Advanced decision-support methods for the design, control, and optimization of perishable products life cycle

In the last decades, global food supply chains had to deal with the increasing awareness of the stakeholders and consumers about safety, quality, and sustainability. In order to address these new challenges for food supply chain systems, an integrated approach to design, control, and optimize product life cycle is required. Therefore, it is essential to introduce new models, methods, and decision-support platforms tailored to perishable products. This thesis aims to provide novel practice-ready decision-support models and methods to optimize the logistics of food items with an integrated and interdisciplinary approach. It proposes a comprehensive review of the main peculiarities of perishable products and the environmental stresses accelerating their quality decay. Then, it focuses on top-down strategies to optimize the supply chain system from the strategical to the operational decision level. Based on the criticality of the environmental conditions, the dissertation evaluates the main long-term logistics investment strategies to preserve products quality. Several models and methods are proposed to optimize the logistics decisions to enhance the sustainability of the supply chain system while guaranteeing adequate food preservation. The models and methods proposed in this dissertation promote a climate-driven approach integrating climate conditions and their consequences on the quality decay of products in innovative models supporting the logistics decisions. Given the uncertain nature of the environmental stresses affecting the product life cycle, an original stochastic model and solving method are proposed to support practitioners in controlling and optimizing the supply chain systems when facing uncertain scenarios. The application of the proposed decision-support methods to real case studies proved their effectiveness in increasing the sustainability of the perishable product life cycle. The dissertation also presents an industry application of a global food supply chain system, further demonstrating how the proposed models and tools can be integrated to provide significant savings and sustainability improvements.

Understanding molecular mechanisms of resistance to immune checkpoint inhibitors in advanced non-small cell lung cancer (NSCLC)

Immune checkpoint inhibitors (ICI) that target PD-1/PD-L1 have recently emerged as an integral component of front-line treatment in metastatic NSCLC patients. The PD-1 inhibitor pembrolizumab is approved as monotherapy for advanced NSCLC with a PD-L1 tumor proportion score (TPS) of ≥1% and in combination with platinum doublet chemotherapy regardless of PD-L1 expression level. However, responses to either regimen occur in only a minority of cases, and PD-L1 TPS is limited as a biomarker in predicting whether a cancer will respond to PD-1 inhibition alone or would be more likely to benefit from PD-1 inhibition plus chemotherapy. Additional biomarkers of immunotherapy efficacy, such as tumor mutational burden (TMB), have not been incorporated into routine clinical practice for treatment selection. The identification of patients who have the greatest likelihood of responding to immunotherapies is critical for guiding treatment decisions. IN addition, early indicators of response could theoretically prevent patients from staying on an ineffective therapy where they might experience complications due to disease progression or develop toxicities from unnecessary exposure to an inactive agent. The aim of this research project is to investigate the clinicopathologic and molecular determinant of response/resistance to the currently available immune checkpoint inhibitors, in order to identify therapeutic vulnerabilities that can be exploited to improve the clinical outcomes of patients with advanced NSCLC.