Protelica Awarded Phase II SBIR Grant by the National Science Foundation
News Jan 20, 2009
Protelica (formerly known as ProtElix, Inc.) has announced that it has received formal notification of Phase II SBIR funding by the National Science Foundation for its scientific project entitled “Bioinformatics knowledge-based, universal library design for a non-immunoglobulin, protein-scaffold.”
The grant, effective January 15, 2009 provides $500,000 for the next 24 months and will allow the Company to continue developing its platform technology, and to fund preclinical studies of its early stage lead candidates for cardiovascular and cancer therapies.
“We are very grateful to NSF for acknowledging our research project as one of the most innovative in the country and for supporting the development of new protein therapeutics that may replace the first generation monoclonal antibody drugs,” states Dr. Roberto Crea, Protelica’s Founder, CEO and Chief Scientific Officer.
“We believe our approach to antibody mimics discovery and optimization will lead to new therapeutics that may be more effective and less expensive. We are pleased to receive the validation of this peer-reviewed grant,” adds Dr. Crea.
The project, which started two years ago, includes a bioinformatics-based understanding of nature’s evolutionary rules, and utilizes Protelica’s proprietary DNA mutagenesis technologies to develop small, specific and potent protein blockers.
“By understanding how nature evolves its protein binding specificity, we are able to introduce new and ‘intelligent’ diversity to human protein scaffolds, like Fibronectin sub-units, and generate billions of new variants. We expect this program to lead to the discovery and clinical development of new protein drugs that combine the exquisite specificity of antibodies with the many clinical and manufacturing advantages typical of small molecules,” explains Dr. Guido Cappuccilli, the project’s Principal Investigator and head of the Bioinformatics Group at Protelica.
MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery.
Biochemists, microbiologists, drug discovery experts and infectious disease doctors have teamed up in a new study that shows antibiotics are not always necessary to cure sepsis in mice. Instead of killing causative bacteria with antibiotics, researchers treated infected mice with molecules that block toxin formation in bacteria.READ MORE
Previous work by the International Multiple Sclerosis Genetics Consortium (IMSGC) has identified 233 genetic risk variants. However, these only account for about 20% of overall disease risk, with the remaining genetic culprits proving elusive. A new study has tracked down four of these hard-to-find genes.READ MORE