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Lewis B Sheiner

Publications and source records attributed to Lewis B Sheiner.

15 recordsLinked to original sources
PubMed2005

Getting the Dose Right: report from the Tenth European Federation of Pharmaceutical Sciences (EUFEPS) conference on optimizing drug development.

This report highlights the main points emerging from a meeting sponsored on "Getting the Dose Right" in clinical development, jointly sponsored by the European Federation of Pharmaceutical Sciences and the European Center of Pharmaceutical Medicine, as part of the Workshop Series on Frontiers in Drug Development, in Basel, Switzerland on December 9-12, 2002.

Clinical Trials as Topic↗
PubMed2005

Analyzing multi-response data using forcing functions.

INTRODUCTION: Two analytic strategies can be taken to the analysis of multi-response data: a multivariate output model can be fit to all the response components simultaneously (SIM), or each response component can be fit separately to a univariate output model, conditioning in some way on the non-modeled components, the so-called forcing function approach (FFA). Focusing on a special case of multi-response model corresponding to a (pharmacokinetic) physiological f low model (PFM), the aims of this study are to (i) provide an algorithm for applying FFA to multi-response data from a PFM; (ii) examine the performance of FFA vs. SIM under optimal conditions for both, and in the presence of model misspecification; (iii) make recommendations regarding the use of FFA for multi-response data analysis. METHODS: The basic PFM we use (variants of the basic model are used for simulation) has four homogenous compartments among which drug distributes. All are sampled arterial blood (A), non-eliminating tissue (N), eliminating tissue (E), and venous blood (V), which is also the drug dosing site. Parameters are blood f low rates to E and N, volumes of distribution of A, E, N, and V, elimination rate constant from E, and observation error variances. Observations from a generic individual under various study designs and parameter values are simulated. Using data-analytic models (DAM) both the same as, and different than the data simulation model (DSM), SIM fits the PFM to all data simultaneously; FFA first fits each type of response (one per tissue) separately, approximating the tissue's input by linearly interpolating the observed concentrations from the donor tissue(s), estimates the identifiable parameter combinations for the response type, and then solves the simultaneous equations linking these across tissues, to obtain the primary model parameters of interest. This simulation and analysis steps are repeated to generate reliable performance statistics. Performances are compared with respect to parameter estimation error (when DAM and DSM are identical), and interpolated prediction error (when DAM and DSM are/are-not identical). The ability of SIM and FFA to identify the correct analytic model is also examined by comparing their failure rates in rejecting the wrong DAM. RESULTS: The parameter estimation errors with FFA are generally about two times greater than those with SIM when the DAM is identical to the DSM. The prediction errors of FFA are about ten times greater than those of SIM when the DAM is identical to the DSM, and are about three times greater when the two are different. However, SIM fails to identify the correct model twice as often as FFA. CONCLUSIONS: Despite its greater convenience for model building, and its clear advantages for model identification, FFA's final parameter estimates cannot be trusted when the multi-response system being modeled involves feedback. The size of the ratio of the two FFA residuals (obtained from the response-specific fits and from predictions made with the final FFA parameters) can, however, be used to indicate when FFA's final estimates may be trustworthy.

Algorithms↗
PubMed2004

Estimating treatment effect in the presence of non-compliance measured with error: precision and robustness of data analysis methods.

Non-compliance with the nominal prescribed dosage causes unintended variability in actual drug exposure during clinical trials. In the ideal case that compliance is not a confounder, and it is known--hence actual dosage is known--true dose-response can be validly estimated. Measuring compliance presents a challenge, however. A simulation study of the case that dosage history questionnaires (C(Q)--usually over-optimistic estimates of actual compliance) are available in all subjects enrolled in a clinical trial, but accurate compliance measurements (C--e.g. from electronic medication event monitors), are only available in a (random) fraction of subjects is reported. It reveals that a 'Maximum Penalized Marginal Likelihood' (MPML) method which uses all compliance data, effectively calibrating C(Q) to C, is superior to other methods which use only one compliance measure, or both, or neither (neither = ITT, intention to treat, which assumes actual dosage equals nominal dosage), but do not calibrate. MPML yields the most precise estimates of dose-response over widely varying clinical trial designs, extremes in quality and quantity of compliance information, and a range of drug effect sizes. It is most beneficial when compliance data are sparse and maintains good performance even when its key assumptions are somewhat violated.

