Human Variability and Noncancer Risk Assessment— An Analysis of the Default Uncertainty Factor

REGULATORY TOXICOLOGY AND PHARMACOLOGY ARTICLE NO.

27, 3–20 (1998)

RT971195

Human Variability and Noncancer Risk Assessment— An Analysis of the Default Uncertainty Factor A. G. Renwick* and N. R. Lazarus† *Clinical Pharmacology Group, University of Southampton, Biomedical Sciences Building, Bassett Crescent East, Southampton SO16 7PX, United Kingdom; and †Open University, South East Region, East Grinstead, West Sussex AH19 1ES, United Kingdom Received October 1, 1997

agencies may vary the uncertainty factor in particular circumstances, for example for an incomplete toxicity database. The use of default uncertainty factors is a central part of risk assessment, but the scientific basis of their original derivation is not clear (Lehman and Fitzhugh, 1954; Vettorazzi, 1987; Lu, 1988; Truhaut, 1991). A number of post hoc analyses have been published which support the default uncertainty factors used for the main areas of uncertainty, i.e., interspecies differences, interindividual differences, subchronic to chronic, and LOAEL (lowest-observed-adverse-effect level) to NOAEL (e.g., Dourson and Stara, 1983; Calabrese, 1985; Hattis et al., 1987; Sheenan and Gaylor, 1990; Lewis et al., 1990; Renwick, 1991; Calabrese et al., 1992; Naumann and Weideman, 1995). The use of a safety factor for nature of toxicity is not related to uncertainty, does not have a sound scientific basis, and has not been used consistently (Renwick, 1995). Most reviews have identified examples or situations where the normal default values may be inappropriate; in such cases there is the need for the use of more relevant factors. A major problem with the use of standard default uncertainty factors has been the absence of a mechanism for allowing compound-specific data in a particular area of uncertainty to contribute to the derivation of a relevant, compound-specific uncertainty factor. Renwick (1991) analyzed the 100-fold safety factor by a consideration of two different aspects: toxicokinetics (the relationship between external dose and internal dose) and toxicodynamics (the relationship between internal dose and effect). A subsequent paper (Renwick, 1993) proposed a scheme in which these two aspects could be used as the basis for subdividing each 10-fold factor into two subfactors (Fig. 1), each of which could be replaced by appropriate specific compound-related data. An advantage of the scheme is that it is compatible with current procedures because the subfactors collapse back to 10-fold in the absence of compound-specific data. The approach was assessed at an interna-

A 10-fold uncertainty factor is used for noncancer risk assessments to allow for possible interindividual differences between humans in the fate of the chemical in the body (kinetics) and target organ sensitivity (dynamics). Analysis of a database on the variability in each of these aspects is consistent with an even subdivision of the 10-fold factor into 100.5 (3.16) for kinetics and 100.5 (3.16) for dynamics. Analysis of the number of subjects in a normally and log-normally distributed population which would not be covered by factors of 3.16 supports this subdivision and also the use of a 10fold factor to allow for both aspects. Analysis of kinetic data for subgroups of the population indicates that the standard default value of 3.16 for kinetics will not be adequate for all routes of elimination and all groups of the population. A scheme is proposed which would allow the selection of appropriate default uncertainty factors based on knowledge of the biological fate and effects of the chemical under review. q 1998 Academic Press Key Words: uncertainty factor; human variability; toxicokinetics; toxicodynamics; interindividual differences.

INTRODUCTION

Uncertainty or safety factors have been used for over 30 years to convert the no-observed-adverse-effect level (NOAEL) for noncancer end points into a human intake believed to be ‘‘without appreciable health risk’’ (WHO, 1987). Despite differences in nomenclature between different agencies, e.g., acceptable or tolerable daily intake vs reference dose and safety factor vs uncertainty factor, there is a consistent approach adopted internationally (Dourson et al., 1996). In all cases, an uncertainty or safety factor is applied to the NOAEL to allow for extrapolation from animals to humans and to allow for human variability. The default factor normally applied to the NOAEL from studies in animals is 100 and from studies in humans is 10, although all 3

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0273-2300/98 $25.00 Copyright q 1998 by Academic Press All rights of reproduction in any form reserved.

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RENWICK AND LAZARUS

FIG. 1. The subdivision of the 100-fold uncertainty factor.

tional workshop (Kroes et al., 1993), which recognized that the scheme allowed the incorporation of scientific data into risk assessment, and also identified clearly the information which would be most relevant to the replacement of uncertainty by data. The scheme was considered at an International Programme on Chemical Safety (IPCS) workshop on the derivation of guidance values (WHO, 1994) and the proposed subdivision of the 10-fold factor for human variability (Renwick, 1993) was revised to allow equal weighting to toxicokinetics and toxicodynamics (Fig. 1). A limited database had been used by Renwick (1993) as the basis for the subdivision of the 10-fold uncertainty factor for human variability, and the present paper considers a more extensive database in relation to both the subdivision of the 10-fold factor for human variability into toxicokinetics and toxicodynamic aspects and the adequacy of the 10-fold factor. DATA ANALYZED

The 10-fold factor for human variability is to allow for interindividual differences in response to the external dose. In classic dose–response terms it is to allow for differences in the position of the dose–response curve for the individual, compared with the population mean. In relation to risk assessment and the ADI/TDI/ RfD, the 10-fold factor allows for interindividual differences in the position of the NOAEL. Assuming parallel dose–response relationships in different individuals, the differences in the NOAEL will be reflected by differences in the response part of the dose–response curve. Differences between dose–response curves are defined by estimates such as the ED50 (the dose in that individual which results in an effect which is 50% of the maximum); for parallel dose–response curves, the difference between individuals will be the same at different effect levels, including the NOAEL. Differences in responses to the same fixed dose do not reflect the differences between individuals, because the magnitude of the differences depends on the dose selected; therefore,

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data from single-dose studies cannot be used to reflect human variability. Subdivision of the 10-fold factor requires separation of the variability in response due to kinetics and that due to dynamics. The kinetic factor is to allow for individual differences between the external dose and the concentration delivered, via the circulation, to the site of action (the internal dose). Since most ADI/TDI/RfDs are based on chronic oral toxicity data, the kinetic factor should reflect the chronic blood concentration or body burden, and therefore measurements such as area under the plasma concentration–time curve (AUC) are of most relevance. For oral administration the AUC is proportional to the dose and the fraction absorbed (bioavailability) and is inversely proportional to clearance. The greatest interindividual variability in oral AUC is shown by compounds which have low oral bioavailabilities (Hellriegel et al., 1996); for poorly absorbed chemicals, such variability may arise from differences in intestinal permeability (Lindahl et al., 1996); for well-absorbed compounds with low oral bioavailability, variability arises from differences in intestinal and hepatic first-pass metabolism. The dynamic factor is to allow for interindividual differences in the tissue and body response in relation to the concentration of the chemical (or active metabolite) in the blood/ plasma and delivered to the site of toxicity. Dynamic data require separation of kinetic variability from the total variability in response to the external dose, a procedure most readily achieved by in vitro or ex vivo data or by pharmacokinetic–pharmacodynamic modeling. Literature searches were undertaken using TOXLINE and MEDLINE with search terms primarily aimed at identifying data on in vivo concentration– effect relationships. Papers giving kinetic data were selected on the basis of the quality and/or size of the study, the interest of the results, and the physiological/ metabolic process determining the kinetic parameter. Papers giving dynamic data were selected on the basis of the adequate separation of variability due to kinetics and dynamics. DATA ANALYSIS

