Protocol for Bayesian combined multi-genotype and concentration informed tacrolimus dosing in paediatric solid organ transplantation (BRUNO-PIC)

Protocol for Bayesian combined multi-genotype and concentration informed tacrolimus dosing in paediatric solid organ transplantation (BRUNO-PIC) | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Protocol for Bayesian combined multi-genotype and concentration informed tacrolimus dosing in paediatric solid organ transplantation (BRUNO-PIC) View ORCID Profile Dhrita Khatri , View ORCID Profile Andreas Halman , Joshua Kausman , View ORCID Profile Jacob Mathew , View ORCID Profile Elizabeth Bannister , View ORCID Profile Tayla Stenta , View ORCID Profile Claire Moore , View ORCID Profile Elizabeth Williams , View ORCID Profile Roxanne Dyas , View ORCID Profile Julian Stolper , View ORCID Profile Cecilia Moore , View ORCID Profile Mark Pinese , View ORCID Profile Rishi S. Kotecha , View ORCID Profile Christopher Gyngell , View ORCID Profile Sebastian Lunke , View ORCID Profile John Christodoulou , View ORCID Profile Amanda Gwee , View ORCID Profile Rachel Conyers , View ORCID Profile David Metz doi: https://doi.org/10.1101/2025.05.06.25327120 Dhrita Khatri 1 Cancer Therapies Group, Murdoch Children’s Research Institute , Parkville, Australia 2 Department of Paediatrics, The University of Melbourne , Parkville, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dhrita Khatri Andreas Halman 1 Cancer Therapies Group, Murdoch Children’s Research Institute , Parkville, Australia 3 Victorian Clinical Genetics Services , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andreas Halman Joshua Kausman 2 Department of Paediatrics, The University of Melbourne , Parkville, Australia 4 Department of Nephrology, The Royal Children’s Hospital , Melbourne, Australia 5 Murdoch Children’s Research Institute , Parkville, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jacob Mathew 6 Department of Cardiology, The Royal Children’s Hospital , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jacob Mathew Elizabeth Bannister 5 Murdoch Children’s Research Institute , Parkville, Victoria, Australia 7 Department of Gastroenterology, The Royal Children’s Hospital , Melbourne, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elizabeth Bannister Tayla Stenta 1 Cancer Therapies Group, Murdoch Children’s Research Institute , Parkville, Australia 2 Department of Paediatrics, The University of Melbourne , Parkville, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tayla Stenta Claire Moore 1 Cancer Therapies Group, Murdoch Children’s Research Institute , Parkville, Australia 2 Department of Paediatrics, The University of Melbourne , Parkville, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Claire Moore Elizabeth Williams 1 Cancer Therapies Group, Murdoch Children’s Research Institute , Parkville, Australia 2 Department of Paediatrics, The University of Melbourne , Parkville, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elizabeth Williams Roxanne Dyas 1 Cancer Therapies Group, Murdoch Children’s Research Institute , Parkville, Australia 2 Department of Paediatrics, The University of Melbourne , Parkville, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Roxanne Dyas Julian Stolper 1 Cancer Therapies Group, Murdoch Children’s Research Institute , Parkville, Australia 2 Department of Paediatrics, The University of Melbourne , Parkville, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julian Stolper Cecilia Moore 2 Department of Paediatrics, The University of Melbourne , Parkville, Australia 8 Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute , Parkville, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cecilia Moore Mark Pinese 9 Children’s Cancer Institute, Lowy Cancer Centre , UNSW Sydney, NSW, Australia 10 School of Clinical Medicine, UNSW, Medicine and Health , UNSW Sydney, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mark Pinese Rishi S. Kotecha 11 Department of Clinical Haematology, Oncology, Blood and Marrow Transplantation, Perth Children’s Hospital , Nedlands, Western Australia, Australia 12 Curtin Medical School, Curtin University , Bentley, Western Australia , Australia 13 Leukaemia Translational Research Laboratory, WA Kids Cancer Centre, The Kids Research Institute Australia, University of Western Australia , Nedlands, Western