Figure S1A . Directed acyclic graph (DAG) representing the
setting to estimate the effect of ABCG2 c.421 SNP, i.e., reduced
(vs. preserved) ABCG2 function resulting from variant allele carriage
(vs. wt homozygosity) on steady-state pharmacokinetics of MPA (MPA PK –
outcome, O). The measured “treatment” – ABCG2
c.421C>A genotype - is an instrument (black circle), since
ABCG2 activity (actual exposure) is not measured. The causal path (thick
black arrow) might be a direct one and/or mediated (dashed black arrow)
through an unmeasured (hypothetical) mediator, i.e., MPAG levels. Pale
red circles represent confounders (ancestors of both the treatment i.e.,
actual exposure, and the outcome), blue circles represent ancestor of
the outcome and green circles represent ancestors of (actual, but
unmeasured) exposure (ABCG2 activity). Gray arrows depict biasing paths.
Gray filled/outlined circles represent unmeasured variables - one is a
suggested but unmeasured confounder – SLCO1B3 c.334 SNP, and one
is unmeasured ancestor of the outcome- UGT1A9 c.98 SNP; the
others indicate transporter/enzyme activities (presumably) affected by
exposure/outcome ancestors (see text for details).
4. Number of baseline covariates that can interfere with the (tested)ABCG2 c.421 (ABCG2 activity) effect is high (although Figure S1A
is somewhat simplified). In a scenario in which the ABCG2 c.421variant would be “treatment”, none of them would meet the
“classical” definition of a confounder, since “treatment” is defined
at conception, and the current knowledge about possible epigenetic
regulation of ABCG2 is virtually non-existing. In such a case,
they would qualify as “ancestors of the outcome” (i.e., factors known
or suspected to affect MPA PK, thorough different mechanisms
[paths]). As illustrated in Figure S1A, when ABCG2 activity is
considered as an actual but unobserved “treatment” (but adequately
represented by an instrument), then some of these variables should
justifiably be considered ancestors of both the “treatment” (may
affect ABCG2 activity) and the outcome (may affect MPA PK, by different
mechanisms); 5. Variables that may be considered ancestors of both the
“treatment” and the “outcome” (depicted in pale red in Figure S1A)
include: i) type of CNI (CsA or tacrolimus). They are both (in
vitro ) potent ABCG2 inhibitors, but it is possible that in vivo(at therapeutic doses) they differ in their inhibitory effect –in vitro , CsA is particularly (and more) potent when the number
of transporter is reduced [12] (as in the case of the ABCG2c.421 SNP). Next, CsA inhibits ABCC2 (ABCC2 activity is another
unmeasured variable in this setting) and affects MPAG/MPA recirculation
and exposure, while tacrolimus does not [13]. Also, both CsA and
tacrolimus may both inhibit and induce ABCB1 activity (a further
unmeasured variable) [14], and may differ in this respect, and MPA
is a substrate of ABCB1 [15]. Also, CsA, but not tacrolimus, is
listed among SLCO inhibitors [15] – thus, it can affect SLCO1B1
and/or 1B3 activity (further unmeasured variables), and MPAG is a
substrate of both [1]; ii) ABCB1 2677/345/1236 SNPs (as
diplotypes, since in LD) reflect on ABCB1 activity (not measured), hence
they affect MPA (outcome), and also the exposure: both CsA and
tacrolimus are also ABCB1 substrates [14], hence altered ABCB1
activity may reflect on their trough concentrations, and this may result
in a variable effect on ABCG2 activity (exposure); iii) ABCC2 -24
or/and 1249 SNPs may reflect on ABCC2 activity and MPAG is an ABCC2
substrate. Also, although ABCC2 is not considered relevant in CsA and
tacrolimus pharmacokinetic pathways [10], ABCC2 (and -24/1249 SNPs)
may affect tacrolimus [16] – hence, affect its concentrations which
might reflect on its effect on ABCG2 activity; iv) donor’s ABCC2
1249 SNP might reflect on MPA (presumably, by affecting MPAG in the
kidney) [17], and, at least theoretically, on tacrolimus [16]
(although renal excretion is of minor relevance for tacrolimus
[10]), and thus contribute to the variability of tacrolimus effect
on ABCG2 activity; v) UGT2B7 -161 SNP is in a complete LD with
the UGT1B7 802 SNP [18], hence it “represents” the 802 SNP.
