[Evolution of insulin therapy: past, present, future].

2021 marks the 100th anniversary of the discovery of insulin, an event that forever changed the lives of people with diabetes mellitus. At present patients around the world experience the miracle of insulin therapy every day. A disease that used to kill children and teenagers in 2 years in 1920 has become a disease that can be controlled with a possibility to lead a long productive life.

[Characteristics of purchases of hypoglycemic agents in pharmacy retail sector in 2016-2020 years dynamics].

Background: Procurement of medicines reflects the demand and frequency of prescribing certain drugs, which makes it possible to assess the quality of medical care and compliance with standards. The Russian pharmaceutical market is dynamically developing and expanding, therefore, the commercial sector of drug circulation is a significant part of it and should be studied along with public procurement. Given the significant number of patients diagnosed with diabetes mellitus (DM) in our country, we considered it appropriate and interesting to analyze the structure and volume of turnover of antidiabetic drugs in the retail trade over five years.

Prediction of Clinical Trials Outcomes Based on Target Choice and Clinical Trial Design with Multi-Modal Artificial Intelligence.

Drug discovery and development is a notoriously risky process with high failure rates at every stage, including disease modeling, target discovery, hit discovery, lead optimization, preclinical development, human safety, and efficacy studies. Accurate prediction of clinical trial outcomes may help significantly improve the efficiency of this process by prioritizing therapeutic programs that are more likely to succeed in clinical trials and ultimately benefit patients. Here, we describe inClinico, a transformer-based artificial intelligence software platform designed to predict the outcome of phase II clinical trials.

Medical concept normalization in clinical trials with drug and disease representation learning.

Motivation: Clinical trials are the essential stage of every drug development program for the treatment to become available to patients. Despite the importance of well-structured clinical trial databases and their tremendous value for drug discovery and development such instances are very rare. Presently large-scale information on clinical trials is stored in clinical trial registers which are relatively structured, but the mappings to external databases of drugs and diseases are increasingly lacking.

Drug prioritization using the semantic properties of a knowledge graph.

Compounds that are candidates for drug repurposing can be ranked by leveraging knowledge available in the biomedical literature and databases. This knowledge, spread across a variety of sources, can be integrated within a knowledge graph, which thereby comprehensively describes known relationships between biomedical concepts, such as drugs, diseases, genes, etc. Our work uses the semantic information between drug and disease concepts as features, which are extracted from an existing knowledge graph that integrates 200 different biological knowledge sources.