The data on kinetics and dynamics were tabulated; the coefficients of variation were averaged for different studies which measured a common end point or for multiple doses which measured the same end point. The primary analysis compared variability in kinetics and dynamics in order to provide a more secure basis for the subdivision of the 10-fold factor. The data were also analyzed using the reported standard deviation and the calculated geometric standard deviation for each parameter to define the Z-score in order to determine the proportion of a normally, or lognormally, distributed population which would be cov-

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HUMAN VARIABILITY AND RISK ASSESSMENT

FIG. 2. The effect of data variability on the proportion of a normal distribution covered by a factor of 3.16 above the mean; a factor of 3.16 is the default for either kinetics or dynamics. Solid line, coefficient of variation at 80% of mean; dotted line, coefficient of variation at 50% of mean; dashed line, coefficient of variation at 30% of mean.

ered by each of the subfactors of 3.16 (100.5). The factor of 3.16 is not intended to cover the whole population and applies only to individuals with higher blood concentrations or greater target organ sensitivity compared with the mean (Fig. 2). The proportion of a population not covered by a factor of 3.16 times the mean parameter estimate depends on the standard deviation or coefficient of variation (Fig. 2). Combination of the results for kinetics and dynamics was used to assess the validity of the 10-fold factor to act as a default for total variability. This analysis assumes that an individual who had a value ú3.16 times the population mean for one aspect and a value õ3.16 times the population mean for the other aspect would have a combined value °10-fold from the population mean for the in vivo response to the external dose. Data for minority groups in a population (poor metabolizers, children, ethnic minorities) were analyzed separately with the assumption that the 3.16-fold factor for kinetics applied to variability around the mean for the majority group. RESULTS

Subdivision of the 10-Fold Factor for Human Variability into Kinetic and Dynamic Aspects Data for the kinetics of 60 compounds were identified which represented a range of pathways of metabolism or clearance (Table 1). These studies represented only a small proportion of the total published literature on the kinetics of xenobiotics in humans. (The potential for greater utilization of human kinetic data for risk

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assessment is considered later.) Although some of the studies contained small numbers of subjects (n õ 10), the standard deviation would be comparable to that for a larger group providing that the subjects studied were representative of the population. The maximum coefficient of variation related to the production of an active metabolite by the gut microflora. In this context it is interesting to note that the interindividual variability in the formation of cyclohexylamine from cyclamate by the gut microflora was so great that it had to be incorporated as a specific factor in the calculation of the ADI for cyclamate. The mean coefficient of variation of the kinetic parameters was 38% with a minimum of 9% and a maximum of 114%. Concentration–effect data, mostly in vivo plasma concentration–response data, were identified for 49 compound-related effects (Table 2). A variety of effects are included in Table 2, but the majority are shortterm changes in cardiovascular or CNS measurements. Concentration–response data in humans are essential to undertake any analysis of the 10-fold uncertainty factor and the vast majority of such data related to therapeutic responses. The use of therapeutic/drug response data can be criticized as not representing adverse effects to which the uncertainty factor is normally applied. This limitation is inevitable, because well-controlled studies on the adverse effects of nontherapeutic compounds in humans are and must remain unethical. However, it is possible that much of the response data in Table 2 would show greater interindividual variability than the response to chronic toxicants, because the effects are frequently related to initial drug–receptor interactions which are modulated by physiological and homeostatic control processes, any of which may add variability to the final outcome. In addition, for the clinical responses in Table 2 (for example, the antipyretic effect of ibuprofen) some interindividual variability may have arisen from differences in the severity of the clinical condition. The mean coefficient of variation in dynamics was 51% with a minimum of 8% and a maximum of 137%. This analysis supports the subdivision proposed by the IPCS Working Group (WHO, 1994), with an equal weighting for kinetics and dynamics (Fig. 1). However, it should be noted that unlike the kinetic data, much of the dynamic data were for the clinical treatment of patients. Therefore, aging and disease processes may have contributed to the greater variability in dynamics compared to kinetics. An alternative avenue for exploring individual variability in response in relation to circulating blood levels is the use of correlation coefficients (r values) for the relationship between a kinetic parameter (such as AUC) and the clinical response or outcome. The r2 value is usually taken as representing the extent to which the parameter accounts for the variability in the data.

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OXID OXID OXID OXID OXID Multiple

AUC (mmol L01 min) AUC oral (ng ml01 h) AUC oral (ng ml01 h) AUC oral (ng ml01 h) CL oral (ml min01 kg01) % Salicylate in urine AUC oral (mg ml01 h) AUC iv (mg ml01 h) AUC oral (mg L01 h) % absorption (% dose) AUC oral (ng ml01 h) % in urine (as parent) CL ip (mg ml01 h) AUC oral (ng ml01 h) AUC oral (ng ml01 h) Fluoride in urine AUC oral (ng ml01 h) AUC oral (mg L01 h) AUC iv (mg L01 h) AUC oral (nM h) AUC oral (ng ml01 h) CL oral (L h01) CLiv (ml min01) AUC oral (mg ml01 h) AUC iv (mmol L01 min) AUC oral (ng ml01 h) CL oral (ml h01 kg01) AUC oral (nmol L01 h) AUC oral (ng ml01 h) AUC oral (ng ml01 h) AUC oral (ng ml01 h) AUC oral (ng ml01 h) AUC oral (mg L01 h) AUC oral (ng ml01 h) AUC oral (ng ml01 min) CL/F (ml min01 kg01) AUC iv (mg L01 h) AUC oral (ng ml01 h) AUC oral (ng ml01 h) CLrenal (ml min01 kg01) Uptake inhalation (%) CL (L h01 kg01) AUC iv ((ng ml01 h) AUC oral (mg L01 h) CL/F (ml min01)

2-Butoxyethanol Adinazolam mesylate Alprozalam Amlodipine Antipyrine Aspirin Bromfenac Cefroxadine Clarithromycin Cyclamate Cyclosporine Debrisoquine Diamminedichloroplatinum Dilevalol Encainide Enflurane Epanolol Ergometrine Etoposide Felodipine Flunitrazepam Haloperidol ICG Idrapil Leu-Dox Lorazepam Methylprednisolone Metoclopramide Morphine Mosapride citrate Moxonidine N-Desmethyladinazolam Naproxen Nifedipine Nisoldipine Nitrazepam Ofloxacin Ondansetron Pethidine Phenylpropranolamine a-Pinene (/)a-Pinene Prednisolone Probenecid Propranolol

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CONJ OXID Multiple CONJ OXID

OXID

OXID Multiple OXID

OXID CYP2E1

OXID OXID RENAL

RENAL OXID

Pathway

Unit

Compound 1040 166 305 152 0.57 83 240 70.9 18.9 27.6 1110 19.5 22.8 261 124 1090 82.0 3.11 190 100.9 176.4 141.7 1189 182 118 371 472 1298 22.2 84 7.4 2474 1140 154 411 1.09 12.2 133 553 7.2 58 1.09 Two doses 603 1873

