3.3.1 Mechanistic modeling

Mechanistic models are defined here as mathematical constructs that describe physiological variables (e.g., plasma drug concentration, tumor size, or biomarkers) and their dynamics based on physical and biological principles (e.g., law of mass conservation). They describe the time profiles of the variables of interest by means of ordinary or partial differential equations (ODEs and PDEs, respectively) and are thus deterministic.

The main challenge of the modeling exercise is to find the appropriate balance between the degree of integration of biological phenomena (model complexity) and granularity of the data available (i.e., sampling time resolution, observed variables, spatio-temporal or only temporal measurements) ensuring the feasibility of parameter estimation. Indeed, cancer biology is extremely complex, involving processes at multiple temporal and spatial scales (intra- and inter-cellular, tissular, organism). It is thus tempting to build intricate models integrating as many phenomena as possible. Along these lines, the last decades have witnessed the proliferation of multiple such complex models. We see two shortcomings to this approach. First, in contrast with models of physical phenomena, the parameters of biological models are often not directly measurable and thus have to be estimated from fitting the models to experimental or clinical data. Therefore, their number has to be commensurate to the available data in order to ensure identifiability. Unfortunately, many complex models from mathematical oncology have too many parameters to be reasonably identified and have thus had a limited application in terms of biological insights or clinical applications. Second, complex, multiscale models are characterized by a reductionist point-of-view whereby general phenomena could be explained by decomposing them into elementary pieces. However, corresponding elementary experiments would not be suitable for quantification of the several homeostatic mechanisms involved in the whole real process. Thereby, we do not adhere to this reductionist vision and for modeling purposes we rather adopt a holistic approach considering the process as an indivisible whole.

To avoid the above-mentioned caveats, our methodology always starts from: 1) a clinically relevant medical problem but more importantly 2) the data available to build models.

In several instances, the mechanistic models are ordinary differential equations (ODEs). This is the case for the simplest type of experimental data that we generate, i.e., tumor growth kinetics. Departing from previous works establishing models for untreated experimental growth, we are now actively engaged into designing pharmacokinetics (PK)/ pharmacodynamics (PD) models of the effect of multiple therapies. These models have to account for the specificities of the drug delivery (e.g., nanoparticles), the biological effect of the treatments (e.g., cytotoxics, antiangiogenics or immunotherapies) and resistance to the therapy (either innate or acquired). The resulting models are novel nonlinear ODEs that need to be validated against the data and, when necessary, theoretically studied for their qualitative behavior. With the advent of immunotherapies, there has been a regain of interest to modeling tumor-immune interactions. Again, despite a wide literature on the subject, very few models have been validated against empirical data. A methodological objective is to establish and validate such models, including effect of immunotherapies.

Description of other phenomena are more adapted to partial differential equations (PDE) models. For instance, following an approach initiated by Iwata et al. 131, structured PDE models can be written for description of a population of metastases (see 4.3.2). Indeed, at the organism scale, cancer diseases are often characterized by a generalized (metastatic) state. However, few modeling efforts are currently focused on this aspect. The only validated models in large cohorts for systemic disease concern the sum of largest diameters as defined by the RECIST criteria 105. We aim to go beyond this state of the art by: 1) providing models of coupled tumor growth with interactions (and quantification of inter-lesion variability) 102, 103 and more importantly 2) developing models accounting not only for growth of the tumors, but also dissemination (birth of new lesions).

To date, most of the available and collectable clinical data about tumor growth and response to therapy consist of scalar data, often even limited to lesion diameters or sum of diameters. This is why we primarily focus our efforts on developing kinetic models of such data, the novelty coming from integrating other longitudinal biological data (e.g., from blood counts). Nevertheless, imaging data are now increasingly accessible and recent advances in image analysis allow the automatic segmentation of lesions make it possible to quantify the spatial shape and texture of tumors without a prohibitive cost for radiologists. This opens the way to develop spatially distributed PDE models of tumor dynamics. The existing models have largely remained unconfronted to data, apart from notable exceptions from the Inria MONC 112 and EPIONE 110 teams, as well as the Swanson 98 and Yankeelov 127 groups. Radiomics approaches quantifying heterogeneity in the images could bring additional information. We will rely on existing or establish collaborations with other dedicated Inria teams (EPIONE, MONC) for such purpose. COMPO would ideally bridge the gap between clinical studies and the Inria ecosystem. Finally, PDE models are also well adapted to describe intra-tumor drug penetration and we have recently developed such models for description of intra-tumor fluid flow and transport of antibody nano-conjugates 149.

3.3.2 Statistical modeling

Statistical models are defined here as mathematical constructs that describe the stochastic sources of variability in the data. They comprise both: 1) classical statistical models defining the functional and probabilistic relationship explicitly and 2) machine learning (ML) algorithms highly based on the data alone (e.g., tree-based models and associated ensemble methods or support vector machines) 124. We use such models for the following purposes: defining appropriate frameworks for parameter estimation; quantitative testing of biological hypothesis; addressing interindividual variability (using nonlinear mixed-effects (NLME) modeling); and building predictive models.

NLME – also termed the population approach in PK/PD modeling, or hierarchical modeling 133 – consists in assuming a statistical distribution of the parameters of the structural (often mechanistic) model, in order to describe longitudinal observations within a population of individuals. Instead of estimating individual parameters on a subject per subject basis – leading to identifiability issues in sparse data situations characteristic of longitudinal measurements in oncology – all data can be pooled together and a joint likelihood is obtained. Likelihood maximization becomes more complex than for classical nonlinear regression, nevertheless this problem has already been addressed by means of algorithms such as the deterministic first-order conditional expansion (FOCE) algorithm 134 or the stochastic approximation of the expectation-maximization algorithm (SAEM) 114. These algorithms are implemented in widely used software in the PMX community such as NONMEM® (Icon) or Monolix® (Lixoft), or R packages (e.g., saemix or nlme). Once the population distribution is estimated, empirical Bayes estimates (EBEs) can be derived for estimation of individual parameters. We also use the language Stan that implements state-of-the art Bayesian methods 106.

Departing from a general distribution of the parameters (often assumed log-normal) with quantified but unexplained interindividual variability, covariates are incorporated to explain this variability and build predictive models. This is traditionally done by means of linear models (possibly up to a functional transformation). However, with the increase in number of such covariates, the traditional tools and algorithms are limited. We thus develop advanced covariate models in NLME incorporating ML algorithms. Such methods require novel contributions. A possible lead is to first identify the EBEs and then use ML algorithms to predict these from the covariates 141. In other cases, ensemble models could be built from the heterogeneous sources of data, integrating one sub-model from EBEs identified from early data. Another, more challenging, avenue would be to adapt the parameter estimation algorithms like SAEM to include ML models in the covariate part.

In addition, because few data have been available longitudinally so far (i.e., small number of quantities measured at each time point), the current use of NLME relies on models with a small number of output variables. In this respect, modern clinical oncology studies bring new modeling and statistical challenges because many more quantitative data are collected at each time point (e.g., hundreds of variables from immuno-monitoring or possibly tens of thousands from circulating DNA, or radiomics features from imaging). Defining high-dimensional ODE models describing all the physiologically meaningful variables becomes intractable, therefore new methods are required. A possible avenue is to have a sequential approach, using first ML methods to reduce the dimension, then model the reduced number of variables. Another, more challenging, avenue would be to perform the two tasks (dimension reduction and temporal modeling) at the same time, and include this in an NLME framework for population estimation. The first part could be done using tools from unsupervised learning such as auto-encoders.

Following the availability of longitudinal tumor measurements, recent developments in the field of NLME have concerned joint modeling 117. This consists in modeling the longitudinal kinetics of a biomarker (e.g., tumor size) together with censored time-to-event data (e.g., overall survival) in a single step. Promising results have been obtained so far and we intend to develop methods beyond the state-of-the art in this area. This includes, in connection with above: 1) extension to models with emergence of new metastases, 2) integration of high-dimensional covariates and 3) high-dimensional longitudinal data. This Bayesian integration of data for updated survival predictions could lead to high impact results, as demonstrated by a recent publication in Cell 132.

Finally, we intend to bring the use of established ML tools to address concrete clinical problems emerging from the data collected in routine or clinical trials. Indeed, such data is so far analyzed using traditional statistical methods. ML algorithms could bring added value for predicting efficacy or toxicity from demographic, clinical and biological data.

3.8.1 Modeling

We plan to achieve two main things on the modeling side: 1) the development of effective numerical tools (either as web applications or as part of simulation software) and 2) the empirical validation of the models.

For 1), this includes a tool able to predict response to ICI monotherapy in NSCLC from baseline and early response data . We will work with our industrial partners (in particular, HalioDX from the PIONeeR consortium), to transfer the tool for commercial use. Second, we plan to have a validated numerical tool able to predict metastatic relapse from clinical biomarkers at diagnosis, using our mechanistic model. This model will integrate the effect of adjuvant therapy (hormonotherapy or cytotoxic therapy) and will be able to simulate the long-term impact of alternative treatments (e.g., number of cycles to be administered to prevent distant relapse). It will have been validated from our local databases and will be implemented in a clinically-usable online tool such as PREDICT 150. The main difference with this tool will be the ability to mechanistically simulate the effect of therapy. We plan to have initiated a larger initiative at the national or European level to collect large data bases, validate further the predictive power of the model, and refine its structure if required. We will also extend this tool to other pathologies that share the same problematic (diagnosis at early-stage, important probability of future distant relapse) such as kidney cancer, following our initial work from preclinical data 99, 104, 113.

The pharmacometric models that will have been developed will also be implemented as clinically effective dose adaptation numerical tools directly usable to personalize the dose and scheduling of multiple anti-cancer agents not only by the clinicians and pharmacists from our group, but also by others, at least at a regional level.

For 2), our strategy of validation is the following. First, during the development phase, a proportion of the dataset (usually, 30%) is left aside unused for establishment of the model and initial calibration, and then used as a test set. When the sample size is too small (n $\le$ 100 patients), only (nested) cross-validation is employed to assess the predictive power of the model. Evaluation metrics will be the classical ones, adapted to the task (classification, regression or survival regression) and include discrimination and calibration. In addition, specific methods will be used when in the context of mixed-effects approach (e.g., visual predictive checks for evaluation at the population level). The second step consists in evaluating the predictive power of the models in retrospective, external data sets. We have for instance initiated a collaboration with Dr C. Scherer (Clermont-Ferrand) to validate our metastatic prediction model on an external database of 3061 patients. The third step is to validate the added value of the model-based approach compared with the standard of care and is a long-term rather than mid-term objective.

3.8.2 Pharmacological and clinical oncology

In axis 2, in addition to standard drugs, developing tools for similarly better understanding the sources of therapeutic and PK variability and understanding the PK/PD relationships of cell therapy in oncology such as CAR-T cell therapies, is a challenging task. The challenge with CAR-T cells is that first, developing bioanalytical tools to monitor them in patients is not trivial, and second, little but nothing is known regarding their PK properties and possible sources impacting on PK/PD relationships such as disease status or immune status of the patient. We aim at developing both a platform to monitor CAR-T cells and future cell therapies in plasma and mechanistic models to describe the disposition of these new therapies in the body.

The nanoPBPK model will be extrapolated to humans and used to determine the specifications of an optimal nanosystem in order to penetrate solid tumors such as pancreatic tumors. The rationally designed nanosystem will be evaluated in vitro and in vivo in order to validate the approach. The nanoPBPK model will be interfaced to become a software and be shared with the scientific community. To achieve this goal, a partnership with ESQlabs, the company developing the opensource PBPK platform PKSim, has already been approved by both sides. The nanoPBPK model will be combined with pharmacodynamic modeling describing the effect of the loaded anticancer drug on tumor growth and metastases spread, on the immune system, and on dose-limiting toxicities.

Our mid-term objective in axis 3 is to assist the design of scheduling regimen for combinatorial treatments in early phase clinical trials, which represent an important clinical challenge of the next 10 years. To do so, we will benefit from our close connection to the INCa-labeled AP-HM's center for early phase clinical trials (CLIP2). Our aim is to design model-based, individualized and adaptive scheduling regimen that depend on the monitoring of the disease evolution. We plan to run phase I/II trials based on the model recommendations. Depending on our achievements and success in phase I/II trials our mid-term goal would be to lead a prospective, randomized, phase III trial comparing a model-based adaptive regimen to the standard of care for combination of immune checkpoint inhibition with chemotherapy and/or anti-angiogenic and targeted therapy. According to our team expertise, the target malignancies would be primarily lung cancer (LG) and head and neck cancer (SS).

3.9.1 Modeling

Our short-term program is devoted to the development of new models and their confrontation to empirical data. The mid-term program will focus on the validation and refinement of these models. In the long-term, we foresee that this will bring novel questions in terms of mathematical analysis of the models. For instance, metastatic modeling will establish validated models for tumor-tumor interactions, including immune-mediated interactions. In turn, this leads to nonlinear, size-structured, renewal PDEs. Study of the asymptotic behavior of such equations is non-trivial.

More generally, we expect that physiologically structured PDEs (psPDE) can become relevant to practical modeling in oncology, from two types of data: flow cytometry and single-cell sequencing. Flow cytometry is currently becoming of increasing relevance to characterize multiple populations of cells, for instance in the context of immuno-oncology. In the QUANTIC project (10.2), we are starting to interact with such data, only by means of scalar quantities so far. However, the structure of this data is to have, for each cell, a quantitative measure (e.g., a surface marker). Measuring these in a population of millions or billions of cells makes it adapted to modeling by such psPDE. Similarly, single-cell sequencing is a technique by which every cell of a population (e.g., in a tumor) is sequenced, thus having mutation information. In turn, this allows to quantify subclones in the population. Such data has already generated fascinating results, for instance in the study of metastatic development theories 123, 129. Although evolutionary modeling is a wide field with established groups (M. Nowak or F. Michor in Harvard, C. Curtis in Stanford, T. Graham at the CRUK), few groups are modeling dynamical data at single-cell resolution. To this regard, the theoretical work initiated by J. Clairambault and B. Perthame (Inria MAMBA) suggesting to use psPDEs to model evolution in cancer cell populations could be appropriate 107. Parameter estimation in such models is a challenging task 118 and data assimilation from flow cytometry or single-cell sequencing data sets would represent an important avenue. Dynamical data can be provided by circulating tumor DNA and we have already initiated contacts with an important clinical and biological study in Marseille on this topic (the SCHISM study, PIs: SS and F. Fina). The recent developments of technology enabling spatial resolution of single-cell sequencing also paves the way to exciting avenues in terms of modeling 139.

We will also build models that can optimize the effectiveness of treatments incorporating new criteria (other than the evolution of tumor mass) of diagnostic and therapeutic evaluation, especially those we have forged around the information provided by functional imaging (T80) computational algorithm time at which 80% of FDG is metabolized 100, 111, 143.

