Design evaluation for a population pharmacokinetic study using clinical trial simulations: a case study

Safety of teriflunomide in Chinese adult patients with relapsing multiple sclerosis: A phase IV, 24-week multicenter study

BACKGROUND

Disease-modifying therapies have been approved for the treatment of relapsing multiple sclerosis (RMS). The present study aims to examine the safety of teriflunomide in Chinese patients with RMS.

METHODS

This non-randomized, multi-center, 24-week, prospective study enrolled RMS patients with variant (c.421C>A) or wild type ABCG2 who received once-daily oral teriflunomide 14 mg. The primary endpoint was the relationship between ABCG2 polymorphisms and teriflunomide exposure over 24 weeks. Safety was assessed over the 24-week treatment with teriflunomide.

RESULTS

Eighty-two patients were assigned to variant (n = 42) and wild type groups (n = 40), respectively. Geometric mean and geometric standard deviation (SD) of pre-dose concentration (variant, 54.9 [38.0] μg/mL; wild type, 49.1 [32.0] μg/mL) and area under plasma concentration-time curve over a dosing interval (AUCtau) (variant, 1731.3 [769.0] μg∙h/mL; wild type, 1564.5 [1053.0] μg∙h/mL) values at steady state were approximately similar between the two groups. Safety profile was similar and well tolerated across variant and wild type groups in terms of rates of treatment emergent adverse events (TEAE), treatment-related TEAE, grade ≥3 TEAE, and serious adverse events (AEs). No new specific safety concerns or deaths were reported in the study.

CONCLUSION

ABCG2 polymorphisms did not affect the steady-state exposure of teriflunomide, suggesting a similar efficacy and safety profile between variant and wild type RMS patients.

REGISTRATION

NCT04410965, https://clinicaltrials.gov.

A genetic-based population PK/PD modeling of methadone in Chinese opiate dependence patients

PURPOSE

The full potential of methadone maintenance treatment (MMT) is often limited by the large inter-individual variability in both pharmacokinetics (PK) and pharmacodynamics (PD), and by the risk of torsade de pointes, a severe adverse effect caused by QTc prolongation. The current study aims to quantitate the contribution of genetic polymorphisms and other variables in PK/PD variability, and their contribution to the QTc interval prolongation in Chinese MMT patients.

METHODS

Population PK models were developed to fit (R)- and (S)-methadone PK data. Hierarchical models were tested to characterize the PK profile, the concentration-QTc relationship, and concentration-urinalysis illicit drug testing relationship, with demographics and genetic variants being included as covariates. Simulation based on the developed PK/PD models was performed to assess the effect of methadone dose and genetic variants on QTc interval prolongation.

RESULTS

The PK data were best-fit by a one-compartment, first-order absorption model. Clearance of (R)- and (S)-methadone was both affected by the weighted activity score derived from genetic variants. A linear model was used to describe both the methadone concentration-urinalysis illicit drug testing relationship and the methadone concentration-QTc relationship. Concentration of (R)- and (S)-methadone exhibits a comparable effect on QTc prolongation. Simulation showed that the percentage of QTc higher than 450 ms was almost doubled in the lowest clearance group as compared the highest when methadone dose was greater than 120 mg.

CONCLUSIONS

The large variability in PK/PD profiles can be partially explained by the genetic variants in an extent different from other population, which confirmed the necessity to conduct such a study in the specific Chinese patients.

Detecting placebo and drug effects on Parkinson's disease symptoms by longitudinal item-score models

This study tested the hypothesis that analyzing longitudinal item scores of the Unified Parkinson's Disease Rating Scale could allow a smaller trial size and describe a drug's effect on symptom progression. Two historical studies of the dopaminergic drug ropinirole were analyzed: a cross-over formulation comparison trial in 161 patients with early-stage Parkinson's disease, and a 24-week, parallel-group, placebo-controlled efficacy trial in 393 patients with advanced-stage Parkinson's disease. We applied item response theory to estimate the patients' symptom severity and developed a longitudinal model using the symptom severity to describe the time course of the placebo response and the drug effect on the time course. Similarly, we developed a longitudinal model using the total score. We then compared sample size needs for drug effect detection using these two different models. Total score modeling estimated median changes from baseline at 24 weeks (90% confidence interval) of -3.7 (-5.4 to -2.0) and -9.3 (-11 to -7.3) points by placebo and ropinirole. Comparable changes were estimated (with slightly higher precision) by item-score modeling as -2.0 (-4.0 to -1.0) and -9.0 (-11 to -8.0) points. The treatment duration was insufficient to estimate the symptom progression rate; hence the drug effect on the progression could not be assessed. The trial sizes to detect a drug effect with 80% power on total score and on symptom severity were estimated (at the type I error level of 0.05) as 88 and 58, respectively. Longitudinal item response analysis could markedly reduce sample size; it also has the potential for assessing drug effects on disease progression in longer trials.

