Lewis B. Sheiner

Nicotine absorption and cardiovascular effects with smokeless tobacco use: Comparison with cigarettes and nicotine gum

Because of recent resurgence in its consumption, the effects and health consequences of smokeless tobacco are of considerable public health interest. We studied the extent and time course of absorption of nicotine and cardiovascular effects of smokeless tobacco (oral snuff and chewing tobacco) and compared it with smoking cigarettes and chewing nicotine gum in 10 healthy volunteers. Maximum levels of nicotine were similar but, because of prolonged absorption, overall nicotine exposure was twice as large after single exposures to smokeless tobacco compared with cigarette smoking. All tobacco use increased heart rate and blood pressure, with a tendency toward a greater overall cardiovascular effect despite evidence of development of some tolerance to effects of nicotine with use of smokeless tobacco. Relatively low levels of nicotine and lesser cardiovascular responses were observed with use of nicotine gum. Adverse health consequences of smoking that are nicotine related would be expected to present a similar hazard with the use of smokeless tobacco.

Simultaneous modeling of pharmacokinetics and pharmacodynamics: Application to d‐tubocurarine

We propose a model of drug pharmacodynamic response that when integrated with a pharmacokinetic model allows characterization of the temporal aspects of pharmacodynamics as well as the time‐independent sensitivity component. The total model can accommodate extremes of effect. It allows fitting of simultaneous plasma concentration (Cp) and effect data from the initial distribution phase of drug administration, or from any non‐equilibrium phase. The model postulates a hypothetical effect compartment, the dynamics of which are adjusted to reflect the temporal dynamics of drug effect. The effect compartment is modeled as an additional compartment linked to the plasma compartment by a first‐order process, but whose exponential does not enter into the pharmacokinetic solution for the mass of drug in the body. The hypothetical amount of drug in the effect compartment is then related to the observed effect by the Hill equation, a nonlinear sigmoid form. Nonlinear least‐squares data fitting is used for parameter estimation. The model is demonstrated on two different sets of Cp and effect data for the drug d‐tubocurarine (dTC). In 7 normal subjects, the (mean ± SD) rate constant for equilibration of dTC effect (paralysis) and Cp is 0.13 ± 0.04 min−1 and the (mean ± SD) steady‐state Cp required to produce 50% paralysis is 0.37 ± 0.05 µg/ml.

Adherence to protease inhibitors, Hiv-1 viral load, and development of drug resistance in an indigent population

ObjectiveTo examine the relationship between adherence, viral suppression and antiretroviral resistance in HIV-infected homeless and marginally housed people on protease inhibitor (PI) therapy. Design and settingA cross-sectional analysis of subjects in an observational prospective cohort systematically sampled from free meal lines, homeless shelters and low-income, single-room occupancy (SRO) hotels. ParticipantsThirty-four HIV-infected people with a median of 12 months of PI therapy. Main outcomesAdherence measured by periodic unannounced pill counts, electronic medication monitoring, and self-report; HIV RNA viral load; and HIV-1 genotypic changes associated with drug resistance. ResultsMedian adherence was 89, 73, and 67% by self-report, pill count, and electronic medication monitor, respectively. Thirty-eight per cent of the population had over 90% adherence by pill count. Depending on the measure, adherence explained 36–65% of the variation in concurrent HIV RNA levels. The three adherence measures were closely related. Of 20 genotyped patients who received a new reverse transcriptase inhibitor (RTI) when starting a PI, three had primary protease gene substitutions. Of 12 genotyped patients who received a PI without a new RTI, six had primary protease gene substitutions (P < 0.03). ConclusionA substantial proportion of homeless and marginally housed individuals had good adherence to PI therapy. A strong relationship was found between independent methods of measuring adherence and concurrent viral suppression. PI resistance was more closely related to the failure to change RTI when starting a PI than to the level of adherence.

Learning versus confirming in clinical drug development

Clinical Pharmacology & Therapeutics (1997) 61, 275–291; doi:

Forecasting individual pharmacokinetics.

Often drug dosage may be chosen rationally by use of plasma concentration (CP) as the “therapeutic” end point. The ability to accurately forecast the CP resulting from a dosage regimen is central to choosing that regimen. Traditionally forecasting has been attempted only by accounting for known influences on pharmacokinetics, such as sex, age, and renal disease. One must also adjust for previously observed CPs. Herein, we discuss and explain an approach to both of these tasks, mainly focusing on the latter. The approach balances observed outcomes against prior expectations taking account of observation CP error. For digoxin, use of 1 measured CP, as opposed to none, improves forecast precision for future CPs by 40% (decrement in variance of forecast error), and 2 CPs improve it by 67%. There is also an increase in forecast accuracy (decrement in mean of forecast error) as the number of CPs used increases. After only 2, forecast accuracy and precision are as good as theoretically possible. Moreover, information from CPs is far more valuable for forecasting than that from observable patient features—sex, age, and the like; use of all the latter information does not improve accuracy and precision as much as only 1 CP.

Evaluating pharmacokinetic/pharmacodynamic models using the posterior predictive check.

