| Literature DB >> 33437865 |
Ehsan Nobakht1, Muralidharan Jagadeesan1, Rohan Paul1, Jonathan Bromberg2, Sherry Dadgar1,3.
Abstract
Desirable outcomes including rejection- and infection-free kidney transplantation are not guaranteed despite current strategies for immunosuppression and using prophylactic antimicrobial medications. Graft survival depends on factors beyond human leukocyte antigen matching such as the level of immunosuppression, infections, and management of other comorbidities. Risk stratification of transplant patients based on predisposing genetic modifiers and applying precision pharmacotherapy may help improving the transplant outcomes. Unlike certain fields such as oncology in which consistent attempts are being carried out to move away from the "error and trial approach," transplant medicine is lagging behind in implementing personalized immunosuppressive therapy. The need for maintaining a precarious balance between underimmunosuppression and overimmunosuppression coupled with adverse effects of medications calls for a gene-based guidance for precision pharmacotherapy in transplantation. Technologic advances in molecular genetics have led to increased accessibility of genetic tests at a reduced cost and have set the stage for widespread use of gene-based therapies in clinical care. Evidence-based guidelines available for precision pharmacotherapy have been proposed, including guidelines from Clinical Pharmacogenetics Implementation Consortium, the Pharmacogenomics Knowledge Base National Institute of General Medical Sciences of the National Institutes of Health, and the US Food and Drug Administration. In this review, we discuss the implications of pharmacogenetics and potential role for genetic variants-based risk stratification in kidney transplantation. A single score that provides overall genetic risk, a polygenic risk score, can be achieved by combining of allograft rejection/loss-associated variants carried by an individual and integrated into practice after clinical validation.Entities:
Year: 2021 PMID: 33437865 PMCID: PMC7793397 DOI: 10.1097/TXD.0000000000001102
Source DB: PubMed Journal: Transplant Direct ISSN: 2373-8731
A panel of genetic predictors for transplant outcomes
| Reference | Gene | Physiologic function | SNP identifier | Associations with clinical outcomes |
|---|---|---|---|---|
| Reeves-Daniel et al[ | ApoL1 | Trypanosome killing function | rs71785313rs60910145rs73885319 | Reduced kidney allograft survival |
| Tonnerre et al[ | MICA | Stress-induced protein regulated at the cell surface | rs2596538rs67841474 | Anti-MICA sensitization and increased proteinuria in kidney transplant recipients and is a predictor of susceptibility to CMV infection |
| Eikmans et al[ | TLR3 | Cell-bound receptor involved in innate immune system | rs3775296 | Increased acute kidney allograft rejection |
| Hwang et al[ | TLR4 | Binds to endogenous ligands released from damaged tissues and exogenous ligands such as lipopolysaccharide | rs10759932 | Increased rejection-free survival rate |
| Eikmans et al[ | FCN2 | Soluble recognition molecule that can engage apoptotic and necrotic cells | rs7851696 | Reduced incidence of severe kidney allograft rejection and graft loss |
| Steers et al[ | LIMS1 | A minor histocompatibility antigen | rs893403 | Increased kidney allograft rejection |
| Oetting et al[ | MIR146A | Modulated Treg and suppression of inflammatory responses | rs2910164 | Increased kidney allograft rejection |
| Moore et al[ | CAV1 | Involved in cholesterol transport and transmembrane signaling | rs4730751 | Increased kidney allograft failure |
| Forconi et al[ | PD-1 | Involved in the dysfunction of HIV-specific T cell response and CMV-specific CD8 T cells | rs11568821 | Improved kidney allograft survival in recipients from CMV-positive donors |
