Variants  associated  with  tacrolimus  troughs  in  European  American  kidney  transplant  recipients:  A  genome  wide  association  study    Jacobson  PA1;  Miller  MB2,  Schladt  D3,  Israni  A4,  Sanghavi  K1,  Dorr  C4,  Remmel  RP5,  Guan  W6;  Matas  AJ7;  Oetting  WS1  for  the  DeKAF  and  GEN03  investigators  

1Experimental  and  Clinical  Pharmacology,  College  of  Pharmacy,  University  of  Minnesota,  2Department  of  Psychology,  University  of  Minnesota,    3Minneapolis  Medical  Research  Foundation,  Hennepin  County  Medical  Center,  Minnesota,    4Department  of  Nephrology,  Hennepin  County  Medical  Center  and  University  of  Minnesota,  5Department  of  Medicinal  Chemistry,  College  of  

Pharmacy,  University  of  Minnesota,    6Department  of  Biostatistics,  University  of  Minnesota    7Department  of  Surgery,  University  of  Minnesota  

RESULTS                                  

CONCLUSION  We   identiTied   CYP3A5*3,   CYP3A4*22   and   CYP3A4*3   as   top   most   variants   important   towards  tacrolimus  trough  using  GWAS.  Days  post-­‐transplant,  recipient  age,  GRF  at  time  of  trough,  weight  at  baseline,    primary  disease,  calcium  channel  blocker  use,  ace-­‐inhibitor  use  and  antiviral  use  were  clinical   factors   signiTicant   towards   tacrolimus   dose-­‐normalized   trough   concentrations.   These  variants  should  be  tested  in  future  studies  in  European  Americans.  

INTRODUCTION  Tacrolimus  is  an  immunosuppressive  agent  highly  dependent  on  CYP3A4  and  CYP3A5  for  its   metabolism.[1,2]   Variability   in   its   pharmacokinetics   and   narrow   therapeutics   index  necessitates   close   monitoring   of   tacrolimus   concentrations.   Compared   to   African  Americans,  European  Americans  require  lower  doses  to  achieve  target  concentrations.[2,3]    In  our  previous   study,   conducted   in  695  kidney   transplant   recipients,   clinical   factors  and  genotypes   together   explained   ~50%   of   variability   in   dose   normalized   trough  concentrations.[4]   Majority   of   European   Americans   (~90-­‐95%)   carry   the   non-­‐functional  CYP3A5*3   allele   and   hence   are   poor   CYP3A5   substrate   metabolizers.   In   our   study   the  CYP3A5*3  was   the  most   inTluential   variant.   [1,4]     After   accounting   for  CYP3A5*3,   a   large  part  of  the  pharmacokinetic  variability  remained  unexplained.    We  hypothesize  that  there  are  additional  genetic  variants  that  further  inTluence  tacrolimus    variability.      The  purpose  of  this  study  was  to  perform  a  genome  wide  search  to  Tind  additional  genetic  variants  speciTic  to  tacrolimus  metabolism  in  the  the  European  American  population.  

                                         

METHODS                            

REFERENCES  1.  Barbarino   JM,   Staatz   CE,   Venkataramanan   R,   Klein   TE,   Altman   RB.   Pharmacogenet  

Genomics.  2013;23:563-­‐85    2.  Staatz  CE,  Tett  SE.  Clin  Pharmacokinet.  2004;43:623-­‐53.    3.  Venkataramanan  R,   Swaminathan   A,   Prasad   T,   Jain   A,   Zuckerman   S,  Warty   V,   et   al.   Clin  

Pharmacokinet.  1995;29:404-­‐30.    4.  Jacobson  PA,  Oetting  WS,  Brearley  AM,  Leduc  R,  Guan  W,  Schladt  D,  et  al.  Transplantation.  

2011;91:300-­‐8    

ACKNOWLEDGMENTS   The   study   was   funded   by   National   Institute   of   Allergy   and  Infectious  Disease.  We  acknowledge   the  dedication   and  hard  work  of   our   coordinators:  Nicoleta  Bobocea,  Tina  Wong,  Adrian  Geambasu,  Alyssa  Sader,  Myrna  Ross,  Kathy  Peters,    Mandi  DeGrote,   Jill  Nagorski,  Lisa  Berndt,  Tom  DeLeeuw,  Wendy  Wallace,  Tammy  Lowe,  Catherine   Barker,   and   Tena   Hilario.   We   also   acknowledge   the   dedicated   work   of   our  research  scientists:  Marcia  Brott,  Becky  Willaert  and  Amutha  Muthuswamy.    