The posterior predictive check (PPC) is a model evaluation tool. It assigns a value (pPPC) to the probability that the value of a given statistic computed from data arising under an analysis model is as or more extreme than the value computed from the real data themselves. If this probability is too small, the analysis model is regarded as invalid for the given statistic. Properties of the PPC for pharmacokinetic (PK) and pharmacodynamic (PD) model evaluation are examined herein for a particularly simple simulation setting: extensive sampling of a single individuals data arising from simple PK/PD and error models. To test the performance characteristics of the PPC, repeatedly, “real” data are simulated and for a variety of statistics, the PPC is applied to an analysis model, which may (null hypothesis) or may not (alternative hypothesis) be identical to the simulation model. Five models are used here: (PK1) mono-exponential with proportional error, (PK2) biexponential with proportional error, (PK2ε) biexponential with additive error, (PD1) Emax model with additive error under the logit transform, and (PD2) sigmoid Emax model with additive error under the logit transform. Six simulation/analysis settings are studied. The first three, (PK1/PK1), (PK2/PK2), and (PD1/PD1) evaluate whether the PPC has appropriate type-I error level, whereas the second three (PK2/PK1), (PK2ε/PK2), and (PD2/PD1) evaluate whether the PPC has adequate power. For a set of 100 data sets simulated/analyzed under each model pair according to a stipulated extensive sampling design, the pPPC is computed for a number of statistics in three different ways (each way uses a different approximation to the posterior distribution on the model parameters). We find that in general; (i) The PPC is conservative under the null in the sense that for many statistics, prob(pPPC≤α)<α for small α. With respect to such statistics, this means that useful models will rarely be regarded incorrectly as invalid. A high correlation of a statistic with the parameter estimates obtained from the same data used to compute the statistic (a measure of statistical “sufficiency”) tends to identify the most conservative statistics. (ii) Power is not very great, at least for the alternative models we tested, and it is especially poor with “statistics” that are in part a function of parameters as well as data. Although there is a tendency for nonsufficient statistics (as we have measured this) to have greater power, this is by no means an infallible diagnostic. (iii) No clear advantage for one or another method of approximating the posterior distribution on model parameters is found.

Modelling of individual pharmacokinetics for computer-aided drug dosage.

Abstract A conceptual scheme and associated statistical methodology is presented which is designed to provide the basis for a clinically useful computer program to suggest optimal dosage regimens for a number of drugs for individual patients. Routinely available clinical observations will be used to predict the parameters of a pharmacokinetic model, and subsequent blood level determinations used to refine these predictions, so that dosages can be designed to produce therapeutically desirable blood levels of agents. The system is intended to deal with most, if not all, of the influences on drug pharmacokinetics, to improve its performance by learning about the individual patient as well as the underlying population, and to modify its suggestions for an individual, should his clinical characteristics change. It may be used to discover new relationships between physiological states and drug pharmacokinetics. The system can exploit prior information in the form of theoretical and published data when its data base is small. An encouraging preliminary test of the system is reported.

Comparison of the Akaike Information Criterion, the Schwarz criterion and the F test as guides to model selection

In pharmacokinetic data analysis, it is frequently necessary to select the number of exponential terms in a polyexponential expression used to describe the concentration-time relationship. The performance characteristics of several selection criteria, the Akaike Information Criterion (AIC), and the Schwarz Criterion (SC), and theF test (α=0.05), were examined using Monte Carlo simulations. In particular, the ability of these criteria to select the correct model, to select a model allowing estimation of pharmacokinetic parameters with small bias and good precision, and to select a model allowing precise predictions of concentration was evaluated. To some extent interrelationships among these procedures is explainable. Results indicate that theF test tends to choose the simpler model more often than does either the AIC or SC, even when the more complex model is correct. Also, theF test is more sensitive to deficient sampling designs. Clearance estimates are generally very robust to the choice of the wrong model. Other pharmacokinetic parameters are more sensitive to model choice, particularly the apparent elimination rate constant. Prediction of concentrations is generally more precise when the correct model is chosen. The tendency for theF test (α=0.05) to choose the simpler model must be considered relative to the objectives of the study.

Simultaneous vs. sequential analysis for population PK/PD data I: best-case performance.

Dose [-concentration]-effect relationships can be obtained by fitting a predictive pharmacokinetic (PK)-pharmacodynamic (PD) model to both concentration and effect observations. Either a model can be fit simultaneously to all the data (“simultaneous” method), or first a model can be fit to the PK data and then a model can be fit to the PD data, conditioning in some way on the PK data or on the estimates of the PK parameters (“sequential” method). Using simulated data, we compare the performance of the simultaneous method with that of three sequential method variants with respect to computation time, estimation precision, and inference. Using NONMEM, under various study designs, observations of one type of PK and one type of PD response from different numbers of individuals were simulated according to a one-compartment PK model and direct Emax PD model, with parameters drawn from an appropriate population distribution. The same PK and PD models were fit to these observations using simultaneous and sequential methods. Performance measures include computation time, fraction of cases for which estimates are successfully obtained, precision of PD parameter estimates, precision of PD parameter standard error estimates, and type-I error rates of a likelihood ratio test. With the sequential method, computation time is less, and estimates are more likely to be obtained. Using the First Order Conditional Estimation (FOCE) method, a sequential approach that conditions on both population PK parameter estimates and PK data, estimates PD parameters and their standard errors about as well as the “gold standard” simultaneous method, and saves about 40% computation time. Type-I error rates of likelihood ratio test for both simultaneous and sequential approaches are close to the nominal rates.

Opportunities for integration of pharmacokinetics, pharmacodynamics, and toxicokinetics in rational drug development

Clinical Pharmacology and Therapeutics (1992) 51, 465–473; doi:10.1038/clpt.1992.47