SandboxAQ, a leading AI solutions company, has been selected by Sanofi to identify new biomarkers for safety and efficacy in clinical development using its Quantitative AI models. This collaboration will leverage SandboxAQ’s Large Quantitative Models (LQMs) to causally filter knowledge graphs, allowing scientists to automatically extract new clinical hypotheses from literature and highlight those that are truly causal.
Nadia Harhen, General Manager of AI Simulation at Sandbox AQ, expressed excitement about the application, which increases their reach into later-stage clinical development and unlocks patient benefits beyond early-stage drug discovery. This partnership is the latest in a series of collaborations for SandboxAQ, including with the University of California San Francisco (UCSF), Novonix, Riboscience, Flagship Pioneering, and SPARK NS.
Collaboration in Biomarker Identification: SandboxAQ and Sanofi Join Forces
The identification of biomarkers is a crucial step in the clinical development of new medicines. Biomarkers serve as indicators of a disease’s presence, progression, or response to treatment, enabling researchers to assess the safety and efficacy of investigational drugs. Recently, SandboxAQ, a company specializing in artificial intelligence (AI) solutions, announced its collaboration with Sanofi, a global pharmaceutical leader, to identify biomarkers during clinical development.
This partnership will leverage SandboxAQ’s Quantitative AI models, specifically designed to filter knowledge graphs causally. This technique allows scientists to automatically extract new clinical hypotheses from literature and highlight those that are truly causal. By applying these models, researchers can gain a deeper understanding of human biology, facilitating the identification of new biomarkers and aiding in the demonstration of mechanism of action, efficacy, and safety of investigational medicines and targets.
SandboxAQ’s Quantitative AI models, part of their growing suite of Large Quantitative Models (LQMs), are trained on multiple data streams, including proprietary data generated internally by SandboxAQ algorithms. This approach enables LQMs to evade the limitations in scale and accuracy inherent to natural language Large Language Models (LLMs) trained on public internet data. The use of LQMs has already shown promise in various areas across the life sciences, including drug repurposing and reverse screening.
The Power of Large Quantitative Models in Biomarker Identification
Large Quantitative Models (LQMs) are a key component of SandboxAQ’s AI solutions. These models are trained on vast amounts of data, allowing them to identify complex patterns and relationships that may not be apparent through traditional analysis methods. In the context of biomarker identification, LQMs can rapidly process large volumes of literature and experimental data, extracting valuable insights that can inform clinical development.
The application of LQMs in biomarker identification offers several advantages over traditional approaches. Firstly, these models can analyze vast amounts of data quickly and accurately, reducing the time and resources required for biomarker discovery. Secondly, LQMs can identify novel biomarkers that may not have been considered through conventional methods, potentially leading to new therapeutic targets and treatment strategies.
The Role of Causal Filtration in Biomarker Identification
Causal filtration is a critical component of SandboxAQ’s Quantitative AI models. This technique enables researchers to distinguish between correlation and causation, allowing them to identify biomarkers that are truly causal. In the context of clinical development, this is essential for demonstrating the safety and efficacy of investigational medicines.
The application of causal filtration in biomarker identification offers several benefits. Firstly, it enables researchers to focus on biomarkers that are most likely to be relevant to disease progression or treatment response. Secondly, it reduces the risk of false positives, which can lead to costly and time-consuming dead ends in clinical development.
The Growing Ecosystem of SandboxAQ Collaborations
SandboxAQ’s collaboration with Sanofi is the latest in a series of partnerships aimed at advancing innovation pipelines across various industries. Last year, the company announced collaborations with the University of California San Francisco (UCSF), Novonix, and Riboscience. In 2024, Flagship Pioneering, SPARK NS, and other organizations joined forces with SandboxAQ to further their innovation goals.
These partnerships demonstrate the growing recognition of the potential of Quantitative AI models in addressing some of the world’s most pressing challenges. As the ecosystem of SandboxAQ collaborations continues to expand, it is likely that we will see significant advances in fields such as life sciences, financial services, navigation, cyber, and others.
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