific arguments for the establishment of a CES, one might propose to use a zero CES to avoid an arbitrary decision. However, considering any non-zero effect as adverse leads to statistical problems and seems unnecessarily conservative (Slob and Pieters, 1998). Therefore, toxicological, biological, and statistical information or partial consensus (within certain expert groups, institutes or member states) on arbitrary chosen values is needed to establish CESs. Partial consensus on CESs with respect to some (structural related) compounds seems no option, since the CES of a certain effect parameter should not depend on the tested substance.

4.3.3. Effect le6el CESs can be defined on three different effect levels, namely: for each toxicological effect parameter, for a combination of toxicological effect parameters, and for all toxicological effect parameters together (one universal CES). In addition the unit of observation can vary. For example in expressing CESs for developmental effect parameters, one can either use the individual animal or the litter as the unit of observation (Allen et al., 1998; Fung et al., 1998). As described in the earlier paragraph the use of toxicological and biological insights are of relevance for the determination of CESs. Using toxicological and biological insights makes the determination of one universal CES almost impossible, because these insights are usually specific for one or a combination of toxicological effect parameters. Besides some expressions (e.g. an absolute CES) can not be used to define a universal CES. Therefore, most experts advise against the use of one universal CES. This was also found in the literature. In order to be useful in human risk assessment, CESs should be postulated for each (combination of) toxicological effect parameter(s), depending on the toxicological and biological information available. Most CESs found in literature or suggested by experts are determined for one specific toxicological effect parameter. Some toxicological effect parameters, however, are so closely related to each other that it is easier to define a CES for a combination of toxicological effect parameters. Frequently, a change in one effect parameter influences or causes a change in another effect parameter. When defining CESs for a combination of effect parameters, these interactions can be taken into account, which may result in more complex situations. Therefore, if there is enough mechanistic knowledge about the sequence of events, only the CES for the most critical effect parameter in that sequence has to be determined. However, it should be noted that depend-

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ing on the mechanism of toxicity, the most sensitive parameter may vary. Therefore, for all (combinations of) toxicological effect parameters, a CES should, for practical reasons be determined. An additional consideration in defining CESs with respect to developmental effects should be intra-litter correlation. It is recommended to treat the litter as unit of observation (or to implement a procedure that accounts for intra-litter correlation), because it is likely that correlations among the developmental effects exist due to genetic similarity and similar treatment conditions (Allen et al., 1998). Therefore, CESs expressed in litter effect should be considered.

4.3.4. Experimental conditions When defining a CES the experimental conditions can be specified or not. The CES of a toxicological effect parameter may be different depending on the experimental conditions, including the animal species and the route, source, and duration of the exposure. When a certain CES is used in the literature, the species and exposure conditions are usually specified. The general opinion in the literature as well as in the analysis of opinions is that distinctions have to be made in CESs for different species, sexes, and possibly age groups. CESs should theoretically be independent of the exposure conditions and routes. All experimental conditions should be taken into account with extrapolation to the human situation. This extrapolation, however, is done after the calculation of the BMD (or other exposure limit) for the experimental animal, which means CESs should be defined for the experimental animal. Human CESs can only be used in combination with sufficient human dose response data, which is seldom available. When extrapolating to the human situation, differences between human situation and the tested species, exposure conditions, and routes should be taken into account. An example of a difference between the human and the tested species is the relevance of the tested effect parameters for humans. Some effects observed in the animal might be irrelevant or not interpretable for the human situation. 4.3.5. Expression Different expressions can be used to define CESs for continuous effect parameters and BMRs for dichotomous effect parameters (ILSI, 1994; US-EPA, 1995; Murrell et al., 1998; Slob and Pieters, 1998). In this respect, both the expressions used to define CESs, as well as BMRs will be discussed, because in case a continuous effect parameter is dichotomised, the determination of the CES and the BMR are closely linked together. 4.3.5.1. Expressions for CESs. CESs for continuous toxicological effect parameters can be expressed in terms of an absolute effect size [ f(d)], an absolute change in effect size [ f(d)−f(0) ], or a relative change in effect size. Considering that adult male rats normally weigh between

350 and 500 g, a CES defined as an absolute effect size [ f(d)] could, for example, be a body weight of 300 g for (exposed) adult male rats (where a body weight 300 g or less is regarded adverse). An example of a CES defined in terms of an absolute change in effect size (compared with the effect size of the controls [ f(d)− f(0) ]), is a difference in body weight (between exposed and non-exposed adult male rats) of 50 g (where a change of 50 g or more is regarded as adverse). There are four different possibilities to express a CES in terms of a relative change in effect size. The relative change can be presented as the ratio of the absolute change in effect size [ f(d)− f(0) ] divided by 1. the effect size of the controls [ f(0)], e.g. a difference in body weight (between exposed and non-exposed adult rats) of 10% of the body weight of the controls (where a difference of 10% or greater is regarded as adverse); 2. the standard deviation of the distribution of the effect size in the experimental control group [S.D.(0)], e.g. a difference in body weight (between exposed and non-exposed adult rats) of 2-times the standard deviation of the body weight in non-exposed rats (where a difference of two S.D. or greater is regarded as adverse); 3. the standard error of the mean effect size in the historical controls [S.E.(0)], e.g. a difference in body weight (between exposed and non-exposed adult rats) greater than 2-times the standard error of the mean body weight in the historical controls; 4. the maximal change in effect size [ f(max)− f(0) ] or [ f(min) − f(0) ]. An example of this way to express a CES is a decrease in body weight (between exposed and non-exposed adult rats) of 10% of the possible (maximal) decrease in body weight of the adult rat (where a decrease of 10% or greater is regarded as adverse). Assuming a normal distribution of the effect size, the second and third possibility correspond with defining a normal range of the effect size observed in the experimental (2) or historical (3) controls. All effect sizes outside the confidence limits of this normal range are regarded as adverse, e.g. every body weight less then the 5th percentile of the body weight of the historical control rats.

