Computational validation of ABCB1 gene polymorphism and its effect on tacrolimus dose concentration/levels in renal transplant individuals of South India

Computational studies on the functional and structural impact of pathogenic mutations in enzymes

Enzymes are critical biological catalysts involved in maintaining the intricate balance of metabolic processes within living organisms. Mutations in enzymes can result in disruptions to their functionality that may lead to a range of diseases. This review focuses on computational studies that investigate the effects of disease-associated mutations in various enzymes. Through molecular dynamics simulations, multiscale calculations, and machine learning approaches, computational studies provide detailed insights into how mutations impact enzyme structure, dynamics, and catalytic activity. This review emphasizes the increasing impact of computational simulations in understanding molecular mechanisms behind enzyme (dis)function by highlighting the application of key computational methodologies to selected enzyme examples, aiding in the prediction of mutation effects and the development of therapeutic strategies.

Optimizing tacrolimus dosage in post-renal transplantation using DoseOptimal framework: profiling CYP3A5 genetic variants for interpretability

BACKGROUND

Achieving optimal tacrolimus dosing is vital for effectively balancing therapeutic efficacy and safety, as CYP3A5 genetic variants and inter-patient variability emphasize the need for precision strategies.

AIM

This study aimed to optimize tacrolimus dosage prediction for renal transplant recipients by incorporating genetic polymorphisms, specifically profiling CYP3A5 genetic variants, within the DoseOptimal framework to enhance interpretability and accuracy of dosing decisions.

METHOD

The dataset comprised clinical, demographic, and CYP3A5 genetic variants information from 1045 stable tacrolimus-treated patients. The DoseOptimal framework was developed by integrating the strengths of the most effective algorithms from fifteen machine learning models. SHapley Additive exPlanations (SHAP) and decision tree insights were incorporated to enhance the framework's interpretability. The framework's performance was assessed using mean absolute error (MAE) and the coefficient of determination (R2 score). The F-statistic and p value were calculated to validate the framework's statistical significance.

RESULTS

The DoseOptimal framework demonstrated robust performance with an R2 score of 0.884 in the training set and 0.830 in the testing set. The MAE was 0.40 mg/day (95% CI 0.38-0.43) in the training set and 0.41 mg/day (95% CI 0.38-0.45) in the testing set. The framework predicted the ideal tacrolimus dosage in 87.6% (n = 275) of the test cohort, with 3.2% (n = 10) underestimation and 9.2% (n = 29) overestimation. The framework's statistical significance was confirmed with an F-statistic of 266.095 and a p value < 0.001.

CONCLUSION

The framework provides precision medicine-based dosing solutions tailored to individual genetic profiles, minimizing dosing errors and enhancing patient outcomes.

Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition

BACKGROUND

Glomerular lesions reflect the onset and progression of renal disease. Pathological diagnoses are widely regarded as the definitive method for recognizing these lesions, as the deviations in histopathological structures closely correlate with impairments in renal function.

METHODS

Deep learning plays a crucial role in streamlining the laborious, challenging, and subjective task of recognizing glomerular lesions by pathologists. However, the current methods treat pathology images as data in regular Euclidean space, limiting their ability to efficiently represent the complex local features and global connections. In response to this challenge, this paper proposes a graph neural network (GNN) that utilizes global attention pooling (GAP) to more effectively extract high-level semantic features from glomerular images. The model incorporates Bayesian collaborative learning (BCL), enhancing node feature fine-tuning and fusion during training. In addition, this paper adds a soft classification head to mitigate the semantic ambiguity associated with a purely hard classification.

RESULTS

This paper conducted extensive experiments on four glomerular datasets, comprising a total of 491 whole slide images (WSIs) and 9030 images. The results demonstrate that the proposed model achieves impressive F1 scores of 81.37%, 90.12%, 87.72%, and 98.68% on four private datasets for glomerular lesion recognition. These scores surpass the performance of the other models used for comparison. Furthermore, this paper employed a publicly available BReAst Carcinoma Subtyping (BRACS) dataset with an 85.61% F1 score to further prove the superiority of the proposed model.

