In simple terms, for photodynamic tumor therapy (PDT) to be successful all it takes is a photosensitizer (PS) at the right place, light and sufficient oxygen. It is the action of the PS on oxygen that is key here, as it converts oxygen into singlet oxygen (1O2).
In contrast to oxygen in the triplet ground state, 1O2 is highly reactive, especially in a biological environment. Mainly proteins, but also lipids and DNA can react with 1O2, which will lead to cell death. Due to its high reactivity, the lifetime of 1O2 in a biological environment was found to be shorter than 400 ns [1]. This means that the toxic effect of PDT treatment is limited to the cell, where the 1O2 is generated and hence has the ability to be a very specific and targeted treatment.
However, PS are usually small, low molecular weight molecules which have the disadvantage of not showing active accumulation in the target tissue, this can lead to inducing sensitization of other tissues.
According to the so-called EPR effect, enhanced permeability and retention, which was discovered more than 30 years ago, molecules with a molecular weight above ~40 kDa are preferentially accumulated in solid tumors due to differences in the structure of tumor capillaries versus those in normal tissues and on the limited lymphatic drainage in solid tumors [2,3]. There is common agreement that it takes a carrier system to exploit this effect and improve accumulation in the target tissue. Nanoparticle-based formulations are a promising option.
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In simple terms, for photodynamic tumor therapy (PDT) to be successful all it takes is a photosensitizer (PS) at the right place, light and sufficient oxygen. It is the action of the PS on oxygen that is key here, as it converts oxygen into singlet oxygen (1O2).
In contrast to oxygen in the triplet ground state, 1O2 is highly reactive, especially in a biological environment. Mainly proteins, but also lipids and DNA can react with 1O2, which will lead to cell death. Due to its high reactivity, the lifetime of 1O2 in a biological environment was found to be shorter than 400 ns [1]. This means that the toxic effect of PDT treatment is limited to the cell, where the 1O2 is generated and hence has the ability to be a very specific and targeted treatment.
However, PS are usually small, low molecular weight molecules which have the disadvantage of not showing active accumulation in the target tissue, this can lead to inducing sensitization of other tissues.
According to the so-called EPR effect, enhanced permeability and retention, which was discovered more than 30 years ago, molecules with a molecular weight above ~40 kDa are preferentially accumulated in solid tumors due to differences in the structure of tumor capillaries versus those in normal tissues and on the limited lymphatic drainage in solid tumors [2,3]. There is common agreement that it takes a carrier system to exploit this effect and improve accumulation in the target tissue. Nanoparticle-based formulations are a promising option.
Poly[N-(2-hydroxypropyl)methacrylamide] (pHPMA) copolymers with hydrophobic load have been shown to exhibit this EPR effect, even when the pHPMA copolymers are smaller than the mentioned molecular weight threshold [4,5]. The reason is the formation of stable micellar structures, resulting in a significantly larger hydrodynamic size of the whole micelle. However, this micelle-formation also has its disadvantages, most importantly, drug accumulation in the liver [6]. To overcome this challenge, research groups are trying to optimize the stability of such micellar pHPMA-PS copolymers by varying size and PS-loading. In this context, it is essential to accurately determine the hydrodynamic size and stability of the forming drug.
Dynamic Light Scattering (DLS), as a non-invasive, solution-based technique, is thus an ideal characterization tool for these materials. However, due to their photoactive nature, measurements may prove difficult.
As an example of the several similar copolymers investigated , a multiple loaded pHPMA copolymer of around 33 kDa was selected for this study. The structure, absorption and fluorescence spectra are shown in Fig. 1.
![[Figure 1 AN220609-DLS-cancer-therapy.jpg] Figure 1 AN220609-DLS-cancer-therapy.jpg](https://p3.aprimocdn.net/malvernpanalytical/49f0262b-8b08-43ad-8466-aeaf00e21795/Figure%201%20AN220609-DLS-cancer-therapy_Original%20file.jpg)
Figure 1. Structure, UV-VIS absorption spectrum (red) and fluorescence of poly[N-(2-hydroxypropyl)methacrylamide] copolymer conjugate with pyropheophorbide-a in 0.5% aqueous solution of Tween80 (blue).
The experimental procedure for these materials is described in reference [7].
