A forward selection algorithm to identify mutually exclusive alterations in cancer studies.

Mutual exclusivity analyses provide an effective tool to identify driver genes from passenger genes for cancer studies. Various algorithms have been developed for the detection of mutual exclusivity, but controlling false positive and improving accuracy remain challenging. We propose a forward selection algorithm for identification of mutually exclusive gene sets (FSME) in this paper. The method includes an initial search of seed pair of mutually exclusive (ME) genes and subsequently including more genes into the current ME set. Simulations demonstrated that, compared to recently published approaches (i.e., CoMEt, WExT, and MEGSA), FSME could provide higher precision or recall rate to identify ME gene sets, and had superior control of false positive rates. With application to TCGA real data sets for AML, BRCA, and GBM, we confirmed that FSME can be utilized to discover cancer driver genes.

Modeling of bounded outcome scores with data on the boundaries: application to disability assessment for dementia scores in Alzheimer's disease.

Mixed-effects beta regression (BR), boundary-inflated beta regression (ZOI), and coarsening model (CO) were investigated for analyzing bounded outcome scores with data at the boundaries in the context of Alzheimer's disease. Monte Carlo simulations were conducted to simulate disability assessment for dementia (DAD) scores using these three models, and each set of simulated data were analyzed by the original simulation model. One thousand trials were simulated, and each trial contained 250 subjects. For each subject, DAD scores were simulated at baseline, 13, 26, 39, 52, 65, and 78 weeks. The simulation-reestimation exercise showed that all the three models could reasonably recover their true parameter values. The bias of the parameter estimates of the ZOI model was generally less than 1%, while the bias of the CO model was mainly within 5%. The bias of the BR model was slightly higher, i.e., less than or in the order of 20%. In the application to real-world DAD data from clinical studies, examination of prediction error and visual predictive check (VPC) plots suggested that both BR and ZOI models had similar predictive performance and described the longitudinal progression of DAD slightly better than the CO model. In conclusion, the investigated three modeling approaches may be sensible choices for bounded outcome scores with data on the edges. Prediction error and VPC plots can be used to identify the model with best predictive performance.

Pharmacokinetic and pharmacodynamic modeling for acute and chronic pain drug assessment.

INTRODUCTION: Population pharmacokinetic and pharmacodynamic (PK/PD) modeling is a critical component of drug development. Quantitative PK/PD models are used in drug development to improve both the design and interpretation of clinical trials across therapeutic areas. AREAS COVERED: In this review, the authors provide an overview of PK/PD modeling approaches and their applications in the management of acute and chronic pain as well as drug assessment. The advantages and limitations of these modeling approaches with regard to handling different end points of pain assessment in monotherapy and combination therapy are highlighted. EXPERT OPINION: New modeling approaches suitable for analgesics used in treatment of acute and chronic pain have started to emerge during the past few years. The application of the clinical utility index is limited but highly encouraged in pain drug assessment as it may inform the optimal window for treatment of pain to attain the best benefit:risk ratio. Owing to the restricted range of pain scores, beta regression models and coarsening models may be more appropriate modeling approaches for the bounded outcome data, rather than regular nonlinear/linear models that assume normal or lognormal error distribution. Additionally, modeling of exposure-response in flexible chronic pain studies remains challenging, and further investigations are needed.

Mixed-effects beta regression for modeling continuous bounded outcome scores using NONMEM when data are not on the boundaries.

Beta regression models have been recommended for continuous bounded outcome scores that are often collected in clinical studies. Implementing beta regression in NONMEM presents difficulties since it does not provide gamma functions required by the beta distribution density function. The objective of the study was to implement mixed-effects beta regression models in NONMEM using Nemes' approximation to the gamma function and to evaluate the performance of the NONMEM implementation of mixed-effects beta regression in comparison to the commonly used SAS approach. Monte Carlo simulations were conducted to simulate continuous outcomes within an interval of (0, 70) based on a beta regression model in the context of Alzheimer's disease. Six samples per subject over a 3 years period were simulated at 0, 0.5, 1, 1.5, 2, and 3 years. One thousand trials were simulated and each trial had 250 subjects. The simulation-reestimation exercise indicated that the NONMEM implementation using Laplace and Nemes' approximations provided only slightly higher bias and relative RMSE (RRMSE) compared to the commonly used SAS approach with adaptive Gaussian quadrature and built-in gamma functions, i.e., the difference in bias and RRMSE for fixed-effect parameters, random effects on intercept, and the precision parameter were <1-3 %, while the difference in the random effects on the slope was <3-7 % under the studied simulation conditions. The mixed-effect beta regression model described the disease progression for the cognitive component of the Alzheimer's disease assessment scale from the Alzheimer's Disease Neuroimaging Initiative study. In conclusion, with Nemes' approximation of the gamma function, NONMEM provided comparable estimates to those from SAS for both fixed and random-effect parameters. In addition, the NONMEM run time for the mixed beta regression models appeared to be much shorter compared to SAS, i.e., 1-2 versus 20-40 s for the model and data used in the manuscript.

