The Therapeutic Target of IBD and the Mechanism of Dipyridamole in Treating IBD Explored by Geo Gene Chips, Network Pharmacology, and Molecular Docking.

BACKGROUND AND AIMS: Inflammatory Bowel Disease (IBD) is a refractory disease with repeated attacks, and there is no accurate treatment target at present. Dipyridamole, a phosphodiesterase (PDE) inhibitor, has been proven to be an effective treatment for IBD in a pilot study. This study explored the therapeutic target of IBD and the pharmacological mechanism of dipyridamole for the treatment of IBD. MATERIALS AND METHODS: The candidate targets of dipyridamole were obtained by searching the pharmMapper online server and Swiss Target Prediction Database. The IBD-related targets were selected from four GEO chips and three databases, including Genecards, DisGeNET, and TTD database. A protein-protein interaction (PPI) network was constructed, and the core targets were identified according to the topological structure. KEGG and GO enrichment analysis and BioGPS location were performed. Finally, molecular docking was used to verify dipyridamole and the hub targets. RESULTS: We obtained 112 up-regulated genes and 157 down-regulated genes, as well as 105 composite targets of Dipyridamole-IBD. Through the PPI network analysis, we obtained the 7 hub targets, including SRC, EGFR, MAPK1, MAPK14, MAPK8, PTPN11, and LCK. The BioGPS showed that these genes were highly expressed in the immune system, digestive system, and endocrine system. In addition, the 7 hub targets had good intermolecular interactions with dipyridamole. The therapeutic effect of dipyridamole on IBD may involve immune system activation and regulation of inflammatory reactions involved in the regulation of extracellular matrix, perinuclear region of cytoplasm, protein kinase binding, and positive regulation of programmed cell death through cancer pathway (proteoglycans in cancer), lipid metabolism, Ras signaling pathway, MAPK signaling pathway, PI3K-AKT signaling pathway, Th17 cell differentiation, and other cellular and innate immune signaling pathways. CONCLUSION: This study predicted the therapeutic target of IBD and the molecular mechanism of dipyridamole in treating IBD, providing a new direction for the treatment of IBD and a theoretical basis for further research.

Initial dosage optimisation of cyclosporine in Chinese paediatric patients undergoing allogeneic haematopoietic stem cell transplantation based on population pharmacokinetics: a retrospective study.

OBJECTIVE: Improved understanding of cyclosporine A (CsA) pharmacokinetics in children undergoing allogeneic haematopoietic stem cell transplantation (allo-HSCT) is crucial for effective prevention of acute graft-versus-host disease and medication safety. The aim of this study was to establish a population pharmacokinetic (Pop-PK) model that could be used for individualised therapy to paediatric patients undergoing allo-HSCT in China. DESIGN, SETTING AND PARTICIPANTS: A retrospective analysis of 251 paediatric HSCT patients who received CsA intravenously in the early post transplantation period at Women and Children's Medical Center in Guangzhou was conducted. ANALYSIS MEASURES: The model building dataset from 176 children was used to develop and analyse the CsA Pop-Pk model by using the nonlinear mixed effect model method. The basic information was collected by the electronic medical record system. Genotype was analysed by matrix-assisted time-of-flight mass spectrometry. The stability and predictability of the final model were verified internally, and a validation dataset of 75 children was used for external validation. Monte Carlo simulation is used to adjust and optimise the initial dose of CsA in paediatric allo-HSCT patients. RESULTS: The typical values for clearance (CL) and volume of distribution ([Formula: see text]) were 14.47 L/hour and 2033.53 L, respectively. The body weight and haematocrit were identified as significant variables for V, while only body weight had an impact on CL. The simulation based on the final model suggests that paediatrics with HSCT required an appropriate intravenous dose of 5 mg/kg/day to reach the therapeutic trough concentration. CONCLUSIONS: The CsA Pop-PK model established in this study can quantitatively describe the factors influencing pharmacokinetic parameters and precisely predict the intrinsic exposure to CsA in children. In addition, our dosage simulation results can provide evidence for the personalised medications TRIAL REGISTRATION NUMBER: ChiCTR2000040561.

