Intra- and inter-individual variability in urinary nicotine excretion and plasma cotinine in adult cigarette smokers

Regulatory Toxicology and Pharmacology 64 (2012) 388–393

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Intra- and inter-individual variability in urinary nicotine excretion and plasma cotinine in adult cigarette smokers Qiwei Liang ⇑, Mohamadi Sarkar Altria Client Services, Richmond, VA 23219, USA

a r t i c l e

i n f o

Article history: Received 29 March 2012 Available online 19 September 2012 Keywords: Nicotine equivalents Plasma cotinine Inter-individual Intra-individual Variability Clinical study Short term Long term Linear mixed model

a b s t r a c t Urinary nicotine equivalents (NE) and plasma cotinine are widely used as a biomarker for exposure to tobacco products, but there is limited information on intra- and inter-individual variability in the literature. Data were gathered from 13 randomized controlled clinical studies sponsored by Philip Morris USA, with study durations between 2 and 8 days for the short term (ST) and 3–12 months for the long term (LT) studies. Coefficients of variation (CV) were compared and a linear mixed model was used to partition the total study variability into inter- and intra-individual variability. In the ST and LT studies respectively, the root–mean–square (RMS) intra-individual CV was 19% and 29% for NE (mg/24 h); 19% and 33% for NE (mg/cig) and 13% and 22% for plasma cotinine. The RSM inter-individual CV was 38% and 38% for NE (mg/ 24 h), 25% and 32% for NE (mg/cig) and 38% and 37% for plasma cotinine, in ST and LT study, respectively. Intra-individual CV was smaller in ST studies than in LT studies, and was significantly less than inter-individual CV in ST studies. Daily cigarette consumption alone could not explain all the variability in NE and plasma cotinine. The variability estimates could be used for clinical study design of clinical and developing regulatory guidance. Ó 2012 Elsevier Inc. All rights reserved.

1. Introduction Cigarette smoking is a leading cause of some preventable diseases including lung cancer, cardiovascular disease and chronic obstructive pulmonary disease (US Department of Health and Human Services, 1998). Cigarette smoke is a complex mixture of thousands of chemicals and the specific chemicals responsible for the smoking related diseases have yet to be determined. Recently we have reported that urinary excretion of nicotine equivalents may be used as a surrogate biomarker for overall smoke exposure (Wang et al., 2011). Nicotine equivalents (NE) is the molar sum of nicotine measured in 24 h urinary excretion of nicotine and its five major metabolites (cotinine and cotinine-N-glucuronide, trans-30 hydroxycotinine and trans-30 -hydroxycotinine-O-glucuronide, and nicotine-N-glucuronide) (Benowitz et al., 1994; Roethig et al., 2005; Scherer et al., 2007). It has been reported that 24 h urinary excretion of NE accounts for an average of about 80–90% of the systemic nicotine intake from smoking or from a nicotine patch (Benowitz et al., 1994; Feng et al., 2007; St Charles et al., 2006). Nicotine equivalents has been widely used as a biomarker for exposure to tobacco and cigarette smoke (Frost-Pineda et al., 2008a, 2008b; Mendes et al., 2008; Roethig et al., 2005, 2007, ⇑ Corresponding author. Address: Altria Client Services, 601 East Jackson Street, Richmond, VA 23219, USA. Fax: +1 804 334 6368. E-mail address: [email protected] (Q. Liang). 0273-2300/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.yrtph.2012.09.006

2008, 2009; Sarkar et al., 2008; Shepperd et al., 2009; St Charles et al., 2006; Stratton et al., 2000; Wang et al., 2011). Cotinine, the major proximate metabolite of nicotine, measured in blood, saliva or urine (Jarvis et al., 1988), has also been widely used as a biomarker of exposure to tobacco (Benowitz, 1996; Benowitz et al., 1994, 2009; Mendes et al., 2008; Nagano et al., 2010; Pérez-Stable et al., 1995; Roethig et al., 2007, 2009). Due to genetic differences in the metabolism of nicotine to cotinine, cotinine levels by itself may be prone to greater inter-individual variability compared to nicotine equivalents (Jatlow et al., 2003). Cigarette smoking is a complex and highly variable behavior which may correspondingly result in highly variable levels of exposure to cigarette smoke in smokers. However there is very limited information comparing intra- and inter-individual variability of nicotine in the literature (Matt et al., 2007; Shepperd et al., 2009; St Charles et al., 2006). In longitudinal studies for human exposure to tobacco use, nicotine equivalents or plasma cotinine has been measured repeatedly over time on the same smokers (Frost-Pineda et al., 2008a, 2008b; Roethig et al., 2005, 2007, 2008; Sarkar et al., 2008; Stratton et al., 2000). In those studies, there are generally two main sources of variability for nicotine equivalents and plasma cotinine: (1) inter-individual (between different individuals) and (2) intra-individual (within the same individual) variability. Inter-individual variability is primarily due to differences among individuals in demographic variables (e.g., age, gender), genetics (e.g., impact of CYP2A6 on cotinine metabolism) and smoking

