Integrating quantitative systems pharmacology (QSP) into pharmacokinetics-pharmacodynamics (PKPD) has resulted in models that are highly complex and often not amenable to further exploration via estimation or design. Since QSP models are usually depicted using nonlinear differential equations it is not straightforward to apply some model reduction techniques, such as proper lumping. In this study, we explore the combined use of linearization and proper lumping as a general method to simplification of a nonlinear QSP model. We illustrate this with a bone biology model and the reduced model was then applied to describe bone mineral density (BMD) changes due to denosumab dosing. The methodologies used in this study can be applied to other multiscale models for developing a mechanism-based structural model for future analyses. This article is protected by copyright. All rights reserved.
Pharmacology, Toxicology and Pharmaceutical Science
7
28%
Mathematics
3
12%
Chemical Engineering
2
8%
Biochemistry, Genetics and Molecular Biology
2
8%
Agricultural and Biological Sciences
2
8%
Other
3
12%
Unknown
6
24%
Attention Score in Context
Attention Score in Context
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 August 2018.
All research outputs
#8,791,092
of 27,521,961 outputs
Outputs from CPT: Pharmacometrics & Systems Pharmacology
#246
of 864 outputs
Outputs of similar age
#129,636
of 346,161 outputs
Outputs of similar age from CPT: Pharmacometrics & Systems Pharmacology
#4
of 23 outputs
Altmetric has tracked 27,521,961 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 864 research outputs from this source. They receive a mean Attention Score of 4.5. This one has gotten more attention than average, scoring higher than 71% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 346,161 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.