4.3.5.2. Expressions for BMRs. A Benchmark Risk for dichotomous effect parameters (or dichotomised continuous effect parameters) can be expressed in terms of an additional risk, or an extra risk. An additional risk can be defined as the additional proportion of the animals that responds in the presence of a certain dose [AR(d)= P(d)− P(0)=P(BMD) − P(0)]. In other words, the additional risk is the added risk of impairment in an exposed population relative to an unexposed population. The extra risk is the fraction of animals

S. Dekkers et al. / En6ironmental Toxicology and Pharmacology 10 (2001) 33–52

that would respond when exposed to a certain dose, among animals who otherwise would not respond [ER(d)= (P(d)−P(0))/(1 −P(0))]. The extra risk is the additional risk divided by the proportion of animals that will not respond in the absence of exposure. Additional risk and extra risk differ quantitatively in the way they incorporate the background response. For example, if a dose increases a response from 0 to 1%, both additional risk and the extra risk is 1%. However, if a dose increases risk form 90 to 91%, the additional risk is still 1%, but the extra risk is 10%.

4.3.5.3. Expressions used and suggested in the literature re6iew and opinion analysis. In the opinion analysis, many experts did not give their opinion on this matter, for example because they were of opinion that this matter was best dealt with by experts statisticians or specialists in the target system involved. Among the experts who did give their opinion on this matter, as well as in the literature review, CESs are most frequently expressed in terms of a relative change in effect size. Especially expressing the CES in terms of a change in effect size relative to the effect size of the controls, [ f(d) − f(0) / f(0)] is a very popular expression. One of the reasons why many scientists favour this expression is that this ‘standardisation’ makes a comparison between different studies possible, by accounting for the natural background of the effect parameter studied. This expression cannot be used when the natural background of an effect parameter is zero (Murrell et al., 1998). It should be noted that a relative change of 5% (compared with the effect size of the controls) in one effect parameter does not have to be comparable to the same change in another effect parameter. Some other expressions, which try to make comparisons between parameters and studies possible, use the variation in either the control group or the historical controls to calculate the CES [ f(d) − f(0) / S.D.(0)] or [ f(d)−f(0) /S.E.(0)]. A disadvantage of these expressions is that the cut-off point distinguishing normal from abnormal is an arbitrary decision, usually based on statistical criteria (Murrell et al., 1998). An objection against using the variation in the control group [S.D.(0)] is that this S.D.(0) heavily depends on the experimental error, which may vary from parameter to parameter and study to study in ways not related to the health importance of the effect (Murrell et al., 1998; Slob and Pieters, 1998). Using the variation (or standard error) of the historical control data makes the CES less dependent of the experimental error, but has the disadvantage of being influenced by sample size in a way unrelated to the toxicological relevance of the effect parameter and not rewarding better experimental designs (Kavlock et al., 1995). Since the limited toxicological relevance of the variation in the control group or population different, CES-values should be determined for each (combination of) toxicological effect parame-

41

ter(s), depending on the adversity of certain changes in the effect parameter. Another way to express CESs as a relative change, which is seldom mentioned in literature or by the consulted experts, is to express the CES relative to the maximal change in effect size [ f(d)− f(0) / f(max)− f(0) ]. To use this expression, the total range in effect size must be known (from historical data), or the tested dose range should include high levels of exposure resulting in very severe toxicity and injury in the tested animals (which adds to the confidence that the highest attainable effect has been reached in the study) (Murrell et al., 1998). Furthermore, one may question the assumption that for all effects saturation occurs at high doses. Saturation is a common phenomenon, but may not be a good indication for the adversity or toxicological relevance of a certain exposure, since a saturation in one effect parameter may lead to other (possibly more severe) effects. Therefore, again, different CES-values should be determined for each (combination of) toxicological effect parameter(s), depending on the adversity of certain changes in the effect parameter. In addition, the limiting mechanisms may vary among the different effect parameters, which makes comparison difficult. An expression in which the toxicological relevance or adversity of a certain effect size can (and should) be incorporated is the absolute (change in) effect size [ f(d)] or [ f(d)− f(0) ]. A disadvantage of this expression is that it often makes comparison between different studies and effect parameters impossible, since each CES can be expressed in a different unit, depending on the unit in which the changes in the toxicological effect parameter is measured. To express each individual toxicological effect parameter in terms of an absolute (change in) effect size a lot of information and discussion is required. Expressing CESs in the absolute effect size [ f(d)], also has the disadvantage of ignoring the contemporaneous control group, which might also show the effect of interest if, for example, diet-related factors are playing a role. In the expert opinion analysis several experts favour a combined use of the expressions, depending on the situation. In some cases, comparing the absolute change in effect size with the effect size of the controls may be sufficient, while in other cases possible distributions of standard deviations have to be included. In some cases, like multi-peak distributions, relative change measures may not provide sufficient information. In such cases absolute measures should be considered. Sometimes the CES is not directly used to estimate the CED from the dose–response curve describing the continuous data but is used to convert the data into dichotomous data, for which a BMR is used to determine the CED from the dose–response curve describing this dichotomous data. The BMRs described in the literature are generally expressed in terms of additional risk. The