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behavior (e.g., number of cigarettes, puff volume and frequency). On the other hand, intra-individual variability mostly arises from differences in cigarette consumption and smoking behavior at different times for each individual. Measurement variability (both analytical and sample collection) is also a contributor. There can also be heterogeneity among individuals in their nicotine and cotinine excretion over time due to temporal variability in smoking behavior and daily life activities. Like other biological measures, measurements of nicotine and its metabolites are also subject to random error. In a statistical model, inter-individual variability can be examined by the introduction of an individual-specific random effect. Unlike fixed effects, random effect facilitates the generalization of a study result to a greater population. Intra- and inter-individual variability in nicotine equivalents or plasma cotinine are needed for sample size estimation for equivalence testing of tobacco products. In addition, this information could help understand the relationship between the exposure and behavioral and health outcome measures including nicotine dependence. The purpose of this analysis was to estimate the intraand inter-individual variability in nicotine equivalents and plasma cotinine based on data from multiple studies sponsored by Philip Morris, USA (Frost-Pineda et al., 2008a, 2008b; Roethig et al., 2005, 2007, 2008; Sarkar et al., 2008). We hypothesized that variability between the individuals would be larger than within the individuals and it would be larger in long term studies than in short term studies. Daily urinary excretion of nicotine equivalents and plasma cotinine were measured as biomarkers of exposure to cigarette smoke. In order to account for the contribution of cigarette consumption to the variability of the biomarker measures, the intra- and inter-individual variability in daily cigarette consumption was also estimated.

2. Materials and methods 2.1. Study data Data from 13 controlled clinical studies were used, including eight short term (2–8 days long) and five long term studies (3– 12 months long). These studies were sponsored by Philip Morris, USA and conducted between 2001 and 2007. The design of the studies was randomized, controlled and parallel group. After baseline biomarker measurements, adult smokers were randomly assigned to a group that either used a test product, stopped smoking or continued to smoke the cigarettes smoked at baseline. Biomarker data from the adult smokers from the latter group were included in the statistical analysis. The last baseline measurement was used in the studies where multiple baseline measurements were determined. In the short term studies, individuals were confined in the clinic for two to eight days which allowed for accurate urine collection over the 24 h period and a number of cigarettes smoked were carefully recorded for each individual. In the long term studies, individuals smoked cigarettes in their normal life settings and were asked to return the cigarette butts that they smoked as a measure of the daily cigarette consumption. The participants were required to collect urine over the 24 h period while smoking cigarettes in the ambulatory setting. The samples were brought to the clinic the following day, during which time the blood samples were collected for other biomarker measurements. Study participants included males and females who were 21 years of age or older and in generally good health. Smoker status was defined as self-reported smoking of at least 10–30 cigarettes per day for at least the past 12 months, and no use of any other nicotine-containing products. Pregnant or nursing women were excluded. All studies were approved by the local Institutional Review Board and conducted in accordance with Good Clinical

