Early Prediction of Tacrolimus-Induced Tubular Toxicity in Pediatric Refractory Nephrotic Syndrome Using Machine Learning.
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ABSTRACT:
SUBMITTER: Mo X
PROVIDER: S-EPMC8430214 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC9706003 | biostudies-literature
Project description:ImportanceCalcineurin inhibitors are an established first-line corticosteroid-sparing therapy for patients with corticosteroid-dependent nephrotic syndrome (CDNS), whereas B-lymphocyte-depleting therapy is mostly used as a rescue for calcineurin inhibitor-resistant cases. The positive efficacy and safety profile of rituximab raises the question of whether it could be used as a first-line alternative to calcineurin inhibitor therapy.ObjectiveTo compare the efficacy of rituximab and tacrolimus in maintaining relapse-free survival among children with CDNS.Design, setting, and participantsA parallel-arm, open-label, randomized clinical trial was performed from May 8, 2015, to September 20, 2016, with 1-year follow-up in a single-center, tertiary care unit. A total of 176 consecutive children aged 3 to 16 years with CDNS not previously treated with corticosteroid-sparing agents were screened for eligibility.InterventionsThe children received either tacrolimus (along with tapering alternate-day prednisolone) for 12 months or a single course of rituximab (2 infusions of 375 mg/m2).Main outcomes and measuresTwelve-month relapse-free survival in the intention-to-treat population.ResultsOf the 176 children screened for eligibility, 120 were randomized and all but 3 patients completed 1 year of follow-up. The groups were comparable, with mean (SD) age of 7.2 (2.8) years, 32 boys (53.3%) in each group, mean (SD) disease duration of 2.5 (1.5) years and 2.3 (1.7) in the tacrolimus and rituximab groups, respectively, disease duration less than 1 year among 15 children (25.0%) in each group, median (interquartile range) of 4 (3-5) relapses in each group, and mean (SD) cumulative prednisolone dose of 246 (48) mg/kg and 239 (52) mg/kg in the prestudy year in the tacrolimus and rituximab groups, respectively. Rituximab therapy was associated with a higher 12-month relapse-free survival rate than tacrolimus (54 [90.0%] vs 38 [63.3%] children; P < .001; odds ratio, 5.21; 95% CI, 1.93-14.07). Among the patients who experienced relapse, median time to first relapse was 40 weeks in the rituximab group and 29 weeks in the tacrolimus group. Only 2 patients in the rituximab group had more than 1 relapse during the study period compared with 10 patients in the tacrolimus group. The cumulative corticosteroid dose during the 12-month study period was lower with rituximab compared with tacrolimus (mean [SD], 25.8 [27.8] vs 86.3 [58.0] mg/kg). Although both treatments were well tolerated, mild to moderate infections were twice as common in the tacrolimus group (26 [43.3%] vs 13 [21.7%] events).Conclusions and relevanceIn children with CDNS, rituximab appears to be more effective than tacrolimus in maintaining disease remission and minimizing corticosteroid exposure and, given its good tolerability and lack of nephrotoxic effects, may be considered as first-line corticosteroid-sparing therapy.Trial registrationClinicalTrials.gov Identifier: NCT02438982; Clinical Trial Registry of India: CTRI/2014/01/004355.
| S-EPMC6142920 | biostudies-literature
Project description:BackgroundIn the absence of effective measures to predict steroid responsiveness, patients with nonhereditary steroid-resistant nephrotic syndrome (SRNS) have a significantly increased risk of progression to end-stage renal disease. In view of the poor outcomes of SRNS, it is urgent to identify the steroid responsiveness of idiopathic nephrotic syndrome (INS) early.MethodsTo build a prediction model for SRNS, we collected 91 subjects; 57 of them had steroid-sensitive nephrotic syndrome, and the others had SRNS. For each subject, 87 clinical variables were measured. In general, only a small part of these variables is informative to SRNS. Thus, we proposed a new variable selection framework including a penalized regression approach (named MLR+TLP) to select variables having a linear effect on the SRNS and a nonparametric screening method (MAC) to select variables having a nonlinear marginal (joint) effect on the SRNS. Thereafter, considering the correlation between selected clinical variables, we used a stepwise method to build our final model for predicting SRNS. In addition, a statistical testing procedure is proposed to test the overfitting of the proposed model.ResultsTwenty-six clinical variables were selected to be informative to SRNS, and an SVM model was built to predict SRNS with a leave-one-out cross-validation (LOO-CV) accuracy of 95.2% (overfitting p value<0.005). To make the model more useful, we incorporate prior medical information into the model and consider the correlation between selected variables. Then, a reduced SVM model including only eight clinical variables (erythrocyte sedimentation rate, urine occult blood, percentage of neutrophils, immunoglobulin A, cholesterol, vinculin autoantibody, aspartate aminotransferase, and prolonged prothrombin time) was built to have a LOO-CV accuracy of 92.8% (overfitting p value<0.005). The validation cohort showed that the reduced model obtained an accuracy of 94.0% (overfitting p value<0.005), with a sensitivity of 90.0% and a specificity of 96.7%. Notably, vinculin autoantibody is the only podocyte autoantibody included in this model. It is linearly related to steroid responsiveness. Finally, our model is freely available as a user-friendly web tool at https://datalinkx.shinyapps.io/srns/.ConclusionThe SRNS prediction model constructed in this study comprehensively and objectively evaluates the internal conditions and disease status of INS patients and will provide scientific guidance for selecting treatment methods for children with nonhereditary SRNS.