Case report: Sideroblastic anemia with B-cell immunodeficiency, periodic fevers, and developmental delay: Three cases and a literature review.

Sideroblastic anemia with B-cell immunodeficiency, periodic fevers, and developmental delay (SIFD) is a serious autosomal recessive syndrome caused by biallelic mutations in cytosine-cytosine-adenosine tRNA nucleotidyltransferase 1 (TRNT1). The main clinical features of SIFD are periodic fevers, developmental delay, sideroblastic or microcytic anemia, and immunodeficiency. Herein, we report three cases of SIFD with compound heterozygous variants of TRNT1. Patients 1 and 2 were siblings; they presented with periodic fevers, arthritis, low immunoglobulin A, bilateral cataracts, anemia, and neurodevelopmental and developmental delay. Patient 3 had severed clinical features with recurrent fever and infections. She was treated with infliximab and symptomatic treatments but without therapeutic effect. She received a stem cell transplantation of umbilical cord blood but died of posttransplant infection and posttransplant graft-vs.-host disease 17 days after transplantation. Finally, a literature review revealed that TRNT1 variants differed among SIFD patients. Our cases and literature review further expand existing knowledge on the phenotype and TRNT1 variations of SIFD and suggest that the early genomic diagnosis of TRNT1 is valuable to promptly assess bone marrow transplantation and tumor necrosis factor inhibitor treatments, which might be effective for the immunodeficiency and inflammation caused by SIFD.

How to promote the social-emotional competence of rural left-behind children? An empirical study based on propensity score matching.

Social-emotional competence (SEC) played an important role in promoting the physical and mental development of children, but there exist huge gaps in SEC development between rural left-behind children. This study used propensity score matching (PSM) to investigate 578 rural children about the effects of being left behind as well as individual characteristics and teacher support on their development of SEC. The results showed that being left behind had significant negative effects on the SEC of rural children. The development of SEC varies among left-behind children of different genders and length of left-behind duration. Teacher support had a significant moderating effect on the influence path of SEC, which could effectively mitigate the negative effects of left-behind children. Therefore, this study played an implicative role in studying the development of left-behind children's SEC. The government and society should provide adequate cultural capital by completing the support system for compensating the lack of cultural capital. Schools and teachers should pay more attention to the development of left-behind children's SEC through curriculum development and performance evaluation to create a positive atmosphere. Parents should promote SEC development for left-behind children by improving their communication and family parenting styles.

Tacrolimus in the treatment of childhood nephrotic syndrome: Machine learning detects novel biomarkers and predicts efficacy.

STUDY OBJECTIVE: The pharmacokinetics and pharmacodynamics of tacrolimus (TAC) vary greatly among individuals, hindering its precise utilization. Moreover, effective models for the early prediction of TAC efficacy in patients with nephrotic syndrome (NS) are lacking. We aimed to identify key factors affecting TAC efficacy and develop efficacy prediction models for childhood NS using machine learning algorithms. DESIGN: This was an observational cohort study of patients with pediatric refractory NS. SETTING: Guangzhou Women and Children's Medical Center between June 2013 and December 2018. PATIENTS: 203 patients with pediatric refractory NS were used for model generation and 35 patients were used for model validation. INTERVENTION: All patients regularly received double immunosuppressive therapy comprising TAC and low-dose prednisone or methylprednisolone. In this observational cohort study of 203 pediatric patients with refractory NS, clinical and genetic variables, including single-nucleotide polymorphism (SNPs), were identified. TAC efficacy was evaluated 3 months after administration according to two different evaluation criteria: response or non-response (Group 1) and complete remission, partial remission, or non-remission (Group 2). MEASUREMENTS: Logistic regression, extremely random trees, gradient boosting decision trees, random forest, and extreme gradient boosting algorithms were used to develop and validate the models. Prediction models were validated among a cohort of 35 patients with NS. MAIN RESULTS: The random forest models performed best in both groups, and the area under the receiver operating characteristics curve of these two models was 80.7% (Group 1) and 80.3% (Group 2). These prediction models included urine erythrocyte count before administration, steroid types, and eight SNPs (ITGB4 rs2290460, TRPC6 rs3824934, CTGF rs9399005, IL13 rs20541, NFKBIA rs8904, NFKBIA rs8016947, MAP3K11 rs7946115, and SMARCAL1 rs11886806). CONCLUSIONS: Two pre-administration models with good predictive performance for TAC response of patients with NS were developed and validated using machine learning algorithms. These accurate models could assist clinicians in predicting TAC efficacy in pediatric patients with NS before utilization to avoid treatment failure or adverse effects.

Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction.

Background and Aim: Tacrolimus (TAC) is a first-line immunosuppressant for the treatment of refractory nephrotic syndrome (RNS), but the pharmacokinetics of TAC varies widely among individuals, and there is still no accurate model to predict the pharmacokinetics of TAC in RNS. Therefore, this study aimed to combine population pharmacokinetic (PPK) model and machine learning algorithms to develop a simple and accurate prediction model for TAC. Methods: 139 children with RNS from August 2013 to December 2018 were included, and blood samples of TAC trough and partial peak concentrations were collected. The blood concentration of TAC was determined by enzyme immunoassay; CYP3A5 was genotyped by polymerase chain reaction-restriction fragment length polymorphism method; MYH9, LAMB2, ACTN4 and other genotypes were determined by MALDI-TOF MS method; PPK model was established by nonlinear mixed-effects method. Based on this, six machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Extra-Trees, Gradient Boosting Decision Tree (GBDT), Adaptive boosting (AdaBoost) and Lasso, were used to establish the machine learning model of TAC clearance. Results: A one-compartment model of first-order absorption and elimination adequately described the pharmacokinetics of TAC. Age, co-administration of Wuzhi capsules, CYP3A5 *3/*3 genotype and CTLA4 rs4553808 genotype were significantly affecting the clearance of TAC. Among the six machine learning models, the Lasso algorithm model performed the best (R2 = 0.42). Conclusion: For the first time, a clearance prediction model of TAC in pediatric patients with RNS was established using PPK combined with machine learning, by which the individual clearance of TAC can be predicted more accurately, and the initial dose of administration can be optimized to achieve the goal of individualized treatment.

Prediction of Tacrolimus Dose/Weight-Adjusted Trough Concentration in Pediatric Refractory Nephrotic Syndrome: A Machine Learning Approach.

PURPOSE: Tacrolimus (TAC) is a first-line immunosuppressant for patients with refractory nephrotic syndrome (NS). However, there is a high inter-patient variability of TAC pharmacokinetics, thus therapeutic drug monitoring (TDM) is required. In this study, we aimed to employ machine learning algorithms to investigate the impact of clinical and genetic variables on the TAC dose/weight-adjusted trough concentration (C0/D) in Chinese children with refractory NS, and then develop and validate the TAC C0/D prediction models. PATIENTS AND METHODS: The association of 82 clinical variables and 244 single nucleotide polymorphisms (SNPs) with TAC C0/D in the third month since TAC treatment was examined in 171 children with refractory NS. Extremely randomized trees (ET), gradient boosting decision tree (GBDT), random forest (RF), extreme gradient boosting (XGBoost), and Lasso regression were carried out to establish and validate prediction models, respectively. The best prediction models were validated on a cohort of 30 refractory NS patients. RESULTS: GBDT algorithm performed best in the whole group (R2=0.444, MSE=591.032, MAE=20.782, MedAE=18.980) and CYP3A5 nonexpresser group (R2=0.264, MSE=477.948, MAE=18.119, MedAE=18.771), while ET algorithm performed best in the CYP3A5 expresser group (R2=0.380, MSE=1839.459, MAE=31.257, MedAE=19.399). These prediction models included 3 clinical variables (ALB0, AGE0, and gender) and 10 SNPs (ACTN4 rs3745859, ACTN4 rs56113315, ACTN4 rs62121818, CTLA4 rs4553808, CYP3A5 rs776746, IL2RA rs12722489, INF2 rs1128880, MAP3K11 rs7946115, MYH9 rs2239781, and MYH9 rs4821478). CONCLUSION: The association between the clinical and genetic variables and TAC C0/D was described, and three TAC C0/D prediction models integrating clinical and genetic variables were developed and validated using machine learning, which may support individualized TAC dosing.

