A Bayesian Implementation of Quality-by-Design for the Development of Cationic Nano-Lipid for siRNA Transfection.

Unlike Quality by Testing approach, where products were tested only after drug manufacturing, Quality by Design (QbD) is a proactive control quality paradigm, which handles risks from the early development steps. In QbD, regression models built from experimental data are used to predict a risk mapping called Design Space in which the developers can identify values of critical input factors leading to acceptable probabilities to meet the efficacy and safety specifications for the expected product. These empirical models are often limited to quantitative responses. Moreover, in practice the smallness and incompleteness of datasets degrade the quality of predictions. In this study, a Bayesian approach including variable selection, parameter estimation and model quality assessment is proposed and assessed using a real case study devoted to the development of a Cationic Nano-Lipid Structures for siRNA Transfection. Two original model structures are also included to describe both binary and percentage response variables. The results confirm the practical relevance and applicability of the Bayesian implementation of the QbD analysis.

Identification of Gender- and Subtype-Specific Gene Expression Associated with Patient Survival in Low-Grade and Anaplastic Glioma in Connection with Steroid Signaling.

Low-grade gliomas are rare primary brain tumors, which fatally evolve to anaplastic gliomas. The current treatment combines surgery, chemotherapy, and radiotherapy. If gender differences in the natural history of the disease were widely described, their underlying mechanisms remain to be determined for the identification of reliable markers of disease progression. We mined the transcriptomic and clinical data from the TCGA-LGG and CGGA databases to identify male-over-female differentially expressed genes and selected those associated with patient survival using univariate analysis, depending on molecular characteristics (IDH wild-type/mutated; 1p/19q codeleted/not) and grade. Then, the link between the expression levels (low or high) of the steroid biosynthesis enzyme or receptors of interest and survival was studied using the log-rank test. Finally, a functional analysis of gender-specific correlated genes was performed. HOX-related genes appeared to be differentially expressed between males and females in both grades, suggesting that a glioma could originate in perturbation of developmental signals. Moreover, aromatase, androgen, and estrogen receptor expressions were associated with patient survival and were mainly related to angiogenesis or immune response. Therefore, consideration of the tight control of steroid hormone production and signaling seems crucial for the understanding of glioma pathogenesis and emergence of future targeted therapies.

Proton MR Spectroscopy and Diffusion MR Imaging Monitoring to Predict Tumor Response to Interstitial Photodynamic Therapy for Glioblastoma.

Despite recent progress in conventional therapeutic approaches, the vast majority of glioblastoma recur locally, indicating that a more aggressive local therapy is required. Interstitial photodynamic therapy (iPDT) appears as a very promising and complementary approach to conventional therapies. However, an optimal fractionation scheme for iPDT remains the indispensable requirement. To achieve that major goal, we suggested following iPDT tumor response by a non-invasive imaging monitoring. Nude rats bearing intracranial glioblastoma U87MG xenografts were treated by iPDT, just after intravenous injection of AGuIX® nanoparticles, encapsulating PDT and imaging agents. Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) allowed us an original longitudinal follow-up of post-treatment effects to discriminate early predictive markers. We successfully used conventional MRI, T2 star (T2*), Diffusion Weighted Imaging (DWI) and MRS to extract relevant profiles on tissue cytoarchitectural alterations, local vascular disruption and metabolic information on brain tumor biology, achieving earlier assessment of tumor response. From one day post-iPDT, DWI and MRS allowed us to identify promising markers such as the Apparent Diffusion Coefficient (ADC) values, lipids, choline and myoInositol levels that led us to distinguish iPDT responders from non-responders. All these responses give us warning signs well before the tumor escapes and that the growth would be appreciated.

Monte Carlo methods for light propagation in biological tissues.

Light propagation in turbid media is driven by the equation of radiative transfer. We give a formal probabilistic representation of its solution in the framework of biological tissues and we implement algorithms based on Monte Carlo methods in order to estimate the quantity of light that is received by a homogeneous tissue when emitted by an optic fiber. A variance reduction method is studied and implemented, as well as a Markov chain Monte Carlo method based on the Metropolis-Hastings algorithm. The resulting estimating methods are then compared to the so-called Wang-Prahl (or Wang) method. Finally, the formal representation allows to derive a non-linear optimization algorithm close to Levenberg-Marquardt that is used for the estimation of the scattering and absorption coefficients of the tissue from measurements.

Tumor vascular responses to antivascular and antiangiogenic strategies: looking for suitable models.

Antiangiogenic and vascular disrupting agents are in the current cancer therapeutic armamentarium. A better understanding of the intricate mechanisms ruling neovessel survival within tumors during or after treatment is needed. Refinement of imaging and a growing knowledge of molecular biology of tumor vascularization provide new insights. It is necessary to define suitable methods for monitoring tumor response and appropriate tools to analyze data. This review compares most commonly used preclinical models, considering their recent improvements, and describes promising new approaches such as microfluidics, real-time electrical impedance based technique and noninvasive imaging techniques. The advantages and limitations of the in vitro, ex vivo and in vivo models are discussed. This review also provides a critical summary of emerging approaches using mathematical modeling.