Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.  The major goal of pharmaceutical research and development is to ensure the continuous availability of new chemical molecular entities (NCEs) which display a high therapeutic efficacy with few or no adverse effects. Efficacy and toxicity are associated with pharmacokinetic profiles governed by absorption, distribution, metabolism, and excretion (ADME). Consequently, poor ADME profiles of NCEs can be a major cause of attrition in drug development . Major cThe most important challenge for in silico ADME screening includes setting up robust models with a high predictability. Recent model construction studies have predominantly utilized artificial intelligence (AI) and machine learning (ML) technologies, including artificial neural networks (ANN), random forests (RF), support vector machines (SVM), and tree-based methods . Furtherhttp://www.simulations-plus.com/ (accessed on 7 November 2023)), BIOVIA Discovery Studio ), and SCIQUICK ). These platforms have emerged as powerful tools for drug discovery in the pharmaceuticals industry. Furthermore, effort has been made by different pharmaceutical companies to construct in-house ADME prediction platforms using datasets based on proprietary chemical libraries and data derived from in-house screening systems with optimized assay conditions. The complementary use of in-house platforms and commercially available suites allows for improved ADME screening and the efficient acceleration of NCE discovery.In silico ADME models are likely to be constructed by players in three major areas: information technology (IT), pharmaceuticals, and academia. IT companies have created predictive platforms for various ADME parameters, including toxicological profiles, such as ADMET Predictor ) [http://www.swissadme.ch/ (accessed on 6 November 2023)) [http://biosig.unimelb.edu.au/pkcsm/ (accessed on 6 November 2023)) [https://www.amed.go.jp/en/ (accessed on 6 November 2023)). The Department of Innovative Drug Discovery and Development (iD3) division of AMED, which supports academic drug discovery, established the Initiative Development of a Drug Discovery Informatics System (iD3-INST) with financial support. The objective of the iD3-INST was mainly to construct a platform for academic drug discovery, which would comprise a database and in silico prediction models for ADME profiles. Attempts have been made in the public sector and in public–private partnerships to overcome the issues described above [To meet social demand, freely available prediction models have been developed, including online chemical modeling environments, such as OCHEM (r 2023)) , SwissADr 2023)) , and pkCr 2023)) . The Japed above .Given that pharmacokinetic behavior is governed by many parameters, an understanding of pharmacokinetics requires a comprehensive evaluation of these parameters. Many in silico models have been reported for each parameter. It is necessary to recognize the significance of these parameters when considering which model is appropriate for screening purposes. While this may be possible for pharmacokinetic researchers, medicinal chemists working in industry and academic biologists often do not have an in-depth understanding of these parameters. Therefore, this review describes the background and significance of evaluating each parameter in pharmacokinetic studies and then introduces the characteristics of various in silico models. Thus, this review will serve as a guide for ADME-based in silico drug discovery for researchers in various fields. The features of the open-access in silico models developed using the iD3-INST are also described. Toward the end, we briefly discuss the ongoing challenges with newly available AI technologies built by industry–academia partnerships, and the future prospective for in silico ADME prediction.We evaluated the predictive models more frequently cited in previous studies. A description of the evaluation metrics is shown in the tables, and their descriptions are given below.Classification model evaluation metrics“Accuracy” measures the proportion of samples correctly classified by the model; however, it may not be suitable for imbalanced datasets. “Precision” calculates the ratio of true positive predictions to all positive predictions made by the model; it is important to minimize false positives. “Recall” calculates the ratio of true positive predictions to all actual positive samples; it is important to minimize false negatives. “F1” is the harmonic mean of precision and recall, and balances the trade-off between these two metrics. In addition, it is useful for assessing the overall model performance. “Receiver operating characteristic area under the curve (ROC-AUC)” quantifies the area under the ROC curve, which plots the true positive rate against the false positive rate at different thresholds. “Cohen’s kappa (kappa)” measures the agreement between observations, and it takes into account the possibility of agreement occurring by chance. It is a credible and dependable indicator of inter-rater agreement.These metrics (other than kappa) are expressed in the range of 0–1; a higher value is desirable. The range of possible values of kappa is from −1 to 1, though it usually falls between 0 and 1.b.Regression model evaluation metrics:2)” and “Cross-validation coefficient of determination (Q2)” represent the proportion of variation in the dependent variable that can be explained by the model, where Q2 is calculated based on a full cross-validation. R2 and Q2 provide a measure of how well the observed outcomes are replicated by the model, based on the proportion of total variation in the outcomes that can be explained by the model.“Mean absolute error (MAE)” is the average of the absolute differences between the actual and predicted values. It quantifies the size of prediction errors without squaring the values and is less sensitive to outliers. “Mean squared error (MSE)” is the average of the squared differences between the actual and predicted values. It quantifies the size of prediction errors and is more sensitive to outliers than the MAE. “Root mean squared error (RMSE)” is the square root of the MSE, providing a similar measure of prediction error with the same units as the target variable. “Coefficient of determination . In the subsequent subsections, we describe the definition, importance in ADME, and determination of each of these properties. Lipophilicity refers to the ability of a compound to interact with non-polar solvents and is a fundamental property for describing hydrophobicity.It has been associated with the ADME, toxicity, and efficacy of NCEs and has been widely exploited when constructing in silico models for ADMET as a chemical descriptor. Increasing the lipophilicity of compounds via chemical modifications tends to enhance their penetration through cell membranes and biological barriers, in addition to their binding to proteins such as serum albumin, but reduces their aqueous solubility.Lipophilicity is typically defined by the partition coefficient (P), which is determined based on the ratio of the concentrations of a solute between the two solvent phases . The logarithm of this ratio (log P) has also been utilized to indicate lipophilicity.The distribution coefficient, log D, is the ratio of the sum of the concentrations of all forms of the compound (ionized plus unionized forms) in each of the two solvents (aqueous phase at a specified pH and n-octanol). Therefore, log D depends on the ionization of the compound and is equal to log P for non-ionizable compounds. For ionizable compounds, log D is preferentially utilized over log P when considering the relationship between lipophilicity and ADME. Solubility refers to the ability of a compound to dissolve in a specific solvent . The solubility of a compound varies greatly depending on its salt and crystal structures and is also influenced by chemical structure, temperature, and the nature of the solvent. Solubility is a critical property in drug development because it directly affects bioavailability. Solubility is also an indicator of absorbability. For poorly soluble compounds, much effort is often needed to improve their intestinal absorption in the nonclinical stages  and to develop appropriate formulations for clinical trials. Solubility can be determined using the shake-flask method. In this method, a compound is added to a known volume of the solvent, and the mixture is shaken until equilibrium is reached. The concentration of the dissolved compound is then determined via various analytical techniques, such as high-performance liquid chromatography. + ions) when in solution. Ionization (indicated using the ionization constant (pKa)) is a measure of the acidity or basicity of a compound, specifically its ability to donate or accept protons . The pH at the inflection point is the pKa, and log D is closely related to pKa.Topology refers to the arrangement or connectivity of atoms in a molecule, specifically in the context of its chemical structure and the spatial relationships between atoms.It is a fundamental concept in medicinal chemistry, and the connectivity and arrangement of atoms in a compound are key factors in determining its chemical and biological properties. Medicinal chemists often design the topology of a compound to optimize its binding affinity to specific targets and improve its pharmacokinetic properties.The MW of a compound, also known as the molecular mass or molar mass, is the sum of the atomic weights (or atomic masses) of all the atoms in a molecule and is typically expressed in Daltons (Da).A compound’s MW is a determinant of its ADME profile, and affects its membrane permeability; this consequently affects its intestinal absorption and tissue distribution, particularly in the context of penetration through the blood–brain barrier (BBB).To calculate the MW, the atomic weights of all the atoms in the chemical formula are added up. Accurate MWs can be obtained using analytical instruments such as mass spectrometers.Oral absorbability, alongside hepatic metabolic stability, is a key criterion for selecting NCEs with desirable pharmacokinetic properties . SolubilThe terms “kinetic solubility” and “thermodynamic solubility” are often used to describe water solubility, and solubilities are assessed during several stages in drug discovery . The kinCell-based models have been used to predict in vivo intestinal drug permeability by utilizing Caco-2 and other epithelial cell lines, such as human primary intestinal cells and induced pluripotent stem cells . Caco-2 Several in silico models for solubility, membrane permeability, and intestinal