 Table  1:  Patient  Characteristics  

.    

    N  No.  of  subjects     1446  No.  of  trough  concentrations     25255  No.  of  male  subjects  (%)   908  (62.79)  Daily  dose  (mg)a   5.50  

(0.10-­‐36.00)  Tacrolimus    trough  (ng/mL)a   8.40  

(0.30-­‐75.90)  No.  of  subjects  in  each  baseline  weight  group  (kg)  (%)    0-­‐69  70-­‐81  82  to  95  >95  

   

350  (24.20)  330(22.82)  370  (25.59)  396  (27.39)  

No.  of  recipients  in  each  age  category  (%)    18-­‐34  years  35-­‐64  years  >64  years  

   165  (11.41)  1048  (72.48)  233  (16.11)  

No.  of  troughs  with  antiviral  drug  (%)   14127  (55.94)  No.  of  troughs  with  steroid  (%)   15915  (63.02)  No.  of  troughs  with  calcium  channel  blocker  (%)  

9213  (36.48)  

No.  of  troughs  with  ace  inhibitor  use  (%)   3238  (12.82)  Diabetes  at  baseline  (%)   564  (39.00)  Simultaneous  kidney-­‐pancreas  transplant  (%)  

120  (8.30)  

Donor  status    Living  (%)  Deceased(%)  

 961  (66.46)  485  (33.54)  

Variable   Group    Estimate  (95%  CI)   p-­‐value  For  each  day  post-­‐transplantb         1.06(1.06-­‐1.07)   7.60X10-­‐70  

Additional  effect  for  each  day  after  day  9  posttransplantc         0.94(0.93-­‐0.95)   9.00X10-­‐68  

Age  in  yrs  compared  to  >64  yrs   18-­‐34     0.75(0.69-­‐0.81)   6.30X10-­‐12  

      35-­‐64     0.87(0.83-­‐0.93)   6.60X10-­‐06  GFR  compared  to  >84  ml/min   55   1.06(1.02-­‐1.09)   4.30X10-­‐04  

      56-­‐68       1.04(1.01-­‐1.07)   2.00X10-­‐03  

       69-­‐84   1.03(1.01-­‐1.06)   1.70X10-­‐03  Weight  in  kg  compared  to  >95  kg    69   1.05(1.01-­‐1.09)   2.50X10-­‐02  

      70-­‐81   1.06(1.02-­‐1.1)   1.20X10-­‐03  

      82-­‐95   1.04(1.01-­‐1.07)   1.30X10-­‐02  

Diabetes  at  time  of  transplant           1.11(1.06-­‐1.16)   7.20X10-­‐06  

Living  donor           1.06(1.01-­‐1.11)   1.40X10-­‐02  

Male  donor           1.03(0.99-­‐1.08)   1.30X10-­‐01  

Steroid  use  at  time  of  trough           0.98(0.95-­‐1.01)   1.10X10-­‐01  

Ca  Channel  blocker  use    at  time  of  trough         1.05(1.03-­‐1.07)   3.10X10-­‐09  

ACE-­‐inhibitor  use  at  time  of  trough         0.97(0.95-­‐0.99)   6.00X10-­‐03  

Antiviral  use  at  time  of  trough         1.04(1.03-­‐1.05)   2.70X10-­‐11  Antibody  induction     combination     0.87(0.76-­‐0.99)   2.90X10-­‐02  

      monoclonal     0.98(0.93-­‐1.04)   5.20X10-­‐01  

      none     1.31(1.16-­‐1.49)   2.10X10-­‐05  

CYP3A5*3  (effect  of    1  G  allele)         1.85  (1.74-­‐1.20)   2.30X10-­‐92  CYP3A4*22  (effect  of    1  T  allele)         1.28(1.2-­‐1.38)   1.90X10-­‐12  CYP3A4*3  (effect  of    1  C  allele)         1.37(1.15-­‐1.62)   3.00X10-­‐04  

Table  3:  Final  regression  model    for  the  tacrolimus  dose-­‐normalized  troughs  in  Virst  6  months  post-­‐transplanta  

