Use Of Computational Models Research Articles

ОСОБЛИВОСТІ ОБСТЕЖЕННЯ НЕЖОРСТКОГО ДОРОЖНЬОГО ОДЯГУ ПРИ ПРОЄКТУВАННІ ЗАХОДІВ РЕМОНТУ ІЗ ЗАСТОСУВАННЯМ ТЕХНОЛОГІЇ ХОЛОДНОГО РЕСАЙКЛІНГУ

Problem Statement. Observation of the performance of objects restored by cold recycling technology shows that more attention should be paid to diagnosing the condition of existing pavement and predicting the processes that cause premature deterioration. Purpose. To find solutions to clarify the condition of the existing pavement and to determine in more detail the factors of deterioration. Research methods. Analytical and experimental with the use of computer modeling. Conclusions. Based on the results of the research, an algorithm for conducting the survey was developed, which consists of two consecutive stages. This algorithm is based on the results of the first stage of the survey that provides that not only data are collected, but also envisages that a program for the second stage of the survey is drawn up. Based on the results of the second stage of the survey, a plan diagram of the same type of road sections is formed, the need for additional preparatory work to eliminate local areas with deterioration is determined, and constructive and technological solutions for the repair of pavement using cold recycling technology are developed with the determination of the depth of milling, the amount and composition of stone materials that are additionally added to the mixture, the composition of the binder, etc.

Road safety impact assessment practices and recommendations

Abstract This article reviews the practices for conducting Road Safety Impact Assessment (RIA). An overview of current research and methodologies related to this field is presented, and various aspects of the preparation of RSIA are analysed. The DIRECTIVE OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on road infrastructure safety management (RISM Directive) defines the procedure “road safety impact assessment” as a strategic comparative analysis of the impact of a new road or a substantial modification to the existing network on the safety performance of the road network”; The RISM Directive provides only the basic framework for conducting such analysis by just pointing out its basic elements without providing specific methodology for it. Some countries have developed their own methodologies, others not. Although the RIA is a procedure, that was included in the initial RISM Directive (2008), Bulgaria has not yet had much experience with it, nor has a developed methodology or recommendations for it. The main aim of this article is to review and analyse international experience in conducting road safety impact assessment and to present some recommendations about it for such procedures from national teams. The article draws attention to the methods and tools used in the preparation of the RIA. This includes the collection and analysis of road infrastructure and traffic data, the study of previous safety analyses, modelling and simulations of the proposed measures, and the assessment of social and economic aspects. One of the main aspects discussed in the article is the possibility of using computer models and artificial intelligence (AI) in the preparation of the RIA. AI can be applied to process large volumes of data and perform complex risk and scenario analyses. This allows practitioners to obtain more accurate and predictable results to serve as a basis for making informed road safety decisions. In conclusion, the article emphasizes the importance of preparing Road safety impact assessment and presents different practices and methodologies that can be used in this process. The use of computer models and AI can improve the efficiency and accuracy of RSIA and contribute to achieving higher standards of road safety.

A primer on the use of computational modelling to investigate affective states, affective disorders and animal welfare in non-human animals.

Objective measures of animal emotion-like and mood-like states are essential for preclinical studies of affective disorders and for assessing the welfare of laboratory and other animals. However, the development and validation of measures of these affective states poses a challenge partly because the relationships between affect and its behavioural, physiological and cognitive signatures are complex. Here, we suggest that the crisp characterisations offered by computational modelling of the underlying, but unobservable, processes that mediate these signatures should provide better insights. Although this computational psychiatry approach has been widely used in human research in both health and disease, translational computational psychiatry studies remain few and far between. We explain how building computational models with data from animal studies could play a pivotal role in furthering our understanding of the aetiology of affective disorders, associated affective states and the likely underlying cognitive processes involved. We end by outlining the basic steps involved in a simple computational analysis.

Potential of computational models in personalized treatment of obstructive sleep apnea: a patient-specific partial 3D finite element study.

