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Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

Overview of attention for article published in Frontiers in Pharmacology, June 2015
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Title
Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes
Published in
Frontiers in Pharmacology, June 2015
DOI 10.3389/fphar.2015.00123
Pubmed ID
Authors

Hannu Raunio, Mira Kuusisto, Risto O. Juvonen, Olli T. Pentikäinen

Abstract

The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP-ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals.

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X Demographics

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 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Brazil 1 1%
Unknown 95 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 16%
Student > Master 16 16%
Student > Bachelor 14 14%
Researcher 12 12%
Professor 5 5%
Other 17 18%
Unknown 17 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 21%
Biochemistry, Genetics and Molecular Biology 16 16%
Pharmacology, Toxicology and Pharmaceutical Science 8 8%
Medicine and Dentistry 7 7%
Chemistry 7 7%
Other 18 19%
Unknown 21 22%
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 06 April 2016.
All research outputs
#16,048,009
of 25,374,917 outputs
Outputs from Frontiers in Pharmacology
#5,787
of 19,717 outputs
Outputs of similar age
#147,433
of 278,770 outputs
Outputs of similar age from Frontiers in Pharmacology
#17
of 64 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 19,717 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has gotten more attention than average, scoring higher than 68% 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 278,770 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 64 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.