Precision Medicine in Kidney Transplantation: Just Hype or a Realistic Hope?

Prediction models for postoperative renal function after living donor nephrectomy: a systematic review

INTRODUCTION

Living-donor nephrectomy (LDN) is the most valuable source of organs for kidney transplantation worldwide. The current preoperative evaluation of a potential living donor candidate does not take into account formal estimation of postoperative renal function decline after surgery using validated prediction models. The aim of this study was to summarize the available models to predict the mid- to long-term renal function following LDN, aiming to support both clinicians and patients during the decision-making process.

EVIDENCE ACQUISITION

A systematic review of the English-language literature was conducted following the principles highlighted by the European Association of Urology (EAU) guidelines and following the PRISMA 2020 recommendations. The protocol was registered in PROSPERO on December 10, 2022 (registration ID: CRD42022380198). In the qualitative analysis we selected the models including only preoperative variables.

EVIDENCE SYNTHESIS

After screening and eligibility assessment, six models from six studies met the inclusion criteria. All of them relied on retrospective patient cohorts. According to PROBAST, all studies were evaluated as high risk of bias. The models included different combinations of variables (ranging between two to four), including donor-/kidney-related factors, and preoperative laboratory tests. Donor age was the variable more often included in the models (83%), followed by history of hypertension (17%), Body Mass Index (33%), renal volume adjusted by body weight (33%) and body surface area (33%). There was significant heterogeneity in the model building strategy, the main outcome measures and the model's performance metrics. Three models were externally validated.

CONCLUSIONS

Few models using preoperative variables have been developed and externally validated to predict renal function after LDN. As such, the evidence is premature to recommend their use in routine clinical practice. Future research should be focused on the development and validation of user-friendly, robust prediction models, relying on granular large multicenter datasets, to support clinicians and patients during the decision-making process.

Update on the reciprocal interference between immunosuppressive therapy and gut microbiota after kidney transplantation

Gut microbiota is often modified after kidney transplantation. This principally happens in the first period after transplantation. Antibiotics and, most of all, immunosuppressive drugs are the main responsible. The relationship between immunosuppressive drugs and the gut microbiota is bilateral. From one side immunosuppressive drugs modify the gut microbiota, often generating dysbiosis; from the other side microbiota may interfere with the immunosuppressant pharmacokinetics, producing products more or less active with respect to the original drug. These phenomena have influence over the graft outcomes and clinical consequences as rejections, infections, diarrhea may be caused by the dysbiotic condition. Corticosteroids, calcineurin inhibitors such as tacrolimus and cyclosporine, mycophenolate mofetil and mTOR inhibitors are the immunosuppressive drugs whose effect on the gut microbiota is better known. In contrast is well known how the gut microbiota may interfere with glucocorticoids, which may be transformed into androgens. Tacrolimus may be transformed by micro biota into a product called M1 that is 15-fold less active with respect to tacrolimus. The pro-drug mycophenolate mofetil is normally transformed in mycophenolic acid that according the presence or not of microbes producing the enzyme glu curonidase, may be transformed into the inactive product.

Pharmacomicrobiomics of Classical Immunosuppressant Drugs: A Systematic Review

The clinical response to classical immunosuppressant drugs (cIMDs) is highly variable among individuals. We performed a systematic review of published evidence supporting the hypothesis that gut microorganisms may contribute to this variability by affecting cIMD pharmacokinetics, efficacy or tolerability. The evidence that these drugs affect the composition of intestinal microbiota was also reviewed. The PubMed and Scopus databases were searched using specific keywords without limits of species (human or animal) or time from publication. One thousand and fifty five published papers were retrieved in the initial database search. After screening, 50 papers were selected to be reviewed. Potential effects on cIMD pharmacokinetics, efficacy or tolerability were observed in 17/20 papers evaluating this issue, in particular with tacrolimus, cyclosporine, mycophenolic acid and corticosteroids, whereas evidence was missing for everolimus and sirolimus. Only one of the papers investigating the effect of cIMDs on the gut microbiota reported negative results while all the others showed significant changes in the relative abundance of specific intestinal bacteria. However, no unique pattern of microbiota modification was observed across the different studies. In conclusion, the available evidence supports the hypothesis that intestinal microbiota could contribute to the variability in the response to some cIMDs, whereas data are still missing for others.