Mean value 11 42 54 30 40 71 35 28 29 57 47 79 25 39 61 17 67 29 31 53 37 43 29 64 29 34 38 44 59 30 66 13 15 40 50 19 14 50 30 26 9 18 13 31 32

Coefficient of variation (SD%) 7 14 22 12 128 129 12 9 12 192 187 43 8 18 5 10 15 6 14 140 20 28 14 5 14 24 6 5 6 20 8 15 10 59 10 12 8 16 9 10 8 8 7 14 13

n

10/8 — 3/7 9/6 6/0 14/0 126/14 11/9 28/0 14/0 5/0 8/6 24/0 4/2 5/0 2/4 20/0 5/3 15/0 4/6 59/0 3/7 0/12 8/0 — 9/0 10/0 8/0 8/0 7/0 7/7 13/0

7/2 12/0 113/79 — 28/15

7/0 14/0 22/0 6/6 44/84 67/62

M/F

Subject

TABLE 1 Variability in Kinetic Parameters

Caucasian data

Average of two doses

Similar data for males Top dose studied

Dose normalized data Hypertensive patients Top dose

Postrenal transplant

Cancer patients

Mixed races Liver blood flow

Elderly with lung cancer Includes elderly

Elderly patients

EM / PM Cancer patients Includes elderly EMs for CYP2D6

Increased AUC in elderly

Hypertensive patients Caucasian / Asian

Inhalation

Notes

Johanson et al. (1986) Fleishaker et al. (1991a) Friedman et al. (1991) Donnelly et al. (1993) Fraser et al. (1979) Hutt et al. (1986) Ho¨gger and Rohdewald (1993) Cado´rniga et al. (1990) Chu et al. (1992) Buss et al. (1992) Lindholm et al. (1992) Ritchie et al. (1986) Furukawa et al. (1993) MacPhee et al. (1991) Wensing et al. (1991) Kharasch et al. (1994) Marlier et al. (1990) de Groot et al. (1994) Miller et al. (1990) Blychert et al. (1991) Grahne´n et al. (1991) Midha et al. (1989) Fleishaker et al. (1991a) Criscuoli et al. (1993) de Jong et al. (1992) Friedman et al. (1991) Tornatore et al. (1993) Graffner et al. (1979) Hoskin et al. (1989) Sakashita et al. (1993) Kirch et al. (1990) Fleishaker et al. (1991b) Vree et al. (1993a) Renwick et al. (1988) van Harten et al. (1989) Greenblatt et al. (1985) Guay et al. (1991) Blackwell and Harding (1989) Chan et al. (1990) O’Connell et al. (1989) Falk et al. (1990) Falk et al. (1990) Wald et al. (1992) Vree et al. (1993b) Sowinski et al. (1995)

Source of data

6 RENWICK AND LAZARUS

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Note. F, female; M, male; n, number of subjects. The data are for healthy young volunteers unless indicated otherwise. Pathway, principal pathway determining the pharmacokinetic parameter. AUC, area under the concentration – time curve (plasma or blood); CL, clearance; CONJ, conjugation; EM, extensive metabolizer; F, fraction absorbed (bioavailability); OXID, oxidation; PM, poor metabolizer; REDN, reduction; RESP, respiration rate/volume; TRA, all-trans-retinoic acid.

Carosella et al. (1989) Buss et al. (1994) 6/5 0/10 11 10 80 74 CL (L h01 kg01) AUC TRA (ng ml01 h)

OXID OXID

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AUC oral (ng ml01 h) Vapiprost

/

0.55 85.9

18/0 613 35 Three doses

CL/F (L h01) CL iv (ml min01 m02) AUC iv (mg ml01 h) Triazolam Trimetrexate glucuronate Tropescomycin

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AUC as active metabolite (TRA) after 50-mg dose

Uematsu et al. (1991)

Gupta et al. (1990) Grochow et al. (1989) Novak et al. (1990)

Cancer patients Eight subjects with eight doses Six subjects with three doses 10 33 818 9 65 17 25.8 31 Eight doses CYP3A

CYP3A4

10/0 — 64/0

Mean of two studies Five to six subjects at eight doses 12/0 1/15 42/0

Subcutaneous dose

Gut microflora

67/62 9/2 9/2 7/30

129 11 11 37 36 12 16 42 24 114 35 17 17 22 45 20 45 31.2 83 1.19 7.71 1136 163.6 0.10 CONJ REDN Multiple OXID

Salicyluric acid (% dose) AUC sulfide (mg ml01 h) AUC parent (mg ml01 h) CL (L min01) AUC oral (mg ml01 h) AUC oral (ng ml01 h) AUC oral (ng ml01 h) AUC oral (mg L01 h) Salicylate Sulfinpyrazone Sulfinpyrazone Sumatriptan Temafloxacin Terfenadine Thiamine Tolcapone

Remoxipride

AUC oral (mmol L01 h)

Multiple

7.29

55

10

10/0

Renal / hepatic oxidation

Grind et al. (1989)

Hutt et al. (1986) Strong et al. (1984) Strong et al. (1984) Visser et al. (1996) Granneman et al. (1991) Rau et al. (1997) Mascher and Kikuta (1993) Dingemanse et al. (1995)

HUMAN VARIABILITY AND RISK ASSESSMENT

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The in vivo cytotoxicity of the anticancer drug etoposide in 14 previously untreated patients (Miller et al., 1990) was correlated against the plasma AUC giving r2 values from 0.4 to 0.92; this indicates that variability in the AUC (kinetics) accounted for 40% or more of the total variability in response. A similar r2 value was found between the plasma concentration of trimetrexate glucuronate 1 h after dosing (kinetics) and leukopenia (r2 Å 0.36) and thrombocytopenia (r2 Å 0.42) (Grochow et al., 1989). These r2 values support an approximately equal subdivision of the 10-fold factor, but a major possible problem with this type of analysis is that no specific kinetic–dynamic model is used. In consequence, clear kinetic contributions to variability in overall response may be missed, if the wrong kinetic parameter is used, or the best correlated kinetic parameter may be purely fortuitous and not a cause and effect relationship. Analysis of the Proportions of a Population Not Covered by the Uncertainty Factors The use of Z-scores to calculate the proportion of a population which would fall more than 3.16-fold away from the mean is presented in Table 3. As expected, the number of subjects not covered by the 3.16-fold factor is directly proportional to the standard deviation for the estimate. It is also interesting to note that the choice of distribution model, i.e., normal or log-normal, has a greater impact when the population estimate is in the tail of the distribution. For kinetics the average number of subjects not covered by a factor of 3.16 away from the mean parameter estimate was about 700 per million of the population assuming a normal distribution and 9000 assuming a log-normal distribution. For dynamics the average number of subjects not covered by a factor of 3.16-fold away from the mean was about 3000 per million of the population assuming a normal distribution and about 19,000 assuming a lognormal distribution. The probability of the same individual falling outside the range for both kinetics and dynamics is presented in Table 3. This analysis assumes that the kinetic and dynamic ‘‘risk factors’’ are independent variables. Independence of kinetics and dynamics is a reasonable assumption, although situations could be envisaged where the increase in risk for kinetics and dynamics shares a common origin, e.g., hepatic impairment with an hepatotoxic chemical. The analysis in Table 3 illustrates the extent to which the normal default factor of 10-fold (3.16 times 3.16) is adequate to allow for both kinetics and dynamics. In common with all mathematical extrapolations away from the biological data, the outcome is highly dependent on the model chosen. On average only 2 persons in a million would not be cov-