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Practice and the principles of the Declaration of Helsinki. Participants were recruited through advertising and written informed consent was obtained from each subject prior to entering the study. Details about the conduct of several studies can be found in the literature (Frost-Pineda et al., 2008a, 2008b; Mendes et al., 2008; Roethig et al., 2005, 2007, 2008; Sarkar et al., 2008). 2.2. Biomarker sampling and analytical methods Twenty-four-hour urine samples were collected from each individual for measurement of urinary nicotine and five of its metabolites. The total volume of urine was measured and recorded prior to removal of any aliquots. Aliquots were removed and stored frozen at 20 °C until analysis. Nicotine, nicotine-N- glucuronide, cotinine, cotinine-N-glucuronide, trans-30 -hydroxycotinine and trans30 -hydroxycotinine-O-glucuronide were measured by liquid chromatography/tandem mass spectrometry (LC–MS/MS) methods as described previously (Roethig et al., 2005; Sarkar et al., 2008). Blood samples were also collected at around 7 PM for plasma cotinine measurement. Plasma cotinine was also measured by LC–MS/ MS. 2.3. Statistical methods A linear mixed model, defined as Yij = B0j + Voi+eij, was used to estimate the intra-individual and inter-individual variability of the biomarkers and daily cigarette consumption (CPD). In the model, Yij was the measure of a biomarker (or daily cigarette consumption) in the ith individual and at the jth time, B0j the overall mean of the biomarker at the jth time, Voi the influence of individual i which was treated as a random effect and eij was the biomarker random error specific to the ith subject at jth time. With Voi as a random effect, the population distribution was assumed to be normally distributed with mean 0 and variance r2v. The term eij was assumed to be normally distributed and conditionally independently distributed with mean 0 and common variance r2e (Hershberger and Moskowitz, 2002). The variance r2v represents interindividual variance and the variance r2e intra-individual variance. The ratio of r2v to the total variance (r2v + r2e) indicates the proportion of variance in the data that is attributed to differences between individuals, which is also called intra-class correlation (ICC). The square root of r2v or r2e was divided by the biomarker mean to get the inter-individual coefficient of variation (CV) or the intraindividual coefficient of variation for the biomarker. The compound symmetry structure (Littell et al., 2006) was used in the linear mixed model for examining covariance. SAS procedure Proc Mixed was uses for the statistical analysis (SAS, Version 9.1). 3. Results The study identifier, year of study conduct and sample size are shown in Table 1. The sample size per study, representing the number of adult smokers who continued to smoke the same cigarette as that smoked at baseline, ranged from 20 to 77 individuals in the ST studies and 15–64 individuals in the LT studies. A total of 411 individuals were included in the statistical analysis. The variation in the levels of nicotine equivalents (mg/24 h) over time for a typical short term study (ST-2) is shown in Fig. 1 and typical long term study (LT-3 study) is shown in Fig. 2. Intraand inter-individual variability was obvious in the plots, with a smaller variability in the ST study than in the LT study. There was a wide range observed in the percent coefficient of variation (% CV) in NE (mg/24 h), NE (mg/cig), plasma cotinine (ng/ml) and daily cigarette consumption among the individual smokers. The% CV for each smoker, representing intra-individual variability, ran-

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Table 1 Characteristics of the selected clinical studies. Studya Year of No. of Post-baseline Subjectsb visit study conduct

Publication

ST-1 ST-2 ST-3 ST-4 ST-5 ST-6 ST-7 ST-8 LT-1

2001 2002 2003 2003 2004 2002 2003 2007 2003

20 20 20 20 37 25 77 30 15

Roethig et al. (2005) Roethig et al. (2007) Sarkar et al. (2008) Frost-Pineda et al. (2008a) No Sarkar et al. (2008) Mendes et al. (2008) No Sarkar et al. (2008)

LT-2

2002

21

LT-3

2002

33

LT-4 LT-5

2004 2003

29 64

Days 1, 7, 8 Days 1–8 Days 1–8 Days 1–8 Days 1, 2, 3 Days 1–8 Days 1–8 Days 7, 8 Wks 0, 4, 8, 12, 16, 20, 24 Wks 0, 4, 8, 12, 16, 20, 24 Wks 1, 2, 4, 8, 13, 17, 21, 26, 39, 52 Wks 1, 4 8, 12 Wks 0, 4, 8, 12, 20, 24

variability, accounting for an average of 79% of the total study variability in NE (mg/24 h), 62% in NE (mg/cig), 88% in plasma cotinine and 85% in daily cigarette consumption in the ST studies and 63% in NE (mg/24 h), 71% in plasma cotinine and 58% in daily cigarette consumption in the LT studies. By biomarker, the RMS intra-individual CV was relatively smaller in plasma cotinine (average 17% over ST and LT studies) than in NE (mg/24 h) (average 24% over ST and LT studies) and NE (mg/cig) (average 26% over ST and LT studies). By study duration, the RMS intra-individual CV for any biomarker was larger in the LT studies than in the ST studies, 36% vs. 19% in NE (mg/24 h), 38% vs. 19% in NE (mg/cig) and 21% vs. 13% in plasma cotinine. This was also true for daily cigarette consumption, 17% vs. 8%.