Algorithms↗
PubMed2004

Population one-compartment pharmacokinetic analysis with missing dosage data.

OBJECTIVE: Our objective was to develop a population 1-compartment pharmacokinetic (PK) method of analysis to deal with suspect or missing prior dosage history. METHODS: Population PK data from a 1-compartment model with first-order elimination and absorption, described by PK parameters clearance, volume of distribution, and absorption rate constant, are simulated. A PK sample is drawn just before a test dose (Dt), followed by a (varying) number of additional samples over 1 interdose interval (tau). For 60% of the subjects, the true history of the scheduled dose (Ds) preceding Dt differs from that prescribed, whereas doses taken before Ds do not. Two settings are evaluated: considerable accumulation of drug in the body (typical drug half-life t1/2 approximately equal to tau) and very little such accumulation (t1/2 approximately equal to tau/5). Precision and bias of several PK analysis methods--Missing Dose Method (MDM), Missing Dose Mixture Method (MDMM) and Extrapolation-Subtraction Method (ESM), all of which essentially do not use prior dose history--are compared with those of the Prescribed Dose Method (PDM), which assumes nominal dosage, and an Ideal Method (IDM), which uses true (but unknown) pre-test dose history. RESULTS: At t1/2 approximately equal to tau, MDM and MDMM are the most precise methods. The accuracy of ESM and PDM is poor. At t1/2 approximately equal to tau/5, no significant differences, in terms of precision or bias, are observed between methods. Misspecification of the structural or statistical model seems not to influence these results. The results of analysis of a real (caffeine) data set are compatible with the findings from the simulations. CONCLUSION: When a test dose is given and a predose baseline observation is taken as part of an "intensive" PK study during outpatient therapy of a 1-compartment drug, an analysis that assumes that the nominal dose history is correct is not robust to past dosage history misspecification, whereas methods that do not do this are robust and reliable.

Absorption↗
PubMed2003

Population pharmacokinetics of enfuvirtide in pediatric patients with human immunodeficiency virus: searching for exposure-response relationships.

OBJECTIVE: Our objective was to describe the population pharmacokinetics and pharmacodynamics of enfuvirtide acting on viral ribonucleic acid in children with human immunodeficiency virus 1. METHODS: A 1-compartment population pharmacokinetic model with first-order absorption and elimination was fit to subcutaneous and intravenous dose pharmacokinetic data from a 2-part study, as follows: an intensive-sample pharmacokinetic design (observed dose) (subcutaneous and intravenous, n = 12) followed by a sparse-sample design (unobserved dose) (subcutaneous, n = 15). The parameters of this model are clearance (CL), volume of distribution (V), absorption rate (k(a)), bioavailability (F), and both interindividual and residual variability. Plasma ribonucleic acid concentrations over time were used to build a population viral pharmacodynamics model with the following parameters: viral clearance (CL(v)), initial viral concentration (A(0)), duration (tau) and rate (R) of preinfected cell-based viral input after enfuvirtide was begun, and interindividual and residual variability. RESULTS: The mean population CL and V of enfuvirtide for a child with a mean body weight of 21.3 kg were 1.42 L/h and 5.67 L, respectively. Patient weight affected CL and V. Volume appeared to differ between intensive and sparse sampling occasions, and this difference also affected the residual error variance. Time after the beginning of therapy did not significantly affect any pharmacokinetic parameter, supporting the absence of metabolic induction and inhibition. Although trends were present, no statistically significant relationship was seen between any pharmacokinetic-based individual enfuvirtide exposure measure and any virologic response measure. CONCLUSIONS: Regarding pharmacokinetics and pharmacodynamics, no statistically significant correlation between exposure measures and viral clearance was observed.

Antiretroviral Therapy, Highly Active↗
PubMed2003

Effect of simultaneous versus staggered dosing on pharmacokinetic interactions of protease inhibitors.

