Purpose

Define user requirements for a standalone FUZKK utility that accepts laboratory CSV data, evaluates the records using limits embedded in code, and produces LabWare-compatible JSON.

Scope

The utility is intended for preliminary QC/QA review, integration testing, LIMS/LabWare flow and evidence-trail preparation. Final release decisions remain under the laboratory's validated procedure and responsible personnel.

Users

QC analyst, QA reviewer, CSV/validation engineer, LIMS/LabWare integration engineer, responsible laboratory specialist.

User requirements

1. The utility shall run without arguments and print its self-description, a built-in input.csv example from GetDemoData(), and demo evaluation for the embedded records.
2. The utility shall run with two arguments: input.csv output.json.
3. The utility shall not read input.csv and shall not write output.json when started without arguments.
4. CSV numeric values shall be parsed using CultureInfo.InvariantCulture.
5. Output shall be generated as LabWare-compatible JSON with Header, Samples, Results, Status, StatusCode, ErrorMessage, Description and DescriptionEN.
6. For PASS records, ErrorMessage shall be an empty string.
7. Embedded limits shall follow this priority: Ph. Eur. → British Pharmacopoeia / UK implementation → EAEU / regional requirements → EMA/ICH/EU guidance → USP fallback.
8. If an exact monograph is not known, strict standard API limits are used where applicable: assay 98–102%, total impurities ≤1.0%, individual impurity ≤0.5%.
9. For biologics and mAb-like products, aggregation, sterility and endotoxin checks shall be included where relevant to the utility purpose.
10. If a parameter may arrive in different units, the unit shall be represented as a separate input field or explicitly reflected in the input.csv field name.

Input CSV

BatchNumber,ADCName,HMW_Aggregates_Percent,Monomer_Percent,LMW_Fragments_Percent,SubVisibleParticles_10um,SubVisibleParticles_25um,Turbidity_NTU,StorageTemp_Celsius,StressTestDays,DAR_Value
ADC-AGG-2026-001,Trastuzumab-Deruxtecan,0.8,98.5,0.7,1200,150,2.5,5.0,0,7.7
ADC-AGG-2026-002,Sacituzumab-Govitecan,1.2,97.8,1.0,2500,300,4.1,25.0,7,7.6
ADC-AGG-2026-003,Polatuzumab-Vedotin,0.5,99.1,0.4,800,100,1.8,5.0,0,3.5

input.csv fields

Utility description

ADC Aggregation Monitor — ADC Aggregation Monitoring

ADC Aggregation Monitor — ADC Aggregation Monitoring

ℹ️ Utility analyzes aggregation profile and particles in ADCs:
• HMW Aggregates ≤1.5%
• Monomer purity ≥98.0%
• LMW Fragments ≤2.0%
• Particles >10 μm ≤6000 particles/mL
• Particles >25 μm ≤600 particles/mL
• Turbidity ≤10 NTU

⚠️ CRITICAL: Hydrophobic toxins increase aggregation risk!
ADC aggregates are highly immunogenic and can cause anaphylaxis.

Usage:
ADCAggregationMonitor.exe → demo mode (console output)
ADCAggregationMonitor.exe input.csv output.json → evaluate your data

Input format:
BatchNumber,ADCName,HMW_Aggregates_Percent,Monomer_Percent,LMW_Fragments_Percent,SubVisibleParticles_10um,SubVisibleParticles_25um,Turbidity_NTU,StorageTemp_Celsius,StressTestDays,DAR_Value

Example:
ADC-AGG-2026-001,Trastuzumab-Deruxtecan,0.8,98.5,0.7,1200,150,2.5,5.0,0,7.7

— WHY IS THIS NEEDED?
Aggregation control is critical for ADC products (Hengrui, BeiGene, Hansoh, etc.):
• Conjugation of hydrophobic toxin disrupts antibody structure, increasing tendency to clump
• Aggregates cause immune response (ADA), drug neutralization, and allergic reactions
• Sub-visible particles can block capillaries or cause inflammatory reactions
• For regulators (NMPA, FDA), particle profile is a key safety parameter

⚠️ CRITICAL:
• HMW Aggregates ≤1.5% — main marker of conjugate stability
• Monomer ≥98.0% — ensures presence of active drug form
• Particles >10 μm ≤6000 — compliance with USP <788> for parenteral forms
• Turbidity ≤10 NTU — indicator of large toxin precipitates
• High DAR (e.g., 7.7 for T-DXd) significantly increases aggregation risk compared to low DAR (3.5)

