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Micropharmacology: An In Silico Approach for Assessing Drug Efficacy Within a Tumor Tissue

Overview of attention for article published in Bulletin of Mathematical Biology, February 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#40 of 1,115)
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

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Title
Micropharmacology: An In Silico Approach for Assessing Drug Efficacy Within a Tumor Tissue
Published in
Bulletin of Mathematical Biology, February 2018
DOI 10.1007/s11538-018-0402-x
Pubmed ID
Authors

Aleksandra Karolak, Katarzyna A. Rejniak

Abstract

Systemic chemotherapy is one of the main anticancer treatments used for most kinds of clinically diagnosed tumors. However, the efficacy of these drugs can be hampered by the physical attributes of the tumor tissue, such as tortuous vasculature, dense and fibrous extracellular matrix, irregular cellular architecture, tumor metabolic gradients, and non-uniform expression of the cell membrane receptors. This can impede the transport of therapeutic agents to tumor cells in sufficient quantities. In addition, tumor microenvironments undergo dynamic spatio-temporal changes during tumor progression and treatment, which can also obstruct drug efficacy. To examine ways to improve drug delivery on a cell-to-tissue scale (single-cell pharmacology), we developed the microscale pharmacokinetics/pharmacodynamics (microPKPD) modeling framework. Our model is modular and can be adjusted to include only the mathematical equations that are crucial for a biological problem under consideration. This modularity makes the model applicable to a broad range of pharmacological cases. As an illustration, we present two specific applications of the microPKPD methodology that help to identify optimal drug properties. The hypoxia-activated drugs example uses continuous drug concentrations, diffusive-advective transport through the tumor interstitium, and passive transmembrane drug uptake. The targeted therapy example represents drug molecules as discrete particles that move by diffusion and actively bind to cell receptors. The proposed modeling approach takes into account the explicit tumor tissue morphology, its metabolic landscape and/or specific receptor distribution. All these tumor attributes can be assessed from patients' diagnostic biopsies; thus, the proposed methodology can be developed into a tool suitable for personalized medicine, such as neoadjuvant chemotherapy.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 26%
Student > Bachelor 2 11%
Student > Doctoral Student 2 11%
Librarian 1 5%
Lecturer 1 5%
Other 1 5%
Unknown 7 37%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 4 21%
Mathematics 3 16%
Biochemistry, Genetics and Molecular Biology 2 11%
Agricultural and Biological Sciences 1 5%
Computer Science 1 5%
Other 1 5%
Unknown 7 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 November 2019.
All research outputs
#2,170,833
of 23,622,736 outputs
Outputs from Bulletin of Mathematical Biology
#40
of 1,115 outputs
Outputs of similar age
#52,794
of 442,080 outputs
Outputs of similar age from Bulletin of Mathematical Biology
#2
of 30 outputs
Altmetric has tracked 23,622,736 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,115 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 96% 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 442,080 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.