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Nonmem version

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NONMEM is a software package for nonlinear mixed-effects modeling. It is used for analyzing pharmacokinetic and pharmacodynamic data.

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Lab products found in correlation

Nonmem version 7, by ICON Development Solutions (1 mentions) Nonmem, by ICON Development Solutions (1 mentions)

5 protocols using nonmem version

1

Population Pharmacokinetic Modeling of Paromomycin

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To allow for analysis of sparse and heterogeneous data, a population approach was applied for pharmacokinetic modelling of paromomycin plasma concentrations. The population pharmacokinetic model was developed using a nonlinear mixed-effects modelling approach (NONMEM, version 7.3; ICON Development Solutions, Ellicott City, MD, USA) using the first-order conditional estimation method with interaction (FOCE-I) and ADVAN13. Tools used to evaluate the model and visualize the data and model output were R (version 3.6.2), RStudio (version 1.2.5033; Boston, MA, USA), PsN (version 4.7.0), and the graphical interface Pirana (version 2.9.9). Model development and evaluation was performed in three steps: (1) selection of a structural and stochastic model; (2) selection of the covariate model; (3) internal model evaluation.
Verrest L., Wasunna M., Kokwaro G., Aman R., Musa A.M., Khalil E.A., Mudawi M., Younis B.M., Hailu A., Hurissa Z., Hailu W., Tesfaye S., Makonnen E., Mekonnen Y., Huitema A.D., Beijnen J.H., Kshirsagar S.A., Chakravarty J., Rai M., Sundar S., Alves F, & Dorlo T.P. (2021). Geographical Variability in Paromomycin Pharmacokinetics Does Not Explain Efficacy Differences between Eastern African and Indian Visceral Leishmaniasis Patients. Clinical Pharmacokinetics, 60(11), 1463-1473.
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2

Pharmacokinetic Modeling with NONMEM and R

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Software packages NONMEM version 7.3 (ICON Development Solutions) was used for this analysis, where first‐order conditional estimation method was used as default.22 To support the analysis, Perl‐speaks‐NONMEM version 4.7.923 and R software (versions 3.2.2, 3.4.1, and 3.6.1)24 were used. The diagnostic assessment was performed using Xpose4.17, 18
Shoji S., Suzuki A., Nouri P., Cai C., Gaitonde P, & Marshall S. (2023). Prediction of relative change in free nerve growth factor following subcutaneous administration of tanezumab, a novel monoclonal antibody to nerve growth factor. CPT: Pharmacometrics & Systems Pharmacology, 12(9), 1358-1370.
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3

Population PK Modeling of GUMDROPS Clinical Trial

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Population PK model development was performed in NONMEM version 7.3.0 (ICON Development Solutions, Ellicott City, MD). Clinical trial data of GUMDROPS was collected in Castor EDC (version 2021.6.2, Castor Electronic Data Capture; Ciwit BV, Amsterdam, The Netherlands). Analysis of the PET images and kinetic modeling was performed using PMOD (version 4.105, PMOD Technologies LLC, Zürich, Switzerland). All data preparation, statistical analysis, and graphical presentation was performed in R version .3.6.3.14
van der Hoek S., Mulder D.J., Willemsen A.T., Visser T., Heeres A., Slart R.H., Elsinga P.H., Heerspink H.J, & Stevens J. (2022). Studying Telmisartan Plasma Exposure, Kidney Distribution, Receptor Occupancy, and Response in Patients With Type 2 Diabetes Using [ 11C]Telmisartan. Clinical Pharmacology and Therapeutics, 112(6), 1264-1270.
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4

Nonlinear Mixed-Effects Pharmacokinetic Modeling

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PopPK analysis was performed using nonlinear mixed-effects modeling software (NONMEM®, version 7.4; ICON Development Solutions, Ellicott City, MD, United States) compiled with gfortran 4.6.0 and interfaced with Perl-speaks-NONMEM (version 4.7.0; uupharmacometrics.github.io/PsN). The NONMEM output was analyzed using R software (version 3.5.1; www.r-project.org). First-order conditional estimation methodology with interaction between interpatient and residual variability was employed for model development.
Cai X.J., Li R.D., Li J.H., Tao Y.F., Zhang Q.B., Shen C.H., Zhang X.F., Wang Z.X, & Jiao Z. (2022). Prospective population pharmacokinetic study of tacrolimus in adult recipients early after liver transplantation: A comparison of Michaelis-Menten and theory-based pharmacokinetic models. Frontiers in Pharmacology, 13, 1031969.
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5

Pharmacokinetics and Safety of Darolutamide

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Patient demographics, comorbidities, and concomitant medications were summarized descriptively for all randomized patients, i.e., the full analysis set. Comedications were categorized according to potential for relevant DDIs (CYP inducers/inhibitors/substrates, UGT substrates, and drug transporter substrates) based on in vitro and phase I clinical studies of darolutamide [21 (link)] and established DDI profiles for the currently available androgen-receptor inhibitors, enzalutamide and apalutamide [11 –14 ].
Pharmacokinetic model development was conducted via nonlinear mixed-effects modelling using NONMEM (version 7.3, ICON Development Solutions plc, Manchester, UK) and R software [22 ]. Full details of the population pharmacokinetic analysis are given in the ESM—Methods.
Safety evaluations were conducted in all patients who were randomized and received at least one dose of study medication.
Shore N., Zurth C., Fricke R., Gieschen H., Graudenz K., Koskinen M., Ploeger B., Moss J., Prien O., Borghesi G., Petrenciuc O., Tammela T.L., Kuss I., Verholen F., Smith M.R, & Fizazi K. (2019). Evaluation of Clinically Relevant Drug–Drug Interactions and Population Pharmacokinetics of Darolutamide in Patients with Nonmetastatic Castration-Resistant Prostate Cancer: Results of Pre-Specified and Post Hoc Analyses of the Phase III ARAMIS Trial. Targeted Oncology, 14(5), 527-539.
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