Subjects  were  European  American  kidney  transplant  recipients  (n=1446)  enrolled  in  our    multicenter  DEKAF    genomics  study  (NCT00270712)    who  received  tacrolimus    maintenance  therapy.    Tacrolimus    trough  concentrations  were  obtained  from  each  subject  in      the  Tirst  6months  (twice  each    week    for  the  Tirst  2  months  and    then  twice  in  each  month    up  to  6    months).  Trough  concentrations    were  targeted  to  8-­‐12  ng/mL  for    the  Tirst  3  months  and  6-­‐10  ng/mL    for  3-­‐6  months  posttransplant.    Table  1  shows  demographic  and  clinical  characteristics  of  the    subjects.  Genotyping:  DNA  was  from    peripheral  blood  and  genotyped  using  a  custom  exome-­‐plus    Affymetrix  TxArray  SNP  chip    containing  450,130    markers  after    QC.    Data  quality  control  was  conducted  using  PLINK  software.      Samples  were  dropped  if  they  had  less  than  98%    call  rate,  were  monomorphic,  did  not  pass  Hardy  Weinberg  equilibrium  testing,  Hapmap  concordance  rate    >  2%,    gender  mismatch,    or  were  identical  by  descent,  had  minor  allele  frequency  <1%.  Principal  component  analysis  and  visual  inspection  was  used  to  conTirm  European  ancestry.    

rs  number  (variant)   Allele    Frequencies  rs776746  (3A5*3)   G=93.20%   A=6.80%  rs35599367  (3A4*22)   C=94.36%   T=5.64%  rs4986910  (3A4*3)   T=99.15%   C=0.85%  

Table  2:  Genotype  frequencies  of  the  top  variants  

Figure  1  shows  the  GWAS  Manhattan  plots  of  association  of  variants  towards  the  tacrolimus  dose  normalized  trough  concentrations.  In  the  initial  unadjusted  analysis,  55  variants  were  signiTicant  (p<10-­‐8,  Figure  1A)    towards  trough  concentrations.    CYP3A5*3  was  the  top  most  signiTicant  variant  (p=6.88X  10-­‐49).    We  then  adjusted  the  analysis  for  CYP3A5*3  and  seven  variants   remained   signiTicant   (Figure   1B).   The   top   variant   was   CYP3A4*22   (p=2.77   X  10-­‐13).      We  then  adjusted  the  analysis  for  CYP3A5*3  and  CYP3A4*22  and    no  variants  were  GWAS  signiTicant  although  CYP3A4*3  was  top  most  although  it  did  not  meet  genome  wide  signiTicance   (p=0.001,  Figure  1C).  The  allele   frequencies   are   shown   in  Table  2.    The   Tinal  parameter  estimates  of  clinical  and  genetic  factors  obtained  after  multivariate  analysis  are  in   Table   3.   Figure   2   shows   the   plots   of   dose   normalized   concentrations   vs   time   by  genotypes.    

Statistics:  Linear  mixed  effects  regression  models  were  used  to  test  for  associations  between  natural  log  (ln)  transformed  dose  normalized  troughs  and  genotypes.    Visual  inspection  showed  that  weight  normalized  trough  concentrations  initially  started  low,  rose  quickly  until  day  9  post-­‐transplant.    Therefore,  we  initially  tested  the  association  of  variants  from  GWAS  with  the  estimated  day  9  trough  levels  from  a  simple  time  trend  model.  For  the  Tinal  model  (Table  3),  the  top  variants  were  adjusted  for  clinical  factors  that  were  identiTied  using  backward  selection  with  a  retention  p-­‐value  of  0.10  

adata  are  median  (range)  in  the  Tirst  6  months  posttransplant  

aData  shown  as  untransformed  estimates,  bFor  each  day  post-­‐transplant  (day  1  to  180)  there  is  a  daily  1.06  (6%)  increase  in  dose-­‐normalized  tacrolimus  troughs.  cThere  is  an  additional  effect  for  each  day  after  day  9  (day  10-­‐180)  where  dose-­‐normalized  tacrolimus  troughs  are  reduced  by  0.94(6%).  

Figure  1.    Manhattan  plots  of  association  of  dose  normalized  troughs  and  variants    

Figure  2:  Dose-­‐normalized  tacrolimus  trough  concentrations  vs  time  by  genotype    

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