Australia, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rishi S. Kotecha Christopher Gyngell 2 Department of Paediatrics, The University of Melbourne , Parkville, Australia 5 Murdoch Children’s Research Institute , Parkville, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christopher Gyngell Sebastian Lunke 3 Victorian Clinical Genetics Services , Melbourne, Australia 5 Murdoch Children’s Research Institute , Parkville, Victoria, Australia 14 Department of Pathology, The University of Melbourne , Parkville, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sebastian Lunke John Christodoulou 2 Department of Paediatrics, The University of Melbourne , Parkville, Australia 3 Victorian Clinical Genetics Services , Melbourne, Australia 5 Murdoch Children’s Research Institute , Parkville, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John Christodoulou Amanda Gwee 2 Department of Paediatrics, The University of Melbourne , Parkville, Australia 15 Infectious Disease Department, The Royal Children’s Hospital , Parkville, Australia 16 Antimicrobials Group, Murdoch Children’s Research Institute , Parkville, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Amanda Gwee Rachel Conyers 1 Cancer Therapies Group, Murdoch Children’s Research Institute , Parkville, Australia 2 Department of Paediatrics, The University of Melbourne , Parkville, Australia 17 Children’s Cancer Centre, The Royal Children’s Hospital , Parkville, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rachel Conyers David Metz 4 Department of Nephrology, The Royal Children’s Hospital , Melbourne, Australia 5 Murdoch Children’s Research Institute , Parkville, Victoria, Australia 18 Department of Paediatrics, Monash University , Melbourne, VIC, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David Metz For correspondence: david.metz{at}mcri.edu.au Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Introduction Tacrolimus is an immunosuppressant used extensively in solid organ transplantation. Whilst highly effective in preventing organ rejection, it has a narrow range of safe and effective concentrations and wide pharmacokinetic variability, which can lead to and suboptimal outcomes or unacceptable toxicities. Importantly, much is known about the sources of pharmacokinetic variability. There is a clear link between body size, pharmacogenetic variants of cytochrome P450 (CYP) 3A4 and CYP3A5 and typical dose requirement. Increasing individualisation of initial dose is expected to increase the proportion of transplant recipients with tacrolimus concentrations within the acceptable range in the initial post-transplant week. Subsequently, maximum a posteriori Bayesian dosing can better maintain concentrations within the acceptable range over time. Together, this should lead to improved patient outcomes. Method and analysis BRUNO-PIC is an open-label trial with a prospective intervention arm and a retrospective standard of care comparator arm. The prospective arm evaluates covariate informed initial dosing combined with Bayesian dose adjustment in children undergoing kidney, liver or heart transplant. CYP3A5 and CYP3A4 genotyping will be combined with allometric size scaling to predict initial tacrolimus dose. Post-transplant dose adjustment will be guided by NextDose, a Bayesian dosing platform that incorporates genotype, clinical characteristics, measured tacrolimus concentrations and a population pharmacokinetic (popPK) model to inform initial dosing and guide dose adjustments following transplant. The primary objective of this study is to determine whether genotype-informed Bayesian dosing of tacrolimus leads to better achievement of tacrolimus concentrations within the acceptable range over the first 8 weeks post-transplant in paediatric solid organ transplant (SOT) recipients (kidney, heart and liver), when compared to a retrospective historical control group using standard of care dosing. The primary outcome is the proportion of each cohort with tacrolimus concentration (steady-state average concentration) within the acceptable range of 80-125% of target at post-transplant dosing day 4 (DD4), at week 3 and at week 8. Secondary outcomes include the proportion with trough concentration within the acceptable range on DD4, the median time to acceptable range, and the time within acceptable range over the first 8-weeks post-transplant. Ethics