Sarkar et al. (2008) Roethig et al. (2008)

4. Discussion

Frost-Pineda et al. (2008b) Mendes et al. (2008)

a

ST = short term study; LT = long term study. For those who continued to smoke the same cigarette type over the study duration. b

ged from 4% to 105% in NE (mg/24 h), 1% to 88% in NE (mg/cig), 5% to 161% in plasma cotinine and 0% to 158% in daily cigarette consumption in the ST studies (Table 2). The% CV ranged from 2% to 82% in NE (mg/24 h), 2% to 105% in NE (mg/cig), 4% to 91% in plasma cotinine and 4% to 98% in daily cigarette consumption in the LT studies (Table 2). In the ST studies, the proportion of inter-individual variance in the total variance, also called intra-class correlation coefficient (ICC), varied between 64% and 88% in NE (mg/24 h), 40% and 81% in NE (mg/cig), 78% and 93% in plasma cotinine and 81% and 93% in daily cigarette consumption (Table 3). In the LT studies, ICC was 42% to 76% in NE (mg/24 h), 36% to 59% in NE (mg/cig), 61% to 87% in plasma cotinine and 34% to 81% in daily cigarette consumption (Table 3). The root–mean–square (RMS) inter-individual CV was similar between NE (mg/24 h) and plasma cotinine (ng/ml) and between NE (mg/cig) and daily cigarette consumption in the ST and the LT studies (Table 3). The inter-individual CV was larger than the corresponding intra-individual CV in all three biomarker measures and daily cigarette consumption in the ST studies as well as the LT studies, with the exception of NE (mg/cig) in the LT studies (Table 3). Inter-individual variability was the major source of the total study

Few studies have conducted multiple measurements of nicotine equivalents and plasma cotinine in the same smokers over several days or weeks. In this study, using data from 13 studies where nicotine equivalents and plasma cotinine were measured daily or biweekly for up to a one year time period (Roethig et al., 2005, 2007, 2008; Mendes et al., 2008; Frost-Pineda et al., 2008a, 2008b; Sarkar et al., 2008), we have provided a rigorous estimation of the intraand inter-individual variability of nicotine equivalents and plasma cotinine. One of the most significant sources of variability in exposure arises from the number of cigarettes smoked. Due to the controlled setting of the ST studies, an accurate measurement of actual number of cigarettes smoked was possible. The ST studies were conducted in a confined clinical environment where the subjects were not allowed to leave the study site over the entire duration of the study. The clinic staff handed out the cigarettes to each participant when they were ready to smoke. Therefore, the impact of number of cigarettes smoked on the overall variability has been accounted for. With the confined clinical setting of the ST studies, it is reasonable to assume that the differences in the daily excretion of nicotine and five of its metabolites between individuals are primarily attributed to differences in the number of cigarettes smoked, product design features, smoking behavior, analytical measurement variability as well as other random sources of error. In addition to daily cigarette consumption, there are other sources of variability. For example, one source of variability could be the analytical measurement variability from the biomarker

70

60

NE (mg/day)

50

40

30

20

10

0

0

1

2

3

4

5

6

7

8

Study Day Fig. 1. Nicotine equivalents (NE) (mg/24 h) in adult smokers over time (day) in a short term study (ST-2), n = 20.

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60

NE (mg/day)

50

40

30

20

10

0

0

1

2

4

8

13

17

21

28

39

52

Study Week

Fig. 2. Nicotine equivalents (NE) (mg/24 h) in adult smokers over time (week) in a long term study (LT-3), n = 33.

measurements. Examination of our internal analytical method validation reports showed that the precision (% CV) ranged from 2.6% to 9.2% for nicotine and 2.3% to 2.4% for cotinine. These values suggest that the analytical variability is an important contributor to the overall variability. The differences due to variability in metabolism could have an influence on serum cotinine levels. However the effect could be dampened when representing nicotine and five of its metabolites as a molar equivalent of nicotine. In a recent publication (Bloom et al., 2011), it was reported that seven polymorphisms in CYP2A6 might account for the variability in nicotine metabolism. However given that the allelic frequency (although higher in specific ethnic groups) is not very common in the general population (<5%) (Zabetian et al., 2000), therefore contribution of the genetic difference in the overall variability may not be that large. Since nicotine equivalent levels were based on a 24-h urine collection, it is possible that a portion of the variability could be attributed to the sample collection process. However the variability due to the 24-h urine collection was minimized at least in the ST study since the participants were confined in the clinic and an accurate 24-h urine collection was ensured by the clinic staff. In the LT studies an algorithm was utilized to ensure complete urine collection based on amount of creatinine excreted. Nevertheless, incomplete urine collection could not be completely ruled out and may have been a contributing factor in the overall variability. In a study involving a total of 23 different commercial cigarette brands from the US market place (Morton and Laffoon, 2008), the

