Nanoparticles interacting with cell surfaces, highlighting adhesion and targeted drug delivery.

Decoding Nanoparticle Dosimetry: Are We Getting the Right Dose?

Nico Varela in Health & Wellness August 2025 • 4 min read.

"New research reveals how particle behavior on cell surfaces impacts the effectiveness and safety of nanomedicine, offering critical insights for better drug delivery design."

Nanoparticles are becoming increasingly important in medicine, from delivering drugs to diagnosing diseases. But to ensure these tiny particles are both effective and safe, we need to fully understand how they interact with our cells. This is where 'dosimetry' comes in – essentially, making sure we're giving the right 'dose' to get the desired effect without harmful side effects.

Traditional methods of measuring this dose often fall short, especially in in vitro (lab-based) studies. Many calculations are based on the nominal dose, i.e., the concentration of particles added to a cell culture. However, this doesn't tell us how many particles actually reach the cells or where they end up. Factors like particle size, shape, and the properties of the cell culture medium all play a role.

Now, a groundbreaking study is shedding light on the complexities of nanoparticle dosimetry. Researchers are using high-resolution scanning electron microscopy to meticulously track particle behavior at the single-cell level. Their findings reveal surprising discrepancies in particle distribution and adhesion, challenging conventional models and paving the way for more accurate and effective nanomedicine.

The Sticky Situation: Why Particle Adhesion Matters

Nanoparticles interacting with cell surfaces, highlighting adhesion and targeted drug delivery.

One of the most striking findings of the study is the uneven distribution of nanoparticles on cell surfaces. The researchers discovered that particles tend to accumulate more in the spaces between cells (intercellular regions) than directly on the cell surfaces themselves.

This unevenness highlights the importance of particle adhesion. Do nanoparticles stick easily to cell membranes, or do they tend to bounce off? To address this, researchers compared their experimental results with two popular particokinetic models: In vitro Sedimentation, Diffusion, and Dosimetry (ISDD) and Distorted Grid (DG). These models help predict particle behavior based on factors like size, diffusion, and importantly, surface 'stickiness'.

ISDD Model: Assumes particles stick readily to surfaces. Best predicts particle distribution on surfaces with high adsorption.
DG Model: Incorporates a 'stickiness' parameter, allowing simulation of different adsorption strengths. More accurately predicts particle behavior when stickiness varies.

The study revealed that when high adhesion (or 'stickiness') was assumed, the ISDD model accurately predicted particle concentrations in intercellular spaces. However, when it came to the cell surfaces themselves, a modified DG model that accounted for lower stickiness provided the most accurate predictions. This suggests that particle adhesion to cell membranes is weaker than previously thought.

Rethinking the Dose: Implications for Future Research

This research underscores that accurately predicting the effective cellular dose is more complex than simply measuring the concentration of nanoparticles added to a cell culture. The spatial distribution of particles, influenced by factors like adhesion, plays a crucial role.

To improve the accuracy of dosimetry models, future research needs to focus on better understanding and quantifying particle adsorption and desorption rates on different surfaces. Techniques like high-resolution optical microscopy and single-particle tracking could provide valuable insights into these dynamic processes.

Ultimately, a more refined understanding of nanoparticle dosimetry will lead to the design of safer and more effective nanomedicines. By precisely controlling the dose delivered to cells, we can maximize therapeutic benefits while minimizing potential side effects.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1186/s12951-018-0426-2, Alternate LINK

Title: Assessment Of In Vitro Particle Dosimetry Models At The Single Cell And Particle Level By Scanning Electron Microscopy

Subject: Pharmaceutical Science

Journal: Journal of Nanobiotechnology

Publisher: Springer Science and Business Media LLC

Authors: Thomas Kowoll, Susanne Fritsch-Decker, Silvia Diabaté, Gerd Ulrich Nienhaus, Dagmar Gerthsen, Carsten Weiss

Published: 2018-12-01

Everything You Need To Know

What is nanoparticle dosimetry, and why is it important in nanomedicine?

Nanoparticle dosimetry is the process of measuring and understanding the dose of nanoparticles that cells are exposed to. It's essential for nanomedicine because it helps ensure that the correct amount of nanoparticles is delivered to achieve the desired therapeutic effect without causing harm. This involves understanding how nanoparticles interact with cells, including factors like particle size, shape, and the cell culture medium properties, as these influence the actual dose received by the cells.

Why does the uneven distribution of nanoparticles matter?

The uneven distribution of nanoparticles matters significantly because it directly impacts the effective dose received by cells. The study revealed that nanoparticles don't distribute uniformly; they accumulate more in intercellular regions. This means that simply measuring the concentration of nanoparticles added to a cell culture (nominal dose) doesn't accurately reflect how much of the nanoparticles actually interact with cell surfaces. This uneven distribution can lead to inaccurate assessments of both efficacy and safety.

How do the In vitro Sedimentation, Diffusion, and Dosimetry (ISDD) model and the Distorted Grid (DG) model differ in this context?

The In vitro Sedimentation, Diffusion, and Dosimetry (ISDD) model assumes that nanoparticles readily stick to cell surfaces, predicting particle distribution. The Distorted Grid (DG) model incorporates a 'stickiness' parameter, simulating different adsorption strengths. The study found that ISDD model accurately predicted particle concentrations in intercellular spaces, when high adhesion was assumed. But the DG model that accounted for lower stickiness was most accurate for predicting what happens on the cell surfaces themselves. The choice of model and the parameters used affect how accurately we understand the distribution and action of nanoparticles.

What is particle adhesion, and why is it relevant?

Particle adhesion refers to how well nanoparticles stick to cell membranes. The study found that particle adhesion to cell membranes is weaker than previously thought. This means that nanoparticles might not bind to cells as strongly as researchers previously believed, impacting their delivery and effectiveness. Understanding this 'stickiness' is important for predicting where the particles go and how they interact with cells, which directly impacts drug delivery.

How might this research change the way we conduct future studies?

This research changes how we view and measure the dose of nanoparticles that cells receive. It emphasizes that accurately predicting the effective cellular dose is more complex than just measuring the nominal dose. It highlights the importance of understanding particle distribution and adhesion. This means future research needs to consider these factors for more accurate predictions of nanoparticle behavior, which is critical for improving the effectiveness and safety of nanomedicine. This could include improved models and experimental approaches to understand and optimize drug delivery.