The Utility of Spatial Transcriptomics for Solid Organ Transplantation

Spatial transcriptomics (ST) measures and maps transcripts within intact tissue sections, allowing the visualization of gene activity within the spatial organization of complex biological systems. This review outlines advances in genomic sequencing technologies focusing on in situ sequencing-based ST, including applications in transplant and relevant nontransplant settings. We describe the experimental and analytical pipelines that underpin the current generation of spatial technologies. This context is important for understanding the potential role ST may play in expanding our knowledge, including in organ transplantation, and the important caveats/limitations when interpreting the vast data output generated by such methodological platforms.

Developing supervised machine learning algorithms to evaluate the therapeutic effect and laboratory-related adverse events of cyclosporine and tacrolimus in renal transplants

BACKGROUND

Single nucleotide polymorphisms influence the effects of tacrolimus and cyclosporine in renal transplants.

AIM

We set out to use machine learning algorithms (MLAs) to identify variables that predict the therapeutic effects and adverse events following tacrolimus and cyclosporine administration in renal transplant patients.

METHOD

We sampled 120 adult renal transplant patients (on cyclosporine or tacrolimus). Generalized linear model (GLM), support vector machine (SVM), artificial neural network (ANN), Chi-square automatic interaction detection, classification and regression tree, and K-nearest neighbors were the chosen MLAs. The mean absolute error (MAE), relative mean square error (RMSE), and regression coefficient (β) with a 95% confidence interval (CI) were used as the model parameters.

RESULTS

For a stable dose of tacrolimus, the MAEs (RMSEs) of GLM, SVM, and ANN were 1.3 (1.5), 1.3 (1.8), and 1.7 (2.3) mg/day, respectively. GLM revealed that the POR*28 genotype and age significantly predicted the stable dose of tacrolimus as follows: POR*28 (β -1.8; 95% CI -3, -0.5; p = 0.006), and age (β -0.04; 95% CI -0.1, -0.006; p = 0.02). For a stable dose of cyclosporine, MAEs (RMSEs) of 93.2 (103.4), 79.1 (115.2), and 73.7 (91.7) mg/day were observed with GLM, SVM, and ANN, respectively. GLM revealed the following predictors of a stable dose of cyclosporine: CYP3A5*3 (β -80.8; 95% CI -130.3, -31.2; p = 0.001), and age (β -3.4; 95% CI -5.9, -0.9; p = 0.007).

CONCLUSION

We observed that various MLAs could identify significant predictors that were useful to optimize tacrolimus and cyclosporine dosing regimens; yet, the findings must be externally validated.

The Gut Microbiota in Kidney Transplantation: A Target for Personalized Therapy?

Kidney transplantation improves quality of life, morbidity, and mortality of patients with kidney failure. However, integrated immunosuppressive therapy required to preserve graft function is associated with the development of post-transplant complications, including infections, altered immunosuppressive metabolism, gastrointestinal toxicity, and diarrhea. The gut microbiota has emerged as a potential therapeutic target for personalizing immunosuppressive therapy and managing post-transplant complications. This review reports current evidence on gut microbial dysbiosis in kidney transplant recipients, alterations in their gut microbiota associated with kidney transplantation outcomes, and the application of gut microbiota intervention therapies in treating post-transplant complications.

Sensitization in transplantation: Assessment of Risk 2022 Working Group Meeting Report

The Sensitization in Transplantation: Assessment of Risk workgroup is a collaborative effort of the American Society of Transplantation and the American Society of Histocompatibility and Immunogenetics that aims at providing recommendations for clinical testing, highlights gaps in current knowledge, and proposes areas for further research to enhance histocompatibility testing in support of solid organ transplantation. This report provides updates on topics discussed by the previous Sensitization in Transplantation: Assessment of Risk working groups and introduces 2 areas of exploration: non-human leukocyte antigen antibodies and utilization of human leukocyte antigen antibody testing measurement to evaluate the efficacy of antibody-removal therapies.