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Record W2268487266

Classification of anticancer drugs based on tissue penetration using a novel in vitro screening assay

2007· article· en· W2268487266 on OpenAlexaboutno aff
Alastair H. Kyle, Andrew I. Minchinton

Bibliographic record

VenueMolecular Cancer Therapeutics · 2007
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
Fundersnot available
KeywordsSpheroidPenetration (warfare)DrugTissue cultureIn vivoCancer researchIn vitroPharmacologyBiologyBiochemistryGenetics
DOInot available

Abstract

fetched live from OpenAlex

A213 Background: The failure of many anticancer drugs to control the growth of solid cancers may stem in part from inadequate delivery to tumor regions distant from vasculature. However, gaining an understanding of diffusion limitations of existing drugs and developing new drugs with improved tumor penetration is often limited by a lack of techniques with which to evaluate drugs. In this study we employ multilayered cell culture (MCC) in combination with a biological endpoint to assess the tissue penetration of a panel of 18 commonly used anti-cancer drugs. MCC is a planar analogue of spheroidal cell culture, in which tumor cells are instead grown into discs of tissue. Due to their 3-D conformation they model many characteristics of the tumor extravascular compartment. A unique property of MCC is that it possesses two populations of rapidly proliferating cells, one on each side of the culture, that are separated by a known thickness of tissue. In tumors and spheroids proliferation status falls off with distance from the vasculature (tumors) or tissue edge (spheroids), which in turn modifies cellular response to drugs. Hence, visualizing the distribution of a drug’s effect within these tissues cannot be used to directly determine actual drug distribution.
 Method: In this study, we exploit the symmetrical growth that occurs within MCCs by exposing them to drugs from one side and then comparing drug effect on the exposed side versus the far side of the cultures. This approach circumvents issues that normally arise from the biochemical gradients that occur with distance into tissue (e.g. changing intrinsic sensitivity of cells to drugs with depth into tissue) and in effect uses the cells themselves as the drug detection endpoint. Using this technique we examined the tissue penetration of representative anti-cancer drugs from a selection of classes, including anthracyclines, microtubule agents, anti-metabolites, platinum based agents and others. The distribution of drug activity within HCT-116 MCCs was assessed 1 to 3 days after a 1-h drug exposure via immunodetection of S-phase cells using bromodeoxyuridine. Using an automated computer analysis routine, the effect of the drugs in the first 30 µm of tissue located on either edge of the cultures relative to controls was assessed.
 Results: Penetration of the agents through HCT-116 MCCs was grouped into four classes: near uniform tissue distribution (cisplatin, 5-FU and vinorelbine), 1-5 fold decrease in drug exposure to cells on the far side versus the exposed side of the cultures (vincristine, vinblastine, paclitaxel, mitomycin C and etoposide), ~10-fold difference (doxorubicin, epirubicin, docetaxel and gemcitabine) and greater than 10-fold (daunorubicin and mitoxantrone). In the case of the anthracyclines and taxanes, the MCC-based assay was validated by comparing the predicted drug distributions with direct visualization of the drugs themselves.
 Conclusion: This model could be applied as a screening system for the discovery of biologically active drugs which exhibit desirable penetration properties.
 This research was supported by the National Cancer Institute of Canada with funds from the Canadian Cancer Society, the Michael Smith Foundation For Health Research and the Canadian Institutes for Health Research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.678
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.054
GPT teacher head0.351
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2007
Admission routes1
Has abstractyes

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