3.9.2 Pharmacological and clinical oncology

A general, challenging, long-term objective, would be to run prospective clinical trials in which a model-informed arm would be compared to the standard of care. In the model-informed arm, therapeutic decision would be based on the recommendation of the model. This applies to the models developed in all axes. For instance, in breast cancer, the number of cycles of chemotherapy would be adapted based on the model indication (axis 1), decision of the maximum tolerated dose in the treatment of leukemia patients would be based on the PGx/PK/PD model (axis 2) or the combination scheduling regimen would be given by model calculations (axis 3).

Regarding nanosystems initially designed by our group based on the nanoPBPK modeling, they will also be tested in early clinical trials. Our group will drive the design of these based on simulations performed with the nanoPBPK and pharmacodynamic model, in order to guarantee the highest chances of success while ensuring patients safety. In particular, nanoparticles specifically transporting cytoxics could replace standard systemic myelo-ablation in hematopoietic stem cell transplantation, a risky strategy with frequent life-threatening, when not lethal, toxicities. Because of the fully controlled distribution phase in the body, nanoparticles encapsulating several drugs could thus be implemented in the preparative regimens for allogeneic stem cell transplantation in leukemia or myeloid malignancies.

In addition, several groups predict that in addition to standard drugs or biologics, or rising gene therapy and cell therapy strategies, new devices such as nanobots will be developed to treat cancers. Nanobots are entities which are not designed to interact with standard pharmacological targets or genes like current anticancer treatments, but could fix the cancer cell, either by providing a missing protein, or ultimately trigger a mechanic cell-death using radiation, thermal wave, or by disrupting cell membrane. These new entities should exhibit totally new pharmacokinetics, because they are unlikely to be metabolized in the liver or to be cleared by the kidney or the biliary tract. Therefore, new models for PK/PD should be developed, because neither behavior in the body nor intrinsic mechanisms of action are known yet. In addition, the issue of nanosafety with such devices will be particularly critical and will require extensive PMX resources, to predict long-term effects or to keep under control the mechanism of action. Should such nanobots be developed, the COMPO joint-team should develop specific resources to monitor their fate in the body, specific resources to write equations describing nanobots/body, nanobots/immune system, and nanobots/cancer cells interactions, plus new global models encapsulating all the interactions, and pharmacodynamics impact of such devices.

Depending on the expertise we gain in the PK/PD knowledge of those cell therapies as part of the mid-term objectives, optimizing combinatorial strategies to such cell therapies will be another long-term objective.

Last, pharmaco-economic studies including impact of quality of life, increase in both tolerability and efficacy will be performed to determine whether PMX-based dosing is a cost-saving strategy. For instance, by refining the scheduling of immune checkpoint inhibitors, such as determining, using modeling and simulation strategies, when the plasma concentration of the drug reaches the threshold in trough levels necessary to ensure maximal target engagement, it will be possible to customize the frequency of the administrations, with possible strong impact on treatment costs. By using real-life data, we propose to use dedicated models to quickly define alternate and more appropriate dosing and/or scheduling with newly marketed anticancer agents, either chemotherapy, targeted therapies or biologics including immune checkpoint inhibitors.

7.1.1 stats_pioneer

• Name:
  Statistical analysis and reporting of the PIONeeR study
• Keywords:
  PIONeeR, Statistical analysis, Biostatistics
• Functional Description:

  This software was built to analyse the PIONeeR (Precision Immuno-Oncology for advanced Non-small cell lung cancer patients with PD-(L) 1 ICI Resistance) data. PIONeeR is a prospective, multicenter study with primary objective being to validate the existence of a hypothetical immune profile explaining resistance to immunotherapy in non-small cell lung cancer patients.

  It initially integrated preprocessing, exploratory data analysis, visualization, statistical analysis, feature selection, machine learning and results generation and reporting. Since, exploratory data analysis, visualization and statistical analysis have been promoted to the COMPO-level `compoEDA` package, feature selection and machine learning to the COMPO-level `ml.tidy` package and .

  This software corresponds to the very first step of the data analysis, which is the preprocessing, and the very last: generation of results. Some of its functions aim at:

  • preprocessing the data (creation of clinical variables, dictionary, outcome variables, data monitoring and corrections, treatment of the variables types)
  • generating the tools to load the data and metadata
  • computing statistical tests, logistic or Cox regression, or performing a correlation analysis
  • visualising the data (boxplots, barplots, survival curves, ROC curves, volcano plots)
  • providing detailed and interactive statistical reports on the data
  • displaying statistical results and visualisation to interactive dashboards
  • performing supervised and unsupervised machine learning modelling
  • providing detailed and interactive machine learning reports
  • displaying all reports on two websites (one private, one public)
  • automating the production of reports and websites using Gitlab CI/CD
• News of the Year:
  New PK data. New biologically informed groups / subgroups for biomarkers. New documentation about these groups. Large cleaning and refactoring of the repository. Other updates included:

  • Preprocess:Toggle sub-subsection view
    • generating new outcomes (primary resistance and secondary resistance)
    • complete documentation of all outcomes
  • Data exploration:Toggle sub-subsection view
    • new reports on imputation methods performance
    • new reports on data correlation
  • Feature selection:Toggle sub-subsection view
    • cleaning feature selection general report
    • investigation of multiple new signatures
    • new report analyzing stability of new signatures
    • new report analyzing impact on feature selection methods' stability of data correlation
  • Statistical analysis:Toggle sub-subsection view
    • cleaning variable summary dashboard
  • Unsupervised machine learning:Toggle sub-subsection view
    • new unsupervised analysis of PIONeeR's dataset taking as input structure of biomarkers (using MFA and HCPC algorithms)
    • new reports presenting results
  • Supervised machine learning:Toggle sub-subsection view
    • factorization of supervised machine learning reports, then promotion to the "compo.tidyML" COMPO-wise package
    • creating generic template report, using compo.tidyML new supervised machine learning pipeline, for performing supervised analysis
    • new reports from this template considering multiples outcomes and group of patients (first line, second line, all lines)
    • incorporating optimism framework for computing performances of models
    • new report presenting learning curves of optimism framework trained models
    • new report presenting optimism framework hyperparameter tuning analysis
    • documentation about optimism code
  • Machine learning:Toggle sub-subsection view
    • new report presenting results for both unsupervised and machine learning models as a companion for ongoing papers
  • Continuous integration / continuous development:Toggle sub-subsection view
    • large refactoring
    • web solution hosting two PIONeeR's websites (one internal, one as a companion for ongoing papers)
    • shiny dashboard added inside both websites
    • complete documentation about new CI/CD
  • Reporting:Toggle sub-subsection view
    • scripts for reproductible figures of two ongoing papers (methodology, biomarkers analysis)
• URL:
  https://­gitlab-int.­inria.­fr/­pioneer/­pioneer
• Publications:
  hal-03926538, hal-03928784
• Contact:
  Sebastien Benzekry
• Partners:
  Veracyte, Innate pharma, APHM

7.1.2 compo.EDA

• Name:
  Exploratory Data Analysis for Clinical Oncology Data
• Keywords:
  Data Exploration, Biostatistics
• Scientific Description:
  This library implements as an R package:

  • Exploratory analysis:Toggle sub-subsection view
    • Clinical characteristics table
    • Kaplan-Meier estimation of the progression-free and overall survival
    • Clinical and biological features distribution
  • Classification analysis:Toggle sub-subsection view
    • Univariate and multivariate logistic regression
    • Odds ratio
    • Area under ROC curve
    • t test / chi2 test
  • Survival analysis:Toggle sub-subsection view
    • Univariate and multivariate Cox regression
    • Hazard ratio
    • Area under ROC curve
    • log-rank test
  • Data visualization:Toggle sub-subsection view
    • Correlation plots (Pearson correlation)
    • Volcano plots (p-value and adjusted p-value)
    • Boxplots (quantitative features) and barplots (qualitative feaures)
    • Kaplan-Meier curves
    • Automatic comprehensive and customizable statistical reports
• Functional Description:

  The package compoEDA aims to provide a comprehensive exploratory analysis of data from clinical studies in oncology. These studies commonly investigate biological markers able to reveal and distinguish different tumor profiles, in order to early adapt the therapeutic strategy for patients.

  The objective of this software is to provide a simplified tool for both computational scientists and clinical researchers to easily generate a graphical results and automatic reports containing the following analyses:

  • overview and visualization of clinical data and biological markers
  • univariate and multivariate classification analysis (logistic regression)
  • univariate and multivariate survival analysis (Cox regression, Kaplan-Meier analysis)
  • correlation analysis
  • statistical tests
  • visualization of markers (boxplots, barplots, volcano plots, forest plots ...).
• Release Contributions:
  APP deposit
• News of the Year:

  Aesthetic and cleaning for reports generation. Add the possibility to generate complete statistical reports or smaller, more concise, ones.

  Other updates include:

  • cleaning functions
  • remove hard coding variables
• URL:
  https://­gitlab.­inria.­fr/­compo/­compo.­eda
• Contact:
  Sebastien Benzekry
• Participants:
  Sebastien Benzekry, Linh Nguyen Phuong, Celestin Bigarre, Paul Dufosse, Melanie Karlsen, Andrea Vaglio

7.1.3 compo.tidyML

• Name:
  Machine learning with tidymodels
• Keywords:
  Survival analysis, Machine learning, Data analysis, Oncology
• Scientific Description:
  This software maximizes the use of the R package tidymodels.
• Functional Description:

  This package provides multiple functions to perform machine learning analysis using the `tidymodels` framework. Tasks include: feature selection, plot feature importances, train, cross-validate, apply supervised machine learning algorithms (classification or survival analyses) and unsupervised machine learning, evaluate metrics of predictive performances, compute learning curves.

  Initial development was part of the `stats_pioneer` package (also called `pioneerPackage`) and `ml.tidy` evolved as a standalone package only in February 2023.

• News of the Year:

  A full generic pipeline for generating automatically a complete machine learning reports was added. It can be used to perform both classification or survival analysis. It is composed of multiple parts:

  • methods description
  • data preprocessing
  • feature selection
  • analysis of generated signature with SHAP plot and heatmaps
  • cross-validation evaluation
  • train/test evaluation
  • stratified Kaplan-Meier survival analysis
  • RADAR significant patients profile displaying

  Other updates include:

  • adding bobolasso as a feature selection method
  • adding xgboost as classifier possible
  • adding leave one out cross validation as evaluation method
  • adding group-based unsupervised clustering method
• URL:
  https://­gitlab.­inria.­fr/­compo/­compo.­tidyml
• Publications:
  hal-03478003, hal-03926538, hal-03928784
• Contact:
  Sebastien Benzekry

7.1.4 compo.NLME

• Name:
  R package for fitting and analyzing Non-Linear Mixed Effects (NLME) models using Monolix.
• Keywords:
  Monolix, Nonlinear mixed effects models, Lixoft, Population approach
• Scientific Description:

  Available features:

  • Structural modelsToggle sub-subsection view
    • constant
    • linear
    • double exponential
    • double exponential with dropout
    • hyperbolic
  • preprocess blood marker datasets
  • preprocess tumor kinetics datasets
  • fit NLME models using monolix API
  • post-process of results

  Available data:

  • Tumor Kinetics with dropout data. A simulated dataset of tumor kinetics following the double-exponential model, with parameters obtained from (Benzekry et al., PAGE 20, 2022), which deals with the RECIST-based sum of largest diameters (SLD, in mm) of lung cancer treated with immune-checkpoint blockade (anti-PDL1 drug atezolizumab). Dropout was also simulated using a Weibull survival model.
  • Tumor and Blood marker Kinetics with dropout data. A simulated dataset of joint tumor and blood markers (albumin C-reactive protein, lactate dehydrogenase, neutrophils) kinetics following the models and parameters established in (Benzekry et al., PAGE 20, 2022). These are monitoring data during immune-checkpoint blockade (anti-PDL1drug atezolizumab) in lung cancer. Dropout was also simulated using a Weibull survival model.
• Functional Description:
  This R package implements a framework to work with Non-linear Mixed effects models in the context of clinical oncology to predict relapse and survival using longitudinal data.
• News of the Year:
  Integration of postprocessing of Monlix runs:

  • computation of ChartsData
  • residual plots
  • observations VS predictions plots
  • individual fits plots
  • VPCs
  • distribution of individual parameters
  • distribution of random effects
• URL:
  https://­gitlab.­inria.­fr/­pioneer/­compo.­nlme
• Publications:
  hal-04375606, hal-03921394
• Contact:
  Sebastien Benzekry
• Participants:
  Sebastien Benzekry, Celestin Bigarre, Linh Nguyen Phuong, Ruben Taieb

7.1.5 compOC

• Name:
  Optimism correction of machine learning models
• Keyword:
  Feature selection
• Functional Description:
  compOC is a Python implementation of the optimism correction bootstrapping framework for evaluating full machine learning pipelines with or without feature selection. There are 40+ FS methods available, such as Lasso, Stability Selection, Stabl, t-test filtering, hierarchical clustering, recursive feature elimination and others.
• News of the Year:
  Initial development of the package.
• URL:
  https://­gitlab.­inria.­fr/­compo/­compoc
• Contact:
  Sebastien Benzekry
• Participants:
  Sebastien Benzekry, Anastasiia Bakhmach, Mohamed Boussena

7.1.6 SChISModeling

• Name:
  SChISM modeling
• Keywords:
  SChISM, Statistical analysis, Biostatistics
• Scientific Description:
  • Preprocess
  • Exploratory data analysis
  • Classification analysis (logistic regression)
  • Survival analysis (Cox regression)
  • Mixed-effects modeling analysis
  • Simulation for ODE models for mechanistic modeling
• Functional Description:

  SChISModeling aims to analyze SChISM data (Size CfDNA Immunotherapies Signature Monitoring). SChISM is a clinical study that introduces an innovative approach to quantify circulating free DNA in cancer patients treated with immunotherapy. The study's objective is to early predict response to immunotherapy in patients at an advanced/metastatic stage according to these quantitative cfDNA data.

