Biomarkers and personalized therapy in chronic kidney diseases

Abstract

Numerous clinical trials are currently evaluating new strategies to halt the progression of renal damage in patients with chronic kidney diseases (CKDs). Unfortunately, none of them have considered that the lack of response to new therapies may be due to the pharmacogenetics/pharmacogenomics profile of the patient. The recent impact of high-throughput technologies used in genomics, proteomics and metabolomics may open a new way for discovering biomarkers that can provide us information about the mechanisms on the progression of renal damage. However, they can also be used for diagnosis and for selecting drugs, leading to personalized tailored therapy. The uses of classifiers formed by a list of genes, proteins and metabolites have been introduced into oncology and organ transplantation. These new approaches have recently also been used in the care of human glomerulonephritis. Integrating the large omic data sets with drug and disease databases could give the prediction of drug efficacy and side effects in CKDs.

Keywords::

• biomarkers
• chronic kidney diseases
• personalized therapy
• pharmacogenetics
• pharmacogenomics

1. Introduction

Cernaro et al. [1] reviewed the scientific literature and the registry of the Clinical Trials.gov website http://clinicaltrials.gov/ct2/home by November 2013 for searching Phase I and II clinical trials on new therapeutic strategies halting the progression of renal damage in chronic kidney diseases (CKDs). They reported the results of numerous clinical trials, many of them were dedicated to reduce proteinuria that is the highest risk factor for progression of renal damage. They also described the results obtained with the administration of antifibrotic, anti-inflammatory and antioxidative drugs. Finally, they reported preliminary data on the use of autologous mesenchymal stem cells that should have a repairing effect on the damaged renal tissue.

In consideration of these results, it is evident that there will be new drugs available for halting the progression of renal damage in all CKDs but we should move towards a new direction that could be the personalized therapy. The goal of this therapy includes three important aims: i) to choose the right drug; ii) for the right patient; and iii) at the right time. Personalized therapy has been actively developed in oncology [2] and then in cardiology [3], but preliminary data have also been published in nephrology [4] and in kidney transplantation [5]. The potential impact of new high-throughput technologies applied in genomics, proteomics and metabolomics has open a new way for discovering biomarkers that may provide us information about the mechanisms on the progression of renal damage but they can also be used for diagnosis and for selecting drugs, leading to personalized tailored therapy.

2. Pharmacogenetics and pharmacogenomics

There is a large interindividual variability in drug response. Therefore, approaches for personalizing the therapy in kidney diseases are based, first, on data obtained from pharmacogenetic and pharmacogenomic studies. The Pharmacogenomics Knowledge Database shows ∼ 50 genes classified as very important pharmacogenes [6]. Table 1 includes some gene/variants selected for this article. The angiotensin-converting enzyme (ACE) gene encodes ACE, an enzyme involved in the renin-angiotensin-aldosterone system and in the kinin–kallikrein pathway. ACE is the target of ACE inhibitors that are drugs currently used in CKDs such as renoprotectives in reducing proteinuria and blood pressure.

Table 1. Selected genes for pharmacogenomics.

Gene	Alleles/variants	Function
ACE	I/D	Response to ACEi
AGT	Met 235 Thr	Response to ARBs
CYP	CYP2B6*5; CYP2C19*2	Influence on CYP therapy
	CYP3A4; CYP3A5	Influence on CNIs therapy
TPMT		Influence on azathioprine therapy
UGT1A9	T-275A; C-2152T	Influence on MMF therapy
MTHFS	Rs 6495446	Related to the progression of renal damage

ACE: Angiotensin-converting enzyme; AGT: Angiotensinogen; ARBs: Angiotensin receptor blockers; CNIs: Calcineurin inhibitors; MMF: Mycophenolate mofetil; MTHFS: Methenyltetrahydrofolate synthase; TPMT: Thiopurine methyl transferase; UGT: Uridine diphosphate glucuronosyl transferase.

