Optimizing doxorubicin-G-CSF chemotherapy regimens for the treatment of triple-negative breast cancer

La chimiothérapie cytotoxique reste une option de traitement de première intention pour la majorité des cancers. Un effet secondaire majeur dans les schémas chimio-thérapeutiques est la neutropénie. La thérapie prophylactique avec le facteur de stimulation des colonies de granulocytes (G-CSF), une cytokine endogène responsable de la régulation de la production de neutrophiles, est administrée en concomitance. Le moment et la dose exacts pour administrer la chimiothérapie et le G-CSF représentent des éléments cruciaux pour obtenir les résultats souhaités du traitement. En nous appuyant sur des travaux antérieurs qui optimisaient les schémas thérapeutiques du G-CSF, nous sommes basés sur une approche de pharmacologie quantitative des systèmes (QSP) pour étudier la fréquence et l’intensité de la dose dans le but de maximiser les effets anti-tumoraux de la chimiothérapie tout en minimisant la neutropénie. Dans ce travail, nous avons effectué une optimisation sur une large gamme de longueurs de cycle et de valeurs des doses de chimiothérapie afin d’identifier les meilleurs schémas en combinaison avec le G-CSF. Nos résultats suggèrent que la doxorubicine 45mg/BSA tous les 14 jours a un impact positif sur le contrôle de la croissance tumorale, et qu’il est préfèrable de retarder l’administration du G-CSF au septième jour après la chimiothérapie et de donner moins de doses pour minimiser le risque de neutropénie et le fardeau de ce médicament. Cette étude suggère des pistes possibles pour des schémas optimaux de chimiothérapie, avec le soutien prophylactique du G-CSF spécifiquement dans le contexte du cancer du sein triple négatif.Cytotoxic chemotherapy continues to be a first-line treatment option for the majority of cancers. A major side effect in chemotherapy regimens is neutropenia. Prophylactic therapy with granulocyte colony stimulating factor (G-CSF), an endogenous cytokine responsible for regulating neutrophil production, is administered concomitantly; the exact timing of the combination chemotherapy and G-CSF is crucial for achieving treatment results. Leveraging on previous work that optimized treatment regimens based on G-CSF timing, we developed a quantitative systems pharmacology (QSP) framework to study dose frequency and intensity of chemotherapy in order to maximize anti-tumor effects while minimizing neutropenia. In this work, we performed an optimization across a wide range of cycle lengths and dose sizes to identify the best cytotoxic chemotherapy regimens with G-CSF support. Our results suggest that doxorubicin 45mg/BSA every 14 days, has a positive impact on tumour growth control, and that to minimize the risk of neutropenia and the burden to patients it is best to delay the administration of G-CSF to day seven after chemotherapy and give fewer doses . This study suggests possible avenues for optimal chemotherapy regimens with prophylactic support of G-CSF in the context of Triple Negative Breast Cancer

Mathematical models of cytotoxic effects in endpoint tumor cell line assays: Critical assessment of the application of a single parametric value as a standard criterion to quantify the dose-response effects and new unexplored proposal formats

The development of convenient tools for describing and quantifying the effects of standard and novel therapeutic agents is essential for the research community, to perform more precise evaluations. Although mathematical models and quantification criteria have been exchanged in the last decade between different fields of study, there are relevant methodologies that lack proper mathematical descriptions and standard criteria to quantify their responses. Therefore, part of the relevant information that can be drawn from the experimental results obtained and the quantification of its statistical reliability are lost. Despite its relevance, there is not a standard form for the in vitro endpoint tumor cell lines' assays (TCLA) that enables the evaluation of the cytotoxic dose-response effects of anti-tumor drugs. The analysis of all the specific problems associated with the diverse nature of the available TCLA used is unfeasible. However, since most TCLA share the main objectives and similar operative requirements, we have chosen the sulforhodamine B (SRB) colorimetric assay for cytotoxicity screening of tumor cell lines as an experimental case study. In this work, the common biological and practical non-linear dose-response mathematical models are tested against experimental data and, following several statistical analyses, the model based on the Weibull distribution was confirmed as the convenient approximation to test the cytotoxic effectiveness of anti-tumor compounds. Then, the advantages and disadvantages of all the different parametric criteria derived from the model, which enable the quantification of the dose-response drug-effects, are extensively discussed. Therefore, model and standard criteria for easily performing the comparisons between different compounds are established. The advantages include a simple application, provision of parametric estimations that characterize the response as standard criteria, economization of experimental effort and enabling rigorous comparisons among the effects of different compounds and experimental approaches. In all experimental data fitted, the calculated parameters were always statistically significant, the equations proved to be consistent and the correlation coefficient of determination was, in most of the cases, higher than 0.98.The authors are grateful to the Foundation for Science and Technology (FCT) of Portugal and FEDER for financial support to CIMO (UID/AGR/00690/2013); and to the Xunta de Galicia for financial support for the post-doctoral research of M. A. Prieto.info:eu-repo/semantics/publishedVersio