The upper airway experiences mechanical loads during breathing. Obstructive sleep apnea is a very common sleep disorder, in which the normal function of the airway is compromised, enabling its collapse. Its treatment remains unsatisfactory with variable efficacy in the case of many surgeries. Finite element models of the upper airway to simulate the effects of various anatomic and physiologic manipulations on its mechanics could be helpful in predicting surgical success. Partial 3D finite element models based on patient-specific CT-scans were undertaken in a pilot study of 5 OSA patients. Upper airway soft tissues including the soft palate, hard palate, tongue, and pharyngeal wall were segmented around the midsagittal plane up to a width of 2.5 cm in the lateral direction. Simulations of surgical interventions such as Uvulopalatopharyngoplasty (UPPP), maxillo-mandibular advancement (MMA), palatal implants, and tongue implants have been performed. Our results showed that maxillo-mandibular advancement (MMA) surgery of 1cm improved the critical closing pressure by at least 212.2%. Following MMA, the best improvement was seen via uvulopalatopharyngoplasty (UPPP), with an improvement of at least 19.12%. Palatal and tongue implants also offered a certain degree of improvement. Further, we observed possible interacting mechanisms that suggested simultaneous implementation of UPPP and tongue stiffening; and palatal and tongue stiffening could be beneficial. Our results suggest that computational modeling is a useful tool for analyzing the influence of anatomic and physiological manipulations on upper airway mechanics. The goal of personalized treatment in the case of OSA could be achieved with the use of computational modeling.

A STUDY OF IMPROVEMENT OF A TECHNOLOGY FOR OBTAINING RADIATION-PROTECTING MATERIALS FOR SPACECRAFT AND ROCKETRY SYSTEMS


 
 
 Abstract. The use of commercial integrated circuits (ICs) in the electronic equipment of the spacecraft entails a certain risk. This is due to the fact that some commercial ICs are not suitable for operating conditions in space, most have a functional failure rate of about 10 krad per total accumulated dose (ie quite low), durability is not controlled from lot to lot, and reliability in extreme operating conditions is not defined. The object of development and research were the processes of interaction of materials with corpuscular radiation in a wide range of energies, and the subject of research was the shielding of electron and proton flows with energies up to 100 MeV by disperse -filled composite materials with a polymer matrix. A promising way to increase the survivability of the spacecraft by reducing the accumulated radiation dose is the use of protective materials that effectively protect the BEA from the radiation flow. The analysis of the current state of protection of radio- electronic equipment of space technology made it possible to establish that the most effective protection can be achieved by optimally combining the content of light and heavy elements in the composite material (CM). Modern research is aimed at obtaining composite materials that have the ability to weaken and disperse both light and heavy elementary particles. In the State University named after O. Honchar developed fundamentally new composite materials, which are superior in protection efficiency to those used today - metal ones, and are not inferior to the best world analogues. Preliminary results of theoretical and experimental research, including the use of computer modeling and simulation experiments on electron and proton accelerators, made it possible to draw conclusions about the possibility of increasing the complex of protective properties and prospects of the materials being created. The flow of high-energy electrons decreases by 1.4 times, and protons by 1.6 times compared to aluminum alloys. Enterprises of the space industry of Ukraine have shown great interest in the use of new protective materials. Analysis of the available information shows that the created materials are superior in operational parameters to those used today in China, Russia, Ukraine and other space countries. According to theoretical calculations, CM samples were developed, which have a higher stopping power of electrons than aluminum or its alloys, which are currently used in space vehicles in almost the entire range of the spectrum characteristic of GSO. Studies using the ELIAS linear electron accelerator showed that the obtained KM samples are more than 1.3 times more effective in terms of protective properties compared to aluminum at electron energies of the order of 2-3 MeV . There are prerequisites for obtaining an even more effective attenuation of the electron flow in comparison with the "reference" aluminum due to the variation of both the qualitative composition of the CM and its structure.
 
