Polydispersity index
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Multiple Species Distinction & Aggregate-Free Accuracy
FIDA’s Polydispersity Index (PDI) feature provides a precise, automated and reliable assessment of sample homogeneity by distinguishing up to three different species of varying Rh and offering an automated polydispersity index readout for samples with higher complexity. This readout is delivered automatically for every sample measured, making the quality control process effortless and quick.
Unlike other technologies, FIDA’s PDI is not skewed by aggregates and large particles, ensuring accurate, reproducible quality control from research to production. This capability supports diverse applications, from structural biology and drug formulation to vaccine development, enabling optimized workflows, reduced sample waste, and faster, consistent production outcomes.
Why measure PDI with FIDA?
Purity Assessment for Structural Biology Assay Optimisation:
FIDA’s polydispersity measurement allows researchers to distinguish between monomers, oligomers, and larger aggregates, ensuring that only homogeneous protein samples are used for crystallography or cryo-EM. This improves the accuracy of protein models and speeds up structural validation, reducing the time spent troubleshooting sample issues. For academic structural biologists, this means fewer false starts and less time spent optimizing sample conditions for high-resolution imaging. For industrial research, especially in drug discovery, this reduces time-to-market by ensuring faster protein characterization.
Small sample sizes maximise data-to-sample used ratio:
FIDA requires only nanoliter to microlite sample volumes, making it one of the most efficient tools available for sample purity control. This is especially beneficial for labs working with precious or limited biological samples, such as patient-derived samples, rare proteins, or costly reagents. Reduced sample consumption, allows for more experiments to be conducted with limited resources, reducing costs and maximizing data output. Thanks to an autosampler (2x96 well plates/2x50 vials) and an ability to measure PDI in unpurified samples FIDA can even be used for drug candidate screening.
Drug stability and efficacy optimisation:
In the formulation of biopharmaceuticals, controlling the polydispersity of protein solutions is essential for ensuring stability and efficacy. FIDA’s ability to provide accurate and detailed polydispersity data enables formulation scientists to optimize buffers, excipients, and storage conditions, leading to better product stability and shelf life. This ensures that biologic drugs maintain efficacy and stability from production to patient delivery, reducing the risk of suboptimal formulations.
Faster Vaccine Development:
FIDA’s polydispersity measurement plays a critical role in ensuring the homogeneity of proteins or nanoparticles in vaccine formulations, which is essential for triggering an effective immune response. A high level of polydispersity can compromise antigen presentation and reduce vaccine stability. With FIDA’s ability to accurately measure and control PDI, vaccine developers can optimize formulation consistency, enhancing both the stability and efficacy of vaccines. This leads to more reliable immune responses and extends the shelf life of vaccine products, ensuring quality from production to administration.
Fermentation & production optimisation:
FIDA’s ability to monitor polydispersity during protein fermentation (in unpurified samples) helps ensure that production yields are consistent and high-quality. Detecting varying species during the production process enables early intervention, reducing waste and improving overall efficiency. For industrial fermentation and production lines, FIDA minimizes process inefficiencies by detecting purity issues early, leading to fewer rejected batches and more consistent outputs.
Batch consistency:
In both research and industrial protein production, purity is paramount. FIDA’s polydispersity measurement ensures that production batches contain uniform protein populations, reducing the risk of contaminants or unwanted protein variants. This is essential for proteins intended for therapeutic applications, where batch-to-batch consistency is critical, or research applications, where purity affects experimental outcomes. Industrial biopharmaceutical companies can rely on FIDA to meet regulatory purity standards for therapeutic proteins, while academic researchers benefit from higher reproducibility in experiments involving protein expression and purification.
FAQs
Would you like to know more? See frequently asked questions below. If you do not find an answer to your question, you can ask a question to one of our scientists.
The polydispersity index and hydrodynamic radius are measured simultaneously. Difference in size is indicative of the species.
Polydispersity Index (PDI) is a crucial parameter that describes the relative distribution of molecular sizes in a sample. Unlike substances with uniform molecular size, some materials, such as aggregates and biopolymers, exhibit a range of lengths and sizes. The PDI quantifies this variability, providing insight into the sample's heterogeneity. A PDI value close to 1 indicates a uniform sample, where all molecules are nearly the same size. Higher PDI values signify a broader distribution of molecular sizes, indicating greater variability within the sample. Understanding PDI is essential in fields like polymer science and biotechnology, where the size distribution of molecules can impact material properties and functionality.
Protein oligomerization refers to the process by which two or more protein molecules (monomers) associate to form a larger, often functional complex called an oligomer. These oligomers can be composed of identical (homooligomers) or different (heterooligomers) protein subunits. This process is critical in various biological functions and is fundamental to the structure and activity of many proteins.
An oligomerization will cause an increase in hydrodynamic radius compared to the monomer. FIDA measures hydrodynamic radius, that it can detect this change. Moreover, as Fida is a first principle technique the expected size of the monomer can be predicted with either the molecular weight of the protein or a given structure available. A size larger than the predicted monomer would indicate oligomerization, which can be validated by diluting the sample and measuring again. If the size of the diluted sample decreases oligomerization has been verified. If a titration of the protein of interest is performed the oligomerization Kd can be determined.
