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Purpose <p>We compare our experience with three pharmacometric modeling workflows for simulating alternative dosing regimens of&#xa0;atezolizumab: (1) the gold-standard, NONMEM software used in combination with R, (2) the R-based package RxODE, and (3) the&#xa0;recently developed Julia-based software Pumas, discussing the advantages and limitations of each.</p> Methods <p>Our prior work demonstrated that an extended-interval dosing regimen (840 mg q6w) following two standard loading doses&#xa0;maintained efficacy while having a nonsignificant exposure-response relationship with adverse events. In the original analysis, the&#xa0;virtual population was generated in R, simulations performed using NONMEM, and data analysis and visualization then conducted in&#xa0;R. In the present study, we perform the full workflow within R using RxODE for simulation and also recreate this workflow using&#xa0;Pumas in Julia. Pharmacokinetic parameters and graphical output, as well as the processing speed for each method were&#xa0;compared.</p> Results <p>All three approaches generated comparable virtual populations, key exposure metrics of CMAX, CMIN, and Weekly AUC,&#xa0;and data visualizations of the simulated serum concentrations. However, there were differences in how quickly each software&#xa0;simulated the entire seven cycle dataset, with Pumas simulating 33,273 obs/second, NONMEM 4,782 obs/sec, and RxODE 251&#xa0;obs/sec. Due to this large difference, the dataset was broken into individual cycles, where NONMEM and RxODE performed&#xa0;comparably at 2041-3337 obs/sec, while Pumas simulated 48,122-69,168 obs/sec.</p> Conclusion <p>All three software produced comparable results. Ultimately, the choice should be based on the modeler’s specific needs&#xa0;and limitations.</p>

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A comparison of pharmacometric software programs for atezolizumab population pharmacokinetic simulation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月19日

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  • Yi Zeng,
  • Oluwatobi Arisa,
  • Natalia Corvalan,
  • Francis Bateman,
  • Keith Schmidt,
  • Cody Peer,
  • William D. Figg

摘要

Purpose

We compare our experience with three pharmacometric modeling workflows for simulating alternative dosing regimens of atezolizumab: (1) the gold-standard, NONMEM software used in combination with R, (2) the R-based package RxODE, and (3) the recently developed Julia-based software Pumas, discussing the advantages and limitations of each.

Methods

Our prior work demonstrated that an extended-interval dosing regimen (840 mg q6w) following two standard loading doses maintained efficacy while having a nonsignificant exposure-response relationship with adverse events. In the original analysis, the virtual population was generated in R, simulations performed using NONMEM, and data analysis and visualization then conducted in R. In the present study, we perform the full workflow within R using RxODE for simulation and also recreate this workflow using Pumas in Julia. Pharmacokinetic parameters and graphical output, as well as the processing speed for each method were compared.

Results

All three approaches generated comparable virtual populations, key exposure metrics of CMAX, CMIN, and Weekly AUC, and data visualizations of the simulated serum concentrations. However, there were differences in how quickly each software simulated the entire seven cycle dataset, with Pumas simulating 33,273 obs/second, NONMEM 4,782 obs/sec, and RxODE 251 obs/sec. Due to this large difference, the dataset was broken into individual cycles, where NONMEM and RxODE performed comparably at 2041-3337 obs/sec, while Pumas simulated 48,122-69,168 obs/sec.

Conclusion

All three software produced comparable results. Ultimately, the choice should be based on the modeler’s specific needs and limitations.