42

S. Dekkers et al. / En6ironmental Toxicology and Pharmacology 10 (2001) 33–52

choice of expressing this BMR in either extra risk or additional risk is to a certain extent based on assumptions about how the background risk should be incorporated in the expression. A disadvantage of both the BMR expressions (extra and additional risk) is that they are dependent on the experimental error in a way comparable to the NOAEL approach (Slob and Pieters, 1998).

4.3.6. Magnitude No suggestion for specific values of CESs were made by the consulted experts, while only few values for CESs are mentioned in the literature. Although the general opinion is that the value of a CES should be based primarily on toxicological and biological understanding and insights, only the CES-values for erythrocyte cholinesterase activity and adult body weight were partly based on biological considerations. Most other CES-values described in literature were based on statistical and/or arbitrary choices. An additional, but often less valid, consideration in establishing CESs (used in the literature and suggested by the consulted experts) is the comparison of the resulting BMDs with the NOAEL. Although comparing BMDs with endpoint specific NOAELs, might be valuable to gain further insight in the use of the Benchmark approach, establishing CESs so that BMDs are comparable to the NOAELs, makes them dependent on the NOAELs. A CES which is dependent on the NOAEL, is also indirectly dependent on several disadvantages of the NOAEL, like dose spacing, experimental design, etc. The value of CESs expressed in terms of a relative change in effect size compared with the effect size in the controls [ f(d)−f(0) /f(0)] described in literature, usually lies between 1 and 10%. Some articles in literature favour the use of 10% because this level appeared to provide BMDs that corresponded more closely to the traditional NOAEL than the use of a 5 or 1% relative change in effect size. However, the suggested values of CES, based on comparisons with NOAELs, vary between the different effect parameters. One of the reasons is that the NOAEL depends on the ability to determine whether or not two groups are significantly different, which is different for each toxicological effect parameter. An advantage of using a relative high CES (like 10% or greater) is that extrapolation below experimental range is usually not necessary. However, a disadvantage of using 10% is that for some parameters this risk is regarded as too high to use as guideline. One document suggested the use of either 5 or 10%, but acknowledged that future research is needed to demonstrate the advisability of selecting one value over another (US-EPA, 1996a). The same suggestion can be done for CES expressed as changes in effect size relative to the maximal change in effect size [ f(d) −f(0) / f(max) − f(0) ]. When CESs are expressed as changes in effect size relative to the variation in the control group [ f(d) − f(0) /S.D.(0)],

expressing CESs in percentiles (in stead of standard deviations) has the advantage of also giving information on whether an increase or a decrease in effect size is examined. The value of CESs expressed in percentiles, is usually an arbitrary or statistical choice. In case a CES is used in combination with a BMR, the value of the BMR should be set in accordance with the value of the CES. In literature usually BMRs between 1 and 10% are used. Some propose a BMR of 1% or even lower when the experimental data are good enough. However, others claim that a BMR of 1% is too low. In establishing the value of the BMRs the comparison with the NOAEL is often used. Instead of estimating the dose associated with extra/additional risk, one can also choose to derive the dose at which the ‘average’ animal studied responded (the ED50) to extrapolate this dose to the dose at which the average human would respond and finally extrapolate the latter to the dose at which the ‘sensitive’ human would respond. Although using an ED50 avoids arbitrary choices of a BMR for some severe toxicological effects a starting-point where 50% of the animals responds might be too high. Appendix B gives an overview of the expressions and values of CESs for continuous toxicological parameters found in literature.

4.3.7. Objecti6ity and subjecti6ity in estimating CESs The opinions on the objectivity or subjectivity of the different aspects in determining CESs vary widely between the different experts, maybe because they interpreted the question differently. The choices made in determining CES should be based on experimental research as much as possible. However, especially when there is insufficient scientific evidence on the adversity of certain changes in effect parameters, it will be impossible to avoid all subjectivity. In the process of reaching consensus on CESs, subjectivity will play an important role, but should be validated with objective information as much as possible. CESs should be established in a consistent and reliable manner, in which expert judgement of various experts will lead to comparable CESs. 4.4. Comparison of the literature and expert opinions When the results of the literature review are compared with the results of the expert opinion analysis no major differences are found. In the literature only two CESs, for which consensus seemed to be reached, were found (IPCS, 1990; Nair et al., 1995; Slob and Pieters, 1998 (erythrocyte cholinesterase activity) and US-EPA, 1995; Haber et al., 1998b (adult body weight)). However, the literature review resulted in many ideas about the expressions to define CESs and the Benchmark approach in general. In the opinion analysis no CESs for which consensus was reached were mentioned, but again many opinions on the expressions used to define CESs and the Benchmark approach in general were gathered.