CONCLUSION

The proposed model not only facilitates precise recognition of glomerular lesions but also serves as a potent tool for diagnosing kidney diseases effectively. Furthermore, the framework and training methodology of the GNN can be adeptly applied to address various pathology image classification challenges.

A Low Tacrolimus Concentration-to-Dose Ratio Increases Calcineurin Inhibitor Nephrotoxicity and Cytomegalovirus Infection Risks in Kidney Transplant Recipients: A Single-Center Study in Japan

BACKGROUND

Tacrolimus (TAC) has several problems due to its narrow therapeutic window and variations pharmacokinetics and pharmacodynamics. Recently, several studies reported that TAC metabolism, defined by TAC blood trough concentration to dose (C/D) ratio, was associated with TAC toxicity. Reports on once-daily extended-release TAC (TAC-ER) are limited. The present study aimed to investigate the effect of the TAC metabolic rate on TAC-ER and compare TAC area under the curve (AUC) between fast and slow metabolizers.

METHODS

A total of 58 recipients were included in this study. The optimal cut-off value and time of the C/D ratio on TAC-ER for fast and slow metabolizers was determined using receiver operating characteristic curve analysis for biopsy-proven calcineurin inhibitor (CNI) nephrotoxicity.

RESULTS

The optimal time to evaluate the C/D ratio was 1 month after kidney transplantation (KT) and the cut-off value was 0.9. The multivariate analysis for CNI nephrotoxicity risk showed that only TAC metabolism was associated with CNI nephrotoxicity (hazard ratio 10.60, P = .005, 95% CI 2.03-55.22). Cytomegalovirus infection occurred more frequently in fast metabolizers when the cut-off value of the C/D ratio was set to 0.9 at 3 months after KT (P = .04). The TAC C₄, AUC_2-8, was higher in fast metabolizers than in slow metabolizers (P < .01, P = .03, respectively).

CONCLUSION

The study revealed that TAC fast metabolizers on TAC-ER may be classified as a high-risk group for CNI nephrotoxicity and cytomegalovirus infection. The result of TAC AUC supported the hypothesis that fast metabolizers tended to be overexposed to immunosuppressive agents early after oral administration.

Effects of ABCB1 DNA methylation in donors on tacrolimus blood concentrations in recipients following liver transplantation

Aims

To investigate the effects of ABCB1 DNA methylation in donors on individual differences in tacrolimus blood concentrations following liver transplantation.

Methods

Twenty‐three donor liver samples carrying the CYP3A5*3/*3 genotype were classified into 2 groups based on their initial tacrolimus blood concentrations (C₀ >10 μg/L or <5 μg/L) following liver transplantation. ABCB1 mRNA levels in liver tissues and HepG2 cells were determined by quantitative reverse transcriptase polymerase chain reaction. DNA methylation status in liver tissues and HepG2 cells was determined using Illumina 850 methylation chip sequencing technology and pyrosequencing. 5‐Aza‐2dC was used to reverse methylation in HepG2 cells. Intracellular tacrolimus concentrations were determined by liquid mass spectrometry.

Results

Genome‐wide methylation sequencing and pyrosequencing analyses showed that the methylation levels of 3 ABCB1 CpG sites (cg12501229, cg00634941 and cg05496710) were significantly different between groups with different tacrolimus concentration/dose (C₀/D) ratios. ABCB1 mRNA expression in donor livers was found to be positively correlated with tacrolimus C₀/D ratio (R = .458, P < .05). After treatment with 5‐Aza‐2‐Dc, the methylation levels of the ABCB1 CpG sites in HepG2 cells significantly decreased, and this was confirmed by pyrosequencing; there was also a significant increase in ABCB1 transcription, which induced a decrease in intracellular tacrolimus concentrations.

Conclusion

ABCB1 CpG site methylation affects tacrolimus metabolism in humans by regulating ABCB1 expression. Therefore, ABCB1 DNA methylation in donor livers might be an important epigenetic factor that affects tacrolimus blood concentrations following liver transplantation.