Repurposing drugs for new indications and routes of administration
How AI generates unprecedented drug repurposing insights, accelerating development and reducing program risk.
Introduction
Bringing a novel drug to market is a slow, costly and laborious endeavor – with a notoriously high risk of failure.
Drug repurposing, in which new uses are discovered for already pre-approved or existing drugs, offers a faster, more streamlined approach to take a new candidate therapy from the scientist’s bench to a patient’s bedside.
Today, advances in technology have enabled systematic approaches to identifying and translating repurposable compounds – giving a second chance to de-risked, ‘old’ medicines, and significantly reducing development costs and timelines along the way.
Though systematic drug repurposing has clear advantages in the context of drug development, some challenges remain. When it comes to drug repurposing, there are two primary categories of approaches: experimental and computational
The VeriSIM Life advantage
Computational and artificial intelligence (AI) technologies have advanced rapidly to meet the challenges of drug repurposing and reformulation head on.
VeriSIM Life’s drug decision engine, BIOiSIM®, is a computational platform deploying advanced artificial intelligence and machine learning, a proprietary big data foundation, and state-of- the-art mechanistic models to develop novel therapies from existing molecules.
BIOiSIM®, and its groundbreaking Translational Index™️ technology
Which advances only the most promising drug candidates through R&D to investigational new drug (IND) application, offers actionable insights of unprecedented value to the drug development industry.
Combining thousands of validation data sets, multi-compartmental models, and its integrated AI/ML engine, BIOiSIM® achieves superior physiological and biological relevance within three classes of therapeutics: small molecules, large molecules, and re-engineered viruses.
AtlasGEN™️ Novel Drug Designer benefits
The BIOiSIM® platform features a
robust data lake foundation, integrating:
Vast chemical search space: 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 1012 compounds.
Super fast discovery: AtlasGEN accurately predicts protein-lygand binding affinity by combining geometric conformer analysis with machine learning based on experimental data X% more efficiently than molecular docking-based approaches.
Pre-validated hits: 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.
1 trillion potential compounds search space for de novo synthesis and structural screening
Physiological data from 7 different animal species, plus humans
Support for genomics data integration
More than 3,000,000 real compounds including proprietary data from multiple partnerships
Proprietary experimental data from scientific literature and other sources
Validation by real-world observed data
For the repurposing of therapeutics with an established mechanism-of-action, BIOiSIM® can also be used to simulate and predict the pharmacokinetics (PK) and pharmacodynamics (PD) of compounds that are known to match the targeted pathway. Similar predictive simulation capabilities can guide the development of new drug combinations – using the application of machine learning to predict drug pathways and anticipated disease response in lieu of experimental data.
Optimizing dosing and route of administration with BIOiSIM®
Challenge
VeriSIM Life’s biotech client needed to determine the feasibility of repurposing and developing a topical formulation for erectile dysfunction, which contained a combination of three known drugs.
Solution
VeriSIM Life’s biotech client leveraged BIOiSIM’s® transdermal model to repurpose compounds for topical application. After a seamless, three-step process, the platform recommended a therapeutic drug window for the three compounds.
Methods
- Data import and automated screening.
- ML model clustering, fine-tuning, and simulation.
- Model evaluation and analysis with test compounds.
Outcome
BIOiSIM® identified a different route of administration, validated therapeutic window and predicted optimal dosing for three compound- mixes, with 80%+ accuracy. Predictions were ultimately validated by a CRO and the client has now filed IP and applied for IND approval.
Without VSL’s insights, we couldn’t have gained the confidence to approach a CRO and invest money in further experiments.
Additional VeriSIM Life Case Studies & Content
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Bring better drugs to market, faster, with AtlasGEN Novel Drug Designer
Now you can accelerate the discovery of new therapies based on existing compounds with VeriSIM Life’s AtlasGEN Drug Designer computational platform – purpose-built to decode chemistry and biology at scale. With the industry’s most generalistic AI platform, your innovation is no longer limited to experimental constraints.
Contact us today to schedule a demonstration of BIOiSIM’s AtlasGEN technology.
Bring better drugs to market, faster, with BIOiSIM®
Now you can accelerate the discovery of new therapies based on existing compounds with VeriSIM Life’s BIOiSIM® computational platform – purpose-built to decode chemistry and biology at scale. With the industry’s most generalistic AI platform, your innovation is no longer limited to experimental constraints.
Contact us today to schedule a demonstration of BIOiSIM®
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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.