  This software corresponds to the very first step of the data analysis, which is the statistical analysis. Some of its functions aim at:

  • preprocessing the data (creation of clinical variables, dictionary, outcome variables, clinical biomarkers, treatment of the variables types)
  • computing statistical tests, logistic or Cox regression, performing a correlation analysis
  • visualizing the data (boxplots, barplots, survival curves, ROC curves, volcano plots)
  • providing detailed and interactive statistical reports on the data
  • simulation for ODE models for mechanistic modeling
• News of the Year:
  • Improved preprocess
  • New functions for data visualization
  • Simulation for mechanistic model
  • New classification outcome added : primary resistance
  • Bootstraps
• URL:
  https://­gitlab-int.­inria.­fr/­phd-projects-linh-nguyen/­schism
• Contact:
  Sebastien Benzekry
• Participants:
  Sebastien Benzekry, Linh Nguyen Phuong, Romain Zakrajsek

7.1.7 metamats

• Keyword:
  Mechanistic modeling
• Functional Description:
  This R package is the implementation of a general framework to build and use models of the metastatic process based on the initial model of Iwata et al. (2000). The family of model that can be built describe the metastatic disease with a partial differential equation (pde) on the size structured distribution of the tumors. These models have three components, a function that characterize the growth of the primary tumor, a function that characterize the growth of the metastases, and a dissemination function that decribes how new metastases are produced.
• Release Contributions:
  Features:

  • Model structure
  • Direct computation of $N\left(t\right)$ (C++)
  • Individual fit of cumulative size distribution (direct only)
  • Many diagnostic plots
• News of the Year:

  Development of the first version of the sofware and several updates.

  This sofware was used and will be described in a work to be published in 2025. The application was the use of this model to describe the dynamics of the brain metastasis disease in small cell lung cancer patient with or without prophylactic cranial irradiation (PCI) and study the impact of PCI on the overall survival of the patients as well as the progression of the brain disease.

• URL:
  https://­gitlab.­inria.­fr/­cbigarre/­metamats
• Contact:
  Celestin Bigarre
• Participants:
  Sebastien Benzekry, Celestin Bigarre

7.1.8 metamatsModels

• Keyword:
  Mechanistic modeling
• Functional Description:
  A collection of models implementation for the metamats R package
• Release Contributions:
  Available models:

  • Gompertz growth model (alpha, mu parametrization)
  • Gompertz growth model (alpha, K parametrization)
  • Gompertz with dormancy growth model (alpha, mu, tau parametrization)
  • Proportional dissemination model
  • Power dissemination model (mu, gamma parametrization)
  • Iwata model object (identical gompertz growth, proportional dissemination)
  • Benzekry model object (identical gompertz growth with dormance, power diss)
• News of the Year:

  Development of the first version of the sofware and several updates.

  This sofware works in combinaison with the metamats R package and was used and will be described in a work to be published in 2025 (see the BIL entry of metamats R package for more details).

• Contact:
  Celestin Bigarre
• Participants:
  Celestin Bigarre, Sebastien Benzekry

Pre- and post-surgical monitoring of experimental primary tumor growth and metastasis under neo-adjuvant treatment

• Contributors:
  Mastri, Michalis (Data collector), Benzekry, Sebastien (Distributor), Ebos, John ML (Contact person)
• Description:
  This comprehensive dataset contains pre-surgical primary tumor volume measurements and pre- and post-surgical records of metastatic burden in 251 mice implanted with human breast cancer cells either untreated or pre-surgically treated with two distinct receptor tyrosine kinase inhibitors (Sunitinib and Axitinib) and multiple dose and scheduling regimen. In addition, the data contains tumor tissue and circulating biomarkers collected at surgery.
• Dataset DOI:
  10.5281/zenodo.10607555
• Publications:
  13
• Contact:
  S. Benzekry

Background

:

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers,toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease.

Objective

:

Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI).

Methods

:

Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly on Zenodo). A nonlinear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (CD31 and Ki67).

Results

:

Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically.

Conclusion

:

Mathematical modeling combined with machine learning techniques represent a novel platform for integrating preclinical surgical metastasis models in outcome prediction of neoadjuvant treatment.

Background

:

Establishing reliable and early predictive biomarkers of response of ICI is essential. Analysis of cfDNA fragmentation profiles (fragmentome) is a promising non-invasive method to do so independently of a specific molecular target, cancer type or treatment. We monitored plasmatic cfDNA concentration and size characteristics of the fragmentome in advanced lung, head and neck, kidney and bladder cancer patients, treated with ICI (n = 111). The aim was to predict EP (defined as progression at the first imaging evaluation) and PFS.

Methods

:

Our novel patented technology made possible to measure accurately cfDNA concentration and size profile directly from tens of microlitres of plasma and without prior DNA extraction (BIABooster system). Statistical association and predictive performances of response from fragmentome-derived metrics (e.g., concentration, size distribution peaks or fragments size ranges) were conducted. The data was split between a training (n=78) and a test (n=33) set. Optimal thresholds were determined through receiver-operator characteristics (ROC) curve analysis, and confidence intervals determined using bootstrap resampling. Classification metrics were assessed in both the training and testing set. The entire process was bootstrapped 100 times to assess the robustness.

Results

:

Quantity of long fragments over 1650 bp (LF) showed the best discriminatory power (AUC = 0.77 (0.65-0.87)) of EP. LF were significantly, strongly and positively associated with non-EP (odd ratio =0.27 (0.14-0.52), p <0.001) and longer PFS (p<0.001, hazard ratio 0.406 (0.274 - 0.599)). The predictive performances of EP were also very high: AUC 0.75 (0.65-0.84), accuracy 71% (95% CI: 63% - 80%), positive predictive value was 0.61 (0.47-0.78), on the test set.

Conclusions

:

These findings highlight a very significant association of cfDNA high-molecular-weight fragments with EP and PFS that outperform the predictive value of the only routinely used marker PDL1.

Objectives

:

Simple blood markers derived from routine hematology or biochemistry have been reported to be prognostic factors of overall survival (OS) in cancer patients. However, most studies only use baseline (BSL) values. The only longitudinal biomarker that has been extensively used and modeled to date is tumor size kinetics (TK), linked to OS by parametric survival models (TK-OS).

The few studies investigating blood marker kinetics (BK) used simplified empirical or non-coupled models. These often fail to capture important correlations and lack a systemic view of the processes at stake. Non-trivial, complex dynamic BK profiles, either under monotherapy or combination therapy remain to be quantitatively modeled.

We propose here an analysis of the combined kinetics between tumor size and three BKs: albumin, LDH and neutrophils.

Specifically, our aims were to:

(1) Develop a mechanistic model coupling TK with albumin, LDH and neutrophil counts kinetics (denoted TALN-k)

(2) Integrate TALN-k into a nonlinear mixed-effects (NLME) modeling framework to account for inter-individual variability

(3) Assess its goodness-of-fit and benchmark TALN-k against empirical models

(4) Use TALN-k to disentangle complex TK-BK interactions in non-small cell lung cancer patients (NSCLC).

(5) Integrate the selected model-based TALN-k parameters into a ML model for prediction of individual OS.

Methods

:

Data:

Monotherapy data consisted of the three phase 2 clinical trials POPLAR + FIR + BIRCH (MONO, 862 patients). Combination therapy data consisted of the phase 3 trial IMpower 150 (COMBO, 1115 patients) with 3 arms composed of combinations of atezolizumab (ATZ) and other agents (bevacizumab and carboplatin + paclitaxel).

TALN-k mechanistic model

TLAN-k is a system of coupled ordinary differential equations (with delay), describing the joint tumor, albumin, LDH and neutrophil counts dynamics under immune-checkpoint inhibition either in monotherapy or in combination with other drugs. The model was derived assuming the following pharmaco-biological hypotheses:

TK : Tumor cells were divided into two subpopulations with linear growth rate : one resistant , the other sensitive, both with different linear death rates. For albumin, LDH and neutrophils, production is regulated and elimination is linear in the absence of tumors. Albumin : Albumin production is impaired by tumor-related inflammation, with logistic effect. Inflammation also increases capillary permeability. LDH: In addition to physiological production, LDH levels increase when tumor cells die (Warburg effect), but also following neighboring stroma damage during invasion. Neutrophils: Progenitor cells production occurs with linear due to hematopoiesis. They further undergo a three-compartments chain of maturation (variables ) with linear transition rate. Precursor cell production increases with tumor-related inflammation. Chemotherapy effect on neutrophil production was considered to be constant over time. It was thus implicitly modeled in the precursor production parameter.

Population Model

Individual parameters were all assumed to have log-normally distributed random effects, with a diagonal variance-covariance matrix. The error models were combined (constant + proportional) (S), constant (A), proportional (L and N), which minimized the corrected Bayesian information criterion.

The TALN-k NLME model was fit on the entire tumor, albumin, LDH and neutrophil longitudinal data, on MONO and COMBO separately (total data points: 67,507 COMBO, 44,911 MONO), using the Monolix software and the SAEM algorithm.

Goodness of fit and identifiability were assessed using diagnostic plots, relative standard error (RSE) values and shrinkage of the different estimates.

Definition of the OS prediction problem from on-treatment data

To avoid time-dependent covariate bias for OS prediction, we placed ourselves at cycle 5 day 1 (C5D1, i.e., 3 months after treatment initiation). For each patient, we used only the longitudinal data available before C5D1, to identify their Empirical Bayes estimates (denoted EBEs4 ; 4 as in after 4 cycles). We then discarded the patients who died before C5D1 and computed the shifted OS with C5D1 as baseline for the remaining ones, denoted OS4.

Survival model

The previously defined individual EBEs4 (p = 26 parameters) were adjuncted to baseline covariates (p = 10) to be used as predictors of OS4. Following previous work, a random survival forest machine learning (ML) model was chosen and the complete prediction model denoted TALN-kML4. The ML model performances were assesed using 10-fold cross-validation of each dataset. The C-index was considered as the main (discrimination) predictive metric. The 12-months survival area under the ROC curve was also evaluated.

Results

:

We found the TALN-k model to fit the data very accurately, with interesting, non-trivial and interpretable individual dynamics and trends, such as early and late peaks of LDH, early drops in albumin, and early drops in neutrophil count.

Despite the relatively large number of parameters (p = 26), practical identifiability was also excellent, with minimal correlation between parameters, low RSEs (all < 27%) and eta-shrinkage (< 9%).

Remarkably, this novel mechanistic model outperformed previous modeling attempts using TK + BK empirical models as evidenced by a substantial decrease in the corrected Bayesian information criterion (difference of 5,883 for COMBO, and 1,819 for MONO).

TLAN-kML4 on COMBO yielded a cross-validation test C-index of 0.67± 0.02 and AUC of 0.78±0.03, versus 0.66 ± 0.04 ; 0.74 ± 0.04 and 0.64 ± 0.04 ; 0.7± 0.06, using TK4 or BSL, respectively.

On MONO, the individual predictions were substantially better than on COMBO. TALN-kml4 yielded a 0.74± 0.02 C-index and 0.83± 0.04 AUC; these dropped to 0.71± 0.03 and 0.78± 0.05 with TK4; and 0.66± 0.02 and 0.72± 0.04 using BSL.

Conclusions

:

TALN-k offers interpretability for combined TK-BK dynamics and outperformed previous empirical models. OS prediction was also improved, with better performances for immuno-monotherapy than for immuno-combotherapy.

Such a modeling framework could have three main concrete applications:

To inform indvidual clinical decisions at bedside (personalized healthcare); To integrate previous trials and early data to anticipate the outcome of a phase 3 trial, and optimize the timing for an interim analysis; To help assess in Phase 1b, whether a subsequent Phase 3 would be relevant.

Background

: Programmed-death 1 (PD1), Cytotoxic T-lymphocyte associated protein 4 (CTLA4) and Lymphocyte-activation gene 3 (LAG3) blockade have shown remarkable efficacy in melanoma patients. Predictive biomarkers are needed to predict response, prevent unneeded toxicity and reduce costs. Despite collective efforts, no biomarker is robust or accessible enough for routine use. Our objective was to evaluate mathematical modeling-based kinetic markers derived from routine biological parameters as early predictors of durable benefit for first-line immunotherapy in advanced melanoma. Our secondary objective was to assess if these modeling could predict progression-free survival (PFS).

Methods

: We retrospectively included all consecutive, treatment-naïve, metastatic or unresectable melanoma patients treated with PD1 blockade alone or in combination with CTLA4 or LAG3 blockade between April 2020 and 2022 in our department. Clinical, biological and radiological data were collected. The primary endpoint was durable clinical benefit (DCB) defined as partial or complete response at 6 months or stable disease of at least 6 months. 11 simple blood markers and their on-treatment kinetics were considered. 4 empirical mathematical models (constant, linear, double exponential and hyperbolic) were fitted to the data, allowing to reject the constant null hypothesis for all of them. The association of the best-models coefficients with DCB and PFS was assessed using logistic and Cox regression. To do so, the data was truncated at 1, 2 and 3 months and the model coefficients were retrieved using Bayesian estimation with a population prior identified on the full-kinetics dataset. This study was approved by our local IRB.

Results

: 61 patients were included with 24 months median follow-up. DCB was observed for 57% of them. A low model-based value at baseline using 1 month truncated data was significantly associated with DCB for neutrophils (OR = 4.54[1.69-12.5], p < 0.0001) and - neutrophils/lymphocytes ratio (NLR) (OR = 3.03 [1.47-6.67], p = 0.003). Model-based results increased AUC versus baseline raw data: 0.755 versus 0.699 for neutrophils and 0.76 versus 0.7 for NLR. An elevated model-based at baseline using 1 month truncated data was significantly associated with PFS for neutrophils (HR = 1.56 [1.25-1.96], p < 0.0001), AST (HR = 1.71 [1.24-2.35], p < 0.0001), LDH (HR = 2.0 [1.44-2.77], p < 0.0001), CPR (HR = 1.75 [1.14-2.67], p = 0.01) and ALT (HR = 1.37 [1.05-1.78], p = 0.02). At 1 month, the kinetics parameters on creatinine (HR = 1.38 [1.05-1.82], p = 0.02), albumin (HR = 0.68 [0.48-0.97], p = 0.03) and monocytes (HR = 1.49 [1.02-2.18], p = 0.04) were significantly associated with PFS.

Conclusions

: Modeling the kinetics of routine biological parameters shows potential ability to predict DCB. Perspectives include model improvement, kinetics machine learning integrative model development as well as a toxicity prediction.

Background

Commercial Paclitaxel (PTX) formulations such as Taxol are associated with adverse drug toxicities related to the added emulsionizer. Therefore, PTX formulations have been explored as they could increase efficacy and have improved pharmacokinetic (PK) properties. Recently our partners have developed a new polymer prodrug of PTX for which we proposed that metronomic dosing could result in an effective and tolerable anticancer treatment that is simultaneously convenient for patients as it allows for subcutaneous (SC) administration.

Objective

The objective of this study was to develop a PK/PD model of a novel polymer prodrug subcutaneously injectable to predict the best administration scheme.

Methods

Pharmacokinetics and pharmacodynamics (PD) studies were done on MCF-7 bearing mice using Monolix software. The PK model was developed on both intravenous (IV) Ptx and SC Ptx-PAAm (7mg/kg) data. The PD model was developed on three PD data sets (control, IV Ptx, SC Ptx-PAAm 15mg/kg), and validated on an independent group (SC Ptx-PAAm 60mg/kg). Simulations explored multiple treatment schedules, and the most effective ones were tested in vivo.

