Turbine Unveils First-Ever Virtual Lab Platform for Cell Simulations in Drug Development
核心洞察
Turbine (搜索) has launched the world's first virtual lab using cell simulations, aligning with FDA's recent move to phase out animal testing requirements in drug development.
The platform enables scientists to simulate experimental outcomes at unprecedented speed, with initial beta modules focusing on antibody-drug conjugate (ADC) payload selection and clinical positioning.
Through a partnership with Champions Oncology, Turbine (搜索) is building a virtual patient library with multi-omics data to predict therapeutic responses across diverse molecular subtypes, potentially transforming oncology (搜索) R&D.
Turbine (搜索) has opened access to its groundbreaking cell simulation platform, previously available only to select pharmaceutical partners, marking a significant advancement in drug discovery technology. The virtual lab allows scientists to design and run experiments in silico, potentially revolutionizing the traditional approach to biological research and drug development.
The platform's launch comes at a pivotal moment as the FDA recently announced plans to phase out animal testing requirements in drug development, encouraging human-relevant computational methods instead. Turbine (搜索)'s technology, developed over a decade, enables researchers to simulate experimental outcomes and understand molecular mechanisms without traditional wet lab constraints.
"For a decade, we've been developing our cell simulations hand in hand with select partners in biopharma R&D globally," said Szabolcs Nagy, Co-founder & CEO at Turbine (搜索). "When we realized that our biologists started rethinking the very process of biological experimentation by integrating simulations into processes, we knew we had to make this available to scientists everywhere."
Initial Beta Modules Target Critical Drug Development Challenges
Turbine (搜索) is releasing two beta modules in 2025, focusing on antibody-drug conjugates (搜索) (ADCs (搜索)) and clinical positioning—areas presenting significant challenges in pharmaceutical development.
The ADC Payload Selector (搜索) addresses a fundamental challenge in ADC development: payload-mediated resistance (搜索). ADCs (搜索) combine antibodies, linkers, and cytotoxic payloads that can be mixed in billions of ways, making optimal payload selection critical yet difficult to predict. The platform allows researchers to:
- Identify promising payload candidates through millions of predictive simulations
- Understand context-specific effects using an in silico library of biological models
- Calculate combination synergy predictions at various doses with detailed profiles
The Clinical Positioning Suite (搜索), developed in collaboration with Champions Oncology, integrates clinically relevant tumor (搜索) data to create a comprehensive virtual patient library. This integration enables researchers to simulate experimental outcomes on virtual patients constructed from real-world data.
"The future of AI- and ML-driven drug discovery is grounded in access to precise, high-quality multi-omics datasets," explained Matt Newman, EVP at Champions Oncology. "Champions is uniquely positioned to meet this need, delivering the deeply characterized, clinically relevant data that partners like Turbine (搜索) are seeking."
Addressing a Critical Industry Challenge
Turbine (搜索)'s technology tackles one of biopharma's most persistent problems: the high failure rate of drug candidates in clinical trials. Despite technological advancements, over 90% of drug candidates fail in clinical trials, resulting in billions in wasted investment.
By enabling computational prediction of therapy effects, the platform allows drug developers to focus resources on the most promising candidates, potentially increasing the likelihood of clinical success. The virtual lab facilitates experimentation using a vast library of cell models, patient-derived samples, and virtual patients.
Validation Through Industry Partnerships
Turbine (搜索)'s simulation technology has been validated through collaborations with leading pharmaceutical and biotech companies, including Bayer, AstraZeneca, Ono (搜索), and Cancer Research Horizons (搜索). The company continues to integrate its platform with other AI-driven discovery tools and contract research organizations worldwide.
The platform represents a significant step toward reducing reliance on traditional experimental methods while accelerating the drug discovery process. By computationally predicting therapeutic responses across diverse patient populations, researchers can potentially identify molecular traits linked to drug sensitivity or resistance, refining eligible patient cohorts for clinical trials.
Scientists interested in accessing the beta modules can request access through Turbine (搜索)'s website. The company plans to make additional assay types available to partners in the coming months, further expanding the platform's capabilities and applications in drug discovery and development.