Interaction Of Agents Research Articles

Abstract A001: Navigating the future of therapeutic development: innovations in safety and efficacy through advanced research methodologies and nanotechnology

Abstract In recent years, the field of therapeutic development has experienced a significant paradigm shift driven by strategic scientific innovations that prioritize safety and efficacy in therapeutic agents. This paper explores the intersection of advanced research methodologies, novel material sciences, and biotechnological advancements that are revolutionizing the approach to drug formulation and delivery systems. The integration of precision medicine principles alongside cutting-edge technologies allows for the design of therapeutics that are not only effective in targeting specific diseases but are also engineered to minimize adverse effects associated with conventional therapies. One focal point of this exploration is the application of nanotechnology in drug delivery systems. By utilizing nanoparticles, researchers can enhance the bioavailability of therapeutic agents while reducing systemic toxicity. This targeted approach enables the delivery of higher concentrations of drugs to the affected areas, thereby maximizing therapeutic efficacy and minimizing side effects. Additionally, the paper highlights the role of computational modeling and simulations in predicting the interaction of therapeutic agents at the molecular level, facilitating the identification of safer compounds through virtual screening methods. Another significant aspect addressed in this paper is the development of biologics and biosimilars as safer alternatives to traditional synthetic drugs. The advancement of biomanufacturing processes has made it feasible to produce complex molecules with high specificity and reduced immunogenicity, paving the way for innovative therapies that cater to individual patient profiles. Furthermore, the strategic incorporation of pharmacogenomics in drug development allows for the customization of therapeutic agents, ensuring that treatment regimens are tailored to the genetic makeup of patients, which can drastically improve safety and treatment outcomes. The paper also discusses the regulatory landscape surrounding the introduction of these innovative therapeutic agents. It emphasizes the need for adaptive regulatory frameworks that keep pace with scientific advancements while ensuring patient safety and efficacy. Collaborative efforts between researchers, regulatory bodies, and pharmaceutical companies are essential to facilitate the translation of innovative therapies from the laboratory to clinical practice. In conclusion, this paper underscores the critical importance of strategic scientific innovations in the quest for safer therapeutic agents. By harnessing the potential of advanced technologies, researchers can develop therapeutics that not only meet the growing demand for efficacy but also uphold the highest standards of safety, ultimately improving patient care and health outcomes. The findings presented advocate for continued investment in research and collaboration across disciplines to drive future advancements in therapeutic development. Citation Format: Peter O. David. Navigating the future of therapeutic development: innovations in safety and efficacy through advanced research methodologies and nanotechnology. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr A001

Swarming network inference with importance clustering of relative interactions

Abstract Swarming is central to many problems in physics, biology, and engineering 
where collective motion and cooperation emerge through interactions of many agents. As a result, inferring network connections underlying swarms from time series data is an important problem with broad application. In this work, we propose a method based on phase-space regression of the observed dynamics with random forest models, from which relative interactions are clustered according to their Gini importance with respect to a given agent. Network connections are inferred by comparing how the statistics of the strongly and weakly important clusters overlap. Because the method entails fitting the dynamics and finding the most important interactions for each agent individually with general swarming assumptions, high accuracy can be maintained with relatively modest amounts of observation data and utilizing only a small number of generalizable hyperparameters across a variety of behavioral patterns, diverse physical ingredients, and heterogeneous network topologies.

Rapid Vehicle Trajectory Prediction Based on Multi-Attention Mechanism for Fusing Multimodal Information

Trajectory prediction plays a crucial role in autonomous driving tasks, as accurately and rapidly predicting the future trajectories of traffic participants can significantly enhance the safety and robustness of autonomous driving systems. This paper presents a novel trajectory prediction model that follows the encoder–decoder paradigm, achieving precise and rapid predictions of future vehicle trajectories by efficiently aggregating the spatiotemporal and interaction information of agents in traffic scenarios. We propose an agent–agent interaction information extraction module based on a sparse graph attention mechanism, which enables the efficient aggregation of interaction information between agents. Additionally, we introduce a non-autoregressive query generation method that accelerates the model inference speed by generating the decoding queries in parallel. Comparative experiments with the existing advanced algorithms show that our method improves the multimodal trajectory prediction metrics for the Minimum Average Displacement Error (minADE), the Minimum Final Displacement Error (minFDE), and the Miss Rate (MR) by an average of 9.1%, 11.8%, and 14.6%, respectively, while the inference time is only 33.7% of the average time taken by the other algorithms. Finally, we demonstrate the effectiveness of the various modules proposed in this paper through ablation studies.

