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PDI vs. SPAN: Which Metric Best Represents Particle Size Distribution?
In the fields of nanotechnology and materials science, particle size distribution (PSD) is a critical parameter that directly influences the performance and applicability of nanomaterials. Two of the most commonly used indicators to evaluate PSD are the Polydispersity Index (PDI) and the SPAN value. While often used interchangeably, these two metrics differ significantly in how they are calculated and interpreted. Understanding the distinction between PDI and SPAN is essential for accurate particle characterization.
What Is Polydispersity Index (PDI)?
The Polydispersity Index (PDI) is a measure of the uniformity of particle size distribution in a sample. It quantifies the deviation from a perfectly monodisperse system, where all particles are of identical size. PDI is commonly derived from Dynamic Light Scattering (DLS) data and reflects both the width and homogeneity of the particle size distribution.
Calculation:
PDI is calculated as follows:
PDI = (σ / ZD)²
Where:
σ = standard deviation of the particle size
ZD = Z-average diameter (intensity-weighted mean size)
Interpretation of PDI values:
PDI = 0: perfectly monodisperse
PDI < 0.1: highly uniform dispersion
PDI > 0.5: broad distribution, highly polydisperse
What Is SPAN?
The SPAN value provides another way to assess the width of particle size distribution, especially for asymmetric or skewed distributions. It is widely used in characterizing complex samples, such as those with irregular or non-normal size distributions.
Calculation:
SPAN = (d90 – d10) / d50
Where:
d10, d50, and d90 represent the diameters at 10%, 50%, and 90% cumulative volume, respectively.
A higher SPAN indicates a wider distribution.
Key Differences Between PDI and SPAN
1. Calculation Approach
PDI is based on the ratio of standard deviation to mean particle size, assuming a symmetric distribution.
SPAN uses percentile values from the cumulative distribution curve and is more suitable for asymmetric or skewed systems.
2. Applicability
PDI is ideal for samples with narrow, symmetric distributions, such as well-controlled synthesis processes.
SPAN excels in describing asymmetrically distributed systems often seen in real-world samples like exosomes, liposomes, or viral vectors.
3. Regulatory Relevance in PBE (Population Bioequivalence)
In nanomedicine and drug delivery applications, PBE analysis typically relies on d10, d50, d90, and SPAN as primary metrics.
D50 and SPAN are considered core BE parameters.
Z-average and PDI are sometimes used as alternatives when DLS is the only available method, but they are not preferred.
Notably, PDI tends to exaggerate test/reference (T/R) differences, making PBE evaluations based on PDI more challenging.
4. Numerical Range
PDI ranges from 0 to 1 and emphasizes uniformity.
SPAN has no fixed range and emphasizes distribution width.
Conclusion: Which One Should You Use?
Choosing the right metric for particle size distribution is crucial for understanding and controlling nanoparticle behavior. While both PDI and SPAN are useful, their interpretive value differs across applications:
For symmetric, well-behaved systems, PDI is convenient and informative.
For real-world, polydisperse samples—common in biologics like exosomes, lipid nanoparticles (LNPs), and viral particles—SPAN provides more robust and representative information.
Recent publications and guidance from the China NMPA suggest that relying solely on DLS-derived PDI is inadequate. DLS cannot provide cumulative values such as d10, d50, or d90, and thus cannot fully represent particle distribution. For a comprehensive characterization, multiple orthogonal techniques are strongly recommended [1].
We hope this article clarifies the roles of PDI and SPAN, empowering researchers and developers to make informed choices in nanoparticle analysis and regulatory submission strategies.
References:
[1] Yang D, Zhao X, Li X, Li M. Research and Quality Control Considerations for Lipid Nanoparticles in mRNA Vaccines. Chinese Journal of New Drugs, 2023, Vol. 32(24).
[2] Pathak SM, Ruby PK, Aggarwal D. In vitro and in vivo equivalence testing of nanoparticulate intravenous formulations. Drug Res (Stuttg). 2014;64(4):169–176. doi:10.1055/s-0033-1357204