Shrinkage in nonlinear mixed-effects population models: quantification, influencing factors, and impact.

Shrinkage of empirical Bayes estimates (EBEs) of posterior individual parameters in mixed-effects models has been shown to obscure the apparent correlations among random effects and relationships between random effects and covariates. Empirical quantification equations have been widely used for population pharmacokinetic/pharmacodynamic models. The objectives of this manuscript were (1) to compare the empirical equations with theoretically derived equations, (2) to investigate and confirm the influencing factor on shrinkage, and (3) to evaluate the impact of shrinkage on estimation errors of EBEs using Monte Carlo simulations. A mathematical derivation was first provided for the shrinkage in nonlinear mixed effects model. Using a linear mixed model, the simulation results demonstrated that the shrinkage estimated from the empirical equations matched those based on the theoretically derived equations. Simulations with a two-compartment pharmacokinetic model verified that shrinkage has a reversed relationship with the relative ratio of interindividual variability to residual variability. Fewer numbers of observations per subject were associated with higher amount of shrinkage, consistent with findings from previous research. The influence of sampling times appeared to be larger when fewer PK samples were collected for each individual. As expected, sample size has very limited impact on shrinkage of the PK parameters of the two-compartment model. Assessment of estimation error suggested an average 1:1 relationship between shrinkage and median estimation error of EBEs.

Population pharmacokinetics of tapentadol immediate release (IR) in healthy subjects and patients with moderate or severe pain.

BACKGROUND: Tapentadol is a new, centrally active analgesic agent with two modes of action--mu opioid receptor agonism and norepinephrine reuptake inhibition--and the immediate-release (IR) formulation is approved in the US for the relief of moderate to severe acute pain. The aims of this analysis were to develop a population pharmacokinetic model to facilitate the understanding of the pharmacokinetics of tapentadol IR in healthy subjects and patients following single and multiple dosing, and to identify covariates that might explain variability in exposure following oral administration. METHODS: The analysis included pooled data from 11,385 serum pharmacokinetic samples from 1827 healthy subjects and patients with moderate to severe pain. Population pharmacokinetic modelling was conducted using nonlinear mixed-effects modelling (NONMEM) software to estimate population pharmacokinetic parameters and the influence of the subjects' demographic characteristics, clinical laboratory chemistry values and disease status on these parameters. Simulations were performed to assess the clinical relevance of the covariate effects on tapentadol exposure. RESULTS: A two-compartment model with zero-order release followed by first-order absorption and first-order elimination best described the pharmacokinetics of tapentadol IR following oral administration. The interindividual variability (coefficient of variation) in apparent oral clearance (CL/F) and the apparent central volume of distribution after oral administration were 30% and 29%, respectively. An additive error model was used to describe the residual variability in the log-transformed data, and the standard deviation values were 0.308 and 0.314 for intensively and sparsely sampled data, respectively. Covariate analysis showed that sex, age, bodyweight, race, body fat, hepatic function (using total bilirubin and total protein as surrogate markers), health status and creatinine clearance were statistically significant factors influencing the pharmacokinetics of tapentadol. Total bilirubin was a particularly important factor that influenced CL/F, which decreased by more than 60% in subjects with total bilirubin greater than 50 micromol/L. CONCLUSIONS: The population pharmacokinetic model for tapentadol IR identified the relationship between pharmacokinetic parameters and a wide range of covariates. The simulations of tapentadol exposure with identified, statistically significant covariates demonstrated that only hepatic function (as characterized by total bilirubin and total protein) may be considered a clinically relevant factor that warrants dose adjustment. None of the other covariates are of clinical relevance, nor do they necessitate dose adjustment.