Key features:
• Comprehensive SEC-HPLC analysis
• Sub-visible particle counting (light scattering or resistive pulse method)
• Assessment of DAR impact on stability
• Stress test monitoring (temperature, storage time)

Critical parameters:
• HMW Aggregates: ≤1.5%
• Monomer: ≥98.0%
• LMW Fragments: ≤2.0%
• Particles >10 μm: ≤6000 particles/mL
• Particles >25 μm: ≤600 particles/mL
• Turbidity: ≤10 NTU

💡 Usage tips:
1. Use SEC-MALS for accurate determination of aggregate molecular weight
2. Avoid filtering samples before analysis unless required by method (filters remove large aggregates)
3. Control sample temperature — heating accelerates aggregation of hydrophobic ADCs
4. Compare aggregation profiles before and after stress tests (freeze/thaw)
5. If high aggregate level (>1.5%) detected, consider optimizing buffer or purification process

⚠️ Note: Unlike standard monoclonal antibodies, ADCs are susceptible to "toxin-induced aggregation". This occurs when hydrophobic toxin molecules seek to minimize contact with water, clumping together even within the protein structure. The utility helps identify such risks at early development stages.

Traceability and limitations

• The URS is used as the source document for functional specification, CSV review and later validation work.
• This document does not replace an approved pharmacopoeial monograph, validated analytical method or internal product specification.
• For product-specific limits, the approved customer specification takes priority.

The functional specification describes the behaviour of the standalone FUZKK console utility, input-data format, evaluation algorithm and output JSON structure.

Functional flow

1. Main() checks the number of arguments.
2. If no arguments are provided: PrintHello() prints the description and built-in input.csv example, then RunDemoEvaluation() executes Evaluate() over GetDemoData() and prints demo JSON.
3. If two arguments are provided: RunWithFiles(input.csv, output.json) reads CSV, evaluates each record and writes LabWare-compatible JSON.
4. LoadData() uses CultureInfo.InvariantCulture and shall not be called in no-arguments mode.
5. Evaluate() returns a named tuple with BatchNumber, ProductName, Parameters, CriticalFailCount, WarningCount, Recommendation and RecommendationEN.
6. GetIssues() builds messages for ErrorMessage in WARNING/FAIL cases.

Evaluation rules

• PASS: CriticalFailCount = 0 and WarningCount = 0.
• WARNING: CriticalFailCount = 0 and WarningCount > 0.
• FAIL: CriticalFailCount > 0.
• ERROR: exception during reading or processing.
• ErrorMessage remains empty for PASS.
• Limits are embedded in Program.cs; no external limit configuration is required.

Input and fields

BatchNumber,ADCName,HMW_Aggregates_Percent,Monomer_Percent,LMW_Fragments_Percent,SubVisibleParticles_10um,SubVisibleParticles_25um,Turbidity_NTU,StorageTemp_Celsius,StressTestDays,DAR_Value
ADC-AGG-2026-001,Trastuzumab-Deruxtecan,0.8,98.5,0.7,1200,150,2.5,5.0,0,7.7
ADC-AGG-2026-002,Sacituzumab-Govitecan,1.2,97.8,1.0,2500,300,4.1,25.0,7,7.6
ADC-AGG-2026-003,Polatuzumab-Vedotin,0.5,99.1,0.4,800,100,1.8,5.0,0,3.5

Output JSON

{
  "Header": {
    "UtilityName": "ADC_Aggregation_Monitor",
    "Version": "1.0.0",
    "Timestamp": "UTC",
    "InstrumentID": "FUZKK-QC-WORKSTATION",
    "OperatorID": "Admin"
  },
  "Samples": [
    {
      "SampleID": "from BatchNumber",
      "BatchNumber": "from CSV",
      "ProductName": "from CSV",
      "TestName": "utility-specific test",
      "AnalysisCode": "utility-specific code",
      "Status": "PASS | WARNING | FAIL | ERROR",
      "StatusCode": "1 | 2 | 0 | -1",
      "ErrorMessage": "",
      "Description": "Russian recommendation",
      "DescriptionEN": "English recommendation",
      "Results": [
        {
          "ParameterName": "parameter",
          "ResultValue": 0.0,
          "UnitOfMeasure": "unit",
          "SpecificationLimit": "limit",
          "IsWithinSpec": true
        }
      ]
    }
  ]
}