smoke chemistry yield of nicotine (mg/cig) was measured over a period of ten years (1998–2008). The within-brand variability separated from variance components of study year and brand was estimated to be 11%. It can be assumed that this represents variability in analytical measurement and smoking machine related variability as well as other random sources of variability. This variability was much smaller than the variability observed in smokers as shown in our analysis, reflecting the addition of smoker-related variability. Nevertheless, the 19% intra-individual CV for NE (mg/24 h) and 8% intra-individual CV for number of cigarettes smoked per day observed in the ST studies therefore suggests that adult smokers smoke their cigarettes reasonably consistently. The 19% intra-individual CV for NE (mg/24 h) is nearly identical to the value from a short term clinical study of 74 individuals (St Charles et al., 2006) which reported a 18.1% intra-individual CV with a range of 4–54%. Intra-individual variability could be reduced with shorter study duration. The intra-subject variability of urinary cotinine levels was examined in a study (Matt et al., 2007) and the variability appeared to increase with increase in study duration. Inter-individual variability in a study where groups of smoker are being compared may be minimized by ensuring that the variability is similar across the groups. This may be achieved by stratification of the participants based on factors such as number of cigarettes smoked per day, gender, age, BMI, race and genetics. Inter-individual variability can also be accounted for and separated from other variability sources by using a crossover study design in which more than one treatment is applied on the same subjects.

Table 2 Range of intra-individual coefficient of variation (CV%) for nicotine equivalents (NE), plasma cotinine and daily cigarette consumption. Study

NE (mg/24 h)

NE (mg/cig)

Plasma cotinine (ng/ml)

Number of cigarettes smoked per day

ST-1 ST-2 ST-3 ST-4 ST-5 ST-6 ST-7 ST-8

7–35 7–48 4–41 11–34 4–73 8–27 7–88 5–105

4–33 7–43 6–43 8–37 1–46 8–26 7–88 1–34

N/A N/A 5–27 4–17 N/A 4–27 5–33 2–161

0–30 2–32 2–14 3–15 0–13 0–13 0–21 0–158

LT-1 LT-2 LT-3 LT-4 LT-5

14–54 12–82 8–48 5–43 2–80

2–69 16–105 11–67 4–47 5–67

4–42 9–58 4–91 N/A 5–56

8–60 3–98 6–61 6–25 4–36

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Table 3 Inter-individual and intra-individual coefficient of variation (CV%) in nicotine equivalents (NE), plasma cotinine and daily cigarette consumption in clinical studies. Study

a b

NE (mg/24 h)

NE (mg/cig)

Plasma cotinine (ng/ml)

Number of cigarettes smoked

Inter-

Intra-

Inter-

Intra-

Inter-

Intra-

Inter-

Intra-

ST-1 ST-2 ST-3 ST-4 ST-5 ST-6 ST-7 ST-8 RMSb

54(85)a 34(75) 37(84) 29(64) 44(88) 39(75) 32(84) 32(75) 38(79)

22(15) 19(25) 15(16) 21(36) 16(12) 15(25) 24(16) 18(25) 19(21)

25(62) 25(59) 25(68) 18(40) 33(81) 30(77) 24(49) 23(63) 25(62)

19(38) 20(41) 17(32) 22(60) 16(19) 16(23) 24(51) 17(37) 19(38)

N/A N/A 41(93) 41(92) N/A 38(92) 34(89) 40(78) 38(880)

N/A N/A 11(7) 11(8) N/A 10(8) 12(10) 21(22) 13(129)

40(93) 19(88) 16(80) 24(87) 21(93) 29(93) 20(89) 25(81) 23(85)

10(7) 7(12) 8(20) 10(13) 5(7) 7(7) 7(11) 11(19) 8(15)

LT-1 LT-2 LT-3 LT-4 LT-5 RMSb

40(59)a 48(64) 35(74) 34(76) 31(42) 38(63)

33(41) 36(36) 21(26) 19(24) 36(58) 29(37)

30(36) 41(56) 28(48) 26(59) 36(46) 32(49)

39(64) 36(44) 30(52) 21(41) 38(52) 33(51)

33(61) 37(67) 46(87) N/A 31(68) 37(71)

26(39) 26(33) 17(13) N/A 21(32) 22(29)

34(40) 28(34) 32(61) 28(81) 31(76) 30(58)

42(60) 39(64) 26(39) 14(19) 17(24) 29(42)

Values in parentheses represent the proportion of the total study variance in the linear mixed model. Root–mean–square for all ST or LT studies.