Remedial dosing recommendations for delayed or missed doses of lamotrigine in pediatric patients with epilepsy using Monte Carlo simulations

OBJECTIVE

This study investigated the effect of delayed or missed doses on the pharmacokinetics (PK) of lamotrigine (LTG) in children with epilepsy and established remedial dosing recommendations for nonadherent patients.

METHODS

The Monte Carlo simulation based on a published LTG population PK model was used to assess the effect of different scenarios of nonadherence and the subsequently administered remedial regimens. The following three remedial approaches were investigated for each delayed dose: A) A partial dose was administered immediately, and the regular dose was administered at the next scheduled time. B) The delayed dose was administered immediately, followed by a partial dose at the next scheduled time. C) The delayed and partial doses were coadministered immediately, the next scheduled dose was skipped, and the regular dosing was resumed at the subsequent scheduled time. The most appropriate remedial regimen was that with the shortest deviation time from the individual therapeutic window.

RESULTS

The effect of nonadherence on PK was dependent on the delay duration and daily dose, and the recommended remedial dose was related to the delay duration and concomitant antiepileptic drugs. Remedial dosing strategies A and B were almost equivalent, whereas C showed a larger PK deviation time. If one dose was missed, double doses were not recommended for the next scheduled time.

CONCLUSIONS

Simulations provide quantitative insight into the remedial regimens for nonadherent patients, and clinicians should select the optimal regimen based on the status of patients.

Population Pharmacokinetics of Vancomycin in Chinese ICU Neonates: Initial Dosage Recommendations

The main goal of our study was to characterize the population pharmacokinetics of vancomycin in critically ill Chinese neonates to develop a pharmacokinetic model and investigate factors that have significant influences on the pharmacokinetics of vancomycin in this population. The study population consisted of 80 neonates in the neonatal intensive care unit (ICU) from which 165 trough and peak concentrations of vancomycin were obtained. Nonlinear mixed effect modeling was used to develop a population pharmacokinetic model for vancomycin. The stability and predictive ability of the final model were evaluated based on diagnostic plots, normalized prediction distribution errors and the bootstrap method. Serum creatinine (Scr) and body weight were significant covariates on the clearance of vancomycin. The average clearance was 0.309 L/h for a neonate with Scr of 23.3 μmol/L and body weight of 2.9 kg. No obvious ethnic differences in the clearance of vancomycin were found relative to the earlier studies of Caucasian neonates. Moreover, the established model indicated that in patients with a greater renal clearance status, especially Scr < 15 μmol/L, current guideline recommendations would likely not achieve therapeutic area under the concentration-time curve over 24 h/minimum inhibitory concentration (AUC24h/MIC) ≥ 400. The exceptions to this are British National Formulary (2016-2017), Blue Book (2016) and Neofax (2017). Recommended dose regimens for neonates with different Scr levels and postmenstrual ages were estimated based on Monte Carlo simulations and the established model. These findings will be valuable for developing individualized dosage regimens in the neonatal ICU setting.

Monte Carlo simulations of the clinical benefits from therapeutic drug monitoring of sunitinib in patients with gastrointestinal stromal tumours

PURPOSE

Therapeutic drug monitoring (TDM) is being considered as a tool to individualise sunitinib treatment of gastrointestinal stromal tumours (GIST). Here, we used computer simulations to assess the expected impact of sunitinib TDM on the clinical outcome of patients with GIST.

METHODS

Monte Carlo simulations were performed in R, based on previously published pharmacokinetic-pharmacodynamic models. Clinical trials with dose-limiting toxicity and patient dropout were simulated to establish the study size required to obtain sufficient statistical power for comparison of TDM-guided and fixed dosing.

RESULTS

The simulations revealed that TDM might increase time to tumour progression by about 1-2 months (15-31 %) in eligible patients. However, the number of subjects required for a sufficient statistical power to quantify clinical benefit of TDM guided is likely to be prohibitively high (>1000).

CONCLUSION

Although data from randomised clinical trials on the clinical impact of sunitinib TDM are lacking, our findings support implementation of sunitinib TDM in clinical practice. For rare cancers with well-defined exposure-response relationships, modelling and simulation might allow the optimisation of dosing strategies when clinical trials cannot be performed due to low number of patients.

