Derivative works — US Patent 11938201

As a Senior Patent Strategist and Research Engineer, I have analyzed the core claims of US Patent 11,938,201. The following defensive disclosure document details a series of derivative inventions and improvements designed to create prior art against future, incremental patent applications by competitors. This disclosure is based on the foundational technology described in the '201 patent but expands it into new materials, applications, and technological integrations.

Defensive Disclosure: Derivatives of FAP-alpha Targeting Agents

Patent Reference: US 11,938,201 B2
Title: Imaging and radiotherapeutics agents targeting fibroblast-activation protein-alpha (FAP-alpha)
Core Claimed Structure: A compound of the formula B-L-A, where A is a FAP-alpha targeting moiety (quinoline-based), L is a linker, and B is an imaging or radiotherapeutic agent.

Analysis of Independent Claim 1

Claim 1 of US 11,938,201 broadly covers a compound B-L-A, where A is a quinoline-based FAP-alpha targeting moiety, L is a linker, and B is an optical or radiolabeled functional group. The following disclosures expand upon this fundamental structure.

Derivative Set 1: Based on Claim 1

1.1. Material & Component Substitution: Alternative Chelators & Radionuclides

Enabling Description: The DOTA chelator specified in the '201 patent (FIG. 1C) is effective for chelating trivalent metals like Indium-111 and Lutetium-177. This disclosure proposes substituting DOTA with alternative chelators to accommodate a wider range of radionuclides with different therapeutic or diagnostic properties. Specifically, the use of desferrioxamine B (DFO) as the chelator for Zirconium-89 (⁸⁹Zr), a positron-emitter with a longer half-life (78.41 hours) suitable for tracking slower biological processes like antibody clearance. The synthesis would involve conjugating a DFO-NHS ester to the terminal amine of the linker (L) on the FAP-alpha targeting moiety (A), followed by radiolabeling with ⁸⁹Zr-oxalate under mild heating in a buffered solution (e.g., HEPES, pH 7.0-7.5). This creates a novel agent for long-term PET imaging of FAP-alpha expression.

Mermaid.js Diagram: Synthesis Flow

graph TD
    A[FAP-alpha Targeting Moiety-Linker-NH2] -- + DFO-NHS Ester --> B(Conjugation Reaction);
    B -- Purification --> C(DFO-L-A Precursor);
    C -- + ⁸⁹Zr-oxalate, pH 7.2 --> D(Radiolabeling);
    D -- Final Purification (SPE) --> E([⁸⁹Zr]DFO-L-A Agent);

1.2. Material & Component Substitution: Non-Peptidic Linkers

Enabling Description: The patent describes amino acid-based linkers. This disclosure proposes using polyethylene glycol (PEG) based linkers of varying lengths (n=4 to 24 units). PEG linkers can improve solubility, reduce immunogenicity, and optimize pharmacokinetics by increasing the hydrodynamic radius of the molecule, leading to reduced renal filtration and longer circulation times. The synthesis involves reacting a mono-protected, amine-terminated PEG-NHS ester with the FAP-alpha targeting moiety. The resulting PEGylated precursor would then be deprotected and conjugated to the imaging/therapeutic moiety (B). This variation allows for fine-tuning of the agent's in-vivo behavior.

Mermaid.js Diagram: Component Architecture

graph LR
    subgraph Compound
        A(Targeting Moiety) --- L(PEG Linker <br> n=4-24);
        L --- B(Imaging/Therapeutic Moiety);
    end

1.3. Operational Parameter Expansion: High-Temperature Stability Formulation

Enabling Description: For deployment in resource-limited or field settings, a thermostable formulation is required. This disclosure describes a lyophilized (freeze-dried) kit formulation of the non-radiolabeled precursor (e.g., DOTA-L-A). The precursor is co-lyophilized with stabilizing excipients such as trehalose or mannitol and a bulking agent like glycine. This powdered formulation is stable at temperatures up to 50°C for extended periods. For use, sterile, pyrogen-free water and a buffered solution containing the radionuclide (e.g., ⁶⁸GaCl₃ eluted from a generator) are added, allowing for rapid, on-site reconstitution and radiolabeling without requiring cold-chain storage for the precursor kit.

