Juvena - Decoding Secreted Proteins for Better Human Health

Secreted Proteins Communicate Naturally to Induce:

• Immune Response
• Repair Processes
• Stem Cell Activation
• Tissue Regeneration

Advantages of Secreted Proteins

• Natural signaling molecules
• Highly specific to their targets
• Evolved potency
• Safer and more cost-effective than cell and gene therapeutics

Juvena’s discovery programs share a unifying theme of diseases which are treatable by trophic and/or immunomodulatory signaling, the core function of human pluripotent and multipotent stem cell- secreted signaling proteins. Human stem cells secrete proteins that modulate regeneration and inflammation with high therapeutic potential (Kim et al. 2022, Yousef et al. 2013, 2011, 2014, Arnold Caplan 2017, Daneshmandi et al. 2020, Li et al. 2019, Kehl et al. 2019, Lopez-Verrilli et al. 2016, Phuong-Uyen et al. 2020).
Juvena’s discovery programs are focused on diseases with:

• Juvena’s discovery programs are focused on diseases with:
• A degenerative (loss of cell number or type) or inflammatory etiology 
• Activity in cell therapy trials
• Tissue targets addressable by a biologic
• Major unmet medical need

Which protein could be the right treatment or target for any given disease when there are over 20,000 protein-coding genes in humans and over 70,000 recognized human diseases? This would lead to >1.4 Billion potential therapeutic mappings to test. 

Despite the various successes that protein therapeutics have witnessed, secreted proteins have been underleveraged by the pharma industry. Underlying this is the challenge to applying computational and high-throughput screening techniques, which have been successful with small molecules, to protein drug discovery.

Juvena’s machine learning-enhanced biologics drug discovery and development platform identifies lead therapeutic candidates from a proprietary pro-regenerative protein library to screen, validate, and engineer into tissue-specific medicines for degenerative diseases.

By testing machine learning-enabled therapeutic protein predictions with the innovative applications of quantitative proteomics, transcriptomics, and computer vision, we feed high-dimensional, multimodal data into a compounding database enabled by our growing human therapeutic protein library derived from mining the secretomes of human pluripotent stem cells.