and dissemination The ethics approval of the trial has been obtained from the Sydney Children’s Ethics Committee (2023/ETH02699). Findings will be disseminated through peer-reviewed publications and professional conference presentations. Trial registration ClinicalTrials.gov NCT 06529536 Strengths This is a prospective study in paediatric solid organ transplant recipients combining (a) pre-transplant CYP3A5 / 3A4 genotyping to improve initial tacrolimus dose, and (b) post-transplant Bayesian individualised tacrolimus dosing to increase time within the safe and effective range. Use of a target concentration approach based on average steady state concentrations is expected to increase exposure in individuals with low tacrolimus clearance and reduce overexposure in individuals with high tacrolimus clearance (e.g. CYP3A5 expressors). Limitations The lack of a randomised comparator arm in this prospective interventional trial precludes unconfounded determination of superiority over standard care. The intervention may not be generalisable to other transplant recipients (e.g. lung, intestinal, hematopoietic stem cell transplant). Protocol Version This trial is approved Sydney Children’s Ethics Committee (2023/ETH02699). The study is currently version 4, 26.05.2025 (Supplementary File 2) Funding BRUNO-PIC is funded by the 2023 Medical Research Future Fund – Genomics Health Future Mission (MARVEL-PIC (MRF/2024900). This funding source had no role in the design of this study and will not have any role during its execution, analyses, interpretation of the data, or decision to submit results. Sponsorship Murdoch Children’s Research Institute 1. INTRODUCTION 1.1 Background and rationale Patient outcomes from solid organ transplantation (SOT) saw major advances in the last two decades of the 20th century, with introduction of highly effective immunosuppressant drugs including tacrolimus, mycophenolate and anti-lymphocyte antibody induction 1 . However, despite appreciable short-term gains, long-term graft and patient outcomes remain suboptimal 2 3 . These suboptimal long-term outcomes are influenced both by immunosuppressant exposure – current and cumulative – in the maintenance phase 2 , but also to alloimmune events and subclinical rejection in the early months post-transplant 4 . Tacrolimus, a calcineurin inhibitor (CNI) immunosuppressant produced by the bacterium Streptomyces tsukubaensis 5 , is a core component of modern SOT immunosuppression. It is used in the majority of recipients, surpassing the CNI cyclosporine after superiority demonstrated in the Elite-Symphony trial in 2007 6 . As a “narrow therapeutic index” drug, tacrolimus requires concentration-controlled dosing 7 for safety and effective use 8 9 . Yet current practice - empiric dose adjustment to attain trough (pre-dose) tacrolimus concentrations within a “therapeutic window” 10 - only partially compensates for tacrolimus dose-response variability. The consequences of suboptimal dosing remain, with treatment failure in some 11 – 16 , unacceptable toxicities in others 2 3 17 . Initial tacrolimus dose Despite low incidence of early acute rejection in contemporary cohorts, acute rejection associated with low tacrolimus concentrations is still seen 18 – 22 . One predictable contributor to initial underexposure – if not accounted for – is the group of individuals with CYP3A5 expression, typically requiring 1.5 to 2-fold higher dose than those with non-expressor genotype 18 – 22 . This is compounded by slow empiric dose titration, with some recipients not reaching target for up to 3 weeks 23 . Greater “individualisation” of initial tacrolimus dosing increases the proportion of recipients within the acceptable concentration range in the first post-transplant week. Meta-analysis of randomised controlled trials (RCTs) demonstrates that CYP3A5 genotype-guided initial dosing increases the proportion of recipients within the acceptable range in the first week post-transplant from 31.5% to 44.1%, along with a reduction in median time to target 24 . Subsequent trials report an even higher proportion of recipients within the acceptable range, 54.8% and 58% respectively, using combined CYP3A4 and CYP3A5 genotypes and superior size scaling in adult kidney transplant recipients 23 25 . This has not been tested in children to date. Finally, whilst tacrolimus