Nonmem version 7

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NONMEM version 7.3 is a software package for nonlinear mixed-effects modeling, which is used for population pharmacokinetic and pharmacodynamic analysis. It is designed to estimate parameters and their uncertainty for complex pharmacological models from data obtained in clinical trials or preclinical studies.

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NONMEM (195 citations) NONMEM 7.4 (78 citations) NONMEM software version 7.3 (7 citations) NONMEM version (5 citations) NONMEM version VII, level 4.3 (3 citations) NONMEM® Version 7.2 (FOCE+I; (2 citations)

133 protocols using nonmem version 7

Venetoclax Population Pharmacokinetics Modeling

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The population PKs of venetoclax was characterized using a nonlinear mixed effects approach and was implemented in NONMEM version 7.3 (Icon Development Solutions, Ellicott City, MD, USA) using data combined from all studies listed in Table1. The NONMEM code for the PK and PD models is included as a [Link], [Link]. The starting model used was adapted from the population PK model used previously to characterize venetoclax PKs in patients with CLL, patients with non‐Hodgkin's lymphoma (NHL), and healthy subjects (Figure1a).24 Briefly, the structural model was a two‐compartment PK model with first‐order absorption and elimination, and interindividual variability modeled exponentially on apparent clearance, apparent central volume of distribution, and bio‐availability. A combined residual error model was used to describe the intraindividual variability observed with venetoclax. The statistical significance of the included covariates was tested by backward elimination and a covariate was retained if P < 0.01. The model was evaluated using goodness‐of‐fit plots.

Dave N., Gopalakrishnan S., Mensing S, & Salem A.H. (2019). Model‐Informed Dosing of Venetoclax in Healthy Subjects: An Exposure−Response Analysis. Clinical and Translational Science, 12(6), 625-632.

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Pharmacometric Data Analysis Workflow

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Data management, statistical calculations, and graphics were performed using R version 3.2.2 (R Core Team 2015, Vienna, Austria),29 data simulation and estimation in NONMEM version 7.3 (Icon Development Solutions, Ellicott City, MD),30 which was run through PsN version 4.5 (Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden).31, 32

Wellhagen G.J., Karlsson M.O, & Kjellsson M.C. (2018). Comparison of Power, Prognosis, and Extrapolation Properties of Four Population Pharmacodynamic Models of HbA1c for Type 2 Diabetes. CPT: Pharmacometrics & Systems Pharmacology, 7(5), 331-341.

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Population Pharmacokinetic Modeling Using NONMEM

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NONMEM version 7.3 (ICON Development Solutions, Ellicott City, MD) was used to develop the model using First Order Conditional Estimation with Interaction. All graphical analyses were performed using R version 3.0.2 (or higher) and Xpose version 4.5.3; bootstrapping and visual predictive checks (VPCs) were conducted using Perl‐speaks‐NONMEM program version 4.4.0.

Zhou J., Curd L., Lohmer L.L., Ossig J., Schippers F., Stoehr T, & Schmith V. (2020). Population Pharmacokinetics of Remimazolam in Procedural Sedation With Nonhomogeneously Mixed Arterial and Venous Concentrations. Clinical and Translational Science, 14(1), 326-334.

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Population PD Analysis of Cilostazol

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NONMEM® version 7.3 (Icon Development solutions, Ellicott City, MD, USA) was used for the population PD analysis of cilostazol and R (version 3.3.2) was used for data exploration and graphics. The first-order conditional estimation with interaction (FOCE-I) was used in all analyses.

Jung Y.S., Chae D, & Park K. (2018). Population pharmacodynamics of cilostazol in healthy Korean subjects. Translational and Clinical Pharmacology, 26(2), 93-98.

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Modeling of Somapacitan's Effect on IGF-I

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The modeling was performed to derive average IGF-I levels over the dosing interval following somapacitan treatment. The population PK/PD modeling was based on somapacitan and IGF-I concentrations and the time since last dose for blood samples, using a previously developed model for somapacitan from trials with full PK/PD profiles. This PK/PD model was a 1-compartment PK model with dual first- and zero-order absorption through a transit compartment, and with saturable elimination with an indirect response PD model for somapacitan effect on IGF-I (14 (link)). Average IGF-I levels were derived from the predicted area under the curve in a dosing interval for each individual treated with somapacitan.
IGF-I SDS profiles for somapacitan were derived by population PK/PD modeling from the REAL 3 trial. Daily GH IGF-I SDS profiles are shown for reference and were derived by population PK/PD modeling of phase 1 data (daily GH model not published).
Population PK/PD modeling was performed in NONMEM version 7.3 (ICON Development Solutions), PsN version 4.6.0 (20 (link)), and R version 3.2.3 (21 ).

Sävendahl L., Battelino T., Brod M., Højby Rasmussen M., Horikawa R., Juul R.V, & Saenger P. (2020). Once-Weekly Somapacitan vs Daily GH in Children With GH Deficiency: Results From a Randomized Phase 2 Trial. The Journal of Clinical Endocrinology and Metabolism, 105(4), e1847-e1861.