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Thromboxane antagonism Loss of neural response Decrease in bp MAO inhibition

AA-2414 Alfentanil Amlodipine Befloxatone Beta receptor density Cocaine N-Desmethyladinazolam Dilevalol Diltiazem Diltiazem Dofetilide Doxacurium Doxazosin Enalapril Etoposide Felodipine Flunitrazepam Ibuprofen Idrapril Labetalol MK-383 Methadone Methylprednisolone Methylprednisolone Methylprednisolone Metipranolol Midazolam Nifedipine Opioids Opioids Opioids Opioids Pinacidil Prednisolone Propofol Propranolol Quinine Rocuronium Salicylate Terbutaline Theophylline Theophylline Tolcapone Tolrestat Triazolam Tubocurarine Vecuronium Xamoterol

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Slope (ng ml01) mm Hg/ng ml01 (ng ml01) bmax (ng ml01) (ng ml01) (ng ml01) (ng ml01) (ng ml01) (ng ml01) (ng ml01) (mm Hg ng01 ml) (ng ml01) (mg L01) (nmol L01) (ng ml01) Slope (ng ml01) (ng ml01) (ng ml01) (mg ml01) (ng ml01) (ng ml01) (ng ml01) (mg L01) See note 1 (mm Hg ng01 ml) See note 2 See note 2 See note 2 (ng ml01) (ng ml01) (ng ml01) (ng ml01) (nmol L01) Slope (mg ml01) (mg L01) (ng ml01) (mg L01) (mg L01) (mg L01) (mg ml01) (ng ml01) (mg ml01) (mg L01) (ng ml01)

Unit

— 51.1 10 – 120 4025 536 0.135 1.1 23.2 4.4 5.06 5.04 1.15 2.0 4.1 – 5.5 0.38 144 33.6

00.48

2.38 476 03.1 4.8 24.5 838 316 17.3 157 110 2.2 54.1 01.8 19.8 2.9 8.0 7.0 0.242 7.0 111 13.6 0.35 10.1 1.2 7.2 3.3

38 34 29 54 38 40 58 70 44 77 27 30 37 66 62 90 39 137 13 44 32 53 42 103 52 64 54 42 29 102 64 68 59 56 19 34 113 15 22 30 36 41 24 65 39 8 26 86

Coefficient of variation (SD%) 39 28 12 11 16 5 12 18 7 32 8 9 10 13 71 13 20 38 5 7 15 15 6 6 6 6 24 14 15 15 15 5 10 7 18 10 6 8 8 7 6 6 48 12 10 20 9 6

n

4/5 6/0

7/7 15/0 15/0 15/0 5/0 — 7/0 0/18 10/0 3/3 2/6 8/0 4/3 4/2 4/2 48/0 12/0 19/0

6/0 6/0 6/0 2/4

12/0 10/8 2/5 29/3 8/0 6/3 4/6 6/7 36/35 — 11/9 — 5/0 0/7 15/0

39/0 13/15 6/6 11/0

M/F

Subject

47 { 9 years old

Average for three effects

Mean for eight doses/six subjects

Mean of two effects

Caucasian data Mean of two parameters Mean for two muscles

Average for three effects

See note 3

Pregnancy hypertension Ex vivo measurement Average value AM data AM data AM data Middle aged (from three different studies) Hypertensives, first dose

Febrile children

Average for diastolic and systolic Hypertensives, first dose Cancer patients Not all data fitted to model

Includes elderly Paroxysmal arrhythmias Elderly subjects

Hypertensive patients Two values for each subject

Ex vivo measurement

Comments

Hussein et al. (1994) Lemmens et al. (1992) Donnelly et al. (1993) Patat et al. (1996) Zhou et al. (1989b) Noe and Kumor (1991) Fleishaker et al. (1991b) MacPhee et al. (1991) Fukuhara et al. (1989) Dias et al. (1992) LeCoz et al. (1995) Gariepy et al. (1993) Donnelly et al. (1989) Donnelly et al. (1990) Karlsson et al. (1995) Blychert et al. (1992) Grahne´n et al. (1991) Kauffman and Nelson (1992) Criscuoli et al. (1993) Saotome et al. (1993) Barrett et al. (1994) Inturrisi et al. (1990) Fisher et al. (1992) Fisher et al. (1992) Fisher et al. (1992) Janku et al. (1992) Note 1 Donnelly et al. (1988) Hill et al. (1990) Hill et al. (1990) Hill et al. (1990) Lemmens et al. (1994) Girard et al. (1993) Wald et al. (1992) Vuyk et al. (1992) Zhou et al. (1989a) Paintaud et al. (1994) Plaud et al. (1995) Day et al. (1989) Braat et al. (1992a) Braat et al. (1992b) Braat et al. (1992b) Dingemanse et al. (1995) Griensven et al. (1995) Gupta et al. (1990) Sheiner et al. (1979) Ducharme et al. (1993) Molajo et al. (1987)

Source of data

Note. ACE, acetylcholine esterase; AV, atrioventricular; bp, blood pressure; CNS, central nervous system; COMT, catechol-O-methyl transferase; dbp, diastolic blood pressure; ECG, electrocardiogram; MAO, monoamine oxidase; NMJ, neuromuscular junction. a The values are the EC50 (concentration giving a 50% response) unless indicated. Note 1: mean value derived from EC50 values reported by Greenblatt et al. (1989), Koopmans et al. (1988), and Mandema et al. (1992). Note 2: average of the coefficients of variation of responses to alfentanil, fentanyl, and morphine. Note 3: average of the coefficients of variation of responses to alfentanil, fentanyl, and trefentanyl measured in the same five subjects on different occasions.

Heart rate Symbol substitution test Decrease in bp AV node Decrease in heart rate ECG changes NMJ blockade Decrease in bp slope ACE inhibition Leukopenia Decrease in bp (Emax) Sedation Antipyretic effect ACE inhibition Decrease in bp Platelet aggregation IC50 Analgesia/sedation Histamine release IC50 Cortisol suppression IC50 T-cell number IC50 Decreased heart rate CNS effects Decrease in bp (linear) Respiratory effects Nausea Mood alertness EEG charges Decrease in dbp Cellular effects IC50 Loss of consciousness Heart rate IC20 Hearing impairment NMJ blockade Hearing loss (unbound) Beta-2 responses Eosinophil effects Hypokalaemia Inhibition of COMT Decrease in rbc sorbitol CNS effects NMJ blockade NMJ blockade Decrease in heart rate

Effect

Compound

Mean valuea

TABLE 2 Variability in Dynamic Parameters

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HUMAN VARIABILITY AND RISK ASSESSMENT

TABLE 3 The Relationship between the Coefficient of Variation and the Validity of the Default Uncertainty Factors Number of subjects not covered by default uncertainty factors (3.16), per million of the population General considerations Coefficient of variation (SD %)

Normal distribution

Log-normal distribution

10 20 30 40 60 80 100 120

0 0 0 0 159 3,467 15,386 35,930

0 0 44 1,402 18,937 50,828 83,357 111,422

Specific considerations of data in Tables 1 and 2 Kinetics (3.16)

Mean Minimum Maximum Mean Minimum Maximum Mean Minimum Maximum

Dynamics (3.16)