dose is typically scaled to size by simple linear function (dose/kg × body weight), linear scaling is inconsistent with the change in drug clearance with body size, leading to underexposure in young children 26 . This has been empirically shown for tacrolimus in paediatric transplantation, with recommendation for higher per-kilogram dose at younger age or lower body weight 27 28 . Alternatively, allometric scaling describes the relationship between size and drug clearance across all body sizes, as well as having robust theoretic underpinnings 26 29 . This allows accurate size scaling of dose from adults through to infancy or early childhood, down to the age at which the drug’s clearance mechanisms have matured (12 months of age for tacrolimus). Tacrolimus pharmacokinetics and initial dose Tacrolimus undergoes hepatic clearance, with metabolism by CYP3A4 , and by CYP3A5 in individuals who express this enzyme 30 31 . These drug-metabolising enzymes reside predominantly in the liver, though are also present in enterocytes, where metabolism and P-glycoprotein-mediated efflux impact bioavailability. All individuals express CYP3A4 , with minimal levels at birth, increasing throughout infancy and reaching full adult values by one year of age 32 . On the other hand, CYP3A5 expression is present in 10-15% of Caucasian populations, 40%-50% of African and 50-70% of Asian populations 33 . Individuals expressing CYP3A5 require higher tacrolimus dosing to achieve equivalent exposure early post-transplant 31 . The Clinical Pharmacogenetics Implementation Consortium (CPIC) and the French National Network of Pharmacogenetics (RNPGx) recommending a 1.5 to 2-fold increase in dose for both heterozygous and homozygous expressors, whilst the Dutch Pharmacogenetics Working Group (DPWG) recommending 1.5-fold increase for heterozygous expressors and 2.5-fold increase for homozygous CYP3A5 expressors 34 . Pharmacogenetic variations in CYP3A4 are quantitatively less pronounced than in CYP3A5 , but a clinically relevant association with the CYP3A4 *22 polymorphism and lower dose requirements is increasingly recognised 35 . The CYP3A4 *22 polymorphism is more common in European populations, where the association has been more clearly demonstrated 36 . In liver transplantation, the picture is more complex. Following transplantation, the recipient’s own CYP3A genotype contributes to CYP expression in enterocytes (influencing tacrolimus bioavailability), whilst the new liver contains the donor’s CYP3A genotype (contributing to both first-pass metabolism and drug clearance) 37 38 . Thus, the influence of recipient CYP3A genotype on tacrolimus disposition is less given impact on enterocyte CYP3A expression only. In popPK analyses in liver transplantation where both donor and recipient genotype were available, with the proportional increase in tacrolimus apparent clearance (CL/F) in recipient CYP3A5 expressers is around half of those with both donor and recipient CPY3A5 expresser status (average across studies, a 38% increase compared to a 78% increase, respectively) 37 – 41 . Thus, whilst there is precedence for applying the same proportion increase in tacrolimus dose for CYP3A5 expressers across all SOT recipients (including liver transplantation) 27 , we favour an empiric reduction (halving) in the influence of recipient genotype in liver transplant recipients, given available data and mechanistic basis. In addition, CYP isoenzyme expression is initially poor due to allograft ischaemia-reperfusion injury 42 , a pro-inflammatory state associated with cellular dysfunction, including downregulation of CYP isoenzyme expression 43 – 45 . Whilst liver transaminases typically start to decline within days post-transplant 46 47 , this does not directly correlate with CYP isoenzyme expression, with tacrolimus CL/F taking up to 2-3 weeks to reach full maturity 39 41 48 . Thus, with tacrolimus CL/F increasing over the initial weeks post liver transplantation (due to an increase in intrinsic hepatic clearance), dose requirement increases too, a dynamic ideally accounted for in dose adjustment predictions. Bayesian dosing to increase time within acceptable range of tacrolimus concentrations In addition to the consequences of low tacrolimus concentrations in the initial post-transplant week in SOT recipients, there is a robust association between