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,ProductName,Residual_Solvents_ppm,Protein_Residue_ppm,Endotoxin_EU_mg,Phenylacetic_Acid_ppm,Unknown_Peaks_Area_Percent,Fermentation_Duration_h,pH_Drift,Max_Solvents,Max_Protein
FERM-IMP-2026-001,Penicillin G Potassium,120.0,50.0,5.0,20.0,0.1,160.0,0.2,500.0,100.0
FERM-IMP-2026-002,Cephalosporin C,80.0,30.0,2.0,5.0,0.05,140.0,0.1,500.0,100.0
FERM-IMP-2026-003,Penicillin G (Poor Extraction),600.0,150.0,15.0,80.0,1.2,165.0,0.8,500.0,100.0

input.csv fields

Utility description

Fermentation Impurity Profile Analyzer — Fermentation Impurity Profile Analyzer

Fermentation Impurity Profile Analyzer — Fermentation Impurity Profile Analyzer

ℹ️ Utility identifies specific biosynthesis impurities:
• Residual extraction solvents
• Protein impurities (fungal)
• Endotoxins
• Precursor residues
• Unidentified metabolic peaks

⚠️ CRITICAL: Protein impurities and endotoxins cause severe reactions!
'Ghost peaks' may indicate strain mutation or contamination.

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

Input format:
BatchNumber,ProductName,Residual_Solvents_ppm,Protein_Residue_ppm,Endotoxin_EU_mg,Phenylacetic_Acid_ppm,Unknown_Peaks_Area_Percent,Fermentation_Duration_h,pH_Drift,Max_Solvents,Max_Protein

Example:
FERM-IMP-2026-001,Penicillin G Potassium,120.0,50.0,5.0,20.0,0.1,160.0,0.2,500.0,100.0

— WHY IS THIS NEEDED?
Fermentation impurity control is critical for antibiotic manufacturers (Shyndec, Sinopharm):
• Antibiotic isolation from broth involves organic solvent extraction
• Fungal proteins and cell walls are strong allergens and pyrogens
• Precursor residues (e.g., phenylacetic acid) can be toxic
• "Ghost peaks" on chromatograms often indicate side metabolites or contamination by another strain
• Regulators require full identification or control of unknown impurities >0.1%

⚠️ CRITICAL:
• Residual solvents (Class 2) must be below ICH Q3C limits
• Protein impurities ≤100 ppm (strictly for parenteral forms)
• Endotoxins ≤10 EU/mg (or stricter depending on dose)
• No unidentified peaks >0.5% area
• Precursor control to prevent side synthesis reactions

Key features:
• Complex analysis of GC-MS (solvents) and HPLC (proteins/precursors) data
• Detection of anomalies in impurity profile ("ghost peaks")
• Purification stage efficiency assessment (extraction, crystallization)
• Producing strain stability monitoring by metabolite profile

Critical parameters:
• Residual Solvents: ≤500 ppm (example)
• Protein Residue: ≤100 ppm
• Endotoxins: ≤10 EU/mg
• Unknown Peaks: ≤0.5%

💡 Usage tips:
1. Use GC-MS for volatile solvents and HPLC-UV/MS for non-volatile impurities
2. Compare profiles with "gold standard" of good fermentation cycle
3. If new peaks appear, sequence strain to exclude mutations
4. Control crystal washing efficiency to reduce protein and solvents
5. Maintain impurity "fingerprint" database for each reactor/tank

⚠️ Note: Unlike synthetic chemistry where impurities are predictable, fermentation is a living process. Impurity profile may change from batch to batch due to slight changes in nutrient media or aeration conditions. The utility helps keep this chaos under control.

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,ProductName,Residual_Solvents_ppm,Protein_Residue_ppm,Endotoxin_EU_mg,Phenylacetic_Acid_ppm,Unknown_Peaks_Area_Percent,Fermentation_Duration_h,pH_Drift,Max_Solvents,Max_Protein
FERM-IMP-2026-001,Penicillin G Potassium,120.0,50.0,5.0,20.0,0.1,160.0,0.2,500.0,100.0
FERM-IMP-2026-002,Cephalosporin C,80.0,30.0,2.0,5.0,0.05,140.0,0.1,500.0,100.0
FERM-IMP-2026-003,Penicillin G (Poor Extraction),600.0,150.0,15.0,80.0,1.2,165.0,0.8,500.0,100.0

Output JSON

{
  "Header": {
    "UtilityName": "Fermentation_Impurity_Profile_Analyzer",
    "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
        }
      ]
    }
  ]
}