Molecular Simulations Inc.
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Microbial drug resistance is a real and growing problem, but drugmakers face disincentives: a plethora of products already on the market, the difficulty of differentiating drugs, and the habit of reserving truly new drugs for emergencies. Big Pharmas are backing out, creating opportunities for small companies who feel they can play successfully. But lack of interest from large partners means biotechs can't access the assets those firms hold, so many start-ups are pairing up with peers. Some firms are building businesses around an abundance of targets derived through genomics. But others are deliberately avoiding working with novel genetic code and instead studying whole cells and physiological changes in organisms. Many firms are addressing the lack of chemical diversity against targets. Some of these are pursuing diversity through natural products like marine microbes, insisting they'll fare better than earlier firms did, in part because of technological advances. Others are trying to create diversity synthetically, by taking structural approaches to understanding targets new and old, as well as compounds. Crystallography, in silico libraries, computational models and mass spectroscopy are key tools in iterative development processes that remain unproven in the anti-infectives field. Some firms are seeking to minimize the risks of novelty, by putting their efforts into developing new versions of antibiotics that worked well before resistance grew. No matter what technological approach start-ups take to developing antibiotics, all face similar challenges external to themselves-primarily in regulatory affairs and funding, but also in hunting Big Pharma partnerships.
Pharmaceutical companies face two giant risks; compound risk and target risk. So, despite an abundance of so-called validated targets emerging from genomics efforts, and novel high throughput tools for compound synthesis and screening that yield plenty of hits against each target, at the end of the day, pharmaceutical company productivity, as measured by approved drugs, remains low. Recently, several former big-pharma executives have founded private companies that hope to speed up the time it takes to come up with optimized small molecule leads. Each has staked out a particular niche where it thinks it can do better than big pharma at coming up with clinic-ready compounds. Kinetix Pharmaceuticals and Triad Pharmaceuticals hope to leverage their knowledge of particular gene families to come up with optimized leads; Enanta Pharmaceuticals hopes to morph peptides into drugs with a combinatorial approach to binding pharmacophores, and Sunesis hopes to tackle some of the targets that prove intractable for others, or in which only large molecules have been able to intervene. The new small molecule companies share a vision of drug discovery that is based on targets, rather than diseases.
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.
GeneFormatics means to develop computational tools that will help the pharmaceutical industry predict the three-dimensional structures and corresponding functions of proteins.
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