Kinetics and dynamics (3.16 times 3.16)

685 0 29,064 2,930 0 57,439 2 0 1,669

8,564 0 103,541 18,896 0 131,363 162 0 13,601

ered by combined factors of (3.16 times 3.16) assuming a normal distribution and 162 persons per million assuming a log-normal distribution. This demonstrates that the 10-fold factor is an adequate default assumption for the types of chemicals and biological effects considered in Tables 1 and 2. The use of standard deviations, geometric standard deviations, and Z-scores involves a number of assumptions (Table 4). An important assumption is that the variability in single measurements (Tables 1 and 2) will be representative of chronic exposure and that outliers on one occasion will remain outliers during chronic treatment. Although few publications give sufficient details to analyze intraindividual variability, the data in Fig. 3 indicate that outliers will not necessarily be the same individual on repeat observations (unless there is some genetically determined reason, such as the ethnic differences for South Asians/Caucasians in Fig. 3). The application of population models and approaches using sparse data can provide useful information in relation to chemical risk assessment (Aarons, 1996). An analysis of concentrations of etoposide associated with leukopenia in 71 patients receiving a total of 118 courses of treatment (Karlsson et al., 1995) showed that interindividual variability (40% of total variability) was less than the interoccasion (intraindividual) variability (60% of total variability). There-

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9

fore, the variability and coefficients of variation in the single estimates in Tables 1 and 2 represent both interand intraindividual variation, and the values in Table 3 for the proportion of a population that would not be covered by factors of 3.16 for kinetics and 3.16 for dynamics are probably overestimations of the situation during chronic administration. Analysis of Special Subgroups of the Population The analyses described above were extended to investigate the proportion of certain subgroups of the population that would not be covered if a 3.16-fold factor was applied to the mean for the major group in the population. The majority of relevant data relate to the kinetics of drugs in Caucasian adults, and therefore this group has been taken as the reference subgroup for comparisons. This is logical because if an interspecies, data-derived toxicokinetic factor were developed on a chemical, the data would probably be comparing the test species with data for Caucasian adults: under such circumstances, the human variability factor would use Caucasian adults as the starting point or reference group. Children. A recent review on the differences in kinetics between adults and children concluded that TABLE 4 Factors to Be Considered in the Interpretation of Population Distribution Analyses i. The data have been analyzed assuming normal and lognormal distributions; this is inappropriate for some parameters which show a skewed distribution (e.g., nifedipine AUC) and the nature of the distribution was not characterized for many parameters. ii. The subjects studied should be representative of the group as a whole; most data refer to healthy young adults. iii. Factors such as aging and disease processes may add to the variability estimation; this would be most important for hepatic and renal changes since these would affect the fate of most xenobiotics. iv. Normal and log-normal distributions do not show upperbound limits; however, many physiological and biochemical processes are under homeostatic control which limits the range of values in a normal population. v. The variability considered in the paper is derived from single observations; repeat observations which would be more relevant for chronic exposures, will tend to reduce interindividual variability and the population distribution. vi. The variability considered in the paper will include variability due to measurement errors; for kinetic parameters, this will largely be analytical errors; for dynamic parameters, these analytical errors will be compounded by response measurement errors and the adequacy of the kinetic–dynamic model. vii. The single-dose measurements analyzed may be modified during chronic exposure; accumulation and enzyme induction or inhibition could affect kinetics; homeostatic control processes, tolerance, or adaptation may affect the dynamic response.

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FIG. 3. Intraindividual variability in kinetic and dynamic estimates. Thiamine AUC oral measured on two separate occasions with different tablets (Mascher and Kikuta, 1993). Bromfenac dose normalized AUC oral measured at three different doses; no indication of saturation kinetics (Ho¨gger and Rohdewald, 1993). Nifedipine dose normalized AUC oral measured at two doses in Caucasians (solid lines in left-hand box and dotted lines in right-hand box) and in South Asians (solid lines in right-hand box) (Ahsan et al., 1993). Flunitrazepam pharmacokinetic–pharmacodynamic modeling undertaken on two occasions. (Grahne´n et al., 1991). Befloxatone EC50 derived from two different doses (Patat et al., 1996).

young children frequently eliminate drugs and foreign compounds more rapidly by metabolism and excretion compared with adults (Renwick, 1998). In consequence, a smaller proportion of a population of children would not be covered by a 3.16-fold factor for kinetics. The data in Table 5 illustrate a number of important aspects. In the case of theophylline, the higher clearance by infants and children (ú6 months old) compared with adults would result in a greater degree of protection, but the immaturity of hepatic metabolism

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and low clearance in preterm infants results in a larger proportion of such infants falling outside the 3.16-fold range. This illustrates the potential vulnerability of preterm infants to foreign compounds. The data for droperidol illustrate three points; first, that when the mean parameter estimate, such as clearance, for a subgroup is a factor of about 3-fold lower than the reference group, then about 50% of the subgroup will not be covered by the 3.16-fold factor applied to the estimate for the main group; second, that

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Pathway RENAL RENAL RENAL RENAL HYDROL HYDROL Multiple Multiple Multiple Multiple OXID OXID OXID OXID OXID OXID OXID OXID OXID OXID OXID OXID

Parameter CL/F (ml min01 kg01) CL/F (ml min01 kg01) CL/F (ml min01 kg01) CL/F (ml min01 kg01) CL (ml min01 kg01) CL (ml min01 kg01) CL (L h01 kg01) CL (L h01 kg01) CL/F (ml min01 kg01) CL/F (ml min01 kg01) Vmax (mg kg01 day01) Vmax (mg kg01 day01) Vmax (mg kg01 day01) Vmax (mg kg01 day01) Vmax (mg kg01 day01) Vmax (mg kg01 day01) CL (L h01 kg01) CL (L h01 kg01) CL (L h01 kg01) CL (L h01 kg01) CL (L h01 kg01) CL (L h01 kg01) 0.5 – 5 years 5 – 10 years 10 years / Adults Children Adults Children 7 – 14 years Adults Children Adults 0.5 – 3 years 4 – 6 years 7 – 9 years 10 – 16 years 8 months – 3 years Adults (18 – 66 years) Preterm infantsb Preterm infantsc Infants õ6 months Infants 6 – 11 months Children 1 – 4 years Young adults 16 – 17 years

Age 3.1 2.5 2.0 1.0 4.7 14.1 0.56 0.49 0.94 1.07 13.8 11.2 9.5 8.0 20.4 8.7 0.0174 0.0384 0.048 0.12 0.102 0.084

Mean 35 36 30 14 49 31 18 24 39 24 31 27 16 21 10 8 34 31 13 25 35 43

SD %

12 10 9 5 10 6 34 24 24 22 40 21 6 8 3 4 10 30

d

17 15 17

n

0 0 0 0 456,663 0 0 0 3 0 0 0 0 0 0 0 845,177 75,772 0 0 0 0

Normal

Note. See Table 1 for abbreviations. HYDROL, hydrolysis. a Data analyzed assuming both normal and log-normal distributions using a 3.16-fold difference from the mean for adults. b At 8 { 4 days old. c At 41 { 12 days old. d Average data for five different doses in two studies cited in the review.

