Evidence map›Paper›PMID 26766116›Full record

ArticleNature protocols2016

Optimization of drug combinations using Feedback System Control.

Patrycja Nowak-Sliwinska, Andrea Weiss, Xianting Ding, Paul J Dyson, Hubert van den Bergh, Arjan W Griffioen, Chih-Ming Ho

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in Nature protocols, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
6.4field-weighted citation impact, top 3% 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

42 citing papers in PubMed, 98 citations in OpenAlex.

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  12. Forcing dividing cancer cells to die; low-dose drug combinations to prevent spindle pole clustering.Apoptosis : an international journal on programmed cell death · 2021
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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

7 authors at 3 institutions in 3 countries.

Patrycja Nowak-SliwinskaDepartment of Medical Oncology, Angiogenesis Laboratory, Vrije Universiteit (VU) University Medical Center, Amsterdam, the Netherlands.
Andrea WeissDepartment of Medical Oncology, Angiogenesis Laboratory, Vrije Universiteit (VU) University Medical Center, Amsterdam, the Netherlands.
Xianting DingMed-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Paul J DysonInstitute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.
Hubert van den BerghInstitute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.
Arjan W GriffioenDepartment of Medical Oncology, Angiogenesis Laboratory, Vrije Universiteit (VU) University Medical Center, Amsterdam, the Netherlands.
Chih-Ming HoDepartment of Mechanical and Aerospace Engineering, University of California, Los Angeles, California, USA.ORCID http://orcid.org/0000-0001-7369-5446
École Polytechnique Fédérale de Lausanne · CHShanghai Jiao Tong University · CNUniversity of California, Los Angeles · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We describe a protocol for the discovery of synergistic drug combinations for the treatment of disease. Synergistic drug combinations lead to the use of drugs at lower doses, which reduces side effects and can potentially lead to reduced drug resistance, while being clinically more effective than the individual drugs. To cope with the extremely large search space for these combinations, we developed an efficient combinatorial drug screening method called the Feedback System Control (FSC) technique. Starting with a broad selection of drugs, the method follows an iterative approach of experimental testing in a relevant bioassay and analysis of the results by FSC. First, the protocol uses a cell viability assay to generate broad dose-response curves to assess the efficacy of individual compounds. These curves are then used to guide the dosage input of each drug to be tested in combination. Data from applied drug combinations are input into the differential evolution (DE) algorithm, which predicts new combinations to be tested in vitro. This process identifies optimal drug-dose combinations, while saving orders of magnitude in experimental effort. The complete optimization process is estimated to take ∼4 weeks. FSC does not require insight into the disease mechanism, and it has therefore been applied to find combination therapies for many different pathologies, including cancer and infectious diseases, and it has also been used in organ transplantation.

Indexed as

Drug SynergismBiological AssayDrug CombinationsDrug Evaluation, PreclinicalDrug Therapy, CombinationHumansDrug Combinations

Identifiers

PMID26766116
OpenAlexW2233789132

What OpenQuestion holds

Textmetadata
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.