Mermaid.js Diagram: Reconstitution Process

sequenceDiagram
    participant User
    participant Kit
    participant Radionuclide
    User->>Kit: Add Sterile Water
    User->>Kit: Add Radionuclide Solution
    Kit->>Kit: Precursor dissolves and chelates radionuclide
    Kit-->>User: Provides injectable radiopharmaceutical

1.4. Cross-Domain Application: Aerospace Material Fatigue Sensor

Enabling Description: Fibroblast activation is analogous to matrix micro-damage and repair initiation in advanced carbon-fiber reinforced polymer (CFRP) composites used in aerospace. This disclosure proposes embedding the FAP-alpha targeting moiety (A) conjugated to a fluorophore (B) that exhibits aggregation-induced emission (AIE). The B-L-A compound is integrated into the epoxy resin matrix of the CFRP. Under normal conditions, the agent is molecularly dispersed and non-emissive. When micro-cracks form due to material fatigue, the exposed polymer chains mimic the fibrotic environment, causing the B-L-A molecules to aggregate at the damage site. This aggregation restricts intramolecular rotation, activating intense fluorescence. The composite can then be inspected under UV light, with damaged areas lighting up brightly long before catastrophic failure occurs.

Mermaid.js Diagram: State Transition

stateDiagram-v2
    [*] --> Dispersed
    Dispersed: Non-fluorescent
    Dispersed --> Aggregated: Material Stress / Micro-crack
    Aggregated: Highly Fluorescent
    Aggregated --> Dispersed: Matrix Self-Healing / Repair

1.5. Cross-Domain Application: Agricultural Plant Stress Imaging

Enabling Description: While FAP-alpha is not present in plants, functionally analogous proteases are upregulated in response to environmental stressors like drought, salinity, or pathogen attack, leading to cell wall remodeling. This disclosure proposes modifying the targeting moiety (A) to bind to these plant-specific stress-associated proteases (SAPs). The modified A is linked to a near-infrared (NIR) fluorescent dye (B) with an emission wavelength (>700 nm) that avoids chlorophyll autofluorescence. The resulting B-L-A' agent can be sprayed onto crops. In-vivo imaging using ground-based or drone-mounted NIR cameras would allow farmers to detect and map plant stress across a field with high precision, enabling targeted irrigation or pesticide application before visible symptoms like wilting occur.

Mermaid.js Diagram: AgTech Application Flow

graph TD
    A[Agent Application <br> (Spray Drone)] --> B{Crop Field};
    B --> C{Stressed Plants <br> (Upregulated SAPs)};
    B --> D{Healthy Plants};
    C -- Agent Binds --> E[NIR Fluorescence];
    D -- No Binding --> F[No Signal];
    E --> G[Imaging Drone <br> (NIR Camera)];
    F --> G;
    G --> H[Stress Map for Precision Agriculture];

1.6. Integration with Emerging Tech: AI-Driven Dosimetry & Treatment Planning

Enabling Description: This disclosure describes a system where PET/CT images generated using a radiolabeled version of the '201 patent's agent (e.g., [⁶⁸Ga]Ga-DOTA-L-A) are fed into a pre-trained convolutional neural network (CNN). The AI model is trained on a large dataset of patient scans correlated with biopsy results and treatment outcomes. The system performs three tasks: 1) Automated segmentation and outlining of FAP-positive tumor volumes and metastases, 2) Calculation of standardized uptake values (SUV) and total tumor burden, and 3) Prediction of the optimal therapeutic dose of a corresponding radiotherapeutic agent (e.g., [¹⁷⁷Lu]Lu-DOTA-L-A) based on tumor uptake, patient biometrics, and kidney clearance rates to maximize efficacy while minimizing off-target toxicity.