OBJECTIVE: The aim of this study was to determine whether pharmacokinetic interactions between the protease inhibitors saquinavir soft gel, nelfinavir, and ritonavir are affected by the timing of administration. STUDY DESIGN: We used an open-label, 6-period, incomplete Latin square crossover study in 18 human immunodeficiency virus-negative subjects. Each received single oral doses of 2 of the 3 protease inhibitors during each of 6 periods. Single doses were given either simultaneously or separated by 4 hours. The order of the periods was balanced, and periods were separated by 2 days. We measured protease inhibitor concentrations over a 24-hour period by HPLC and estimated pharmacokinetic parameters by noncompartmental methods. RESULTS: Median saquinavir area under the curve (AUC) increased by 62-fold when ritonavir was coadministered, by 50-fold when ritonavir was given 4 hours earlier, and by 16-fold when saquinavir preceded ritonavir by 4 hours. Saquinavir AUC increased by 7-fold when nelfinavir was coadministered. Nelfinavir AUC increased by 2.5-fold with coadministration of ritonavir and by 1.8- and 2.1-fold when ritonavir was administered before nelfinavir and after nelfinavir, respectively. Ritonavir AUCs were unaffected by coadministration of the other drugs. The effect of ritonavir on the kinetics of saquinavir persisted for at least 48 hours after a single dose of ritonavir, suggesting the possibility of metabolic intermediates that form inhibitory complexes. CONCLUSION: Except for saquinavir followed by ritonavir, there is little difference in protease inhibitor exposure for simultaneous or staggered doses. The persistent effect of ritonavir suggests the possibility that lower doses and longer dosing intervals might be effective when ritonavir is used to boost concentrations of other protease inhibitors.

Adult↗
PubMed2003

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 befit simultaneously to all the data ("simultaneous" method), or first a model can befit 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.

Computer Simulation↗
PubMed2003

Population pharmacokinetics and pharmacodynamics of efavirenz, nelfinavir, and indinavir: Adult AIDS Clinical Trial Group Study 398.

The present population pharmacokinetic (PK) and pharmacodynamic (PD) study modeled the effects of covariates including drug adherence and the coadministration of protease inhibitors (PIs) on the pharmacokinetics of efavirenz (EFV) and the relationship between EFV exposure and virological failure in patients who failed initial PI treatment in Adult AIDS Clinical Trial Group (AACTG) study 398. We also report on the population PKs of the PIs nelfinavir (NFV) and indinavir (IDV). AACTG study 398 patients received EFV, amprenavir, adefovir dipivoxil, and abacavir and were randomized to take, in addition, one of the following: NFV, IDV, saquinavir (SQV), or placebo. The PK databases consisted of 531 EFV concentrations (139 patients), 219 NFV concentrations (75 patients), and 66 IDV concentrations (11 patients). Time to virological failure was ascertained for all patients in the PK databases. PK data were fit with a population PK model that assumed exclusive hepatic elimination (the well-stirred model). Notable findings with respect to EFV PK and PD are as follows. (i) The hepatic clearance of EFV is unaltered by NFV, IDV, or SQV coadministration. (ii) The hepatic clearance of EFV appears to be 28% higher in white non-Hispanics than in African Americans and Hispanics (P = 0.03). (iii) Higher adherence scores (as measured with the Medication Event Monitoring System) are associated with marginally increased levels of exposure to EFV. (iv) In patients with no prior experience with nonnucleoside reverse transcriptase inhibitors (NNRTIs), a given percent increase in the oral clearance (CL/F) of EFV is associated with a greater percent increase in the hazard of virological failure (P < 0.0003). Among NNRTI-experienced patients, however, hazard is relatively uncorrelated with EFV CL/F.

Acquired Immunodeficiency Syndrome↗
PubMed2003

Modeling of transfer kinetics at the serum-cerebrospinal fluid barrier in rabbits with experimental meningitis: application to grepafloxacin.