S. Dekkers et al. / En6ironmental Toxicology and Pharmacology 10 (2001) 33–52

4.5. Shortcomings of the study Many of the shortcomings of this study are related to subjectivity. Subjective elements, judgements, and assumptions, however, are inherent in human risk assessment (Kraus et al., 1992). The first subjective elements have been introduced with the selection of the literature and the experts. The selection of the literature was done by reading only the abstracts of the original documents, which may have caused some selection bias. Most experts were selected based on the recommendation of one experienced Dutch toxicologist. The subjectivity in this recommendation may have introduced a selection bias. Furthermore, response bias may have occurred, while experts favouring the use of CESs and/or the Benchmark approach might have returned the questionnaire easier than experts disapproving this approach. Also, information bias, as well as observer bias may have been introduced due to subjective opinions of the responding experts and the observer. Since it is demonstrated that large differences in opinion between toxicologists working in industry, academia, and government exists about fundamental issues in human risk assessment (Kraus et al., 1992), experts working in the industry, academia, and government were selected. The co-operating experts represented all three disciplines and the observed differences in their opinion were not structurally related to their discipline. However, generalisation of the results of the opinion analysis is difficult, because specific and unique experiences of the experts may have determined or affected the opinion of the responding experts. Furthermore, it can not be excluded that experts favouring the use of CESs and/or the Benchmark approach give more extensive answers to the questions than experts disapproving this approach. In addition, the experience and knowledge of various experts may have influenced their reaction. In order to limit the observer bias in the literature review, the CESs found in literature were only evaluated on the predetermined aspects. The observer bias in processing of the expert opinion analysis was reduced by using multiple choice questions next to open ended questions.

5. Conclusions From the results of the literature review and expert opinion analysis it can be concluded that reaching consensus on CESs is not yet possible. Before consensus can be reached, a lot of scientific information has to be gathered and a lot of discussion has to take place among various scientists and non-scientists in the route of determining HLVs (including politicians, industrial parties, and scientists specialised in human risk assessment, specific toxicological effect parameters, specific

43

species, statistics, etc.). The advantages of reaching consensus on CESs and their surplus value in human risk assessment should be weighted against the time, effort and money needed to reach consensus and the costs of inadequately established limit values. Aspects like the possibility of estimating the risk when the exposure limits are exceeded should be considered next to aspects like the research needed to establish scientifically based CESs. Without sufficient scientific evidence on the adversity of certain changes in effect parameters, total international consensus on this CES will probably be impossible. Even if it would be possible, than the surplus value of the use of those CESs will probably be limited. BMDs derived with these CESs, will only have certain advantages over a NOAEL (like, taking into account all dose–response data). For some toxicological effect parameters gathering enough scientific knowledge within a reasonable amount of time will be practically impossible. For other effect parameters, like body weight or erythrocyte cholinesterase activity, it might already be possible to reach consensus based on the available scientific evidence. Therefore, reaching international consensus on CESs can probably only be achieved for a few toxicological effect parameters. For most toxicological effect parameters it might be possible, to reach partial consensus within a smaller context like one or several institutes or countries. For the parameters for which no scientific evidence is available to decide on a CES, one may consider to postulate a default CES for the time being and evaluate the outcome of the toxicological studies evaluated with the Benchmark approach on a case by case basis. On the long-term, if it will be possible to collect enough scientific knowledge on the induction and development of all toxicological effects in relation to their physiological control mechanisms, biologically and/or toxicologically based CESs can be defined for most toxicological effect parameters. With respect to the other aspects related to the determination of CESs for toxicological effect parameters commonly used in human risk assessment, the following can be concluded: “ the (value of) CESs should ideally be based on a combination of biological and toxicological considerations specific to the toxicological effect parameter(s) involved, and statistical information; “ CES should be defined for each individual toxicological effect parameter, or for certain combinations of effect parameters; “ different CESs should be defined for different species, sexes and age groups; “ CESs can best be expressed in terms of relative changes in effect size compared with the effect size of the controls.

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S. Dekkers et al. / En6ironmental Toxicology and Pharmacology 10 (2001) 33–52

Based on the conclusions drawn from this study, it is recommended to set up Task groups on CESs, preferably at WHO-level, in order to reach consensus on the definition of CES for all relevant toxicological end points. Since the choice of CESs is a complex problem it requires extensive discussions among all experts involved in human risk assessment in a step-wise approach. Finally, every toxicological endpoint for which consensus on the CES is reached is a step forward, because this CES can facilitate the discussion on the adversity and relevance of certain changes in that effect parameter during the risk assessment process (both using the NOAEL and the Benchmark approach). This will make the final decisions on adversity more transparent and less ad hoc.

Acknowledgements This study was funded by the Dutch Ministry of Social Affairs and Employment (SZW) and earlier described in TNO report V99.195: ‘Critical Effect Sizes in Toxicological Risk Assessment: Establishing the breaking point between adverse and non-adverse changes in toxicological effect parameters’ by Dekkers, S., de Heer, C., and Rennen, M.A.J. (2000). We would like to thank Professor W.F. Passchier and Dr T.M.C.M. de Kok from the Department of Health Risk Analysis and Toxicology of the University of Maastricht for their valuable advice. Furthermore, we wish to thank all the respondents for their co-operation.