Unlocking AI Creativity: A Multi-Agent Approach with CrewAI

By automating processes that traditionally require human intelligence, such as learning, reasoning, and problem-solving, Artificial Intelligence (AI) has transformed many industries. Even with these developments, System 1 thinking is quick, providing instinctive reactions, while System 2 thinking entails thorough analysis and reasoned decision-making. AI systems still have a long way to go before they can replicate System 2 thinking—deliberate, analytical, and essential for managing complex problems. This study investigates how Multi-Agent Systems (MAS) integrate System 2 thinking into AI, concentrating on CrewAI, a no-code framework designed to improve AI creativity and decision-making. Unlike TensorFlow Agents and OpenAI’s Gym, which are limited to single-agent reinforcement learning, CrewAI excels in handling multi-agent, real-world tasks through collaboration. This study explores practical applications of CrewAI, such as intelligent grid management, automated customer support, and advertising. These examples highlight how CrewAI promotes AI creativity and problem-solving through cooperative agent interactions, leveraging System 2 thinking. Problems like scalability and coordination are also addressed, with solutions such as dynamic role assignment and hierarchical task management. In summary, the integration of System 2 thinking into MAS frameworks like CrewAI signifies progress toward creating intelligent, dependable AI systems capable of tackling the complexities of real-world problems.

Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light Intersections

To tackle the energy-saving optimization issue of plug-in hybrid electric trucks traversing multiple traffic light intersections continuously, this paper presents a double-layer energy management strategy that utilizes the dynamic programming–twin delayed deep deterministic policy gradient (DP-TD3) algorithm to synergistically optimize the speed planning and energy management of plug-in hybrid electric trucks, thereby enhancing the vehicle’s passability through traffic light intersections and fuel economy. In the upper layer, the dynamic programming (DP) algorithm is employed to create a speed-planning model. This model effectively converts the nonlinear constraints related to the position, phase, and timing information of each traffic signal on the road into time-varying constraints, thereby improving computational efficiency. In the lower layer, an energy management model is constructed using the twin delayed deep deterministic policy gradient (TD3) algorithm to achieve optimal allocation of demanded power through the interaction of the TD3 agent with the truck environment. The model’s validity is confirmed through testing on a hardware-in-the-loop test machine, followed by simulation experiments. The results demonstrate that the DP-TD3 method proposed in this paper effectively enhances fuel economy, achieving an average fuel saving of 14.61% compared to the dynamic programming–charge depletion/charge sustenance (DP-CD/CS) method.

Bayesian Fictitious Play in Oligopoly: The Case of Risk-Averse Agents

A number of learning models have been suggested to analyze the repeated interaction of boundedly rational agents competing in oligopolistic markets. The agents form a model of the environment that they are competing in, which includes the market demand and price formation process, as well as their expectations of their rivals’ actions. The agents update their model based on the observed output and price realizations and then choose their next period output levels according to an optimization criterion. In previous works, the global dynamics of price movement have been analyzed when risk-neutral agents maximize their expected rewards at each round. However, in many practical settings, agents may be concerned with the risk or uncertainty in their reward stream, in addition to the expected value of the future rewards. Learning in oligopoly models for the case of risk-averse agents has received much less attention. In this paper, we present a novel learning model that extends fictitious play learning to continuous strategy spaces where agents combine their prior beliefs with market price realizations in previous periods to learn the mean and the variance of the aggregate supply function of the rival firms in a Bayesian framework. Next, each firm maximizes a linear combination of the expected value of the profit and a penalty term for the variance of the returns. Specifically, each agent assumes that the aggregate supply of the remaining agents is sampled from a parametric distribution employing a normal-inverse gamma prior. We prove the convergence of the proposed dynamics and present simulation results to compare the proposed learning rule to the traditional best response dynamics.

Cash flow modeling in the open innovation market system

The process of innovation activity is considered from the point of view of agency presence. The open innovation market system is described, including an investor, a developer and a user. The cash flows between the specified agents of the system, arising in the process of creating and commercializing an innovative product, are considered. For each agent, a profit function has been developed, which is a criterion for its optimal activity. An economic and mathematical model has been developed that describes the interaction of agents in the system.