Methods and strategies for assessing uncontrolled drug-drug interactions in population pharmacokinetic analyses: results from the International Society of Pharmacometrics (ISOP) Working Group

The purpose of this work was to present a consolidated set of guidelines for the analysis of uncontrolled concomitant medications (ConMed) as a covariate and potential perpetrator in population pharmacokinetic (PopPK) analyses. This white paper is the result of an industry-academia-regulatory collaboration. It is the recommendation of the working group that greater focus be given to the analysis of uncontrolled ConMeds as part of a PopPK analysis of Phase 2/3 data to ensure that the resulting outcome in the PopPK analysis can be viewed as reliable. Other recommendations include: (1) collection of start and stop date and clock time, as well as dose and frequency, in Case Report Forms regarding ConMed administration schedule; (2) prespecification of goals and the methods of analysis, (3) consideration of alternate models, other than the binary covariate model, that might more fully characterize the interaction between perpetrator and victim drug, (4) analysts should consider whether the sample size, not the percent of subjects taking a ConMed, is sufficient to detect a ConMed effect if one is present and to consider the correlation with other covariates when the analysis is conducted, (5) grouping of ConMeds should be based on mechanism (e.g., PGP-inhibitor) and not drug class (e.g., beta-blocker), and (6) when reporting the results in a publication, all details related to the ConMed analysis should be presented allowing the reader to understand the methods and be able to appropriately interpret the results.

Evaluation of Approaches to Deal with Low-Frequency Nuisance Covariates in Population Pharmacokinetic Analyses

Clinical studies include occurrences of rare variables, like genotypes, which due to their frequency and strength render their effects difficult to estimate from a dataset. Variables that influence the estimated value of a model-based parameter are termed covariates. It is often difficult to determine if such an effect is significant, since type I error can be inflated when the covariate is rare. Their presence may have either an insubstantial effect on the parameters of interest, hence are ignorable, or conversely they may be influential and therefore non-ignorable. In the case that these covariate effects cannot be estimated due to power and are non-ignorable, then these are considered nuisance, in that they have to be considered but due to type 1 error are of limited interest. This study assesses methods of handling nuisance covariate effects. The specific objectives include (1) calibrating the frequency of a covariate that is associated with type 1 error inflation, (2) calibrating its strength that renders it non-ignorable and (3) evaluating methods for handling these non-ignorable covariates in a nonlinear mixed effects model setting. Type 1 error was determined for the Wald test. Methods considered for handling the nuisance covariate effects were case deletion, Box-Cox transformation and inclusion of a specific fixed effects parameter. Non-ignorable nuisance covariates were found to be effectively handled through addition of a fixed effect parameter.

Optimal design in pediatric pharmacokinetic and pharmacodynamic clinical studies

It is not trivial to conduct clinical trials with pediatric participants. Ethical, logistical, and financial considerations add to the complexity of pediatric studies. Optimal design theory allows investigators the opportunity to apply mathematical optimization algorithms to define how to structure their data collection to answer focused research questions. These techniques can be used to determine an optimal sample size, optimal sample times, and the number of samples required for pharmacokinetic and pharmacodynamic studies. The aim of this review is to demonstrate how to determine optimal sample size, optimal sample times, and the number of samples required from each patient by presenting specific examples using optimal design tools. Additionally, this review aims to discuss the relative usefulness of sparse vs rich data. This review is intended to educate the clinician, as well as the basic research scientist, whom plan on conducting a pharmacokinetic/pharmacodynamic clinical trial in pediatric patients.

Covariate selection in pharmacometric analyses: a review of methods

Covariate selection is an activity routinely performed during pharmacometric analysis. Many are familiar with the stepwise procedures, but perhaps not as many are familiar with some of the issues associated with such methods. Recently, attention has focused on selection procedures that do not suffer from these issues and maintain good predictive properties. In this review, we endeavour to put the main variable selection procedures into a framework that facilitates comparison. We highlight some issues that are unique to pharmacometric analyses and provide some thoughts and strategies for pharmacometricians to consider when planning future analyses.

Ethnic and genetic factors in methadone pharmacokinetics: a population pharmacokinetic study

BACKGROUND

Treatment of opiate use disorders with methadone is complicated by wide interindividual variability in pharmacokinetics. To identify potentially contributing covariates in methadone pharmacokinetics, we used population pharmacokinetic modeling to estimate clearance (CL/F) and volume of distribution (V/F) for each methadone enantiomer in an ethnically diverse methadone maintained population.

METHODS

Plasma levels of the opiate-active R-methadone and opiate-inactive S-methadone were measured in 206 methadone maintained subjects approximately two and twenty-three hours after a daily oral dose of rac-methadone. A linear one-compartment population pharmacokinetic model with first-order conditional estimation with interaction (FOCE-I) was used to evaluate methadone CL/F and V/F. The influence of covariates on parameter estimates was evaluated using stepwise covariate modeling. Covariates included ethnicity, gender, weight, BMI, age, methadone dose, and 21 single nucleotide polymorphisms in genes implicated in methadone pharmacokinetics.

