Partnering for Cures is an opportunity to find nontraditional allies who can help you advance your medical research goals, and a platform for solutions needed to accelerate progress.

View videos and summaries of panels and presentations →

Convened by FasterCures, a Center of the Milken Institute, Partnering for Cures 2011 featured:

participants from all sectors of the medical research enterprise, across therapeutic areas, and across geographic boundaries

speakers who have shaped markets engaged in dynamic, candid discussions on issues that matter (75% of speakers are new to this meeting)

plenary and breakout panel discussions that focused on transformative ideas, best practices, and lessons learned

collaborative programs presented their novel approaches to commercialization, academic translational efforts, data sharing initiatives, drug repurposing programs, clinical trials innovations, and intellectual property management

experts on-site provided one-on-one pro-bono consultations with the goal of paying it forward and accelerating the process by making their expertise accessible

individual one-on-one pre-scheduled meetings that connected leaders from across sectors, disciplines, and areas of expertise

Numerous possibilities for productive collisions and results-oriented networking

Priming The Pump:
Filling the Therapeutic Pipeline

Intersection of Human, Social, and
Financial Capital

Back to Basics: Patient Activism
Thirty Years After HIV/AIDS

Who Will Pay for Tomorrow's Medical
Breakthroughs?

Partnering for Cures Outcomes

In its 3rd year, Partnering for Cures has led to a number of tangible successes:
•	An investment partnership between a nonprofit and a biotech company that allowed for early stage research on a cancer drug candidate to move forward.
•	A joint program between a pharmaceutical company and nonprofit research foundation to support novel research resulting in joint grants to three young investigators.
•	A transformative partnership between two nonprofits to pool and analyze large clinical and molecular datasets to better understand disease.