Particle Size Meets Chemistry

Particle size distribution (PSD) describes the range and proportion of particle sizes within a sample, typically reported using metrics such as D10, D50, and D90. 

In pharmaceutical systems, PSD is a critical quality attribute (CQA) because it directly influences drug performance, manufacturability, and consistency. However, PSD alone does not reveal what the particles are made of—only how large they are.

Particle size plays a central role in dissolution rate, bioavailability, content uniformity, and processability.

Smaller particles generally dissolve faster due to increased surface area, while larger particles may improve flow properties. However, optimal performance depends on balancing these effects within a multicomponent system.

Particle size alone cannot distinguish between different materials in a formulation, such as active pharmaceutical ingredients (API) and excipients.

In complex blends, multiple components can share overlapping size distributions. Without chemical identification, it is impossible to determine which material contributes to specific regions of the PSD, limiting interpretability and decision-making.

Particle size refers to the physical dimensions of a particle, while particle chemistry defines its molecular composition.

Both are essential for understanding pharmaceutical behavior. Size influences physical performance, while chemistry determines functionality, efficacy, and regulatory compliance.

D10, D50, and D90 are statistical descriptors of a particle size distribution:

• D10: 10% of particles are smaller than this size
• D50: Median particle size
• D90: 90% of particles are smaller than this size

These metrics summarize distribution shape but do not provide information about particle identity.

Conventional methods such as laser diffraction and dynamic light scattering (DLS) measure particle size efficiently but lack chemical specificity. 

Key limitations include:

• Inability to differentiate API from excipients
• No insight into contamination sources
• Difficulty interpreting multimodal distributions
• Assumptions about particle shape and optical properties

As a result, these techniques often provide incomplete or ambiguous data in multicomponent systems.

Particle Correlated Raman Spectroscopy (PCRS) is an analytical technique that combines particle imaging with Raman spectroscopy to deliver a highly sensitive characterization without sample consumption at the individual particle level.

This approach enables simultaneous physical and chemical characterization, providing a more complete understanding of particulate systems.

Operating via a two-step integrated workflow, PCRS combines automated optical imaging for physical size detection with Raman spectroscopy for exact molecular identification of individual particles. The result is a chemically annotated particle dataset, where each particle is defined by both its size and its molecular identity.

PCRS addresses the key limitation of conventional methods by linking size data with chemistry.

This enables:

• Identification of which particles are API vs. excipient
• Separation of overlapping size distributions by material
• Detection of contaminants or foreign particles
• More accurate interpretation of PSD trends

Instead of a single bulk distribution, PCRS produces component-resolved particle size distributions.

PCRS assigns a chemical identity to each measured particle, allowing PSDs to be calculated for individual components.

For example, separate distributions can be generated for:

• API particles
• Individual excipients
• Agglomerates or contaminants

This transforms PSD analysis from a bulk measurement into a material-specific diagnostic tool.

Particle size influences the rate at which a drug dissolves, which directly impacts bioavailability. 

• Smaller particles → faster dissolution → improved absorption
• Larger particles → slower dissolution → potential variability

However, these effects must be interpreted in the context of chemical identity, since only API particles contribute to therapeutic performance.

PCRS enables root cause analysis by linking particle size features to specific materials. 

For example: 

• A coarse tail may be traced to excipient agglomeration
• Excess fines may originate from API milling
• Unexpected particles may indicate contamination

This allows scientists to move from observation to actionable insight. 

Multimodal distributions occur when multiple particle populations are present, often corresponding to different materials or processing conditions.

Without chemical identification, these populations cannot be definitively assigned. PCRS resolves this by separating distributions based on composition.

Differences in particle size and density between components can lead to segregation during processing. This affects content uniformity, dose accuracy, and formulation physical stability. By identifying the size distribution of each component, PCRS helps predict and mitigate segregation risks.

Advanced particle analysis provides deeper insight into formulation behavior and manufacturing consistency. 

With PCRS, quality control teams can: 

• Verify API distribution within a blend
• Detect batch-to-batch variability
• Identify contamination early
• Support regulatory documentation with chemically resolved data

Traditional PSD cannot determine the origin of fines or large particles. 

PCRS solves this by assigning chemical identity, enabling direct attribution: 

• API-derived fines vs excipient fines
• Agglomerates vs primary particles
• Foreign contaminants vs formulation components

Particle shape and morphology influence both measurement accuracy and material performance.

Irregular shapes can distort size measurements in traditional techniques, while also affecting flow and compaction. Imaging-based approaches like PCRS provide more accurate size characterization alongside morphology insight.

The primary advantage is contextualized data.

Instead of asking “What is the particle size?”, scientists can ask:

• Which material is responsible for this size fraction?
• How does each component behave independently?
• What is the root cause of observed distribution changes?

This leads to faster development, improved quality, and reduced risk.