Real-world use of sorafenib for advanced renal cell carcinoma patients with cardiovascular disease: nationwide survey in Japan

Novel Drugs with High Efficacy against Tumor Angiogenesis

Angiogenesis is involved in physiological and pathological processes in the body. Tumor angiogenesis is a key factor associated with tumor growth, progression, and metastasis. Therefore, there is great interest in developing antiangiogenic strategies. Hypoxia is the basic initiating factor of tumor angiogenesis, which leads to the increase of vascular endothelial growth factor (VEGF), angiopoietin (Ang), hypoxia-inducible factor (HIF-1), etc. in hypoxic cells. The pathways of VEGF and Ang are considered to be critical steps in tumor angiogenesis. A number of antiangiogenic drugs targeting VEGF/VEGFR (VEGF receptor) or ANG/Tie2, or both, are currently being used for cancer treatment, or are still in various stages of clinical development or preclinical evaluation. This article aims to review the mechanisms of angiogenesis and tumor angiogenesis and to focus on new drugs and strategies for the treatment of antiangiogenesis. However, antitumor angiogenic drugs alone may not be sufficient to eradicate tumors. The molecular chaperone heat shock protein 90 (HSP90) is considered a promising molecular target. The VEGFR system and its downstream signaling molecules depend on the function of HSP90. This article also briefly introduces the role of HSP90 in angiogenesis and some HSP90 inhibitors.

Outcomes of sorafenib treatment of advanced renal cell carcinoma according to International Metastatic Renal Cell Carcinoma Data Consortium risk criteria: analysis of Japanese real-world data from postmarketing all-patient surveillance of sorafenib

Aim: To assess sorafenib survival outcomes in renal cell carcinoma patients using standard International Metastatic Renal Cell Carcinoma Data Consortium (IMDC) risk criteria. Patients & methods: The authors restratified a real-world cohort of 3255 advanced renal cell carcinoma patients, obtained from Japanese sorafenib postmarketing surveillance, to assess survival outcomes using IMDC criteria; intermediate risk was subdivided into Int-1 and Int-2 (one and two risk factors, respectively). Results: Overall, 2225 (68%) IMDC-evaluable patients were reclassified as favorable (17%), intermediate (62%) and poor (21%) risk, with median progression-free survival of 10.4, 8.1 and 3.4 months, respectively. Int-1 (36%) and Int-2 (26%) subgroups had median progression-free survival of 10.1 and 6.0 months, respectively. Sorafenib had acceptable safety/tolerability. Conclusion: Sorafenib effectiveness was promising for IMDC intermediate risk, particularly Int-1, warranting further investigation.

Machine Learning in Causal Inference: Application in Pharmacovigilance

Monitoring adverse drug events or pharmacovigilance has been promoted by the World Health Organization to assure the safety of medicines through a timely and reliable information exchange regarding drug safety issues. We aim to discuss the application of machine learning methods as well as causal inference paradigms in pharmacovigilance. We first reviewed data sources for pharmacovigilance. Then, we examined traditional causal inference paradigms, their applications in pharmacovigilance, and how machine learning methods and causal inference paradigms were integrated to enhance the performance of traditional causal inference paradigms. Finally, we summarized issues with currently mainstream correlation-based machine learning models and how the machine learning community has tried to address these issues by incorporating causal inference paradigms. Our literature search revealed that most existing data sources and tasks for pharmacovigilance were not designed for causal inference. Additionally, pharmacovigilance was lagging in adopting machine learning-causal inference integrated models. We highlight several currently trending directions or gaps to integrate causal inference with machine learning in pharmacovigilance research. Finally, our literature search revealed that the adoption of causal paradigms can mitigate known issues with machine learning models. We foresee that the pharmacovigilance domain can benefit from the progress in the machine learning field.