Comparing Model Performance in Characterizing the PK/PD of the Anti‐Myostatin Antibody Domagrozumab
Modeling and simulation provides quantitative information on target coverage for dose selection. Optimal model selection often relies on fit criteria and is not necessarily mechanistically driven. One such case is discussed where healthy volunteer data of an anti‐myostatin monoclonal antibody domagr...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Clinical and Translational Science |
| Online Access: | https://doi.org/10.1111/cts.12693 |
| _version_ | 1818290414854078464 |
|---|---|
| author | Abhinav Tiwari Indranil Bhattacharya Phylinda L.S. Chan Lutz Harnisch |
| author_facet | Abhinav Tiwari Indranil Bhattacharya Phylinda L.S. Chan Lutz Harnisch |
| author_sort | Abhinav Tiwari |
| collection | DOAJ |
| description | Modeling and simulation provides quantitative information on target coverage for dose selection. Optimal model selection often relies on fit criteria and is not necessarily mechanistically driven. One such case is discussed where healthy volunteer data of an anti‐myostatin monoclonal antibody domagrozumab were used to develop different target‐mediated drug disposition models; a quasi‐steady state (QSS) rapid binding approximation model, a Michaelis−Menten (MM)‐binding kinetics (MM‐BK) model, and an MM‐indirect response (MM‐IDR) model. Whereas the MM‐BK model was identified as optimal in fitting the data, with all parameters estimated with high precision, the QSS model also converged but was not able to capture the nonlinear decline. Although the least mechanistic model, MM‐IDR, had the lowest objective function value, the MM‐BK model was further developed as it provided a reasonable fit and allowed simulations regarding growth differentiation factor‐8 target coverage for phase II dose selection with sufficient certainty to allow for testing of the underlying mechanistic assumptions. |
| first_indexed | 2024-12-13T02:27:48Z |
| format | Article |
| id | doaj.art-4f4b37dcf4334c428334cc71d9862696 |
| institution | Directory Open Access Journal |
| issn | 1752-8054 1752-8062 |
| language | English |
| last_indexed | 2024-12-13T02:27:48Z |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Science |
| spelling | doaj.art-4f4b37dcf4334c428334cc71d98626962022-12-22T00:02:35ZengWileyClinical and Translational Science1752-80541752-80622020-01-0113112513610.1111/cts.12693Comparing Model Performance in Characterizing the PK/PD of the Anti‐Myostatin Antibody DomagrozumabAbhinav Tiwari0Indranil Bhattacharya1Phylinda L.S. Chan2Lutz Harnisch3Department of Clinical Pharmacology Pfizer Cambridge Massachusetts USAQuantitative Clinical Pharmacology Takeda Pharmaceuticals International Co Cambridge Massachusetts USADepartment of Clinical Pharmacology/Pharmacometrics Pfizer SandwichKent UKDepartment of Clinical Pharmacology/Pharmacometrics Pfizer SandwichKent UKModeling and simulation provides quantitative information on target coverage for dose selection. Optimal model selection often relies on fit criteria and is not necessarily mechanistically driven. One such case is discussed where healthy volunteer data of an anti‐myostatin monoclonal antibody domagrozumab were used to develop different target‐mediated drug disposition models; a quasi‐steady state (QSS) rapid binding approximation model, a Michaelis−Menten (MM)‐binding kinetics (MM‐BK) model, and an MM‐indirect response (MM‐IDR) model. Whereas the MM‐BK model was identified as optimal in fitting the data, with all parameters estimated with high precision, the QSS model also converged but was not able to capture the nonlinear decline. Although the least mechanistic model, MM‐IDR, had the lowest objective function value, the MM‐BK model was further developed as it provided a reasonable fit and allowed simulations regarding growth differentiation factor‐8 target coverage for phase II dose selection with sufficient certainty to allow for testing of the underlying mechanistic assumptions.https://doi.org/10.1111/cts.12693 |
| spellingShingle | Abhinav Tiwari Indranil Bhattacharya Phylinda L.S. Chan Lutz Harnisch Comparing Model Performance in Characterizing the PK/PD of the Anti‐Myostatin Antibody Domagrozumab Clinical and Translational Science |
| title | Comparing Model Performance in Characterizing the PK/PD of the Anti‐Myostatin Antibody Domagrozumab |
| title_full | Comparing Model Performance in Characterizing the PK/PD of the Anti‐Myostatin Antibody Domagrozumab |
| title_fullStr | Comparing Model Performance in Characterizing the PK/PD of the Anti‐Myostatin Antibody Domagrozumab |
| title_full_unstemmed | Comparing Model Performance in Characterizing the PK/PD of the Anti‐Myostatin Antibody Domagrozumab |
| title_short | Comparing Model Performance in Characterizing the PK/PD of the Anti‐Myostatin Antibody Domagrozumab |
| title_sort | comparing model performance in characterizing the pk pd of the anti myostatin antibody domagrozumab |
| url | https://doi.org/10.1111/cts.12693 |
| work_keys_str_mv | AT abhinavtiwari comparingmodelperformanceincharacterizingthepkpdoftheantimyostatinantibodydomagrozumab AT indranilbhattacharya comparingmodelperformanceincharacterizingthepkpdoftheantimyostatinantibodydomagrozumab AT phylindalschan comparingmodelperformanceincharacterizingthepkpdoftheantimyostatinantibodydomagrozumab AT lutzharnisch comparingmodelperformanceincharacterizingthepkpdoftheantimyostatinantibodydomagrozumab |