RESULTS

In the final model, for each enantiomer, Hmong ethnicity reduced CL/F by approximately 30% and the rs2032582 (ABCB1 2677G>T/A) GG genotype was associated with a 20% reduction in CL/F. The presence of the rs3745274 minor allele (CYP2B6 515G>T) reduced CL/F by up to 20% for S-methadone only. A smaller effect of age was noted on CL/F for R-methadone.

CONCLUSION

This is the first report showing the influence of the rs2032582 and rs3745274 variants on methadone pharmacokinetics rather than simply dose requirements or plasma levels. Population pharmacokinetics is a valuable method for identifying the influences on methadone pharmacokinetic variability.

Model-Based Optimal Design and Execution of the First-Inpatient Trial of the Anti-IL-6, Olokizumab

The first-in-patient study for olokizumab (OKZ) employed model-based, optimal design and adaptive execution to define the concentration–C-reactive protein (CRP) suppression response. Modeling and exploratory statistics activities involved: reverse engineering of first-in-class (tocilizumab) pharmacokinetic/pharmacodynamic (PK/PD) models, adaptation of models to OKZ with a priori knowledge and preclinical data translation, application of multidimensional Desirability Index for optimal study design, sample size reestimation based on new information, optimization of second study part via Bayesian analysis of interim data, and interim and final analysis for PK/PD objective attainment. Design work defined a dose window (0.1–3 mg/kg) for CRP suppression exploration and suggested 72 patients in five single-dose levels would suffice. During execution, new information resulted in reestimating the study size to half. Halting the first part and conducting interim analysis for second part optimization followed. Second interim and final analyses confirmed attainment of study objective, illustrating efficiency and optimality of the study.

Utility of population pharmacokinetic modeling in the assessment of therapeutic protein-drug interactions

Assessment of pharmacokinetic (PK) based drug-drug interactions (DDI) is essential for ensuring patient safety and drug efficacy. With the substantial increase in therapeutic proteins (TP) entering the market and drug development, evaluation of TP-drug interaction (TPDI) has become increasingly important. Unlike for small molecule (e.g., chemical-based) drugs, conducting TPDI studies often presents logistical challenges, while the population PK (PPK) modeling may be a viable approach dealing with the issues. A working group was formed with members from the pharmaceutical industry and the FDA to assess the utility of PPK-based TPDI assessment including study designs, data analysis methods, and implementation strategy. This paper summarizes key issues for consideration as well as a proposed strategy with focuses on (1) PPK approach for exploratory assessment; (2) PPK approach for confirmatory assessment; (3) importance of data quality; (4) implementation strategy; and (5) potential regulatory implications. Advantages and limitations of the approach are also discussed.

Model-based drug development applied to oncology

Model-based drug development (MBDD) is an approach that is used to organize the vast and complex data streams that feed the drug development pipelines of small molecule and biotechnology sponsors. Such data streams are ultimately reviewed by the global regulatory community as evidence of a drug's potential to treat and/or harm patients. Some of this information is captured in the scientific literature and prescribing compendiums forming the basis of how new and existing agents will ultimately be administered and further evaluated in the broader patient community. As this data stream evolves, the details of data qualification, the assumptions and/or critical decisions based on these data are lost under conventional drug development paradigms. MBDD relies on the construction of quantitative relationships to connect data from discrete experiments conducted along the drug development pathway. These relationships are then used to ask questions relevant at critical development stages, hopefully, with the understanding that the various scenarios explored represent a path to optimal decision making. Oncology, as a therapeutic area, presents a unique set of challenges and perhaps a different development paradigm as opposed to other disease targets. The poor attrition of development compounds in the recent past attests to these difficulties and provides an incentive for a different approach. In addition, given the reliance on multimodal therapy, oncological disease targets are often treated with both new and older agents spanning several drug classes. As MBDD becomes more integrated into the pharmaceutical research community, a more rational explanation for decisions regarding the development of new oncology agents as well as the proposed treatment regimens that incorporate both new and existing agents can be expected. Hopefully, the end result is a more focussed clinical development programme, which ultimately provides a means to optimize individual patient care.

Considerations in Analyzing Single-Trough Concentrations Using Mixed-Effects Modeling

The purpose of this study was to assess the effect of trial design and data analysis choices on the bias and precision of pharmacokinetic (PK) parameter estimation. NONMEM was used to simulate and analyze plasma concentrations collected according to a dense (five samples) or sparse (single-trough samples) sampling scheme for a one-compartment open model with intravenous administration. The results indicated that the bias on estimates of CL with only single-trough data was 17% compared to less than 1% for only dense data. The estimates of CL were improved by fixing all other parameters and estimating only mean and variance of CL (-11% to 1.4%, depending on the estimation method). Adding dense data led to further improvements (-2.3% to 0.3%, depending on further improvements). In these cases, first-order conditional estimation (FOCE) methods resulted in better estimates of CL than first-order (FO) methods. These steps also improved the Bayesian estimates of CL. These studies support the following recommendations: (1) avoid collecting single-trough concentrations unless there is reasonable knowledge about the PK of the drug; (2) if collecting single-trough concentrations is inevitable, avoid estimating all parameters when modeling single-trough concentration data; (3) use prior information by modeling the single-trough concentration data along with dense data from other studies; and (4) use Bayes estimates if the PK model and its parameters are known with reasonable certainty.