 

Study of the Migrating Mine Gas Piston Effect during Reactivation of Tectonic Faults

The hypothesis of the piston effect during mine gas migration caused by fault reactivation was studied, with the use of computer modeling, to explain cases of a sudden appearance of mine gases on the earth surface in coal mining areas. The study is based on the factual data of the mode and amplitudes of subsidence along faults during mining, the morphology of the fault planes, and the theoretical ideas about the discrete nature of the fault wall displacement along uneven contact surfaces. It is taken into account that the walls of the fault, due to the asperity types “ridge” and “sag”, form contacts of the “ridge–ridge” and “ridge–sag” patterns. This study examines the situation where gas pressure in the fracture space can sharply increase due to the jerky displacement of reactivated tectonic fault walls with a rough fault plane. It is assumed that, in the first phase of reactivation, the fracture space expands as a result of the displacer opening and the fact that fault plane asperities engage in the “ridge–ridge” type of contact. With the subsequent relative displacement of the fault walls in the second phase of reactivation, the contact changes into the “ridge–sag” type and a sharp reduction in the fracture space volume occurs. It is shown that a “piston effect” emerges due to the reduction in fracture space and that it promotes an increase in gas pressure and stimulates gas movement to the surface through the available channels. The resulting “piston effects” may also be responsible for the suddenly raised gas content recorded in the air of surface structures and recurrent mine gas migration onto the surface. The findings expand our understanding of the sudden gasification of the earth surface and living spaces in coal mining areas and contribute to the understanding of the gas migration process, thereby helping to monitor hazards.

Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Assessing Model Calibration Workflows and Outcomes.

Model reproducibility is a point of emphasis for the National Institutes of Health (NIH) and in science, broadly. As the use of computational modeling in biomechanics and orthopedics grows, so does the need to assess the reproducibility of modeling workflows and simulation predictions. The long-term goal of the KneeHub project is to understand the influence of potentially subjective decisions, thus the modeler's "art", on the reproducibility and predictive uncertainty of computational knee joint models. In this paper, we report on the model calibration phase of this project, during which five teams calibrated computational knee joint models of the same specimens from the same specimen-specific joint mechanics dataset. We investigated model calibration approaches and decisions, and compared calibration workflows and model outcomes among the teams. The selection of the calibration targets used in the calibration workflow differed greatly between the teams and was influenced by modeling decisions related to the representation of structures, and considerations for computational cost and implementation of optimization. While calibration improved model performance, differences in the postcalibration ligament properties and predicted kinematics were quantified and discussed in the context of modeling decisions. Even for teams with demonstrated expertise, model calibration is difficult to foresee and plan in detail, and the results of this study underscore the importance of identification and standardization of best practices for data sharing and calibration.

O043 Investigating Upper-Airway Mechanics with Computational Tongue Models

Abstract With the use of computational models of the oral cavity, we aim to understand how changes in the morphology of the oral cavity and changes the stiffness of the tongue affect the movement of the posterior surface of the tongue. Anatomical and diffusion weighted imaging from 20 healthy individuals (10M:10F, Age: 22-54years, BMI: 17-30kg/m²), were used to develop 3D models of the tongue including the fibre orientations of the tongue muscles. All models were aligned and scaled to match the volume of the average subject's mandible, so that only differences in shape were modelled. The muscles of the tongue were modelled as transversely isotropic, with a shear moduli of 2.9 and 1.0kPa, parallel and perpendicular to the muscle fibre direction, respectively. Each individual participant model was prescribed an active contraction producing 50kPa of stress in the horizontal component of the genioglossus. The influence of initial hyoid position on the average anterior displacement of the posterior surface of the tongue was assessed with linear regression. On average the contraction moved the posterior tongue 4.0±0.7mm anteriorly. For every millimetre of hyoid elevation, anterior tongue displacement (i.e. airway dilation) decreased by 0.1mm. These initial models demonstrate that the automated modelling pipeline can identify how structural differences alter the efficacy of dilator muscles. Future implementations of these models will also incorporate experimentally measured pharyngeal pressures, and temporal and spatial patterns of muscle activations based on experimental data, to assess the how variation in the coordinated contractions contributes to airway collapse.