absorption have been developed, and −2 produced a coefficient determinant (R2) of 0.87 and sensitivity and specificity of 0.96 and 0.80, respectively, for the internal test set. It should be noted that their groups intensively summarized existing in silico classification and QSAR models. A novel prediction approach for biorelevant solubility was recently reported, which was based on a thermodynamic cycle comprising solid-state crystal lattice packing and the dissociation of molecules from the lattice and solvation [Most available in silico solubility predictors are classification models rather than quantitative structure–activity relationship (QSAR) models. Newby et al.  developeolvation . This apPapp) predictions between laboratories, which can be attributable to different assay conditions and/or heterogenous cell lines [Papp of reference compounds is necessary. Therefore, it is difficult to develop QSAR models with a high accuracy using large datasets comprising Papp data collected by various laboratories. Smaller datasets tend to be used when constructing in silico models based on Caco-2 cells to predict permeability. An attempt utilizing an in-house dataset by Kamiya et al. [2 = 0.91). Brocatelli et al. [Papp originating from the Caco-2 cell assay was correlated with that from the MDCK cells, with an R2 of 0.60.The Caco-2 cell assay is a standard method for evaluating the membrane permeability of molecules in the pharmaceutical industry. Large variabilities in membrane permeability  for PAMPA was employed by Williams et al. [PAMPA is widely utilized for early screening in drug discovery due to its simple mechanism, resulting in the development of in silico QSAR models. QSAR models for PAMPA developed by Nakao et al.  and Chi y et al.  updated Several attempts have been made to identify the molecular descriptors that are associated with intestinal absorption as well as membrane permeability in cell-based assays ,38,39. Aa) are summarized in a in humans and Papp in Caco-2 cells with an R of 0.61 was demonstrated by Kamiya et al. [Papp in Caco-2 cells, and when high and low HIA classes were defined based on a cut-off of 30%, an accuracy of 0.80 was achieved. Various binary classification models have been developed based on data in the literature and/or FDA-approved drugs. The classification model employed a cut-off of 30% in many cases. The classification models by Newby et al. [Existing in silico predictive models for human intestinal absorption  in addition to total clearance (CL). The Vdss can be predicted by either empirical approaches based on the allometry concept or physiological-based pharmacokinetic (PBPK) models based on the physiological concept. The PBPK approach generally employs mechanistic tissue composition models that are essentially represented by the model described by Poulin and Theil; this approach considers the unbound fractions of the test compound in plasma and tissues. The model described by Rodgers and Rowland [dss for a wide variety of drugs (36 drugs) in rats. Their results implied that the predictive performance based on unbound fractions in plasma and tissues combined with the estimate effect of the pH partition hypothesis was higher than the performance determined using the methods described by Poulin and Theil and Rodger and Rowland [Predicting the plasma concentration profiles of drugs in humans is important and can be achieved by integrating in vitro and/or in vivo pharmacokinetic data from the nonclinical stages. As a simplified approach, the human profile can theoretically be described by a steady-state distribution volume  in rats by subjecting Kp,t data corresponding to 1460 specimens to an ANN algorithm; the predictive performance was estimated to be 0.909 for the training set and 0.896 for the validation set. Similar models based on QSAR models use multiple linear networks (MLRs). ANNs and SVMs were constructed by Louis et al. [2 of 0.621. In a unique comparative study—between empirical and in silico predictions of Vdss—by Fagerfolm et al. [In silico linear and nonlinear prediction models of Vi et al. . They aci et al.  developes et al. , who dism et al. , no signd) and total clearance (CLtot) of drugs, which govern their pharmacokinetic profiles [Plasma protein binding is one of the key factors considered when carrying out pharmacokinetic, pharmacodynamic, and toxicological evaluations of NCEs. Only the unbound (free) drug is capable of interacting with pharmacological and toxicological targets , renal glomerular filtration, and hepatic metabolism, and of diffusing between the plasma and organs/tissues. From a pharmacokinetic viewpoint, plasma protein binding affects the volume of distribution  to obtain exact predictions of plasma protein binding . The uti2) of 0.69, which was similar to the predictive performance achieved in other studies. The latter study focused on a dataset of 132 diverse acidic drugs with a wide range of binding capacities. An in silico prediction model for fu,p was developed using 178 molecular descriptors based on a genetic algorithm with stepwise regression, and an R2 of 0.771 was achieved. As a guide to assess fu,p for acidic compounds, the following checklist criteria were developed: log P ≥ 3, ≥2 aromatic nonconjugated rings, ≥1 cyano groups, ≥3 H-bond donors and acceptors separated by four or five skeletal bonds, ≥1 tertiary C atoms, ≥1 four-membered rings, and ≥1 iodine atoms. Different approaches to optimizing the human serum albumin binding affinity of chemical structures have been proposed. Zhang et al. attempted an in silico docking analysis [2 of 0.869 for MLR and 0.904 for SVM.Small dataset-derived in silico models were created by Fagerholm et al.  and Zhivanalysis , and ideanalysis  demonstrThe prediction of drug–brain penetration is an essential component when assessing the in vivo pharmacological activities for drugs targeting the CNS ,65; thisDrug distribution can generally be defined by the free drug hypothesis, where only drugs without binding to proteins, lipids, or other tissue components can distribute in target tissues; unbound drug molecules are thus able to interact with pharmacological targets . Unboundp,uu,brain) and unbound volume of distribution , under the assumption that the unbound drug is in equilibrium between the brain and ISF [Three experimental methods can be used to evaluate drug distribution in the brain, i.e., microdialysis (MD), brain slice, and brain homogenate. The MD method has been utilized to determine the concentration of compounds within the interstitial fluid (ISF) in the brain , which e and ISF . This me and ISF , the necu,brain values by measuring the amount of drug present in a brain slice when the drug is incubated with a buffer containing the slice. As the slice maintains the basic structure of the blood and brain tissue, the results obtained using this method tend to account for the influence of nonspecific binding and lysosome trapping. However, this method is not readily replicable in all laboratories because a highly specialized instrument is required for slicing the raw tissue.The in vitro brain-slice method (using rats and mice) can yield Vu,brain) with a relatively high throughput. As the brain homogenate contains tissues with ruptured structures, the fu,brain value is likely to be underestimated . This underestimation can be attributed to additional binding sites of the drug in the collapsed tissues. fu,brain values are generally evaluated using the brain homogenates of rodents, and there are no reported species differences in fu,brain values.The brain-homogenate binding assay estimates the unbound fraction of the drug in homogenized brain tissue . Both parameters represent the extent of the compounds passing through the BBB, which is formed by the endothelial cells of the capillaries in the brain. As studies using mice or rats are low throughput due to their labor-intensive and time-consuming nature, many investigators have constructed qualitative or quantitative computational models (classification or QSAR models) and adapted the methods used from MLRs and PLSRs to SVMs and ANNs, following the development of new technologies. Many researchers have conducted in vivo brain exposure studies which have yielded total brain-to-plasma concentration ratios, denoted by K2, HBAs of <7, HBDs of <3, and RBs of <3. Several QSAR models have been constructed, some of which are introduced here  are critical parameters used to decide poor or good BBB permeability. A pioneer study by Young et al.  demonstrced here . LanevskShaker et al.  pointed Ding et al.  reportedp,uu,brain values in most compounds are not in unity due to the tight junction and barrier system. Therefore, the Kp,uu,brain is a crucial parameter for investigating the ability of a drug to penetrate the BBB [p,uu,brain using in vivo study methods. Several researchers have collected experimental data for fu,brain and Vu,brain based on estimates using the brain homogenate and brain-slice methods, respectively. fu,brain and Vu,brain are explainable parameters for Kp,uu,brain, as evidenced via the validation of 81–1121 compounds from the literature or in-house databases [u,brain and Kp,uu,brain are described in Based on the free theory concept, the unbound drug concentrations in tissues are generally similar to those in the plasma due to the rapid equilibrium of compounds; however, the K the BBB . Due to atabases ,88,89,90p,uu,brain was negatively correlated with hydrogen binding, such as that to PSA or to HBA or HBD. They successfully built a QSAR model using PLSR and 16 descriptors, which achieved a Q2 of 0.452. Chen et al. [2 of 0.94–0.96 and Q2 of 0.73–0.80, respectively).Fridén et al.  demonstrn et al.  and Varan et al.  utilizedu,brain from brain slices is one of the approaches for estimating the Kp,uu,brain via indirect methods, which was built on a physiological model based on lipid binding and pH partitioning. This model involves the partition of a drug into lipids, ISF, and intracellular compartments of the brain, and the outcomes are in good agreement with experimental data [u,brain and fu,brain, which were attributed to the involvement of pH partitioning in slices with a preserved cellular structure. An indirect method employed by Chen et al. [p,uu,brain, and achieved an R2 of 0.94 for RF and 0.90 for SVM.The determination of Vtal data . Similartal data  indicaten et al.  computatu,brain is the brain-homogenate method, which has a relatively high-throughput potential in drug discovery. An advantage of this binding assay that no species differences have been observed; the fu,brain showed a one-by-one correspondence curve between mouse and rat