Lulu Chu

Lymphodepletion (LD) or conditioning is an essential step in the application of currently used autologous and allogeneic chimeric antigen receptor T-cell (CAR-T) therapies as it maximizes engraftment, efficacy and long-term survival of CAR-T. Its main modes of action are the depletion and modulation of endogenous lymphocytes, conditioning of the microenvironment for improved CAR-T expansion and persistence, and reduction of tumor load. However, most LD regimens provide a broad and fairly unspecific suppression of T-cells as well as other hematopoietic cells, which can also lead to severe side effects, particularly infections. We reviewed 1271 published studies (2011-2023) with regard to current LD strategies for approved anti-CD19 CAR-T products for large B cell lymphoma (LBCL). Fludarabine (Flu) and cyclophosphamide (Cy) (alone or in combination) were the most commonly used agents. A large number of different schemes and combinations have been reported. In the respective schemes, doses of Flu and Cy (range 75-120mg/m2 and 750-1.500mg/m2) and wash out times (range 2-5 days) differed substantially. Furthermore, combinations with other agents such as bendamustine (benda), busulfan or alemtuzumab (for allogeneic CAR-T) were described. This diversity creates a challenge but also an opportunity to investigate the impact of LD on cellular kinetics and clinical outcomes of CAR-T. Only 21 studies explicitly investigated in more detail the influence of LD on safety and efficacy. As Flu and Cy can potentially impact both the in vivo activity and toxicity of CAR-T, a more detailed analysis of LD outcomes will be needed before we are able to fully assess its impact on different T-cell subsets within the CAR-T product. The T2EVOLVE consortium propagates a strategic investigation of LD protocols for the development of optimized conditioning regimens.

Unmanned aerial vehicles (UAVs) have become important tools for power transmission line inspection. Cameras installed on the platforms can efficiently obtain aerial images containing information about power equipment. However, most of the existing inspection systems cannot perform automatic real-time detection of transmission line components. In this paper, an automatic transmission line inspection system incorporating UAV remote sensing with binocular visual perception technology is developed to accurately detect and locate power equipment in real time. The system consists of a UAV module, embedded industrial computer, binocular visual perception module, and control and observation module. Insulators, which are key components in power transmission lines as well as fault-prone components, are selected as the detection targets. Insulator detection and spatial localization in aerial images with cluttered backgrounds are interesting but challenging tasks for an automatic transmission line inspection system. A two-stage strategy is proposed to achieve precise identification of insulators. First, candidate insulator regions are obtained based on RGB-D saliency detection. Then, the skeleton structure of candidate insulator regions is extracted. We implement a structure search to realize the final accurate detection of insulators. On the basis of insulator detection results, we further propose a real-time object spatial localization method that combines binocular stereo vision and a global positioning system (GPS). The longitude, latitude, and height of insulators are obtained through coordinate conversion based on the UAV’s real-time flight data and equipment parameters. Experiment results in the actual inspection environment (220 kV power transmission line) show that the presented system meets the requirement of robustness and accuracy of insulator detection and spatial localization in practical engineering.

BACKGROUND: Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies. METHODS: A quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx. RESULTS: The model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1. CONCLUSIONS: This study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.

Chimeric antigen receptor T cell (CAR-T cell) therapies have shown significant efficacy in CD19+ leukemias and lymphomas. There remain many challenges and questions for improving next-generation CAR-T cell therapies, and mathematical modeling of CAR-T cells may play a role in supporting further development. In this review, we introduce a mathematical modeling taxonomy for a set of relatively simple cellular kinetic-pharmacodynamic models that describe the in vivo dynamics of CAR-T cell and their interactions with cancer cells. We then discuss potential extensions of this model to include target binding, tumor distribution, cytokine-release syndrome, immunophenotype differentiation, and genotypic heterogeneity.