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Platform model describing pharmacokinetic properties of vc-MMAE antibody–drug conjugates

Overview of attention for article published in Journal of Pharmacokinetics and Pharmacodynamics, September 2017
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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1 patent

Citations

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23 Mendeley
Title
Platform model describing pharmacokinetic properties of vc-MMAE antibody–drug conjugates
Published in
Journal of Pharmacokinetics and Pharmacodynamics, September 2017
DOI 10.1007/s10928-017-9544-y
Pubmed ID
Authors

Matts Kågedal, Leonid Gibiansky, Jian Xu, Xin Wang, Divya Samineni, Shang-Chiung Chen, Dan Lu, Priya Agarwal, Bei Wang, Ola Saad, Neelima Koppada, Bernard M. Fine, Jin Y. Jin, Sandhya Girish, Chunze Li

Abstract

Antibody-drug conjugates (ADCs) developed using the valine-citrulline-MMAE (vc-MMAE) platform, consist of a monoclonal antibody (mAb) covalently bound with a potent anti-mitotic toxin (MMAE) through a protease-labile vc linker. Recently, clinical data for a variety of vc-MMAE ADCs has become available. The goal of this analysis was to develop a platform model that simultaneously described antibody-conjugated MMAE (acMMAE) pharmacokinetic (PK) data from eight vc-MMAE ADCs, against different targets and tumor indications; and to assess differences and similarities of model parameters and model predictions, between different compounds. Clinical PK data of eight vc-MMAE ADCs from eight Phase I studies were pooled. A population PK platform model for the eight ADCs was developed, where the inter-compound variability (ICV) was described explicitly, using the third random effect level (ICV), and implemented using LEVEL option of NONMEM 7.3. The PK was described by a two-compartment model with time dependent clearance. Clearance and volume of distribution increased with body weight; volume was higher for males, and clearance mildly decreased with the nominal dose. Michaelis-Menten elimination had only minor effect on PK and was not included in the model. Time-dependence of clearance had no effect beyond the first dosing cycle. Clearance and central volume were similar among ADCs, with ICV of 15 and 5%, respectively. Thus, PK of acMMAE was largely comparable across different vc-MMAE ADCs. The model may be applied to predict PK-profiles of vc-MMAE ADCs under development, estimate individual exposure for the subsequent PK-pharmacodynamics (PD) analysis, and project optimal dose regimens and PK sampling times.

Mendeley readers

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 %
Researcher 4 17%
Student > Master 3 13%
Student > Ph. D. Student 3 13%
Lecturer 1 4%
Student > Doctoral Student 1 4%
Other 3 13%
Unknown 8 35%
Readers by discipline Count As %
Chemistry 6 26%
Medicine and Dentistry 3 13%
Pharmacology, Toxicology and Pharmaceutical Science 2 9%
Biochemistry, Genetics and Molecular Biology 2 9%
Decision Sciences 1 4%
Other 1 4%
Unknown 8 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 22 April 2021.
All research outputs
#8,537,346
of 25,382,440 outputs
Outputs from Journal of Pharmacokinetics and Pharmacodynamics
#130
of 477 outputs
Outputs of similar age
#120,168
of 309,735 outputs
Outputs of similar age from Journal of Pharmacokinetics and Pharmacodynamics
#1
of 8 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 477 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 41st percentile – i.e., 41% of its peers scored the same or lower than it.
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 309,735 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 53% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them