The Growth of Cross‐Border Regions Toward “Two Countries, One Park”: A Case Study of the Mohan–Boten Cross‐Border Cooperation Zone

ABSTRACTCross‐border regions have been the frontier areas of regional and subregional cooperation, the development of which involves the interaction of multiple scales and various agents. Existing studies focus mainly on cross‐border economic cooperation but pay less attention to issues such as urban construction and community development, which may lead to the neglect of the interests of local communities in the development of cross‐border regions. Based on a case study of the Mohan–Boten Cross‐border Cooperation Zone (CCZ), this study argues for the integrated development toward “Two Countries, One Park” through the functional integration of urban planning, industrial investment, public services, and community development. The study finds that the success of the “Two Countries, One Park” pattern relies on the coordination of multi‐scalar governance and multi‐agent cooperation, whereas the “scale paradox” caused by the asymmetric distribution of power and responsibility among different governance scales and the relevant agents is a major hindrance to functional integration and cross‐border cooperation. Addressing the “scale paradox” is, therefore, crucial for successful cross‐border development.

Exploring agent interaction patterns in the comment sections of fake and real news.

User comments on social media have been recognized as a crucial factor in distinguishing between fake and real news, with many studies focusing on the textual content of user reactions. However, the interactions among agents in the comment sections for fake and real news have not been fully explored. In this study, we analyse a dataset comprising both fake and real news from Reddit to investigate agent interaction patterns, considering both the network structure and the sentiment of the nodes. Our main findings reveal that, compared with fake news, where users generate more negative sentiment, real news tends to elicit more neutral and positive sentiments. Additionally, nodes with similar sentiments cluster together more tightly than anticipated. From a dynamic perspective, we found that the sentiment distribution among nodes stabilizes early and remains stable over time. These findings have both theoretical and practical implications, particularly for the early detection of real and fake news within social networks.

Effect of dwell time on consensus in multiagent systems with time‐varying topologies

AbstractThis paper studies the effect of dwell time on consensus of high‐order multiagent systems with time‐varying topologies. In contrast to existing literature that only focuses on the convergence of consensus, the proposed work focuses on the sensitivity of consensus on the dwell time and studies its relationship with consensus behaviors. In particular, it is shown that dwell time determines the duration of agent interactions, resulting in different state dynamics, consensus values, and convergence speeds under different switching topologies. Such effects can impact the dynamical performance of the multiagent systems. To quantitatively analyze the effect, a procedure is proposed to estimate the consensus values with respect to the dwell time and switching topologies, and a condition is obtained to find the topologies that can speed up the convergence. The proposed study is applied to the regulation of microgrids, which proves to be effective in improving the performance and efficiency of controllers.

Digital transformation of the Centre for Advanced Professional Training of Kuzbass: approaches and solutions

Russian education system is currently undergoing a phase of advancement that is associated with distinctive features of post-industrial society, the main characteristic of which is informatization and digitalization of economy. The efficiency of business processes and the validity (legitimacy) of enterprises and organizations are directly correlated with their digital transformation. These global trends cannot be ignored by these educational organizations. The most untouched issue concerns the creation of digital services for facilitating interaction between educational institutions and the labor market. The purpose of research: development and implementation of the Digital Transformation Strategy of the Centre for Advanced Professional Training (CAPT) of Kuzbass. The leading methods of research include general scientific methods, methods of systematicity and analysis of scientific and methodological literature and regulatory and legal documentation, as well as empirical methods (in the collection, accumulation and recording of data), and rational method (in the interpretation of research data). The article presents the principles and mechanisms of the digital platform interaction with the external environment are described. The author examines the internal conditions for digital services transformation. The necessity of creating and utilizing a digital replica of the HR service of enterprise on the digital platform is substantiated. The presented approaches pertain to the implementation of the Strategy for the development of digital resources of the Centre for Advanced Professional Training of Kuzbass, along with the development of a digital platform with an enhanced version, which is focused on more effective interaction of labour market agents in the region, contribute to the resolution of the most pressing issues of socio-economic development of the region. These techniques can also be applied to employment assistance services and career centers of educational organizations for secondary vocational and higher education.

Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agents

Growing concerns about safety and alignment of AI systems highlight the importance of embedding moral capabilities in artificial agents: a promising solution is the use of learning from experience, i.e., Reinforcement Learning. In multi-agent (social) environments, complex population-level phenomena may emerge from interactions between individual learning agents. Many of the existing studies rely on simulated social dilemma environments to study the interactions of independent learning agents; however, they tend to ignore the moral heterogeneity that is likely to be present in societies of agents in practice. For example, at different points in time a single learning agent may face opponents who are consequentialist (i.e., focused on maximizing outcomes over time), norm-based (i.e., conforming to specific norms), or virtue-based (i.e., considering a combination of different virtues). The extent to which agents' co-development may be impacted by such moral heterogeneity in populations is not well understood. In this paper, we present a study of the learning dynamics of morally heterogeneous populations interacting in a social dilemma setting. Using an Iterated Prisoner's Dilemma environment with a partner selection mechanism, we investigate the extent to which the prevalence of diverse moral agents in populations affects individual agents' learning behaviors and emergent population-level outcomes. We observe several types of non-trivial interactions between pro-social and anti-social agents, and find that certain types of moral agents are able to steer selfish agents towards more cooperative behavior.

Synergistic interactions of biocontrol agents and chemical fungicides enhance the disease resistance in Vigna radiata (L.) against Macrophomina phaseolina-associated with dry root rot

Macrophomina phaseolina, a necrotrophic fungus responsible for dry root rot disease in Vigna radiata L. (greengram) causes extensive yield losses. Biocontrol agents are reported to suppress phytopathogens, mitigate biotic stress, and improve growth and yield of plants. Here, Bacillus subtilis PSB-5 and Trichoderma sp. TD-6 along with chemical fungicides; mancozeb (MZB) and carbendazim (CBZM) were evaluated alone/or in combination for their effectiveness against M. phaseolina-induced root rot disease in greengram. B. subtilis and Trichoderma sp. suppressed phyto-fungal growth by 72 and 78 %, respectively. Both bioagents synthesized indole-3-acetic acid, ammonia, and solubilized insoluble phosphate, released antimicrobial substances including siderophore, hydrogen cyanide, and various extracellular lytic enzymes. Further, efficacy of bioagents and fungicides (at 50 and 100 ppm) were tested (single and combined) against M. phaseolina-infected greengram cultivated in pot soils. While evaluating, bioagents and fungicides increased plant height, biomass, and vigor indices of infected greengram in order: PSB-5 + TD-6 > PSB-5 > TD-6 > MZB + CBZM (100 ppm) > CBZM (100 ppm) > MZB (100 ppm). Further, among treatment, dual inoculation of bioagents (B. subtilis + Trichoderma), maximally increased total chlorophyll (59.4 %), carotenoid (68 %), root phosphorous (69.6 %), root nitrogen (65.7 %), total soluble protein (80 %), soluble sugar (76 %), pod yield (82.3 %) and seed protein (68 %) over infected plants. Moreover, biotic stress-induced oxidative stress biomarkers (proline, malondialdehyde, electrolyte leakage) and reactive oxygen species were significantly (p ≤ 0.05) lowered in fungal-infected greengram following fungicides and bioagents application. Furthermore, combined inoculation of bioagents significantly (p ≤ 0.05) reduced percent disease index (PDI) and increased % protection in biotic-stressed greengram. Besides, bioagents potentially increased disease resistance in M. phaseolina-infected greengram plants by modulating defense responses such as activities of β-1, 3 glucanase, catalase, phenylalanine ammonia lyase, polyphenol oxidase, peroxidase, and superoxide dismutase which significantly protected plants from fungal pathogen. Soil populations of pathogen and bioagents declined and considerably increased, respectively, at harvest. Application of antagonistic bacterial and fungal strains enhances greengram resistance to M. phaseolina while concurrently offering agriculture an environmentally friendly choice. Also, these strains can be applied as biocontrol agents in disease suppression over chemical fungicides.

Quercetin: Potential antidiabetic effects through enzyme inhibition and starch digestibility