the proportion of time with tacrolimus concentrations outside the acceptable range over the first 6-months, and negative clinical outcomes. This includes association with acute rejection 11 49 – 51 , donor-specific antibody formation 12 14 – 16 52 and post-transplant malignancy 53 , as well as overall poorer long-term patient and graft outcomes 50 52 54 – 58 . Yet maintaining tacrolimus concentrations within the acceptable range can be challenging, particularly in the initial post-transplant months. Published experience reports less than 60% of measurements within a trough concentration based therapeutic window 59 using a therapeutic window approach for dose adjustment (TWA) 7 . This is in part due to substantial between-occasion variability in tacrolimus PK early post-transplant, with contribution from variability in body size, haematocrit (HCT), prednisolone dose, as well as time-post transplant, an empirical predictor of early increased oral bioavailability 49 59 – 61 . In addition, empiric titration of dose based on TCA is both highly user-dependant and less accurate than pharmacokinetic-driven techniques 10 . Increasing time in range has been achieved using maximum a posteriori Bayesian estimation (Bayesian dosing), with superiority to TWA reported in two RCTs in adult kidney transplant recipients 62 63 . Bayesian dosing is a pharmaco-statistical technique that predicts a drug’s pharmacokinetic parameters in the individual - hence dose requirement - by leveraging prior population information about a drugs pharmacokinetic properties (a popPK model) with their measured drug concentrations and clinical characteristics. Maximum likelihood estimation is used to determine the “best fit” concentration time course to observed concentrations, from which is derived individual estimates of a drug’s PK parameters. There are then used to calculate dose required to achieve desired target concentration 10 . Although Bayesian dosing is routine in some transplanting centres 64 65 , these remain in the minority 66 67 . Barriers to implementation include (a) an evolving regulatory framework 68 ; (b) clinician acceptance, including choice of Bayesian platform and (c) lack of clinical trial evidence showing improvement in long-term outcomes 67 . On the final point, however, it is crucial to note the substantial challenge in proving clinical superiority by RCT in modern transplant cohorts, given low rates of events in the first 3-6 months post-transplant (rejection, graft failure) 64 65 69 . Whilst clearly advantageous, the low prevalence of hard endpoints in the first post-transplant year has made it harder to prove value of new interventions difficult, with very large numbers required for power to detect superiority 2 3 . This has led to academic 3 and regulatory calls 70 for innovative approaches, including surrogate biomarkers of long-term outcome. In this context, tacrolimus Bayesian dose adjustment has longstanding precedence for use in some parts of the world such as Europe and Asia 62 63 , along with RCT evidence in adult kidney transplant recipients showing increased time within the acceptable range in the immediate post-transplant week and over the subsequent 3 months 62 63 . This has not been evaluated in children to date. Bayesian dosing and NextDose There are a range of platforms for Bayesian dosing 59 67 . The use of NextDose, noting the developer (Prof Nick Holford) is a co-investigator on BRUNO-PIC 71 , allows for iterative refinement of the underlying model as required (e.g. for non-renal SOT recipient). Furthermore, there is published evidence supporting superiority of the tacrolimus model within NextDose. The Størset tacrolimus model used by NextDose was developed from a large population dataset, with 242 kidney transplant recipients and 3100 tacrolimus whole blood concentrations. This included 64 full PK profiles (> 8 concentrations per occasion), over 153 limited sampling profiles and 1546 trough concentrations 61 . It was developed by combining two separate populations, the pooled model outperforming those from which it was developed in external validation 61 . The model was developed using principles of mechanistic modelling for both structure and covariate introduction 61 72 , a principle with clear basis in pharmacometrics (and statistical modelling more generally) to reduce bias and enhance