Tofacitinib effectiveness in Blau syndrome: a case series of Chinese paediatric patients.

OBJECTIVE: Blau syndrome (BS), a rare, autosomal-dominant autoinflammatory syndrome, is characterized by a clinical triad of granulomatous recurrent uveitis, dermatitis, and symmetric arthritis and associated with mutations of the nucleotide-binding oligomerization domain containing 2 (NOD2) gene. Aim of this study was to assess the efficacy of tofacitinib in Chinese paediatric patients with BS. METHODS: Tofacitinib was regularly administered to three BS patients (Patient 1, Patient 2, and Patient 3) at different dosages: 1.7 mg/day (0.11 mg/kg), 2.5 mg/day (0.12 mg/kg), and 2.5 mg/day (0.33 mg/kg). The clinical manifestations of the patients, magnetic resonance imaging results, serological diagnoses, therapeutic measures and outcomes of treatments are described in this report. RESULTS: The clinical characteristics and serological diagnoses of all BS patients were greatly improved after the administration of tofacitinib treatment. All patients reached clinical remission of polyarthritis and improvements in the erythrocyte sedimentation rate (ESR) and levels of C-reactive protein (CRP) and inflammatory cytokines. CONCLUSION: Tofacitinib, a Janus kinase (JAK) inhibitor, is a promising agent for BS patients who have unsatisfactory responses to corticosteroids, traditional disease-modifying antirheumatic drugs, and biological agents.

Early Prediction of Tacrolimus-Induced Tubular Toxicity in Pediatric Refractory Nephrotic Syndrome Using Machine Learning.

Background and Aims: Tacrolimus(TAC)-induced nephrotoxicity, which has a large individual variation, may lead to treatment failure or even the end-stage renal disease. However, there is still a lack of effective models for the early prediction of TAC-induced nephrotoxicity, especially in nephrotic syndrome(NS). We aimed to develop and validate a predictive model of TAC-induced tubular toxicity in children with NS using machine learning based on comprehensive clinical and genetic variables. Materials and Methods: A retrospective cohort of 218 children with NS admitted between June 2013 and December 2018 was used to establish the models, and 11 children were prospectively enrolled for external validation. We screened 47 clinical features and 244 genetic variables. The changes in urine N- acetyl- ß-D- glucosaminidase(NAG) levels before and after administration was used as an indicator of renal tubular toxicity. Results: Five machine learning algorithms, including extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), extremely random trees (ET), random forest (RF), and logistic regression (LR) were used for model generation and validation. Four genetic variables, including TRPC6 rs3824934_GG, HSD11B1 rs846910_AG, MAP2K6 rs17823202_GG, and SCARB2 rs6823680_CC were incorporated into the final model. The XGBoost model has the best performance: sensitivity 75%, specificity 77.8%, accuracy 77.3%, and AUC 78.9%. Conclusion: A pre-administration model with good performance for predicting TAC-induced nephrotoxicity in NS was developed and validated using machine learning based on genetic factors. Physicians can estimate the possibility of nephrotoxicity in NS patients using this simple and accurate model to optimize treatment regimen before administration or to intervene in time after administration to avoid kidney damage.

Simultaneous determination of erythrocyte methotrexate polyglutamates by a novel and simple HPLC-MS/MS method with stable isotope-labeled internal standards.