Appendix A. Terms and abbreviations Absolute critical effect size (Absolute CES): A CES expressed in terms of an absolute (change in) effect size. Adverse effect: 1. a change in morphology, physiology, growth, development or life span of an organism, which results in impairment of functional capacity or impairment of capacity to compensate for additional stress or increase in susceptibility to the harmful effects of other environmental influences (IPCS, 1994); 2. a biochemical change, functional impairment, or pathological lesion that either singly or in combination adversely affects the performance of the whole organism or reduces an organism’s ability to respond to an additional environmental challenge (US-EPA, 1995). Benchmark dose (BMD): 1. a statistical lower confidence limit on the dose producing a predetermined, altered response for an

effect. (US-EPA, 1995); 2. the lower confidence limit of the dose calculated to be associated with a given incidence (e.g. 5 or 10% incidence) of effect estimated from all toxicity data on that effect within that study (Crump, 1984). Benchmark response (BMR): a predetermined level of altered response or risk at which the benchmark dose is calculated (US-EPA, 1995). Critical effect: 1. the adverse effect judged to be most appropriate for determining a Human Limit Value (IPCS, 1994); 2. the first adverse effect, or its known precursor, that occurs as the dose rate increases (US-EPA, 1995). Critical effect size (CES): 1. the maximal change in a specific (combination of) effect parameters, which is not regarded harmful for the tested animal; 2. the maximum (change in the) magnitude of a specific (combination of) toxicological effect parameters(s), which is assumed to be non-adverse (Slob and Pieters, 1998); 3. value of effect-size below which there is no reason for concern (Vermeire et al., 1999). Critical effect dose (CED): the dose at which the average animal shows the (postulated) CES defined for a particular endpoint (Vermeire et al., 1999). Dose–response relationship: a relationship between (1) the dose, either ‘administered dose’ (i.e., exposure) or absorbed dose; and (2) the extent of toxic injury produced by that chemical. Response can be expressed either as the severity of injury or proportion of exposed subjects affected (US-EPA, 1995). Endpoint: an observable or measurable biological or chemical event used as an index of the effect of a chemical on a cell, tissue organ, organism, etc. (USEPA, 1995). Human limit value (HLV): a general term covering various limit values such as ADI, RfD, etc. (Vermeire et al., 1999). Relative critical effect size (Relative CES): a CES expressed in terms of a relative change in effect size.

Appendix B. Evaluation of CESs found in the literature In case, the CES is used to dichotomise the continuous data, both the CES and the BMR are described, because these are usually defined in combination with each other. Although the evaluation was restricted to CES defined on the basis of animal experiments also human CESs found during the literature search are incorporated in the literature evaluation since these might include useful information and/or considerations (Table 1).

Table 1 Evaluation of CESs (for continuous effect parameters) Experimental conditions (tested species)

( 9 c)

Expression (formula)

Value (%, Px, SDs, etc.)

Reference

Blood parameters par: neutrophils in blood

arb

Rat

+

arb

Rat

+

par: T-lymphocytes in blood par: red blood cell count

arb arb com

Not specified Rat Rat

+ + +

5 or 10% 2 S.D. 5 or 10% 2 S.D. 10% 5% P5

Woutersen et al. (unpublished data)

par: lymphocytes in blood

par: haemoglobin par: methemoglobin

arb arb

Rat Not specified

+ +

Not specified Rat/mice Rat Rat Rat Rat Rat Rat Rat Rat Not specified Not specified Not specified

− + + + + + + + + + − − +

f(d)−f(0) /f(0) f(d)−f(0) /l(0) f(d)−f(0) /f(0) f(d)−f(0) /l(0) f(d)−f(0) /f(0) f(d)−f(0) /f(0) CES: f(d)−f(0) /l(0) in percentile of controls BMR: (P(d)−P(0))/(1−P(0)) f(d)−f(0) /f(0) CES: f(d)−f(0) /l(0) BMR: (P(d)−P(0))/(1−P(0)) f(d)−f(0) /f(0) f(d)−f(0) /f(0) f(d)−f(0) / f(max)−f(0) f(d)−f(0) / f(max)−f(0) f(d)−f(0) / f(max)−f(0) f(d)−f(0) /f(0) f(d)−f(0) / f(max)−f(0) f(d)−f(0) /f(0) f(d)−f(0) / f(max)−f(0) f(d)−f(0) / f(max)−f(0) f(d)−f(0) /f(0)

10% 5% 2 S.D. 10% 10% 10% 5 or 10% 5 or 10% 5 or 10% 30% 5% 30% 5% 5% 20% 2 S.D. 10% 2 S.D. 10% 5% 10% 20%

Murrell et al., 1998 McGrath et al., 1996 Vermeire et al., 1999

10%

Haag-Gronlund et al., 1995

com: immune suppression par: enzyme induction par: plasma retinol par: TT4 activity par: FT4 activity par: ALAT activity par: par: par: par:

arb arb com com com com com ASAT activity com S DeH (DEN/SAL) com Alk Phos. (DEN/SAL) com erythrocyte cholinesterase activity tox tox arb

par: plasma cholinesterase activity

arb

Not specified

+

par: Triglycerides par: LDH

com sta com

Rat Rat Not specified

+ + −

CES: f(d)−f(0) /l(0) BMR: (P(d)−P(0))/(1−P(0)) CES: f(d)−f(0) /l(0) BMR: (P(d)−P(0))/(1−P(0)) f(d)−f(0) / f(max)−f(0) f(d)−f(0) /f(0) f(d)−f(0) /f(0)