Antiemetic effects of sclareol, possibly through 5-HT3 and D2 receptor interaction pathways: In-vivo and in-silico studies

BackgroundEmesis is a complex physiological phenomenon that serves as a defense against numerous toxins, stressful situations, adverse medication responses, chemotherapy, and movement. Nevertheless, preventing emesis during chemotherapy or other situations is a significant issue for researchers. Hence, the majority view contends that successfully combining therapy is the best course of action. In-vivo analysis offers a more comprehensive grasp of how compounds behave within a complex biological environment, whereas in-silico evaluation refers to the use of computational models to forecast biological interactions. ObjectivesThe objectives of the present study were to evaluate the effects of Sclareol (SCL) on copper sulphate-induced emetic chicks and to investigate the combined effects of these compounds using a conventional co-treatment approach and in-silico study. MethodsSCL (5, 10, and 15 mg/kg) administered orally with or without pre-treatment with anti-emetic drugs (Ondansetron (ODN): 24 mg/kg, Domperidone (DOM): 80 mg/kg, Hyoscine butylbromide (HYS): 100 mg/kg, and Promethazine hydrochloride (PRO): 100 mg/kg) to illustrate the effects and the potential involvement with 5HT3, D2, M3/AChM, H1, or NK1 receptors by SCL. Furthermore, an in-silico analysis was conducted to forecast the role of these receptors in the emetic process. ResultsThe results suggest that SCL exerted a dose-dependent anti-emetic effect on the chicks. Pretreatment with SCL-10 significantly minimized the number of retches and lengthened the emesis tendency of the experimental animals. SCL-10 significantly increased the anti-emetic effects of ODN and DOM. However, compared to the ODN-treated group, (SCL-10 + ODN) group considerably (p < 0.0001) extended the latency duration (109.40 ± 1.03 s) and significantly (p < 0.01) decreased the number of retches (20.00 ± 0.70), indicating an anti-emetic effect on the test animals. In in-silico analysis, SCL exhibited promising binding affinities with suggesting receptors. ConclusionSCL-10 exerted an inhibitory-like effect on emetic chicks, probably through the interaction of the 5HT3 and D2 receptors. Further studies are highly appreciated to validate this study and determine the precise mechanism(s) behind the anti-emetic effects of SCL. We expect that SCL-10 may be utilized as an antiemetic treatment in a single dosage form or that it may function as a synergist with other traditional medicines.

Computer simulation as one of the ways to increase the efficiency of training in higher mathematics in a technical university

Introduction. The article considers one of the ways to improve the effectiveness of teaching higher mathematics taught at a technical university using the Maple 18 computer program as a computer simulation. The use of procedures, some functions of the program and materials increases visibility, however, in our opinion, there are other methods for improving the effectiveness of teaching, which are discussed in pedagogical practice, but nevertheless are not widely used in the classroom. The research methods of this work are the analysis of the learning outcomes of students of technical universities and teachers taught this subject using computer modeling, who are interested in improving the effectiveness of learning and teaching using the Maple 18 computer program. The basis of this study is the process of mastering knowledge in higher mathematics of students of engineers of a technical university using computer simulation. The use of computer models, including the Maple 18 computer program, showed the best results in higher mathematics classes and, with the use of modeling, had a positive effect on the quality of teaching knowledge of engineering students and the results of assessing their learning were observed for several years. The most important stages of computer modeling and research questions with engineers in order to check the mastery of the subject in the classroom of higher mathematics are considered. To increase the effectiveness of training and the use of computer simulation, including the Maple 18 program, it is advisable to switch to an active learning method, for which it is advisable to use computer simulation and the Maple 18 computer program.

Recent Deep Learning Applications to Structure-Based Drug Design.

Identification and optimization of small molecules that bind to and modulate protein function is a crucial step in the early stages of drug development. For decades, this process has benefitted greatly from the use of computational models that can provide insights into molecular binding affinity and optimization. Over the past several years, various types of deep learning models have shown great potential in improving and enhancing the performance of traditional computational methods. In this chapter, we provide an overview of recent deep learning-based developments with applications in drug discovery. We classify these methods into four subcategories dependent on the task each method is aiming to solve. For each subcategory, we provide the general framework of the approach and discuss individual methods.