Safety and population pharmacokinetic analysis of intravenous acetaminophen in neonates, infants, children, and adolescents with pain or Fever

OBJECTIVES

The administration of acetaminophen via the oral and rectal routes may be contraindicated in specific clinical settings. Intravenous administration provides an alternative route for fever reduction and analgesia. This phase 1 study of intravenous acetaminophen (Ofirmev, Cadence Pharmaceuticals, Inc., San Diego, CA) in inpatient pediatric patients with pain or fever requiring intravenous therapy was designed to assess the safety and pharmacokinetics of repeated doses over 48 hours.

METHODS

Neonates (full-term to 28 days) received either 12.5 mg/kg every 6 hours or 15 mg/kg every 8 hours. Infants (29 days to <2 years), children (2 to <12 years) and adolescents (≥12 years) received either 12.5 mg/kg every 4 hours or 15 mg/kg every 6 hours. Both noncompartmental and population nonlinear mixed-effects modeling approaches were used. Urinary metabolite data were analyzed, and safety and tolerability were assessed.

RESULTS

Pharmacokinetic parameters of acetaminophen were estimated using a two-compartment disposition model with weight allometrically expressed on clearances and central and peripheral volumes of distribution (Vds). Postnatal age, with a maturation function, was a significant covariate on clearance. Total systemic normalized clearance was 18.4 L/hr per 70 kg, with a plateau reached at approximately 2 years. Total central and peripheral Vds of acetaminophen were 16 and 59.5 L/70 kg, respectively. The drug was well tolerated based on the incidence of adverse events. The primary and minor pathways of elimination were acetaminophen glucuronidation, sulfation, and glutathione conjugate metabolites across all age groups.

CONCLUSIONS

Intravenous acetaminophen in infants, children, and adolescents was well tolerated and achieved plasma concentrations similar to those achieved with labeled 15 mg/kg body weight doses by oral or rectal administration.

From adults to children: simulation-based choice of an appropriate sparse-sampling schedule

BACKGROUND

According to the International Conference on Harmonisation guideline E11, pharmacokinetic (PK) bridging studies can be applied to support pediatric drug development. However, for PK studies in infants and children the sampling schedule needs to be optimized to minimize the number of blood samples per individual.

OBJECTIVE

The aim of this study was to describe how clinical trial simulations (CTS) based on adult data were used to select an appropriate sparse-sampling schedule for a future pediatric population PK (popPK) study.

METHODS

A popPK model for gadobutrol (Gadovist®) was developed using data from a phase I study in adults. This model was used for CTS to select the most appropriate sparse-sampling schedule that met predefined acceptance criteria. This sampling schedule was applied in a pediatric clinical phase I/III study. Non-linear mixed-effects modeling was used for PK modeling and simulations.

RESULTS

An appropriate sampling schedule requiring only three blood samples per patient was selected and successfully applied in a pediatric study with a gadobutrol standard dose of 0.1 mmol/kg bodyweight. A popPK analysis was performed to determine individual PK parameters in the pediatric study population.

CONCLUSIONS

A priori evaluation of selected sampling schedules by simulation from adult data provides a useful tool for efficient planning of pediatric studies.

Rapid sample size calculations for a defined likelihood ratio test-based power in mixed-effects models

Efficient power calculation methods have previously been suggested for Wald test-based inference in mixed-effects models but the only available alternative for Likelihood ratio test-based hypothesis testing has been to perform computer-intensive multiple simulations and re-estimations. The proposed Monte Carlo Mapped Power (MCMP) method is based on the use of the difference in individual objective function values (ΔiOFV) derived from a large dataset simulated from a full model and subsequently re-estimated with the full and reduced models. The ΔiOFV is sampled and summed (∑ΔiOFVs) for each study at each sample size of interest to study, and the percentage of ∑ΔiOFVs greater than the significance criterion is taken as the power. The power versus sample size relationship established via the MCMP method was compared to traditional assessment of model-based power for six different pharmacokinetic and pharmacodynamic models and designs. In each case, 1,000 simulated datasets were analysed with the full and reduced models. There was concordance in power between the traditional and MCMP methods such that for 90% power, the difference in required sample size was in most investigated cases less than 10%. The MCMP method was able to provide relevant power information for a representative pharmacometric model at less than 1% of the run-time of an SSE. The suggested MCMP method provides a fast and accurate prediction of the power and sample size relationship.

