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.

Single-Cell Quantitative Phenotyping via the Aptamer-Mounted Nest-PCR (Apt-nPCR).

Analyzing single-cell phenotypes is increasingly required in biomedical studies, for non-genetic understanding of cellular activities and the biological significance of rare cell subpopulations. However, as compared to the genotypic analysis, single-cell phenotype analysis is technically more challenging. Herein, a tractable method that allows quantitative phenotyping of single cell is developed in this work, termed as the aptamer-mounted nest-PCR (Apt-nPCR). In specific, only two rounds of PCR reactions are required to complete the analysis, where aptamers (short oligonucleotides that bind to specific target molecules) are used as the recognition elements to bind antigens and also as the templates of nPCR for multiplexed and quantitative detection. So, quantitative information of these target antigens can be revealed by quantitative PCR analysis of these aptamers, which can thus be used to interpret cell phenotypes in a quantitative-to-qualitative way. By addressing two technical issues that are involved in single-cell phenotype analysis─multiplexed detection plus high sensitivity, we have shown the availability of this method for single-cell phenotyping. Therefore, the Apt-nPCR method may represent a tractable method to facilitate the single-cell phenotype analysis, which can be used as a complementary method against these single-cell genotyping methods in our daily research.

Direct acupuncture of nitric oxide by an electrochemical microsensor with high time-space resolution.

Measurement of signal molecule is critically important for understanding living systems. Nitric oxide (NO) is a key redox signal molecule that shows diverse roles in virtually all life forms. However, probing into NO's activities is challenging as NO has restricted lifetime (<10 s) and limited diffusion distance (usually <200 µm). So, for the direct acupuncture of NO within the time-space resolution, an electrochemical microsensor has been designed and fabricated in this work. Fabrication of the microsensor is achieved by (1) selective assembly of an electrocatalytic transducer, (2) attaching the transducer on carbon fiber electrode, and (3) covered it with a screen layer to reduce signal interference. The fabricated microsensor exhibits high sensitivity (LOD, 13.5 pM), wide detection range (100 pM-5 µM), and good selectivity. Moreover, studies have revealed that the availability of the sensor for efficient detection of NO is due to the formation of a specific DNA/porphyrin hybrid structure that has synergetic effects on NO electrocatalysis. Therefore, NO release by cells and tissues can be directly and precisely traced, in which we have obtained the release pattern of NO by different cancer cell lines, and have known its dynamics in tumor microenvironment. The fabricated electrocatalytic microsensor may provide a unique and useful tool for the direct assay of NO with high time-space resolution, which promisingly gives a technical solution for the bioassay of NO in living systems.