Rediscovery of mononuclear phagocyte system blockade for nanoparticle drug delivery

Rapid uptake of nanoparticles by mononuclear phagocyte system (MPS) significantly hampers their therapeutic efficacy. Temporal MPS blockade is one of the few ways to overcome this barrier – the approach rediscovered many times under different names but never extensively used in clinic. Using meta-analysis of the published data we prove the efficacy of this technique for enhancing particle circulation in blood and their delivery to tumours, describe a century of its evolution and potential combined mechanism behind it. Finally, we discuss future directions of the research focusing on the features essential for successful clinical translation of the method.

2023 (July 1 st ).We identified and reviewed 153 works and performed the meta-analysis of the studies which employed nanoparticles and cells as blocking agents since they have similar mechanisms of action.
Following data were extracted and summarized in the Supplementary Datasets 1 and 2: properties of the blocking and tracer nanoparticles (composition, size, injected dose), time between injection of blocking and tracer particles, presence of targeting strategy for tracer particles, animal model, presence and type of tumour, blood pharmacokinetics parameters (t1/2 increase after the blockade induction, AUC0-t increase after the blockade induction), tissue biodistribution of the particles (concentration increase in tumour, spleen, liver and lungs after the blockade induction).Supplementary Dataset 1 describes all values for the MPS blockade studies, where the blockade was induced with nanoparticles or cells.Supplementary Dataset 2 describes all the reported studies, in which an improvement in therapeutic efficacy was observed after the blockade induction (tumour inhibition growth or animal survival prolongation).Where possible, the data were gathered directly from the papers, if only the graphs were reported, PlotDigitizer Online app was used to manually collect the values.
For the blood pharmacokinetics analysis from Supplementary Dataset 1, we used half-life time (t1/2) ratio of nanoparticles after and before the blockade.If the data were reported in graphical form, the t1/2 was calculated from Elimination rate constant (Kel) using equation:  1/2 = ln(2) /  .If the MPS blockade effect was reported at several time points, data point with maximum efficacy was used for comparison.For Supplementary Dataset 2, we used AUC0-t ratio values for blood circulation comparison since in most cases the blood pharmacokinetics significantly deviated from monoexponential behaviour.
For biodistribution studies "tumour and tissue delivery increase" shows the ratio of nanoparticle concentration at certain time-point after and before the MPS blockade.Delivery efficacy was analysed by AUC0-t using trapezoidal model if several data points were reported.
We plotted the results as Tukey-type box graphs, describing median and 25-75% percentiles, whiskers show 1.5-fold interquartile range.We used median values for comparison as it depends less on the variability of data than mean value.

Supplementary Table 1. Results of Kolmogorov-Smirnov normality test before and after Box-Cox data transformation
After the Box-Cox transformation, Kolmogorov-Smirnov normality test shows normal distribution of transformed variables (Supplementary Table1), as well as Q-Q plots of F(y) have linear dependence (Supplementary Figures1-5).Hence, for statistical comparison, the analysis of variance (ANOVA) with Tukey's post-hoc test was applied for p values calculation.pvalueless than 0.05 was determined statistically significant.Supplementary Tables2-5show descriptive statistics, as well as p-values for the analysed data.

Table 3 .
Descriptive statistics of Supplementary Dataset 1 for "tumour delivery increase" variables.Bold ANOVA values show statistically significant difference (p < 0.05).

Table 4 .
Descriptive statistics of Supplementary Dataset 1 for "tissue delivery increase" variables.Bold ANOVA values show statistically significant difference (p < 0.05).