Perl-speaks-NONMEM (PsN)—a Perl module for NONMEM related programming

Unbound drug is the pharmacodynamically relevant concentration. This study aimed to determine if chronologic age or markers of biologic aging, such as the frailty phenotype and p16INK4a gene expression, altered unbound pharmacokinetics (PKs) of efavirenz (EFV) and atazanavir/ritonavir (ATV/RTV). Sixty human immunodeficiency virus (HIV)‐infected participants receiving EFV and 31 receiving ATV/RTV provided 1 to 11 samples to quantify total and unbound plasma concentrations. Population PK models with total and unbound concentrations simultaneously described are developed for each drug. The unbound fractions for EFV, ATV, and RTV are 0.65%, 5.67%, and 0.63%, respectively. Covariate analysis suggests RTV unbound PK is sensitive to body size; unbound fraction of RTV is 34% lower with body mass index (BMI) above 30 kg/m2. No alterations in drug clearance or unbound fraction with age, frailty, or p16INK4a expression were observed. Assessing functional and physiologic aging markers to inform potential PK changes is necessary to determine if drug/dosing changes are warranted in the aging population.

Section: Methodsmentioning

confidence: 99%

Population Pharmacokinetics Modeling of Unbound Efavirenz, Atazanavir, and Ritonavir in HIV‐Infected Subjects With Aging Biomarkers

Dumond

Chen

Cottrell

et al. 2016

“…The third scenario used example PK data and NONMEM code provided with Perl‐Speaks‐NONMEM (PsN version 4.6.0),5, 6 which is freely available. The data consisted of 59 neonates who received intravenous phenobarbital.…”

Section: Methodsmentioning

confidence: 99%

“…The hypothetical PK and PK‐VK data were generated with the mrgsolve package in R25; analyses and simulations were performed in NONMEM19 via PsN version 3.7.6 5, 6. Analyses and simulations for the warfarin PK‐PD scenario were performed in NONMEM via Wings for NONMEM26 using previously written control streams 20.…”

Section: Methodsmentioning

confidence: 99%

A Regression Approach to Visual Predictive Checks for Population Pharmacometric Models

Jamsen

Patel

Nieforth

et al. 2018

A visual predictive check (VPC) is a common diagnostic procedure for population pharmacometric models. Typically, VPCs are generated by specifying intervals, or “bins”, of an independent variable (e.g., time). However, bin specification is not always straightforward and the choice of bins may affect the appearance, and possibly conclusions, of VPCs. The objective of this work was to demonstrate how regression techniques can be used to derive VPCs and prediction‐corrected VPCs (pcVPCs) for population pharmacometric models. This alternative approach negates the need for empirical bin selection. The proposed method utilizes local and additive quantile regression. Implementation is straightforward and computationally acceptable. This work provides support for deriving VPCs and pcVPCs via regression techniques.

“…A statistical package R version 3.0.2/3.1.2 was used for performing the exploratory graphical analysis, summarizing the analysis, and assisting the model development and simulations 22. Perl‐speaks‐NONMEM version 3.5.4/4.2.0 and Xpose version 4.4.1/4.5.3 were used for model evaluations 19, 23, 24, 25…”

Section: Methodsmentioning

confidence: 99%

Dissociated Agonist of Glucocorticoid Receptor or Prednisone for Active Rheumatoid Arthritis: Effects on P1NP and Osteocalcin Pharmacodynamics

Shoji

Suzuki

Conrado

et al. 2017

Fosdagrocorat (PF‐04171327), a dissociated agonist of the glucocorticoid receptor, has potent anti‐inflammatory activity in patients with rheumatoid arthritis with reduced adverse effects on bone health. To identify fosdagrocorat doses with bone formation marker changes similar to prednisone 5 mg, we characterized treatment‐related changes in amino‐terminal propeptide of type I collagen (P1NP) and osteocalcin (OC) with fosdagrocorat (1, 5, 10, or 15 mg) and prednisone (5 or 10 mg) in a phase II randomized trial (N = 323). The time course of markers utilized a mixed‐effects longitudinal kinetic‐pharmacodynamic model. Median predicted changes from baseline at week 8 with fosdagrocorat 5, 10, and 15 mg were −18, −22, and −22% (P1NP), and −7, −13, and −17% (OC), respectively. Changes with prednisone 5 and 10 mg were −15% and −18% (P1NP) and −10% and −17% (OC). The probability of fosdagrocorat doses up to 15 mg being noninferior to prednisone 5 mg for P1NP and OC changes was >90%.