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

Development of a docetaxel-trastuzumab immunoliposome in breast cancer

Les nanotechnologies appliquées à la médecine, et plus particulièrement à l’oncologie, ont permis le développement d’une nouvelle classe d’entités, appelées communément nanomédicaments ou médicaments vectorisés. Ce projet de recherche a pour objectif d’encapsuler du docétaxel dans un vecteur lipidique unilamellaire furtif, puis de greffer en surface le trastuzumab afin d’en améliorer le profil pharmacocinétique, notamment en optimisant la spécificité de la phase de distribution. Les résultats obtenus montrent qu’il est possible de développer un immunoliposome furtif de 140 nm encapsulant 90 % de docétaxel avec un taux de greffage de trastuzumab de 30 %. L’approche en cytométrie de flux que nous avons développée et appliquée a permis une quantification absolue du nombre d’anticorps présents en surface. In vitro, un double screening en culture 2D et en sphéroïde 3D a démontré la supériorité antiproliférative de l’immunoliposome comparativement à tous les autres traitements, indépendamment du statut Her2 des lignées étudiées. Les études in vivo ont confirmé cette supériorité, y compris comparativement au T-DM1, l’antibody-drug conjugate de référence dans la pathologie. Les études de biodistribution ont montré que l’accumulation de notre forme vectorielle dépendait de la taille et du degré de vascularisation des tumeurs, plus que statut Her2 tumoral. En conclusion, nous avons démontré l’intérêt thérapeutique de développer des formes vectorielles dans la prise en charge du cancer du sein, comparativement aux traitements standard. Une optimisation de la phase de distribution explique la supériorité antiproliférative obtenue avec l’immunoliposome.The application of nanotechnology in medicine, especially oncology, has allowed for the development of a new class of entities, commonly called nanomedicine or vectorized medicine.This research project aims to encapsulate docetaxel in a stealthy, unilamellar, lipidic vector, then graft trastuzumab onto its surface to improve its pharmacokinetic profile, specifically by optimizing the specificity of the distribution phase.The results show that it is possible to develop a stealthy immunoliposome of 140 nm encapsulating 90% docetaxel and a trastuzumab grafting rate of 30 %. The flow cytometry approach that we developed and applied allowed for an absolute quantification of the number of antibodies present on the surface. In vitro, a double screening in 2D culture and in 3D spheroid showed the antiproliferative superiority of the immunoliposome compared to all the other treatments, regardless of the Her2 status in the cells studied. In vivo studies have confirmed said superiority compared to T-DM1; the benchmark antibody-drug conjugate for this pathology. Biodistribution studies have shown that the accumulation of our vector depends moreover on the size and degree of tumor vascularization than its Her2 status. In conclusion, we have demonstrated the therapeutic value of developing vector forms in the management of breast cancer therapy compared to standard treatments. The optimization of the distribution phase explains the antiproliferative superiority obtained by using the immunoliposome

Nanotherapeutics Plus Immunotherapy in Oncology: Who Brings What to the Table?

International audienceWhile 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

Résultats et complications des trous maculaires idiopathiques opérés par vitrectomie transconjonctivale 25-gauge sans suture

Evaluer la sûreté et l efficacité de la vitrectomie 25gauge (G) transconjonctivale dans le traitement des trous maculaires (TM) idiopathiques. Matériel et Méthodes : Dans cette étude rétrospective, concernant une série de 53 patients (55 yeux) présentant un trou maculaire idiopathique, nous avons étudié la fermeture anatomique du trou à l OCT, l acuité visuelle préopératoire et postopératoire, les complications per et postopératoires. Chez tous les patients, une vitrectomie complète a été réalisée avec pelage de la membrane limitante interne et un tamponnement par gaz. Un positionnement tête vers le sol a été instauré pour une durée moyenne de 5 à 7 jours dans tous les cas. On constate une fermeture du trou à l OCT dans 91% des cas. L acuité visuelle moyenne de loin préopératoire était de 1 logMAR soit 1/10ème et s est améliorée de manière significative (p<0,05) avec une acuité visuelle postopératoire de 0,44 logMAR soit 4/10ème (suivi minimum de 12 mois). Une amélioration de l acuité visuelle de loin d au moins 2 lignes est notée dans 87% des cas et une amélioration de près de 2 niveaux dans 71% des cas. Les complications peropératoires ont été marquées par un taux de déchirures peropératoires de 9%. Les complications postopératoires sont marquées par 3 cas de décollement de rétine (5,4%), une déchirure rétinienne inférieure, 2 cas d hypotonie inférieure à 6 mm Hg à J1 d évolution favorable sans traitement. La vitrectomie 25G transconjonctivale est une technique sûre, efficace dans le traitement des trous maculaires idiopathiques. Elle permet d autre part d améliorer le confort post opératoire du patient.PARIS6-Bibl.Pitié-Salpêtrie (751132101) / SudocSudocFranceF

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

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

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

PK/PD modelling to advance the preclinical development of a novel polymer prodrug in oncology

International audienceBackground pharmacodynamics medthods : Taxanes such as Paclitaxel (PTX) are considered essential chemotherapeutics for the treatment of solid tumors. PTX formulations have been explored to increase efficacy, to reduce toxicities and have improved pharmacokinetics (PK).Treatment simulation - Results - Outline - PK/PD modeling: dose regimen optimizatio

Tumor growth monitoring in breast cancer xenografts: A good technique for a strong ethic

International audiencePurpose 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 1500mm 3 , 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