Showing papers by "Mehdi Bouhaddou published in 2016"
TL;DR: Two metrics of drug responsiveness (slope and area under the curve) that are derived from the original CCLE and CGP data are examined and find reasonable and statistically significant consistency, reviving confidence that the CCLEand CGP drug dose–response data are of sufficient quality for meaningful analyses.
Abstract: The Cancer Cell Line Encyclopedia1 (CCLE) and Cancer Genome Project2 (CGP) are two independent large-scale efforts to characterize genomes, mRNA expression, and anti-cancer drug dose–responses across cell lines, providing a public resource relating cellular biochemical context to drug sensitivity. A recent study3 analysed correlations between reported dose–response metrics and found inconsistency between CCLE and CGP, thus questioning the validity of not only these, but also other current and future costly large-scale studies. Here, we examine two metrics of drug responsiveness (slope and area under the curve) that we derive from the original CCLE and CGP data, and find reasonable and statistically significant consistency. Our results revive confidence that the CCLE and CGP drug dose–response data are of sufficient quality for meaningful analyses. There is a Reply to this Comment by Safikhani, Z. et al. Nature 540, http://dx.doi.org/10.1038/nature20581 (2016). CCLE and CGP share 2,520 dose–responses across 285 cell lines and 15 drugs, but cells were treated with different dose ranges. To compare
58 citations
01 Jan 2016
TL;DR: Current methods used to build kinetic models of biochemical signaling networks are described and strengths and weaknesses of the various approaches are highlighted, as well as areas that need more research to drive the field towards influencing these important potential applications.
Abstract: Kinetic models of biochemical signaling networks are a mechanistic description of pharmacodynamics, and thus are potentially well-poised to fill gaps in the drug development pipeline by: (i) allowing putative drugs to be tested via simulations for efficacy and safety before expensive experiments and failed clinical trials; (ii) providing a framework for personalized and precision medicine that incorporates genomic information into a prediction of drug action in an individual; and (iii) interfacing with traditional pharmacokinetic models to yield computable yet mechanistic simulations that can inform drug dosing and frequency. However, biochemical signaling networks are currently incompletely understood on a basic level and are extremely complex compared to traditional applications of kinetic modeling. Herein, we describe current methods used to build such models and highlight strengths and weaknesses of the various approaches, as well as identify areas that need more research to drive the field towards influencing these important potential applications.
2 citations