ParticleSizeDistributionChecker

Particle Size Distribution

dermal fillers dissolution EAEU endotoxins hyaluronic acid impurities injectables microbiology

Particle Size Distribution Checker — Particle Size Analysis (ISO 13485, Ph. Eur.)

ℹ️ The utility checks D10, D50, D90, max size, agglomerates, Span (homogeneity).
ℹ️ Does not use machine learning — rule-based logic only.
⚠️ CRITICAL! D_Max > 800 µm needle clogging risk. Agglomerates > 1% granuloma risk!

Usage:
ParticleSizeDistributionChecker.exe → demo mode
ParticleSizeDistributionChecker.exe input.csv output.json → evaluate data

Input format:
BatchNumber,D10_um,D50_um,D90_um,D_Max_um,Agglomerates_Percent,Span_Value,Concentration_ParticlesPerMl,Viscosity_Pa_s,pH_Value,SterilityTest,Endotoxin_EU_ml,Measurement_Method

Example:
PSD-HA-2026-001,45.0,150.0,350.0,480.0,0.3,2.03,50000,20.0,7.1,0,0.1,LaserDiffraction

— WHY IS THIS NEEDED?
Particle size distribution — critical quality parameter for homogenized fillers (HA, CaHA, PLLA):
• Determines passability through thin needles (27G, 30G)
• Affects injection smoothness and risk of lumps/granulomas
• D50 (median) characterizes average particle size
• D90 and D_Max control presence of large outlier particles
• Span ((D90-D10)/D50) shows distribution homogeneity (lower is better)
⚠️ CRITICAL:
• D_Max ≤ 800 µm — larger particles may clog needle or cause embolism
• Agglomerates ≤ 1% — clumped particles are main cause of granulomas and skin irregularities
• D50 in range 20–400 µm — optimal for most facial areas
• Span ≤ 2.5 — ensures predictable gel behavior
• Sterility and endotoxins — mandatory for all injectable forms
⚠️ Note: Measurement performed by laser diffraction, dynamic light scattering, or automated microscopy. Important to control not only averages but also distribution "tails" (D90, D_Max), as large particles cause injection problems. Agglomerates may form during improper storage or synthesis. Homogeneity (low Span) critical for uniform filler distribution in tissues.

input.csv

BatchNumber,D10_um,D50_um,D90_um,D_Max_um,Agglomerates_Percent,Span_Value,Concentration_ParticlesPerMl,Viscosity_Pa_s,pH_Value,SterilityTest,Endotoxin_EU_ml,Measurement_Method
PSD-HA-2026-001,45.0,150.0,350.0,480.0,0.3,2.03,50000,20.0,7.1,0,0.1,LaserDiffraction

URS & FS — user requirements and functional specification

This document defines user requirements and functional specification for the quality-control utility. It supports deployment discussion, IQ/OQ preparation and QC workflow integration.

Utility: ParticleSizeDistributionCheckerAPI / object: Particle Size DistributionCategory: Dermal fillers

1. Scope

The Particle Size Distribution Checker utility is used for Particle Size Distribution. The actual input.csv is the source of truth for the input structure; control criteria are defined by the executable and the domain description.

The utility does not use machine learning; decisions are produced by deterministic rules.

Categories: Dermal fillers, EAEU pharmacopoeia

Tags: dermal fillers, dissolution, EAEU, endotoxins, hyaluronic acid, impurities, injectables, microbiology, particles, pharmacopoeia, rheology, specification, sterility

2. Execution modes

ParticleSizeDistributionChecker.exe                            → demo mode
ParticleSizeDistributionChecker.exe input.csv output.json      → evaluate data

3. Key controlled areas

assay / content
dissolution / disintegration
microbiology and pathogens
pH and physicochemical parameters

4. Domain limits and critical parameters

⚠️ CRITICAL! D_Max > 800 µm needle clogging risk. Agglomerates > 1% granuloma risk!
⚠️ CRITICAL:
D_Max ≤ 800 µm — larger particles may clog needle or cause embolism
Agglomerates ≤ 1% — clumped particles are main cause of granulomas and skin irregularities
D50 in range 20–400 µm — optimal for most facial areas
Span ≤ 2.5 — ensures predictable gel behavior
Sterility and endotoxins — mandatory for all injectable forms
⚠️ Note: Measurement performed by laser diffraction, dynamic light scattering, or automated microscopy. Important to control not only averages but also distribution "tails" (D90, D_Max), as large particles cause injection problems. Agglomerates may form during improper storage or synthesis. Homogeneity (low Span) critical for uniform filler distribution in tissues.

