The plant Cannabis sativa L. has been used as an herbal remedy for centuries and is the most important source of phytocannabinoids. The endocannabinoid system (ECS) consists of receptors, endogenous ligands (endocannabinoids) and metabolizing enzymes, and plays an important role in different physiological and pathological processes. Phytocannabinoids and synthetic cannabinoids can interact with the components of ECS or other cellular pathways and thus affect the development/progression of diseases, including cancer. In cancer patients, cannabinoids have primarily been used as a part of palliative care to alleviate pain, relieve nausea and stimulate appetite. In addition, numerous cell culture and animal studies showed antitumor effects of cannabinoids in various cancer types. Here we reviewed the literature on anticancer effects of plant-derived and synthetic cannabinoids, to better understand their mechanisms of action and role in cancer treatment. We also reviewed the current legislative updates on the use of cannabinoids for medical and therapeutic purposes, primarily in the EU countries. In vitro and in vivo cancer models show that cannabinoids can effectively modulate tumor growth, however, the antitumor effects appear to be largely dependent on cancer type and drug dose/concentration. Understanding how cannabinoids are able to regulate essential cellular processes involved in tumorigenesis, such as progression through the cell cycle, cell proliferation and cell death, as well as the interactions between cannabinoids and the immune system, are crucial for improving existing and developing new therapeutic approaches for cancer patients. The national legislation of the EU Member States defines the legal boundaries of permissible use of cannabinoids for medical and therapeutic purposes, however, these legislative guidelines may not be aligned with the current scientific knowledge.

Drug-drug interaction (DDI) is a change in the effect of a drug when patient takes another drug. Characterizing DDIs is extremely important to avoid potential adverse drug reactions. We represent DDIs as a complex network in which nodes refer to drugs and links refer to their potential interactions. Recently, the problem of link prediction has attracted much consideration in scientific community. We represent the process of link prediction as a binary classification task on networks of potential DDIs. We use link prediction techniques for predicting unknown interactions between drugs in five arbitrary chosen large-scale DDI databases, namely DrugBank, KEGG, NDF-RT, SemMedDB, and Twosides. We estimated the performance of link prediction using a series of experiments on DDI networks. We performed link prediction using unsupervised and supervised approach including classification tree, k-nearest neighbors, support vector machine, random forest, and gradient boosting machine classifiers based on topological and semantic similarity features. Supervised approach clearly outperforms unsupervised approach. The Twosides network gained the best prediction performance regarding the area under the precision-recall curve (0.93 for both random forests and gradient boosting machine). The applied methodology can be used as a tool to help researchers to identify potential DDIs. The supervised link prediction approach proved to be promising for potential DDIs prediction and may facilitate the identification of potential DDIs in clinical research.

The (TAAAA)(n) SHBG gene polymorphism might be an important predictor for serum SHBG levels and, consequently, for hyperandrogenaemic clinical presentation of PCOS.

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