external validity 73 . Finally, its superiority has been reported in a published systematic review and external validation, e.g. “the model by Størset et al, which was comparatively the best of all 16 published models… ” 72 . Whilst the NextDose tacrolimus model was developed in an adult kidney transplant population, this is not a barrier to use in children who have reached an age when the clearance mechanisms are mature after which, from a PK perspective, children are “small adults” 26 . Full maturation of clearance mechanisms occurs before 1-2 years of age for tacrolimus. Therefore, application of allometric size scaling can be used to predict dose requirement in children using models developed with adult popPK (popPK) data 26 74 . Further, research groups have developed separate popPK models in children and in adults (excluding infancy) demonstrating similar PK parameters and concentration-time course (scaled for size) 64 . For use of NextDose in non-kidney transplantation, a further validation step is underway. Importantly, whilst the tacrolimus popPK model for NextDose was developed in a kidney transplant population, there is published precedence for pooling of popPK models between kidney and non-kidney transplant recipients 65 75 76 . These population “meta-models” show liver transplant recipients initially have 50% lower tacrolimus CL/F recovering to stable values after 2 weeks (mechanisms discussed above). Importantly, by adding a parameter to account for reduced clearance early post liver transplantation, models initially developed in kidney transplant recipients have been shown to be able to predict tacrolimus concentrations in liver transplant recipients 75 76 . Finally, both liver and heart transplant recipients have lower tacrolimus CL/F beyond the initial weeks 75 77 when compared with kidney transplant recipients 78 . This published data has been used to add additional parameters for reduced clearance in NextDose, to allow for first dose prediction in liver and heart transplant recipients following internal validation. Concentration target for dose titration: trough or average concentration The long-accepted paradigm for tacrolimus dose titration is to use a concentration measured just before a dose (Ctrough). However, whether this is sufficient to optimise safety and effectiveness of tacrolimus is being increasingly challenged 79 . Whilst Ctrough is a pragmatic target in clinical care 80 , it is used as a surrogate for overall drug exposure (the concentration area under the curve, AUC), the latter more directly linked with clinical effect. This is well established for cyclosporine, the predominant CNI before prior to tacrolimus 81 . For tacrolimus, whilst most of the exposure-response data is with Ctrough, the same principle is assumed to apply given same downstream pharmacodynamic mechanism 81 . This is supported by early exposure-response data linking AUC with drug effect 82 . Subsequently, real-world data has shown substantial between-subject variability in AUC at a given trough concentration 83 , with more recent data showing AUC as more predictive of late rejection than Ctrough in kidney transplantation 84 . There is substantial precedence for AUC-guided dosing as standard of care, from over two decades of use in certain regions with local expertise (e.g. Europe) 17 83 – 88 , though without broader global uptake. Those most likely to benefit from the more precise exposure-guided dosing are pharmacokinetic ‘outliers’, i.e. CYP3A5 expressers or those with other cause for high tacrolimus clearance 33 79 87 . These individuals typically require substantially higher dose to achieve the same tacrolimus Csstrough 89 90 , often translating to higher peak concentrations and higher overall tacrolimus systemic exposure 33 87 91 . Higher tacrolimus exposure despite Csstrough within acceptable range explain why these individuals see an increase toxicity including CNI nephrotoxicity 89 92 – 96 , BK virus nephropathy 97 – 99 and other toxicities 33 90 100 . Thus, with current tacrolimus dosing practice, CYP3A5 expressors are at risk of rejection if they fail to achieve acceptable exposure in the early post-transplant period, and toxicities over time due to chronic over-exposure, with overall inferior graft and patient survival 33 79 90 100 . Greater individualisation of initial dose, followed by Bayesian dosing to exposure (AUC), has potential to ameliorate both of these issues, in CYP3A5 expressors, and in PK outliers of other aetiology. 