Agenda:

1. Introduction - Paolo Bossi, MD (Chair)

2. The Experience of Nausea and Vomiting: How to Design a Clinical Predictor of a Treatment-Induced Adverse Effect - George Dranitsaris, PhD

Objectives:

• Understand the steps in developing toxicity risk prediction tools in oncology, with a focus on nausea and vomiting
• Illustrate the clinical application of toxicity prediction tools in oncology
• Demonstrate how toxicity prediction tools can be used to improve the efficiency of clinical trials in oncology

3. Predicting Oral and GI Toxicities in Supportive Oncology: Leveraging Clinical Data to Prevent Nutritional and Pain Consequences - Mellar Davis, MD

Objectives:

• Drug-Related Genetic Risk Assessment: identify DPYD variants associated with 5-FU and capecitabine toxicity UGT1A1 conjugate associated with irinotecan mucositis and diarrhea
• Individual Genomic Risk Factors: name at least 3 of the 4 categories of individual genetic risks: drug transporters, genes involved in DNA repair, inflammatory signalling pathways, epithelial healing genes
• Clinical Application: correctly sequence opioid selection as: First line: Morphine (preferred over oxycodone). Second line: Buprenorphine and hydromorphone (preferred over methadone, all three preferred over fentanyl). Contraindicated: Codeine and tramadol (due to CYP2D6 dependence)
• Treatment Protocol: identify benzylamine as the preferred initial intervention before morphine (where available)

4. Emerging Predictors of Oral and GI Toxicities: From Biological and Microbiome-based Models to AI Models - Akshat Singhal, PhD    

Objectives:

• Describe the significance of biologically-informed AI models in patient care, including treatment and toxicity prediction
• Examine a chemotherapy resistance model and discuss methods to extend such models for toxicity prediction

5. Q&A

6. Closing Remarks

Financial support provided by Nestlé Health Science.