Results

The optimal model included a two-compartment PK structure and drug resistance in the PD component. The in vivo results demonstrated excellent agreement with the model predictions. A loading dose followed by daily administration achieved 60% complete response, outperforming previous internal results (tumor volume reduced to 60 versus 1350 mm${}^3$), without additional toxicity.

Conclusion

Our PK/PD model showed SC Ptx-PAAm with optimized regimens significantly increased efficacy over standard schedules.

Background

To evaluate the effectiveness of a Bayesian adaptive dosing strategy in achieving target busulfan exposure in adult patients undergoing haematopoietic cell transplantation (HCT).

Patients and Methods

This study included 71 adult patients scheduled to receive high-dose busulfan. Busulfan was administered to achieve a cumulative area under the curve (AUC) of 66.0 mg/L/h (16 000 μM/min), 82.60 mg/L/h (20 000 μM/min) or 87.6 mg/L/h (21 200 μM/min) depending on the regimen. Individual pharmacokinetic (PK) parameters of busulfan were estimated from three blood samples using a one-compartment model and Bayesian estimation after the first standard dose. Individual PK parameters were used to adjust subsequent doses to achieve the target exposure.

Results

All patients had their dose adjusted after the first dose administration. The final deviation from the target AUC was significantly improved compared to the initial deviation after standard mg/kg dosing (mean absolute deviation 19.5% vs 11.7%, P $<.01$). In addition, the proportion of patients with marked deviation from target exposure (ie, $>25$%) decreased significantly from 31% after standard dosing to 10% after PK-guided dosing (P $<.01$). Canonical busulfan-related toxicity, specifically veno-occlusive disease, was observed in 5% of patients who achieved successful PK-guided dosing. In contrast, one-third of patients with off-target exposure with poor dosing experienced toxicity.

Conclusion

The Bayesian adaptive dosing strategy significantly improves the accuracy of achieving the target busulfan AUC in patients undergoing HCT. This approach not only reduces marked deviations from target exposure, but also reduces the incidence of busulfan-related toxicity, thereby maintaining a favorable toxicity/efficacy ratio.

Background

Many tumors are refractory to immune checkpoint inhibitors, but their combination with cytotoxics is expected to improve sensitivity. Understanding how and when cytotoxics best re-stimulate tumor immunity could help overcome resistance to immune checkpoint inhibitors.

Methods

In vivo studies were performed in C57BL/6 mice grafted with immune-refractory LL/2 lung cancer model. A longitudinal immunomonitoring study on tumor, spleen, and blood after multiple treatments including Cisplatin, Pemetrexed, and anti-VEGF (either alone or in combination) was performed, spanning a period of up to 21 days, to determine the optimal time window during which immune checkpoint inhibitors should be added. Finally, an efficacy study was conducted comparing the antiproliferative performance of various schedules of anti-VEGF, Pemetrexed-Cisplatin doublet, plus anti-PD-1 (i.e., immunomonitoring-guided scheduling, concurrent dosing or a random sequence), as well as single agent anti-PD1.

Results

Immunomonitoring showed marked differences between treatments, organs, and time points. However, harnessing tumor immunity (i.e., promoting CD8 T cells or increasing the T CD8/Treg ratio) started on D7 and peaked on D14 with the anti-VEGF followed by cytotoxics combination. Therefore, a 14-day delay between anti-VEGF/cytotoxic and anti-PD1 administration was considered the best sequence to test. Efficacy studies then confirmed that this sequence achieved higher antiproliferative efficacy compared to other treatment modalities (i.e., -71% in tumor volume compared to control).

Conclusions

Anti-VEGF and cytotoxic agents show time-dependent immunomodulatory effects, suggesting that sequencing is a critical feature when combining these agents with immune checkpoint inhibitors. An efficacy study confirmed that sequencing treatments further enhance antiproliferative effects in lung cancer models compared to concurrent dosing and partly reverse the resistance to cytotoxics and anti-PD1.

Background

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.

Patients and Methods

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 synthesized using identical molar ratios and by varying the microfluidic parameters TFR, FRR and buffer.

Results

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).

Conclusions

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.

Background

Imatinib is the treatment of elderly or frail patients with chronic myeloid leukemia (CML). Trough levels of around 1000 ng/ml are considered as the target exposure.

Methods

We searched for baseline parameters associated with imatinib pharmacokinetics, and studied the clinical impact of subsequent adaptive dosing. We present data from 60 adult CML patients upon imatinib with therapeutic drug monitoring (TDM) and adaptive dosing.

Results

Mean trough levels after treatment initiation were 994.2 ± 560.6 ng/ml (with 56% inter-patient variability). Only 29% of patients were in the therapeutic range. Body weight, height, body surface area, body mass index (BMI), and age were associated with imatinib plasma levels on univariate analysis. Age and BMI remained the only parameters associated with imatinib trough levels on multivariate analysis. As severe toxicities have been previously reported in patients with low BMI treated with standard imatinib, we evaluated the extent to which low BMI may lead to plasma overexposure. We found a statistically significant difference in trough imatinib levels in patients with BMI $<18.5$ kg/m2, with exposure +61.5% higher than in patients with 18.5 $<$ BMI $\le$ 24.9 and +76.3% higher than in patients with BMI $\ge$ 25. After TDM with adaptive dosing, a statistically significant difference in dosing between patients was observed, with doses ranging from 200 to 700 mg. No difference in toxicity or efficacy was observed regardless of BMI after adaptive dosing.

Conclusion

Our data suggest that low BMI has a significant impact on imatinib exposure but that pharmacokinetically-guided dosing limits its clinical impact in patients.

Background

We report the case of an adult patient diagnosed with Hodgkin's lymphoma who was scheduled for Pembrolizumab after failure of standard therapy. After three well-tolerated courses of Pembrolizumab, a PET scan showed a favorable outcome and a fourth course of Pembrolizumab was started. Unexpectedly, extremely severe toxicities (i.e., autoimmune peripheral hypothyroidism, rhabdomyolysis and severe acute renal failure) occurred after this last course, requiring transfer to the intensive care unit.

Methods

Therapeutic drug monitoring was performed to measure residual Pembrolizumab levels at intervals from the last dose (i.e., 120 and then 170 days), as well as pharmacogenetics investigations on the FCγR gene.

Results

Pembrolizumab plasma concentrations that were still pharmacologically active months after the last administration, suggesting impaired elimination of Pembrolizumab in this patient. Further pharmacokinetic modeling based on the population approach showed that both half-life (47.8 days) and clearance (0.12 L/day) values were significantly different from the standard values usually reported in patients. Further in silico simulations showed that pharmacologically active concentrations of Pembrolizumab were maintained for up to 136 days after the last dose. The search for possible polymorphisms affecting the genes coding for FCγR (i.e., rs1801274 on FCGR2A and rs396991 on FCGR3A gene) was negative. Further TDM showed that Pembrolizumab could be detected up to 263 days after the last administration.

Conclusion

This case report suggests that persistent overexposure in plasma could lead to life-threatening toxicities with Pembrolizumab.

Background

Mutations in spliceosome genes (SRSF2, SF3B1, U2AF1, ZRSR2) correlate with inferior outcomes in patients treated with intensive chemotherapy for Acute Myeloid Leukemia. However, their prognostic impact in patients treated with less intensive protocols is not well known.

Methods

This study aimed to evaluate the impact of Spliceosome mutations in patients treated with Venetoclax and Azacitidine for newly diagnosed AML. 117 patients treated in 3 different hospitals were included in the analysis.

Results

Thirty-four harbored a mutation in at least one of the spliceosome genes (splice-mut cohort). K/NRAS mutations were more frequent in the splice-mut cohort (47% vs 19%, p=0.0022). Response rates did not differ between splice-mut and splice-wt cohorts. With a median follow-up of 15 months, splice mutations were associated with a lower 18-month LFS (p=0.0045). When analyzing splice mutations separately, we found SRSF2 mutations to be associated with poorer outcomes (p=0.034 and p=0.037 for OS and LFS respectively). This negative prognostic impact remained true in our multivariate analysis.

Conclusion

We believe this finding should warrant further studies aimed at overcoming this negative impact.

10.1.1 Visits of international scientists

10.1.2 Visits to international teams

PIONeeR - biomarkers

• Title:
  Precision Immuno-Oncology for advanced Non-small cell lung cancer patients with PD-1 ICI Resistance
• Partner Institutions:
  • AP-HM, Marseille
  • 13 national cancer centers, including Centre Lyon Bérard and IUCT in Toulouse
  • Marseille Immmunopôle (immuno-monitoring platform)
  • Vasculo-monitoring platform of AP-HM
  • Inserm
  • Aix-Marseille University
  • Veracyte, Marseille
  • InnatePharma, Marseille
• Date/Duration:
  2018 - 2024
• Funding:
  $\simeq$ 8 M€, ANR
• Principal investigator:
  F. Barlesi (IGR)
• COMPO members involved:
  S. Benzekry, J. Ciccolini, L. Greillier, P. Dufosse, M. Boussena, M. Hamimed, M. Karlsen, S. Marolleau, C. Marin, A. Vaglio.

LUCA-pi RHU

• Title:
  Lung cancer prevention and interception
• Partner Institutions:
  • Gustave Roussy Institute (G. Kroemer, L. Zitvogel)
  • Therapanacea (N. Paragios)
  • CIML (P. Milpied)
• Date/Duration:
  2023 - 2028
• Funding:
  $\simeq$ 10 M€, ANR
• Principal investigator:
  D. Boulate (COMPO)
• COMPO seniors:
  D. Barbolosi, S. Benzekry

AML

• Title:
  Prédiction de la Toxicité du Venetoclax dans la Population de Patients Agés traités pour une LAM
• Partner Institutions:
  • AP-HM, Marseille
• Date/Duration:
  2023 - 2026
• Funding:
  $\simeq$ 30k€, GIRCI
• Principal investigator:
  Sylvain Garciaz (IPC, Marseille)
• COMPO members involved:
  S. Benzekry, R. Fanciullino, A. Bakhmach

DIGPHAT PEPR Digital Health

• Title:
  Digital pharmacological twin
• Partner Institutions:
  • JB Woillard (CHU Limoges)
  • C. Battail (CEA Grenoble)
  • M. Ursino + S. Zohar (HEKA, Inria, Paris)
  • J. Josse (PREMEDICAL, Inria, Montpellier)
  • E. Chatelut + M. White-Koning (IUCT, Toulouse)
• Date/Duration:
  2023 - 2028
• Funding:
  Total 1.8 M€, COMPO 251k€
• Principal investigator:
  JB Woillard (CHU Limoges)
• COMPO senior:
  S. Benzekry.
• COMPO junior:
  A. Bakhmach

COPYCAT

• Title:
  Combining Organoid technology with Mathematics to develop innovative models mimicking tumor cellular heterogeneity and plasticity for pediatric oncology
• Partner Institutions:
  • L. Broutier (CRCL, Inserm, CNRS, UCBL, Lyon)
  • E. Pasquier (CRCM)
  • R. Mounier (INMG, Inserm, CNRS, UCBL, Lyon)
• Date/Duration:
  2023 - 2027
• Funding:
  Total 922k€, COMPO 115k€ (INCa)
• Principal investigator:
  L. Broutier (CRCL, Inserm, CNRS, UCBL, Lyon)
• COMPO members involved:
  S. Benzekry, E. Ventre

SChISM

• Title:
  Size CfDNA Immunotherapies Signature Monitoring
• Partner Institutions:
  • APHM
  • M. Lavielle (XPOP – Inria)
  • Adelis,
  • F. Fina (ID-Solutions Oncology)
• Date/Duration:
  2022 - 2025
• Funding:
  $\simeq$ 120k€, APHM + PhD grant ICI - Laennec
• Principal investigator:
  S. Benzekry, S. Salas
• COMPO junior:
  L. Nguyen Phuong

METAMATS

• Title:
  Mechanistic modeling for the prediction of metastatic relapse in breast cancer
• Partner Institutions:
  • F. Bertucci (IPC, Marseille)
  • G. MacGrogan (Institut Bergonié , Bordeaux)
• Date/Duration:
  2020 - 2024
• Funding:
  $\simeq$ 100k€, Inria-Inserm PhD grant
• Principal investigator:
  S. Benzekry, X. Muracciole
• COMPO members involved:
  C. Bigarre

PhD H. Hamdache

• Title:
  Improving therapeutic efficacy and managing side effects and sequelae in pediatric cancer through improved and personalized nutritional programs using computer simulations
• Partner Institutions:
  • V. Pancaldi (IUCT, Inserm, Toulouse)
• Date/Duration:
  2023 - 2026
• Funding:
  120k€, Inria-Inserm PhD grant
• Principal investigator:
  V. Pancaldi (IUCT, Inserm, Toulouse)
• COMPO members involved:
  S. Benzekry (co-supervisor).

South-ROCK

• Title:
  South-research on cancer for kids
• Partner Institutions:
  28 constitutive teams

  • P. Mehlen (Centre Léon Bérard + Hospices Civils de Lyon)
  • E. Pasquier (CRCM, Marseille)
  • M. Castets (CRCL, Lyon)
• Date/Duration:
  2023 - 2028
• Funding:
  Total 2 M€
• Principal investigators:
  P. Mehlen (CLB + HCL, Lyon), E. Pasquier (CRCM, Marseille), M. Castets (CRCL, Lyon)
• COMPO seniors:
  S. Benzekry, F. Gattacceca, J.Ciccolini

THERMONANO

• Title:
  Nanoassemblies for the subcutaneous self-administration of anticancer drugs
• Partner Institution:
  • Institut Galien Paris-Saclay (UMR CNRS 8612)
• Date/Duration:
  2019 - 2024
• Funding:
  1.8 M€, ERC
• Principal investigator:
  J. Nicolas (Institut Galien, Paris-Sud)
• COMPO members involved:
  A. Rodallec, S. Benzekry, S. Marolleau.

