Last updated: August 9, 2025
Introduction
The World Intellectual Property Organization (WIPO) patent WO2007119249, titled "Method for predicting drug efficacy and toxicity," represents a pivotal development within the intersection of computational biology, pharmacology, and drug discovery. This patent illustrates innovative methods for predicting drug efficacy and toxicity through computational models, primarily focusing on in silico techniques. Analyzing its scope, claims, and the broader patent landscape offers insights into technological evolution, competitive positioning, and strategic patenting in the biopharmaceutical sector.
Scope of Patent WO2007119249
Broad Geographical and Technological Scope
WO2007119249 is a WIPO international patent application filed via the Patent Cooperation Treaty (PCT), with the intent to secure patent rights across multiple jurisdictions. Its scope primarily encompasses:
- Methodologies for predicting drug responses: Utilization of computational models, such as machine learning algorithms, to forecast efficacy and toxicity profiles.
- Use of biological data: Incorporation of gene expression, proteomic, or chemical data to inform predictive models.
- Applications in early-stage drug development: Aimed at preclinical assessment, reducing reliance on animal models, and streamlining clinical trial planning.
- Data integration approaches: Combining multi-omics datasets to enhance predictive accuracy.
- Tools for personalized medicine: Facilitating tailored therapeutic strategies based on patient-specific data.
The scope emphasizes methods, systems, and tools for in silico prediction, rather than specific chemical entities or drug molecules. This broad approach positions the patent as potentially applicable across a wide spectrum of therapeutic areas, including oncology, neurology, and infectious diseases.
Claims Analysis
Overview of Claims
The claims define the legal boundaries of the patent, with WO2007119249 consolidating core innovations around computational prediction methods.
Key Claims Breakdown:
-
Methodology for predicting drug efficacy or toxicity
Encompasses steps involving the collection of biological data (e.g., gene expression profiles), application of computational models (machine learning algorithms, neural networks), and outputting predictions regarding drug response.
-
Use of specific biological markers
Claims include the utilization of particular biomarkers or molecular signatures as inputs for the predictive model, thereby narrowing or tailoring the scope toward certain diseases or biological pathways.
-
Data integration and processing techniques
Covers datasets from various sources—genomic, proteomic, chemical—and their integration into a unified model, impacting the predictive accuracy.
-
Predictive system comprising hardware and software components
Describes a tangible system that incorporates data input modules, computational units, and output interfaces.
-
Application in drug screening and development
Asserts that the system/method can be employed during different stages of drug development, emphasizing its commercial and practical utility.
Claim Scope and Limitations
While extensive, the claims are generally directed toward methods involving computational modeling, which are susceptible to subject matter eligibility challenges in jurisdictions like the US. However, their broad language may encompass various implementations, providing extensive protection yet potentially facing challenges under patent novelty and inventive step analyses if similar prior art exists.
Patent Landscape Context
Prior Art and Related Patents
The landscape for predictive computational methods in drug development includes several key patents and publications:
-
Early Computational Drug Discovery Patents: Prior to 2007, companies like Schering-Plough and AstraZeneca filed patents covering the use of machine learning for drug target validation ([2], [3]).
-
Bioinformatics and Systems Biology Patents: Multiple filings with overlapping claims regarding data integration and biomarker identification, e.g., WO2006107020 (Fitzgerald et al.), which addresses biomarker-based drug response prediction.
-
Academic Publications: Prior to the filing, numerous scientific articles (e.g., by Lamb et al., 2006) detailed in silico prediction models, which may serve as prior art challenges.
Market Players and Patent Activity
-
Pharmaceutical Companies: Large players like Novartis, Pfizer, and Merck have filed related patents on computational approaches to predict drug toxicity and efficacy.
-
Technology Providers: Companies providing drug discovery software (e.g., Schrödinger, ChemAxon) focus on modeling platforms compatible with WO2007119249.
-
Academic and Public Sector: Universities and research institutes have contributed foundational work, often foundational to subsequent patent filings.
Patent Strength and Challenges
-
The broad claims of WO2007119249 offer strategic coverage but may face122 challenges from prior art or lack of inventive step under certain patent regimes.
-
The temporal proximity to early bioinformatics disclosures indicates that some claims could be narrowed if prior art demonstrates similar methodologies.
-
The emphasis on data integration and machine learning aligns with a current trend, potentially strengthening the patent's relevance in the evolving precision medicine field.
Implications for Stakeholders
-
Patent Holders: Secure foundational claims in predictive modeling, establishing a vantage point to claim innovations in drug efficacy and toxicity prediction.
-
Competitors: Must navigate existing disclosures, potentially designing around broad claims or focusing on specific novel biomarkers or algorithms not covered.
-
Regulators and Legal Bodies: May scrutinize the patent for subject matter eligibility, especially concerning whether algorithms or abstract methods qualify for patent protections.
-
Innovators: Can leverage WO2007119249 as a reference point for developing advanced, non-infringing predictive models, emphasizing specific technical improvements or novel data sources.
Conclusion
WO2007119249 exemplifies an early-stage, broad patent claiming innovative computational methodologies for predicting drug efficacy and toxicity. Its scope encompasses predictive models leveraging biological data integration, providing strategic patent coverage in the burgeoning field of computational drug discovery. The patent landscape underscores a competitive environment wherein subsequent filings refine or design around such broad claims, with continued importance placed on specific biomarkers, datasets, and algorithms. Stakeholders must critically assess prior art, jurisdictional nuances, and technological advancements when evaluating infringement risks or licensing opportunities.
Key Takeaways
-
Broad Claim Coverage: The patent claims methods integrating multi-omics data and machine learning to predict drug responses, offering wide applicability but potentially facing patentability challenges.
-
Strategic Positioning: Secures foundational rights in an emerging segment of personalized medicine, enabling licensing or enforcement opportunities.
-
Landscape Complexity: Overlapping patents and academic disclosures necessitate thorough freedom-to-operate analyses.
-
Innovation Trends: Emphasizes the importance of data integration and computational algorithms in modern drug discovery pipelines.
-
Regulatory and Legal Environment: Evolving patent standards for software-related inventions require ongoing strategic patent drafting and prosecution.
FAQs
1. What types of methods does WO2007119249 cover?
It covers computational methods utilizing biological datasets—such as gene expression profiles—and machine learning algorithms to predict drug efficacy and toxicity.
2. How does this patent fit into the broader drug discovery landscape?
It represents a critical step toward integrating computational approaches in early drug development, aligning with trends toward personalized medicine and reducing reliance on traditional experimental assays.
3. Are the claims limited to specific diseases or drugs?
No, the claims are broad and applicable across multiple diseases and drug classes, focusing on the methodology rather than specific compounds.
4. What challenges might this patent face?
Potential challenges include prior art that predates the filing, questions around patent eligibility (particularly for algorithms), and the novelty or inventive step of the claimed methods in light of existing scientific knowledge.
5. How can competitors navigate around this patent?
Competitors can develop alternative models with different data sources, novel biomarkers not covered by claims, or implement proprietary algorithms that differ significantly from claimed methods.
References
[1] WIPO Patent Application WO2007119249, "Method for predicting drug efficacy and toxicity".
[2] US Patent US2006310584A1, "Machine learning methods for drug discovery".
[3] WO2006107020A1, "Biomarker-based prediction of drug response".
