Efficient simulation of clinical target response surfaces
Abstract Simulation of combination therapies is challenging due to computational complexity. Either a simple model is used to simulate the response for many combinations of concentration to generate a response surface but parameter variability and uncertainty are neglected and the concentrations are...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-04-01
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| Series: | CPT: Pharmacometrics & Systems Pharmacology |
| Online Access: | https://doi.org/10.1002/psp4.12779 |
| _version_ | 1818060161552482304 |
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| author | Daniel Lill Anne Kümmel Venelin Mitov Daniel Kaschek Nathalie Gobeau Henning Schmidt Jens Timmer |
| author_facet | Daniel Lill Anne Kümmel Venelin Mitov Daniel Kaschek Nathalie Gobeau Henning Schmidt Jens Timmer |
| author_sort | Daniel Lill |
| collection | DOAJ |
| description | Abstract Simulation of combination therapies is challenging due to computational complexity. Either a simple model is used to simulate the response for many combinations of concentration to generate a response surface but parameter variability and uncertainty are neglected and the concentrations are constant—the link to the doses to be administered is difficult to make—or a population pharmacokinetic/pharmacodynamic model is used to predict the response to combination therapy in a clinical trial taking into account the time‐varying concentration profile, interindividual variability (IIV), and parameter uncertainty but simulations are limited to only a few selected doses. We devised new algorithms to efficiently search for the combination doses that achieve a predefined efficacy target while taking into account the IIV and parameter uncertainty. The result of this method is a response surface of confidence levels, indicating for all dose combinations the likelihood of reaching the specified efficacy target. We highlight the importance to simulate across a population rather than focus on an individual. Finally, we provide examples of potential applications, such as informing experimental design. |
| first_indexed | 2024-12-10T13:28:01Z |
| format | Article |
| id | doaj.art-d297c53fa9a14a1691c16fe7a86e1619 |
| institution | Directory Open Access Journal |
| issn | 2163-8306 |
| language | English |
| last_indexed | 2024-12-10T13:28:01Z |
| publishDate | 2022-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | CPT: Pharmacometrics & Systems Pharmacology |
| spelling | doaj.art-d297c53fa9a14a1691c16fe7a86e16192022-12-22T01:47:05ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062022-04-0111451252310.1002/psp4.12779Efficient simulation of clinical target response surfacesDaniel Lill0Anne Kümmel1Venelin Mitov2Daniel Kaschek3Nathalie Gobeau4Henning Schmidt5Jens Timmer6IntiQuan GmbH Basel SwitzerlandIntiQuan GmbH Basel SwitzerlandIntiQuan GmbH Basel SwitzerlandIntiQuan GmbH Basel SwitzerlandMedicines for Malaria Venture Geneva SwitzerlandIntiQuan GmbH Basel SwitzerlandInstitute of Physics University of Freiburg Freiburg GermanyAbstract Simulation of combination therapies is challenging due to computational complexity. Either a simple model is used to simulate the response for many combinations of concentration to generate a response surface but parameter variability and uncertainty are neglected and the concentrations are constant—the link to the doses to be administered is difficult to make—or a population pharmacokinetic/pharmacodynamic model is used to predict the response to combination therapy in a clinical trial taking into account the time‐varying concentration profile, interindividual variability (IIV), and parameter uncertainty but simulations are limited to only a few selected doses. We devised new algorithms to efficiently search for the combination doses that achieve a predefined efficacy target while taking into account the IIV and parameter uncertainty. The result of this method is a response surface of confidence levels, indicating for all dose combinations the likelihood of reaching the specified efficacy target. We highlight the importance to simulate across a population rather than focus on an individual. Finally, we provide examples of potential applications, such as informing experimental design.https://doi.org/10.1002/psp4.12779 |
| spellingShingle | Daniel Lill Anne Kümmel Venelin Mitov Daniel Kaschek Nathalie Gobeau Henning Schmidt Jens Timmer Efficient simulation of clinical target response surfaces CPT: Pharmacometrics & Systems Pharmacology |
| title | Efficient simulation of clinical target response surfaces |
| title_full | Efficient simulation of clinical target response surfaces |
| title_fullStr | Efficient simulation of clinical target response surfaces |
| title_full_unstemmed | Efficient simulation of clinical target response surfaces |
| title_short | Efficient simulation of clinical target response surfaces |
| title_sort | efficient simulation of clinical target response surfaces |
| url | https://doi.org/10.1002/psp4.12779 |
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