Next-Generation CDK Inhibition Systems
Legal Citation
Summary of the Inventive Concept
This inventive concept envisions a paradigm shift in CDK inhibition technology, leveraging machine learning, neural networks, and advanced delivery systems to create a new generation of APPAMP compounds with improved efficacy, precision, and adaptability.
Background and Problem Solved
The original patent disclosed CDK inhibitors as 4-[[(7-aminopyrazolo[1,5-a]pyrimidin-5-yl)amino]methyl]piperidin-3-ol compounds, but these have limitations in terms of efficacy, toxicity, and patient specificity. The new inventive concept addresses these limitations by introducing a system-level approach that integrates AI-driven optimization, personalized medicine, and advanced delivery technologies.
Detailed Description of the Inventive Concept
The inventive concept comprises a machine learning module that iteratively refines CDK inhibition optimization, a neural network-based method for predicting efficacy in patient populations, a pharmaceutical composition featuring a combination of APPAMP compounds with distinct inhibition profiles, and a computer-implemented method for designing novel compounds using generative adversarial networks. Additionally, the concept includes a point-of-care diagnostic device for detecting CDK activity using a novel APPAMP compound-based biosensor.
Novelty and Inventive Step
The new claims introduce a system-level approach to CDK inhibition, integrating AI-driven optimization, personalized medicine, and advanced delivery technologies, which is a significant departure from the original patent's focus on individual compounds. The use of machine learning, neural networks, and generative adversarial networks to optimize CDK inhibition is a novel and non-obvious advancement.
Alternative Embodiments and Variations
Alternative embodiments of the inventive concept could include the use of other AI techniques, such as reinforcement learning or transfer learning, to optimize CDK inhibition. Variations could also involve the integration of additional data sources, such as genomic or proteomic data, to further personalize CDK inhibition.
Potential Commercial Applications and Market
The next-generation CDK inhibition systems envisioned in this inventive concept have the potential to revolutionize the treatment of cancer and other diseases, with a market size projected to be in the billions of dollars. The target industries include pharmaceutical, biotechnology, and healthcare, with potential applications in personalized medicine, precision oncology, and diagnostic devices.
Section 103 Obviousness Analysis (PHOSITA)
Field of Art
Pharmaceutical chemistry, specifically cyclin-dependent kinase (CDK) inhibition technologies, with expertise in medicinal chemistry, computational drug design, and molecular biology
Person of Ordinary Skill (PHOSITA) Profile
A researcher with advanced degree in pharmaceutical sciences or chemical engineering, proficient in computational modeling, drug design techniques, machine learning applications in pharmaceutical research, and understanding of molecular targeting strategies
Obviousness Rationale
A person having ordinary skill in the art would recognize that applying machine learning and computational techniques to optimize CDK inhibition represents a predictable extension of existing pharmaceutical research methodologies. The source patent's focus on specific APPAMP compounds provides a clear foundation for exploring advanced optimization strategies using computational approaches. The PTD's proposed AI-driven methods represent a natural progression in drug discovery technologies that would be obvious to a skilled researcher seeking to improve compound design and efficacy.
Obvious Combinations & Variations
Original Patent Information
| Patent Number | US 11,857,552 |
|---|---|
| Title | 4-[[(7-aminopyrazolo[1,5-a]pyrimidin-5-yl)amino]methyl]piperidin-3-ol compounds as CDK inhibitors |
| Assignee(s) | CARRICK THERAPEUTICS LIMITED |