Mechanistic Pharmacokinetic/Pharmacodynamic Modeling in Support of a Patient-Convenient, Longer Dosing Interval for Carfilzomib, a Covalent Inhibitor of the Proteasome.

BACKGROUND: Carfilzomib is an irreversible second-generation proteasome inhibitor that has a short elimination half-life but much longer pharmacodynamic (PD) effect based on its irreversible mechanism of action, making it amenable to longer dosing intervals. A mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model was built using a bottom-up approach, based on the mechanism of action of carfilzomib and the biology of the proteasome, to provide further evidence of the comparability of once-weekly and twice-weekly dosing. METHODS: The model was qualified using clinical data from the phase III ENDEAVOR study, where the safety and efficacy of bortezomib (a reversible proteasome inhibitor) and carfilzomib were compared. Simulations were performed to compare the average proteasome inhibition across five cycles of treatment for the 20/70 mg/m2 once-weekly (70 QW) and 20/56 mg/m2 twice-weekly (56 BIW) regimens. RESULTS: Results indicated that while 70 QW had a higher maximum concentration (Cmax) and lower steady-state area under the concentration-time curve (AUC) than 56 BIW, the average proteasome inhibition after five cycles of treatment between the regimens was comparable. Presumably, the higher Cmax of carfilzomib from 70 QW compensates for the lower overall AUC compared with 56 BIW, and hence 70 QW is expected to have comparable proteasome inhibition, and therefore comparable efficacy, to 56 BIW. The comparable model-predicted proteasome inhibition between 70 QW and 56 BIW also translated to comparable clinical response, in terms of overall response rate and progression-free survival. CONCLUSION: This work provides a framework for which mechanistic PK/PD modeling can be used to guide optimization of dosing intervals for therapeutics with significantly longer PD effects than PK, and help further justify patient-convenient, longer dosing intervals.

Toward a diagnostic toolkit for linear models with Gaussian-process distributed random effects.

Gaussian processes (GPs) are widely used as distributions of random effects in linear mixed models, which are fit using the restricted likelihood or the closely related Bayesian analysis. This article addresses two problems. First, we propose tools for understanding how data determine estimates in these models, using a spectral basis approximation to the GP under which the restricted likelihood is formally identical to the likelihood for a gamma-errors GLM with identity link. Second, to examine the data's support for a covariate and to understand how adding that covariate moves variation in the outcome y out of the GP and error parts of the fit, we apply a linear-model diagnostic, the added variable plot (AVP), both to the original observations and to projections of the data onto the spectral basis functions. The spectral- and observation-domain AVPs estimate the same coefficient for a covariate but emphasize low- and high-frequency data features respectively and thus highlight the covariate's effect on the GP and error parts of the fit, respectively. The spectral approximation applies to data observed on a regular grid; for data observed at irregular locations, we propose smoothing the data to a grid before applying our methods. The methods are illustrated using the forest-biomass data of Finley et al. (2008).

Adaptive predictive principal components for modeling multivariate air pollution.

Air pollution monitoring locations are typically spatially misaligned with locations of participants in a cohort study, so to analyze pollution-health associations, exposures must be predicted at subject locations. For a pollution measure like PM2.5 (fine particulate matter) comprised of multiple chemical components, the predictive principal component analysis (PCA) algorithm derives a low-dimensional representation of component profiles for use in health analyses. Geographic covariates and spatial splines help determine the principal component loadings of the pollution data to give improved prediction accuracy of the principal component scores. While predictive PCA can accommodate pollution data of arbitrary dimension, it is currently limited to a small number of pre-selected geographic covariates. We propose an adaptive predictive PCA algorithm, which automatically identifies a combination of covariates that is most informative in choosing the principal component directions in the pollutant space. We show that adaptive predictive PCA improves the accuracy of multi-pollutant exposure predictions at subject locations.

Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models.

Piecewise growth mixture models are a flexible and useful class of methods for analyzing segmented trends in individual growth trajectory over time, where the individuals come from a mixture of two or more latent classes. These models allow each segment of the overall developmental process within each class to have a different functional form; examples include two linear phases of growth, or a quadratic phase followed by a linear phase. The changepoint (knot) is the time of transition from one developmental phase (segment) to another. Inferring the location of the changepoint(s) is often of practical interest, along with inference for other model parameters. A random changepoint allows for individual differences in the transition time within each class. The primary objectives of our study are as follows: (1) to develop a PGMM using a Bayesian inference approach that allows the estimation of multiple random changepoints within each class; (2) to develop a procedure to empirically detect the number of random changepoints within each class; and (3) to empirically investigate the bias and precision of the estimation of the model parameters, including the random changepoints, via a simulation study. We have developed the user-friendly package BayesianPGMM for R to facilitate the adoption of this methodology in practice, which is available at https://github.com/lockEF/BayesianPGMM . We describe an application to mouse-tracking data for a visual recognition task.

Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) Study.

BACKGROUND: DNA methylation is a widely studied epigenetic phenomenon; alterations in methylation patterns influence human phenotypes and risk of disease. As part of the Atherosclerosis Risk in Communities (ARIC) study, the Illumina Infinium HumanMethylation450 (HM450) BeadChip was used to measure DNA methylation in peripheral blood obtained from ~3000 African American study participants. Over 480,000 cytosine-guanine (CpG) dinucleotide sites were surveyed on the HM450 BeadChip. To evaluate the impact of technical variation, 265 technical replicates from 130 participants were included in the study. RESULTS: For each CpG site, we calculated the intraclass correlation coefficient (ICC) to compare variation of methylation levels within- and between-replicate pairs, ranging between 0 and 1. We modeled the distribution of ICC as a mixture of censored or truncated normal and normal distributions using an EM algorithm. The CpG sites were clustered into low- and high-reliability groups, according to the calculated posterior probabilities. We also demonstrated the performance of this clustering when applied to a study of association between methylation levels and smoking status of individuals. For the CpG sites showing genome-wide significant association with smoking status, most (~96%) were seen from sites in the high reliability cluster. CONCLUSIONS: We suggest that CpG sites with low ICC may be excluded from subsequent association analyses, or extra caution needs to be taken for associations at such sites.