Comparative validation of two patient-specific modelling pipelines for predicting knee joint forces during level walking

Over the past few years, the use of computer models and simulations tailored to the patient's physiology to assist clinical decision-making has increased enormously.While several pipelines to develop personalized models exist, their adoption on a large scale is still limited due to the required niche computational skillset and the lengthy operations required. Novel toolboxes, such as STAPLE, promise to streamline and expedite the development of image-based skeletal lower limb models. STAPLE-generated models can be rapidly generated, with minimal user input, and present similar joint kinematics and kinetics compared to models developed employing the established INSIGNEO pipeline. Yet, it is unclear how much the observed discrepancies scale up and affect joint contact force predictions. In this study, we compared image-based musculoskeletal models developed (i) with the INSIGNEO pipeline and (ii) with a semi-automated pipeline that combines STAPLE and nmsBuilder, and assessed their accuracy against experimental implant data.Our results showed that both pipelines predicted similar total knee joint contact forces between one another in terms of profiles and average values, characterized by a moderately high level of agreement with the experimental data. Nonetheless, the Student t-test revealed statistically significant differences between both pipelines. Of note, the STAPLE-based pipeline required considerably less time than the INSIGNEO pipeline to generate a musculoskeletal model (i.e., 60 vs 160 min). This is likely to open up opportunities for the use of personalized musculoskeletal models in clinical practice, where time is of the essence.

Mathematical and computational models in spheroid-based biofabrication.

Ubiquitous in embryonic development, tissue fusion is of interest to tissue engineers who use tissue spheroids or organoids as building blocks of three-dimensional (3D) multicellular constructs. This review presents mathematical models and computer simulations of the fusion of tissue spheroids. The motivation of this study stems from the need to predict the post-printing evolution of 3D bioprinted constructs. First, we provide a brief overview of differential adhesion, the main morphogenetic mechanism involved in post-printing structure formation. It will be shown that clusters of cohesive cells behave as an incompressible viscous fluid on the time scale of hours. The discussion turns then to mathematical models based on the continuum hydrodynamics of highly viscous liquids and on statistical mechanics. Next, we analyze the validity and practical use of computational models of multicellular self-assembly in live constructs created by tissue spheroid bioprinting. Finally, we discuss the perspectives of the field as machine learning starts to reshape experimental design, and modular robotic workstations tend to alleviate the burden of repetitive tasks in biofabrication. STATEMENT OF SIGNIFICANCE: Bioprinted constructs are living systems, which evolve via morphogenetic mechanisms known from developmental biology. This review presents mathematical and computational tools devised for modeling post-printing structure formation. They help achieving a desirable outcome without expensive optimization experiments. While previous reviews mainly focused on assumptions, technical details, strengths, and limitations of computational models of multicellular self-assembly, this article discusses their validity and practical use in biofabrication. It also presents an overview of mathematical models that proved to be useful in the evaluation of experimental data on tissue spheroid fusion, and in the calibration of computational models. Finally, the perspectives of the field are discussed in the advent of robotic biofabrication platforms and bioprinting process optimization by machine learning.

Thoracoabdominal asynchrony in a virtual preterm infant: computational modeling and analysis.

Thoracoabdominal asynchrony (TAA), the asynchronous volume changes between the rib cage and abdomen during breathing, is associated with respiratory distress, progressive lung volume loss, and chronic lung disease in the newborn infant. Preterm infants are prone to TAA risk factors such as weak intercostal muscles, surfactant deficiency, and a flaccid chest wall. The causes of TAA in this fragile population are not fully understood and, to date, the assessment of TAA has not included a mechanistic modeling framework to explore the role these risk factors play in breathing dynamics and how TAA can be resolved. We present a dynamic compartmental model of pulmonary mechanics that simulates TAA in the preterm infant under various adverse clinical conditions, including high chest wall compliance, applied inspiratory resistive loads, bronchopulmonary dysplasia, anesthesia-induced intercostal muscle deactivation, weakened costal diaphragm, impaired lung compliance, and upper airway obstruction. Sensitivity analyses performed to screen and rank model parameter influence on model TAA and respiratory volume outputs show that risk factors are additive so that maximal TAA occurs in a virtual preterm infant with multiple adverse conditions, and addressing risk factors individually causes incremental changes in TAA. An abruptly obstructed upper airway caused immediate nearly paradoxical breathing and tidal volume reduction despite greater effort. In most simulations, increased TAA occurred together with decreased tidal volume. Simulated indices of TAA are consistent with published experimental studies and clinically observed pathophysiology, motivating further investigation into the use of computational modeling for assessing and managing TAA.NEW & NOTEWORTHY A novel model of thoracoabdominal asynchrony incorporates literature-derived mechanics and simulates the impact of risk factors on a virtual preterm infant. Sensitivity analyses were performed to determine the influence of model parameters on TAA and respiratory volume. Predicted phase angles are consistent with prior experimental and clinical results, and influential parameters are associated with clinical scenarios that significantly alter phase angle, motivating further investigation into the use of computational modeling for assessing and managing thoracoabdominal asynchrony.