Low-dose methotrexate is the first-line therapy for juvenile idiopathic arthritis. In vivo, methotrexate is converted into a series of methotrexate polyglutamates whose intracellular levels contribute significantly to its efficacy and toxicity. In this study, a novel high-performance liquid chromatography-tandem mass spectrometry method was developed and validated to simultaneously determine erythrocyte methotrexate polyglutamates using stable isotope-labeled internal standards. Erythrocyte samples were precipitated by perchloric acid and then determined on an XBridge BEH C18 column with an XP vanguard precolumn in 12 min. The mobile phase consisted of 10 nM ammonium acetate (pH 10) and methanol under gradient elution. The detection was carried out in multiple reaction monitoring mode via an electrospray ionization source in positive ionization mode. The calibration curve for each metabolite was linear from 2.0 to 500.0 nmol/L (r2  > 0.99). The intraday and interday accuracies were between 93.0 and 107.0%, and the corresponding precisions were between 0.8 and 5.2%. The relative recovery ranged from 82.7 to 105.1%, and the relative matrix effect varied from 96.5 to 104.4%. The erythrocyte metabolites were stable for 30 days at -80°C. This simple and accurate method is applicable to routine monitoring of the concentration of erythrocyte methotrexate polyglutamates in patients to achieve individualized treatment.

Marked decrease of tacrolimus blood concentration caused by compound Chinese herbal granules in a patient with refractory nephrotic syndrome.

WHAT IS KNOWN AND OBJECTIVE: The blood concentration of tacrolimus can be affected by co-administrated drugs. The objective is to draw more attention to herb-drug interactions in China, where herbal medicines are commonly used. CASE DESCRIPTION: The blood concentration of tacrolimus in a girl with refractory nephrotic syndrome decreased nearly a half despite no change in dose. Nebulizer therapy, cyclophosphamide and a compound Chinese herbal medicine were the only additional treatments than usual. WHAT IS NEW AND CONCLUSION: The most possible cause of the decrease in tacrolimus concentration was the administration of Radix Astragali among compound Chinese herbal medicine granules.

Early Prediction of Clinical Response to Etanercept Treatment in Juvenile Idiopathic Arthritis Using Machine Learning.

BACKGROUND AND AIMS: At present, there is a lack of simple and reliable model for early prediction of the efficacy of etanercept in the treatment of juvenile idiopathic arthritis (JIA). This study aimed to generate and validate prediction models of etanercept efficacy in patients with JIA before administration using machine learning algorithms based on electronic medical record (EMR). MATERIALS AND METHODS: EMR data of 87 JIA patients treated with etanercept between January 2011 and December 2018 were collected retrospectively. The response of etanercept was evaluated by using DAS44/ESR-3 simplified standard. The stepwise forward and backward method based on information gain was applied to select features. Five machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extremely Random Trees (ET) and Logistic Regression (LR) were used for model generation and validation with fifty-fold stratified cross-validation. EMR data of additional 14 patients were collected for external validation of the model. RESULTS: Tender joint count (TJC), Time interval, Lymphocyte percentage (LYM), and Weight were screened out and included in the final model. The model generated by the XGBoost algorithm based on the above 4 features had the best predictive performance: sensitivity 75%, specificity 66.67%, accuracy 72.22%, AUC 79.17%, respectively. CONCLUSION: A pre-administration model with good prediction performance for etanercept response in JIA was developed using advanced machine learning algorithms. Clinicians and pharmacists can use this simple and accurate model to predict etanercept response of JIA early and avoid treatment failure or adverse effects.

Kidney podocyte-associated gene polymorphisms affect tacrolimus concentration in pediatric patients with refractory nephrotic syndrome.