Li6er toxicity par: enzyme activity in liver

com

Rat/mice

+

f(d)−f(0) /f(0)

Woutersen et al. (unpublished data) McGrath et al., 1996 Woutersen et al. (unpublished data) Haber et al., 1998a

Woutersen et al. (unpublished data) Nair et al., 1995 McGrath et al., 1996 McGrath et al., 1996 Murrell et al., 1998 Murrell et al., 1998 Murrell et al., 1998 Woutersen et al. (unpublished data) Murrell et al., 1998 Woutersen et al. (unpublished data) Murrell et al., 1998 Murrell et al., 1998 IPCS, 1990 Slob and Pieters, 1998 Nair et al., 1995

S. Dekkers et al. / En6ironmental Toxicology and Pharmacology 10 (2001) 33–52

Information (tox/arb/ot/ com b)

Effect level (per/com/un: parameter)a

Nair et al., 1995

45

46

Table 1 (Continued) Information (tox/arb/ot/ com b)

Experimental conditions (tested species)

( 9 c)

Expression (formula)

Value (%, Px, SDs, etc.)

Reference

par: glucose-6-phosphatase activity in liver

arb

Mice

+

CES: f(d)−f(0) /d(0)

2.33 S.D.

Barton and Das, 1996

par: CYP1A1 activity in liver

com arb com arb

Rat/mice Rat Rat/mice Mice

+ + + +

Andersen et al., 1995 McGrath et al., 1996 Murrell et al., 1998 Vogel et al., 1997

com com com com arb arb

Rat/mice Rat/mice Rat/mice Rat/mice Rat Mice

+ + + + + +

par: MROD activity in liver

arb

Mice

+

par: total Ah receptor binding par: liver labelling index par: liver fat content

com com arb arb

Mice Rat Rat Rat

+ + − +

par: cell necrosis in liver par: DNA content in liver par: peroxisome proliferation in liver

com com com

Rat/mice Rat/mice Rat/mice

+ + +

f(d)−f(0) / f(max)−f(0) CES: f(d)−f(0) /l(0) BMR: (P(d)−P(0))/P(0) f(d)−f(0) / f(max)−f(0) f(d)−f(0) / f(max)−f(0) f(d)−f(0) / f(max)−f(0) f(d)−f(0) / f(max)−f(0) f(d)−f(0) /f(0) CES: f(d)−f(0) /l(0) BMR: (P(d)−P(0))/P(0) CES: f(d)−f(0) /l(0) BMR: (P(d)−P(0))/P(0) f(d)−f(0) / f(max)−f(0) f(d)−f(0) / f(max)−f(0) Not specified CES: f(d)−f(0) /l(0) in percentiles BMR: (P(d)−P(0))/P(0) f(d)−f(0) /f(0) f(d)−f(0) /f(0) f(d)−f(0) /f(0)

5% 10% 5 or 10% 2 S.D. 1% 5 or 10% 5 or 10% 5% 5% 10% 2 S.D. 1% 2 S.D. 1% 5% 5% 1% P1 or P5 10% 10% 10% 10%

Lung parameters par: IL-1beta activity in lung

arb

Mice

+

par: LDH in BALF lymphocytes par: protein in BALF lymphocytes par: FEV1

com com com

Rat Rat Human

+ + −

CES: f(d)−f(0) /l(0) BMR: (P(d)−P(0))/P(0) f(d)−f(0) /f(0) f(d)−f(0) /f(0) f(d)−f(0) f(d)−f(0) /f(0) CES: f(d)−f(0) /f(0) BMR: P(d)−P(0)/P(0) BMR: (P(d)−P(0))/(1−P(0))

2 S.D. 1% 10% 10% 50, 100, or 708 ml 10 or 20% 20% 50, 10, 1, or 0.1% 10, 1, or 0.1%

Neurotoxicity par: eye-hand co-ordination

com

Human

+

par: par: par: par: par: par:

4OH-AA activity in liver T4UGT activity in liver hepatic retinol activity liver hepatic retinyl-palmitate AHH activity in liver IL-1beta activity in liver

BMR: not specified f(d)−f(0) /f(0)

CES: f(d)−f(0) /l(0) in percentiles of P95 controls BMR: not specified 5 or 10%

Murrell et al., 1998 Murrell et al., 1998 Murrell et al., 1998 Murrell et al., 1998 McGrath et al., 1996 Vogel et al., 1997 Vogel et al., 1997 Murrell Murrell Crump, Crump,

et al., 1998 et al., 1998 1984 1995

Haag-Gronlund et al., 1995 Haag-Gronlund et al., 1995 Haag-Gronlund et al., 1995 Vogel et al., 1997 Malsch et al., 1994 Malsch et al., 1994 Bailer et al., 1997

Davis et al., 1998

S. Dekkers et al. / En6ironmental Toxicology and Pharmacology 10 (2001) 33–52

Effect level (per/com/un: parameter)a

Table 1 (Continued) Information (tox/arb/ot/ com b)

Experimental conditions (tested species)

( 9 c)