The ACE gene has an insertion (I)/deletion (D) polymorphism of 287 bases in the intron 16. The response to ACE inhibitors is dependent on the DD genotype that is associated with increased ACE activity and high-circulating angiotensin levels. Thus, these individuals are more prone to develop CKDs [7]. The gene encoding angiotensinogen (AGT) is also involved in the renin-angiotensin system. The Thr/Thr genotype of the AGT polymorphism Met235 Thr is associated with high AGT levels, increased risk of concentric left ventricular hypertrophy and vascular complications, both in nonrenal and renal populations [8]. These results suggest the use of genotype study in individuals not responsive to angiotensin receptor blockers.

Moving to the immunosuppressive drugs, cyclophosphamide (CYP) therapy is influenced by two genetic variants such as CYP2B6*5, in which a cysteine replaces the arginine at position 847 in the CYP2B6 allele 5, and CYP2C19*2 that lead to the inactivation of enzymes CYP2B6 and CYP2C19, respectively, involved in the cytochrome P450 enzyme system that metabolizes CYP. Therefore, the non-conversion of CYP in its active metabolites makes inactive the drug. Lupus nephritis patients with CYP2B6*5 or CYP2C19*2 homozygosity are at high risk to be nonresponsive to CYP therapy [9].

The cytochrome P450 enzyme system is also involved in the metabolism of some calcineurin inhibitors (CNIs) such as cyclosporine and tacrolimus. The CYP3A4 and CYP3A5 enzymes and the p-glycoprotein pump modulated by the multi-drug resistance 1 gene with their variants seem to influence the pharmacokinetics of the CNIs [10].

The homozygous defect of the thiopurine methyl transferase gene influences the metabolism of 6-mercaptopurine that is the active metabolite of azathioprine. The uridine diphosphate glucuronosyl transferase (UGT) is encoded by the UGT1A9 gene that has several promoter polymorphisms such as T-275A and C-2152T. These single-nucleotide polymorphisms (SNPs) are associated with higher hepatic expression of UGT1A9, which correlates with reduced immunosuppressive effect of mycophenolate mofetil [11].

The polymorphism of the amino acid at position 158 of Immunoglobulin Fc receptor (FcϒRIIIa), characterized by a valine, increases 10-fold the binding affinity of rituximab. This indicates that these polymorphisms may influence the efficacy of the drug [12].

The genotyping of these polymorphisms has not achieved widespread use in the clinical practice, but this approach could be used in patients with CKDs or transplantation who are not responsive to these drugs.

3. Omics biomarkers

Transcriptomics with proteomics and metabolomics can be considered a new way to detect unknown biomarkers of CKDs. Granata et al. in a whole transcriptomic analysis of peripheral blood mononuclear cells (PBMCs) from patients with CKDs demonstrated an high expression of genes of the oxidative phosphorylation system that evidenced the occurring presence of a mitochondrial dysregulation and oxidative stress [13]. These pathways should be considered for a targeted therapy in patients with CKDs. Three genes like macrophage migration inhibitory factor, IL-8 receptor β and chemokine ligand 12 have been identified in a transcriptome study on PBMCs of uremic patients undergoing periodic hemodialysis or peritoneal dialysis [14]. They may be considered as potential targets for therapeutic approaches to reduce inflammation in dialysis patients.

A few studies on transcriptomics of PBMCs have been carried out in renal diseases such as IgA nephropathy and lupus nephritis, and in renal transplantation but some of them, miss the validation step or have not been confirmed by other studies. However, only transcriptomics cannot provide sufficient information, because translational and post-translational modifications can cause functional changes in proteins that are the real executors of biological events.

Genetic differences, for example, gene expression analysis in large populations, and linkage studies in families indicate most genes of particular value as a tool for hypothesis generation. These results should be confirmed at protein level and by in vitro or in vivo activity for gene function. In addition, the most promising candidate SNPs and/or variants, evidenced by high statistical significance, should be selected for replication in an independent large population of patients. To avoid the effects of environmental confounders, a Mendelian randomization approach should also be used.

