Drug-drug interactions
How AI addresses common pain points experienced in the area of drug-drug interactions (DDI) in drug development.
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Introduction
In the process of drug development, accounting for potential drug-drug interactions (DDIs) is a critical step. DDIs are defined as “two or more drugs interacting in such a manner that the effectiveness or toxicity of one or more drugs is altered.”1 Computational methods of identifying possible DDIs are growing in prominence throughout the industry, as their predictive ability allows researchers to identify these interactions sooner, saving time, money and other resources in the drug development process to lead to faster IND approval and improved ROI.
Adverse drug reactions (ADRs) are one of the major roadblocks to having a new drug approved, and aside from possibly preventing the drug from making it to market, not properly accounting for ADRs will result in patient harm and other serious consequences for a drug manufacturer down the road.
The VeriSIM Life advantage
Today, there’s good news for pharma researchers embarking on the process of predicting drug-drug interactions. Innovations in AI-driven technologies have allowed for the streamlining and de-risking of many aspects of early-stage drug development, including the process of predicting DDIs.
VeriSIM Life (VSL)’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 predict and account for possible drug-drug interactions for a particular drug candidate, among other drug discovery-related tasks. This includes identifying new drugs as inhibitors, substrates, inducers of interaction with targets, pathways and enzymes of interest, such as potentially problematic classes, and those identified by the FDA of particular concern.
BIOiSIM, and VSL’s associated services, can be used to problem-solve some of the most significant challenges facing early-stage drug R&D – meeting development timelines faster, at lower costs, and with better results than traditional approaches.
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/gene therapy.
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
In Silico Development of Combinatorial Therapeutic Approaches Targeting Key Signaling Pathways in Metabolic Syndrome
Challenge
A common and an initial approach to improve and or reverse the deleterious nature of clinical risks in metabolic syndrome (MetS) is changes in lifestyle diet with increased physical activity. Nevertheless, often these lifestyle modifications are not enough to sway the balance towards normality in patients, hence, treatment may require a different therapeutic approach, a multi-drug therapy regimen. However, a multi-drug regimen or polypharmacy has been known to be a major problem for the treatment of patients with MetS due to suboptimal patient compliance, off-target effects, and potential drug-drug interactions.
Solution
We used BIOiSIM to take a DPP4 inhibitor, a small molecule, and anti-IL-17a, a biologic with a completely distinct mechanism of action to address and combat outcomes of dysregulation of a wide range of key metabolic pathways involved in insulin resistance and inflammation. Specifically, the key dysregulated metabolic pathways in part, originate from impaired glucose control and disorders in lipid metabolism. This leads to elevated blood lipid level resulting in insulin resistance and chronic inflammation due to the release of proinflammatory cytokines.
Methods
- Targets were chosen based on the clustered clinical risks in metabolic syndrome: dyslipidemia, insulin resistance, impaired glucose control, and chronic inflammation.
- Drug development platform, BIOiSIM, was used to narrow down two different drug classes with distinct modes of action and modalities.
- Pharmacokinetic and pharmacodynamic profiles of the most promising drugs were modeled showing predicted outcomes of combinatorial therapeutic interventions.
Outcome
Overall, our results suggest that simultaneously targeting lipid metabolic pathways, impaired glucose control, insulin resistance, and chronic inflammation with DPP-4 inhibitor Evogliptin and IL-17A inhibitor Secukinumab will likely provide a high likelihood of ameliorating a significant portion of the clustered clinical risk associated with MetS. Read the full article.
For this publication, BIOiSIM™ inputs include subject-specific parameters (organ volumes, blood flow rates, tissue composition, enzyme expression levels), relevant PK mechanisms (clearance, drug dissolution, permeability), among others.
Additional VeriSIM Life Case Studies & Content
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.