Expression (formula)

com

Human

+

par: hand steadiness

com

Human

+

par: visual reaction time

com

Human

+

par: scholastic/psychological test score

com

Human

+

par: neurologic score

com

Human

+

par: age first walked

com

Human

+

par: duration of wakefulness par: motor conduction velocity

com com

Rat Human

+ +

par: sensor conduction velocity

com

Human

+

par: amplitude ratio

com

Human

+

par: fatty-acid composition hippocampus par: fatty-acid composition cerebellum cortex par: degenerated axons par: silver strain grain counts

com com

Mongolian gerbils + Mongolian gerbils +

CES: f(d)−f(0) /l(0) in percentiles BMR: (P(d)−P(0))/P(0) 10% CES: f(d)−f(0) /l(0) in percentiles of P95 controls BMR: not specified 5 or 10% f(d)−f(0) Latency increased each 2 min with an 8-min testing period CES: f(d)−f(0) /l(0) in percentiles of P95 controls BMR: not specified 5 or 10% CES: f(d)−f(0) /l(0) 2 S.D. BMR: (P(d)−P(0))/P(0) 10% CES: f(d)−f(0) /l(0) in percentiles of P5 controls BMR: (P(d)−P(0))/P(0) 10% CES: f(d)−f(0) /l(0) in percentiles of P1 or P5 controls BMR: (P(d)−P(0))/P(0) 5 or 10% CES: f(d)−f(0) /l(0) in percentiles of P1 or P5 controls BMR: (P(d)−P(0))/P(0) 5 or 10% f(d) 10% CES: f(d)−f(0) /l(0) in percentiles of P99 controls BMR: (P(d)−P(0))/P(0) 10% CES: f(d)−f(0) /l(0) in percentiles of P99 controls BMR: (P(d)−P(0))/P(0) 10% CES: f(d)−f(0) /l(0) in percentiles of P99 controls BMR: (P(d)−P(0))/P(0) 10% f(d)−f(0) /f(0) 10% f(d)−f(0) /f(0) 10%

arb arb

Rat Monkey

Effect level (per/com/un: parameter)a

Mongolian gerbils + Rat/monkey +

f(d)−f(0) /l(0) 3 S.D. CES: f(d)−f(0) /l(0) in percentiles of P99, 0 controls BMR: (P(d)−P(0))/P(0) 10% f(d)−f(0) /f(0) 10% f(d)−f(0) /l(0) 3 S.D.

Davis, et al., 1998

Davis et al., 1998

Crump et al., 1998

Crump et al., 1995

Crump et al., 1995

Clewell et al., 1997 Price et al., 1997

Price et al., 1997

Price et al., 1997

S. Dekkers et al. / En6ironmental Toxicology and Pharmacology 10 (2001) 33–52

par: alternations glutathione hippocampus com par: serotonin (5HT) hippocampus arb

+ +

Value (%, Px, SDs, etc.) Reference

Haag-Gronlund et al., 1995 Haag-Gronlund et al., 1995 Woutersen et al. (unpublished data) Slikker et al., 1998

Haag-Gronlund et al., 1995 Gaylor and Slikker, 1990 47

48

Table 1 (Continued) Experimental conditions (tested species)

( 9 c)

Expression (formula)

Value (%, Px, SDs, etc.) Reference

arb

Rat

+

P01

Gaylor et al., 1998

par: serum PRL (biomarker of dopaminergic dysfunction)

arb

Human

+

5% P95

Mutti and Smargiassi, 1998

par: dopamine depletion

arb arb

Mice Mice/monkey

−

CES: f(d)−f(0) /l(0) in percentile of controls BMR: P(d)−P(0) CES: f(d)−f(0) /l(0) in percentile of controls BMR: P(d)−P(0) f(d)−f(0) /l(0) CES: f(d)−f(0) /l(0) in percentile of controls BMR: P(d)−P(0)

10% 2.33 S.D. P99

Kodell et al., 1995 Slikker et al., 1996

Body weight par: body weight

Organ weight par: thymus weight

par: spleen weight

par: liver weight

5%

ot com com com

Rat Rat Not specified Rat

− − −

Not specified f(d)−f(0) /f(0)

1% 5% 10%

arb arb

Not specified Rat

− +

com

Rat

+

f(d)−f(0) /l(0) CES: f(d)−f(0) /l(0) BMR: (P(d)−P(0))/(1−P(0)) CES: f(d)−f(0) /l(0) in percentlile of controls BMR: (P(d)−P(0))/(1−P(0))

2 S.D. 2.5 S.D. 5% P95 or P5

f(d)−f(0) /f(0)

10% 5 or 10% 2 S.D. 1% 10%

com com

Rat Rat

+ +

ot com com com

Rat Rat Rat Rat

− + − +

com com

Rat Rat

+ +

f(d)−f(0) /l(0) Not specified f(d)−f(0) /f(0) f(d)−f(0) /l(0) f(d)−f(0) /f(0) f(d)−f(0) /l(0)

Kavlock et al., 1995 Woutersen et al. (unpublished data) Woutersen et al. (unpublished data) Haber et al., 1998b US-EPA, 1995 Woutersen et al. (unpublished data) Gaylor et al., 1998 Haber et al., 1998b

10%

5 or 10% 2 S.D. 10% 5 or 10% 2 S.D.

