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Integration of preclinical and clinical knowledge to predict intravenous PK in human: Bilastine case study

Overview of attention for article published in European Journal of Drug Metabolism and Pharmacokinetics, April 2013
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Title
Integration of preclinical and clinical knowledge to predict intravenous PK in human: Bilastine case study
Published in
European Journal of Drug Metabolism and Pharmacokinetics, April 2013
DOI 10.1007/s13318-013-0131-3
Pubmed ID
Authors

Valvanera Vozmediano, Ignacio Ortega, John C. Lukas, Ana Gonzalo, Monica Rodriguez, Maria Luisa Lucero

Abstract

Modern pharmacometrics can integrate and leverage all prior proprietary and public knowledge. Such methods can be used to scale across species or comparators, perform clinical trial simulation across alternative designs, confirm hypothesis and potentially reduce development burden, time and costs. Crucial yet typically lacking in integration is the pre-clinical stage. Prediction of PK in man, using in vitro and in vivo studies in different animal species, is increasingly well theorized but could still find wider application in drug development. The aim of the present work was to explore methods for bridging pharmacokinetic knowledge from animal species (i.v. and p.o.) and man (p.o.) into i.v. in man using the antihistamine drug bilastine as example. A model, predictive of i.v. PK in man, was developed on data from two pre-clinical species (rat and dog) and p.o. in man bilastine trials performed earlier. In the knowledge application stage, two different approaches were used to predict human plasma concentration after i.v. of bilastine: allometry (several scaling methods) and a semi-physiological method. Both approaches led to successful predictions of key i.v. PK parameters of bilastine in man. The predictive i.v. PK model was validated using later data from a clinical study of i.v. bilastine. Introduction of such knowledge in development permits proper leveraging of all emergent knowledge as well as quantification-based exploration of PK scenario, e.g. in special populations (pediatrics, renal insufficiency, comedication). In addition, the methods permit reduction or elimination and certainly optimization of learning trials, particularly those concerning alternative off-label administration routes.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Unknown 30 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 19%
Student > Bachelor 4 13%
Student > Ph. D. Student 4 13%
Student > Master 4 13%
Other 3 10%
Other 4 13%
Unknown 6 19%
Readers by discipline Count As %
Medicine and Dentistry 7 23%
Pharmacology, Toxicology and Pharmaceutical Science 5 16%
Biochemistry, Genetics and Molecular Biology 3 10%
Agricultural and Biological Sciences 2 6%
Chemistry 2 6%
Other 5 16%
Unknown 7 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 26 August 2013.
All research outputs
#14,751,991
of 22,708,120 outputs
Outputs from European Journal of Drug Metabolism and Pharmacokinetics
#293
of 422 outputs
Outputs of similar age
#115,670
of 194,058 outputs
Outputs of similar age from European Journal of Drug Metabolism and Pharmacokinetics
#3
of 4 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 422 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 28th percentile – i.e., 28% 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 194,058 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.