brains [u,brain found among seven species, including humans. Thus, the fu,brain in rodents is suggested to be an essential parameter for computational models for predicting the Kp,uu,brain in humans, in combination with in silico Kp prediction. Lanevskij et al. [u,brain using nonlinear least-squares minimization with a dataset of 470 compounds, which had a good predictability with an R2 of 0.75. Moreover, in silico models for fu,brain were constructed using SVMs by Wan et al. [2 vales of 0.782 and 0.64, respectively. Interestingly, Kosugi et al. [p,uu,brain values that correlated well with in vivo data when compared to ML models. Another approach to evaluating the ft brains . A similt brains , with noj et al.  developen et al.  and Dolgn et al. , both ofi et al.  demonstr2 and basic pKas < 8 had a decreased distribution. Whether NCEs are subject to P-gp-mediated efflux is generally evaluated using a Caco-2 monolayer or LLC-PK-1 transfected with human P-gp. In the latter case, an ER can be estimated by comparing the Papp ratio between Basal (B) to Apical (A) and A to B in LLC-PK-1 cell monolayers with or without over-expressed human P-gp; consequently, P-gp substrates can be defined by net ER ≥ 2, while non-P-gp substrates are defined by net ER < 2. Gunaydin et al. [2, and clogP of >3. In addition, QSAR analyses identified a relationship between net ER  and the computationally derived solvation free energy difference, ΔGH2O−CHCl3. Moreover, Chen et al. [2 of 0.96 for the training set (n = 50).P-gp, which is a member of the ATP-binding cassette transporter family with a broad substrate specificity, negatively impacts the absorption and distribution of its substrates. This is because P-gp functions as a barrier against xenobiotics in the body. Particularly, P-gp is considered to play a central role in the BBB. Whether NCEs are substrates of P-gp is an important factor when selecting brain-targeting compounds, and some in silico discrimination models have been developed for P-gp efflux. Desai et al.  establisn et al.  revealedn et al.  developeDrug metabolism studies comprise several components, including the identification enzymes responsible for drug metabolism, the site of metabolism, metabolic inhibition (a drug–drug interaction), and metabolic stability. Among them, metabolic stability can be regarded as the most important parameter governing in vivo pharmacokinetics, which affects in vivo efficacy and toxicity. Thus, the assessment of metabolic stability has been implemented in high-throughput screening (HTS) processes for identifying and optimizing lead structures in drug discovery. Technical supports based on in silico models for metabolic stability have been necessary to reduce the number of screening compounds in HTS with large compound libraries, and guide the direction of structures synthesized during lead optimization. int) [1/2) [In vitro metabolic stability is evaluated using human liver microsomes or human liver S9 fractions. Much attention has been paid to the development of robust in silico models to assess metabolic stability in recent decades. These studies share several common features, i.e., (1) in-house datasets, (2) the use of either intrinsic clearance (CLint) ,102,103 t) [1/2) ,105,106 int using an RF or SVM for a relatively large dataset of 1952 proprietary compounds, which achieved a sensitivity of >0.9 and specificity of >0.6. Large dataset-derived two-classification models were reported by Lee et al. [int) that categorized low (CLint < 9.2 μL/mg/min), moderate (9.2 < CLint < 48), and high (CLint > 48) risk, and had a high predictive performance shown by RF or SVM.First, we assessed the prediction models developed by major global pharmaceutical companies. Sakiyama et al.  developee et al.  and Gupte et al. . The fore et al.  developeThis relatively high predictability might be due to the use of standardized protocols that ensure consistent data in each individual laboratory. Sasahara et al. employed an intriguing prediction strategy ,103. The1/2, which can be directly determined as an endpoint in substrate depletion assays. Liu et al. [1/2 using datasets from ChEMBL, an open database. The models by Liu et al. of human metabolic stability achieved a sensitivity of 0.73–0.78 and specificity of 0.85–0.88 for the nearest neighbor. The in silico predictive models by Li et al. yielded an AUC of 0.84 for rat metabolic stability and 0.86 for human metabolic stability. Furthermore, the National Center for Advancing Translational Sciences aimed to build global and local models to accommodate for the large and unique dataset of >24,000 compounds from more than 250 projects that covered a wide range of pharmacological targets and biological pathways [https://opendata.ncats.nih.gov/adme (accessed on 6 November 2023)). However, t1/2-based models have a disadvantage with respect to applicability relative to CLint-based models. The t1/2 parameter depends on experimental conditions such as microsomal concentrations and incubation time in metabolic stability studies; in contrast, CLint is normalized in terms of microsomal concentration and incubation time. Thus, similar to CLint-based models, the predictive outcomes from t1/2-based in silico models provide insights into chemical structure design; however, recalculation based on differences in experimental conditions is required. Many researchers have employed tu et al.  and Li eu et al.  construcpathways . The locint. The total clearance values predicted by the RF and radial basis function models were within two-fold of the observed values for 67.7 and 71.9% of the test set compounds, respectively; both models also showed potential for improvement via the incorporation of in vitro parameters such as fu,p and CLint. It should be noted that the predictability of the in silico approach based on total clearance leads to the possibility of a paradigm shift occurring with respect to the metabolic stability-based optimization process in the early stages of drug discovery.An alternative computational approach is to directly predict total clearance. When elimination from the body only occurs hepatically, the hepatic clearance is equal to the total clearance. Berellini et al.  developeWith recent scientific and technological advances, protein structures have been widely used to predict the binding modes of ligands or designed compounds in docking algorithms for rational drug design. As shown in CYP3A4, a major drug-metabolizing enzyme, is involved in the metabolism of 30% of the drugs in current use ; this cau,p [u,p*GFR; however, both secretory and reabsorption processes sometimes occur simultaneously to different extents.Renal clearance refers to the excretion of unchanged drugs through the kidneys and represents the major elimination route for drugs with negligible metabolism and biliary excretion. Renal excretion is an integrated process that comprises glomerular filtration, tubular secretion, and reabsorption . Glomeruu,p . TubularBoth renal clearance (CLr) and the fraction of compounds excreted in their unchanged form in the urine (fe) are important predictive indicators for renal drug excretion. CLr is a governed parameter for pharmacokinetic profiles and is calculated using the proportionality term between the urinary excretion rate and plasma concentration of unchanged compounds . The fe 2 of 0.844 and 0.720 when using Volsurf and Molcom-Z descriptors, respectively. Chen et al. [2 of 0.77 and 0.81, respectively. Furthermore, it was pointed out that fu,p was a key parameter in CLr prediction, which may be explained by the fact that unbound compounds only undergo filtration and secretion in the kidney.Doddareddy et al.  developen et al.  generaten et al.  was ableu,p, MW, and log D of 41 drugs, and the predictive performance was estimated to be 90% for recall. Similar investigations were conducted by Toshimoto et al. [As reported in the prediction of absorption fraction, the proportion of urinary excretion is usually discussed qualitatively, leading to the development of classification approaches based on the excretion fraction criterion. Criteria setting is likely to differ between researchers. Kusama et al.  computato et al.  and Wakao et al. . Wakayamo et al.  utilizedTwo major families of transporters, ATP-binding cassette (ABC) transporters and solute carrier (SLC) transporters, are known to govern the pharmacokinetic profiles of their substrates. Multidrug-resistance-associated proteins (MRP2/3/4)  were utilized as basic parameters. Much effort by the author’s group was made to compile a negative dataset for non-substrates of each transporter, owing to the paucity of data in public databases. Expert-involved curation was conducted to generate a dataset of putative non-substrates for each transporter based on the personal opinions of 11 researchers in the field. The developed model correctly predicted 111 of 136 compounds as substrates for an external dataset. This information will be useful for the prediction of renal clearance by in silico models because SCL transporters are often involved in renal excretion processes.Kusama et al. attempted to perform an in silico prediction of hepatic uptake via OATPs, and employed rectangular boxes with MW, log D, and fely 0.67 . Toshimoely 0.67 . Importaely 0.67 . Four phThe safety of NCEs in humans must be ensured based on results from various toxicological studies during drug discovery. Recently, ML has become increasingly prominent in predictive toxicology as toxicological assessments have shifted from in vivo studies to in silico studies via in vitro studies. Various models have been used to predict toxicological endpoints. Wang et al. summarized in silico models to predict various toxicity endpoints, such as cardiotoxicity, hepatotoxicity, genotoxicity, immunotoxicity, acute oral toxicity, and developmental toxicity via various ML modalities . HoweverThe importance of curation when creating high-quality training datasets was demonstrated by Alves et al., who showed that the inclusion of a large number of duplicated data in data repositories artificially yielded a high predictability in the case of no curation . The curThe goal in toxicological evaluations has shifted from evaluating toxicity alone to mechanistically explaining any toxic outcomes, which allows researchers to improve the safety profiles of drugs . In geneThere are several commercial software packages available for predicting ADME parameters, including ADMET Predictor, BIOVIA Discovery Studio, and SCIQUICK. These suites are widely used in the pharmaceutical industries during the early stages of research and development. Furthermore, in silico ADME models constructed