TORNADO

• Title:
  TOols for Rational NAnoDrugs Optimization: development of a physiologically-based pharmacokinetic model for nanomedicines
• Partner Institution:
  • P. Garrigue (CERIMED, Marseille)
  • S. Legeay (MINT, Univ Angers)
  • E. Roger (MINT, Univ Angers)
• Date/Duration:
  2021-2024
• Funding:
  $\simeq$ 120k€ (Doctoral grant AMU ED62)
• Principal investigator:
  F Gattacceca
• COMPO junior:
  J. Ou (PhD student), M. Mahdjoub (M2 intern)

PEMBOV

• Title:
  Pembrolizumab in Combination With Bevacizumab and Pegylated Liposomal Doxorubicin in Patients With Ovarian Cancer
• Partner Institution:
  • Institut Gustave Roussy (IGR)
• Date/Duration:
  2021-2024
• Funding:
  300 K€, INCa
• Principal investigator:
  J. Michels (IGR)
• COMPO members involved:
  J. Ciccolini, M. Hamimed

REZOLVE

• Title:
  A phase 2 trial of intraperitoneal bevacizumab to treat symptomatic ascites in patients with chemotherapy-resistant, epithelial ovarian cancer
• Date/Duration:
  2020-2025
• Funding:
  total funding undisclosed, COMPO funding 40 K€
• Principal investigator:
  S. Yip (Sydney Medical Center Australia), J.Ciccolini
• COMPO senior:
  J.Ciccolini
• COMPO junior:
  C. Marin

COMPLICITY

• Title:
  COMPutationaL tools for NanoBooster In Cancer ImmunoTherapY
• Partner Institution:
  • Pharmaceutical Institute, Bonn University, GERMANY: A Lamprecht M Shetab Boushehri
• Date/Duration:
  2023-2026
• Funding:
  25k€ (Amidex Pepiniere), 30K€ (ARC), 2K€ (DAAD)
• Principal investigator:
  A. Rodallec
• COMPO senior:
  A. Rodallec, F. Gattacceca
• COMPO junior:
  C. Perez

Paris Saclay Cancer Cluster

• Title:
  Clinical Pharmacokinetics Platform
• Partner Institution:
  • Paris Saclay
• Date/Duration:
  2025-2029
• Funding:
  Paris Saclay
• Principal investigator:
  E. Vivier (PSCC)
• COMPO members involved:
  J. Ciccolini

Prevalung

• Title:
  Epidemiological Study to Assess the Prevalence of Lung Cancer in patients with smoking-associated atherosclerotic cardiovascular diseases
• Partner Institution:
  • APHM, Gustave Roussy Institute
• Date/Duration:
  2019-2025
• Funding:
  $\simeq$ 7M€ Horizon Europe
• Principal investigator:
  D. Boulate
• COMPO members involved:
  D. Boulate, D. Barbolosi, C. Buton

PETRA Network

• Title:
  PE“TRANSLA” : translational research in neuro-oncology
• Partner Institution:
  • Assistance Publique Hôpitaux de Marseille (APHM)
• Date/Duration:
  2023-2027
• Funding:
  Canceropole PACA
• Principal investigator:
  E. Tabouret (APHM)
• COMPO members involved:
  J. Ciccolini

VENETACIBLE

• Title:
  Etude PK/PD du Venetoclax dans les leucémies aigues myéloides
• Date/Duration:
  2023-2025
• Funding:
  to be determined
• Principal investigator:
  G. Venton
• COMPO senior:
  R. Fanciullino, J.Ciccolini
• COMPO junior:
  L. Osanno

CEREAL

• Title:
  Allogreffe de cellules souches hématopoïétiques après conditionnement à toxicité réduite associant cladribine, fludarabine et busulfan chez les patients atteints d'hémopathies malignes réfractaires (Phase I)
• Date/Duration:
  2020-2025
• Funding:
  to be determined
• Principal investigator:
  R. Devillier (IPC), J.Ciccolini
• COMPO senior:
  J.Ciccolini
• COMPO junior:
  D. Protzenko

MOIO

• Title:
  A non-inferiority randomized phase III trial of standard immunotherapy by checkpoint inhibitors vs. reduced dose intensity in responding patients with metastatic cancer: the MOIO protocol study
• Date/Duration:
  2022-2027
• Funding:
  $\simeq$ 1M€ total (INCA, PHRC + BMS grant), COMPO funding undetermined yet
• Principal investigator:
  G. Gravis (IPC Marseille), J.Ciccolini
• COMPO members involved:
  J.Ciccolini

IMHOTEP

• Title:
  étude de phase 2, avec 4 cohortes évaluant l’efficacité du pembrolizumab MK-3475 chez des patient(e)s atteints d'une tumeur MSI/dMMR ou d'un cancer gastrique EBV+, résécable non prétraité.
• Date/Duration:
  2024-2027
• Funding:
  $\simeq$ 1M€ total (INCA, PHRC), COMPO funding undetermined yet
• Principal investigator:
  C. de la Fouchardière (IPC Marseille), J.Ciccolini
• COMPO members involved:
  J.Ciccolini

PKID

• Title:
  Population pharmacokinetic approach centered on the patient: a patient pharmacokinetic ID from one drug to predict the PK of the next one
• Partner Institution:
  • R. Guilhaumou (APHM, Marseille)
• Date/Duration:
  2021-2024
• Funding:
  APHM (hospital intern)
• Principal investigator:
  F Gattacceca
• COMPO junior:
  A. Deschamps

PhD M. Boussena

• Title:
  Machine learning methods for clinical oncology data: application to the prediction of immunotherapy response in lung cancer
• Partner Institutions:
  • J. Josse (PREMEDICAL, Inria)
  • GFPC
  • CHU Brest
  • Inserm Brest
• Date/Duration:
  2023 - 2026
• Funding:
  120k€, Institut Laennec
• Principal investigator:
  S. Benzekry, L. Greillier
• COMPO members involved:
  M. Boussena

PICOMALE

• Title:
  Phase I Clinical trials Oncology & Machine Learning
• Partner Institutions:
  • CLIP: N. Andre, P. Tomasini
  • LIS (Laboratoire d'informatique et des systèmes): C. Roman, S. Sellami, A. Bouchra
• Date/Duration:
  2023 - 2026
• Funding:
  Total 100k€, COMPO 66k€
• Principal investigator:
  N. Andre
• COMPO senior:
  S. Benzekry.
• COMPO junior:
  C. Bigarre

COALA

• Title:
  Cure Oncogene-Addicted Lung Adenocarcinoma
• Date/Duration:
  2024 - 2029
• Funding:
  3M total, $\simeq$ 130k€ COMPO
• Principal investigator:
  J. Mazières (CRCT)
• COMPO members involved:
  S. S. Benzekry, D. Barbolosi

Brain meta SCLC

• Title:
  Modeling the impact of prophylactic whole brain radiotherapy to prevent brain metastases in SCLC
• Date/Duration:
  2022 - 2023
• Funding:
  Inria – Inserm PhD + APHM for AD
• Principal investigator:
  S. Benzekry, X. Muracciole, L.Padovani
• COMPO members involved:
  C. Bigarre, A. Daumas

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• S. Benzekry
  • September, 2024. Twelfth International Workshop on Pharmacodynamics of Anticancer Agents. "Use of machine learning for predicting cancer outcomes", Ponta Delgada, Portugal.
  • July, 2024. ECMTB congress (European Conference on Mathematical and Theoretical Biology). "Mechanistic learning for metastasis modeling and prediction", talk within the mini-symposium "Mechanistic learning in mathematical oncology" organized by A. Kohn-Luque and S. Haupt, Toledo, Spain.
  • July, 2024. ECMTB congress (European Conference on Mathematical and Theoretical Biology). "Integrating kinetics and machine learning modeling for prediction of outcome following immunotherapy in lung cancer", talk within the mini-symposium "Digital twins for clinical oncology and cancer research" organized by G. Lorenzo, Toledo, Spain.
  • July, 2024. SMB congress (Society of Mathematical Biology). "Integrative kinetics and machine learning modeling for prediction of outcome following immunotherapy in lung cancer". Talk within the mini-symposium "Mathematical modeling of cancer treatment" organized by E. Kim, Seoul, Korea.
  • June, 2024. BIOMAT summer school. Mini-course "Mechanistic learning to predict response and survival in immuno-oncology.", Granada, Spain.
  • March, 2024. Mechanistic learning for clinical cancer research. CANSEARCH, Geneva University, Switzerland.
  • March, 2024. Keynote invited speaker in the session "Machine Learning for Rare Disease Applications: from Limited to In Silico Trials" of the Applied machine learning days (AMLD2024), "Mechanistic learning to predict the results of clinical trials", EPFL, Lausanne.
  • January, 2024. EORTC-PAMM, The PIONeeR trial, Marseille.
• A. Rodallec
  • September, 2024. Pharmacometric days, Paris, France.
• L. Nguyen Phuong
  • October, 2024. Groupe de Métabolisme et Pharmacocinétique. "Computational modeling approaches for circulating cell-free DNA in oncology" within the symposium "Biomarker strategy for dose selection/optimization", Lyon, France.
• J. Ciccolini
  • December, 2024. Groupe Français de Pneumo-Cancérologie. "E-R relationships with immunotherapy?", Paris, France.
  • November, 2024. GPCO-Unicancer Workshop"Place des anticorps conjugués dans le carcinome urothélial?" Paris, France.
  • November, 2024. Cours Saint-Paul en Oncologie Digestive"PK/PD relationships with immune checkpoint inhibitors: is the earth flat?" Nice, France.
  • October, 2024. Symposium Clinique Waters"Bioanalyse des biothérapies: application au STP en oncologie clinique de routine" Paris, France.
  • October, 2024. 61th Oncology congress "Clinical pharmacology of Antibody Drug conjugates" Belgrade, Serbia.
  • October, 2024. Management du MTXHD - Partage d'expérience sur les hémopathies en région PACA "Retards d'élimination du MTX: Aspects pharmacométriques" Marseille, France.
  • September, 2024. Twelfth International Workshop on Pharmacodynamics of Anticancer Agents. "Pharmacometrics as a decision-making tool with immune checkpoint inhibitors: finding the perfect blend?", Ponta Delgada, Portugal.
  • June, 2024. Science, Society and Values Symposium "Is biology soluble into ideology?" Bordeaux, France.
  • June, 2024. Imagining the future of cancer treatments Mini-Symposium "ADC in oncology, reality beyond the myth?" Marseille, France.
  • June, 2024. 7e édition des IFODS (Journées Franco-Internationales d'Oncologie) "Nanoparticules en Oncologie: enjeux & Perspectives" Paris, France.
  • March, 2024. Fondazione Pisana per la Scienza "Anti-Body Drug Conjugates: why «Magic Bullet» is not «Magic Pharmacology»", Pisa, Italy.
  • February, 2024. CISAM/RDV Innovation Santé "Individualisation des posologies en oncologie: apport de la modélisation mathématique en pratique clinique de routine à l'APHM" Marseille, France.
  • February, 2024. PAMM-EORTC 44th Winter Meeting "BRAF inhibitors: pharmacology and pharmacokinetics considerations" Marseille, France.
  • January, 2024. Triple Negative Breast Cancer : A dialog between basic science and clinical practice Workshop "Antibody-Drug Conjugates ; beyond the hype, which pharmacology in triple negative breast cancer?" Nantes, France.

11.1.5 Scientific expertise

• J. Ciccolini: Expert at Institut National du Cancer (INCA) PHRC-K, CIVIS, Ligue Régionale contre le Cancer, Cancéropôle CLARA, Cancéropôle GSO, Cancéropôle IdF, GIRCI-MED, Groupe Français des Myelodysplasies (GFM-ONUVEN-MDS), University of Peshawar, Pakistan, University of Sydney, Australia.
• R. Fanciullino: Expert at Association pour la Recherche contre le Cancer (ARC), CN5.
• F. Gattacceca: Scientific expert at ANSM (Agence Nationale de Sécurité du Médicament, national drug agency), member of the permanent scientific committee "Quality and safety of drugs"

11.1.6 Research administration

• A. Rodallec: Website and Social Media coordinator for the CRS benelux Local Chapter
• J. Ciccolini: Member of the Scientific Committee of the School of Pharmacy, Marseille France.
• J. Ciccolini: Member of the Steering Committee & Copil H&N of the Head and Neck Cancer Taskforce, Unicancer Paris France.
• J. Ciccolini: Member of the Scientific Committee of the Immuno-Oncology Group, Unicancer Paris France.
• J. Ciccolini: Member of the ADC TaskForce at EORTC, Brussels Belgium.
• J. Ciccolini: Co-Director of the TRANSLATE-IT Department at CRCM, Inserm U1068, Marseille France.
• J. Ciccolini: Member of the PETRA (PrEclinical and TRAnslational Network neuro-oncology research) Network, PACA France.
• F. Gattacceca: Member of the scientific committee of the school of pharmacy

11.2.1 Teaching

• S. Benzekry: M2 Biologie Santé – Parcours IA biomarqueurs (6h). M2 "Pharmacokinetics" (6h).
• S. Benzekry: M2 PK "Fundamentals for modeling and simulation in pharmacokinetics/pharmacodynamics" (6h).
• A. Rodallec: lectures in MSc in Pharmacokinetics, MSc in Digipharm, MSc in Innovative Diagnostic and therapeutic Drug Products, DESU "Advances courses in pharmacometrics", DES in animal experiments, Pharm.D. studies (2nd, 3rd, 4th and 6th year), odonthology studies -> 190 h a year + additional lectures at University of Paris Saclay and Wuhan University (China).
• J. Ciccolini Lectures at Aix Marseille Univ in: MSc (2nd year) in Oncology, MSc (2nd year) in Oncogenetics, MSc (2nd year) in Pharmacokinetics, MSc (2nd year) in Digipharm, MSc (1st year) in Drugs & Health Products, D.U. in Animal Experiments, D.U. in Genetic Councelling, Master Class in Lung Cancer, Pharm.D. studies (2nd, 3rd, 4th and 6th year) -> 216 h a year.
• J. Ciccolini Teaching outside of AMU: additional lectures in pharmacokinetics at Université Catholique de Lyon, University of Amsterdam NL, University of Pisa Italy, School of Pharmacy of Belgrade, Serbia, and the International School of Metronomics. Lectures for Cours National Approfondissement DES Oncologie Médicale and Phase d'Approfondissement Docteur Junior, Paris Saclay University.
• J. Ciccolini Founder and co-Chair of the "Digital Tools for Pharmaceutical Sciences (Digipharm)"" Master Degree, Aix Marseille Univ.
• R. Fanciullino: Lectures in: MSc (2nd year) in Pharmacokinetics, MSc (2nd year) in Digipharm, CESU in Oncogeriatry, DES in PK Variability, Pharm.D. studies (3rd, 4th and 5th year) -> 190 h a year .
• F. Gattacceca: lectures in pharmacokinetics and pharmacometrics at Aix-Marseille University school of pharmacy (305h), teaching in other universities (50h: Nîmes, Angers, Montpellier): 90% at a post-graduate level.
• F. Gattacceca: Director of the master program "Pharmacokinetics". Director of two international post-graduate university diplomas: "Modeling and simulation: population approaches in pharmacokinetics/pharmacodynamics" and "Modeling and simulation: physiologically-based pharmacokinetic modeling for pharmacology and toxicology". Member of the national reflection committee for the industry pharmacy studies, of the national and local committees for a research option in pharmacy studies. Member of the training steering committee of ICI (Immunology Cancer Institute)
• F. Gattacceca: tutor in the first (2024) edition of the "Pharmacometrics Africa" training in French
• F. Gattacceca: CIVIS International Summer School "Drug Design and Discovery", Tübingen, Germany, July 2024 (Lectures and hands-ons)
• L. Greillier: M2 Recherche clinique et Simulation en Santé.
• L. Greillier: oncology and pulmonology for 3rd – 11th year medical students.
• X. Muracciole: DIU radio-urology for resident medical student.
• X. Muracciole: DCIU radio surgery for resident medical student
• S. Salas: Medical study/initial training. Seminary: palliative care Therapeutic module: pains Oncodigestive module Cancerology (44h), for 3rd - 6th year medical students
• S. Salas: Medical, paramedical study. DU Supportive care in oncology and palliative medicine. DU Wounds and healing. DIU Supportive care in oncology and palliative medicine. Master's in Advance Practice Nursing: Cancerology module, General module, pains. CEU Service providers at home, Home-based cancer care. DU Ambulatory shift, Oncology module. (37h)
• L. Nguyen Phuong: CESU "Fundamentals for modeling and simulation in pharmacokinetics/pharmacodynamics". Introduction to R for pharmacokinetic modeling. (9h).
• L. Nguyen Phuong: M2 PK. Introduction to R for pharmacokinetic modeling. (20h).