Limits stated in the description must be verified against the current approved specification, pharmacopoeial monograph and registration dossier before production use.

5. URS — user requirements

ID	Requirement	Criticality	Acceptance criterion
URS-001	The system shall accept an `input.csv` file for Particle Size Distribution with the exact columns listed in the “Input data contract” section.	High	A file with the correct header is processed without manual editing; missing mandatory columns produce FAIL/import error.
URS-002	The system shall support execution without arguments in demo mode and execution with `input.csv output.json` for user data.	Medium	Both execution scenarios produce a predictable result or clear diagnostic error.
URS-003	The system shall perform rule-based controls for: assay / content, dissolution / disintegration, microbiology and pathogens, pH and physicochemical parameters.	High	Each controlled parameter receives a status and message; the result does not depend on hidden Excel formulas or ML.
URS-004	The system shall preserve traceability between batch/lot, source values, applied rules and final verdict.	High	Output includes batch identifier, source values, parameter statuses and critical findings.
URS-005	The system shall generate machine-readable `output.json` for LIMS/ELN/MES, batch record and QA/QC review.	High	JSON contains overall status, check array, warnings, failures and source-file reference.
URS-006	The system shall support use in the client validation package: URS/FS, IQ/OQ preparation, installation and operational scenario checks.	High	The document, test scenarios and reproducible CSV/JSON flow are suitable for audit and internal approval.
URS-007	The system shall clearly separate technical data errors from specification nonconformities.	Medium	Schema/type errors are not mixed with pharmacopoeial deviations and are reported separately.

6. input.csv data contract

The source of truth for the input schema is the actual input.csv header. Column names are technical identifiers and are not translated.

#	Column	Type	Unit	Description	Sample	Control rule
1	`BatchNumber`	identifier	as specified	Batch Number	`PSD-HA-2026-001`	mandatory field; used for batch/lot traceability
2	`D10_um`	decimal	µm	D10 um	`45.0`	mandatory numeric value; rule comparison is performed by the utility
3	`D50_um`	decimal	µm	D50 um	`150.0`	mandatory numeric value; rule comparison is performed by the utility
4	`D90_um`	decimal	µm	D90 um	`350.0`	mandatory numeric value; rule comparison is performed by the utility
5	`D_Max_um`	decimal	µm	D Max um	`480.0`	mandatory numeric value; rule comparison is performed by the utility
6	`Agglomerates_Percent`	decimal	%	Agglomerates %	`0.3`	mandatory numeric value; rule comparison is performed by the utility
7	`Span_Value`	decimal	as specified	Span Value	`2.03`	mandatory numeric value; rule comparison is performed by the utility
8	`Concentration_ParticlesPerMl`	decimal	as specified	Concentration Particles per Ml	`50000`	numeric value; compared with assay/purity limit from specification or method
9	`Viscosity_Pa_s`	decimal	as specified	Viscosity Pa s	`20.0`	mandatory numeric value; rule comparison is performed by the utility
10	`pH_Value`	decimal	pH	p H Value	`7.1`	mandatory numeric value; rule comparison is performed by the utility
11	`SterilityTest`	boolean / flag	0/1	Sterility Test	`0`	valid flag required; prohibited organisms and growth are normally expected as 0 / absent
12	`Endotoxin_EU_ml`	decimal	as specified	Endotoxin EU ml	`0.1`	numeric value; critical safety attribute compared with endotoxin limit
13	`Measurement_Method`	text	as specified	Measurement Method	`LaserDiffraction`	mandatory value; format and acceptability are checked by the utility

CSV example

BatchNumber,D10_um,D50_um,D90_um,D_Max_um,Agglomerates_Percent,Span_Value,Concentration_ParticlesPerMl,Viscosity_Pa_s,pH_Value,SterilityTest,Endotoxin_EU_ml,Measurement_Method
PSD-HA-2026-001,45.0,150.0,350.0,480.0,0.3,2.03,50000,20.0,7.1,0,0.1,LaserDiffraction