Malsch et al., 1994 Woutersen et al. (unpublished data) Crump, 1984 Woutersen et al. (unpublished data) Malsch et al., 1994 Woutersen et al. (unpublished data) Woutersen et al. (unpublished data) Woutersen et al. (unpublished data)

S. Dekkers et al. / En6ironmental Toxicology and Pharmacology 10 (2001) 33–52

Information (tox/arb/ot/ com b)

Effect level (per/com/un: parameter)a

Table 1 (Continued) Effect level (per/com/un: parameter)a

Information (tox/arb/ot/ com b)

Experimental conditions (tested species)

( 9 c)

Rat

+

par: liver weight/body weight

com

Human

+

par: kidney weight

com com

Rat/mice Rat

+ +

par: lung weight

com

Rat

+

com

Rat

+

com arb com com com

Rat/mice/rabbit Rat Not specified Rat Rat/mice/rabbit/ hamster Not specified Rat/mice/rabbit/ hamster Rat/mice/rabbit Not specified Rat/mice/rabbit/ hamster Rat Rat

Foetal weight par: foetal weight

com com com com com com arb arb com com com com

Rat Not specified Rat/mice/rabbit/ hamster Not specified Rat/mice/rabbit/ hamster

Value (%, Px, SDs, etc.) Reference

CES: f(d)−f(0) /l(0) BMR: not specified CES: f(d)−f(0) /l(0) in percentiles BMR: (P(d)−P(d))/(1−P(0)) CES: f(d)−f(0) /l(0) in percentiles BMR: (P(d)−P(d))/(1−P(0)) f(d)−f(0) /f(0)

2.33 S.D. 5% P1 or P01 1 or 5% P1 5% 10% 5 or 10% 2 S.D. 10% P95 or P5

f(d)−f(0) /l(0) f(d)−f(0) /f(0) CES: f(d)−f(0) /l(0) in percentiles of controls BMR: (P(d)−P(d))/(1−P(0))

10%

− − − +

f(d)−f(0) /f(0)

5%

− −

f(d)−f(0) /l(0) In percentile of controls

− − −

5 or 10%

Haag-Gronlund et al., 1995 Barton and Das, 1996 Barton and Das, 1996 Haag-Gronlund et al., 1995 Woutersen et al. (unpublished data) Malsch et al., 1994 Haber et al., 1998b

Kimmel et al., 1995 Foster and Auton, 1995 Allen et al., 1993 Allen et al., 1996

P5, P10 or P25

Allen et al., 1993 Kavlock et al., 1995

f(d)−f(0) /l(0)

P25 0.05, 0.1, 1, or 2 S.D.

Kimmel et al., 1995 Allen et al., 1993 Kavlock et al., 1995

CES: f(d)−f(0) /l(0) BMR: not specified

0.5 S.D. 2 S.D. 5%

Allen et al., 1996 Mantovani et al., 1998

− −

f(d)−f(0) /S.E.(0)

2 SES

Foster and Auton, 1995 Allen et al., 1993 Kavlock et al., 1995

−

CES: f(d)−f(0) /l(0) in percentiles of controls BMR: (P(d)−P(0))/(1−P(0))

P1 or P25, 5 or 10%

Allen et al., 1993

P5 or P10, 1 or 25%

Kavlock et al., 1995

+ +

−

S. Dekkers et al. / En6ironmental Toxicology and Pharmacology 10 (2001) 33–52

com

Expression (formula)

49

50

Table 1 (Continued) Experimental conditions (tested species)

( 9 c)

com com com com com

Rat Rat – – Rat/rabbit/mice

+ + − − +

De6elopmental parameters com: developmental

arb

Not specified

−

Reproducti6e parameters com: reproductive parameters

com

par: rib count

com com com arb

Not specified Not specified Rat Rabbits Rat Rat

− − + − + −

par: fertility index

com

Rat

Other effect parameters par: cell proliferation par: max EGF receptor par: spleen PFC/106 cells

ot com com

Not specified Rat Mice

par: sperm morphology par: sperm counts

a

Expression (formula)

Value (%, Px, SDs, etc.) Reference

P5 5%

Allen et al., 1996 Kavlock et al., 1996 Kavlock et al., 1994 Kavlock and Schmid, 1994 Kimmel, 1996

f(d)−f(0) /f(0)

1, 5 or 10%

Allen et al., 1994a,b

f(d)−f(0) /f(0)

Allen et al., 1994a,b Pease et al., 1991 Murrell et al., 1998 Pease et al., 1991 Murrell et al., 1998 Allen et al., 1996

+

f(d)−f(0) / f(max)−f(0) f(d)−f(0)/f(0) f(d)−f(0) / f(max)−f(0) CES: f(d)−f(0) /l(0) BMR: not specified ( f(d)−f(0) )/( f(max)−f(0))

1, 5 or 10% 10% 5% 10% 5% 0.5 S.D. 5 or 10% 5%

− + +

Not specified f(d)−f(0) / f(max)−f(0) f(d)−f(0) / f(max)−f(0)

1% 5% 5%

Gaylor and Zheng, 1996 Murrell et al., 1998 Murrell et al., 1998

par, per parameter; com, per combination of toxicological parameters; un, universal CESa. Tox, toxicological or biological information; arb, arbitrarily; ot, other kind of information; com, a combination of the possibilities mentioned. c 9 , experimental conditions specified. b

Murrell et al., 1998

S. Dekkers et al. / En6ironmental Toxicology and Pharmacology 10 (2001) 33–52

Information (tox/arb/ot/ com b)

Effect level (per/com/un: parameter)a

S. Dekkers et al. / En6ironmental Toxicology and Pharmacology 10 (2001) 33–52

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