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Duncan I. Jodrell

dblp:185/8546 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2017
0000-0001-9360-1670ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › drug discovery
drug combination analysis
0.212016
Combenefit: an interactive platform for the analysis and visualization of drug combinations · Bioinform. 2016
Bioinformatics and computational biology › drug discovery › drug combination analysis
synergy quantification
0.212016
Combenefit: an interactive platform for the analysis and visualization of drug combinations · Bioinform. 2016

Methods — techniques the papers use, named apart from their topics

loewe synergy model · 0.2bliss synergy model · 0.2HSA synergy model · 0.2
YearPublicationVenuePosition
2017 Modelling of the cancer cell cycle as a tool for rational drug development: A systems pharmacology approach to cyclotherapy
abstract
The dynamic of cancer is intimately linked to a dysregulation of the cell cycle and signalling pathways. It has been argued that selectivity of treatments could exploit loss of checkpoint function in cancer cells, a concept termed "cyclotherapy". Quantitative approaches that describe these dysregulations can provide guidance in the design of novel or existing cancer therapies. We describe and illustrate this strategy via a mathematical model of the cell cycle that includes descriptions of the G1-S checkpoint and the spindle assembly checkpoint (SAC), the EGF signalling pathway and apoptosis. We incorporated sites of action of four drugs (palbociclib, gemcitabine, paclitaxel and actinomycin D) to illustrate potential applications of this approach. We show how drug effects on multiple cell populations can be simulated, facilitating simultaneous prediction of effects on normal and transformed cells. The consequences of aberrant signalling pathways or of altered expression of pro- or anti-apoptotic proteins can thus be compared. We suggest that this approach, particularly if used in conjunction with pharmacokinetic modelling, could be used to predict effects of specific oncogene expression patterns on drug response. The strategy could be used to search for synthetic lethality and optimise combination protocol designs.
Robert C. Jackson, Giovanni Y. Di Veroli, Siang-Boon Koh, Ian Goldlust, Frances M. Richards, Duncan I. Jodrell
PLoS Comput. Biol.6
2016 Combenefit: an interactive platform for the analysis and visualization of drug combinations
abstract
MOTIVATION: Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies. RESULTS: We present Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism. Data from combinations assays can be processed using classical Synergy models (Loewe, Bliss, HSA), as single experiments or in batch for High Throughput Screens. This user-friendly tool provides laboratory scientists with an easy and systematic way to analyze their data. The companion package provides bioinformaticians with critical implementations of routines enabling the processing of combination data. AVAILABILITY AND IMPLEMENTATION: Combenefit is provided as a Matlab package but also as standalone software for Windows (http://sourceforge.net/projects/combenefit/). CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Giovanni Y. Di Veroli, Chiara Fornari, Dennis Wang, Séverine Mollard, Jo L. Bramhall, Frances M. Richards, Duncan I. Jodrell
Bioinform.7