7. FS — functional specification

ID	Function	Implementation description
FS-001	CLI entry point	The executable `ParticleSizeDistributionChecker.exe` supports demo mode and `input.csv output.json` processing mode.
FS-002	CSV parser	The import module reads CSV, validates header, column presence/order, value count and encoding. Decimal values are expected with a dot separator.
FS-003	Field conversion	Each column is converted to the expected type: identifier, text, decimal number, date/time or boolean flag.
FS-004	Domain rule engine	For Particle Size Distribution, explicit rules are applied: range, minimum, maximum, absence of prohibited flag, data completeness or calculation-based check.
FS-005	Criticality handling	Critical violations produce FAIL; non-critical deviations and incomplete data produce WARNING; full conformance produces PASS.
FS-006	JSON writer	`output.json` stores overall status, per-parameter results, source values, warnings, failures and diagnostic messages.
FS-007	Integration contract	The CSV → JSON format is stable for invocation from LIMS/ELN/MES, scheduled task or wrapper service.
FS-008	Error handling	Schema error, missing file, non-numeric value or JSON write failure returns diagnosable error without silent PASS.

8. output.json contract

The output file must be suitable for automated processing, audit review and correlation with the source input.csv row.

{
  "utility": "ParticleSizeDistributionChecker",
  "api": "Particle Size Distribution",
  "batchNumber": "PSD-HA-2026-001",
  "overallStatus": "PASS|WARNING|FAIL",
  "checkedAtUtc": "2026-05-18T00:00:00Z",
  "checks": [
    {
      "parameter": "BatchNumber",
      "value": "PSD-HA-2026-001",
      "unit": "as specified",
      "status": "PASS|WARNING|FAIL",
      "message": "Rule-based check result"
    },
    {
      "parameter": "D10_um",
      "value": "45.0",
      "unit": "µm",
      "status": "PASS|WARNING|FAIL",
      "message": "Rule-based check result"
    },
    {
      "parameter": "D50_um",
      "value": "150.0",
      "unit": "µm",
      "status": "PASS|WARNING|FAIL",
      "message": "Rule-based check result"
    },
    {
      "parameter": "D90_um",
      "value": "350.0",
      "unit": "µm",
      "status": "PASS|WARNING|FAIL",
      "message": "Rule-based check result"
    },
    {
      "parameter": "D_Max_um",
      "value": "480.0",
      "unit": "µm",
      "status": "PASS|WARNING|FAIL",
      "message": "Rule-based check result"
    },
    {
      "parameter": "Agglomerates_Percent",
      "value": "0.3",
      "unit": "%",
      "status": "PASS|WARNING|FAIL",
      "message": "Rule-based check result"
    },
    {
      "parameter": "Span_Value",
      "value": "2.03",
      "unit": "as specified",
      "status": "PASS|WARNING|FAIL",
      "message": "Rule-based check result"
    },
    {
      "parameter": "Concentration_ParticlesPerMl",
      "value": "50000",
      "unit": "as specified",
      "status": "PASS|WARNING|FAIL",
      "message": "Rule-based check result"
    }
  ],
  "criticalFindings": [],
  "sourceFile": "input.csv"
}

9. OQ/PQ test scenarios

ID	Scenario	Expected result
TC-001	Valid CSV with expected header and sample row	All rows are processed; output contains PASS/WARNING/FAIL and parameter-level detail.
TC-002	A mandatory input.csv column is missing	Import is rejected or the row receives FAIL with schema reference.
TC-003	A numeric field contains text or a blank value	Type conversion error is recorded; the result is not hidden as PASS.
TC-004	A parameter is outside specification or critical limit	Critical parameters produce FAIL; non-critical deviations produce WARNING according to the rule.
TC-005	Positive pathogen/microbiological flag or microbiological limit excursion	Critical FAIL is produced with the offending parameter.

10. QA/QC, CSV and change control

Before production use, the client records executable version, checksum, specification/monograph version, test CSV, expected JSON and IQ/OQ results.
Column names must not be changed without updating the validator and test scenarios.
The source CSV, output.json and utility version should be stored together as an evidence package.
Any change in control rules must go through change control and repeated OQ scenario verification.

Included in packages

Dental & Dental Materials QC Suite

QC utility package for dental medicinal products, oral-care products and dental materials: anaesthetics, antiseptics, analgesic/anti-infective products, polymers/minerals, resins, bonding workflows, microbiology, stability and material QC.

Open

Dermal Fillers QC Suite

Package for injectable implants and dermal fillers: hyaluronic acid, CaHA, PLLA, PMMA, collagen and related tests.

Open

EAEU Pharmacopoeia QC Suite

Package for EAEU pharmacopoeial methods: specifications, dissolution, microbiology, impurities and general tests.

Open