All measurements were performed on a Zetasizer Ultra Red label using a disposable sizing cuvette (DTS0012) at a backscattering collection angle. Samples were equilibrated at 25°C for 120 s and all settings were left in automatic. The narrowband filter (NBF) has a central wavelength of 635 nm with a total bandpass of 15 nm (+/-7.5) i.e. wavelengths of 627.5 nm to 642.5 nm, with a transmission greater than 90% at 633 nm (laser source wavelength). The filter was set to disabled/enabled as described in the results.
Fluorescent samples can be challenging to measure by Dynamic Light Scattering. To obtain size results from DLS, the scattered photons’ decay rates are correlated over very short time scales, typically from high nanoseconds to milliseconds. When fluorescence occurs, photons will be emitted at a different wavelength of that of the laser source as well as at different timescales. This will lead to an increase in the experimental noise collected, which in turn will decrease the signal-to-noise (S/N) ratio or intercept of the autocorrelation function. The drop in the autocorrelation function S/N ratio will be proportional to the fluorescence quantum yield and the absorption of the nanoparticles [8].
The samples were analyzed in a Zetasizer Ultra Red label before and after the NBF was enabled. The material’s high absorption bands and high photoluminescence quantum yields can produce very noisy DLS results with low correlation coefficients rendering the analysis very difficult, as demonstrated in Figure 2.
![[Figure 2 AN220609-DLS-cancer-therapy.jpg] Figure 2 AN220609-DLS-cancer-therapy.jpg](https://p3.aprimocdn.net/malvernpanalytical/a11e1522-60de-41a9-816f-aeaf00e21714/Figure%202%20AN220609-DLS-cancer-therapy_Original%20file.jpg)
Figure 2. (a) Autocorrelation functions (a) and particle size distributions by Intensity (b) for pHPMA conjugated with pyropheophorbide. The fluorescent filter was disabled.
By enabling the NBF, fluorescence is mostly excluded from the analysis as it will only allow through light within the bandpass of the NBF. Pyropheophorbide emits at longer wavelengths (Figure 1, blue line) and consequently, only the scattered photons, which are on the same range with the wavelength of the laser source, are collected by the detector. Thus, the NBF significantly improves the intercept of the correlogram denoting a higher signal/noise ratio, as shown in Figure 3 a. With the clear improvement of the photon-correlation and decrease in experimental noise, the particles size distribution also improved drastically showing high repeatability with a narrow size distribution.
![[Figure 3 AN220609-DLS-cancer-therapy.jpg] Figure 3 AN220609-DLS-cancer-therapy.jpg](https://p3.aprimocdn.net/malvernpanalytical/e0a00421-29ef-47c7-9e43-aeaf00e2185b/Figure%203%20AN220609-DLS-cancer-therapy_Original%20file.jpg)
Figure 3. (a) Autocorrelation functions (a) and particle size distributions by Intensity (b) for pHPMA conjugated with pyropheophorbide. The fluorescence filter was enabled.
In the Zetasizer Advance products, the Data Quality Guidance comes integrated with the ZS XPLORER software. The approach used in this new system is based on machine learning (artificial neural networks) to classify data as “No data quality issues detected” or to highlight to the user what is negatively impacting the quality of the results. The user can then infer the quality of the results by using the color-coded Quality Indicator in the Analyze tab. Figure 4 illustrates how the Data Quality Guidance system may be used. First, it highlights what are the most likely causes for poorer quality data – incoherent light. Both multiple scattering phenomena and fluorescence emission affect the photo-correlation process and can significantly affect the quality of the data. Then it displays to the user how to address these issues. Hence, if the user suspects that the samples can be fluorescent, enabling the NBF should improve the quality of the results.
![[Figure 4 AN220609-DLS-cancer-therapy.jpg] Figure 4 AN220609-DLS-cancer-therapy.jpg](https://p3.aprimocdn.net/malvernpanalytical/675d3cff-400b-4290-8024-aeaf00e217f8/Figure%204%20AN220609-DLS-cancer-therapy_Original%20file.jpg)
Figure 4. Comparison between results obtained before and after NBF was enabled - Data quality guidance highlighting the main causes for the poor data quality (a) and the clear improvement in the PSD (b) and correlogram (c) once the fluorescence has been filtered.
The narrowband filter available in the Zetasizer Pro and Ultra models allows for the measurement of fluorescent nanoparticles without the need to compromise on the sensitivity for other non-fluorescent and weakly scattering samples, i.e. by having the NBF permanently fitted. The data quality guidance can be used to identify any potential issues with fluorescence and the NBF can be easily enabled in the method and thus improve the signal to noise ratio of the autocorrelation results and obtain meaningful and repeatable results even for challenging samples.