| S-EPMC9911108 | biostudies-literature
Project description:Background: Early detection of pediatric severe sepsis is necessary in order to optimize effective treatment, and new methods are needed to facilitate this early detection. Objective: Can a machine-learning based prediction algorithm using electronic healthcare record (EHR) data predict severe sepsis onset in pediatric populations? Methods: EHR data were collected from a retrospective set of de-identified pediatric inpatient and emergency encounters for patients between 2-17 years of age, drawn from the University of California San Francisco (UCSF) Medical Center, with encounter dates between June 2011 and March 2016. Results: Pediatric patients (n = 9,486) were identified and 101 (1.06%) were labeled with severe sepsis following the pediatric severe sepsis definition of Goldstein et al. (1). In 4-fold cross-validation evaluations, the machine learning algorithm achieved an AUROC of 0.916 for discrimination between severe sepsis and control pediatric patients at the time of onset and AUROC of 0.718 at 4 h before onset. The prediction algorithm significantly outperformed the Pediatric Logistic Organ Dysfunction score (PELOD-2) (p < 0.05) and pediatric Systemic Inflammatory Response Syndrome (SIRS) (p < 0.05) in the prediction of severe sepsis 4 h before onset using cross-validation and pairwise t-tests. Conclusion: This machine learning algorithm has the potential to deliver high-performance severe sepsis detection and prediction through automated monitoring of EHR data for pediatric inpatients, which may enable earlier sepsis recognition and treatment initiation.
| S-EPMC6798083 | biostudies-literature
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress
Project description:Children with steroid-resistant nephrotic syndrome (SRNS) are at risk of developing renal failure. We report here the results of a single-center retrospective observational study of the remission rate in pediatric patients with SNRS receiving tacrolimus. Serial renal biopsies from children on tacrolimus therapy were evaluated for tubulointerstitial fibrosis and transforming growth factor-beta immunostaining. Of the 16 children with SRNS, 15 went into complete remission after a median of 120 days of therapy. Nine children were able to stop steroids, while the others were on tapering doses. Forty-seven percent had relapses, most of which were steroid-responsive. Serial renal biopsies were obtained from seven children after a median treatment duration of 24 months; two of these children had increased tubulointerstitial fibrosis and four showed increased transforming growth factor-beta tissue staining. Children with worsening histological findings were younger. There was no significant association between tacrolimus exposure and biopsy changes, although the average trough level was higher in those children with worsening histological findings. In conclusion, tacrolimus may be a safe and effective alternative agent for inducing remission in children with SRNS. However, caution needs to be taken when prescribing this agent due to its narrow therapeutic index. Serial renal biopsies are necessary to check for subclinical nephrotoxicity, especially in younger children and those with higher trough levels.
| S-EPMC2697358 | biostudies-other
Project description:AimsNephrotic syndrome (NS) is the most common clinical manifestation of glomerular disease in children. Currently, tacrolimus (TAC) is widely used in children with NS. However, pharmacokinetic data in children with nephrotic syndrome is limited. This study was intended to evaluate the population pharmacokinetics (PPK) of TAC in paediatric NS and to optimize dosing regimen.MethodsBlood samples from NS children treated with TAC were collected and the blood concentrations of TAC were detected using HPLC-MS/MS. A PPK model was developed using NONMEM software. Pharmacogenetic analysis was carried out in the CYP3A5 gene.ResultsThe data from 28 children were used for PPK analysis. A one-compartment model and first-order elimination were accorded with the TAC data in paediatric NS. A covariate analysis showed that body weight and CYP3A5 genotype significantly affected TAC pharmacokinetics. Monte Carlo simulation indicated that NS children with CYP3A5*3/*3 receiving 0.10 mg kg-1 dose-1 twice daily and NS children with CYP3A5*1 receiving 0.25 mg kg-1 dose-1 twice daily TAC could achieve the target concentrations of 5-10 ng ml-1 .ConclusionThe PPK of TAC was estimated in children with NS and a CYP3A5 genotype-based dosing regimen was set up based on simulations.