by various researchers have been based on datasets comprising in-house data from institutes or data extracted from commercially available databases, and the calculation tools of descriptors and algorithms in the commercially available software are utilized. This can be attributed to several reasons; this increases the predictive performance of in silico models, utilizes reliable software  for both calculating the descriptors and producing the prediction model, and constructs a model using a dataset selected from a chemical space congruent to its own library. However, high licensing fees for those packages preclude their use by academic researchers; therefore, the construction of a well-established environment for using such prediction tools is essential for academic drug discovery. To meet such a demand, freely available prediction models have been developed, including OChEM , SwissADhttp://www.ebi.ac.uk/chembl/ (accessed on 6 November 2023)) and manually processed the data by checking the experimental protocols, values, units, and other details. The experimental in-house data for some parameters were obtained under unified conditions. Moreover, the iD3-INST originally acquired the experimental data regarding the net ER in P-gp-overexpressed LLC-PK1 cells. Using integrated publicly available databases, in silico prediction models were developed for the following nine parameters: solubility, membrane permeability, HIA [Our group compiled ADME data from ChEMBL  less than 0.1. Furthermore, predicting brain distribution may require in silico models to assess P-gp efflux against screening compounds. Much effort has been made to obtain data regarding net ER by comparing Papp in LLC-PK1 cells with and without P-gp overexpression, thereby successfully allowing for the development of a three-classification model for ER with a relatively high predictive performance [https://drumap.nibiohn.go.jp/ (accessed on 6 November 2023)).Despite our use of free software and public data, our in silico models appeared to yield predictive performances comparable to those of the reported models that were based on in-house databases or commercially available tools. Particularly, some in silico models were created based on the pharmacokinetic screening aspect of drug discovery. In general, in silico models for plasma protein binding allow for the prediction of a range of binding or unbound fractions. However, NCEs tend to be highly lipophilic and often exhibit a high binding affinity to plasma proteins; therefore, to address this issue, in silico models have been generated using compounds with unbound fractions  consortium, comprising public (NIBIOHN and RIKEN) and private sectors  which hold high-quality data, was organized by the iD3-INST. With the dedicated support of AMED, discussion at the iP3 consortium has led to an agreement for a blueprint. Based on the blueprint, the iD3-INST received data on solubility, human plasma protein binding, and metabolic stability from the private sectors and also obtained data for net ER using compounds provided by the private sectors . The est in rats . These iIn August 2020, iD3 launched the “Development of a Next-generation Drug Discovery AI (DAIIA) through Industry-Academia Collaboration” initiative, which aims to improve the efficiency of drug discovery in academia and efficiently create NCEs from drug discovery research in industry. DAIIA aims to develop a next-generation drug discovery AI platform via industry–academia partnership, and has combined data on the binding affinity between drug discovery target biomolecules and compounds held by both industry and academia. Furthermore, empirical knowledge on structural optimization held by drug discovery chemists in industries and other data from multiple aspects of drug discovery research have been shared in the partnership. Nearly 18 member industries of the Japan Pharmaceutical Manufacturers Association Research and Development Committee are participants in the DAIIA project, and the full-scale operation of the industry–academia partnership is ongoing. By using leading-edge AI technologies, DAIIA is developing predictive models for compound design, binding affinity, and ADMET, which will have practical applications in the field of drug discovery for both industry and academia. To address issues regarding the predictability of ADME profiles, many researchers have improved upon existing computational ADME models with the advent of AI, and, recently, graph convolutional networks have been equipped with the ability to predict protein functions by exploiting sequence features extracted from a protein language model and protein structures . The predss, via allometric  or IVIVE  approaches. Some research has suggested an alternative evaluation approach for predictive performance in in silico prediction models toward relevant in vivo parameters, such as CL and CLr; outcomes from the in silico prediction are evaluated by comparing the corresponding data estimated using the allometric and IVIVE methods. These methods are based on the allometry concept and physiological modeling, respectively. This trend has emerged due to unsatisfactory results from predictive models for in vivo human parameters, such as CL and Vapp, CLint, and fu,p, but also in vitro experimental data and in silico-derived parameters in a physiologically based pharmacokinetic model [dss with high prediction accuracies. Other investigations have suggested that predictions of in vivo pharmacokinetic profiles can be assessed by integrating not only various in silico-based parameters, such as Pic model ,145. Uniic model  demonstric model  toward pic model . This pric model  focused This review highlights the innovative technologies that have been incorporated into the field of in silico ADME prediction. Furthermore, in silico modeling is expanding from the prediction of individual in vitro parameters in drug discovery to in vivo pharmacokinetic prediction in nonclinical studies. In silico models are utilized in a wider range of areas than before, and are becoming a crucial tool in drug research and development. In the future, sharing pharmacokinetic and toxicological data in clinical trials between industries and academia will allow academia to develop more useful models for human applications.Given that academic research occupies a central position in making breakthroughs in drug discovery, it is important that future models support academic drug discovery. AMED launched two projects to improve the academic drug discovery environment by constructing a platform consisting of an open-access database and software package, and the models showed acceptable predictive performances. This platform will enable the acceleration of the production of NCEs with desirable profiles (including ADME) in academic drug discovery. Future efforts are required to continue integrating high-quality screening data from industries and cutting-edge technologies from academia. The expansion of similar initiatives is expected to strengthen the AI-based environment of computational ADME prediction in the academic drug discovery field. Finally, the prediction of in vivo pharmacokinetic profiles rather than individual ADME parameters will become a useful tool for academic drug discovery researchers who may have limited resources.


The height of the duodenal villi was significantly reduced (p < 0.001), leading to decreased intestinal sugar absorption. At the same time, MFT alleviates the imbalance of intestinal flora caused by high blood sugar, promotes the growth of beneficial bacteria , and inhibits the reproduction of harmful bacteria . MFT helps reduce the secretion of toxic substances , decreases oxidative stress and inflammation, mitigates organ damage, and improves symptoms of diabetes. Finally, the random blood glucose value of GK rats dropped from 22.79 mmol/L to 14.06 mmol/L. In summary, mulberry leaf fu tea can lower sugar absorption in diabetic rats, reduce the body’s oxidative stress and inflammatory response, regulate intestinal flora, and reduce blood sugar levels in GK rats. It is hinted that mulberry leaf fu tea could be used as a functional drink to help prevent the occurrence of diabetes.Type 2 diabetes mellitus is a disease caused by hyperglycemia, an imbalance in the intestinal flora and disruption of the endocrine system. At present, it is primarily controlled through drug treatment and an improved diet. Mulberry leaf and fu brick tea were considered to have excellent hypoglycemic effects. This study used mulberry leaves and fu brick tea as raw materials to develop a dietary regulator that can assist in the prevention and alleviation of diabetes. The experiment used the Goto-Kakizaki (GK) rat model to investigate the hypoglycemic effect of mulberry leaf fu tea (MFT) and its influence on the intestinal flora of diabetic rats through methods including ELISA, tissue section observation and 16S RNA microbial sequencing. The results showed that, compared with the GK group, the intervention of mulberry leaf fu tea significantly reduced the activities of α-glucosidase ( Type 2 diabetes mellitus (T2DM), also known as adult-onset diabetes, is a complex metabolic disease characterized by elevated blood glucose and lipid abnormalities . The conFirmicutes , Bacteroidetes, Proteobacteria (Enterobacterium), Actinobacteria (Bifidobacterium), Fusobacteria, Verrucomicrobia, etc. . The temperature of the animal room was maintained at 24–26 °C, the light on time/the light of time was 12/12 h cycle, and there was free access to water and food. Food, distilled water, and bedding were changed once a day to keep the cage clean and hygienic. After all rats had been adaptively fed and adopted the diet for one week, the random blood glucose levels of the rats in each group were measured. The successfully modeled GK rats were randomly divided into two groups: the hyperglycemia model group (GK) and the MFT group (T). In addition, 8 Wistar rats comprised the normal control group (NC). Wistar rats were fed standard chow . GK rats were fed a high-fat diet . MFT was used for continuous intervention for 4 weeks, and random blood glucose levels were measured twice a week. At the end of the experiment, the rats fasted for 12 h and were then anesthetized via intraperitoneal injection of sodium pentobarbital (30 mg/kg). Blood was collected from the aorta, stored in a refrigerator at 4 °C for 2 h and centrifuged at 3500 rpm in a high-speed centrifuge at 4 °C for 10 min. The top serum was aspirated, aliquoted into EP tubes, and frozen at −80 °C for later use . The visThe activities of α-amylase and α-glucosidase in the duodenum of rats in each group