Pharmacogenetics of immunosuppressant drugs: A new aspect for individualized therapy

Towards Allograft Longevity: Leveraging Omics Technologies to Improve Heart Transplant Outcomes

PURPOSE OF REVIEW

Heart transplantation (HT) remains the optimal therapy for patients living with end-stage heart disease. Despite recent improvements in peri-transplant management, the median survival after HT has remained relatively static, and complications of HT, including infection, rejection, and allograft dysfunction, continue to impact quality of life and long-term survival.

RECENT FINDINGS

Omics technologies are becoming increasingly accessible and can identify novel biomarkers for, and reveal the underlying biology of, several disease states. While some technologies, such as gene expression profiling (GEP) and donor-derived cell-free DNA (dd-cfDNA), are routinely used in the clinical care of HT recipients, a number of emerging platforms, including pharmacogenomics, proteomics, and metabolomics, hold great potential for identifying biomarkers to aid in the diagnosis and management of post-transplant complications. Omics-based assays can improve patient and allograft longevity by facilitating a personalized and precision approach to post-HT care. The following article is a contemporary review of the current and future opportunities to leverage omics technologies, including genomics, transcriptomics, proteomics, and metabolomics in the field of HT.

The Value of Pharmacogenomics for White and Indigenous Americans after Kidney Transplantation

BACKGROUND

There is a paucity of evidence to inform the value of pharmacogenomic (PGx) results in patients after kidney transplant and how these results differ between Indigenous Americans and Whites. This study aims to identify the frequency of recommended medication changes based on PGx results and compare the pharmacogenomic (PGx) results and patients' perceptions of the findings between a cohort of Indigenous American and White kidney transplant recipients.

METHODS

Thirty-one Indigenous Americans and fifty White kidney transplant recipients were studied prospectively. Genetic variants were identified using the OneOme RightMed PGx test of 27 genes. PGx pharmacist generated a report of the genetic variation and recommended changes. Pre- and post-qualitative patient surveys were obtained.

RESULTS

White and Indigenous American subjects had a similar mean number of medications at the time of PGx testing (mean 13 (SD 4.5)). In the entire cohort, 53% received beta blockers, 30% received antidepressants, 16% anticoagulation, 47% pain medication, and 25% statin therapy. Drug-gene interactions that warranted a clinical action were present in 21.5% of patients. In 12.7%, monitoring was recommended. Compared to the Whites, the Indigenous American patients had more normal CYP2C19 (p = 0.012) and CYP2D6 (p = 0.012) activities. The Indigenous American patients had more normal CYP4F2 (p = 0.004) and lower VKORC (p = 0.041) activities, phenotypes for warfarin drug dosing, and efficacy compared to the Whites. SLC6A4, which affects antidepressant metabolism, showed statistical differences between the two cohorts (p = 0.017); specifically, SLC6A4 had reduced expression in 45% of the Indigenous American patients compared to 20% of the White patients. There was no significant difference in patient perception before and after PGx.

CONCLUSIONS

Kidney transplant recipients had several drug-gene interactions that were clinically actionable; over one-third of patients were likely to benefit from changes in medications or drug doses based on the PGx results. The Indigenous American patients differed in the expression of drug-metabolizing enzymes and drug transporters from the White patients.

Pancreas Rejection in the Artificial Intelligence Era: New Tool for Signal Patients at Risk

INTRODUCTION

Pancreas transplantation is currently the only treatment that can re-establish normal endocrine pancreatic function. Despite all efforts, pancreas allograft survival and rejection remain major clinical problems. The purpose of this study was to identify features that could signal patients at risk of pancreas allograft rejection.

METHODS

We collected 74 features from 79 patients who underwent simultaneous pancreas-kidney transplantation (SPK) and used two widely-applicable classification methods, the Naive Bayesian Classifier and Support Vector Machine, to build predictive models. We used the area under the receiver operating characteristic curve and classification accuracy to evaluate the predictive performance via leave-one-out cross-validation.

RESULTS

Rejection events were identified in 13 SPK patients (17.8%). In feature selection approach, it was possible to identify 10 features, namely: previous treatment for diabetes mellitus with long-term Insulin (U/I/day), type of dialysis (peritoneal dialysis, hemodialysis, or pre-emptive), de novo DSA, vPRA_Pre-Transplant (%), donor blood glucose, pancreas donor risk index (pDRI), recipient height, dialysis time (days), warm ischemia (minutes), recipient of intensive care (days). The results showed that the Naive Bayes and Support Vector Machine classifiers prediction performed very well, with an AUROC and classification accuracy of 0.97 and 0.87, respectively, in the first model and 0.96 and 0.94 in the second model.

CONCLUSION

Our results indicated that it is feasible to develop successful classifiers for the prediction of graft rejection. The Naive Bayesian generated nomogram can be used for rejection probability prediction, thus supporting clinical decision making.

Pharmacogenomics in allogeneic hematopoietic stem cell transplantation: Implications on supportive therapies and conditioning regimens

Allogeneic hematopoietic stem cell transplantation mortality has declined over the years, though prevention and management of treatment-related toxicities and post-transplant complications remains challenging. Applications of pharmacogenomic testing can potentially mitigate adverse drug outcomes due to interindividual variability in drug metabolism and response. This review summarizes clinical pharmacogenomic applications relevant to hematopoietic stem cell transplantation, including antifungals, immunosuppressants, and supportive care management, as well as emerging pharmacogenomic evidence with conditioning regimens.

Therapeutic drug monitoring of immunosuppressive drugs in hepatology and gastroenterology

Immunosuppressive drugs have been key to the success of liver transplantation and are essential components of the treatment of inflammatory bowel disease (IBD) and autoimmune hepatitis (AIH). For many but not all immunosuppressants, therapeutic drug monitoring (TDM) is recommended to guide therapy. In this article, the rationale and evidence for TDM of tacrolimus, mycophenolic acid, the mammalian target of rapamycin inhibitors, and azathioprine in liver transplantation, IBD, and AIH is reviewed. New developments, including algorithm-based/computer-assisted immunosuppressant dosing, measurement of immunosuppressants in alternative matrices for whole blood, and pharmacodynamic monitoring of these agents is discussed. It is expected that these novel techniques will be incorporate into the standard TDM in the next few years.