Design of pharmacokinetic studies for latent covariates

Latent covariates are covariates that are known to exist but are either observable but unavailable or unobservable at the time of the clinical study. Designs to account for latent covariates must incorporate both uncertainty in the prevalence of the covariate and the data-type of the covariate. The informativeness of the covariate will then depend on whether the covariate data is continuous, ordinal or nominal. In this work we consider designs for latent covariates that may either directly influence the parameter of interest, or indirectly via actions on an observable covariate which then influences the parameter of interest. We consider a motivating example based on the effect of a genetic polymorphism on the influence of a continuous covariate (age) on drug clearance (CL). The polymorphism could take the case of a haplotype with many variant alleles, or a copy number variation in genes with different phenotypic expressions which could be treated as continuous data, or as a bi- or tri-allelic single nucleotide polymorphism that could form either an ordinal or nominal covariate on drug CL. The aim of this study was to investigate designs for clinical studies for latent covariates that accommodate both unknown prevalence and unknown data-type. Initially, the informativeness of a covariate was explored using linear regression assuming the three data-types continuous, ordinal and nominal. The linear covariate model was then considered within a nonlinear mixed effects modelling framework. Two simulation scenarios were considered: (1) the influence of the latent covariate directly on the parameter of interest and (2) the influence of the latent covariate on an observable non-latent continuous covariate, which was assumed to follow a normal or stratified distribution, and the effect of this covariate on the parameter of interest. A power analysis for population PK modelling (1) where the latent covariate had direct influence on the parameter also showed similar behaviour to the linear regression solution. When the influence of the latent covariate was mediated via another observable non-latent continuous covariate, the power for the continuous model was highest but the power of the ordinal model was indistinguishable from that of the nominal model. Stratification of the observable non-latent continuous covariate did not appreciably change the power to identify the latent covariate from that when we assumed the observable covariate conformed to a normal distribution. It was found that parameter estimation is generally at least 1.5 to 7 fold more precise for continuous models than for categorical models.

Customized in silico population mimics actual population in docetaxel population pharmacokinetic analysis

Population pharmacokinetic (PK) analyses have been successfully incorporated into drug dosing optimization; however, these analyses necessitate relatively large patient cohorts that many clinical trials do not have the luxury of affording. To address this problem, we developed an approach that utilizes physiologically based pharmacokinetic (PBPK) modeling coupled with Monte Carlo simulation to generate a virtual population, complete with associated patient characteristics and PK data, for population PK analysis. For this work, we used a previously published PBPK model for docetaxel and found that the systemic clearance of this drug was significantly affected by blood volume, slowly perfused tissue volume, and two liver metabolic parameters--the maximum rate of liver metabolism and the Michaelis constant for liver metabolism. These findings, as well as the PK variability predictions, are consistent with those previously associated with docetaxel clearance in population PK analyses performed with actual patient populations, namely plasma protein levels, body size, and hepatic function. Thus, this in silico exercise demonstrates the utility of simulation modeling coupled to population PK analysis for the estimation of PK variability and the identification of patient characteristics that affect a drug's PK in the absence of data assembled from large clinical trials.

Population pharmacokinetics of sirolimus in de novo Chinese adult renal transplant patients

AIMS

This study was aimed at determining the population pharmacokinetics of sirolimus and identifying factors that explain pharmacokinetic variability in de novo Chinese adult renal transplant patients.

METHODS

Data were retrospectively extracted from a formal multicentre clinical trial, which was originally designed to evaluate the safety and efficacy of cyclosporin dose reduction and cyclosporin elimination in patients receiving sirolimus. All patients received 12-month treatment, i.e. induction therapy with cyclosporin, sirolimus and corticosteroids during the first 3 months followed by either cyclosporin dose reduction or cyclosporin discontinuation thereafter. Eight-hundred and four sirolimus trough blood concentrations (C(0)) from 112 patients were used to develop a population pharmacokinetic model using the NONMEM program. A one-compartment model with first-order absorption and elimination was selected as the base model. The influence of demographic characteristics, biochemical and haematological indices, cyclosporin daily dose, cyclosporin C(0) as well as other commonly used co-medications were explored.

RESULTS

The typical values with interindividual variability for apparent clearance (CL/F) and apparent volume of distribution (V/F) were 10.1 l h(-1) (23.8%) and 3670 l (56.7%), respectively. The residual variability was 29.9%. CL/F decreased significantly with silymarin or glycyrrhizin co-therapy in hepatically impaired patients, and with increasing total cholesterol levels or cyclosporin C(0). Moreover, CL/F increased nonlinearly with increasing sirolimus daily dose. The median parameter estimates from a nonparametric bootstrap procedure were comparable and within 5% of the estimates from NONMEM.

