Evidence map›Paper›PMID 26392291›Full record

ArticleScientific reports2015

In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index.

M Kashif, C Andersson, S Hassan, H Karlsson, W Senkowski, M Fryknäs, P Nygren, R Larsson, M G Gustafsson

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
4.7field-weighted citation impact, top 5% 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

17 citing papers in PubMed, 29 citations in OpenAlex.

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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

9 authors at 1 institution in 1 country.

M KashifUppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden.
C AnderssonUppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden.
S HassanUppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden.
H KarlssonUppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden.
W SenkowskiUppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden.
M FryknäsUppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden.
P NygrenUppsala University, Dept of Immunology, Genetics and Pathology (Experimental and Clinical Oncology), Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden.
R LarssonUppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden.
M G GustafssonUppsala University, Dept of Medical Sciences, Cancer Pharmacology and Computational Medicine, Akademiska Sjukhuset, SE-751 85 Uppsala, Sweden.
Uppsala University Hospital · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In vitro-based search for promising anti-cancer drug combinations may provide important leads to improved cancer therapies. Currently there are no integrated computational-experimental methods specifically designed to search for combinations, maximizing a predefined therapeutic index (TI) defined in terms of appropriate model systems. Here, such a pipeline is presented allowing the search for optimal combinations among an arbitrary number of drugs while also taking experimental variability into account. The TI optimized is the cytotoxicity difference (in vitro) between a target model and an adverse side effect model. Focusing on colorectal carcinoma (CRC), the pipeline provided several combinations that are effective in six different CRC models with limited cytotoxicity in normal cell models. Herein we describe the identification of the combination (Trichostatin A, Afungin, 17-AAG) and present results from subsequent characterisations, including efficacy in primary cultures of tumour cells from CRC patients. We hypothesize that its effect derives from potentiation of the proteotoxic action of 17-AAG by Trichostatin A and Afungin. The discovered drug combinations against CRC are significant findings themselves and also indicate that the proposed strategy has great potential for suggesting drug combination treatments suitable for other cancer types as well as for other complex diseases.

Indexed as

Drug Resistance, NeoplasmAlgorithmsAntineoplastic AgentsAutomation, LaboratoryCell Line, TumorCluster AnalysisColorectal NeoplasmsDrug Screening Assays, AntitumorDrug Therapy, CombinationGene Expression Regulation, NeoplasticHumansIn Vitro TechniquesSpheroids, CellularTumor Cells, CulturedAntineoplastic Agents

Identifiers

PMID26392291
PMCPMC4585751
OpenAlexW1886613766

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.