AbstractDiabetes mellitus involves high blood sugar levels due to insufficient insulin action. Furthermore, enzymes such as α‐amylase and α‐glucosidase break down carbohydrates into glucose, leading to postprandial hyperglycemia. Flavonoids, particularly quercetin, inhibit these enzymes, slowing carbohydrate digestion and reducing glucose absorption. Quercetin has significant hypoglycemic effects with inhibitory concentration (IC50) values comparable to acarbose, a standard inhibitor, suggesting its potential as a natural alternative for diabetes management. In silico models, including molecular docking, molecular dynamics (MD) simulations, and quantitative structure‐activity relationship (QSAR) approaches, help researchers understand the molecular interactions of therapeutic agents. These techniques identify potential inhibitors, determine enzyme‐inhibitor structures, and calculate binding energies, correlating findings with in vitro or in vivo data. Molecular docking predicts molecular orientations, MD simulations offer insights into enzyme–inhibitor dynamics, and QSAR models predict inhibitory potential based on structural properties. Studies have shown that quercetin effectively inhibits α‐glucosidase and α‐amylase by forming hydrogen bonds with specific amino acid residues. Quercetin interacts with starches and reduces their digestibility, increases the formation of resistant starch, lowers the glycemic index, and inhibits digestive enzymes. Studies show that the effects of quercetin on starch digestion vary with concentration and type of starch, and its incorporation into foods such as bakery products, pasta, etc. can significantly decrease starch hydrolysis. The incorporation of quercetin into starch matrices may aid in the development of functional foods aimed at improving glycemic control.

Emergency department physiotherapists: consideration of perceived barriers and facilitators to help optimise their role in the Australian emergency department

QuestionsWhat are the current barriers and facilitators to the role of ED physiotherapists? How do ED physiotherapists believe their role may be optimised within the context of the ED?DesignMixed methods study using a cross sectional survey.ParticipantsAustralian physiotherapists currently providing services to patients within an Australian emergency department.InterventionN/A.Outcome measuresThe survey included questions related to the level of integration of ED physiotherapy into emergency department teams and wider health system, and open answer questions to identify the factors which impact and influence ED physiotherapy practice, and the future of ED physiotherapy.Results1 - Organisational culture, 2 - training and credentialling, 3 - governance, legislation and policies, 4 - funding, and 5 - advocacy and research were the five major themes generated from participant comments on existing facilitators and barriers to their role and the role of ED physiotherapists nationally. Six themes were generated from participant comments regarding the strategies to overcome barriers and facilitate an increased contribution by ED physiotherapists in the future: 1 - Training opportunities and specialisation pathways, 2 - Organisational culture, 3 - Governance, legislation and policies, 4 - Funding, 5 - Advocacy, 6 - Medicolegal Risks.ConclusionAustralian emergency department physiotherapists perceive their roles and emergency physiotherapy service provision to be impacted by complex and multi-factorial influences. The overall contribution of ED physiotherapy is susceptible to influence from non-linear interactions of various agents and factors which span all levels of the health system.Trial registrationN/A.

Interaction of Gadolinium-Based Contrast Agents with Endogenous Compounds: Effect on Stability

Interaction of Gadolinium-Based Contrast Agents with Endogenous Compounds: Effect on Stability

РЕЄСТРАЦІЯ ПРАВА ІНТЕЛЕКТУАЛЬНОЇ ВЛАСНОСТІ НА ОСНОВІ ТЕХНОЛОГІЇ БЛОКЧЕЙН

The widespread implementation of blockchain technologies fundamentally changes many processes in the socio-economic and cultural life of modern society. The high level of investment in the implementation of this technology, especially in the field of digital art, confirms the perspective and high interest of business in the further development of the blockchain. At the same time, the appearance of a large number of digital works of art and the possibility of electronic access to the latest developments actualize the issue of copyright protection and quick obtaining of intellectual property (IP) documentation. This issue is also important for teachers and educational institutions because it allows them to protect the results of their work, promote innovation and knowledge in the field. The use of non-fungible tokens is one of the most promising directions for the protection of intellectual property. The article analyzes the experience of implementing blockchain technologies in the process of obtaining intellectual property documents and patents, and copyright protection in general, and also considers systems and platforms designed for copyright protection. We present the model of authorship registration and obtaining IP documents based on blockchain technology. The user’s authorship will be represented in the form of a non-fungible token (NFT), which contains all information about this intellectual property and confirms the user's right to it. Each NFT is unique, allowing creators to present their creations in digital form with guaranteed proof of authorship. In addition, the article presents a fragment of the requirements for the tokenomics model and a diagram of the interaction of agents in the context of the functioning of tokenomics and the model as a whole. During the research, an NFT model was developed. It represents an intellectual property document certifying the authors’ rights to intellectual property objects. We implemented a smart contract that manages the creation and transfer of NFTs using the Cadence language on the Flow blockchain. In addition, the key functional requirements for the system were implemented, among them: registration of authors and experts (reviewers), adding keywords for the selection of experts (reviewers), searching for relevant experts by keywords, implementation of payment functions for reviewers for work performed.