CONCLUSIONS

These results provide important information for clinicians to optimize sirolimus regimens in Chinese renal transplant patients.

Simulation of Correlated Continuous and Categorical Variables using a Single Multivariate Distribution

Clinical trial simulations make use of input/output models with covariate effects; the virtual patient population generated for the simulation should therefore display physiologically reasonable covariate distributions. Covariate distribution modeling is one method used to create sets of covariate values (vectors) that characterize individual virtual patients, which should be representative of real subjects participating in clinical trials. Covariates can be continuous (e.g., body weight, age) or categorical (e.g., sex, race). A modeling method commonly used for incorporating both continuous and categorical covariates, the Discrete method, requires the patient population to be divided into subgroups for each unique combination of categorical covariates, with separate multivariate functions for the continuous covariates in each subset. However, when there are multiple categorical covariates this approach can result in subgroups with very few representative patients, and thus, insufficient data to build a model that characterizes these patient groups. To resolve this limitation, an application of a statistical methodology (Continuous method) was conceived to enable sampling of complete covariate vectors, including both continuous and categorical covariates, from a single multivariate function. The Discrete and Continuous methods were compared using both simulated and real data with respect to their ability to generate virtual patient distributions that match a target population. The simulated data sets consisted of one categorical and two correlated continuous covariates. The proportion of patients in each subgroup, correlation between the continuous covariates, and ratio of the means of the continuous covariates in the subgroups were varied. During evaluation, both methods accurately generated the summary statistics and proper proportions of the target population. In general, the Continuous method performed as well as the Discrete method, except when the subgroups, defined by categorical value, had markedly different continuous covariate means, for which, in the authors' experience, there are few clinically relevant examples. The Continuous method allows analysis of the full population instead of multiple subgroups, reducing the number of analyses that must be performed, and thereby increasing efficiency. More importantly, analyzing a larger pool of data increases the precision of the covariance estimates of the covariates, thus improving the accuracy of the description of the covariate distribution in the simulated population.

Sample size calculations based on generalized estimating equations for population pharmacokinetic experiments

We present a method for calculating the sample size of a pharmacokinetic study analyzed using a mixed effects model within a hypothesis testing framework. A sample size calculation method for repeated measurement data analyzed using generalized estimating equations has been modified for nonlinear models. The Wald test is used for hypothesis testing of pharmacokinetic parameters. A marginal model for the population pharmacokinetic is obtained by linearizing the structural model around the subject specific random effects. The proposed method is general in that it allows unequal allocation of subjects to the groups and accounts for situations where different blood sampling schedules are required in different groups of patients. The proposed method has been assessed using Monte Carlo simulations under a range of scenarios. NONMEM was used for simulations and data analysis and the results showed good agreement.

Recommended reading in population pharmacokinetic pharmacodynamics

Developing the skills or expertise to create useful population pharmacokinetic-pharmacodynamic models can be a daunting task-the level of mathematical and statistical complexity is such that newcomers to the field are frequently overwhelmed. A good place to start in learning the field is to read articles in the literature. However, the number of articles dealing with population pharmacokinetic pharmacodynamics is exponentially increasing on a yearly basis, so choosing which articles to read can be difficult. The purpose of this review is to provide a recommended reading list for newcomers to the field. The list was chosen based on perceived impact of the article in the field, the quality of the article, or to highlight some important detail contained within the article. After reading the articles in the list, it is believed that the reader will have a broad overview of the field and have a sound foundation for more-detailed reading of the literature.

Statistical methods for HIV dynamic studies in AIDS clinical trials

Studies of HIV dynamics in AIDS research are very important for understanding pathogenesis of HIV infection and for assessing the potency of antiviral therapies. Since the viral dynamic results from clinical data were first published by Ho et al. and Wei et al., the study of HIV-1 dynamics in vivo has drawn a great attention from AIDS clinicians and researchers. Although the important findings from HIV dynamic studies have been published in many prestigious scientific journals, statistical methods for estimating viral dynamic parameters have not been paid enough attention by HIV dynamic investigators. The estimation methods in many viral dynamic studies are very crude and inefficient. In this paper, we review the statistical methods and mathematical models for HIV dynamic data analysis developed in recent years. We also address some practical issues and share our experiences in the design and analysis of viral dynamic studies. Some principles and guidelines for the design and analysis of viral dynamic studies are provided. The methodologies reviewed in this paper are also applicable to studies of other viruses such as hepatitis B virus or hepatitis C virus. We also pose some challenging statistical problems in this area in order to stimulate further study by the statistical research community.

Population pharmacokinetics III: design, analysis, and application of population pharmacokinetic Studies

OBJECTIVE

To present a framework within which population pharmacokinetic (PPK) studies should be designed and analyzed and discuss the application of developed PPK models.

