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Wyeth Purchases Fujifilm AP-3000 Label-Free SPR System for Screening Chemical Fragments
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Wyeth Purchases Fujifilm AP-3000 Label-Free SPR System for Screening Chemical Fragments

Wyeth Purchases Fujifilm AP-3000 Label-Free SPR System for Screening Chemical Fragments
News

Wyeth Purchases Fujifilm AP-3000 Label-Free SPR System for Screening Chemical Fragments

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FUJIFILM Corporation announced that Wyeth has purchased an AP-3000 label-free surface plasmon resonance (SPR) detection system. The AP-3000 will support Wyeth’s medicinal chemistry department in screening small chemical fragments during drug discovery.

Using a proprietary, high-sensitivity SPR biosensor, the AP-3000 can detect small fragments with higher throughput than other SPR detection systems. The system is fully automated, making it easy to run all steps of the SPR process unattended, including protein immobilization, compound addition, sensor regeneration and binding data capture.

“We have seen growing interest from pharmaceutical companies in label-free technology, particularly in the area of fragment and small molecule screening,” said Don Janezic, Business Development Manager, SPR of Fujifilm Life Science. “What makes the AP-3000 particularly attractive to researchers is that it’s easy to use, requires very little user intervention and is capable of high throughput SPR screening of thousands of analytes per day.”

Capable of the simultaneous measurement of up to six samples, the AP-3000 enables label-free screening of up to 3,840 samples per day, a substantial increase in throughput over conventional SPR systems. This increased throughput is helpful for hit confirmation, lead optimization, focused library screening and verification of in silico derived structures, making the AP-3000 ideal for drug discovery.

The AP-3000 Version 2 application software, powered by Spotfire, makes analysis easy and includes a new applications wizard for setting up both the immobilization and screening parameters. Spotfire DecisionSite® application software is used to predict a new drug's chances for success in order to reduce time to market for the best candidates and minimize spending on the poor candidates.
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