and the lipopolysaccharide (LPS), superoxide dismutase (SOD), glutathione peroxidase (GSH-PX) in the serum, GSH, malondialdehyde (MDA) content were determined using kit methods.The duodenum, kidney, and other tissues were fixed in 4% paraformaldehyde for 24 h. Subsequently, the tissue was dehydrated, paraffin-embedded, and sectioned. The tissue sections were then stained with Hematoxylin-eosin (H&E). The morphological changes of the duodenum and kidneys in each group were observed under a microscope and photographed for documentation.The intestinal contents were resuspended in sterile water, and bacterial DNA was extracted using a commercial kit  following the kit instructions, followed by agarose gel electrophoresis, and Nanodrops were used to determine the purity and concentration of the DNA. Primers F341 (5′-CTAYGGGRBGCASCAG-3′) and R806 (5′-GGACTACNNGGGTATCTAAT-3′) were used to amplify the V3–V4 domain of the bacterial 16S rRNA gene. Mix the PCR products from the previous step in a 1:1 mass ratio, as determined using the electrophoresis quantification results. After mixing, use the OMEGA DNA Purification Column for column purification. After electrophoresis on a 1.8% agarose gel at 120 V for 40 min, the target fragment is excised and recovered.Purified libraries were sequenced using the Illumina PE250bp . After quality filtering, the raw reads were clustered into operational taxonomic units (OTUs) using UPARSE. The species sequence alignments were then annotated using the Ribosomal Database Project (RDP). A rare analysis of the Shannon index of the gut microbiota was performed using Mothur. R packages were used to perform Principal Coordinate Analysis (PCoA) and Hierarchical Cluster Analysis .p < 0.05, * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001.All experiments were performed with three biological replicates, and the data are expressed as the mean ± standard deviation. SPSS 26.0 software was used to conduct a one-way analysis of variance and an LSD test to analyze the data differences between different groups. Graphs were drawn using GraphPad Prism 9.5. There is a statistically significant difference when The major constituents of MFT extract are shown in p < 0.0001). However, the blood glucose levels of rats in the T group receiving MFT extract were significantly reduced (p < 0.001) and reached their lowest level in the fourth week. These results indicate that MFT extract can effectively reduce blood glucose levels in GK rats.T2DM is a systemic metabolic and endocrine disease characterized by the dysregulation of glucose and lipid metabolism. This study used the spontaneous diabetic GK rat model to investigate the effect of MFT extract on blood glucose levels in diabetic rats. The results are shown in A-glucosidase and α-amylase are critical enzymes for carbohydrate digestion in the body and are essential for the breakdown and metabolism of carbohydrates. Their activity is closely related to postprandial blood glucose levels in individuals with diabetes. We monitored the levels of α-glucosidase and α-amylase in the duodenum of rats in each group. The results showed that the levels of α-glucosidase and α-amylase in the duodenum of rats in the GK group were higher than those in the NC group. However, in the T group of rats given MFT extract, the levels of these two enzymes were significantly reduced, thereby decreasing the intestinal absorption of sugar.p < 0.05). However, the intervention of MFT significantly reduced the serum MDA level of the hyperglycemic rats (p < 0.0001), even falling below the level of the NC group (p < 0.0001). GSH-Px is an important peroxide-decomposing enzyme that is widely distributed in the body. Serum GSH-Px levels were often elevated in diabetic patients. As can be seen from p < 0.001) compared to the NC group, whereas the MFT intervention significantly decreased the GSH-Px level in the serum of the hyperglycemic rats (p < 0.0001) and reduced it to the level of the NC group (p < 0.05). As shown in p < 0.05) and GSH (p < 0.05) in the serum of hyperglycemic rats. These results show that MFT intervention can significantly improve systemic oxidative stress damage in hyperglycemic rats, thereby reducing the incidence of diabetic complications.In a state of insulin resistance, pancreatic beta cells continue to produce excessive insulin, resulting in oxidative stress. This intense oxidative stress is not only associated with impaired glucose metabolism, insulin resistance, and the development of diabetes, but it also promotes the further development of complications related to diabetes. To investigate the effect of MFT extract on the antioxidant status of diabetic rats, we measured the activities of SOD and GSH-PX and the levels of MDA in the serum. As shown in LPS is a biomolecule that can trigger the body’s inflammatory response, and its role is to release inflammatory mediators, ultimately leading to insulin resistance . We measHigh blood glucose is one of the main symptoms of diabetes, and long-term, uncontrolled high blood glucose can cause damage to the kidneys. To further analyze the effect of dark tea on the kidney structure of diabetic rats, we used H&E staining to stain these two organs. The results are shown in The duodenum is the leading site of digestion and absorption in the gastrointestinal tract . H&E secDiabetes is closely associated with intestinal microorganisms. We sequenced the V3–V4 hypervariable region of 16S rRNA from the gut flora of rats treated for 4 weeks and analyzed the effect of MFT extract on the gut flora of diabetic rats. As shown in Additionally, with the intervention of MFT extract, the GK group and the T group exhibited significant division in PCOA and stump clustering. These findings indicate that the intestinal flora of diabetic rats was significantly improved after the intervention with MFT extract. With the intervention, the diversity and richness of the intestinal microbiota in diabetic rats were partially restored.Bacteroidetes, Firmicutes, Verrucomicrobia, Proteobacteria, and Actinobacteria are commonly dominant bacterial phyla. However, in the diabetic GK group, the abundance of Firmicutes, Bacteroidetes, and Verrucomicrobia was higher than that of the NC group. On the contrary, the abundance of Proteobacteria and Cyanobacteria was significantly higher in the GK group than in the NC group, leading to alterations in the Firmicutes/Bacteroidetes ratio. However, after oral administration of MFT to the GK group, there was no significant increase in the Firmicutes. But the Firmicutes/Bacteroidetes ratio showed a tendency similar to that of the NC group. This suggests that MFT may help restore the balance of the gut microbiota in GK diabetic rats and inhibit the proliferation of cyanobacteria. In addition, significant changes were observed in certain bacterial groups at the family level following diabetes and MFT treatments.At the phylum level A, the guLachnospiraceae, Ruminococcaceae, Erysipelotrichaceae, Bacteroidaceae, Eubacteriaceae, Lactobacillaceae, Porphyromonadaceae, and Bifidobacteriaceae dominate the gut microbiota. Diabetes may lead to a significant increase in the abundance of some bacterial families, including Lachnospiraceae, Erysipelotrichaceae, Eubacteriaceae, Bifidobacteriaceae, and Helicobacteraceae. Meanwhile, some other family levels of bacteria may be reduced in diabetes . However, after the administration of MFT extract, the abundance of these bacterial families returned to levels close to the NC group  value indicates the degree of contribution of the microorganism to differentiating between groups. The larger the LDA value, the more significant the role of the microorganism in the differences between groups. We set an LDA threshold of 3 to identify species with significant differences in microbiota abundance. The results are shown in Alistipes, Peptococcus, Roseburia, Clostridium-XVIII, etc. These bacteria may be involved in the inflammatory response. Flavobacterium, Ruminococcus, Os-cillibacter, Phascolarctobacterium, and other bacterial genera were negatively correlated with IL-1β. These bacteria may play a role in the inflammatory response. Peptococcus, Clostridium-XlVb, Roseburia, and Faecalicoccus are positively associated with MDA levels, and these bacteria may be related to oxidative stress. LPS was negatively correlated with Oscillibacter and Flavobacterium. In contrast, LPS was positively correlated with duodenal villus height and glomerular area, suggesting that LPS may be related to the structure of the duodenum and kidneys. α-Glucosidase is negatively correlated with Lactobacillus and Clostridium-XlVb, and these bacteria may be associated with sugar metabolism. Duodenal villus height is positively correlated with Peptococcus, Alistipes, Desulfovibrio, Roseburia, Clostridium-XlVb, and Faecalicoccus. This may indicate that these bacteria are related to the size of the villi in the small intestine. The levels of MDA and IL-1β were positively correlated with duodenal villus height and glomerular area, indicating a potential relationship between MDA and IL-1β and alterations in the villus and glomerular area of the small intestine. These results suggest that in diabetic mouse models, there is an enrichment of bacterial flora associated with glucose and lipid metabolism, barrier dysfunction, inflammatory response, and oxidative stress.To further explore the potential role of different bacterial genera in diabetes, the Spearman correlation analysis was used to examine the correlation between other bacterial genera between the GK group and the T group, as well as the physiological indicators of mice . The resT2DM is a prevalent chronic metabolic disease characterized by high blood glucose (hyperglycemia) and insulin resistance. Treatment approaches for T2DM currently include lifestyle interventions and pharmacotherapy, with a reduction in carbohydrate and sugar intake playing a pivotal role in mitigating disease progression . In partp < 0.0001), below the level of the NC group (p < 0.0001). In addition, MFT intervention can increase the levels of SOD (p < 0.05) and GSH (p > 0.05) in the serum of hyperglycemic rats . The duodenum is the central part of the body where sugar is digested and absorbed. Hyperglycemia can lead to changes in the structure and function of the duodenal villi. These villi are tiny projections responsible for absorbing and transporting nutrients, and changes in these villi can affect how efficiently sugar and other nutrients are absorbed ,55. AccoHyperglycemia increases the filtration load on the glomerulus, resulting in an increase in the glomerular filtration rate and glomerular swelling . In