In a longitudinal study to estimate cigarette smoke exposure in German smokers (Shepperd et al., 2009), three sources of variability (within-brand, between-individual and within-individual) were considered in a nested effect statistical model. Similar to our results, the study showed a larger inter-individual variability than intra-individual variability for NE (mg/24 h) and plasma cotinine. The 21% intra-individual CV for NE (mg/24 h) and 18% CV for plasma cotinine was not much different from our estimates. Yet, our estimation was based on multiple studies and can be considered more robust. The small difference might have been due to the fact that different cigarette products were used in that study, whereas in our studies, only the smoker group which continued to smoke the same cigarette product as smoked at the baseline was used. Similar to our study, this study showed that the intra-individual CV in plasma cotinine was smaller than that of NE (mg/24 h). This observation was contrary to that expected, given that cotinine levels could be susceptible to genetic and pharmacokinetic variability (based on time of sampling). It is possible that the evening plasma cotinine levels reach a steady state and therefore are not prone to pharmacokinetic variability. Additionally, the plasma levels tend to be relatively more accurate than 24-h urinary collection, thereby explaining the lower intra-individual CV in plasma cotinine relative to that for NE. Intra-subject variability has been used to define the bioequivalence criteria for pharmaceuticals with confidence interval determined by the intra-subject variability and the number of subjects in bioequivalence studies (FDA, 2003). Based on the FDA guidance, bioequivalence is established if the confidence interval for the test product is within 20% to +25% of the reference product. For drugs with highly variable pharmacokinetics (defined by intra-subject variability >30%), the criteria for comparison between test and reference product can be extended beyond the normal bioequivalence limit. In the recently issued guidance on demonstrating substantial equivalence for tobacco products (FDA, 2011a), the Center for Tobacco Products at FDA has stated ‘‘a new tobacco product is substantially equivalent to a predicate tobacco product if the new product has the same characteristics as a predicate tobacco product’’. This guidance also mentioned that ‘‘FDA may request additional data needed to make a substantial equivalence determination. Examples of additional data that may be requested include: Clinical data – data comparing the biomarkers of exposure. . .’’. In the guidance, FDA recommends reporting 95% confidence intervals (a statistics related to intra- and inter- individual variability) for individual smoke constituents. FDA has also provided additional information by issuing responses to frequently

asked questions about the guidance (FDA, 2011b). Therefore, it is reasonable to believe that similar information may be required for a biomarker such as NE. Furthermore, for smoke constituents like nicotine, the relatively larger variability observed in humans should be considered when demonstrating equivalence based on machine derived yields of smoke constituents. Recently, there have been recommendations that FDA should impose product standards on the nicotine levels in cigarettes (Hatsukami et al., 2011). We believe that the information regarding intra-individual and interindividual variability in nicotine and plasma cotinine would be important to consider. Funding This work was supported by Philip Morris USA, Inc. Competing interests All authors are current employees of Altria Client Services. Acknowledgment We thank Mr. Lonnie Rimmer for creating a SAS analysis dataset for the statistical analysis. References Benowitz, N., Dains, K., Dempsey, D., Herrera, B., Yu, L., Jacob, P., 2009. Urine nicotine metabolite concentrations in relation to plasma cotinine during lowlevel nicotine exposure. Nicotine and Tobacco Research 11, 954–960. Benowitz, N., 1996. Cotinine as a biomarker of environmental tobacco smoke exposure. Epidemiologic Review 18, 188–204. Benowitz, N., Jacob 3rd., P., Fong, I., Gupta, S., 1994. Nicotine metabolic profile in man: comparison of cigarette smoking and transdermal nicotine. Journal of Pharmacology and Experimenal Therapeutics 268, 296–303. Bloom, J., Hinrichs, A.L., Wang, J.C., von Weymarn, L.B., Kharasch, E., Bierut, B., Goate, A., Murphy, S., 2011. The contribution of common CYP2A6 alleles to variation in nicotine metabolism among European–Americans. Pharmacogenet Genomics 21, 403–416. FDA. 2011a. Guidance for Industry and FDA Staff Section 905(j) Reports: Demonstrating Substantial Equivalence for Tobacco Products. Available at . FDA, 2011b. Draft Guidance for Industry and FDA Staff: Demonstrating the Substantial Equivalence of a New Tobacco Product: Responses to Frequently Asked Questions. Available at . FDA, 2003. Guidance for Industry Bioavailability and Bioequivalence Studies for Orally Administered Drug Products-General Considerations.

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