Excitation-Inhibition Imbalance in Migraine: From Neurotransmitters to Brain Oscillations.

Migraine is among the most common and debilitating neurological disorders typically affecting people of working age. It is characterised by a unilateral, pulsating headache often associated with severe pain. Despite the intensive research, there is still little understanding of the pathophysiology of migraine. At the electrophysiological level, altered oscillatory parameters have been reported within the alpha and gamma bands. At the molecular level, altered glutamate and GABA concentrations have been reported. However, there has been little cross-talk between these lines of research. Thus, the relationship between oscillatory activity and neurotransmitter concentrations remains to be empirically traced. Importantly, how these indices link back to altered sensory processing has to be clearly established as yet. Accordingly, pharmacologic treatments have been mostly symptom-based, and yet sometimes proving ineffective in resolving pain or related issues. This review provides an integrative theoretical framework of excitation-inhibition imbalance for the understanding of current evidence and to address outstanding questions concerning the pathophysiology of migraine. We propose the use of computational modelling for the rigorous formulation of testable hypotheses on mechanisms of homeostatic imbalance and for the development of mechanism-based pharmacological treatments and neurostimulation interventions.

Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling.

The urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming on antimicrobial drugs' efficacy. The metabolic state of microbial cells has been shown to undergo several antimicrobial-induced modifications and, at the same time, to be a good predictor of the outcome of an antimicrobial treatment. Metabolism is a promising reservoir of potential drug targets/adjuvants that has not been fully exploited to date. One of the main problems in unraveling the metabolic response of cells to the environment resides in the complexity of such metabolic networks. To solve this problem, modeling approaches have been developed, and they are progressively gaining in popularity due to the huge availability of genomic information and the ease at which a genome sequence can be converted into models to run basic phenotype predictions. Here, we review the use of computational modeling to study the relationship between microbial metabolism and antimicrobials and the recent advances in the application of genome-scale metabolic modeling to the study of microbial responses to antimicrobial exposure.

Use of computer models in cardiovascular therapy to advance precision medicine.

Use of computer models in cardiovascular therapy to advance precision medicine.

A model of zinc dynamics evoked by intense stimulation at the cleft of hippocampal mossy fiber synapses.

Zinc is a transition metal that is particularly abundant in the mossy fibers of the hippocampal CA3 area. Despite the large number of studies about the zinc role in mossy fibers, the action of zinc in synaptic mechanisms is only partly known. The use of computational models can be a useful tool for this study. In a previous work, a model was developed to evaluate zinc dynamics at the mossy fiber synaptic cleft, following weak stimulation, insufficient to evoke zinc entry into postsynaptic neurons. For intense stimulation, cleft zinc effluxes must be considered. Therefore, the initial model was extended to include postsynaptic zinc effluxes based on the Goldman-Hodgkin-Katz current equation combined with Hodgkin and Huxley conductance changes. These effluxes occur through different postsynaptic escape routes, namely L- and N-types voltage-dependent calcium channels and NMDA receptors. For that purpose, various stimulations were assumed to induce high concentrations of cleft free zinc, named as intense (10μM), very intense (100μM) and extreme (500μM). It was observed that the main postsynaptic escape routes of cleft zinc are the L-type calcium channels, followed by the NMDA receptor channels and by N-type calcium channels. However, their relative contribution for cleft zinc clearance was relatively small and decreased for higher amounts of zinc, most likely due to the blockade action of zinc in postsynaptic receptors and channels. Therefore, it can be concluded that the larger the zinc release, the more predominant the zinc uptake process will be in the cleft zinc clearance.

