A Phase I Trial of Trametinib in Combination with Sorafenib in Patients with Advanced Hepatocellular Cancer

Lessons Learned

• The combination of trametinib and sorafenib has an acceptable safety profile, albeit at doses lower than approved for monotherapy.
• Maximum tolerated dose is trametinib 1.5 mg daily and sorafenib 200 mg twice daily.
• The limited anticancer activity observed in this unselected patient population does not support further exploration of trametinib plus sorafenib in patients with hepatocellular carcinoma.

BackgroundThe RAS/RAF/MEK/ERK signaling pathway is associated with proliferation and progression of hepatocellular carcinoma (HCC). Preclinical data suggest that paradoxical activation of the MAPK pathway may be one of the resistance mechanisms of sorafenib; therefore, we evaluated trametinib plus sorafenib in HCC.MethodsThis was a phase I study with a 3+3 design in patients with treatment‐naïve advanced HCC. The primary objective was safety and tolerability. The secondary objective was clinical efficacy.ResultsA total of 17 patients were treated with three different doses of trametinib and sorafenib. Two patients experienced dose‐limiting toxicity, including grade 4 hypertension and grade 3 elevation of aspartate aminotransferase (AST)/alanine aminotransferase (ALT)/bilirubin over 7 days. Maximum tolerated dose was trametinib 1.5 mg daily and sorafenib 200 mg twice a day. The most common grade 3/4 treatment‐related adverse events were elevated AST (37%) and hypertension (24%). Among 11 evaluable patients, 7 (63.6%) had stable disease with no objective response. The median progression‐free survival (PFS) and overall survival (OS) were 3.7 and 7.8 months, respectively. Phosphorylated‐ERK was evaluated as a pharmacodynamic marker, and sorafenib plus trametinib inhibited phosphorylated‐ERK up to 98.1% (median: 81.2%) in peripheral blood mononuclear cells.ConclusionTrametinib and sorafenib can be safely administered up to trametinib 1.5 mg daily and sorafenib 200 mg twice a day with limited anticancer activity in advanced HCC.     

Elevated glycohemoglobin HbA1c is associated with low back pain in nonoverweight diabetics

Background Context

Low back pain (LBP) is a common complaint in clinical practice of multifactorial origin. Although obesity has been thought to contribute to LBP primarily by altering the distribution of mechanical loads on the spine, the additional contribution of obesity-related conditions such as diabetes mellitus (DM) to LBP has not been thoroughly examined.

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.