Building Academic-Industry Partnerships

Host: Kimberly Reid (OHRI)
Sponsored by: Canadian RNA Biomanufacturing Co-op (merging with The Society for RNA Therapeutics)
Support from: BioCanRx

Video found below.

Key Themes

Key Themes (Categorized)

1. Getting Started: Breaking into Industry Partnerships

• Start with scientific alignment and mutual
• Finding the right person is the biggest hurdle—requires persistence and repetition
• Networking matters: conferences, informal conversations, ongoing engagement
• Funding ecosystems are complex—understanding grants, cash vs. in-kind, incentives is a barrier
• Mitacs and similar organizations can act as connectors/sounding boards

2. Building the Relationship (Not Just a Project)

• Strong partnerships are relationship-driven, not transactional
• Reputation = a long-term funding mechanism (easy to work with = repeat partnerships)
• Avoid dependence on a single champion—engage multiple stakeholders
• Start small → de-risk → scale up

3. Structuring a Successful Collaboration

• Align early on:
  • IP (background vs. foreground)
  • Publication expectations
  • Roles and responsibilities
  • Reporting cadence
  • Commercialization pathways
• Clear expectations + operational structure + communication framework are critical
• Academic–industry projects are co-developed, not dictated

4. Where Partnerships Go Wrong

• Mismatch between expectations and reality (scope, timelines, deliverables)
• Timeline misalignment:
  • Industry = fiscal year cycles
  • Academia = grant and trainee timelines
• Over-scoped projects vs. available talent (e.g., undergrad vs. postdoc capacity)
• Rigidity vs. flexibility mismatch (industry vs. research exploration)

5. The Role of IP & Publishing

• Tension exists but is often overstated
• Start with smaller projects to build trust and avoid legal bottlenecks
• Separate:
  • Background IP (protected)
  • Foreground IP (negotiated)
• Publishing is increasingly valuable to both sides (including companies)

6. Value of Collaboration: Why It Works

• Academia = fast experimentation, higher risk tolerance
• Industry = focus on commercialization and scale
• Trainees are a critical bridge (skills + talent pipeline)
• Collaboration helps de-risk innovation before scaling

7. Commercialization & Translation Mindset

• “Start with the end in mind” (manufacturability matters early)
• Balance needed:
  • Academic freedom to explore
  • Industry need for scalable, reproducible solutions
• Some transformative ideas may look unmanufacturable early—but still succeed

8. Workforce & Skills of the Future

• Core skills:
  • Adaptability & “learning to learn”
  • Cross-sector communication
  • Critical thinking (especially in AI-driven workflows)
  • Delivering under commercial timelines
• Internships are high-value: real-world training + talent pipeline