| S-EPMC6046506 | biostudies-literature
Project description:Background: Pediatric myocarditis is a rare disease. The etiologies are multiple. Mortality associated with the disease is 5-8%. Prognostic factors were identified with the use of national hospitalization databases. Applying these identified risk factors for mortality prediction has not been reported. Methods: We used the Kids' Inpatient Database for this project. We manually curated fourteen variables as predictors of mortality based on the current knowledge of the disease, and compared performance of mortality prediction between linear regression models and a machine learning (ML) model. For ML, the random forest algorithm was chosen because of the categorical nature of the variables. Based on variable importance scores, a reduced model was also developed for comparison. Results: We identified 4,144 patients from the database for randomization into the primary (for model development) and testing (for external validation) datasets. We found that the conventional logistic regression model had low sensitivity (~50%) despite high specificity (>95%) or overall accuracy. On the other hand, the ML model struck a good balance between sensitivity (89.9%) and specificity (85.8%). The reduced ML model with top five variables (mechanical ventilation, cardiac arrest, ECMO, acute kidney injury, ventricular fibrillation) were sufficient to approximate the prediction performance of the full model. Conclusions: The ML algorithm performs superiorly when compared to the linear regression model for mortality prediction in pediatric myocarditis in this retrospective dataset. Prospective studies are warranted to further validate the applicability of our model in clinical settings.
| S-EPMC8102689 | biostudies-literature
Project description:Nephrotic syndrome (NS) is relatively common in children, with most of its histological types being minimal changed disease. Its etiology has long been attributed to lymphocyte (especially T-cell) dysfunction, while T-cell-mediated vascular hyperpermeability increases protein permeability in glomerular capillaries, leading to proteinuria and hypoproteinemia. Based on this etiology, steroids and immunosuppressive drugs that are effective against this disease have also been considered to correct T-cell dysfunction. However, in recent years, this has been questioned. The primary cause of NS has been considered damage to glomerular epithelial cells and podocyte-related proteins. Therefore, we first describe the changes in expression of molecules involved in NS etiology, and then describe the mechanism by which abnormal expression of these molecules induces proteinuria. Finally, we consider the mechanism by which infection causes the recurrence of NS.
| S-EPMC8477269 | biostudies-literature
Project description:Different types of chemicals and products may exhibit various health risks when administered into the human body. For toxicity reasons, the number of new drugs entering the market through the conventional drug development process has been reduced over the years. However, with the advent of big data and artificial intelligence, machine learning techniques have emerged as a potential solution for predicting toxicity and ensuring efficient drug development and chemical safety. An ML model for toxicity prediction can reduce experimental costs and time while addressing ethical concerns by drastically reducing the need for animals and clinical trials. Herein, MolToxPred, an ML-based tool, has been developed using a stacked model approach to predict the potential toxicity of small molecules and metabolites. The stacked model consists of random forest, multi-layer perceptron, and LightGBM as base classifiers and Logistic Regression as the meta classifier. For training and validation purposes, a comprehensive set of toxic and non-toxic molecules is curated. Different structural and physicochemical-based features in the form of molecular descriptors and fingerprints were employed. MolToxPred utilizes a comprehensive feature selection process and optimizes its hyperparameters through Bayesian optimization with stratified 5-fold cross-validation. In the evaluation phase, MolToxPred achieved an AUROC of 87.76% on the test set and 88.84% on an external validation set. The McNemar test was used as the post-hoc test to determine if the stacked models' performance was significantly different compared to the base learners. The developed stacked model outperformed its base classifiers and an existing tool in the literature, reaffirming its better performance. The hypothesis is that the incorporation of a diverse set of data, the subsequent feature selection, and a stacked ensemble approach give MolToxPred the edge over other methods. In addition to this, an attempt has been made to identify structural alerts responsible for endpoints of the Tox21 data to determine the association of a molecule with a plausible downstream pathway of action. MolToxPred may be helpful for drug discovery and regulatory pipelines in pharmaceutical and other industries for in silico toxicity prediction of small molecule candidates.
| S-EPMC10826801 | biostudies-literature