Delivering an Automated and Integrated Approach to Combination Screening Using Acoustic-Droplet Technology

被引:5
作者
Cross, Kevin [1 ]
Craggs, Richard [2 ]
Swift, Denise [1 ]
Sitaram, Anesh [1 ]
Daya, Sandeep [1 ]
Roberts, Mark [2 ]
Hawley, Shaun [1 ]
Owen, Paul [1 ]
Isherwood, Bev [1 ]
机构
[1] AstraZeneca R&D, Discovery Sci, Macclesfield SK10 4TG, Cheshire, England
[2] Tessella Plc, Abingdon, Oxon, England
来源
JALA | 2016年 / 21卷 / 01期
关键词
laboratory informatics; combinations; acoustic droplet technology; apothecary; assay-ready plate; high-content screening; sample management; DRUG-COMBINATIONS; THERAPEUTICS; RELEVANT; DESIGN; MODEL;
D O I
10.1177/2211068215579163
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Drug combination testing in the pharmaceutical industry has typically been driven by late-stage opportunistic strategies rather than by early testing to identify drug combinations for clinical investigation that may deliver improved efficacy. A rationale for combinations exists across a number of diseases in which pathway redundancy or resistance to therapeutics are evident. However, early assays are complicated by the absence of both assay formats representative of disease biology and robust infrastructure to screen drug combinations in a medium-throughput capacity. When applying drug combination testing studies, it may be difficult to translate a study design into the required well contents for assay plates because of the number of compounds and concentrations involved. Dispensing these plates increases in difficulty as the number of compounds and concentration points increase and compounds are subsequently rolled onto additional labware. We describe the development of a software tool, in conjunction with the use of acoustic droplet technology, as part of a compound management platform, which allows the design of an assay incorporating combinations of compounds. These enhancements to infrastructure facilitate the design and ordering of assay-ready compound combination plates and the processing of combinations data from high-content organotypic assays.
引用
收藏
页码:143 / 152
页数:10
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