On the Evolution of Institutional Design in the Context of Economic Fragmentation

An attempt is made to substantiate the categories of "institutional design" and "economic fragmentation" in the context of the evolution of state regulation. The characteristic of the processes of economic fragmentation is given, indicating the need for transformation in the formation of the basic institutional matrix of society. The interaction of economic agents is carried out in the implementation of their motivational aspects, but with a significant influence of external factors. In this regard, the focus of institutional design is based on the development of key parameters that affect the possibilities of subordination of interests at the micro, meso, macro and mega levels of the economic system. A special place in the study is devoted to development institutions that form the institutional shell for the implementation of the innovative component of economic modernization at the "center-regions" levels. The change of subjects forming systemic interaction contributes to the evolution of the mechanism of institutional design and transforms the fullness of state regulation instruments in conditions of economic fragmentation.

Exploiting macrocyclic polylactam for sensing V-series nerve agents: A computational study

The nerve agents are organophosphorus compounds (OPs) developed as chemical warfare agents (CWAs) that inhibit acetylcholinesterase (AChE) and lead to impeding neurological signals in mammals. Therefore, detecting such nerve agents is important, and efforts to study the fundamental interactions of host systems with CWAs are scarce in the literature. In this report, the cyclic 18-membered tetralactam [Ⅰ] as a host molecule has been chosen to sense V-series nerve agents. The DFT calculated results reveal that VM, VG, and VX, bind strongly with the core unit of the host molecule tetralactam [Ⅰ] with a preference for VM. The remarkably higher binding affinity of VM is governed by more hydrogen bonding interactions with the core of the cyclic 18-membered tetralactam [Ⅰ]. The nerve agent Russian VX has been found to bind weakly with the receptor molecule. The hydrogen bonding interactions of these nerve agents have further been analyzed with non-covalent interactions (NCI) and atoms in molecule (AIM) calculations. The tetralactam moiety exhibits the binding with V-series nerve agents and the absorption spectral results can be used as diagnostic signatures for sensing of such nerve agents in useful applications.

Gastroduodenal effects of leflunomide under experimental adjuvant-induced rheumatoid arthritis and comorbid arterial hypertension

Use of leflunomide (LEF), a synthetic immunosuppressor, for the treatment of rheumatoid arthritis (RA), has become an innovation in the management of the disease. Despite the widely and successfully use of the LEF for RA treatment, its pharmacological properties and side effects are not well understood. The aim of the study was to examine the gastroduodenal effects of prolonged enteral use of the LEF in adjuvant-induced RA and comorbid arterial hypertension (AH). The experiments were carried out on male and female mature nonlinear rats, which were divided into five groups. The animals of control group (n = 10) were treated with starch suspension, the animals of the second group (n = 15) were injected with complete Freund’s adjuvant – adjuvant-induced arthritis (AA), the third group (n = 15) AA and comorbid AH (salt-loading model), the fourth group (n = 15) consisted of animals with AA treated with LEF, the fifth group (n = 15) – animals with comorbid condition (AA + AH), which were treated by LEF. Administering of starch-LEF suspension into the stomach was carried out through a special metal probe to rats with adjuvant-induced arthritis and comorbid pathology once daily for the first 3 days at a dose of 15 mg/kg (shock dose) and then at a dose of 1.5 mg/kg (therapeutic dose) for the next 53 days. After finishing the experiments, gastroduodenal toxic effects of the drug were determined according to the frequency of rats with pathological lesions (hyperemia, petechiae, edema, ulcers) and intensity of the damages using a point scale. It has been established that prolonged use of the LEF leads to high levels of gastroduodenal toxicity in rats with adjuvant-induced arthritis and combined pathological condition: an increase in the frequency of rats with gastroduodenal mucosal pathology and intensity of various types of damages (petechiae, edema, ulcers). LEF induced higher prevalence of duodenal than gastric toxicity in rats with both adjuvant-induced arthritis and arterial hypertension comorbidity. Thus, gastroduodenal mucosa pathology after prolonged use of LEF in adjuvant-induced arthritis and comorbid state increases a need for adjuvant therapy to prevent such adverse effects. Moreover, further pharmacological research in order to introduce appropriate drugs is recommended. Furthermore, conducting preclinical and clinical studies to understand the pharmacokinetics, mechanisms of action, and interaction of LEF, in particular, with antihypertensive and gastroprotective agents seem necessary. Implementation of the results of the study in healthcare practice will help to optimize the treatment regimen for rheumatoid arthritis and comorbidities.