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In Defense of Antisense

Authors: R K, Koehn; J, Rossi; C A, Stein;

In Defense of Antisense

Abstract

In his article “A faster way to shut down genes” (News of the Week, 25 May, p. [1469][1]), R. John Davenport describes a promising technique called RNA interference for selectively silencing genes in a range of organisms. But in comparing the new approach to antisense methods, he makes a false assertion: “Fifteen years ago, antisense methods for gene silencing and gene therapy offered similar hopes, but that has been largely a bust.” Like any new method, antisense has faced significant and complex methodological and practical challenges since the first useful demonstration of this method in 1978. Although important issues remain, problems such as drug stability, deliverability, and targetting have been significantly addressed, if not solved. As evidence for these points, there are numerous successful companies that have been built partially or completely on antisense technology. Several have antisense drugs in the clinical pipeline that treat, for example, devastating forms of cancer and inflammatory disease. One antisense drug for the treatment of cytomegalovirus is in the market today. Monoclonal antibodies were trumpeted as new “miracle” drugs when the method to produce them first appeared nearly 30 years ago. They are just now appearing in the pharmaceutical market. Antisense has not been a “bust.” Rather, the development of antisense methods is following a characteristically difficult, expensive, and highly regulated path from laboratory to clinic. [1]: /lookup/doi/10.1126/science.292.5521.1469

Related Organizations
Keywords

Antisense Elements (Genetics), Humans, Technology, Pharmaceutical, Gene Silencing, Genetic Therapy

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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