Synthesis of 2-phenyl- and 2-benzyl derivatives of 4(3Н)-quinazolinone with analgesic activity

Quinazolin-4(3Н)-one derivatives are characterized by a wide range of pharmacological properties, among which the most significant one is a pronounced effect on the central nervous system. In this regard, a molecular design of biologically active compounds that have an analgesic activity due to the formation of ligand-receptor complexes with nociceptive and dopamine receptors, has been performed.The aim of the study was a molecular design and a subsequent targeted synthesis of 2-phenyl- and 2-benzyl derivatives of 4(3H)-quinazolinone with an analgesic activity, as well as the creation of a mathematical model in order to identify significant molecular descriptors.Materials and methods. A molecular design was carried out by a logical-structural approach using the PASS program with the identification of the biological activity of the predicted structures, as well as the energy calculation of the ligand-receptor interaction. The synthesis of 2-phenyl derivatives of 4(3H)-quinazolinone was carried out by the reaction of 2-aminobenzamide with aromatic aldehydes in polyphosphoric acid when heated, while the 2-benzyl derivatives were synthesized by fusing amides of anthranilic and homoveratric acids followed by sulfonation with sulfuric acid. The analgesic activity of the synthesized compounds was studied in the models of nociceptive reactions induced by chemical stimuli (a formalin test and “acetic acid writhings”).Results. A molecular design made it possible to identify promising structures in the series of 4(3H)-quinazolinone derivatives that affect nociceptive and dopamine receptors and have an analgesic activity. A modification was made to the synthesis of 2-phenyl- and 2-benzyl derivatives of 4(3H)-quinazolinone in order to increase the yield of the target products by a simpler and more cost-effective method. The predicted compounds were synthesized by cyclocondensation of anthranilic acid amide with aromatic aldehydes or with homoveraic acid amide. It follows from the primary pharmacological studies results that the synthesized substances are promising from the point of view of creating painkillers based on them. A structure-activity relationship between the molecular descriptors, which are largely responsible for the analgesic activity, and the results of biological tests, has been revealed.Conclusion. The use of computer modelling made it possible to identify the amino acid residues involved in the formation of the ligand-receptor complex with the nociceptive receptor, and to construct a mathematical model to explain the analgesic activity of 2-phenyl- and 2-benzyl derivatives of 4(3H)-quinazolinone. Modified procedures for the synthesis of target compounds have been proposed. The obtained coefficients of the approximation between the theoretical values and the data of the pharmacological experiment make it possible to state a sufficient reliability of the carried out studies.

Mapping the use of computational modelling and simulation in clinics: A survey.

In silico medicine describes the application of computational modelling and simulation (CM&S) to the study, diagnosis, treatment or prevention of a disease. Tremendous research advances have been achieved to facilitate the use of CM&S in clinical applications. Nevertheless, the uptake of CM&S in clinical practice is not always timely and accurately reflected in the literature. A clear view on the current awareness, actual usage and opinions from the clinicians is needed to identify barriers and opportunities for the future of in silico medicine. The aim of this study was capturing the state of CM&S in clinics by means of a survey toward the clinical community. Responses were collected online using the Virtual Physiological Human institute communication channels, engagement with clinical societies, hospitals and individual contacts, between 2020 and 2021. Statistical analyses were done with R. Participants (n = 163) responded from all over the world. Clinicians were mostly aged between 35 and 64 years-old, with heterogeneous levels of experience and areas of expertise (i.e., 48% cardiology, 13% musculoskeletal, 8% general surgery, 5% paediatrics). The CM&S terms "Personalised medicine" and "Patient-specific modelling" were the most well-known within the respondents. "In silico clinical trials" and "Digital Twin" were the least known. The familiarity with different methods depended on the medical specialty. CM&S was used in clinics mostly to plan interventions. To date, the usage frequency is still scarce. A well-recognized benefit associated to CM&S is the increased trust in planning procedures. Overall, the recorded level of trust for CM&S is high and not proportional to awareness level. The main barriers appear to be access to computing resources, perception that CM&S is slow. Importantly, clinicians see a role for CM&S expertise in their team in the future. This survey offers a snapshot of the current situation of CM&S in clinics. Although the sample size and representativity could be increased, the results provide the community with actionable data to build a responsible strategy for accelerating a positive uptake of in silico medicine. New iterations and follow-up activities will track the evolution of responses over time and contribute to strengthen the engagement with the medical community.