Optimizing Druggability through Liposomal Formulations: New Approaches to an Old Concept

Developing innovative delivery strategies remains an ongoing task to improve both efficacy and safety of drug-based therapy. Nanomedicine is now a promising field of investigation, rising high expectancies for treating various diseases such as malignancies. Putting drugs into liposome is an old story that started in the late 1960s. Because of the near-total biocompatibility of their lipidic bilayer, liposomes are less concerned with the safety issue related to the possible long-term accumulation in the body of most nanoobjects currently developed in nanomedicine. Additionally, novel techniques and recent efforts to achieve better stability (e.g., through sheddable coating), combined with a higher selectivity towards target cells (e.g., by anchoring monoclonal antibodies or incorporating phage fusion protein), make new liposomal drugs an attractive and challenging opportunity to improve clinical outcome in a variety of disease. This review covers the physicochemistry of liposomes and the recent technical improvements in the preparation of liposome-encapsulated drugs in regard to the scientific and medical stakes

A reduced Gompertz model for predicting tumor age using a population approach

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 inter-animal 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

A reduced Gompertz model for predicting tumor age using a population approach

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 inter-animal 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

Is There Any Room for Pharmacometrics With Immuno-Oncology Drugs? Input from the EORTC-PAMM Course on Preclinical and Early-phase Clinical Pharmacology

International audienceAs part of the Pharmacology & Molecular Mechanisms (PAMM) Group, European Organization for Research and Treatment on Cancer (EORTC) 2019 winter Meeting Educational sessions, special focus has been placed on strategies to be undertaken to reduce the attrition rate when developing immune-oncology drugs. Immune checkpoint inhibitors have been game-changing drugs in several settings over the past decade such as melanoma and lung cancer. However, during the last years a rising number of studies failing to further improve clinical outcome in patients with cancer was recorded. Extensive pharmacometrics such as pharmacokinetics/ pharmacodynamics modeling support should help to overcome the current glass ceiling that has apparently been reached with immuno-oncology drugs (IOD). In particular, it should help in the issue of setting up combinatorial regimen (i.e. combining immune checkpoint inhibitors with cytotoxics, anti-angiogenesis or targeted therapies) that can no longer be addressed when following standard trial-and-error approaches, but rather by using mathematical-derived algorithms as decision-making tools by investigators for rational design. In routine clinical setting, developing therapeutic drug monitoring of immune checkpoint inhibitors with adaptive dosing strategies has been a long-neglected strategy. Still, substantial improvements might be achieved using dedicated tools for precision medicine and personalized medicine in immunotherapy

Seek and destroy: improving PK/PD profiles of anticancer agents with nanoparticles

Introduction: The Pharmacokinetics/pharmacodynamics (PK/PD) relationships with cytotoxics are usually based on a steepening concentration-effect relationship; the greater the drug amount, the greater the effect. The Maximum Tolerated Dose paradigm, finding the balance between efficacy, while keeping toxicities at their manageable level, has been the rule of thumb for the last 50-years. Developing nanodrugs is an appealing strategy to help broaden this therapeutic window. The fact that efficacy and toxicity with cytotoxics are intricately linked is primarily due to the complete lack of specificity toward the tumor tissue during their distribution phase. Because nanoparticles are expected to better target tumor tissue while sparing healthy cells, accumulating large amounts of cytotoxics in tumors could be achieved in a safer way. Areas covered: This review aims at presenting how nanodrugs present unique features leading to reconsidering PK/PD relationships of anticancer agents. Expert commentary: The constant interplay between carrier PK, interactions with cancer cells, payload release, payload PK, target expression and target engagement, makes picturing the exact PK/PD relationships of nanodrugs particularly challenging. However, those improved PK/PD relationships now make the once contradictory higher efficacy and lower toxicities requirement an achievable goal in cancer patients

Pharmacokinetics variability: why nanoparticles are not magic bullets in oncology

Developing nanoparticles to improve the specificity of anticancer agents towards tumor tissues and to better control drug delivery is a rising strategy in oncology. An increasing number of forms (e.g., conjugated nanoparticles, liposomes, immunoliposomes…) are now made available on the shelves and numerous other scaffolds (e.g., dendrimeres, nanospheres, squalenes…) are currently at various stages of development. However, the attrition rate when developing nanoparticles is particularly high and several promising forms showing excellent behavior and efficacy in preclinical studies failed to succeed in subsequent first-in-man studies or later in phase-II trials. The issue of pharmacokinetic variability is a major, yet largely underestimated issue with nanoparticles. A wide variety of causes (e.g; tumor type and disease staging, comorbidities, patient’s immune system) can explain this variability, which can in return impact negatively on pharmacodynamic endpoints such as lack of efficacy or severe toxicities. This review aims at covering the main causes for erratic pharmacokinetics observed with most nanoparticles. Should the main causes of such variability be identified, specific studies in non-clinical or clinical development stages could be undertaken using dedicated models (i.e., mechanistic or semi-mechanistic mathematical models such as PBPK approaches) to better describe nanoparticles pharmacokinetics and decipher PK/PD relationships. In addition, identifying relevant biomarkers or parameters likely to impact on nanoparticles pharmacokinetics would allow either modifying their characteristics to reduce the influence of the expected variability during development phases, or developing biomarker-based adaptive dosing strategies to maintain an optimal efficacy/toxicity balance. Overall, we call of developing comprehensive distribution studies and state-of-the-art modeling support to help better picture and anticipate nanoparticles pharmacokinetics

Like a Rolling Stone: Sting-Cgas Pathway and Cell-Free DNA as Biomarkers for Combinatorial Immunotherapy

International audienceCombining immune checkpoint inhibitors with other treatments likely to harness tumor immunity is a rising strategy in oncology. The exact modalities of such a combinatorial regimen are yet to be defined, and most attempts have relied so far on concomitant dosing, rather than sequential or phased administration. Because immunomodulating features are likely to be time-, dose-, and-schedule dependent, the need for biomarkers providing real-time information is critical to better define the optimal time-window to combine immune checkpoint inhibitors with other drugs. In this review, we present the various putative markers that have been investigated as predictive tools with immune checkpoint inhibitors and could be used to help further combining treatments. Whereas none of the current biomarkers, such as the PDL1 expression of a tumor mutational burden, is suitable to identify the best way to combine treatments, monitoring circulating tumor DNA is a promising strategy, in particular to check whether the STING-cGAS pathway has been activated by cytotoxics. As such, circulating tumor DNA could help defining the best time-window to administrate immune checkpoint inhibitors after that cytotoxics have been given