Prostate cancer (PCa) is the second most common cancer in men worldwide and the fifth leading cause of death by cancer. The overexpression of TCTP protein plays an important role in castration resistance. Over the last decade, antisense technology has emerged as a rising strategy in oncology. Using antisense oligonucleotide (ASO) to silence TCTP protein is a promising therapeutic option—however, the pharmacokinetics of ASO does not always meet the requirements of proper delivery to the tumor site. In this context, developing drug delivery systems is an attractive strategy for improving the efficacy of ASO directed against TCTP. The liposome should protect and deliver ASO at the intracellular level in order to be effective. In addition, because prostate cancer cells express Her2, using an anti-Her2 targeting antibody will increase the affinity of the liposome for the cell and optimize the intratumoral penetration of the ASO, thus improving efficacy. Here, we have designed and developed pegylated liposomes and Her2-targeting immunoliposomes. Mean diameter was below 200 nm, thus ensuring proper enhanced permeation and retention (EPR) effect. Encapsulation rate for ASO was about 40%. Using human PC-3 prostate cancer cells as a canonical model, free ASO and ASO encapsulated into either liposomes or anti-Her2 immunoliposomes were tested for efficacy in vitro using 2D and 3D spheroid models. While the encapsulated forms of ASO were always more effective than free ASO, we observed differences in efficacy of encapsulated ASO. For short exposure times (i.e., 4 h) ASO liposomes (ASO-Li) were more effective than ASO-immunoliposomes (ASO-iLi). Conversely, for longer exposure times, ASO-iLi performed better than ASO-Li. This pilot study demonstrates that it is possible to encapsulate ASO into liposomes and to yield antiproliferative efficacy against PCa. Importantly, despite mild Her2 expression in this PC-3 model, using a surface mAb as targeting agent provides further efficacy, especially when exposure is longer. Overall, the development of third-generation ASO-iLi should help to take advantage of the expression of Her2 by prostate cancer cells in order to allow greater specificity of action in vivo and thus a gain in efficacy.

Purpose: Although recent regulations improved conditions of laboratory animals, their use remains essential in cancer research to determine treatment efficacy. In most cases, such experiments are performed on xenografted animals for which tumor volume is mostly estimated from caliper measurements. However, many formulas have been employed for this estimation and no standardization is available yet. Methods: Using previous animal studies, we compared all formulas used by the scientific community in 2019. Data were collected from 93 mice orthotopically xenografted with human breast cancer cells. All formulas were evaluated and ranked based on correlation and lower mean relative error. They were then used in a Gompertz quantitative model of tumor growth. Results: Seven formulas for tumor volume estimation were identified and a statistically significant difference was observed among them (ANOVA test, p < 2.10⁻¹⁶), with the ellipsoid formula (1/6 π × L × W × (L + W)/2) being the most accurate (mean relative error = 0.272 ± 0.201). This was confirmed by the mathematical modeling analysis where this formula resulted in the smallest estimated residual variability. Interestingly, such result was no longer valid for tumors over 1968 ± 425 mg, for which a cubic formula (L x W x H) should be preferred. Main findings: When considering that tumor volume remains under 1500mm³, to limit animal stress, improve tumor growth monitoring and go toward mathematic models, the following formula 1/6 π × L × W x (L + W)/2 should be preferred.

The development of nanoparticles could help to improve the efficacy/toxicity balance of drugs. This project aimed to develop liposomes and immunoliposomes using microfluidic mixing technology.Various formulation tests were carried out to obtain liposomes that met the established specifications. The liposomes were then characterized in terms of size, polydispersity index (PDI), docetaxel encapsulation rate and lamellarity. Antiproliferative activity was tested in human breast cancer models ranging from near-negative (MDA-MB-231), positive (MDA-MB-453) to HER2 positive. Pharmacokinetic studies were performed in C57BL/6 mice.Numerous batches of liposomes were synthesised using identical molar ratios and by varying the microfluidic parameters TFR, FRR and buffer. All synthesized liposomes have a size < 200 nm, but only Lipo-1, Lipo-6, Lipo-7, Lipo-8 have a PDI < 0.2, which meets our initial requirements. The size of the liposomes was correlated with the total FRR, for a 1:1 FRR the size is 122.2 ± 12.3 nm, whereas for a 1:3 FRR the size obtained is 163.4 ± 34.0 nm (p=0.019. Three batches of liposomes were obtained with high docetaxel encapsulation rates > 80%. Furthermore, in vitro studies on breast cancer cell lines demonstrated the efficacy of liposomes obtained by microfluidic mixing technique. These liposomes also showed improved pharmacokinetics compared to free docetaxel, with a longer half-life and higher AUC (3-fold and 3.5-fold increase for the immunoliposome, respectively).This suggests that switching to the microfluidic process will produce batches of liposomes with the same characteristics in terms of in vitro properties and efficacy, as well as the ability to release the encapsulated drug over time in vivo. This time-efficiency of the microfluidic technique is critical, especially in the early stages of development.

