Epix Closes In on the Clinic
“The PREDICT Methodology represents an innovation in small molecule selection,” according to Sharon Shacham, Ph.D., svp for drug development at Epix Pharmaceuticals (www.epixpharma.com). In her presentation, Dr. Shacham described how the company’s platform has resulted in four novel drug candidates in less than four years.
Focusing on a rational drug design technology, Epix has developed a suite of modeling and optimization algorithms leading to rapid lead discovery and optimization. The discovery program is aimed at GPCRs, which rarely have well-described 3-D protein structures given the extreme difficulty of isolation, purification, and crystallization of hydrophobic molecules.
To overcome this daunting roadblock, the company’s Predict technology was designed to encompass the in silico screening of a library of more than four millionprospective compounds. Epix seeks to target proteins with clear biological validation that are on the market or in development, with the goal of improving on existing selectivity, half-life, and pharmacology.
Applicable to all GPCRs, each new model undergoes a validation process to establish the internal consistency of structure. This encompasses agreement with available site-directed mutagenesis data and binding pocket location data including correct interactions with key residues in the binding pocket.
Another component of the platform is a reconstruction protocol in which success in an enrichment experiment proves that the docking procedure is able to pick out known ligands that have been embedded in a large and random 10,000 member drug-like compound library. The validity of this approach is proven by the fact that more than 20 candidates have successfully passed through this validation process, according to Dr. Shacham.
The next phase of the program involves the in silico screening process in which the candidate receptors are confronted with the virtual compound library, and in which the Predict program looks for virtual hits using the criteria of Ki< 10 mM, a validated dose response, and a clear intellectual property search.
Subsequently, the qualifying candidates are put through an optimization program featuring extensive use of computational tools including 3-D structures and predictive ADME to navigate the multiple possible optimization pathways. These prioritize choices to synthesize or not to synthesize. “This is an efficient process, robust in theory, and agnostic to the receptor class,” Dr. Shacham said. “It is hypothesis driven so there must be a specific reason for each synthesized compound; those that don’t fit the model binding site or bind to an off-target structure will not be synthesized.”
Speeding Up the Process
So what role does high-content screening play when it comes to drug discovery? In theory, technology that pole vaults over in vivo and in vitro testing of vast numbers of compounds should go a long way toward optimizing the discovery process, saving time and money. However, in silico results are not definitive, since they do not cover a plethora of factors that influence the fate of a project. These include biophysical properties of the compound, strategic and safety issues, and, frequently, IP considerations.
Bender argued that “high-content imaging fares rather well when it comes to the success of HTS campaigns, and potentially, by integrating in silico tools such as ligand-target prediction, those campaigns may fare even better in the future.”
Delphic prophesies predicting breakthroughs in drug development using in silico modeling technologies abound.
A recent Deloitte report, The Changing Face of R&D in the Future Pharmaceutical Landscape, bemoans the current state of the pharmaceutical industry and predicts that companies will exploit “virtual R&D processes with significant outsourcing to maximize flexibility and manage development risk.”
While in silico modeling holds much promise, it still remains an unproven technology, as there are no drugs that have traveled the epic voyage from lead compound optimization to the marketplace. A number of anti-HIV drugs have been developed through programs that include in silico modeling, however.
As pharma companies are understandably reticent to reveal the details of their discovery programs, it is not surprising that the precise role of in silico modeling in drug development is somewhat opaque. Moreover, the very long gestation period required in today’s world of drug development means that new strategies may take years to see their effects realized. With many small and large players in the field, the next few years should see the vindication of this approach.
K. John Morrow Jr., Ph.D., is president of Newport Biotech. Web:www.newportbiotech.com. Email: firstname.lastname@example.org.