| Vu et al[ | IFN-γ | Involved in immune response to viral and bacterial infections | rs2430561 | Increased risk for the CMV infection |
| Moore et al[ | ABCB1 | An efflux pump for intestinal transport of medications including tacrolimus | rs1045642rs2229109 | Increased risk of renal allograft loss |
| Dessing et al[ | NLRP3 | NOD-like receptor family, pyrin domain containing 3 is a member of inflammasome family with a causal role in several inflammatory disorders | rs35829419rs6672995 | Increased acute kidney allograft rejection with rs35829419 andReduced acute kidney allograft rejection with rs6672995 |
| Abdi et al[ | CCR5 | Chemokine receptor specific for the proinflammatory chemokines | rs1799987 | Increased acute kidney allograft rejection |
| Abdi et al[ | CCR2 | Involved in immune response including monocyte recruitment and T cell proliferation | rs1799864 | Increased acute kidney allograft rejection |
| Park et al[ | IL2RB | Stimulating T-cell proliferation through complex of IL2RA-IL2RB-IL2 | rs228942rs228953 | Increased acute kidney allograft rejection episodes |
| Marshall et al[ | IL6 | A pleiotropic cytokine with proinflammatory and anti-inflammatory properties | rs1800795 | Increased acute kidney allograft rejection |
| Sankaran et al[ | IL10 | An immunomodulatory cytokine with anti-inflammatory effects | rs1800896 | Increased acute kidney allograft rejection |
| Alakulppi et al[ | TNF-α | Proinflammatory cytokine | rs1800629 | (rs1800629 in Donor and Recipient)Increased acute kidney allograft rejection episodes(rs1800629 in Recipient)Modulates the effect of ATG treatment |
| Tinckam et al[ | TGF-β | Anti-inflammatory but profibrotic cytokine | rs1982073rs1800471 | Reduced risk of late acute kidney allograft rejections with rs1800471 and increased kidney allograft subclinical rejection with rs1982073 |
| Pawlik et al[ | CD 28 | A costimulatory molecule involved in T cell-mediated immune response | rs3116496 | Increased acute kidney allograft rejection |
| Golshayan et al[ | MBL2 | Complement-activating MBL, a soluble pattern recognition receptor | rs7096206 rs5030737 rs1800450 rs1800451 | Increased acute kidney allograft rejection |
| Canossi et al[ | CTLA4 | CTLA4 transduces signals that inhibit lymphocyte activation | rs231775 rs3087243 | Reduced acute kidney allograft rejection with rs231775 and increased acute kidney allograft rejection with rs3087243 |
| Heidenreich et al[ | Factor II | Prothrombotic factor | rs1799963 | Increased acute kidney allograft rejection, especially vascular rejections, and early allograft failure |
| Heidenreich et al[ | Factor V Leiden | Prothrombotic factor | rs6025 | Increased acute kidney allograft rejection especially vascular rejections |
| Heidenreich et al[ | MTHFR | Prothrombotic factor | rs1801133 | Increased acute kidney allograft rejection, especially vascular rejections |
| Cartron et al[ | FCGR3A | Encodes the IgG Fc receptor | rs396991 | Increased risk of infection following Rituximab in recipients of liver transplant |
The panel is not exhaustive of all published literature.
ABCB1, ATP binding cassette subfamily B member 1; ApoL1, Apolipoprotein 1; ATG, antithymocyte globulin; CAV1, caveolin-1; CCR, chemokine receptor; CMV, cytomegalovirus; CTLA, cytotoxic T-lymphocyte antigen; IFN-γ, interferon-gamma; IL2RB, IL2 Receptor Beta; MBL, mannose-binding lectin; MICA, MHC class I-related chain A; MiR, microRNA; MTHFR, methylenetetrahydrofolate reductase; NLRP3, NOD-like receptor family, pyrin domain containing 3; SNP, single nucleotide polymorphism; TGF, transforming growth factor; TLR, Toll-Like Receptor; TNF-α, tumor necrosis factor-alpha; Treg, regulatory T cells.
