| Title |
Deterministic identifiability of population pharmacokinetic and pharmacokinetic–pharmacodynamic models
|
|---|---|
| Published in |
Journal of Pharmacokinetics and Pharmacodynamics, June 2017
|
| DOI | 10.1007/s10928-017-9530-4 |
| Pubmed ID | |
| Authors |
Vijay K. Siripuram, Daniel F. B. Wright, Murray L. Barclay, Stephen B. Duffull |
| Abstract |
Identifiability is an important component of pharmacokinetic-pharmacodynamic (PKPD) model development. Structural identifiability is concerned with the uniqueness of the model parameters for a set of perfect input-output data and deterministic identifiability with the precision of parameter estimation given imperfect input-output data. We introduce two subcategories of deterministic identifiability, external and internal, and consider factors that distinguish between these forms. We define external deterministic identifiability as a function of externally controllable variables, i.e., the design, and internal deterministic identifiability as a function of the model and its parameter values. The concepts are explored using three common PK and PKPD models, and verified for their precision for the selected set of parameter values under optimal design. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| New Zealand | 2 | 33% |
| Netherlands | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 50% |
| Members of the public | 3 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 26% |
| Researcher | 4 | 17% |
| Student > Bachelor | 4 | 17% |
| Professor > Associate Professor | 2 | 9% |
| Student > Master | 2 | 9% |
| Other | 1 | 4% |
| Unknown | 4 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Pharmacology, Toxicology and Pharmaceutical Science | 10 | 43% |
| Biochemistry, Genetics and Molecular Biology | 2 | 9% |
| Medicine and Dentistry | 2 | 9% |
| Mathematics | 1 | 4% |
| Computer Science | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 6 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 12 October 2017.
All research outputs
#6,850,695
of 25,382,440 outputs
Outputs from Journal of Pharmacokinetics and Pharmacodynamics
#94
of 477 outputs
Outputs of similar age
#101,634
of 331,711 outputs
Outputs of similar age from Journal of Pharmacokinetics and Pharmacodynamics
#2
of 3 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 477 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 80% 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 331,711 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 69% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.