Publications & Blog
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FDA Signals a New Era for Drug Development — VeriSIM Life Is Already There
From the beginning, VSL has pioneered a mission-driven approach rooted in the belief that advanced simulations can replace outdated, animal-centric paradigms that have limited applicability to humans. The FDA now explicitly affirms that AI-based computational modeling can reliably simulate how monoclonal antibodies (mAbs) distribute through the human body, predict side effects based on molecular features, and accelerate therapeutic delivery without compromising safety. That’s exactly what VSL already enables and more!
FDA Signals a New Era for Drug Development — VeriSIM Life Is Already There
From the beginning, VSL has pioneered a mission-driven approach rooted in the belief that advanced simulations can replace outdated, animal-centric paradigms that have limited applicability to humans. The FDA now explicitly affirms that AI-based computational modeling can reliably simulate how monoclonal antibodies (mAbs) distribute through the human body, predict side effects based on molecular features, and accelerate therapeutic delivery without compromising safety. That’s exactly what VSL already enables and more!
VeriSIM Life’s Alignment with Recent FDA Draft Guidelines
VeriSIM Life's BIOiSIM platform is highly aligned with the core principles of new FDA documents, as it leverages advanced artificial intelligence (AI) and machine learning (ML) approach to simulate complex pharmacokinetics and pharmacodynamics models. This approach ensures robust predictions of drug behavior in diverse patient populations, aligning directly with the guideline's emphasis on physiologically based pharmacokinetics (PBPK) and quantitative systems pharmacology.
VeriSIM Life’s Alignment with Recent FDA Draft Guidelines
VeriSIM Life's BIOiSIM platform is highly aligned with the core principles of new FDA documents, as it leverages advanced artificial intelligence (AI) and machine learning (ML) approach to simulate complex pharmacokinetics and pharmacodynamics models. This approach ensures robust predictions of drug behavior in diverse patient populations, aligning directly with the guideline's emphasis on physiologically based pharmacokinetics (PBPK) and quantitative systems pharmacology.
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Knowledge-enhanced AI to Supercharge ADC Development for Treatment of Cancer
The use of Artificial Intelligence (AI) is becoming increasingly prevalent in drug discovery and development and holds the potential to address the challenges in ADC development. AI requires substantial amounts of high-quality data to make reliable predictions of a novel ADC’s safety and efficacy. However, since this therapeutic area is still fairly new, there is limited data available to meet all the challenges of ADC development through AI. In such a scenario, enriching AI with knowledge (hybrid AI) is a very promising approach and provides high-accuracy predictions even in data-limited scenarios.
Knowledge-enhanced AI to Supercharge ADC Development for Treatment of Cancer
The use of Artificial Intelligence (AI) is becoming increasingly prevalent in drug discovery and development and holds the potential to address the challenges in ADC development. AI requires substantial amounts of high-quality data to make reliable predictions of a novel ADC’s safety and efficacy. However, since this therapeutic area is still fairly new, there is limited data available to meet all the challenges of ADC development through AI. In such a scenario, enriching AI with knowledge (hybrid AI) is a very promising approach and provides high-accuracy predictions even in data-limited scenarios.
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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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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.