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Pharmacokinetic Analysis of Antiretroviral Drugs

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The phenotypes in this study were the post hoc PK parameters. The PK analysis was performed as previously described.8, 9 Briefly, drug concentrations were measured in the University of North Carolina Center for AIDS Research Clinical Pharmacology and Analytical Chemistry Laboratory. Total and intracellular drug concentrations were measured by validated liquid‐chromatography tandem mass spectrometry. Unbound EFV, ATV, and RTV concentrations were analyzed using rapid equilibrium dialysis followed by liquid‐chromatography tandem mass spectrometry. Population PK models were developed for each drug using nonlinear mixed effects modeling program NONMEM version 7.3 (ICON Development Solutions, Hanover, MD). For the current study, individual PK parameters of interest included clearance of the unbound drug for EFV, ATV, and RTV, clearance of the parent drug for TFV and FTC, and the rate constant of intracellular metabolite conversion and elimination for TFV‐DP and FTC‐TP. These estimates were obtained using the Bayesian post hoc estimation method based on the final models, including the covariate effects of creatinine clearance on TFV and FTC clearance and p16^INK4a expression on TFV‐DP and FTC‐TP disposition.

Chen J., Akhtari F.S., Wagner M.J., Suzuki O., Wiltshire T., Motsinger‐Reif A.A, & Dumond J.B. (2017). Pharmacogenetic Analysis of the Model‐Based Pharmacokinetics of Five Anti‐HIV Drugs: How Does This Influence the Effect of Aging?. Clinical and Translational Science, 11(2), 226-236.

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Estimating GFR and Drug Clearance

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The eGFR was calculated using the equations as listed in Supplementary Table 1. Body surface area (BSA) was calculated using the Du Bois & Du Bois formula: BSA = 0.007184 × Height^0.725 × (Body Weight)^0.425. The 5% – 95% range of age-appropriate normal GFR was calculated as mean ± 1.96 × SD based on published data.^{7 (link)} The individual drug CL values were determined with the observed PK data of renally-eliminated drugs using a standard population PK analysis approach.^{6 (link)} For the equations that calculate eGFR in the unit of mL/min/1.73m², the obtained eGFR values were divided by 1.73 and multiplied by individual BSA, to be consistent with the unit of CL which is mL/min. The eGFR/CL ratio was subsequently calculated.
Data analysis was performed using NONMEM® version 7.3 (ICON Development Solutions, Ellicott City, MD, USA) and R version 3.5.1 (https://cran.r-project.org/).

Zhang Y., Sherwin C.M., Gonzalez D., Zhang Q., Khurana M., Fisher J., Burckart G.J., Wang Y., Yao L.P., Ganley C.J, & Wang J. (2020). Creatinine-Based Renal Function Assessment in Pediatric Drug Development: An Analysis Using Clinical Data for Renally Eliminated Drugs. Clinical pharmacology and therapeutics, 109(1), 263-269.

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Pharmacokinetic Analysis of Rat Brain Data

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The PK analysis was performed using NONMEM version 7.3 (ICON Development Solutions, Hanover, MD, USA) (35 ). For the brain PK modeling of rat data, the extended least squares estimation method was applied. Other analyses were performed by using the first-order conditional estimation method with interaction (FOCE-I). The compartmental models were defined using the ADVAN6 differential equation solver in NONMEM (35 ). The plots and the statistical analysis were conducted using R (Version 3.2.5; R Foundation for Statistical Computing, Vienna, Austria) (36 ).

Yamamoto Y., Välitalo P.A., van den Berg D.J., Hartman R., van den Brink W., Wong Y.C., Huntjens D.R., Proost J.H., Vermeulen A., Krauwinkel W., Bakshi S., Aranzana-Climent V., Marchand S., Dahyot-Fizelier C., Couet W., Danhof M., van Hasselt J.G, & de Lange E.C. (2016). A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations. Pharmaceutical Research, 34(2), 333-351.

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Dabigatran Concentration-Clotting Time Modeling

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Mixed-effects modelling was performed using NONMEM version 7.3 (Icon Development Solutions, Ellicott City, Maryland, United States). Data preparation, graphical summaries and non-parametric regressions of dabigatran concentration–clotting time parameters against age were performed using the R statistical environment version 3.0.3 (R Foundation for Statistical Computing, Vienna, Austria).

Maas H., Gropper S., Huang F., Stangier J., Tartakovsky I., Brueckmann M., Halton J.M, & Mitchell L.G. (2018). Anticoagulant Effects of Dabigatran in Paediatric Patients Compared with Adults: Combined Data from Three Paediatric Clinical Trials. Thrombosis and Haemostasis, 118(9), 1625-1636.

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Stochastic Simulation for Venom PK

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Both aims of this study were addressed using a stochastic simulation estimation (SSE) study using MATLAB version 2018a (The MathWorks, Inc., Natick, MA, USA) for simulation and NONMEM version 7.3 (ICON Development Solutions, Ellicott City, MD, USA) for population PK modelling and estimation using first-order conditional estimation method with interaction.
In this SSE study a venom ‘dose’ was constructed from a set of characteristic proteins and then administered to each virtual subject. We use the term characteristic to denote that these are proteins that have similar molecular weights that are characteristic of typical toxins. Each simulated venom consisted of a discrete set of toxins, each with a different mixture of molecular weights. The study consisted of 100 virtual patients who provided an intensive sampling protocol of 12 blood samples for total venom concentration (the sum of all toxins). Note this study is designed to evaluate the best-case scenario, and we do not anticipate that such a study would necessarily be practical in the clinical/field setting. The resultant timed venom concentrations were then analysed using compartmental pharmacokinetic models in NONMEM.

Sanhajariya S., Isbister G.K, & Duffull S.B. (2020). The Influence of the Different Disposition Characteristics of Snake Toxins on the Pharmacokinetics of Snake Venom. Toxins, 12(3), 188.

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