Pharmacology Journal

There are more than 4000 chemicals found in cigarette smoke. Cigarette smoking is associated with an increased incidence of both respiratory and cardiovascular disease, but the relationship of specific constituents with disease has not yet been established. Carbon monoxide (CO) is one of the cigarette smoke constituents that has a very high affinity for haemoglobin (Hb) relative to that for oxygen (approximately 200-fold). This results in an acute effect of decrease in oxygen-carrying capacity of Hb and a leftward shift of the oxyhaemoglobin dissociation curve, which reduces the release of oxygen to tissues. CO also binds with other haemoproteins such as myoglobin, which abounds in skeletal muscles, causing dysfunction by impairing its oxygen-carrying capacity and the transportation of oxygen from the blood to the mitochondria.

CO exposure is often estimated by either CO concentrations in exhaled breath or from CO bound to Hb. There are several reports in the literature regarding mathematical modelling of carboxyhaemoglobin (COHb) in humans, but none in adult smokers. Some of the models, e.g. the Coburn–Forster–Kane (CFK) equation, were developed to predict the rate of endogenous CO production, which has also been used to predict the rate of COHb formation during inhalation exposure to CO. Cigarette smoking involves multiple short and rapid inhalations over the entire smoking period, resulting in a COHb steady state, followed by a period when there is no smoking, resulting in dissociation of CO from haemoglobin. This process has not been systematically characterized in the population of smokers. Since it is not practical to obtain extensive blood sampling from a large population of adult smokers, a population pharmacokinetic (PK) analysis approach was employed. The objectives of this population PK analysis were to characterize the PK and variability of COHb concentrations in adult smokers, and to identify factors which influence COHb disposition.

Study conduct

This analysis examined data from adult smokers of different conventional cigarettes in three open-label, randomized, controlled, forced-switching, parallel group studies. These studies were conducted to evaluate the effect of switching adult smokers to test cigarettes; however, only the data from smokers of conventional reference cigarettes and those who stopped smoking were used for the model building. The studies were conducted at MDS Pharma Services Inc., Lincoln, Nebraska after approval of the study protocol by the local Internal Review Board. After signing the informed consent and passing screening for inclusion/exclusion criteria, adult male and female smokers of 10–30 conventional cigarettes [Federal Trade Commission (FTC) tar delivery 11 mg (CC1) or 6 mg (CC2)] per day were enrolled. Subjects were confined to the clinic during the entire course of the studies.

Materials

The products used in these studies were: CC1, Marlboro Lights cigarettes, tar = 11 mg, nicotine = 0.8 mg, CO = 12 mg; CC2, Marlboro Ultra Lights cigarettes, tar = 6 mg, nicotine = 0.5 mg and CO = 7 mg; CC3, Merit Ultima cigarettes, tar = 1 mg, nicotine = 0.1 mg and CO = 4 mg. The tar values reported were based on FTC smoking methods.

Study design

Study 1 examined healthy adults who smoked CC1 at baseline (n = 100). Following baseline investigations, subjects were randomly assigned to continue to smoke CC1 (n = 20), switch to CC3 (n = 20) or to stop smoking (n = 20) over a period of eight consecutive days. Study 2 included data from 50 healthy adults who smoked CC2 at baseline and were subsequently randomized to continue smoking their original brand (n = 25) or to stop smoking (n = 25) for a period of 8 days. In study 3, data were used from healthy adult smokers of CC1, of whom 40 were randomized either to continue smoking CC1 (n = 20) or to stop smoking (n = 20) for 8 days.

Days −2 and −1 were designated as an acclimat@
Ú< which was followed by randomization to respective smoking groups on day 1. The study design did not include a day 0. During the acclimatization phase subjects were monitored for cigarette consumption in order to determine their daily allotment of cigarettes for the remainder of the study. Subjects continued with their assigned smoking groups through the end of day 8. COHb percentage saturations were evaluated at 07.00, 11.00, 15.00, 19.00 and 23.00 h on baseline (day −1) and day 8 in study 1 and on days −1, 3 and 8 in studies 2 and 3. Smoking was controlled (as described by Roethig et al.) and monitored in all three studies. All three studies collected several biomarkers, but only COHb concentrations were evaluated in this modelling analysis.

Analytical methods

COHb quantification Blood samples (10 ml) for determination of COHb concentrations were drawn in K₃EDTA vacutainer tubes at protocol-specified times. COHb in whole blood was assayed spectrophotometrically with a CO oximeter (IL Multi-4; Instrumentation Laboratory, Lexington, MA, USA) at Covance Central Clinical Laboratory (Indianapolis, IN, USA). Anticoagulated whole blood was aspirated into the CO-oximeter, mixed with diluent, haemolysed with a non-ionic surfactant and brought to a constant temperature in the cuvette. Absorbance of a monochromatic light source passed through the cuvette was measured at six specific wavelengths. The limit of quantification was 0.3%, linear calibration range was 0.3–64.7%. The between-run precision (% coefficient of variation) was <5%.

