Evidence map›Paper›PMID 29423880›Full record

ArticleBulletin of mathematical biology2019

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

Aleksandra Karolak, Katarzyna A Rejniak

Open access · greenAbstract read
In one paragraph

Article in Bulletin of mathematical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.7field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 24 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Synthetic living materials in cancer biology.Nature reviews bioengineering · 2023
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Hybrid modeling frameworks of tumor development and treatment.Wiley interdisciplinary reviews. Systems biology and medicine · 2020
    Review
  14. What Can Mathematics Do for Drug Development?Bulletin of mathematical biology · 2019
    Article
  15. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

Aleksandra KarolakIntegrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.ORCID 0000-0001-8430-7138
Katarzyna A RejniakIntegrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. kasia@rejniak.net.ORCID 0000-0003-2093-2422
Moffitt Cancer Center · USUniversity of South Florida · US

Funding

TRANSLATIONAL RESEARCHP30CA076292 · NCI · UNIVERSITY OF SOUTH FLORIDA · PI Christine O'Connell · 1998 to 2026
$93.5M
"Research Supplement to Promote Diversity in Health-Related Research", as part of Moffitt PS-OC, "Cancer as a Complex adaptive System"U54CA193489 · NCI · H. LEE MOFFITT CANCER CTR & RES INST · PI ANDERSON, ALEXANDER ROBERTSON ALLAN, GATENBY, ROBERT A · 2015 to 2020
$12.5M
Causes & Consequences of Acid pH in TumorsR01CA077575 · NCI · UNIVERSITY OF ARIZONA · PI GILLIES, ROBERT J. · 1999 to 2018
$8.5M
Physical Dynamics of Cancer Response to Chemotherapy in 3D MicroenvironmentsU01CA202229 · NCI · VANDERBILT UNIVERSITY · PI MARKOV, DMITRY, MCCAWLEY, LISA JOY · 2015 to 2019
$3.0M
NCI NIH HHS P30 CA076292NCI NIH HHS R01 CA077575NCI NIH HHS U01 CA202229NCI NIH HHS U54 CA193489
6 · The paper itself

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.

Indexed as

Models, BiologicalAntineoplastic AgentsBiological Transport, ActiveComputer SimulationDrug Delivery SystemsHumansLigandsMathematical ConceptsNeoplasmsReceptors, Cell SurfaceSingle-Cell AnalysisSpatio-Temporal AnalysisTreatment OutcomeTumor MicroenvironmentAntineoplastic AgentsLigandsReceptors, Cell SurfaceDrug penetrationHypoxia-activated pro-drugsmicroPKPDSingle-cell pharmacologyTargeted therapeuticsTumor tissue architecture

Identifiers

PMID29423880
PMCPMC6082744
OpenAlexW2790788848

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.