Psychopharmacology and Addiction Biology

This brief scientific-analytical review examines the limitations of traditional categorical diagnostic systems—the International Classification of Diseases (ICD) and the Diagnostic and Statistical Manual of Mental Disorders (DSM)—in forming homogeneous groups for biological research and applying the obtained results to address clinical challenges. As alternatives, we considered new psychopathological frameworks: the Research Domain Criteria (RDoC) and the Hierarchical Taxonomy of Psychopathology (HiTOP). The review primarily focuses on the HiTOP framework, which is structured hierarchically. Within this system, clinical presentation is viewed as a multi-level structure where the top level is occupied by a general psychopathology factor termed the P factor. The middle level consists of psychopathological domains or spectra encompassing more specific phenotypic manifestations: depression, cognitive impairments, impulsivity, and psychosis. The bottom level comprises individual symptoms (e.g., insomnia or suicidality) characteristic of a particular case but not defining the overall clinical picture. Additionally, the framework accounts for the degree of patient maladaptation associated with a specific symptom, as well as the influence of external factors on symptom severity and the extent of maladaptation. As an illustration of the rationality of this approach, the review presents knockout models of two domains—cognitive and anxiety-related. Using the example of the knockout of several genes (Kmo, Gabrb2, Grm2/3, Grm1, Plcb1, Taar1, Disc1, Slc6a4, Htr1a, Htr2c, Slitrk5), we showed that specific genetic disruptions lead to the manifestation of diverse behavioral phenotypes, particularly various combinations of cognitive and emotional impairments, which often occur as transdiagnostic symptoms across different conditions. The review discusses the potential applications of the HiTOP approach and data obtained from knockout animal studies for the personalization of therapy and the development of more precise biological models of mental disorders.

BACKGROUND: Alcohol withdrawal delirium is one of the most severe forms of alcohol withdrawal syndrome, occurring in 5%–12% of patients with alcohol dependence. Delirium can have serious consequences and is associated with a high risk of developing multiple organ pathology, death, pronounced cognitive impairments, or psycho-organic/amnestic syndrome. Therefore, identifying effective approaches to the prevention and treatment of alcohol withdrawal delirium is extremely important for addiction medicine practice. To achieve this goal, the development of reliable tools for predicting the onset of alcohol withdrawal delirium is necessary. A promising direction in this field is the application of machine learning-based algorithms.

AIM: This study aimed to develop a method for predicting the onset of alcohol withdrawal delirium in patients with early manifestations of alcohol withdrawal syndrome based on mathematical analysis using artificial intelligence.

METHODS: To build predictive models, four laboratory parameters were used: blood platelet count and serum levels of potassium, sodium, and chloride. The probabilistic model was based on the Multilayer Perceptron (MLP) algorithm, while the binary model was based on the Random Forest (RF) algorithm. For model training, an anonymized database of patients with alcohol withdrawal syndrome was used, comprising 498 individuals: 295 patients with developed alcohol delirium and 203 ones with uncomplicated alcohol withdrawal syndrome.

RESULTS: Both models demonstrated good results on the test dataset: the average prediction accuracy for the model based on the MLP algorithm was 84%, and 83% for the RF algorithm. The MLP model was validated in an addiction treatment hospital setting, with an accurate prediction rate of 84.4%.

CONCLUSION: The use of artificial intelligence opened the possibility to identify a risk group for alcohol delirium based on a scientific approach of objective and rapid laboratory parameters.

BACKGROUND: The dopamine transporter (DAT, gene SLC6A3) is a crucial and promising target in psychopharmacology. Selecting an appropriate model organism is a critical step in planning preclinical in vivo studies. In recent years, Danio rerio have been used with increasing frequency in such experiments. However, it remains unknown whether D. rerio is a suitable model for in vivo studies of substances affecting DAT.

AIM: This study aimed to describe potential interspecies differences between DAT in humans and Danio rerio using bioinformatic approaches to investigate the evolutionary history of theSLC6A3 gene and the structure of the DAT protein.

METHODS: The study employed ortholog identification, phylogenetic tree reconstruction, sequence alignment and comparison, three-dimensional modeling of human and D. rerio DAT, molecular docking of DAT inhibitors (fonturacetam, mesocarb, and CE-123), and calculation of binding free energies.

RESULTS: No deletions or duplications of SLC6A3 were identified in the D. rerio genome. Human and D. rerio dopamine transporter sequences differ at 23% of positions. Docking analysis revealed only minor differences in ligand-protein interaction interfaces and binding energies.

CONCLUSION: This study found only minor differences between the human and D. rerio DATs, suggesting that this species may serve as a suitable model for experimental studies of dopamine transporter-targeting compounds. Further research will include molecular dynamics simulations of ligand–protein complexes to provide a detailed characterization of the interactions between various ligands and the human and D. rerio DATs.

The name of Alexander P. Nelyubin (1785–1858), professor of the Imperial Medico-Surgical Academy, is well known in Russian historiography. However, his research has been covered relatively one-sidedly. This article aims to describe in more detail the activities of A.P. Nelyubin as a polymath of the first half of the 19th century. A.P. Nelyubin is primarily known as a researcher of the Caucasian Mineral Waters, having developed his own numbering system for them, which remains partially in use to this day. A.P. Nelyubin was one of the prominent intellectuals of the first half of the 19th century, more accurately described as a polymath. His scientific interests extended not only to the study of mineral waters but also encompassed many issues in pharmacology, pharmacy, forensic chemistry and medicine, obstetrics, and healthcare organization. A.P. Nelyubin wrote one of the first Russian-language textbooks on pharmacology, which went through five editions. All of A.P. Nelyubin’s activities characterize him as one of the founders of Russian experimental pharmacology, as well as a prominent figure in Russian chemistry, and forensic and rehabilitation medicine. Through the efforts of A.P. Nelyubin and his followers, original directions in pharmacology and related disciplines were formed. He and his students consistently opposed reactionary doctrines in medicine, fighting against them productively. Furthermore, A.P. Nelyubin proved himself a talented organizer of a major scientific school of researchers in pharmacology, pharmacy, pharmacognosy, and medicine as a whole.