The most significant association with CKD has been found for the intronic SNP rs 6495446 located in the methenyltetrahydrofolate synthase (MTHFS) gene [15]. This SNP appears to be related to the progression of CKD because the ala/valine amino-acid exchange in the 677 C/T SNP reduces the enzymatic activity of MTHFS. Next step should consider the effect of drugs on this SNP.

The use of validate biomarkers for measuring the positive effect of drugs is lacking in all Phase I and II clinical trials described by Cernaro et al. [1]. The benefit of drugs has been considered on the base of the estimated glomerular filtration rate (GFR) value obtained by calculating serum creatinine or cystatin C. It is hoped for future trials that investigators may use additional new biomarkers obtained by high-throughput technologies applied in genomics, proteomics and metabolomics studies of biological fluids as serum and urine, and renal biopsy tissue. This approach may individualize noncompliant patients who can benefit of other drug molecules, and move towards personalized medicine with new treatment strategies designed according to the genetic background of patients. Over the years, research in the omics field has progressed from the identification of one candidate gene to many genes with high false discovery rate that form the classifier. The latter performs significantly better than one gene.

In complex diseases, like CKDs, one biomarker may not be sufficient for monitoring the therapy but a panel of biomarkers could be more effective. In this way, we can have biomarkers that predict the development of a disease and suggest initiating the therapy or targeted biomarkers that indicate the appropriate effect of therapy for a patient.

In conclusion, the genotyping of patients enrolled in the randomized clinical trials and the effect of administered drug on gene expression of PBMCs should be systematically included in a randomized clinical trial for studying the interindividual variability to drug response.

4. Expert opinion

Cernaro et al. [1] describe in their review data on new therapeutic strategies halting the progression of renal damage in CKDs and on the use of autologous mesenchymal stem cells that should have a repairing effect on the damaged renal tissue. Weakness points of the reported Phase I and II clinical trials are represented by the estimation of GFR and the measurement of daily proteinuria for evaluating the benefit of administered drugs. These classical biomarkers have poor sensitivity and specificity for the detection of renal injury [16]. Moreover, the infusion of stem cells is replaced by a new approach that consists in administering vesicles obtained from cultured stem cells [17] or proteins/genetic material obtained from microvesicles [18].

Our recent experience on transcriptomics suggests that omics are available for creating specific classifiers in different diseases [19,20]. They are formed by a list of genes that are involved in the pathogenesis of the disease and they can be considered targeted genes for specific drugs [21]. Classifiers are also formed by a list of proteins (proteome) or metabolites (metabolome) that can be used as target for personalized therapy. This approach has been realized in oncology [2] and in organ transplantation [5]. Recent publications [22,23] demonstrate that in the last decade omics, such as proteomics and metabolomics, have been considered an unbiased new approach to detect unidentified biomarkers of CKDs. Integrating the large omic data sets with drug and disease databases could guide the prediction of drug efficacy and side effects. Moving from the empirical to individualized therapy, three key factors are needed in order to define specific treatments: refined molecular fingerprints, clinical biomarkers and multiple therapeutic options [24].

These approaches have the potential to diagnose CKDs with a higher degree of accuracy than traditional diagnostic methods. Moreover, it is possible to provide a powerful tool for development of new therapeutic strategies and for monitoring the benefit of administered drugs. Biomarkers that predict response to therapy could be used to choose the most appropriate regimen for individual patient. Therefore, it is hoped for providing a framework of omic data that expands research into individualized therapies for kidney diseases. This approach could help the clinicians to reduce the chance of fails when they move to clinical trials of new drugs from Phase II to Phase III.

Future clinical trials on new therapeutic strategies should be based not only on the evaluation of serum creatinine and estimated GFR and daily proteinuria but also on the use of omics classifiers that represent a new approach to predict the drug efficacy.

Declaration of interest

This paper has been partially supported by grants PS 144/07 and 44/09 of the Puglia Region and by grant PON-REC 02-00134/11 of the Ministry of University, Italy and by the Schena Foundation, Valenzano, Italy. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.