Hyperbolastic type-III diffusion process: Obtaining from the generalized Weibull diffusion process

The modeling of growth phenomena has become a matter of great interest in many different fields of application and research. New stochastic models have been developed, and others have been updated to this end. The present paper introduces a diffusion process whose main characteristic is that its mean function belongs to a wide family of curves derived from the classic Weibull curve. The main characteristics of the process are described and, as a particular case, a di usion process is considered whose mean function is the hyperbolastic curve of type III, which has proven useful in the study of cell growth phenomena. By studying its estimation we are able to describe the behavior of such growth patterns. This work considers the problem of the maximum likelihood estimation of the parameters of the process, including strategies to obtain initial solutions for the system of equations that must be solved. Some examples are provided based on simulated sample paths and real data to illustrate the development carried out.This work was supported in part by the Ministerio de Economía, Industria y Competitividad, Spain, under Grant MTM2017-85568-P

Modelling the impact of treatment uncertainties in radiotherapy

Uncertainties are inevitably part of the radiotherapy process. Uncertainty in the dose deposited in the tumour exists due to organ motion, patient positioning errors, fluctuations in machine output, delineation of regions of interest, the modality of imaging used, and treatment planning algorithm assumptions among others; there is uncertainty in the dose required to eradicate a tumour due to interpatient variations in patient-specific variables such as their sensitivity to radiation; and there is uncertainty in the dose-volume restraints that limit dose to normal tissue. This thesis involves three major streams of research including investigation of the actual dose delivered to target and normal tissue, the effect of dose uncertainty on radiobiological indices, and techniques to display the dose uncertainty in a treatment planning system. All of the analyses are performed with the dose distribution from a four-field box treatment using 6 MV photons. The treatment fields include uniform margins between the clinical target volume and planning target volume of 0.5 cm, 1.0 cm, and 1.5 cm. The major work is preceded by a thorough literature review on the size of setup and organ motion errors for various organs and setup techniques used in radiotherapy. A Monte Carlo (MC) code was written to simulate both the treatment planning and delivery phases of the radiotherapy treatment. Using MC, the mean and the variation in treatment dose are calculated for both an individual patient and across a population of patients. In particular, the possible discrepancy in tumour position located from a single CT scan and the magnitude of reduction in dose variation following multiple CT scans is investigated. A novel convolution kernel to include multiple pretreatment CT scans in the calculation of mean treatment dose is derived. Variations in dose deposited to prostate and rectal wall are assessed for each of the margins and for various magnitudes of systematic and random error, and penumbra gradients. The linear quadratic model is used to calculate prostate Tumour Control Probability (TCP) incorporating an actual (modelled) delivered prostate dose. The Kallman s-model is used to calculate the normal tissue complication probability (NTCP), incorporating actual (modelled) fraction dose in the deforming rectal wall. The impact of each treatment uncertainty on the variation in the radiobiological index is calculated for the margin sizes.Thesis (Ph.D.)--Department of Physics and Mathematical Physics, 2002

Statistical inference of the mechanisms driving collective cell movement

Numerous biological processes, many impacting on human health, rely on collective cell movement. We develop nine candidate models, based on advection-diffusion partial differential equations, to describe various alternative mechanisms that may drive cell movement. The parameters of these models were inferred from one-dimensional projections of laboratory observations of Dictyostelium discoideum cells by sampling from the posterior distribution using the delayed rejection adaptive Metropolis algorithm (DRAM). The best model was selected using the Widely Applicable Information Criterion (WAIC). We conclude that cell movement in our study system was driven both by a self-generated gradient in an attractant that the cells could deplete locally, and by chemical interactions between the cells

T-Growth Stochastic Model: Simulation and Inference via Metaheuristic Algorithms

The main objective of this work is to introduce a stochastic model associated with the one described by the T-growth curve, which is in turn a modification of the logistic curve. By conveniently reformulating the T curve, it may be obtained as a solution to a linear differential equation. This greatly simplifies the mathematical treatment of the model and allows a diffusion process to be defined, which is derived from the non-homogeneous lognormal diffusion process, whose mean function is a T curve. This allows the phenomenon under study to be viewed in a dynamic way. In these pages, the distribution of the process is obtained, as are its main characteristics. The maximum likelihood estimation procedure is carried out by optimization via metaheuristic algorithms. Thanks to an exhaustive study of the curve, a strategy is obtained to bound the parametric space, which is a requirement for the application of various swarm-based metaheuristic algorithms. A simulation study is presented to show the validity of the bounding procedure and an example based on real data is provided.Ministerio de Economía, Industria y Competitividad, Spain, under Grant MTM2017-85568-PFEDER/Junta de Andalucía-Consejería de Economía y Conocimiento, Spain, Grant A-FQM-456-UGR1