Background —Standard treatment of inoperable hepatocellular carcinoma has not been established. Somatostatin has been shown to possess antimitotic activity against a variety of non-endocrine tumours. Aims —To assess the presence of somatostatin receptors in human liver and to treat advanced hepatocellular carcinoma with the somatostatin analogue, octreotide. Methods —Somatostatin receptors were measured in liver tissue homogenates from patients with acute and chronic hepatitis, cirrhosis, and hepatocellular carcinoma. Fifty eight patients with advanced hepatocellular carcinoma were randomised to receive either subcutaneous octreotide 250 μg twice daily, or no treatment. Groups were comparable with respect to age, sex, Okuda classification, presence of cirrhosis, and liver biochemistry and virology. Results —Various amounts of somatostatin receptors were identified in liver tissue of all patients including those with hepatocellular carcinoma. Treated patients had an increased median survival (13 months versus four months, p=0.002, log rank test) and an increased cumulative survival rate at six and 12 months (75% versus 37%, and 56% versus 13% respectively). Octreotide administration significantly reduced α fetoprotein levels at six months. When a multivariable Cox’s proportional hazards model was fitted, variables associated with increased survival were: treatment administration, absence of cirrhosis, increased serum albumin, and small tumours. Treated patients clearly had a lower hazard (0.383) in the multivariate analysis. Conclusions —Octreotide administration significantly improves survival and is a valuable alternative in the treatment of inoperable hepatocellular carcinoma.

The drug discovery process is a rigorous and time-consuming endeavor, typically requiring several years of extensive research and development. Although classical machine learning (ML) has proven successful in this field, its computational demands in terms of speed and resources are significant. In recent years, researchers have sought to explore the potential benefits of quantum computing (QC) in the context of machine learning (ML), leading to the emergence of quantum machine learning (QML) as a distinct research field. The objective of the current study is twofold: first, to present a review of the proposed QML algorithms for application in the drug discovery pipeline, and second, to compare QML algorithms with their classical and hybrid counterparts in terms of their efficiency. A query-based search of various databases took place, and five different categories of algorithms were identified in which QML was implemented. The majority of QML applications in drug discovery are primarily focused on the initial stages of the drug discovery pipeline, particularly with regard to the identification of novel drug-like molecules. Comparison results revealed that QML algorithms are strong rivals to the classical ones, and a hybrid solution is the recommended approach at present.