addiFirmicutes and Clostridia were significantly reduced in patients with diabetes. Furthermore, the reduction in Bacteroidetes and Proteobacteria, along with the increase in the ratio of Betaproteobacteria, was positively correlated with plasma glucose levels [The gut is the most significant internal environment in the human body. It is prosperous in terms of the number and types of microorganisms and is closely linked to human health. Recent studies have shown that an imbalance in gut microbial communities is nearly related to the onset and development of T2DM. Intestinal microorganisms influence the body’s metabolism and immune function by participating in various physiological processes such as glucose metabolism, lipid metabolism, and immune regulation. However, after MFT, the morphology of the kidney tissue was similar to that of the rats in the NC group, and no abnormalities were found. The situation was obviously different from that of rats in the GK group . There ie levels . These cHyperglycemia can lead to reduced diversity of gut microbiota and dysbiosis in rats . HoweverBlautia, Klebsiella, Roseburia, Eubacterium, Helicobacter, and Alistipes while reducing the abundance of genera such as Clostridium-XlVa, Ruminococcus, Flavonifractor, Lactobacillus, and Oscillibacter. However, MFT extract seems to reverse this trend. It should be noted that Akkermansia was considered a beneficial genus of bacteria, whereas Desulfovibri-onaceae is a potentially harmful bacteria. Research shows that diabetes can promote the growth of bacterial groups such as Desulfovibrionaceae, while inhibiting the growth of bacterial groups such as Akkermansia. However, after administering the MFT extract, the abundance of Desulfovibrionaceae and Akkermansia did not seem to return to normal levels, which may mean that the MFT extract has a limited effect on these bacterial genera and cannot regulate the abundance of these two genera  while inhibiting the proliferation of harmful bacteria , helping to reduce the secretion of toxic substances. MFT helps to reduce oxidative stress and inflammation in the body, minimize organ damage, improve insulin resistance, and ultimately achieve the goal of lowering blood sugar.In conclusion, drinking MFT could have a number of beneficial effects on GK diabetic rats. First, MFT reduced the activity of α-glucosidase and α-amylase in the duodenum, inhibited the elongation of duodenal villi, and reduced sugar absorption. At the same time, the intervention of MFT improved the intestinal microbiota disruption caused by hyperglycemia and increased the diversity and stability of microorganisms. It promotes the growth of beneficial bacteria (


Gaming addiction is a compulsive mental health condition that can have severe negative consequences on a person’s life. As online gaming has increased during the COVID-19 pandemic, studies have shown a heightened risk of mental health issues. This study aims to assess the prevalence of severe phobia and addiction to online gaming among Arab adolescents and identify risk factors associated with these disorders.This cross-sectional study was conducted across 11 Arab nations. Participants were recruited using convenience sampling through an online survey distributed on social media platforms in 11 Arab countries. The survey included demographic questions, the Nine-item Internet Gaming Disorder Scale-Short Form (IGDS-SF9) to measure participants’ online gaming addiction, the Social Phobia Scale (SPS), and questions assessing the impact of the COVID-19 pandemic on the prevalence of internet gaming addiction. The data were analyzed using SPSS win statistical package version 26.Out of 2,458 participants, 2,237 were included in the sample due to non-response and missing data. The average age of the participants was 19.9 ± 4.8 years, and the majority were Egyptian and unmarried. 69% of the participants reported playing more than usual since the COVID-19 pandemic, as they were confined to their homes. Higher social phobia scores were associated with being single, male, and Egyptian. Participants from Egypt and those who felt that the pandemic significantly increased their gaming time had higher scores for online gaming addiction. Several major criteria, such as playing hours per day and beginning gaming at an early age, were associated with a higher level of online gaming addiction with social phobia.The study’s findings suggest that there is a high prevalence of internet gaming addiction among Arab adolescents and young adults who play online games. The results also indicate a significant association between social phobia and several sociodemographic factors, which may inform future interventions and treatments for individuals with gaming addiction and social phobia. The AraTo mitigate the impact of the pandemic on mental health, the World Health Organization (WHO) has recommended video games as part of its “Healthy at Home” program . HoweverMoreover, prolonged stress during the pandemic may lead to significant emotional distress, including anxiety and depression , 14. AltThis aim of this study is to assess the impact of the COVID-19 pandemic on the prevalence of IGD and SPS among online gamers in the Arab world as well as dentifying risk factors for IGD negative consequences associated with these disorders.2.2.1.This study aimed to assess the prevalence of internet gaming addiction (IGA) and its association with social phobia among Arab adolescents and young adults aged 15 to 30 who played online games. The cross-sectional study was conducted between September 2021 and March 2022 in 11 Arab countries, namely Egypt, Syria, Palestine, Jordan, Tunisia, Iraq, Morocco, Algeria, Sudan, Libya, and Lebanon.2.2.Convenience sampling, a non-probability sampling method, was used to collect data from responders. Participants were recruited through an online survey using Google Forms, which was placed on Arab online gaming and social forums. The survey was designed to be completed in approximately 10 min and distributed on social media platforms such as Facebook, WhatsApp, Twitter, and Telegram to reach a substantial sample size.The questionnaire included three sections: the first section gathered information on the participants’ demographics, the second section used the Nine-item Internet Gaming Disorder Scale-Short Form (IGDS-SF9) to measure participants’ online gaming addiction, and the third section included the Social Phobia Scale (SPS) and three questions assessing the impact of the COVID-19 pandemic on the prevalence of internet gaming addiction.2.3.The first section of the questionnaire gathered information on the participants’ demographics, including age, sex, marital status, level of education, drug use history, level of smoking, country of residence, and gaming status.The Nine-item Internet Gaming Disorder Scale-Short Form (IGDS-SF9) was used to measure participants’ online gaming addiction. The scale consists of nine items and assesses the symptoms of internet gaming addiction, including salience, mood modification, tolerance, withdrawal, conflict, relapse, problems, deception, and loss of control. Participants rated each item on a five-point Likert scale ranging from 1 (never) to 5 (very often). The scores ranged from 9 to 45, with higher scores indicating a higher degree of addiction .The Social Phobia Scale (SPS) was used to measure social phobia. The SPS is a 20-item self-report measure that assesses the fear and avoidance of social situations. Participants rated each item on a five-point Likert scale ranging from 0  to 4 (extremely). The scores ranged from 0 to 80, with higher scores indicating a higher degree of social phobia (2.4.n = [(Za/2)2. P (1-P)]/d2]. Assuming a prevalence of 50% in the population, a confidence level of 95%, a margin of error of 5%, and a 5% addition for the non-response rate, the recommended sample size was 384 individuals. A total of 2,458 people participated in the survey. After excluding 76 participants who declined to complete the survey and 145 instances of missing data, the final sample size was 2,237.The sample size for this study was determined using the formula described by Swinscow and Cohen2.5.To ensure the survey’s comprehensibility and to avoid errors, a pilot test was conducted with 50 participants. Cronbach’s alpha values indicated strong internal consistency for the IGA and SPS scales .2.6.U test (non-parametric t-test) as appropriate. Comparison between more than two groups for numerical variables was done using ANOVA or non-parametric Kruskal-Wallis as appropriate. The relationship between qualitative data was analyzed using the Chi-square test or Fisher’s exact test as appropriate. Binary logistic regression was used to determine the potential association between the risk of developing a high degree of internet gaming addiction (IGA score >) and social phobia (SPS score >), based on the participants’ baseline characteristics.The data were stored in an Excel sheet and analyzed using SPSS win statistical package version 26. Numerical data were summarized as means and standard deviations or medians and ranges as appropriate. Qualitative data were described as frequencies and percentages. Comparison between two groups for numerical variables was done using either the student t-test or the Mann–Whitney 3.3.1.In this study, 2,237 individuals were included in the analysis, with a majority being male , and a mean age of 19.9 years. Most of the participants were from Egypt (76.7%), single (87.5%), and 2,102 of them were gamers. The study of gamers revealed that 73.8% played for less than 5 hours per day, and the majority (84%) started gaming before the age of 12 years. Approximately 75% of gamers reported watching streamers, with 35% indicating they watched “a lot.” Since the COVID-19 pandemic, 69% of participants reported playing more than usual since they were unable to leave their homes .3.2.p < 0.001). Being single, Egyptian, or male was associated with higher social phobia scores. Education level, age of first gameplay, and gaming hours also demonstrated significant differences (p < 0.001). Additionally, the study found a significant difference in self-reported changes in gaming behavior during the pandemic (p < 0.004), with those who reported “significantly increased” playing having a slightly higher SPS score than those whose behavior did not change , changes in play behavior during the pandemic (p < 0.001), duration of gaming per day (p < 0.001), and following streamers (p < 0.001). Specifically, participants who played games for five to 10 hours per day had scores almost 5 points higher on the IGD scale compared to those who played for half an