METHODS

Information on PPK was retrieved from a MEDLINE search (1979-December 2003) of the literature and a bibliographic evaluation of review articles and books. This information is used in conjunction with experience to explain the design and analysis of PPK studies. Also, examples are included to demonstrate the usefulness of PPK.

SYNTHESIS

A great deal of thought must be given to the design and analysis of PPK studies (ie, development of PPK models). Models are of 2 primary types--descriptive and predictive--and the process applied to these models is necessarily different. An approach that ensures model applicability is presented.

CONCLUSIONS

PPK models have great utility, and the applications are many. They are very different from single-subject pharmacokinetic models and therefore require different approaches to model estimation.

Statistical Evaluation of Clinical Trial Design for a Population Pharmacokinetic Study —A Case Study—

A population pharmacokinetic substudy design of a new chemical entity was evaluated based on the bias in parameter estimates and the power of detecting a specific subpopulation showing different clearance using a clinical trial simulation approach. The effect of analysis algorithms on type I error was also assessed. The design factors included the number of patients (n=100-300) and the number of sampling points per patient (n=2-6). Simulation data were generated from a model developed based on a Phase I study. The power was evaluated for a percentile of test statistics obtained by the simulation study. The clearance (CL) related parameters were estimated with sufficient accuracy in all study designs and all analysis algorithms: the first order (FO), first order conditional estimation (FOCE) and first order conditional estimation with interaction (FOCE-INTER) methods. With the FO and FOCE methods, the type I error rate increased as the frequency of sampling from each patient became higher, but such increase was hardly observed with the FOCE-INTER method. The power tended to depend on the size of the subpopulation. A large difference was found in the power of detecting a specific subpopulation showing a clearance decrease of 30% or 50%. Therefore, the most dominant factors controlling power would be the size of the subpopulation and the decreasing ratio of CL in the subpopulation. These findings obtained by the clinical trial simulation approach are useful for optimization of study design and determination of the limits of evaluation.

Population pharmacokinetics of carbamazepine in Chinese epilepsy patients

AIM

To investigate the pharmacokinetic profile of carbamazepine (CBZ) in Chinese epilepsy patients.

MATERIALS AND METHODS

Serum samples through concentrations at steady state (n = 687) were collected prospectively from 585 patients during routine clinical care. Data were analyzed by the non-linear mixed-effect modeling (NONMEM) technique with a one-compartment model of first-order absorption and elimination.

RESULTS

The important determinants of clearance (CL) were total body weight (TBW); dose; patient age over 65 years (E); and comedication with phenytoin (PHT), phenobarbital (PB), or valproic acid (VPA) when VPA daily dose was greater than 18 mg/kg. The final pharmacokinetic model for relative CL and apparent distribution volume (V) were: Equation

CONCLUSION

A population pharmacokinetic model was proposed to estimate the individual CL for Chinese patients receiving CBZ in terms of patient's dose, TBW, and comedications to establish a priori dosage regimens.

Pharmacokinetic-pharmacodynamic guided trial design in oncology

The application of pharmacokinetic (PK) and pharmacodynamic (PD) modeling in drug development has emerged during the past decades and it is has been suggested that the investigation of PK-PD relationships during drug development may facilitate and optimize the design of subsequent clinical development. Especially in oncology, well designed PK-PD modeling could be extremely useful as anticancer agents usually have a very narrow therapeutic index. This paper describes the application of the current insights in the use of PK-PD modeling to the design of clinical trials in oncology. The application of PK-PD modeling in each separate stage of (pre)clinical drug development of anticancer agents is discussed. The implementation of this approach is illustrated with the clinical development of docetaxel.

Population kinetics, efficacy, and safety of dichloroacetate for lactic acidosis due to severe malaria in children

The authors conducted a randomized, double-blind, placebo-controlled trial of intravenous dichloroacetate (DCA) for the purpose of treating lactic acidosis in 124 West African children with severe Plasmodium falciparum malaria. Lactic acidosis independently predicts mortality in severe malaria, and DCA stimulates the oxidative removal of lactate in vivo. A single infusion of 50 mg/kg DCA was well tolerated. When administered at the same time as a dose of intravenous quinine, DCA significantly increased the initial rate and magnitude of fall in blood lactate levels and did not interfere with the plasma kinetics of quinine. The authors developed a novel population pharmacokinetic-pharmacodynamic indirect-response model for DCA that incorporated characteristics associated with disease reversal. The model describes the complex relationships among antimalarial treatment procedures, plasma DCA concentrations, and the drug's lactate-lowering effect. DCA significantly reduces the concentration of blood lactate, an independent predictor of mortality in malaria. Its prospective evaluation in affecting mortality in this disorder appears warranted.