Theophylline

Phenytoin

Metronidazole

Metoclopramide

Droperidol

Ceftibuten

Compound

0 0 0 0 455,408 73 0 1 3,329 1 0 0 0 0 0 0 899,951 112,329 3 0 38 2,608

Log-normal

No. of subjects not covered by a 3.16-fold factor (per million)a

TABLE 5 Kinetics in Infants and Children Compared with Adults

Kearns et al. (1991) Kearns et al. (1991) Kearns et al. (1991) Klepser et al. (1995) Grunwald et al. (1993) Fischler et al. (1986) Bateman (1983) Bateman (1983) Amon et al. (1983) Amon et al. (1983) Chiba et al. (1980) Chiba et al. (1980) Chiba et al. (1980) Chiba et al. (1980) Blain et al. (1981) Blain et al. (1981) Kearns and Read (1989) Kearns and Read (1989) Kearns and Read (1989) Kearns and Read (1989) Kearns and Read (1989) Kearns and Read (1989)

Source of data

HUMAN VARIABILITY AND RISK ASSESSMENT

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under such circumstances, the analysis is essentially independent of the population model; and third, that such data need critical analysis and validation; for example, in the case of droperidol the data for children and adults are not ideal because they are from different studies in different establishments, although the data appear valid. Ethnic differences. Ethnicity would need to be considered when the interspecies default factor for kinetics is replaced by a data-derived estimate based on data for the test species and Caucasian subjects. Ethnic differences can arise from genetic and environmental factors and result in differences in both kinetics and response (Renwick, 1996). In many cases, differences in mean kinetic parameter estimates between different ethnic groups are small (Table 6) and ethnicity would not influence the validity of the default factor of 3.16. However, a 3.16-fold factor applied to the mean for Caucasians would be less adequate in cases where a different ethnic group showed a decrease in clearance, combined with an increase in variability, e.g., desipramine, diazepam, methylprednisolone, and nifedipine. It is clear that ethnicity should be considered for some P450-mediated oxidation reactions, although this may need to be on a case-by-case basis. Polymorphic metabolism. Calabrese (1985) illustrated that genetically determined biochemical differences could exceed greatly the 10-fold factor for human variability. That analysis interpreted variability in enzyme activities in relation to the full 10-fold factor and included in vitro estimations of activity as well as diagnosed clinical conditions. In cases of diagnosed clinical conditions, specific advice can be given; for example, phenylketonurics are advised that the sweetener aspartame is a source of phenylalanine. Of greater potential concern are undiagnosed and unrecognized sources of variability, such as genetically determined differences in enzymes affecting kinetics, which must be covered by the default uncertainty factor. The data in Table 7 illustrate the potential impact of polymorphic oxidation on the kinetics of lipid-soluble, low-molecular-weight drugs; similar conclusions would be reached for other well-recognized polymorphisms, e.g., acetylation and glucose-6-phosphate dehydrogenase. It is clear that genetic polymorphisms can have a profound influence on the validity of a 3.16fold default uncertainty factor (Table 7). The numbers of subjects in the whole population not covered by a factor of 3.16 applied to the mean for the extensive metabolizers (EMs) would have to take into account the incidence of poor metabolizer (PM) status. For example, for fluoxetine the incidence outside a factor of 3.16 would be 85% of the incidence of PM subjects plus a small number of EM subjects, i.e., up to about 8% of the whole population.

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Genetically determined differences are of greatest relevance to risk assessment when the polymorphic pathway represents the major route of elimination. PM subjects would be at greater risk if the polymorphic pathway resulted in detoxication but at less risk than the EM group in cases where the pathway is involved in a bioactivation process leading to toxicity. Examples where genetically determined pathways which can result in a significant proportion of PM subjects being greater than 3.16-fold away from the mean parameter estimate for EMs include CYP2D6 and CYP2C19 (Table 7). The AUC of dextromethorphan after an oral dose was 150 times higher in six PM subjects (1362 mg L01 h; range 1126–1605) compared with six EM subjects (9.0 mg L01 h; range 1.2–160) of CYP2D6 substrates (Capon et al., 1996) (these data could not be included in Table 7 because the SD was not reported). For many compounds the presence of alternative or multiple pathways of elimination means that PM subjects would show little or no increase in plasma concentrations of the parent compound compared to EMs. Therefore, knowledge that a chemical is a substrate for a metabolic pathway which shows polymorphic expression raises questions about the validity of the 3.16-fold default uncertainty factor for kinetics (and therefore the combined 10-fold factor for human variability), but does not automatically invalidate the default values. CONCLUSIONS AND FUTURE DIRECTIONS

The present analysis is consistent with a subdivision of the 10-fold uncertainty factor for human variability which gives equal weighting to kinetic and dynamic aspects (WHO, 1994). The slightly greater variability in the dynamic data (Table 2) compared with kinetics (Table 1) is not sufficient to warrant an unequal subdivision weighted in favor of dynamics, because the dynamic data included a number of receptor-mediated, clinical responses in patients, for which interindividual differences in disease severity may have added to the variability. The use of Z-scores to determine the numbers of subjects not covered by the 3.16-fold factors for kinetics and dynamics has provided interesting insights, both in relation to the main database (Table 3) and the analysis of specific subgroups (Tables 5–7). The number of subjects per million of population not covered by the default uncertainty factors depends on the standard deviation of the data and the choice of a normal or lognormal distribution. The choice of distribution model is particularly important in the analysis of the tails of distributions, i.e., at 3.16 times the mean parameter estimate. Although there are a number of assumptions in such an analysis (Table 4), it is clear from the data in Tables 1–3 that in nearly all cases the likelihood of the value for an individual exceeding 3.16 times the

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Pathway OXID OXID OXID OXID OXID OXID OXID OXID CONJ CONJ CYP3A4 CYP3A4 CYP3A4 GSHd GSHd GSHd GSHd GSHd GSHd Multiplee Multiplee Multiplee OXID OXID OXID OXID OXID OXID CYP3A CYP3A

Parameter CL oral (L h01) CL oral (L h01) CL (ml min01 kg01) CL (ml min01 kg01) CL oral (ml h01 kg01) CL oral (ml h01 kg01) CL (ml min01 kg01) CL (ml min01 kg01) CL (ml min01 kg01) CL (ml min01 kg01) AUC oral (ng ml01 h) AUC oral (ng ml01 h) AUC oral (ng ml01 h) % Dose in urine % Dose in urine % Dose in urine % Dose in urine % Dose in urine % Dose in urine AUC oral (ng ml01 h) AUC oral (ng ml01 h) AUC oral (ng ml01 h) CL oral (ml min01 kg01) CL oral (ml min01 kg01) CL oral (ml min01 kg01) CL oral (ml min01 kg01) CL oral (ml min01 kg01) f CL oral (ml min01 kg01) f AUC oral (ng ml01 min) AUC oral (ng ml01 min) Caucasian Chinese Caucasianb Orientalb Whitec Blackc White Black Caucasian Chinese Caucasian South Asian Nigerian Causian, mercapturate Caucasian, cysteine Ghanaian, mercapturate Ghanaian, cysteine Kenyan, mercapturate Kenyan, cysteine Caucasian Chinese Indian White Black White Black Caucasian Chinese Caucasian South Asian

Subjects 123 73.5 0.40 0.29 472 234 8.3 8.8 20.0 29.9 323 802 808 5.0 4.3 2.3 2.9 2.3 2.1 553.3 421.7 500.9 20.6 27.6 27.7 42.3 27.4 59.8 1383 1616