1.2 OBJECTIVES Primary Objective To determine whether genotype-informed Bayesian dosing of tacrolimus in paediatric SOT recipients (kidney, heart and liver) increases time with tacrolimus concentrations within the acceptable range over the first 8 weeks post-transplant, compared with a retrospective historical control group using standard of care dosing. Secondary objectives To determine the safety and effectiveness of using a genotype-informed Bayesian dosing of tacrolimus in SOT within the initial 8 weeks post-transplant (prospective arm only, descriptive) To compare achievement and maintenance of acceptable concentrations achieved with genotype-informed dosing of tacrolimus with a retrospective group using standard of care dosing. To evaluate the feasibility of using NextDose for concentration guided dosing in the context of paediatric SOT. 1.3 METHODS: TRIAL DESIGN BRUNO-PIC is a prospective, open-label interventional trial, with retrospective cohort as standard of care comparator. The prospective arm will recruit 45 children undergoing kidney, liver or heart transplantation, who will have pre-transplant genotyping of CYP3A4 and CYP3A5 , and initial tacrolimus dose using genotype and allometric size scaling. Subsequent dosing over the first 8 weeks post-transplant will be by Bayesian estimation using NextDose. The retrospective cohort will consist of approximately 120 eligible paediatric recipients of kidney, liver, or heart transplants at the RCH over a 5-year period (January 2019 to April 2024). This cohort represents current tacrolimus dosing practice at the Royal Children’s Hospital (RCH), which involves a standard mg/kg protocol followed by trough concentration measurement and empiric dose adjustment using the TWA. The ability of genotype-informed Bayesian-dosing to optimise tacrolimus exposure (concentrations within the acceptable range initially and over time) will be compared to the retrospective cohort. 2. METHODS: PARTICIPANTS, INTERVENTIONS, AND OUTCOMES 2.1. Trial setting BRUNO-PIC will be conducted at a single tertiary institute in Victoria, Australia, enrolling children undergoing SOT (either kidney, liver or heart) at the RCH. 2.2. Eligibility criteria Inclusion criteria ▪ Age 1-18 years of age ▪ Kidney, liver or heart transplant recipients ▪ Participant and/or parent consent to the study (prospective arm only) Exclusion criteria ▪ Previous liver transplant. ▪ Lung OR Intestinal transplant. ▪ Insufficient time before transplant for pharmacogenomic analysis (prospective arm only) ▪ Immunosuppressant regimen not containing tacrolimus immediate release product ▪ Known hypersensitivity to tacrolimus and/or its formulation. Participant enrolment: phase 1 and 2 A total of 45 individuals will be recruited prospectively to the trial, with phased opening of cohorts based on SOT type: Phase 1 : Enrolling kidney transplant recipients, the population in whom the popPK model used by NextDose was developed. Phase 2: Enrolling other SOT recipients (liver and heart). This second phase will commence following evaluation of the NextDose model with adjustment for organ type (liver or heart), with acceptability of predicted doses for non-kidney SOT recipients based on virtual performance using retrospective data. Evaluation of NextDose for non-renal organ transplant recipients Additional parameters have been added to the NextDose tacrolimus model to account for reduced tacrolimus CL/F in heart and liver recipients in the first post-transplant week, based on published population PK models (including models pooling and comparing organ types) 65 75 76 . Prior to use of the adjusted model in prospective heart and liver transplant recipients, the model performance will be evaluated using retrospective data, by assessing iterative dose recommendations over the first post-transplant week. Performance will be assessed against achieved tacrolimus concentrations, and with reference to accepted standards for bias and precision: bias by mean prediction error (percentage) ±15-20% and precision by mean absolute percentage error ≤20% 101 . 