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Computing optimal drug dosing with OptiDose: implementation in NONMEM

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Abstract

Determining a drug dosing recommendation with a PKPD model can be a laborious and complex task. Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal doses for any pharmacometrics/PKPD model for a given dosing scenario. In the present work, we reformulate the underlying optimal control problem and elaborate how to solve it with standard commands in the software NONMEM. To demonstrate the potential of the OptiDose implementation in NONMEM, four relevant but substantially different optimal dosing tasks are solved. In addition, the impact of different dosing scenarios as well as the choice of the therapeutic goal on the computed optimal doses are discussed.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, University of Konstanz, PO Box 195, 78457, Konstanz, Germany

    Freya Bachmann & Johannes Schropp

  2. Pediatric Pharmacology and Pharmacometrics, University of Basel Children’s Hospital, Spitalstrasse 33, 4056, Basel, Switzerland

    Gilbert Koch, Britta Steffens & Marc Pfister

  3. ICON Clinical Research LLC, Blue Bell, PA, USA

    Robert J. Bauer

  4. Pediatric Endocrinology and Diabetology, University of Basel Children’s Hospital, Spitalstrasse 33, 4056, Basel, Switzerland

    Gabor Szinnai

  5. Department of Clinical Research, University of Basel and University Hospital Basel, Basel, Switzerland

    Gabor Szinnai & Marc Pfister

Authors
  1. Freya Bachmann
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  2. Gilbert Koch
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  3. Robert J. Bauer
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  4. Britta Steffens
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  5. Gabor Szinnai
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  6. Marc Pfister
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  7. Johannes Schropp
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Corresponding author

Correspondence to Gilbert Koch.

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This study was supported by a Grant awarded to GK from the Swiss National Science Foundation; Grant No. 179510. This work has been supported by the DFG, project no. SCHR 692/3-1 (Germany).

Appendices

Appendix 1

Schematic overview on how to implement OptiDose in NONMEM

figure h

Appendix 2

Biomarker Indirect Response Model. The data file biomarker.dat for dosing scenario A, B, C with IV bolus administration

figure i

Appendix 3

Biomarker Indirect Response Model, dosing scenario A with quadratic or linear reference function. The control file biomarker.ctl

figure j

Appendix 4

Biomarker Indirect Response Model. The data file biomarkerinf.dat for dosing scenario A with IV infusion administration

figure k

Appendix 5

Biomarker Indirect Response Model, dosing scenario A with IV infusion. The control file biomarkerinf.ctl

figure l

Appendix 6

Biomarker Indirect Response Model, dosing scenario B and C. The control file biomarker2.ctl

figure m

Appendix 7

Bispecific Antibody Example. The data file antibody.dat

figure n

Appendix 8

Bispecific Antibody Example. The control file antibody.ctl

figure o

Appendix 9

Target AUC Example. The data file auc.dat

figure p

Appendix 10

Target AUC Example. The control file auc.ctl

figure q

Appendix 11

Rheumatic Arthritis DDE Example. The data file ra.dat

figure r

Appendix 12

Rheumatoid Arthritis DDE Example. The control file ra.ctl

figure s

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Bachmann, F., Koch, G., Bauer, R.J. et al. Computing optimal drug dosing with OptiDose: implementation in NONMEM. J Pharmacokinet Pharmacodyn 50, 173–188 (2023). https://doi.org/10.1007/s10928-022-09840-w

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  • DOI: https://doi.org/10.1007/s10928-022-09840-w

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