Mean 46 53 26 37 38 53 42 44 20 33 36 43 31 38 37 57 52 43 62 30 27 16 34 30 31 43 42 71 43 14

% SD 16 14 12 13 6 6 10 9 8 8 27 16 12 111 111 67 67 20 20 9 9 9 13 13 13 13 9 10 8 8

n 1 46,867 0 242 0 142,518 0 0 0 0 0 263,004 197,916 0 0 0 0 0 0 0 0 0 0 0 0 0 247 4,007 0 0

Normal 4,317 100,697 3 10,325 866 183,466 2,155 2,026 0 1 491 279,146 220,242 866 659 140 797 1 530 44 0 0 252 0 73 66 21,826 23,095 2,608 0

Log-normal

No. of subjects not covered by a 3.16-fold factor (per million)a Source of data Rudorfer et al. (1984) Rudorfer et al. (1984) Ghoneim et al. (1981) Ghoneim et al. (1981) Tornatore et al. (1993) Tornatore et al. (1993) Rutledge et al. (1989) Rutledge et al. (1989) Zhou et al. (1993) Zhou et al. (1993) Ahsan et al. (1993) Ahsan et al. (1993) Sowunmi et al. (1995) Critchley et al. (1986) Critchley et al. (1986) Critchley et al. (1986) Critchley et al. (1986) Critchley et al. (1986) Critchley et al. (1986) Chan et al. (1990) Chan et al. (1990) Chan et al. (1990) Johnson and Burlew (1992) Johnson and Burlew (1992) Johnson and Burlew (1992) Johnson and Burlew (1992) Zhou et al. (1989a) Zhou et al. (1989a) Kinirons et al. (1996) Kinirons et al. (1996)

Note. See Table 1 for abbreviations. a Data analyzed assuming both normal and log-normal distributions, using a 3.16-fold difference from the mean of the White/Caucasian subjects. b Not classified with respect to genetic differences in P450 activities (see Table 7). c Renal transplant patients. d Metabolites formed via glutathione (GSH) conjugation of the reactive metabolite. e Studied under condition of urine acidification. f Racemic compound.

Triazolam

Propanolol

Propranolol (d)

Propranolol (l)

Pethidine

Paracetamol

Nifedipine

Morphine

Metoprolol

Methylprednisolone

Diazepam

Desipramine

Compound

TABLE 6 Kinetics in Subjects of Different Ethnic Origins

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CL total (L h01) CL total (L h01) CL2OH (L h01) CL2OH (l h01) CL/F (ml min01) CL/F (ml min01) AUC oral (ng ml01 h) AUC oral (ng ml01 h) CL renal (ml min01) CL renal (ml min01) CL metabolic (ml min01) CL metabolic (ml min01) AUC oral (mg L01 h) AUC oral (mg L01 h) AUC oral (mg ml01 h) AUC oral (mg ml01 h) AUC oral (ng ml01 h) AUC oral (ng ml01 h) CL/F (L h01) CL/F (L h01) AUC oral (mg L01 h) AUC oral (mg L01 h) AUC oral (ng ml01 h) AUC oral (ng ml01 h)

Parameter

RENAL RENAL OXIDc OXIDc OXIDc OXIDc OXIDb OXIDb OXIDc OXIDc OXIDb OXIDb OXIDc OXIDc OXIDc OXIDc

OXID OXID OXIDc OXIDc OXIDb OXIDb

Pathway Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Asian EM Asian PM Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian Caucasian EM PM EM PM EM PM EM PM

EM PM EM PM EM PM EM PM EM PM EM PM EM PM

Subjects 113.9 67.7 6.7 0.7 25.7 12.1 860 1462 315 308 726 292 481 1871 2.9 13.7 640 3769 44.5 16.7 441 447 394 1590

Mean 40 45 61 68 21 9 30 28 22 23 33 47 51 18 176 103 — — 16 16 52 36 61 71

% SD 10 9 10 9 13 3 5 5 5 5 5 5 10 10 8 8 2 2 8 7 10 10 6 6

n

0 25,490 199 837,706 0 0 0 1,080 0 0 0 282,132 11 851,373 109,860 626,066 — — 0 122,659 16 0 199 620,035

Normal

1,411 71,069 20,394 963,877 0 5 44 12,019 0 0 173 295,735 8,357 877,716 166,318 681,845 — — 0 141,783 9,340 563 20,394 649,063

Log normal

b

Data analyzed assuming both normal and log-normal distributions, using a 3.16-fold difference from the mean of the extensive metabolizers. EM and PM are extensive and poor metabolizers of mephenytoin (CYP2C19). c EM and PM are extensive and poor metabolisers of debrisoquine/sparteine (CYP2D6).

a

Timolol

Sertraline

Moclobemide

Methoxyphenamine

Lansoprazole

Fluoxetine

Flecainide

Diazepam

Clomipramine

Compound

No. of subjects not covered by a 3.16-fold factor (per million)a

TABLE 7 Kinetics in Subjects with Genetically Determined Differences in Xenobiotic Oxidation

Nielsen et al. (1994) Nielsen et al. (1994) Nielsen et al. (1994) Nielsen et al. (1994) Bertilsson et al. (1989) Bertilsson et al. (1989) Mikus et al. (1989) Mikus et al. (1989) Mikus et al. (1989) Mikus et al. (1989) Mikus et al. (1989) Mikus et al. (1989) Hamelin et al. (1996) Hamelin et al. (1996) Sohn et al. (1997) Sohn et al. (1997) Roy et al. (1985) Roy et al. (1985) Gram et al. (1995) Gram et al. (1995) Hamelin et al. (1996) Hamelin et al. (1996) McGourty et al. (1985) McGourty et al. (1985)

Source of data

14 RENWICK AND LAZARUS

HUMAN VARIABILITY AND RISK ASSESSMENT

15

FIG. 4. Possible future refinements of uncertainty factors based on an analysis of relevant data. ?, a value which is currently being developed on the basis of a comprehensive review of existing published data. In each box i is a chemical specific value and ii, iii, and iv are default values which can be selected dependent on the extent of knowledge of the compound under evaluation. The uncertainty factor within any box would be at the highest level that could be justified scientifically. The general default values (which collapse back to 100) would be the fallback position in the absence of specific/relevant data.

population average for either kinetics or dynamics is less than 1 in 100 assuming a normal distribution and less than 1 in 20 assuming a log-normal distribution. In relation to the use of a composite 10-fold factor for risk assessment to allow for both kinetics and dynamics, the proportion of a population not covered by the default factor can be estimated by multiplication of the proportions not covered for each of the 3.16-fold subfactors. This analysis indicates that the composite 10-fold factor would cover the vast majority (ú99.9%) of the population (Table 3), assuming either normal or log-normal distribution. The simplistic analysis in Table 3 is probably an underestimate because it assumes that the extent above 3.16-fold for one aspect is compensated quantitatively by the extent that individual is below 3.16-fold for the other aspect. However, a more sophisticated analysis is not warranted, given the caveats in Table 4, especially the impact of repeated measures on the interindividual variability (see Fig. 3), and the potential for disease processes to affect the kinetics for many xenobiotics. The analysis of subgroups (Tables 5–7) has explored the ability of the 3.16-fold factor for kinetics to cover the whole population. Other aspects warranting detailed analysis include aging and disease. Infants and children do not generally represent a special group from a kinetic viewpoint (Renwick, 1998)