Gene-drug pairs with sufficient evidence for at least 1 prescribing action to be recommended
| Author | Gene | Medication | Pharmacogenetics implications |
|---|---|---|---|
| Birdwell et al[ | CYP3A5 | Tacrolimus | Higher starting dose at 1.5–2 times standard dose, not exceeding 0.3 mg/kg/d in CYP3A5 extensive metabolizer or intermediate metabolizer. |
| Birdwell et al[ | CYP3A4 | Tacrolimus | Higher starting dose as above |
| Elens and Haufroid[ | POR | Tacrolimus | POR*28 homozygosity is associated with a significant higher CYP3A4 activity in those who are CYP3A5 nonexpressers |
| Relling et al[ | TPMT | Azathioprine | Reduce initial dose in TPMT heterozygous with 1 of alleles *2, *3A, *3B, *3C, and *4 |
| Relling et al[ | NUDT15 | Azathioprine | Reduce initial dose for NUDT15 intermediate metabolizer. Consider an alternative agent for NUDT15 poor metabolizer |
| CPIC[ | HPRT1 | Mycophenolic acid | Consider using alternative agent in HGPRT deficiency |
| Crews et al[ | CYP2D6 | Codeine Oxycodone | Use alternative analgesics in CYP2D6 poor metabolizers or ultra-rapid metabolizers |
| Moriyama et al[ | CYP2C19 | Voriconazole Clopidogrel | Use an alternative agent other than voriconazolein CYP2C19 ultra-rapid or rapid or poor metabolizersUse an alternative agent other than Clopidogrel in patients with at least 1 decreased function allele |
| Johnson et al[ | VKORC1 | Warfarin | Consider an alternative oral anticoagulant/calculate warfarin dosing according to CPIC guideline pharmacogenetic algorithm |
| Johnson et al[ | CYP2C19 | Warfarin | Consider an alternative oral anticoagulant/calculate warfarin dosing according to CPIC guideline pharmacogenetic algorithm |
| Johnson et al[ | CYP4F2 | Warfarin | Consider an alternative oral anticoagulant/calculate warfarin dosing according to CPIC guideline pharmacogenetic algorithm |
| SEARCH Collaborative Group[ | SLCO1B1 | Simvastatin | Use an alternative agent or a reduced dose of simvastatin in patients with at least 1 reduced function allele |
| Hershfield et al[ | HLA-B*58:01 | Allopurinol | Avoid allopurinol in patients with at least 1 HLA-B*58:01 allele |
aCPIC guideline pharmacogenetic algorithm https://cpicpgx.org/content/guideline/publication/warfarin/2017/28198005.pdf.
CPIC, Clinical Pharmacogenetics Implementation Consortium; CYP, Cytochrome P450; HGPRT, hypoxanthine-guanine phosphoribosyl-transferase; NUDT15, nucleoside diphosphate linked moiety X-type motif 15; SLCO1B1, solute carrier organic anion transporter family member 1B1; TPMT, thiopurine methyltransferase; VKORC1, vitamin K epoxide reductase complex subunit 1.
FIGURE 1.A panel of genetic variants for transplant recipients and donors. This panel functions as an additional tool at disposition of transplant physicians to provide individualized care. Clinical validation through prospective trials supporting the clinical decision outlined is required. ABCB1, ATP binding cassette subfamily B member 1; ApoL1, Apolipoprotein L1; CAV1, caveolin-1; CCR, chemokine receptor; CTLA, Cytotoxic T-Lymphocyte Antigen; IFN-γ, interferon-gamma; IL2RB, IL2, Receptor Beta; MBL, mannose-binding lectin; MICA, MHC class I-related chain A; MiR, microRNA; MTHFR, methylenetetrahydrofolate reductase; NLRP3, NOD-like receptor family, pyrin domain containing 3; TGF-β, transforming growth factor; TLR, Toll-Like Receptor; TNF-α, tumor necrosis factor-alpha.
FIGURE 2.Integration of clinical decision support (CDS) tools with electronic health records (EHRs) can facilitate the use of available actionable genetic information. Adjustment of dose of a drug according to glomerular filtration rate (GFR) through an alert system in EHR is an example of precision prescribing. Similarly, relevant genetic information can be incorporated to EHR and provide guidance to clinicians for precision prescribing. Further studies are required to validate the proposed model.
FIGURE 3.Recognition of interindividual differences is becoming possible through integration of pharmacogenetics, pharmacoproteomics, epigenetics, and noncoding RNAs data into clinical practice. Further studies are required to validate the proposed model. ADR, adverse drug reaction.
FIGURE 4.A single score that provides overall genetic risk, a polygenic risk score (PRS) can be achieved by combining of allograft rejection/loss associated-variants carried by an individual and in conjuncture with pharmacogenetics may be integrated into practice after clinical validation through prospective clinical trial supporting the clinical decision outlined. CMV, cytomegalovirus; CYP, Cytochrome P450.