Few studies have investigated the correlation between pharmacogenomics and tacrolimus pharmacokinetics in patients with nephrotic syndrome (NS). This study evaluated the influences of genetic polymorphisms of metabolic enzymes, transporters, and podocyte-associated proteins on tacrolimus concentration in Chinese pediatric patients with refractory NS. A total of 167 pediatric patients with refractory NS were included from July 2013 to December 2017. Age of onset was restricted to <14 years of age. Dose-adjusted tacrolimus trough concentration (C0/D) on the third month was calculated, and 20 single-nucleotide polymorphisms in sixteen genes were genotyped. Age was correlated with tacrolimus C0/D (p = 0.006, r = 0.213). Tacrolimus C0/D was higher in CYP3A5 nonexpressers than in CYP3A5 expressers (p = 0.003). ACTN4 rs62121818, MYH9 rs2239781, CYP3A5*3, and age explained 20.5% interindividual variability of tacrolimus concentration in the total cohort. In CYP3A5 nonexpressers, ACTN4 rs62121818 and MYH9 rs2239781 together explained 14.6% variation of tacrolimus C0/D. MYH9 rs2239781, LAMB2 rs62119873 and age together explained 22.3% variability of tacrolimus level in CYP3A5 expressers. CYP3A5*3 was still an important factor affecting tacrolimus concentration in patients with NS. Podocyte-associated gene polymorphisms, especially ACTN4 rs62121818 and MYH9 rs2239781, were the other most important biomarkers for tacrolimus whole blood levels. Genotyping of CYP3A5, ACTN4, and MYH9 polymorphisms may be helpful for better guiding tacrolimus dosing in pediatric patients with refractory NS.

Early and Accurate Prediction of Clinical Response to Methotrexate Treatment in Juvenile Idiopathic Arthritis Using Machine Learning.

Background and Aims: Accurately predicting the response to methotrexate (MTX) in juvenile idiopathic arthritis (JIA) patients before administration is the key point to improve the treatment outcome. However, no simple and reliable prediction model has been identified. Here, we aimed to develop and validate predictive models for the MTX response to JIA using machine learning based on electronic medical record (EMR) before and after administering MTX. Materials and Methods: Data of 362 JIA patients with MTX mono-therapy were retrospectively collected from EMR between January 2008 and October 2018. DAS44/ESR-3 simplified standard was used to evaluate the MTX response. Extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), and logistic regression (LR) algorithms were applied to develop and validate models with 5-fold cross-validation on the randomly split training and test set. Data of 13 patients additionally collected were used for external validation. Results: The XGBoost screened out the optimal 10 pre-administration features and 6 mix-variables. The XGBoost established the best model based on the 10 pre-administration variables. The performances were accuracy 91.78%, sensitivity 90.70%, specificity 93.33%, AUC 97.00%, respectively. Similarly, the XGBoost developed a better model based on the 6 mix-variables, whose performances were accuracy 94.52%, sensitivity 95.35%, specificity 93.33%, AUC 99.00%, respectively. Conclusion: Based on common EMR data, we developed two MTX response predictive models with excellent performance in JIA using machine learning. These models can predict the MTX efficacy early and accurately, which provides powerful decision support for doctors to make or adjust therapeutic scheme before or after treatment.

Determination of erythrocyte methotrexate polyglutamates by liquid chromatography/tandem mass spectrometry after low-dose methotrexate therapy in Chinese patients with rheumatoid arthritis.

Methotrexate (MTX) is currently one of the most widely used drugs for treatment of rheumatoid arthritis (RA) through polyglutamation of methotrexate polyglutamates (MTXPGs), a process attaching sequential γ-linked glutamic residues to MTX. A new and sensitive LC/MS/MS method was developed and validated for determination of whole-blood MTX and total MTX (MTX+MTXPGs), and then concentration of MTXPGs was calculated. To determine whole-blood MTX, whole blood was precipitated with 50% trifluoroacetic acid, and extraction was performed using ethyl acetoacetate. Analytes were subjected to LC/MS/MS analysis using positive electrospray ionization. To determine whole-blood total MTX, whole blood was incubated with ascorbic acid (200 mM) at 37°C for 3h to enzymatically convert the MTXPGs to MTX, and then processed with the same method mentioned above. Recoveries of spiked MTX at ppb (ng/mL) level were between 26.2% and 37.8% with intra- and inter-day precision less than 15.8% and 11.8%, respectively. The lower limit of detection (LLOD) and lower limit of quantitation (LLOQ) were 0.5 ng/mL and 1 ng/mL, respectively. The sensitive LC/MS/MS method was fully validated with high selectivity and acceptable accuracy and precision, which was successfully applied to determine the erythrocyte methotrexate polyglutamates in patients with RA.