The goals of the present study were to model the population kinetics of in vivo influx and efflux processes of grepafloxacin at the serum-cerebrospinal fluid (CSF) barrier and to propose a simulation-based approach to optimize the design of dose-finding trials in the meningitis rabbit model. Twenty-nine rabbits with pneumococcal meningitis receiving grepafloxacin at 15 mg/kg of body weight (intravenous administration at 0 h), 30 mg/kg (at 0 h), or 50 mg/kg twice (at 0 and 4 h) were studied. A three-compartment population pharmacokinetic model was fit to the data with the program NONMEM (Nonlinear Mixed Effects Modeling). Passive diffusion clearance (CL(diff)) and active efflux clearance (CL(active)) are transfer kinetic modeling parameters. Influx clearance is assumed to be equal to CL(diff), and efflux clearance is the sum of CL(diff), CL(active), and bulk flow clearance (CL(bulk)). The average influx clearance for the population was 0.0055 ml/min (interindividual variability, 17%). Passive diffusion clearance was greater in rabbits receiving grepafloxacin at 15 mg/kg than in those treated with higher doses (0.0088 versus 0.0034 ml/min). Assuming a CL(bulk) of 0.01 ml/min, CL(active) was estimated to be 0.017 ml/min (11%), and clearance by total efflux was estimated to be 0.032 ml/min. The population kinetic model allows not only to quantify in vivo efflux and influx mechanisms at the serum-CSF barrier but also to analyze the effects of different dose regimens on transfer kinetic parameters in the rabbit meningitis model. The modeling-based approach also provides a tool for the simulation and prediction of various outcomes in which researchers might be interested, which is of great potential in designing dose-finding trials.

Animals↗
PubMed2002

Effect of coadministration of nelfinavir, indinavir, and saquinavir on the pharmacokinetics of amprenavir.

OBJECTIVE: Pharmacokinetic interactions are expected when human immunodeficiency virus (HIV) protease inhibitors are coadministered because many are both substrates for and inhibitors of CYP3A4. The goal of this model-based pharmacokinetic analysis was to describe the differences observed in amprenavir pharmacokinetics among treatment arms in the Adult AIDS Clinical Trial Group (AACTG) study protocol 398 and to propose mechanisms to account for them. METHODS: One hundred seventy-six HIV-positive subjects receiving 1200 mg amprenavir twice daily as part of AACTG protocol 398 were included in the pharmacokinetic study. All patients also received background medications efavirenz, adefovir dipivoxil, and abacavir and, depending on the study arm, placebo or one of the following protease inhibitors: nelfinavir, indinavir, or saquinavir. A population pharmacokinetic model was fitted to a total of 565 amprenavir concentration measurements. The blood samples for concentration measurements were drawn at week 2 (12-hour pharmacokinetic study, approximately 7 samples per study; 46 patients) and at week 24 (6-hour pharmacokinetic study, approximately 5 samples per study; 10 patients). In addition, samples were collected at 1 or more follow-up visits (population pharmacokinetic study, 1 to 3 occasions per patient; 150 patients). RESULTS AND CONCLUSION: Amprenavir intrinsic clearance was significantly reduced relative to placebo by nelfinavir (-41%) and indinavir (-54%) but not by saquinavir. The absolute magnitude of amprenavir intrinsic clearance suggests that CYP3A4 inhibition by nelfinavir and indinavir is balanced by enzymatic induction in the presence of the background drug(s), most likely efavirenz. Amprenavir intrinsic clearance apparently increases by more than 30% between weeks 2 and 24, possibly because of the time course of CYP3A4 induction.

Adult↗
PubMed2002

Model-based analysis of the pharmacokinetic interactions between ritonavir, nelfinavir, and saquinavir after simultaneous and staggered oral administration.

Eighteen healthy human immunodeficiency virus-negative subjects participated in an open-label, six-period, incomplete Latin-square crossover pharmacokinetic study. Each subject received two of the three possible pair-wise combinations of single-dose oral ritonavir (R) (400 mg), nelfinavir (N) (750 mg), and saquinavir (S) (800 mg), each pair on three occasions (simultaneous or staggered administration), each occasion at least 2 days after the last. A model-based analysis reveals the following major drug interactions under the conditions of this study: 1). R given simultaneously with S decreases S hepatic intrinsic clearance almost 50-fold relative to that predicted for S given alone and increases its gut bioavailability 90% (but decreases its rate of absorption 40%) relative to when N is given simultaneously; 2). N given simultaneously with S decreases S hepatic intrinsic clearance 10-fold relative to that predicted for S given alone; and 3) R inhibits S hepatic intrinsic clearance even after R plasma levels have become undetectable (>48 h after dosing), implying that R, when used as a pharmacokinetic enhancer, can be dosed less frequently than might be predicted from the duration of detectable systemic concentrations.

Administration, Oral↗