Misuse of Substitution Drugs in the Substitution-Based Therapy.

BACKGROUND: The reasons for, and the extent of, misuse of prescribed substitution medication as well as parallel consumption of other drugs during substitution-based therapy have still not been adequately researched in Germany. METHODS: This study examines the use of substitution medication in German substitution clinics utilizing a nationwide survey with anonymised questionnaires. RESULTS: The analysis of the 605 questionnaires showed a 30-day consumption prevalence of 8.8% with regard to misuse of substitution substances. The lack of available heroin (38%) and the lack of open spots in treatment programs (21%) were quoted as being the main reasons for the misuse of substitution medication. CONCLUSION: Although the misuse of substitution medications is considered an important problem, our study showed that the current misuse was prevalent only among a minority of the patients. German regulations focused on the avoidance of misuse might be partially contributing to the problem.

Interpretable molecular encodings and representations for machine learning tasks.

Molecular encodings and their usage in machine learning models have demonstrated significant breakthroughs in biomedical applications, particularly in the classification of peptides and proteins. To this end, we propose a new encoding method: Interpretable Carbon-based Array of Neighborhoods (iCAN). Designed to address machine learning models' need for more structured and less flexible input, it captures the neighborhoods of carbon atoms in a counting array and improves the utility of the resulting encodings for machine learning models. The iCAN method provides interpretable molecular encodings and representations, enabling the comparison of molecular neighborhoods, identification of repeating patterns, and visualization of relevance heat maps for a given data set. When reproducing a large biomedical peptide classification study, it outperforms its predecessor encoding. When extended to proteins, it outperforms a lead structure-based encoding on 71% of the data sets. Our method offers interpretable encodings that can be applied to all organic molecules, including exotic amino acids, cyclic peptides, and larger proteins, making it highly versatile across various domains and data sets. This work establishes a promising new direction for machine learning in peptide and protein classification in biomedicine and healthcare, potentially accelerating advances in drug discovery and disease diagnosis.