The Data Generation Core will focus on measuring the effect of drug combinations and individual drugs on cell types associated with hepatic toxicity, cardiac toxicity and peripheral neuropathy adverse events observed at the organism level. These are all serious adverse events that alter drug usage and development. A novel feature of our experimental design is our selection of drug combinations. Most Americans take multiple drugs. Exhaustively testing all potential drug combinations is intractable due to the combinatorial explosion of possibilities and has not been done by any previous large scale effort. Our preliminary data based on analysis of serious drug adverse events as reported in FAERS has revealed 120 drug combinations involving 130 individual drugs whereby addition of a second drug significantly mitigates the serious toxicity induced by the first drug. We will use a panel of human cardiomyocytes, hepatocytes, and neuronal cell lines from primary/established lines, as well as those derived from human induced pluripotent stem cells. The total of number of cell lines is 18. The experimental data will be high-throughput, producing greater than 1 million data points per year. It will be on the genome-wide whole cell level and include changes in mRNA levels (microarray) and changes in protein levels (mass spec). We expect that a combination of mRNA and protein level data will synergize in signature generation. Our use of established yet cost effective methodologies is desirable since it enables immediate production and facilitates more widespread uptake and reproduction. These data together will be used to develop cellular signatures for the drugs or drug combinations in the Data Analysis and Signature Generation Core. The signatures will highlight key pathways that inform targeted, medium-throughput measurements of protein states by microwestern array. These targeted measurements will characterize dose and dynamic responses of biological states relevant to our drugs and drug combinations. Throughout we focus on careful annotation and quality control for dissemination of these data to the community, and also are open to community feedback in altering our preliminary plan.
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|Gallo, James M; Birtwistle, Marc R (2015) Network pharmacodynamic models for customized cancer therapy. Wiley Interdiscip Rev Syst Biol Med 7:243-51|
|Klinke 2nd, David J; Birtwistle, Marc R (2015) In silico model-based inference: an emerging approach for inverse problems in engineering better medicines. Curr Opin Chem Eng 10:14-24|
|Birtwistle, Marc R (2015) Analytical reduction of combinatorial complexity arising from multiple protein modification sites. J R Soc Interface 12:|