11.2.2 Supervision

$•$ Postdoc

• S. Benzekry
  • Paul Dufossé (Institut Laennec, AMU) : “Quantitative modeling and machine learning for prediction of the response to immunotherapy in lung cancer”.
• A. Rodallec
  • P. Piris, (Thermonano)

$•$ Engineers

• S. Benzekry
  • R. Zakrasjek (SChISM)
  • A. Vaglio (RHU PIONeeR)
• J. Ciccolini
  • C. Perez (RHU PIONeeR)

$•$ PhD students

• S. Benzekry
  • A. Bakhmach (PEPR Santé Numérique DIGPHAT), 2023 - 2026: “Modelling and statistical learning for pharmacology in oncology”, co-supervision with R. Fanciullino (COMPO, APHM) and S. Garciaz (IPC)
  • M. Boussena (Institut Laënnec, AMU), 2023 - 2026: “Machine learning methods for clinical oncology data: application to the prediction of immunotherapy response in lung cancer”, co-supervision with J. Josse (Premedical, Inria) and L. Greillier (COMPO, APHM)
  • C. Bigarré, 2020 - 2024: "Mathematical modeling for prediction of metastatic relapse in breast cancer", co-supervision X. Muracciole, funding Inria – Inserm
  • L. Nguyen Phuong, 2022 - 2025, SChISM: "Mechanistic modeling of circulating DNA combined to machine learning for prediction of response and survival following immunotherapy", co-supervision S. Salas, funding Amidex ICI (Institute for Cancer Immunotherapy)
  • H. Hamdache, 2023 - 2026: "Improving therapeutic efficacy and managing side effects and sequelae in pediatric cancer through improved and personalized nutritional programs using computer simulations", co-supervision V. Pancaldi (IUCT, Inserm, Toulouse), funding Inria–Inserm.
• E. Ventre
  • C. Berthaud, 2024-2027: "Bioinformatic approach to the impact of modulation of the respiratory chain SDH complex on cell states dynamics in pediatric gliomas and rhabdomyosarcomas", co-supervision M. Castets (Inserm, CRCL), funding Ligue contre le cancer
• D. Boulate
  • C. Buton (Institut Laënnec, AMU), 2024-2027: "Development of interactive expert software stratifying the risk of lung cancer diagnosis in the setting of lung cancer screening based on mathematical modeling and machine learning approaches", co-supervision with D. Barbolosi (COMPO)
  • A. Todesco (CRCM - E19 - SMARTc), 2023-2026: "Study of the maintenance of pulmonary and systemic vascular permeability: from physiology to pathology", co-supervision with P. Habert (APHM)
  • E. Armand (CRCM), 2023-2026: "French screening programme: development of risk stratification tools"
• J. Ciccolini
  • C. Marin, CetuxIMAX, funding APHM & Merck Serono
  • A. Ronda, Pembro Monitoring in real-world patients, funding APHM
  • G. Kallee, DPD status and clinical outcome, funding APHM
  • D. Protzenko, PK-guided dosing in HCT conditioning, funding APHM
• F. Gattacceca
  • A. Deschamps, 2019-2024, funding APHM, co-supervision R. Guilhaumou (APHM)
  • J. Ou, 2021-2024, funding AMU doctoral school 62, co-supervision P. Garrigue (AMU)
  • S. Benamara, 2023-2026, funding CIFRE, co-supervision D. Teutonico (Sanofi)
• R. Fanciullino
  • M. Dacos, Nanimmuno funding APHM & Institut Roche
  • L. Osanno, Venaza project funding APHM

$•$ Interns (Master 2)

• S. Benzekry
  • A. Boniffay (M2 AI4PH AMU, funding Laënnec)
  • A. Daumas (radiotherapist, M2 AI and biomarkers AMU)
  • A. Ouloum (Centrale Marseille, sup. S. Benzekry)
  • R. Taïeb (M2, ENS Lyon)
  • R. Zakrasjek (M2 PK AMU, funding ICI, sup. S. Benzekry)
• A. Rodallec
  • O. Jaonino, immunomodulation of nanoparticles, University of Bonn
• J. Ciccolini, R. Fanciullino
  • E. Diroff, nanoparticles in pancreatic cancer, Aix Marseille Univ funding Inserm
• J. Ciccolini
  • Q. Gerbault, PK/PD of lirilumab in paediatric cancers, funding APHM
• F. Gattacceca
  • M. Hammami, Development of a physiologically-based pharmacokinetic model of fludarabine in cancer patients, funding COMPO

11.2.3 Juries

• S. Benzekry: reviewer of the PhDs of Virginie Montalibet (MONC, Inria Bordeaux), Florian Jeanneret (CEA Grenoble) and Beatriz Ocaña-Tienda (Universidad de Castilla La Mancha, Spain)
• A. Rodallec: Jury of Pharm.D. Thesis and DES Hospital Pharmacist thesis, School of Pharmacy of Marseille (approx. 5 thesis/year)
• J. Ciccolini:
  • Reviewer for PhD Defense: Thao Nguyen Pham, Approches biomathématiques sur les effets immunitaires systémiques induits par les radiations dans les cancers du cerveau et de la tête & cou en utilisant des modèles précliniques et cliniques. Université de Caen Normandie. October 2024
  • President for PhD Defense: Arthur Millet, Dosage d’anticorps médicaments utilisés en cancérologie par spectrométrie de masse. Application à une étude clinique. Université Claude Bernard Lyon 1. January 2024
  • Jury of Pharm.D. Thesis and DES Hospital Pharmacist thesis, School of Pharmacy of Marseille (approx. 30 thesis/year)
• R. Fanciullino:
  • Jury of Pharm.D. Thesis and DES Hospital Pharmacist thesis, School of Pharmacy of Marseille (approx. 15 thesis/year)
• S. Salas
  • President for PhD defense: Clémence Marin.
  • Reviewer for Pharm.D. defense: Clara Boeri, Relation PK/PD du Nivolumab dans les cancers ORL.
• F. Gattacceca
  • Reviewer for PhD Defense: Blaise Pasquier, Apport de la modélisation PKPD dans l’extrapolation interespèce et dans l’adaptation de posologie en pratique clinique des anticorps monoclonaux thérapeutiques. Université Paris Cité. October 2024
  • Member of PhD scientific evaluation committees: I. Benbouziane (Jagiellonian University in Krakow, Poland), B. Cardozo (Aix-Marseille Université), A. Baillod (Université de Poitiers), Céline Delansay (Université Paris-Saclay), Magali Godard (Université de Montpellier)
  • Jury of Pharm.D. thesis: Aura Seroussi.

11.3.1 Productions (articles, videos, podcasts, serious games, ...)

• Joseph Ciccolini:
  • October 2024: Serious Game TRANSFORM-O, Société Française du Cancer et Association Enseignement Recherche Internes en Oncologie (AERIO) "Développement clinique en oncologie" Montpellier, France.