By affecting UGT2B7 activity (not measured), it would affect MPA
glucuronidation. On the other hand, one study demonstrated direct
glucuronidation of CsA and tacrolimus in human gut and liver by UGT2B7
[19] – hence, it might affect CNI concentrations, and,
consequently, their effect on ABCG2 activity; vi) SCLO1B1 521 SNP
(and linked SNPs) – may affect SLCO1B1 activity (not measured) and MPAG
is a substrate to SLCO1B1. However, SLCO1B1 may also transport
tacrolimus [20], hence affect its concentrations and the effect on
ABCG2; vii) serum albumin levels and diseases that might interfere with
pharmacokinetics of MPA and of CNI – Figure S1A is simplified in that
these factors were considered jointly (since also possibly
inter-related): hypoalbuminemia is known factor affecting exposure to
MPA, and various systemic conditions in the early post-transplant stage
can be reasonably considered as factors that could affect both exposure
to MPA and CNI levels (and, thus, CNI effects on ABCG2 activity); viii)
Food (concomitant) may interfere with absorption of both MPA and CNIs
(and their concentrations); ix) Renal function and its commonly
measurable “proxy” – estimated creatinine clearance (eCrCl) – may
reflect on bioavailability of MPA and of CNI (although, this is a minor
pathway for CNIs [10]), hence on both “exposure” and the
“outcome”; x) age, body mass index (Figure S1B is simplified in that
it combines these two demographic factors and omits all possible
interconnections between demographics, concomitant morbidity, liver and
renal function) – have been found related to bioavailability of all
immunosuppressants (at least to some extent; e.g., by reflecting on
renal function, liver function, or in any other way), hence they affect
both the “exposure” and the “outcome”. 6. Unobserved (known)
confounder – SLCO1B3 c.334 SNP (and linked SNPs). In complex
pharmacogenetic settings, a number of unmeasured/unknown confounders are
possible. SLCO1B3 c.334 SNP would qualify as a known (although
not unambiguously) possible confounder which however remained unobserved
(patients were not genotyped for this SNP). By (presumably) affecting
SLCO1B3 activity it would reflect on MPAG (MPAG is a substrate), but it
may also reflect on tacrolimus concentrations [20], and thus on its
effect on ABCG2 activity. 7. Ancestors of the outcome (depicted in blue
in Figure S1A). A number of covariates qualified as ancestors of the
outcome (they can be plausibly related to MPA PK, but not to
“exposure”, i.e., ABCG2 activity): i) drugs affecting MPA PK (by
effects on UGTs, transporters or by any other mechanism) – Figure S1A
is simplified in that it considers all such drugs jointly and omits
their relationship to “effector molecules”, e.g., UGT enzymes, ABCB1,
ABCC2, SLCO1B1/B3 or others; ii) MPA formulation and MPA dose – IR MMF
and EC-MPA formulations are not bioequivalent; they deliver different
molar doses and MPA concentration-time profiles are not equivalent
[21]. Clearly, choice of formulation (specific molar dose and
release particulars) affects bioavailability of MPA; iii) UGT1A9
-2152/-275 SNPs (as diplotypes, since in complete LD) reflect on the
enzyme activity (unmeasured), and thus on MPA PK; 8. Unobserved
(known) ancestor of the outcome – UGT1A9 c.98T>C :
variant allele carriage has been suggested (although with high
uncertainty) associated with higher exposure to MPA, but its prevalence
is very low. 9. Ancestors of exposure (depicted in Figure S1A in green).
Variables that affect exposure and have no effect on the outcome (apart
that executed through their effect on exposure) are instrumental
variables. When actual exposure is quantified, adjustment for
instruments worsens or introduces bias, does not remove it [5-7]. In
the present study, actual exposure is unobserved, and we use an
instrument (ABCG2 c.421C>A genotype). In such a
setting, accounting for other instrumental or near-instrumental
variables (affect “exposure”, while effect on the outcome is minor) is
needed in order for the instrument to adequately represent the (actual)
exposure: i) drugs that interfere with ABCG2 activity (beyond CNIs).
Figure S1A is simplified in that it considers all such drugs jointly. It
also allows for a possibility that these drugs could (by any mechanism)
affect CNI concentrations (which could also reflect on ABCG2 activity);
ii) CNI concentration (morning trough at the beginning of the 12-hour
MPA sampling period) regardless of the CNI type [ln(tacrolimus)
troughs rescaled to ln(CsA troughs) scale by linear transformation].
CNI concentration is a descendant of several variables that may affect
it. Putting both “CNI type” and “CNI concentration” into the network
is reasonable: in some aspects, tacrolimus and CsA differ qualitatively
(e.g., CsA inhibits ABCC2 and SLCO1B1, tacrolimus does not), while the
effect on “exposure” (ABCG2 activity) might be concentration-dependent
(along with a possibility of a qualitative difference between the two);
iii) CNI dose directly affects CNI concentrations; iv) CYP3A4/5SNPs (those genotyped in the present sample and other potentially
relevant [10]) may affect CNI concentrations; v) drugs affecting
pharmacokinetics of CNIs – Figure S1A is simplified in that it
considers all such drugs jointly and omits their mechanisms (e.g.,
effects on CYPs, transporters or any other “effector”).
Figure S1B contains all the same elements as Figure S1A, but depicts the
minimal adjustment set required (and sufficient) to block biasing paths,
as to (unbiasedly) estimate the causal effect of treatment (ABCG2
c.421C>A variant allele – reduced transporter function)
on the outcome (MPA PK): adjustment (by different means) for
outcome/exposure ancestors (but not for colliders) [5-7]. The
minimal adjustment set includes (variables depicted in Figure S1B as
open, black-outlined circles): i) ABCB1 2677/3435/1236 SNPs
considered as diplotypes (since in strong LD; 3 levels based on the
number of variant alleles) – by matching; ii) ABCC2 -24 and1249 SNPs and also donor’s ABCC2 1249 SNP, dichotomized as
variant carriers and wt subjects – by matching (the latter also by
statistical adjustment); iii) UGT2B7 -161 SNP (represents the 802
SNP since in a complete LD), dichotomized as variant allele or wt – by
matching; iv) UGT1A9 -2152 and -275 SNPs considered as
diplotypes (since in complete LD), dichotomized as variant or wt
diplotype – by matching; v) SLCO1B1 521 SNP (may include other
SNPs in a complete LD), dichotomized as variant or wt – by matching;
vi) Food-drug interaction – by clinical procedure: blood sampling after
an overnight fast; vii) MPA formulation (IR MMF or EC-MPA) – by
matching; viii) MPA dose – all PK parameters calculated using
dose-adjusted MPA concentrations; ix) CNI type (CsA or tacrolimus) – by
matching; x) CNI concentration (trough) – by matching; xi) Age, BMI –
by matching (latter also by statistical adjustment); xii) Renal function
– by inclusion criteria (patients had to have by at least 33% improved
creatinine vs. post-operative Day 1 with absolute value <300
µmol/L and stable diuresis);