Verisimlife

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.

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Situation

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

In experimental approaches, techniques like affinity chromatography, Surface Plasmon Resonance (SPR) and mass spectrometry can be used to identify novel targets of known drugs through binding assays to identify relevant target interactions. Phenotypic screening is another experimental approach, in which high-throughput phenotypic screening of compounds is achieved using in vitro or in vivo disease models to indicate potential for clinical evaluation.

Computational approaches offer a faster, more cost-efficient alternative. These approaches typically screen chemical structure and/or omic data to find characteristics relevant to predicting a new therapeutic purpose. To be successful, these methods require comprehensive compound and protein target data libraries (taking into consideration the heterogeneity of big data from experimental, clinical trials, biobanks, electronic health records and other sources).

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.

Proof of Value

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.

CEO, Biotech Client

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Evolve your pipeline

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.