International journals

• 10 articleL.Lyria Amari, P.Pascale Tomasini, E.Emmanuelle Dantony, G.Gaëlle Rousseau-Bussac, C.Charles Ricordel, L.Laurence Bigay Game, D.Dominique Arpin, H.Hugues Morel, R.Remi Veillon, G.Grégoire Justeau, E.Eric Huchot, P.Pierre Fournel, A.Alain Vergnenègre, A.Acya Bizeux, F.Fabien Subtil, B.Benedicte Clarisse, C.Chantal Decroisette, C.Christos Chouaïd, L.Laurent Greillier and O.Olivier Bylicki. Safety and Patient-Reported outcomes of atezolizumab plus chemotherapy with or without bevacizumab in stage IIIB/IV non-squamous non-small cell lung cancer with EGFR mutation, ALK rearrangement or ROS1 fusion progressing after targeted therapies (GFPC 06-2018 study).Lung Cancer1932024, 107843HALDOI
• 11 articleK.Kevin Atsou, A.Anne Auperin, J.Jôel Guigay, S.Sébastien Salas and S.Sébastien Benzekry. Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma.CPT: Pharmacometrics and Systems PharmacologyDecember 2024HALDOIback to text
• 12 articleS.Sébastien Benzekry, M.Mélanie Karlsen, C.Célestin Bigarré, A. E.Abdessamad El Kaoutari, B.Bruno Gomes, M.Martin Stern, A.Ales Neubert, R.René Bruno, F.François Mercier, S.Suresh Vatakuti, P.Peter Curle and C.Candice Jamois. Predicting Survival in Patients with Advanced NSCLC Treated with Atezolizumab Using Pre‐ and on‐Treatment Prognostic Biomarkers.Clinical Pharmacology and Therapeutics1164July 2024, 1110–1120HALDOIback to text
• 13 articleS.Sébastien Benzekry, M.Michalis Mastri, C.Chiara Nicolò and J.John Ebos. Machine-learning and mechanistic modeling of primary and metastatic breast cancer growth after neoadjuvant targeted therapy.PLoS Computational Biology205May 2024, e1012088HALDOIback to textback to text
• 14 articleG.Guillaume Berton, B.Bochra Sedaki, E.Erwann Collomb, S.Sami Benachour, M.Michael Loschi, B.Bilal Mohty, C.Colombe Saillard, Y.Yosr Hicheri, C.Camille Rouzaud, V.Valerio Maisano, F.Ferdinand Villetard, E. D.Evelyne D.'Incan Corda, A.Aude Charbonnier, J.Jerome Rey, M.-A.Marie-Anne Hospital, A.Antoine Ittel, N.Norman Abbou, R.Raphaëlle Fanciullino, B.Bérengère Dadone-Montaudié, N.Norbert Vey, G.Geoffroy Venton, T.Thomas Cluzeau, A.-S.Anne-Sophie Alary and S.Sylvain Garciaz. Poor prognosis of SRSF2 gene mutations in patients treated with VEN-AZA for newly diagnosed acute myeloid leukemia.Leukemia Research141June 2024, 107500HALDOI
• 15 articleJ.-Y.Jean-Yves Blay, C.C. Schiffler, O.O. Bouché, M.M. Brahmi, F.F. Duffaud, M.M. Toulmonde, B.B. Landi, W.W. Lahlou, D.D. Pannier, E.E. Bompas, F.F. Bertucci, L.L. Chaigneau, O.O. Collard, M.M. Pracht, C.C. Henon, I.I. Ray-Coquard, K.K. Armoun, S.S. Salas, M.M. Spalato-Ceruso, A.A. Adenis, B.B. Verret, N.N. Penel, C.C. Moreau-Bachelard, A.A. Italiano, A.A. Dufresne, S.S. Metzger, S.S. Chabaud, D.D. Perol and A.A. Le Cesne. A randomized study of 6 versus 3 years of adjuvant imatinib in patients with localized GIST at high risk of relapse.Annals of Oncology3512December 2024, 1157-1168HALDOI
• 16 articleO.Olivier Bylicki, F.Florian Guisier, A.Arnaud Scherpereel, C.Catherine Daniel, A.Aurélie Swalduz, E.Emmanuel Grolleau, M.Marie Bernardi, S.Stephane Hominal, J.Jean.Briac Prevost, G.Guillaume Pamart, M.Marie.Héléne Marques, N.Nicolas Cloarec, S.Simon Deshayes, J.Judith Raimbourg, R.Rémi Veillon, Y.Youssef Oulkhouir, C.Clarisse Audigier Valette, F.Fabien Subtil, C.Christos Chouaïd and L.Laurent Greillier. Real-World efficacy and safety of combination nivolumab plus ipilimumab for Untreated, Unresectable, pleural Mesothelioma: The Meso-Immune (GFPC 04–2021) trial.Lung Cancer194August 2024, 107866HALDOI
• 17 articleJ.Joseph Ciccolini. Metastatic renal cell carcinoma: individualisation of doses, pharmacokinetics monitoring and role in therapeutic de-escalation.La Lettre du Cancérologue5May 2024HAL
• 18 articleD.David Combarel, L.Léa Dousset, S.Stéphane Bouchet, F.Florent Ferrer, P.Pauline Tetu, C.Céleste Lebbe, J.Joseph Ciccolini, N.Nicolas Meyer and A.Angelo Paci. Tyrosine kinase inhibitors in cancers: Treatment optimization – Part I.Critical Reviews in Oncology/Hematology199July 2024, 104384HALDOI
• 19 articleM.Mathilde Dacos, B.Benoît Immordino, E.Erwan Diroff, G.Guillaume Sicard, A.Artemis Kosta, A.Anne Rodallec, S.Sarah Giacometti, J.Joseph Ciccolini and R.Raphaëlle Fanciullino. Pegylated liposome encapsulating docetaxel using microfluidic mixing technique: Process optimization and results in breast cancer models.International Journal of Pharmaceutics656May 2024, 124091HALDOI
• 20 articleM.Margaux Damerval, M.Mohammed Bennani, C.Catherine Rioufol, S.Selim Omrani, M.Margaux Riboulet, N.Nelly Etienne-Selloum, A.Audrey Saint-Ghislain, F.Fanny Leenhardt, A.Antonin Schmitt, N.Nicolas Simon, A.-L.Anne-Laure Clairet, A.Aurélia Meurisse, V.Virginie Andre, J.Jeanne Briet, M.Michael Bringuier, R.Régine Chevrier, F.Florian Correard, A.Amélie Cransac, A.Alice Danckaert, F.Françoise Decrozals, E.Elise Deluche, C.Catherine Devys, N.Nelly Etienne-Selloum, R.Raphaëlle Fanciullino, J.Julie Fulcrand, V.Vincent Goldschmidt, J.Jérémy Jost, M.Murielle Laudet, F.Fanny Leenhardt, B.Barbara Lortal, I.Isabelle Madelaine, P.Pierre Nizet, S.Selim Omrani, E.Emeline Orillard, G.Germain Perrin, S.Sophie Potin, F.Florent Puisset, L.Liliane Remenieras, F.Fanny Rethouze, C.Catherine Rioufol, A.Audrey Saint-Ghislain, A.Antonin Schmitt, N.Nicolas Simon, F.Florian Slimano, G.Geoffrey Strobbe, A.Aurélie Terrier-Lenglet, A.Audrey Thomas, J.Julie Vardanega, E.Erika Viel-Truong and V.Virginie Nerich. Attributes for a discrete-choice experiment on preferences of patients for oncology pharmacy consultations.Supportive Care in Cancer325April 2024, 318HALDOI
• 21 articleA.Alice Daumas, C.Celestin Bigarre, M.Mohamed Boucekine, A.Audrey Zaccariotto, B.Bertrand Kaeppelin, A.Alice Mogenet, E.Etienne Gouton, J.Johan Pluvy, P.Pascale Tomasini, X.Xavier Muracciole, S.Sébastien Benzekry, L.Laurent Greillier and L.Laetitia Padovani. Lack of Prophylactic Cranial Irradiation for Extensive Small-Cell Lung Cancer in Real Life, with the Emergence of Immunotherapy.Cancers1623December 2024, 4122HALDOI
• 22 articleC.Chantal Decroisette, L.Laurent Greillier, H.Hubert Curcio, M.Maurice Pérol, C.Charles Ricordel, J.-B.Jean-Bernard Auliac, L.Lionel Falchero, R.Remi Veillon, S.Sabine Vieillot, F.Florian Guisier, M.Marie Marcq, G.Grégoire Justeau, L.Laurence Bigay-Game, M.Marie Bernardi, H.Hélène Doubre, J.Julian Pinsolle, K.Karim Amrane, C.Christos Chouaïd and R.Renaud Descourt. Three-Year Overall Survival of Patients With Advanced Non–Small-Cell Lung Cancers With ≥50% PD-L1 Expression Treated With First-Line Pembrolizumab Monotherapy in a Real-World Setting (ESCKEYP GFPC Study).Journal of Immunotherapy471January 2024, 16-20HALDOI
• 23 articleR.R. Descourt, C.C. Decroisette, J.J. Pinsolle, C.C. François, Y.Y. Simonneau, S.S. Vieillot, E.E. Huchot, R.R. Lamy, K.K. Amrane, D.D. Moreau, L.L. Thibonnier, R.R. Schott, C.C. Locher, N.N. Delberghe, J.J. Le Treut, C.C. Audigier-Valette, O.O. Bylicki, C.C. Chouaïd and L.L. Greillier. Explore ALK : Alectinib chez les patients atteints de cancer du poumon non à petites cellules métastatique ALK+: une analyse nationale en vie réelle (GFPC 03-2019).Revue des Maladies Respiratoires Actualites161January 2024, 66-67HALDOI
• 24 articleM.Melissa Dolan, Y.Yuhao Shi, M.Michalis Mastri, M.Mark Long, A.Amber Mckenery, J.James Hill, C.Cristina Vaghi, S.Sébastien Benzekry, J.Joseph Barbi and J.John Ebos. A senescence-mimicking (senomimetic) VEGFR TKI side-effect primes tumor immune responses via IFN/STING signaling.Molecular Cancer TherapeuticsMay 2024, Online ahead of printIn press. HALDOI
• 25 articleM.-C.Marie-Christine Etienne-Grimaldi, B.Bernard Royer, M.Manon Launay, A.Antonin Schmitt, F.Fabienne Thomas and J.Joseph Ciccolini. Large-Scale DPD Testing Should Be More Than an Option.JCO Oncology PracticeDecember 2024HALDOI
• 26 articleF.Florent Ferrer, P.Pauline Tetu, L.Léa Dousset, C.Céleste Lebbe, J.Joseph Ciccolini, D.David Combarel, N.Nicolas Meyer, A.Angelo Paci and S.Stéphane Bouchet. Tyrosine kinase inhibitors in cancers: Treatment optimization – Part II.Critical Reviews in Oncology/Hematology200August 2024, 104385HALDOI
• 27 articleC.C. Gaudy-Marqueste and J.J. Ciccolini. CDA deficiency is not associated with higher sensitivity to immune checkpoint inhibitors in melanoma patients.European journal of cancer skin cancer22024, 100265HALDOI
• 28 articleV. S.Vimal Scott Kapoor, J.Joseph Ciccolini and S.Sunil Kapoor. A Big Problem With a Feasible Solution, Not a Small Problem With a Complex Solution.JCO Oncology PracticeDecember 2024HALDOI
• 29 articleH.Hervé Léna, L.Laurent Greillier, C.Claire Cropet, O.Olivier Bylicki, I.Isabelle Monnet, C.Clarisse Audigier-Valette, L.Lionel Falchero, A.Alain Vergnenègre, P.Pierre Demontrond, M.Margaux Geier, F.Florian Guisier, S.Stéphane Hominal, C.Chrystèle Locher, R.Romain Corre, C.Christos Chouaid and C.Charles Ricordel. Nivolumab plus ipilimumab versus carboplatin-based doublet as first-line treatment for patients with advanced non-small-cell lung cancer aged ≥70 years or with an ECOG performance status of 2 (GFPC 08–2015 ENERGY): a randomised, open-label, phase 3 study.The Lancet Respiratory MedicineOctober 2024HALDOI
• 30 articleP.Paul Maroselli, R.Raphaelle Fanciullino, J.Julien Colle, L.Laure Farnault, P.Pauline Roche, G.Geoffroy Venton, R.Régis Costello and J.Joseph Ciccolini. Body Mass Index affects Imatinib exposure: real-world evidence from TDM with adaptive dosing.Fundamental & Clinical Pharmacology2024. In press. HAL
• 31 articleA.Alice Mogenet, L.Laurent Greillier and P.Pascale Tomasini. Predictive biomarkers for immune checkpoint efficacy: is multi-omics breaking the deadlock?Translational Lung Cancer Research1310October 2024, 2856-2860HALDOI
• 32 articleL.Linh Nguyen Phuong, S.Sébastien Salas and S.Sébastien Benzekry. Computational modeling for circulating cell-free DNA in clinical oncology.JCO Clinical Cancer Informatics9March 2025HALDOI
• 33 articleH.Hans Prenen, S.Sanjeev Deva, B.Bhumsuk Keam, C.Colin Lindsay, I.Iwona Lugowska, J.James Yang, F.Federico Longo, M.Maria de Miguel, M.Mariano Ponz-Sarvise, M.-J.Myung-Ju Ahn, M.Mahmut Gumus, S.Stephane Champiat, A.Antoine Italiano, S.Sébastien Salas, R.Ruth Perets, C.Cagatay Arslan, B.Byoung Cho, S.Stefan Evers, C.Christophe Boetsch, D.Daniel Marbach, D.David Dejardin, N.Nassim Sleiman, C.Caroline Ardeshir, M.Muriel Richard, J.Jehad Charo, A.Anton Kraxner, N.Nino Keshelava, V.Volker Teichgräber and V.Victor Moreno. Phase II Study to Determine the Antitumor Activity and Safety of Simlukafusp Alfa (FAP-IL2v) Combined with Atezolizumab in Esophageal Cancer.Clinical Cancer Research3014July 2024, 2945-2953HALDOI
• 34 articleD.Dorian Protzenko, B.Benjamin Bouchacourt, L.Laure Carriat, P.Paul Maroselli, C.Clara Boéri, R.Raynier Devillier and J.Joseph Ciccolini. Adaptive dosing of high‐dose busulfan in real‐world adult patients undergoing haematopoietic cell transplant conditioning.British Journal of Clinical PharmacologyNovember 2024HALDOI
• 35 articleJ.Jordi Remon, B.Benjamin Besse, S. P.Santiago Ponce Aix, A.Ana Callejo, K.Kamal Al-Rabi, R.Reyes Bernabe, L.Laurent Greillier, M.Margarita Majem, N.Noemi Reguart, I.Isabelle Monnet, S.Sophie Cousin, P.Pilar Garrido, G.Gilles Robinet, R. G.Rosario Garcia Campelo, A.Anne Madroszyk, J.Julien Mazières, H.Hubert Curcio, B.Bartosz Wasąg, Y.Yassin Pretzenbacher, F.Fanny Grillet, A.-M.Anne-Marie Dingemans and R.Rafal Dziadziuszko. Overall Survival From the EORTC LCG-1613 APPLE Trial of Osimertinib Versus Gefitinib Followed by Osimertinib in Advanced EGFR -Mutant Non–Small-Cell Lung Cancer.Journal of Clinical Oncology4212April 2024, 1350-1356HALDOI
• 36 articleC.Christian Rolfo, L.Laurent Greillier, R.Remi Veillon, F.Firas Badin, F.Francois Ghiringhelli, N.Nicolas Isambert, A.Astrid Paulus, S. P.Surendra Pal Chaudhary, Y.Yulia Vugmeyster, M.Masashi Sato and S.Sandrine Hiret. Efficacy and Safety of Bintrafusp Alfa, a Bifunctional Fusion Protein Targeting Transforming Growth Factor-β and Programmed Death-Ligand 1, Plus Chemotherapy in Patients With Stage IV NSCLC.JTO Clinical and Research Reports61January 2025, 100748HALDOI
• 37 articleH.Hugo Roux, F.Franck Touret, A.Antonio Coluccia, P.Pietro Scio, H. S.Hawa Sophia Bouzidi, C.Carole Di Giorgio, F.Florence Gattacceca, O.Omar Khoumeri, R.Romano Silvestri, P.Patrice Vanelle and M.Manon Roche. Design and synthesis of novel thioether analogs as promising antiviral agents: In vitro activity against enteroviruses of interest.European Journal of Medicinal Chemistry288April 2025, 117395HALDOI
• 38 articleY.Yuhao Shi, A.Amber Mckenery, M.Melissa Dolan, M.Michalis Mastri, J. W.James W Hill, A.Adam Dommer, S.Sébastien Benzekry, M.Mark Long, S.Scott Abrams, I.Igor Puzanov and J. M.John M.L. Ebos. Acquired resistance to PD-L1 inhibition enhances a type I IFN-regulated secretory program in tumors.EMBO Reports262024, 521 - 559HALDOI
• 39 articleG.Guillaume Sicard, D.Dorian Protzenko, S.Sarah Giacometti, F.Fabrice Barlési, J.Joseph Ciccolini and R.Raphaelle Fanciullino. Overcoming immuno-resistance by rescheduling anti-VEGF/cytotoxics/anti-PD-1 combination in lung cancer model.Cancer Drug ResistanceMarch 2024HALDOI
• 40 articleL. B.Laura Bender Somme, C.Christos Chouaid, F.Fabien Moinard-Butot, J.-B.Jean-Baptiste Barbe-Richaud, L.Laurent Greillier and R.Roland Schott. Antibody-Drug Conjugates as Novel Therapeutic Agents for Non-Small Cell Lung Carcinoma with or without Alterations in Oncogenic Drivers.BioDrugs384May 2024, 487-497HALDOI
• 41 articleA.A. Todesco, J.J. Grynblat, K. K.K. K. F. Akoumia, D.D. Bonnet, P.P. Mendes-Ferreira, S.S. Morisset, D.D. Chemla, M.M. Levy, M.M. Meot, S. G.S. G. Malekzadeh-Milani, B.B. Tielemans, B.B. Decante, C.C. Vastel-Amzallag, P.P. Habert, M. R.M. R. Ghigna, M.M. Humbert, D.D. Montani, D.D. Boulate and F.F. Perros. Pulmonary Hypertension Induced by Right Pulmonary Artery Occlusion: Hemodynamic Consequences of Bmpr2 Mutation.Journal of the American Heart Association13142024, e034621HALDOI

Invited conferences

• 42 inproceedingsS.Sébastien Benzekry. Integrating kinetics and machine learning modeling for prediction of outcome following immunotherapy in lung cancer.European Conference on Theoretical and Mathematical Biology (ECMTB)Toledo (Spain), SpainJuly 2024HAL
• 43 inproceedingsS.Sébastien Benzekry. Integrative kinetics and machine learning modeling for prediction of outcome following immunotherapy in lung cancer.SMB 2024 - Joint annual meeting of the Korean Society for Mathematical Biology and the Society for Mathematical BiologySeoul, South KoreaJune 2024HAL
• 44 inproceedingsS.Sébastien Benzekry. Machine Learning to Predict Survival and Resistance to Immune Checkpoint Inhibition.Twelfth International Workshop on Pharmacodynamics of Anticancer AgentsPonta Delgada (Açores), PortugalSeptember 2024HAL
• 45 inproceedingsS.Sébastien Benzekry. Mechanistic Learning to predict the results of clinical trials.AMLD 2024 - Applied Machine Learning DaysLausanne, SwitzerlandMarch 2024HAL
• 46 inproceedingsS.Sébastien Benzekry. Mechanistic learning to predict response and survival in immuno-oncology: A modeling journey in immuno-oncology.BIOMAT 2024 - 19th International Summer SchoolGranada (Spain), SpainJune 2024HAL
• 47 inproceedingsS.Sébastien Benzekry. Precision Immuno-Oncology in NSCLC: Challenges in machine learning biomarkers research.EORTC PAMM 2024 - 44th Meeting of the European Organization of Research and Treatment of Cancer (EORTC) – Pharmacology and Molecular Mechanisms (PAMM) GroupMarseille, FranceFebruary 2024HAL
• 48 inproceedingsL.Linh Nguyen Phuong. Computational modeling approaches for circulating cellfree DNA in oncology.Groupe de métabolisme et pharmacocinétiqueLyon, FranceOctober 2024HAL

International peer-reviewed conferences

• 49 inproceedings J.Joseph Ciccolini. Pharmacometrics as a decision-making tool with immune checkpoint inhibitors: finding the perfect blend? 12th International Workshop on Pharmacodynamics of Anticancer Agents Porto del Gado, Portugal September 2024 HAL
• 50 inproceedingsR.Ruben Taieb, R.René Bruno, J.Jin Jin, P.Pascal Chanu and S.Sébastien Benzekry. Mechanistic modelling of tumor kinetics coupled with biomarker dynamics for survival prediction in non-small cell lung cancer patients.PAGEPAGE meeting XXXII - 2024 Population approach group meetingAbstract 10812Roma, France2024HALback to text

