Last updated: July 31, 2025
Introduction
China Patent CN113613671, titled "Method and Device for Predicting Drug-Induced Liver Injury," represents a significant innovation within the realm of pharmacovigilance and personalized medicine. This patent focuses on leveraging computational models to predict the potential hepatotoxicity of pharmaceutical compounds, addressing a critical challenge during drug development and post-market surveillance. This analysis delves into the patent’s scope, claims, and the broader patent landscape, providing insights for industry stakeholders regarding its strategic significance and competitive positioning.
Scope of CN113613671
Innovative Focus and Technical Field
CN113613671 pertains to the intersection of cheminformatics, bioinformatics, and pharmacology, specifically aiming to predict drug-induced liver injury (DILI). The scope encompasses the development of algorithms and devices to analyze drug properties, genetic data, and biological markers to assess hepatotoxicity risks preemptively.
Technical Objectives
The patent embodies an integrated computational framework intended to:
- Collect and analyze multi-dimensional data (chemical structures, pharmacokinetics, genetic predispositions, etc.)
- Build predictive models (machine learning or statistical) for hepatotoxicity
- Develop devices or systems embedded with these algorithms for real-time or batch prediction
Target Applications
While primarily aimed at pharmaceutical companies during the drug discovery phases, the patent’s scope could extend to clinical settings, drug regulatory agencies, and post-market surveillance systems aiming for early drug withdrawal or safety alerts.
Claims Analysis
The patent's claims define the legal boundaries of the invention. They typically articulate the subject matter’s novelty, inventive step, and practical implementation. Here is a detailed breakdown:
Independent Claims
Claim 1:
Describes a method for predicting drug-induced liver injury, comprising steps of collecting drug data, genetic and biological data, applying a predictive model, and outputting a hepatotoxicity risk assessment.
- Scope: It lays the foundation for an end-to-end framework involving data collection, model application, and risk output.
Claim 2:
Defines a device or system that implements the method of Claim 1, comprising modules for data acquisition, processing, and prediction.
- Scope: Extends to hardware/software systems supporting the predictive method.
Dependent Claims
Claims 3-10 specify particular data types, model architectures, algorithms (e.g., machine learning techniques like Random Forest, deep learning models), data preprocessing steps, and validation methods.
- These narrow the scope but enhance patent defensibility by covering various embodiments.
Key aspects include:
- Use of genetic markers linked to hepatotoxicity.
- Incorporation of in vitro or in vivo biological assay data.
- Specific algorithms and feature selection methods.
- Validation procedures ensuring model robustness.
Claims Strategy Insights
- The claims aim to protect both the conceptual method and concrete device embodiments.
- The inclusion of various algorithms provides broad coverage against potential design-around attempts.
- The patent emphasizes multi-source data integration to improve accuracy.
Patent Landscape and Competitiveness
Existing Patents and Technologies
The landscape for DILI prediction technologies includes:
- Computational models for toxicity prediction: Several patents and publications focus on in silico models for DILI, e.g., European Patent EP3219110A1, which discusses toxicity prediction using QSAR models.
- Biomarker-based diagnostics: US patents like US20200225044A1 cover biomarkers for hepatotoxicity.
- Machine Learning in Toxicology: Multiple patents target ML algorithms for toxicity classification, but fewer integrate multi-omics data as comprehensively as CN113613671.
Compared to these, CN113613671's uniqueness lies in its integrated framework that combines diverse data types within a device/system, enhancing predictive accuracy and practical deployment.
Strategic Positioning
- Innovation at the intersection of AI and pharmacology: The patent’s multi-modal data approach aligns with the current trend toward precision medicine and safer drug development.
- Coverage breadth: Protects multiple embodiments of the predictive method and device, discouraging competitors from simple design-around strategies.
- Potential for licensing or corporate acquisition: Given its targeted application, the patent has strategic value for pharmaceutical and biotech firms seeking competitive advantage or licensing opportunities.
Legal and Regional Considerations
- CN113613671’s enforceability depends on the novelty over prior Chinese and international publications.
- Since Chinese patents offer a strong domestic shield, companies operating in China should consider licensing or around-avoidance strategies.
- Non-Chinese counterparts should evaluate whether similar patents exist and if this patent impacts global R&D strategies.
Implications for Industry Stakeholders
- Pharmaceutical R&D: The patent signals an increasing emphasis on AI-driven safety profiling early in drug discovery, potentially reducing late-stage attrition.
- Regulatory agencies: The proprietary algorithms could inform regulatory assessments, enabling more nuanced safety evaluations.
- Competitors: Must innovate around multi-source data integration or focus on specific aspects like novel biomarkers to avoid infringement.
- Patent strategy: Companies should examine this patent to adjust their IP filings, possibly designing around claims centered on data types or algorithms.
Key Takeaways
- Scope covers an integrated computational system and method for early prediction of drug-induced liver injury, emphasizing multi-modal data analysis.
- Claims encompass both procedural and device embodiments, with a broad range of algorithmic and data collection techniques, fortifying the patent against infringement.
- The patent landscape shows active development in AI-enabled toxicity prediction, with CN113613671 standing out due to its comprehensive approach that combines genetic, biological, and chemical data.
- Strategic importance lies in aiding safer drug development, potentially reducing costs and risks associated with hepatotoxicity.
- Legal positioning favors China-based entities, though global expansion will require careful consideration of the patent’s scope vis-à-vis international patents.
FAQs
Q1: How does CN113613671 differentiate itself from existing toxicity prediction patents?
It uniquely combines multiple data sources—chemical, genetic, biological—and embeds them within an integrated system or device, enhancing predictive robustness beyond traditional QSAR or single-data-type models.
Q2: Can this patent be utilized in clinical practice?
Yes, especially in personalized medicine settings, for risk assessment in patients or during clinical trials to evaluate hepatotoxicity likelihood before medication approval or prescription.
Q3: What should competitors consider to avoid infringement?
Focus on developing models that do not incorporate the specific combination of multi-modal data or specific algorithms claimed, or engineer alternative predictive frameworks and data integration approaches.
Q4: Is this patent applicable outside China?
While enforceable in China, similar patents may exist internationally. Companies should conduct comprehensive global patent searches, and if necessary, seek local patent protection for jurisdictions of interest.
Q5: How might this patent impact drug safety regulations?
It could influence regulators to incorporate AI-based hepatic risk assessments into approval workflows, encouraging industry adoption of advanced predictive tools.
References
- [1] China Patent CN113613671. Method and Device for Predicting Drug-Induced Liver Injury.
- [2] European Patent EP3219110A1. Methods for Predicting Toxicity.
- [3] US20200225044A1. Biomarkers for Hepatotoxicity.
- [4] Relevant literature on AI and toxicity prediction models.
- [5] Industry reports on drug safety and pharmacovigilance advancements.
