Blog
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Solving First in Human Dosing Challenges with AI
One major difficulty in FIH dosing is considering variability in human response. The complexity of possible individual responses is challenging to account for using traditional FIH dosing methods, and failure to properly consider these possible variations can result in heightened risk of adverse effects in clinical trial participants. It can also increase the already high cost/time investment of clinical trials. This underscores the need for more sophisticated approaches in FIH dosing that can account for the myriad factors that influence individual human response.
Solving First in Human Dosing Challenges with AI
One major difficulty in FIH dosing is considering variability in human response. The complexity of possible individual responses is challenging to account for using traditional FIH dosing methods, and failure to properly consider these possible variations can result in heightened risk of adverse effects in clinical trial participants. It can also increase the already high cost/time investment of clinical trials. This underscores the need for more sophisticated approaches in FIH dosing that can account for the myriad factors that influence individual human response.
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How AI Enables the Use of Real World Evidence in Drug Development
Artificial intelligence (AI) has emerged as a game-changer in unlocking the potential of real-world evidence (RWE) to revolutionize the way we design, test, and deliver new treatments. RWE, which encompasses a wealth of data from electronic health records, patient-reported outcomes, and other sources, offers invaluable insights into drug safety and effectiveness in real-world settings. AI is becoming a transformative force in harnessing RWE to improve drug development and for addressing challenges that come with RWE such as data quality, patient privacy, and bias.
How AI Enables the Use of Real World Evidence in Drug Development
Artificial intelligence (AI) has emerged as a game-changer in unlocking the potential of real-world evidence (RWE) to revolutionize the way we design, test, and deliver new treatments. RWE, which encompasses a wealth of data from electronic health records, patient-reported outcomes, and other sources, offers invaluable insights into drug safety and effectiveness in real-world settings. AI is becoming a transformative force in harnessing RWE to improve drug development and for addressing challenges that come with RWE such as data quality, patient privacy, and bias.
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Enhancing FDA-Approved AI/ML Methods: VeriSIM Life's Comprehensive Approach to Drug Development and Patient Selection
The FDA's development and use of an AI/ML-based scoring rule for patient selection in the Emergency Use Authorization (EUA) of anakinra to treat COVID-19 marks a significant milestone in the integration of advanced technologies in drug development and patient care. This article explores the FDA's approach, its requirements, and how VeriSIM Life's cutting-edge techniques offer an even more informative and powerful methodology for drug development and patient selection.
Enhancing FDA-Approved AI/ML Methods: VeriSIM Life's Comprehensive Approach to Drug Development and Patient Selection
The FDA's development and use of an AI/ML-based scoring rule for patient selection in the Emergency Use Authorization (EUA) of anakinra to treat COVID-19 marks a significant milestone in the integration of advanced technologies in drug development and patient care. This article explores the FDA's approach, its requirements, and how VeriSIM Life's cutting-edge techniques offer an even more informative and powerful methodology for drug development and patient selection.
How AI Assists Small Molecule Drug Design and Development
From identifying novel drug targets to optimizing the manufacturing process, AI is transforming the way small molecule drugs are discovered, designed, and developed. AI-driven drug development holds remarkable potential for accelerating the availability of life-changing therapies through small molecule drug development. Read on to connect the linkages between small molecule drug chemistry, the drug discovery and development process, and the application of AI to deliver safe and effective drugs for clinical use in patients.
How AI Assists Small Molecule Drug Design and Development
From identifying novel drug targets to optimizing the manufacturing process, AI is transforming the way small molecule drugs are discovered, designed, and developed. AI-driven drug development holds remarkable potential for accelerating the availability of life-changing therapies through small molecule drug development. Read on to connect the linkages between small molecule drug chemistry, the drug discovery and development process, and the application of AI to deliver safe and effective drugs for clinical use in patients.
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Introducing AtlasGEN™ Novel Drug Designer
AtlasGEN Novel Drug Designer efficiently identifies new molecules based on favorable chemical properties and simultaneously evaluates them for clinical safety and effectiveness based on biology using VeriSIM Life’s groundbreaking Translational Index™ technology.
Iterative ranking of target engaging hits by their Translational Index values reduces the time and cost associated with other computational hit discovery approaches, while compressing the hit-to-lead refinement and optimization process by pre-validating hits for translatability. This unique integration dramatically reduces the number of compounds to evaluate in subsequent experimental research.
The ability to generate target engaging compound hits depends largely on the size of the molecular search library. The AtlasGEN search space is supported by more than 1 trillion compounds. Generative AI and deep learning techniques deliver unprecedented discovery capability.
AtlasGEN accurately predicts protein-lygand binding affinity by combining geometric conformer analysis with machine learning based on experimental data 72% more efficiently than molecular docking-based approaches.