Mermaid.js Diagram: Data Flow

graph TD
    A[PET/CT Scanner] -- DICOM Images --> B(AI Platform);
    B --> C{CNN Model};
    C -- Task 1 --> D[Automated Tumor Segmentation];
    C -- Task 2 --> E[Tumor Burden Calculation];
    C -- Task 3 --> F[Optimal ¹⁷⁷Lu Dose Prediction];
    D & E & F --> G(Oncologist's Dashboard);

1.7. Integration with Emerging Tech: Blockchain for Radiopharmaceutical Supply Chain

Enabling Description: To ensure the authenticity and quality of radiopharmaceuticals, which have short half-lives and are produced on-demand, this disclosure proposes a blockchain-based tracking system. Each batch of the FAP-targeting precursor (e.g., DOTA-L-A) is assigned a unique hash on a permissioned blockchain. At each step—synthesis, QC testing, radiolabeling at the radiopharmacy, dose calibration, and administration to the patient—a transaction is recorded on the blockchain ledger. This creates an immutable, time-stamped audit trail. Hospitals can scan a QR code on the dose vial to instantly verify its entire lifecycle, confirming it is an authentic product from a licensed manufacturer and that the radioactivity matches the prescribed dose at the time of injection.

Mermaid.js Diagram: Blockchain Ledger

sequenceDiagram
    participant Manufacturer
    participant Radiopharmacy
    participant Hospital
    participant Blockchain
    Manufacturer->>Blockchain: Create Batch (Precursor Hash)
    activate Blockchain
    Radiopharmacy->>Blockchain: Record Radiolabeling Event (¹⁷⁷Lu Lot #)
    Radiopharmacy->>Blockchain: Record QC Results
    Hospital->>Blockchain: Record Dose Administration (Patient ID Hash)
    deactivate Blockchain

1.8. The "Inverse" or Failure Mode: FAP-alpha Activated Pro-Drug

Enabling Description: To improve the tumor-to-background ratio and reduce systemic toxicity, this disclosure describes a "pro-drug" therapeutic agent. The cytotoxic payload (B), instead of being a simple radionuclide, is a potent chemotherapeutic agent like a duocarmycin analogue. It is attached via a specialized linker (L) that is specifically cleavable by the enzymatic activity of FAP-alpha. Furthermore, a temporary "caging" group is attached to the cytotoxic agent, rendering it inert. The entire B-L-A compound circulates systemically in its inactive state. Upon reaching a FAP-positive tumor, the FAP-alpha enzyme cleaves the linker, releasing the caged cytotoxic agent. A secondary, tumor-specific condition (e.g., hypoxia or low pH) then cleaves the caging group, activating the payload only within the precise tumor microenvironment. This dual-check system ensures the agent fails safely (remains inert) if it does not localize to the target.

Mermaid.js Diagram: Activation Sequence

graph TD
    A[Systemic Circulation <br> (Inactive Prodrug)] -- Step 1: Localization --> B{FAP-alpha+ Tumor};
    B -- Step 2: FAP-alpha enzymatic cleavage --> C[Linker Cleavage <br> (Release of caged payload)];
    C -- Step 3: Tumor Microenvironment <br> (e.g., Hypoxia) --> D[Decaging & Activation];
    D -- Step 4 --> E[Localized Cytotoxicity];

Combination Prior Art Scenarios

Combination with DICOM Standard: The imaging data (PET, SPECT, CT) generated by the agents described in US 11,938,201 is formatted and transmitted according to the open-source DICOM (Digital Imaging and Communications in Medicine) standard (ISO 12052). This allows for seamless integration with existing hospital Picture Archiving and Communication Systems (PACS) and radiology workstations, making the image analysis interoperable with standard clinical software tools and AI algorithms built on the DICOM framework.
Combination with Open Babel Cheminformatics Standard: The chemical structures of all disclosed derivatives, including the quinoline core (A), linkers (L), and functional groups (B), are encoded using the open-source Simplified Molecular-Input Line-Entry System (SMILES) and processed using the Open Babel chemistry toolbox. This enables the creation of searchable chemical databases, facilitates computational modeling of structure-activity relationships (SAR), and allows for the use of open-source software for virtual screening of new targeting moieties or linkers.
Combination with TensorFlow/PyTorch for Predictive Modeling: The AI-driven diagnostic and dosimetry system described in derivative 1.6 is built using the open-source TensorFlow or PyTorch machine learning frameworks. The trained CNN models, along with the training and validation scripts, are made publicly available. This allows other researchers to replicate the results, fine-tune the models on their own datasets, and develop new predictive algorithms for FAP-alpha targeted theranostics, thereby rendering minor improvements to the AI model itself as obvious extensions of this work.