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and they would be covered adequately by a 3.16-fold factor applied to the mean data for adults. Special consideration of children in relation to chemical safety should therefore focus on the differences and the variability in sensitivity compared with adults, e.g., the sensitivity of developing organ systems compared with adults as well as differences in sources and extents of exposure. Different ethnic groups may show differences in both kinetics and therapeutic responses (Renwick, 1996), but in most cases the default value of 3.16 would cover adequately most ethnic groups in a population. The data in Table 6 indicate that special consideration may need to be given to some substrates oxidized by CYP3A. The need for detailed consideration of xenobiotics eliminated primarily by a single pathway which shows polymorphic expression, such as an isoenzyme of cytochrome P450, is shown by the data in Table 7. In conclusion, the data and analyses in this paper indicate that the 10-fold factor for human variability is an appropriate default value, but as with previous reviews it has identified a number of circumstances where the value may be inadequate. The subdivision of the 10-fold factor into kinetic and dynamic aspects allows the possibility for greater refinement of uncertainty factors in the future. The massive published pharmacokinetic and toxicokinetic data-

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base in humans could be used to a greater extent to provide a basis for the definition of better defaults for the potential interindividual variability in major pathways (Fig. 4). The uncertainty factor for interindividual variability in kinetics for any compound under evaluation could then be selected based on the best information available, i.e., (i) a data-derived value if an adequate human study has been performed on the chemical being assessed (Renwick, 1993); (ii) a default based on the known variability of the processes determining the kinetics of that chemical, e.g., a ‘‘renal’’ default (see Fig. 4); or (iii) the standard default of 3.16 in the absence of relevant toxicokinetic information. Pathways of metabolism for a chemical can be identified by simple in vitro systems, and the role of specific isoenzymes can be defined by the use of heterologously expressed human isoenzymes of cytochrome P450. This approach could also be used to develop better defaults for kinetic differences between test species and humans. Uncertainty factors are usually applied to the external dose expressed on the basis of mg/kg body wt, irrespective of which test species shows the critical effect, i.e., mouse, rat, or dog. It is difficult to justify the use of the same default uncertainty factor for the three main test species used in chronic studies (Calabrese et al., 1992). In vivo kinetic data in test species and humans could be used as the basis for the development of suitable species-specific defaults and possibly even species- and route-specific defaults. As described above for human variability, the uncertainty factor selected could be the best of (i) a data-derived value, (ii) a species- and route-specific default, (iii) a species-specific default, or (iv) the general default of 4.0 (Fig. 4). In principle, a similar approach could be adopted for toxic processes and responses. Data for humans are essential to undertake such an analysis and fortunately there are only limited data on human variability or species differences in toxic responses to nontherapeutic agents. However, it may be possible to develop specific defaults for some classes of effect which could be used to replace the general default values for dynamics shown in Fig. 1. Relevant information could relate to in vitro responses of animal and human tissues (for example, peroxisome proliferators) or to in vivo clinical toxicity (for example, leukopenia). Theoretically, it may be possible to develop specific defaults for dynamics in different species compared to humans, but it is unlikely that there will be adequate data on human variability in dynamics for nontherapeutic agents. Therefore, it is likely that the majority of chemicals will require the use of general defaults for dynamics as shown in both Figs. 1 and 4. The use of separate factors for kinetics and dynamics will allow a knowledge of the fate of the chemical in the body to have a greater, and possibly quantitative, impact in the determination of safe levels of exposure

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for humans. In addition, simple in vitro screens of pathways of metabolism will help to identify chemicals for which the common defaults may be inadequate. ACKNOWLEDGMENTS The authors are grateful to ILSI Europe for financial support for sabbatical leave (A. G. Renwick) and for support for assistance with some of the preliminary literature analyses (N. Lazarus), to Dr. M. E. Meek and Health Canada for assistance with literature sources, and to Dr. D. Hattis for advice.

REFERENCES Aarons, L. (1996). Population approaches/sparse data analysis for human variability in kinetics and dynamics. Environ. Toxicol. Pharmacol. 2, 197–199. Ahsan, C. H., Renwick, A. G., Waller, D. G., Challenor, V. F., George, C. F., and Amanullah, M. (1993). The influences of dose and ethnic origins on the pharmacokinetics of nifedipine. Clin. Pharmacol. Ther. 54, 329–338. Amon, I., Amon, K., Scharp, H., Franke, G., and Nagel, F. (1983). Disposition kinetics of metronidazole in children. Eur. J. Clin. Pharmacol. 24, 113–119. Barrett, J. S., Murphy, G., Peerlinck, K., De-Lepeleire, I., Gould, R. J., Panebianco, D., Hand, E., Deckmyn, H., Vermylen, J., and Arnout, J. (1994). Pharmacokinetics and pharmacodynamics of MK-383, a selective non-peptide platelet glycoprotein–IIb/IIIa receptor antagonist, in healthy men. Clin. Pharmacol. Ther. 56, 377– 388. Bateman, D. N. (1983). Clinical pharmacokinetics of metoclopramide. Clin. Pharmacokinet. 8, 523–529. Bertilsson, L., Henthorn, T. K., Sanz, E., Tybring, G., Sa¨we, J., and Vilte´n, T. (1989). Importance of genetic factors in the regulation of diazepam metabolism: Relationship to S-mephenytoin but not debrisoquin hydroxylation phenotype. Clin. Pharmacol. Ther. 45, 348–355. Blackwell, C. P., and Harding, S. M. (1989). The clinical pharmacology of ondansetron. Eur. J. Cancer Clin. Oncol. 25, S21–S24. Blain, P. G., Mucklow, J. C., Bacon, C. J., and Rawlins, M. D. (1981). Pharmacokinetics of phenytoin in children. Br. J. Clin. Pharmacol. 12, 659–661. Blychert, E., Edgar, B., Elmfeldt, D., and Hedner, T. (1991). A population study of the pharmacokinetics of felodipine. Br. J. Clin. Pharmacol. 31, 15–24. Blychert, E., Edgar, B., Elmfeldt, D., and Hedner, T. (1992). Plasma concentration–effect relationships for felodipine: A meta analysis. Clin. Pharmacol. Ther. 52, 80–89. Braat, M. C. P., Jonkers, R. E., and Van Boxtel, C. J. (1992a). Quantification of metoprolol beta2-adrenoceptor antagonism in asthmatic patients by pharmacokinetic–pharmacodynamic modelling. Pulmonary Pharmacol. 5, 31–38. Braat, M. C. P., Jonkers, R. E., Bel, E. H., and Van Boxtel, C. J. (1992b). Quantification of theophylline-induced eosinopenia and hypokalaemia in healthy volunteers. Clin. Pharmacokinet. 22, 231–237. Buss, N. E., Renwick, A. G., Donaldson, K. M., and George, C. F. (1992). The metabolism of cyclamate to cyclohexylamine and its cardiovascular consequences in human volunteers. Toxicol. Appl. Pharmacol. 115, 199–210. Buss, N. E., Tembe, E. A., Prendergast, B. D., Renwick, A. G., and George, C. F. (1994). The teratogenic metabolites of vitamin A in

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