Machine yield of carbon monoxide The amount of CO formed and tar content of each cigarette type were determined under the experimental conditions under FTC conditions, reported in detail elsewhere, carried out at Philip Morris Product Testing Laboratories (Richmond, VA, USA). Briefly, cigarettes were smoked in a Filtrona/Cerulean Smoking Machine (Cerulean Corp., Richmond, VA, USA), models 400-450, equipped with harmonized smoking hood and CO analyser. The standard test protocol was used utilizing 35 ml puff volume over a 2-s puff duration collected every 60 s. CO was analysed in the mainstream smoke by Fourier transform infrared spectroscopy connected to the smoke machine.

Data analysis

Creating the database The final database used for this analysis consisted of 1960 COHb percent saturation observations obtained from 190 subjects. Smoking history, machine yield tar and CO concentrations (FTC conditions), time and duration for each cigarette smoked, demographics and COHb data from all three studies were combined into a single database. Smoking information consisted of the FTC tar and CO yield per cigarette, as well as time and duration of each cigarette smoked in two studies, and the total number of cigarettes smoked per day for the third study. Therefore, for approximately two-thirds of the subjects, each cigarette smoked was treated as a separate dose record. For the remaining individuals (study 3), all cigarette consumption was assumed to occur over a 12-h period.

Since all subjects were confirmed smokers and required to smoke the reference cigarette for at least 4 weeks prior to enrolment as part of the inclusion criteria, they were assumed to be at steady state with regard to COHb concentrations prior to study entry. Each cigarette was assumed to provide a unit dose of COHb, which implies an assumption of a linear relationship between the cigarette and parts per million (p.p.m.) CO available for inhalation, as well as a linear relationship between p.p.m. CO and COHb percent saturation. Law et al. have reported that the relationship between the biochemical markers of smoking and the number of cigarettes smoked is approximately linear for consumption of up to 20 cigarettes per day. For heavy smokers (>20 cigarettes/day) the relationship was no longer linear and exposure was lower than anticipated. For the purpose of this evaluation, in which subjects were enrolled who smoked ≤30 cigarettes a day, the assumption of linearity is supported.

The effect of possible covariates included in the model were age, body weight, ideal body weight (IBW), body mass index (BMI), gender and race. FTC tar was examined only as a covariate on the relative fraction of CO absorbed. Other covariates, such as age and body weight, were examined for potential effect on the rate constants and baseline COHb, in addition to the relative fraction of CO absorbed (F1). The addition of a covariate was accepted only if it resulted in a reduction in the objective function by at least 10.8 points (P < 0.001). In addition, the criteria for the addition of a covariate factor included improvement in one or more of the following: prediction of the observed COHb percent saturation, minimization of the interindividual variance terms, and reduction in the magnitude of the residual variability.

Pharmacokinetic analysis COHb percent saturation–time data were analysed using the nonlinear mixed-effects modelling program, NONMEM, v.5, Level 1 with the Compaq Digital Visual Fortran 6.6C compiler. The First Order Conditional Estimation (FOCE) method with interaction was implemented for all models tested because the comparison of objective functions (likelihood ratio test) from nested models is not reliably χ² distributed under the first order (FO) method, whereas the FOCE method with interaction is generally more reliable for such comparisons. Standard model-building approaches were employed during model development. Several structural models, including one- and two-compartment models with different input functions, were investigated during this evaluation. A structural model was identified, followed by refinement of the variance–covariance matrix, and then covariate identification.

Final model evaluation

Once a final model was identified, a nonparametric bootstrap analysis was performed to establish 95% confidence intervals (CI) for parameter estimates. Additionally, a limited visual predictive check was conducted for representative individuals in each FTC group using the final model. Individuals were selected in order to represent key covariates adequately, such as age, IBW and the number of cigarettes smoked per day. Dense sample times were generated for the individuals in these visual predictive check databases. Two hundred and fifty simulated replicates were generated, and the 95% prediction intervals were calculated from the Winsorized distributions of the simulated data. Winsorizing is a method used to eliminate possible outliers by setting the values equal to, or more extreme than a selected quantile to that of the selected quantile. By trimming the data in this fashion, the distorting effects of influential outliers could be abrogated prior to further processing. Winsorizing also preserves the general distributional characteristics of the data. Following the generation of these intervals, the observed data were overlaid on the prediction intervals and the distributions of observed and simulated data were visually compared.

Filed under: Pharmacokinetics