PIttsburgh, PA (PRWEB) June 06, 2013
Qrono Inc., today announced that the National Institute of General Medical Sciences (NIGMS), National Institutes of Health (NIH), awarded Qrono a Small Business Technology Transfer (STTR) Phase I grant for $256,000 to improve the treatment options for wet age-related macular degeneration (wet AMD) and fund further development of the company’s predictive modeling technology for the design of long-acting injectable (LAI) drug formulations. The research will be conducted in collaboration with The Little Lab at the Swanson School of Engineering at the University of Pittsburgh.
Wet AMD is a leading cause of vision loss among older adults and can progress very quickly due to abnormal blood vessel growth behind the retina. Ranibizumab and bevacizumab are two drugs that target the proteins that cause this abnormal growth. However, because treatments with these drugs require a monthly eye injection, patient non-adherence (up to 67%) is a major problem. Qrono’s new LAI formulation for these drugs will reduce the injection frequency to once every three months or even six months which should improve patient adherence and thereby enable better patient outcomes.
“We are grateful that the grant reviewers recognized the potential of our technology,” said Larry Zana, Qrono CEO & Co-Founder, “and we appreciate the support of NIGMS for this research.” The grant will also enable Qrono to demonstrate that its predictive modeling technology, called QronoMetricsTM, can be used to produce LAI formulations for a wide range of target pharmaceuticals in an unprecedented, rapid period of time. In Phase I, Qrono will develop LAI formulations for ranibizumab, bevacizumab, and two other active pharmaceutical ingredients. Upon successful completion of Phase I, Qrono will be eligible to apply for Phase II funding that will extend its predictive models to cover preclinical and clinical pharmacokinetic data.
QronoMetricsTM offers three key advantages in the production of custom controlled-release and microencapsulated systems:
- Removes the trial and error required by traditional design techniques, resulting in faster time-to-market, reduced development cost and reduced risk.
- Reduced active pharmaceutical ingredient costs, up to 60% less.
- Critical parameters automatically identified for quality by design (QbD) manufacture
Dr. Steven Little, Professor and Chairman of the Department of Chemical and Petroleum Engineering at the University of Pittsburgh, said, “This grant will further enable research that builds upon our ongoing collaboration with Qrono. It is an excellent example of how an academic-industry collaboration can enable better medications.”
Fully operational since 2012, Qrono Inc. is a specialty pharmaceutical company enabling better medications, stronger patient adherence, improved patient outcomes, and faster time-to- market for new medications using an innovative technology to create long-acting injectable (LAI) formulations. These long-acting drug formulations enable a single administration of active pharmaceutical ingredient to provide a therapeutic effect ranging from several days to many weeks or months. Our pipeline strategy focuses on LAI controlled release formulations of drugs with known safety profiles in therapeutic areas with either high non-adherence (e.g., antipsychotics and ophthalmology), or where LAIs can add therapeutic value (e.g., medical countermeasures and oncology).
About The Little Lab:
The Little Lab explores synthetic drug delivery strategies that mimic those of cells and tissues in order to enhance (or alternatively endow new) biological functionality. To this end, researchers explore new ways to produce complex presentations of bio-active molecules over time and space. The mission is twofold. Specifically, researchers aim to utilize biomimetic delivery systems to achieve both: 1) enhanced therapeutic efficiency for future drug formulations (e.g. “medicine that imitates life”) as well as 2) understanding of basic biological processes that are otherwise obscured without engineering tools that can be tuned to replicate multi-modal cellular “language”.