This article was originally published in Start Up
Thanks to high throughput screening, more than 10,000 compounds with biological activity against specific targets are entering the drug discovery process each week. But unless those compounds can pass the hurdles of bioavailability and safety, comprising a series of tests known as ADMET (for absorption, metabolism, distribution, elimination and toxicity), they will never be successful drugs. Today, the tests that make up ADMET evaluation are low throughput, and are apparently not informative or accurate enough to predict a drug's probability of success, given the high failure rate of compounds at all stages of development. Drug discovery companies are therefore looking to re-engineer the ADMET process, moving it up the early discovery chain. The goal is to predict, very early in the process, perhaps even before compounds are synthesized, which compounds pass the test for a good drug. Doing so will require new assays, new computer models, and large volumes of consistent, high quality data on drugs in man, across diverse sets of chemistries. No one company has it all; partnerships and consortiums aim to bring together the necessary resources to integrate absorption, metabolism, and toxicity into a single platform.
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Libraria Inc. is using computational techniques to discover new small-molecule drugs that can be administered orally. Its proprietary technology enables the company and its partners to leverage known molecular structures, chemistry protocols, and bioactivity data to speed the chemistry phase of drug discovery by up to 50%.
Amedis Pharmaceuticals Ltd., based in Cambridge, UK, has developed a range of tools designed to help overcome the bottleneck in the drug development process. Established in April 2000, teh company has devised two core platform technologies based on a combination of chemistry, biology and bioinformatics.
To address the problem of attrition during lead compound discovery and early development, some large drug firms - Pfizer perhaps both the most vocal and committed -- are banking on file enrichment and selection. Better-informed compound library construction and more efficient lead selection could double the useful hit rate, they say, in effect doubling the number of successful new drug introductions downstream. There's room for combining empirical and rational approaches; for example, combining novel fragments to extend diversity with privileged fragments (or scaffolds) that reflect rational assumptions about drug motifs. For now, however, the proposition of file enrichment is much more theory than reality, until it is shown to actually reduce attrition both before and during clinical development, and so improve productivity.
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