Qiagen announced this week that it has taken a worldwide license to a novel siRNA selection algorithm designed by Swiss drug maker Novartis.
According to Eric Lader, associate director of business development at Qiagen, Novartis designed the algorithm using a collection of about 3,000 randomly designed synthetic siRNAs against 34 targets provided by Qiagen. The randomness of the siRNAs, which allowed them to be chosen in an unbiased manner, is what makes Novartis’ algorithm different from methods others use to select siRNAs, he added.
“There are people who are retrospectively looking at the sequences of active versus inactive siRNAs,” Lader told RNAi News, citing Amgen and Dharmacon as examples. The methodology used by these companies for selecting siRNAs, he said, involves looking for similarities between active siRNAs and differences they have with inactive siRNAs.
“Up until now, what people have typically done is look at existing siRNAs that have been designed using some guidelines, and then compared ones that were active versus ones that were inactive and tried to find differences between those two sets,” Lader said. Since these siRNAs were not selected randomly, he added, it isn’t possible to “survey all possible designs and ask whether there are some features that can be discovered by looking at random design sequences that make an siRNA more active.”
But what Novartis has done is take a large set of randomly designed siRNAs, analyze them for activity and potency, and then use the resultant information to develop rules for siRNA selection, Lader stated. “There was no bias in the set they looked at — that’s an essential difference in what they’ve done and what other people have done,” he said.
Patrick Weiss, vice president of gene silencing at Qiagen, characterized the Novartis algorithm as not relying on “rules” for selecting siRNAs, but using a “pattern recognition program. You feed it what works, and it kind of makes its own rules to find out why that actually works,” he told RNAi News.
He said that the algorithm picks up features of active siRNAs that are already well known, but it also picks up things that have heretofore been unnoticed. In terms of efficacy, Lader said that siRNAs designed using the algorithm have proven to be able to knock down genes that have been resistant to silencing by both in-house and competitors siRNAs.
Lader declined to be more specific about efficacy, and Weiss declined to comment further on the design of the algorithm. Weiss noted that details about the algorithm are being prepared for submission to peer-reviewed journals in the next few weeks. He added that a patent application covering the algorithm was submitted roughly one week ago.
The development and licensing of the algorithm coincided with a partnership Qiagen and Novartis struck in August to design a human genome-wide set of siRNAs for use in drug discovery.
That genome-wide siRNA collection is being developed against a map of the sequenced human genome resulting from a collaboration between Novartis and Compugen, Weiss noted.
“[Novartis] doesn’t trust [human genome data] out there, and they definitely don’t trust [it] for their own research,” he said. “There are a couple of public databases available, [but] the trouble with public databases for pharmaceutical companies is that they’re not very accurate, not very exact, and often they’re a big mess.”
“For them to approach the idea of developing a genome-wide library … [Novartis] felt that they had to have the best methodology to design active siRNAs and to design those siRNAs against the best copy of the human genome that they could,” Lader noted.
Weiss said that Qiagen remains on track to deliver the siRNA library to Novartis before the end of the year, about a two-month delay over the end-of-October timeframe he gave to RNAi News’ sister publication GenomeWeb News in August. “We’re shipping half [of the library] today, actually,” he said Tuesday.