Conferences without proceedings

• 51 inproceedingsJ.Joseph Ciccolini. ADC in oncology, reality beyond the myth.Imagining the future of cancer treatments Mini-SymposiumMarseille, FranceJuly 2024HAL
• 52 inproceedingsJ.Joseph Ciccolini. Antibody Drug Conjugates: why magic bullet does not mean magic pharmacology.61th Oncology CongressBelgrade, SerbiaNovember 2024HAL
• 53 inproceedingsJ.Joseph Ciccolini. Antibody-Drug Conjugates in Oncology.Scientific Symposium Fondazione Pisana per la Scienza ONLUS (FPS)Pisa (IT), ItalyMarch 2024HAL
• 54 inproceedings J.Joseph Ciccolini. Antibody-Drug Conjugates ; beyond the hype, which pharmacology in triple negative breast cancer? Triple Negative Breast Cancer : A dialog between basic science and clinical practice Workshop Nantes, France January 2024 HAL
• 55 inproceedingsJ.Joseph Ciccolini. Bioanalyse des biothérapies: application au STP en oncologie clinique de routine.Symposium Clinique WatersParis, FranceOctober 2024HAL
• 56 inproceedingsJ.Joseph Ciccolini. Bispecific Antibodies In Multiple Myeloma: Benchmarking &amp; Pharmacological Consideration.Onco-Hematology MeetingParis, FranceNovember 2024HAL
• 57 inproceedingsJ.Joseph Ciccolini. E-R relationships with immune checkpoint inhibitors.18èmes Journées Clinico-Pathologique GFPCParis, FranceDecember 2024HAL
• 58 inproceedingsJ.Joseph Ciccolini. Individualisation des posologies en oncologie: apport de la modélisation mathématique en pratique clinique de routine à l'APHM.CISAM/RDV innovation santéMarseille, FranceFebruary 2024HAL
• 59 inproceedings J.Joseph Ciccolini. Is Biology soluble in Ideology? Science, Society and Values Symposium Bordeaux, France June 2024 HAL
• 60 inproceedingsJ.Joseph Ciccolini. Modèles mathématiques et médecine de précision en oncologie: individualisation des traitements en pratique clinique de routine.Séminaire Médecine & SciencesCorté, FranceJune 2024HAL
• 61 inproceedingsJ.Joseph Ciccolini. Nanoparticules en Oncologie: enjeux &amp; Perspectives.7e édition des IFODS (Journées Franco-Internationales d'Oncologie)Paris, FranceJune 2024HAL
• 62 inproceedings J.Joseph Ciccolini. PK/PD relationships with immune checkpoint inhibitors: is the earth flat? Cours Saint Paul en Oncologie Digestive Nice, France November 2024 HAL
• 63 inproceedingsJ.Joseph Ciccolini. Place des anti-BRAF dans le cancer colorectal.JFHOD 2024 - Journées Francophones d’Hépato-gastroentérologie et d’Oncologie DigestiveParis, FranceMarch 2024HAL
• 64 inproceedingsJ.Joseph Ciccolini. Place des anticorps conjugués dans le carcinome urothélial.Workshop GPCO-UnicancerParis, FranceNovember 2024HAL
• 65 inproceedingsJ.Joseph Ciccolini. Retards d’élimination du MTX: Aspects pharmacométriques.Management du MTXHD - Partage d'expérience sur les hémopathies en région PACAMarseille, FranceNovember 2024HAL
• 66 inproceedings J.Joseph Ciccolini. Suivi Thérapeutique Pharmacologique des inhibiteurs de point de contrôle de l'immunité: quel niveau de preuve? Journée d’innovation en immunologie Paris, France October 2024 HAL
• 67 inproceedingsJ.Joseph Ciccolini. Treatment of BRAF-mut cancers: pharmacology and pharmacokinetics considerations.44th PAMM-EORTC Winter MeetingMarseille, FranceFebruary 2024HAL
• 68 inproceedingsR.Raphaëlle Fanciullino. Chimiothérapies et Virage Ambulatoire.20éme Congrès de la Sciété Française de Pharmacie CliniqueToulouse, FranceMarch 2024HAL
• 69 inproceedings R.Raphaëlle Fanciullino. Les Biosimilaires en oncologie: copie ou pas copie? Cours Saint Paul Oncologie Digestive Nice, France November 2024 HAL
• 70 inproceedingsR.Raphaëlle Fanciullino. Pharmacie clinique et Intervention Pharmaceutique en Oncologie Clinique.XVéme Journées de la Société Française des Pharmaciens OncologuesParis, FranceOctober 2024HAL
• 71 inproceedingsL.Linh Nguyen Phuong, F.Frederic Fina, L.Laurent Greillier, C.Caroline Gaudy-Marqueste, J.-L.Jean-Laurent Deville, J.-C.Jean-Charles Garcia, S.Sébastien Salas and S.Sébastien Benzekry. Integrating tumor kinetics and circulating cell-free DNA markers through mechanistic modeling for early prediction of immunotherapy response.KSMB-SMB 2024 - Joint annual meeting of the Korean Society for Mathematical Biology and the Society for Mathematical BiologySeoul, South KoreaJuly 2024HALback to text
• 72 inproceedingsA.Anne Rodallec. Model-driven optimization of the scheduling of a novel anti-cancer prodrug administered subcutaneously.Pharmacometrics in FranceParis, FranceSeptember 2024HALback to text
• 73 inproceedingsS.Sébastien Salas, L.Laurence Bréau, L.Laura Estienne and A.Alexia Bonnaire. Représentations sociales de l’obstination déraisonnable et de l’abandon thérapeutique : démarche comparative auprès des patients hospitalisés en oncologie et des médecins impliqués dans la prise en charge de patients avec un pronostic défavorable..1er Colloque International ICARES : Pour une approche intégrative de la santé : Perspectives croisées en SHS.Montpellier, FranceNovember 2024HAL

Doctoral dissertations and habilitation theses

• 74 thesisA.Anthéa Deschamps. Patient-centered population pharmacokinetic approach : Proof of concept and application in critical care.Aix Marseille UniversitéDecember 2024HALback to text
• 75 thesisC.Clémence Marin. Pharmacokinetics of monoclonal antibodies: bioanalytical development and application to therapeutic drug monitoring in oncology.Aix Marseille University (AMU)October 2024HALback to text
• 76 thesisJ.Jessica Ou. Development of PBPK models for nanomedicines optimization.Aix Marseille UniversitéNovember 2024HALback to text

Reports & preprints

• 77 miscC.Célestin Bigarré, A.Alice Daumas, L.Laurent Greillier, X.Xavier Muracciole, L.Laeticia Padovani and S.Sébastien Benzekry. Metamats: A mechanistic software for the simulation, inference and prediction of clinical metastasis.February 2025HAL
• 78 miscA.Anne Rodallec, R.Randy Lee, J.Jingming Cao, S.Sophie Marolleau, J.Julien Nicolas and S.Sébastien Benzekry. Model-driven scheduling of a novel anti-cancer polymer prodrug administered subcutaneously.2025HAL
• 79 miscB.Benjamin Schneider, S.Sébastien Benzekry and J.Jonathan Mochel. Optimizing First-Line Therapeutics in Non-Small Cell Lung Cancer: Insights from Joint Modeling and Large-Scale Data Analysis.2024HAL
• 80 miscC.Cristina Vaghi, A.Anne Rodallec, J.Joseph Ciccolini, R.Raphaëlle Fanciullino and S.Sébastien Benzekry. Pharmacokinetic-pharmacodynamic modeling of antibody nanoconjugates in breast cancer treatment.2025HAL

Other scientific publications

• 81 inproceedingsJ.J. Ciccolini, M.M. Hamimed, S.S. Marolleau, C.C. Perez, S.S. Benzekry, M.M. Le Ray, A.A. Boyer, C.C. Audigier-Valette, S.S. Martinez, H.H. Pegliasco, P.P. Ray, L.L. Falchero, A.A. Serre, N.N. Cloarec, L.L. Lebas, S.S. Hominal, J.J. Mazieres, M.M. Pérol, L.L. Greillier and F.F. Barlesi. 1380P PK/PD analysis of pembrolizumab, nivolumab and atezolizumab in NSCLC patients: The PIONeeR trial.ESMO Congress 202435Barcelone, SpainSeptember 2024, S866-S867HALDOI
• 82 inproceedingsM.Mathilde Dacos, E.Erwan Diroff, S.Sarah Giacometti, J.Joseph Ciccolini and R.Raphaelle Fanciullino. Abstract 7173: Animal pharmacokinetics of an anti-Her2+ immunoliposome of docetaxel.AACR Annual Meeting846_SupplementSan Diego, United StatesMarch 2024, 7173-7173HALDOI
• 83 inproceedingsM.Mathilde Dacos, B.Benoit Immordino, E.Erwan Diroff, S.Sarah Giacometti, J.Joseph Ciccolini and R.Raphaelle Fanciullino. 76P- Pegylated immunoliposome encapsulating docetaxel: In vitro activity and optimal pharmacokinetics in a breast cancer model.ESMO Breast Cancer 2024 annual congress9suppl_4Berlin (DE), Germany2024, 1-34HAL
• 84 inproceedingsJ.Jessica Forestier, A. J.Alice Julie Daumas, M.-A.Marie-Aleth Richard, C.Caroline Gaudy-Marqueste, S.Sébastien Benzekry and N.Nausicaa Malissen. Mathematical modeling of routine biological parameters kinetics in order to predict durable benefit for first-line immunotherapy in metastatic or unresectable melanoma..2024 ASCO Annual Meeting - American Society of Clinical Oncology Annual meeting 20244216_supplChicago (USA), United StatesJune 2024, e21549-e21549HALDOIback to text
• 85 inproceedingsS.Sophie Marolleau, A.Alice Mogenet, C.Clara Boeri, M.Mourad Hamimed, J.Joseph Ciccolini and L.Laurent Greillier. Standard atezolizumab leads to severe overexposure in real-world patients with lung cancer: How far could we go in extending dosing intervals and saving money?2024 ASCO Annual Meeting4216_supplChicago, United StatesJune 2024, 3085-3085HALDOI
• 86 inproceedingsS.Sophie Marolleau, A.Alice Mogenet, M.Mourad Hamimed, C.Clara Boeri, L.Laurent Greillier and J.Joseph Ciccolini. Abstract 7162: Killing a fly with a sledgehammer: standard flat-dosing administration of Atezolizumab leads to marked overexposure in real-world lung cancer patients.AACR Annual Meeting846_SupplementSan Diego, United StatesMarch 2024, 7162-7162HALDOI
• 87 inproceedingsP.Paul Maroselli, R.Raphaelle Fanciullino, J.Julien Colle, L.Laure Farnault, P.Pauline Rocher, G.Geoffroy Venton, R.Régis Costello and J.Joseph Ciccolini. Relationship of body mass index and plasma imatinib levels in CML patients and mitigation of clinical impact using PK-guided dosing strategy..2024 ASCO Annual Meeting4216_supplChicago, United StatesJune 2024, e18522-e18522HALDOI
• 88 miscM.Michalis Mastri, S.Sébastien Benzekry and J. M.John Ml Ebos. Pre- and post-surgical monitoring of experimental primary tumor growth and metastasis under neo-adjuvant treatment.2024HALDOI
• 89 inproceedingsL.Linh Nguyen Phuong, F.Frederic Fina, L.Laurent Greillier, C.Caroline Gaudy-Marqueste, J.-L.Jean-Laurent Deville, A.Audrey Boutonnet, F.Frédéric Ginot, S.Sébastien Salas and S.Sébastien Benzekry. Circulating cell-free DNA size distribution as a prediction marker for early progression undergoing immune checkpoint inhibitors.PAGE 2024 - Annual meeting of the Population Approach Group EuropeRome, ItalyJune 2024HALDOIback to text
• 90 inproceedingsA.Anne Rodallec, O.Olivio Jaonina, M.Maryam Shetab Boushehri and A.Alf Lamprecht. SCREENING OF LIPOSOME FEATURES FOR IMMUNOMODULATION.First BNLF local chapter metting : Drug and Nucleic Acid DeliveryGhent, BelgiumOctober 2024HAL
• 91 inproceedingsA.Anne Rodallec, O.Olivio Jaonina, M.Maryam Shetab Boushehri and A.Alf Lamprecht. Screening of liposome features for immunomodulation.CRS 2024 Annual Meeting and Expo : integrating Delivery Science across disciplinesBologna, ItalyJuly 2024HAL
• 92 inproceedingsA.Anne Rodallec, R.Randy Lee, S.Sophie Marolleau and S.Sébastien Benzekry. PK/PD modelling to advance the preclinical development of a novel polymer prodrug in oncology.CRS 2024 Annual Meeting and Expo : integrating Delivery Science across disciplinesBologna, ItalyJuly 2024HALback to text
• 93 inproceedingsA.Antonin Ronda, L.Laurent Bouguignon, F.Florian Correard, J.Joseph Ciccolini and R.Raphaëlle Fanciullino. High-Dose Methotrexate in hematologic malignancies : covariates of interest to prevent toxicities.44th EORTC-PAMM Winter MeetingPAMM oral communications and postersMarseille, FranceFebruary 2024HAL
• 94 inproceedingsS.Sébastien Salas, L.Linh Nguyen Phuong, J.-C.Jean-Charles Garcia, L.Laurent Greillier, C.Caroline Gaudy-Marqueste, J.-L.Jean-Laurent Deville, A.Audrey Boutonnet, F.Frédéric Ginot, F.Frédéric Fina and S.Sébastien Benzekry. Long cell-free DNA fragments predict early-progression in patients with advanced or metastatic cancer treated with immune-checkpoint inhibition..2024 ASCO annual meeting - American Society of Clinical Oncology Annual meeting 20244216_supplChicago, United StatesJune 2024, e14550-e14550HALDOIback to text
• 95 inproceedingsS.Sébastien Salas, L.Linh Nguyen Phuong, F.Frédéric Ginot, L.Laurent Greillier, P.Pascale Tomasini, A.Audrey Boutonnet, J.-L.Jean-Laurent Deville, F.Frederic Fina and S.Sébastien Benzekry. Long circulating-free DNA fragments predict early-progression (EP) and progression-free-survival (PFS) in advanced carcinoma treated with immune-checkpoint inhibition (ICI): a new biomarker.European Society for Medical Oncology Annual Meeting 2024Barcelone, SpainSeptember 2024HALback to text
• 96 inproceedingsG.Guillaume Sicard, D.Dorian Protzenko, S.Sarah Giacometti, R.Raphaelle Fanciullino, F.Fabrice Barlesi and J.Joseph Ciccolini. Abstract 4037: Optimizing the efficacy of anti-VEGF/cytotoxics/anti-PD-1 scheduling in a lung cancer mice model through B and T cells monitoring.AACR Annual Meeting846_SupplementSan Diego, United StatesMarch 2024, 4037-4037HALDOI