Key Takeaways
- • Altis Labs closed a US$25 million Series A funding round co-led by OrbiMed and Qiming Venture Partners USA, with participation from Innovation Endeavors, Benchstrength, Fusion Fund, and Lumira Ventures via the Cancer Breakthrough Fund.
- • The company will deploy capital to expand its AI prognostic models across additional cancer types, scale commercial partnerships with global biopharmaceutical companies, and establish AI endpoints as a clinical trial standard in oncology.
- • Altis Labs has trained its proprietary IPRO AI model on the industry's largest multimodal oncology database, comprising longitudinal imaging, molecular, clinical, and outcomes data spanning over 500,000 patient years.
- • The funding addresses a critical gap in drug development: while AI has advanced drug candidate discovery, clinical validation in human trials remains a bottleneck; Altis targets oncology endpoints where traditional imaging measures like objective response rate (ORR) provide incomplete patient outcome data.
- • CEO Felix Baldauf-Lenschen leads Altis Labs, which operates as an AI-native clinical development platform in partnership with leading cancer research centers.
Summary
Altis Labs, Inc., an AI-focused clinical development company, has completed a US$25 million Series A financing round aimed at accelerating oncology drug trials through artificial intelligence-powered endpoints. The round was co-led by OrbiMed and Qiming Venture Partners USA, with additional backing from Innovation Endeavors, Benchstrength, Fusion Fund, and Lumira Ventures operating through its Cancer Breakthrough Fund.
The company, led by founder and CEO Felix Baldauf-Lenschen and based in Toronto, operates at the intersection of oncology, clinical development, and machine learning. Altis has developed IPRO, a proprietary prognostic AI model trained on what the company describes as the industry's largest multimodal oncology database. This dataset aggregates longitudinal imaging, molecular, clinical, and outcomes information spanning over 500,000 patient years, providing a substantial foundation for model training and validation.
The capital deployment strategy focuses on three core objectives: expanding the IPRO model's applicability across additional cancer types beyond its current scope, scaling commercial relationships with global biopharmaceutical partners to integrate AI endpoints into active development programs, and positioning AI-derived endpoints as a new standard for oncology trial design. The funding directly addresses a documented inefficiency in pharmaceutical development: traditional oncology trial endpoints, particularly objective response rate (ORR) based on imaging assessments, have provided incomplete prognostic value regarding actual patient survival and long-term outcomes. This measurement gap has slowed clinical development timelines and reduced trial efficiency.
The Series A reflects institutional confidence in AI's potential to optimize the later-stage drug development process. While artificial intelligence has demonstrated clear utility in early-stage drug candidate identification, clinical validation in human subjects has remained a slower-moving frontier. Altis Labs targets this bottleneck by providing biopharma companies with AI-derived endpoint tools that may improve trial design, patient stratification, and outcome prediction—ultimately accelerating the path from candidate to approval.
The company, led by founder and CEO Felix Baldauf-Lenschen and based in Toronto, operates at the intersection of oncology, clinical development, and machine learning. Altis has developed IPRO, a proprietary prognostic AI model trained on what the company describes as the industry's largest multimodal oncology database. This dataset aggregates longitudinal imaging, molecular, clinical, and outcomes information spanning over 500,000 patient years, providing a substantial foundation for model training and validation.
The capital deployment strategy focuses on three core objectives: expanding the IPRO model's applicability across additional cancer types beyond its current scope, scaling commercial relationships with global biopharmaceutical partners to integrate AI endpoints into active development programs, and positioning AI-derived endpoints as a new standard for oncology trial design. The funding directly addresses a documented inefficiency in pharmaceutical development: traditional oncology trial endpoints, particularly objective response rate (ORR) based on imaging assessments, have provided incomplete prognostic value regarding actual patient survival and long-term outcomes. This measurement gap has slowed clinical development timelines and reduced trial efficiency.
The Series A reflects institutional confidence in AI's potential to optimize the later-stage drug development process. While artificial intelligence has demonstrated clear utility in early-stage drug candidate identification, clinical validation in human subjects has remained a slower-moving frontier. Altis Labs targets this bottleneck by providing biopharma companies with AI-derived endpoint tools that may improve trial design, patient stratification, and outcome prediction—ultimately accelerating the path from candidate to approval.
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