hour. Moreover, gamers who watched “a lot” of streamers had scores two points higher on the IGD scale than those who did not watch. Additionally, Egyptian participants and those who reported a significant increase in gaming time due to the COVID-19 epidemic had the highest IGD scores . However, the study did observe significant differences in IGD scores based on nationality  and Social Phobia Scale (SPS) scores among gamers. The findings suggest that sociodemographic factors play an important role in predicting levels of IGD and SPS, indicating the complex and multifaceted nature of these conditions.In terms of IGD, the study found that daily playing hours and following streamers were significant predictors of high levels of internet addiction. These findings suggest that certain behaviors, such as excessive gaming or following online influencers, may lead to increased risk of developing IGD. It is important to note that excessive gaming has been associated with a range of negative outcomes, including poor academic performance, social isolation, and decreased physical activity. As such, clinicians should be aware of the potential risks associated with excessive gaming and work with patients to establish healthy gaming habits.The study also found that sociodemographic factors, such as nationality and the impact of COVID-19 on gaming attitudes, were significant predictors of IGD. Specifically, individuals from Egypt and those who reported a significant increase in gaming time due to the pandemic had the highest IGD scores. The COVID-19 pandemic has led to significant changes in daily routines and social interactions, and as such, it is not surprising that there has been an increase in gaming activity. However, clinicians should be aware of the potential negative impact of increased gaming on mental health and wellbeing.In terms of SPS, the study found that gender, nationality, and daily playing hours were significant predictors of high levels of social phobia. These findings suggest that there may be a complex interplay between gaming behavior, social anxiety, and sociodemographic factors. Social phobia can be a debilitating condition that can significantly impact an individual’s quality of life. As such, clinicians should be aware of the potential risk factors associated with social phobia, such as excessive gaming and certain sociodemographic factors.Overall, the present study highlights the complex and multifaceted nature of IGD and social phobia. The findings suggest that sociodemographic factors play an important role in predicting levels of these conditions, and as such, clinicians should take into account the individual’s sociodemographic background when assessing and treating these conditions. Additionally, it is important for clinicians to be aware of the potential negative impact of excessive gaming and work with patients to establish healthy gaming habits.The findings of this study have important theoretical implications for the field of psychiatry. The diagnostic criteria for Internet Gaming Disorder (IGD) according to the DSM-5 are based on the presence of symptoms such as preoccupation, withdrawal, tolerance, loss of control, giving up other activities, continuation, deception, escape, and negative consequences . Our stuAlthough gaming can be a healthy way to relieve stress in the moment, it should not be used as a long-term solution , 26. DevWe found that increasing playing hours was associated with increasing social phobia scores and aggravating the severity of social phobia . MoreoveThis study’s practical implications highlight the need to monitor the increasing prevalence of IGD since the beginning of COVID-19, as it can cause further damage to the psychosocial situation, which is already damaged by the pandemic , 13, 30.4.1.There are several strengths and limitations to this study. It is the first study to investigate the impact of the COVID-19 pandemic on gamers in the Arab population, and the sampling method employed ensured that a comparatively representative sample was collected. Additionally, all related factors were assessed using validated questionnaires. However, the limitations of the study include the inherent nature of the data collection tool, which depends on the participant’s ability to recall and complete the self-administered questionnaire. Despite using a validated Arabic version of the questionnaire, there is still a risk of recall bias. Furthermore, the response rate of males who completed the survey was much higher than the response rate of females, indicating that the number of males who are addicted to online gaming may be greater than the number of females. Finally, the study did not examine other variables that may affect IGD and social phobia, such as depression, anxiety, and stress, which may influence the prevalence of IGD (39).In conclusion, our study highlights the increasing prevalence of IGD during the COVID-19 pandemic in the Arab population and underscores the importance of monitoring its impact on mental health. The findings of this study may have implications for clinicians and policymakers working to mitigate the negative impact of the pandemic on mental health.4.2.The current study highlights the importance of addressing the potential negative impact of COVID-19 on online gaming behavior and the development of IGD among the general population. To mitigate this impact, it is recommended to provide mental health support and counseling to individuals who are at risk of developing IGD due to prolonged isolation and boredom. This can be achieved through various means, such as online counseling, support groups, and educational campaigns to raise awareness about the risks associated with excessive gaming. Moreover, parents and caregivers should monitor and regulate their children’s online gaming activities, especially during prolonged isolation periods, to prevent the development of IGD.5.The present study adds to the growing body of literature on the impact of COVID-19 on mental health and online gaming behavior. Our findings suggest that the COVID-19 pandemic has led to an increase in online gaming activities, and this increase is associated with an increased risk of developing IGD and social phobia symptoms. Therefore, it is crucial to address this issue and provide adequate support to individuals who are at risk of developing IGD. Future studies should investigate the long-term impact of the pandemic on gaming behavior and the effectiveness of different interventions in mitigating the risk of IGD. Overall, this study highlights the importance of recognizing the potential negative impact of COVID-19 on mental health and emphasizes the need for proactive measures to address this issue.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee, Faculty of Medicine, Alexandria University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.AS and NS contributed equally to the conceptualization, methodology, formal analysis, writing-original draft, review, editing, and data collection. ME, OA, AM, SaS, AR, MM, AZ, and ShS contributed to writing-review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1071764/full#supplementary-materialThe Supplementary material for this article can be found online at: Click here for additional data file.


Gait and dynamic balance are two main goals in neurorehabilitation that mHealth systems could address. To analyze the impact of using mHealth systems on gait and dynamic balance outcomes in subjects with neurological disorders. Randomized controlled trials (RCT) published in PubMed, Web of Science, Scopus, and PEDro databases were searched up to April 2023. Studies including adults with neurological disorders, analyzing the effectiveness of mHealth systems on gait and dynamic balance compared with conventional therapy and/or not intervention, were included. The PEDro scale and the Cochrane Collaboration’s 2.0 tool were used for the methodological quality and risk of bias assessment. The Review Manager 5.4 software was used to obtain meta-analyses. 13 RCT were included in the systematic review and 11 in the meta-analyses, involving 528 subjects. A total of 21 mobile applications were identified for gait and balance training, and to enhance physical activity behaviors. There were significant differences in gait parameters, speed by 0.10 s/m =0.07,0.13;p<0.001), cadence by 8.01 steps/min , affected step length by 8.89 cm , non-affected step length by 8.08 cm , and in dynamic balance, Timed Up and Go by -7.15 s , and mobility subscale of Posture Assessment Scale for Stroke by 1.71 points . Our findings suggested the use of mHealth systems for improving gait in subjects with neurological disorders, but controversial results on dynamics balance recovery were obtained. However, the quality of evidence is insufficient to strongly recommend them, so further research is needed.The online version contains supplementary material available at 10.1007/s10916-023-01963-y. Motor impairments represent a leading cause for the limitation of functional activities and disability in subjects with neurological disorders –4. In paapps” [In that way, mHealth, mobile device-based healthcare , involveapps” . In the apps” , detect apps” –18, tracapps” –21, treaapps” –25, amonapps”  reviewedapps”  suggesteapps” .Concerning the current scientific literature, some reviews are analyzing the potential use of apps in subjects with neurological disorders , 29–31. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews of randomized controlled trials  was follThe inclusion criteria were defined according to the PICOS framework  (PopulatThe exclusion criteria were: i) Systems based on videoconferencing platforms , virtual reality glasses, or websites, ii) Studies related to cost-effectiveness and usability of mHealth systems.Two authors (M.M.L and J.A.M.M.) retrieved and screened the studies by the titles and abstracts independently, removing duplicate articles and identifying potentially relevant articles according to the pre-established criteria. Subsequently, full texts of the retrieved records were downloaded and analyzed for final inclusion in the systematic review. An additional reviewer (D.L.A.) was considered for consensus when needed. Some information was extracted to detail the studies characteristics: authors, year of publication, country, characteristics of the participants , and the intervention characteristics . In addition, each mHealth system employed as an intervention was identified, specifying its purpose and main features.The PEDro scale  was used2 statistic  [Several meta-analyses were performed with The Review Manager 5.4 software to summarize the comparisons of the use of mHealth systems  versus a control group. The studies were classified based on the measuring instrument to assess gait or dynamic balance. The effect measure was the mean difference (MD), reported along with their 95% confidence intervals (CI). The instructions of the Cochrane Handbook for Systematic Reviews of Interventions for obtaining standard deviation from confidence intervals and change-from-baseline standard deviation were employed when needed. After checking for homogeneity with the chi-square test and the Igeneity) , a fixedA total of 594 articles were retrieved during the initial search and 13 RCT were included in the systematic review and 11 in the meta-analysis  ZyMiMetronome App  and GotRRegarding the control interventions, conventional physical and occupational therapies were included, such as specific gait and balance training, home-based PA programs, and active behavior recommendations. None included study compared the mHealth group with no intervention or waitlist.Concerning the measuring instruments, several motion analysis systems were used , implying a moderate quality of the articles included in this review. Ten studies , 48–50 o2=49% and I2=47%, respectively), while PASS (mobility) presented substantial heterogeneity (I2=75%). We found significant differences in temporal gait parameters, speed  , are shown in Figs. The present systematic review and meta-analysis were performed to analyze the impact of the use of mHealth systems on gait and dynamic balance outcomes in subjects with neurological disorders. To our knowledge, this is the first systematic review and meta-analysis analyzing the use of mHealth systems for improving gait and balance in neurorehabilitation, providing the first evidence-based findings supporting its clinical application. As a result, 13 studies were reviewed and 11 were examined in this meta-analysis. Therefore, and according to our results, there was some evidence of the use of mHealth systems which can impact positively on gait and balance outcomes in subjects with neurological disorders.There are some reviews , 29–31 oGait recovery is the key goal in neurorehabilitation , and newGait impairments are closely related to decreased motor trunk control and postural balance. The involvement of similar brain centers and neural pathways could explain this relationship for integrating neuromuscular and sensory signals . TherefoThe improvements in gait and balance using mHealth could be enhanced by several strategies, such as auditory feedback, visual feedback, or incentive-based approaches. Concerning the ZyMi Metronome App  and GotRConcerning the baseline characteristics of the participants, eight studies , 42–47 iAiming to complete our insight about the analyzed topic, the minimum clinically important differences (MCID) were considered evaluating the clinical relevance of our results. According to the speed gait parameter, it was estimated that the MCID in subjects with stroke is 0.16 m/s  for subaFinally, regarding the role of the neurorehabilitation professionals, they have an active role on guiding patients on the effective use of mHealth tools and ensuring that the interventions are safe and well-conducted . By doinAlthough this review and meta-analysis provide an evidence-based overview of the use of mHealth in subjects with neurological disorders, some limitations should be remarked. Our results should be taken with caution because of the heterogeneity of neurological disorders and the variety of assessment tools used. Likewise, several intervention protocols were used in experimental and control groups. These facts could influence the comprehension and interpretation of findings. Thus, protocols standardization is needed for future studies. Another common limitation, along with the studies included, was the lack of assessor blinding for group allocation, resulting in a high risk of bias in this domain. As well as stated previously, the sample size involved in the studies was limited, making the generalization of the obtained results difficult. Finally, it is necessary to perform trials with long-term follow-up to assess whether the mHealth effects are maintained in neurorehabilitation.Nevertheless, despite of those limitations, this systematic review and meta-analysis provides the first evidence-based findings about the use of mHealth systems for gait and balance recovery in neurorehabilitation. Therefore, this manuscript could be used as the base of future clinical studies and discover the necessity of further research that will allow the stratified analysis of the results.In conclusion, our study showed some evidence that mHealth systems can be a useful tool for subjects with neurological disorders to improve gait, and controversial results were found on dynamic balance recovery. In that way, the use of smartphones and tablets for performing clinical or home-based rehabilitation is still in its first stages, and the quality of evidence is not enough to merge the effectiveness and strongly recommend the use of these systems in neurorehabilitation. Thus, it is needed to conduct clinical trials with higher methodological quality, involving a larger sample size, homogenous studied groups, standardized protocols, and outcome measures to recommend mHealth as an alternative or reliable complement to clinical neurorehabilitation.Supplementary file1 (PDF 79 KB)Supplementary file2 (PDF 75 KB)Supplementary file3 (PDF 54.1 KB)Supplementary file4 (PDF 357 KB)Below is the link to the electronic supplementary material.


Couvelaire uterus (CU) is a rare complication in the life-threatening placental abruption (PA) that consists of a state of blood infiltration of the uterine myometrium and serosa. The incidence is around 1% and the treatment of choice is obstetric hysterectomy, however, in some cases, close monitoring and timely decision-making can prevent hysterectomy. Herein, we present a rare and serious case of CU with uterus preservation in a young multiparous with a high-risk pregnancy. Couvelaire uterus (CU) or uteroplacental apoplexy was first described in 1911 by Dr. Alexandre Couvelaire. CU is one of the rare nonfetal complications of severe abruption and is associated with placental abruption (PA); the incidence and maternal mortality are around 1% and 5% .CU manifests when a ruptured decidual spiral artery causes hemorrhage and bleeds into the decidua basalis and myometrium, followed by blood penetration into the uterine serous layer causing a blue-violet ecchymosis . A hysteOur case report describes a 28-year-old female patient  with monochorionic diamniotic twin Gestation, in the 30th week (30 + 6) who presented to Fatemieh Hospital (Hamadan) Emergency with hypertension (blood pressure 140/90) and was hospitalized with a diagnosis of preeclampsia, other vital signs of the patient were normal at the time of admission to the emergency room and the nonstress test (NST) of the twins was reactive. The patient did not report any underlying disease other than chronic hypertension for which no drug was used. She did not mention her history of smoking, alcohol, or drugs.https://uupload.ir/view/vid-20220422-wa0026_q6n.mp4/)).The morning after admission, the patient suddenly experienced sweating, anxiety, pain, and tetanic uterine contractions without vaginal bleeding. On examination, the patient had a heart rate 100 per minute, blood pressure 90/70, and respiratory rate 20 per minute. On suspicion of PA, the patient was asked for an obstetric ultrasound, and according to the diagnosis of PA and fetal bradycardia (90–100 beat/min), the patient was immediately transferred to the operating room for cesarean section (CS). The abdominal layers were opened with a Pfannenstiel incision, Kerr incision was made on the uterus. The first infant with an Apgar score of zero and the second infant with an Apgar score of 3–5, expired despite advanced resuscitation, and the placenta was more than 90% detached. After the placenta was removed, large clots were seen behind it, all of which were removed. The patient's uterus was soft, pasty, blue, and Couvelaire. Diffuse hematomas were also seen with spreading in the pampiniform plexus, broad ligament, and posterior uterus. , and tranexamic acid (1 gr IV) were prescribed to the patient. After controlling the bleeding, the uterus was returned to the abdomen and a drain was inserted into the patient. According to the goal of fluid and electrolyte correction and due to the continuation of anuria (Creatinine = 2.8), a central venous line was inserted for the patient and a high-dose diuretic (Lasix), labetalol, serum nitro-glycerine, and hydralazine were prescribed to control the patient's accelerated hypertension, the patient's echocardiography was normal; therefore, the patient was transferred to the intensive care unit (ICU) and hospitalized in the ICU for two weeks.Due to continuous bleeding and anuria, transfusion of 3 packed red blood cells, 3 fresh frozen plasma (FFP), and 3 cryoprecipitate (Cry) were started for the patient. Also, to maintain the uterus and control its atony, compressive sutures were applied and oxytocin (80 U/L IV), misoprostol , the patient received 11 pack cells, 35 platelets, 20 FFP, 8 Cry, IVIG [After two weeks, with a gradual decrease in creatinine and improvement of tests, hemodialysis and antibiotics were stopped and the patient was transferred to the ward, and after a week, the patient was discharged from the hospital with Cr = 1/3.We present an unusual case of a CU that despite unstable homeostasis and severe bleeding in the myometrium and subserosal, without the need for hysterectomy, the patient was successfully treated. CU was associated in different reports with PA, placenta previa, amniotic fluid embolism, and preeclampsia . In our The complete or partial separation of site implanted placental before delivery is defined as PA. It happens in 0.8 to 1% of births . The etiThere is currently no established diagnostic clinical criterion for PA. The most common reason for a clinical diagnosis of abruption, according to the New Jersey-Placental Abruption study , was retThe management of PA with CU depends on the patient's status. There have been cases of successful conservative management of CU . The uteThe surgical management of the Couvelaire uterus is by hysterotomy or hysterectomy. Hysterectomy can be considered when there is profound myometrial damage or uncontrolled bleeding despite conservative measures by hemostatic brace sutures or uterotonics . In our The unpredictability of this clinical condition and the importance of close vigilance and timely decision-making can prevent adverse maternal and fetal outcomes.