Tumor growth curves are classically modeled by ordinary differential equations. In analyzing the Gompertz model several studies have reported a striking correlation between the two parameters of the model. We analyzed tumor growth kinetics within the statistical framework of nonlinear mixed-effects (population approach). This allowed for the simultaneous modeling of tumor dynamics and interanimal variability. Experimental data comprised three animal models of breast and lung cancers, with 843 measurements in 94 animals. Candidate models of tumor growth included the Exponential, Logistic and Gompertz. The Exponential and – more notably – Logistic models failed to describe the experimental data whereas the Gompertz model generated very good fits. The population-level correlation between the Gompertz parameters was further confirmed in our analysis (R ² > 0.96 in all groups). Combining this structural correlation with rigorous population parameter estimation, we propose a novel reduced Gompertz function consisting of a single individual parameter. Leveraging the population approach using bayesian inference, we estimated the time of tumor initiation using three late measurement timepoints. The reduced Gompertz model was found to exhibit the best results, with drastic improvements when using bayesian inference as compared to likelihood maximization alone, for both accuracy and precision. Specifically, mean accuracy was 12.1% versus 74.1% and mean precision was 15.2 days versus 186 days, for the breast cancer cell line. These results offer promising clinical perspectives for the personalized prediction of tumor age from limited data at diagnosis. In turn, such predictions could be helpful for assessing the extent of invisible metastasis at the time of diagnosis. Author summary: Mathematical models for tumor growth kinetics have been widely used since several decades but mostly fitted to individual or average growth curves. Here we compared three classical models (Exponential, Logistic and Gompertz) using a population approach, which accounts for inter-animal variability. The Exponential and the Logistic models failed to fit the experimental data while the Gompertz model showed excellent descriptive power. Moreover, the strong correlation between the two parameters of the Gompertz equation motivated a simplification of the model, the reduced Gompertz model, with a single individual parameter and equal descriptive power. Combining the mixed-effects approach with Bayesian inference, we predicted the age of individual tumors with only few late measurements. Thanks to its simplicity, the reduced Gompertz model showed superior predictive power. Although our method remains to be extended to clinical data, these results are promising for the personalized estimation of the age of a tumor from limited measurements at diagnosis. Such predictions could contribute to the development of computational models for metastasis.

While the number of oncology-related nanotherapeutics and immunotherapies is constantly increasing, cancer patients still suffer from a lack of efficacy and treatment resistance. Among the investigated strategies, patient selection and combinations appear to be of great hope. This review will focus on combining nanotherapeutics and immunotherapies together, how they can dually optimize each other to face such limits, bringing us into a new field called nano-immunotherapy. While looking at current clinical trials, we will expose how passive immunotherapies, such as antibodies and ADCs, can boost nanoparticle tumor uptake and tumor cell internalization. Conversely, we will study how immunotherapies can benefit from nanotherapeutics which can optimize their lipophilicity, permeability, and distribution (e.g., greater tumor uptake, BBB crossing, etc.), tumor, tumor microenvironment, and immune system targeting properties.

The limitations of chemotherapy (e.g., toxicities, limited efficacy) have led to the development of nanocarriers for drug delivery to improve pharmacokinetics (PK) and therapeutic outcomes. However, optimizing dosing regimens remains challenging. Moreover, since chemotherapy is mainly administered intravenously (IV), this results in patient discomfort and high treatment costs. To address these issues, we used PK/pharmacodynamics (PD) modeling and applied it to subcutaneously (SC) injectable water‐soluble polymer prodrug based on paclitaxel (Ptx) and polyacrylamide (PAAm), synthesized by “drug‐initiated” controlled radical polymerization. PK/PD studies were performed on MCF‐7 tumor‐bearing mice. The PK model was developed on IV Ptx and SC Ptx‐PAAm data. The PD model was developed on control, IV Ptx, and SC Ptx‐PAAm groups (15 mg/kg), and validated on an independent group (SC Ptx‐PAAm at 60 mg/kg). A series of optimal dosing regimens identified in silico were then validated in vivo with excellent agreement. Among them, a dosing regimen combining a loading dose and daily injections achieved a 60% complete response rate without added toxicity, outperforming prior results. To our knowledge, this is the first validated PK/PD model for nanocarriers, offering a framework for more effective, cost‐efficient, and ethically refined drug development.