2.3. Intervention Initial dose Initial dose in BRUNO-PIC will use allometric size scaling from adult dose, with adjustment based on genotype ( CYP3A4 & CYP3A5 ). Dose is scaled for size from a standard adult dose administered every 12 h. Taking the example of kidney transplantation, this is 5 mg in a 70 kg adult. Size scaling is by allometry, with fat free mass (FFM) as size descriptor, using the equation: where FFM STD is 56.1 kg, based on a 70 kg 176 cm male adult . Genotyping of CYP3A4 and CYP3A5 will occur prior to transplantation in the prospective intervention arm. Genotypes of relevance to tacrolimus are: CYP3A4 Homozygous or heterozygous for *22 (e.g. *1/*22, *22/*22) CYP3A4 No *22 allele (e.g. *1/*1) CYP3A5 Normal or intermediate metaboliser (e.g. *1/*1, *1/*3) CYP3A5 Poor metaboliser (e.g. *3/*3, *6/*7) Genotyping will be performed using Illumina’s genome-wide genotyping array (Infinium Global Screening Array). Pre-transplant genotyping will test for CYP3A5 *3, *6, *7, *8 and *9 alleles, and will test for CYP3A4 *22 only (with CYP3A4 * 1 reported if no variant corresponding to *22 was present). The results for CYP3A4 and CYP3A5 will be automatically extracted from the array data using an in-house program. The determined diplotype for CYP3A5 will be matched with the predicted phenotype using the CPIC proposed genotype-to-phenotype translation table. The assignment of the phenotype is outlined in the CPIC guidelines 31 . In addition, influence of CYP3A4 will be incorporated based on recent literature and interventional trials, see section 2.3 and Supplementary File 2 for detail. Though both CYP3A5 *6 and *7 are rare in the population, they result in nonfunctional protein, and their impact on tacrolimus clearance and dose-adjusted trough concentrations is therefore presumed to be equivalent to *3 31 , thus treated here as functionally equivalent to *3 for dose adjustments 61 . Based on the literature for CYP3A5 61 and CYP3A4 23 102 in kidney transplant recipients , the group differences in tacrolimus CL/F and drug dose are as follows: CYP3A4 no *22 allele present & CYP3A5 non-expressor: Dose x 1 (No change) CYP3A4 homozygous or heterozygous for *22: Dose x 0.74 CYP3A5 expressor (normal or intermediate metaboliser): Dose x 1.59 (fCL/F=1.3/0.82) CYP3A4 homozygous or heterozygous for *22 allele and CYP3A5 expressor: Dose x 1.18 (0.74 x 1.59) It is assumed that the genotype effects on tacrolimus CL/F are the same for heart transplant recipients , consistent with published data 103 . For liver transplant recipients , the impact of recipient genotype will be reduced by 50%, as follows: CYP3A4 no *22 allele present & CYP3A5 non-expressor: Dose x 1 (No change) CYP3A4 homozygous or heterozygous for *22: Dose x 0.87 CYP3A5 expressor (normal or intermediate metaboliser): Dose x 1.29 CYP3A4 homozygous or heterozygous for *22 allele and CYP3A5 expressor: Dose x 1.09 The BRUNO-PIC first dose should commence following transplantation as soon as able and/or consistent with clinical unit guidelines (delayed until day 4 post-transplant in for liver transplant recipients on “renal-sparing” protocol). The dose immediately prior to transplant is 50% of maintenance dose, given the known concentration-dependent vasoconstrictive effect of tacrolimus on renal microvasculature 104 . Subsequent dosing Subsequent dosing will be guided by Bayesian estimation using NextDose. Estimation will occur with every dosing occasion over the first 8 weeks that contains a tacrolimus concentration, commencing on post-transplant dosing day 4 (DD4). In addition, on DD4, additional samples will be taken for tacrolimus concentration measurement: immediately pre-dose, then, 1 h, 2 h, and 3 h after the morning dose. Tacrolimus concentrations are measured daily for the first 2-4 weeks post-transplant in all SOT types, then with reducing frequency (eventually twice or thrice weekly). Samples are taken at between 7-9 am each morning, prior to the morning tacrolimus dose, with exact time taken documented. For each dose estimation, tacrolimus concentrations from the day of dosing, and the prior 7 days, will be used within NextDose (excluding concentrations prior to DD4). A unique patient identifier, date of birth (or current age), sex and genotype along with time associated dose and observations (such as weight, concomitant steroid use, transplant date, transplant organ type) are recorded in NextDose, along with tacrolimus concentrations as above. Following dose prediction on each dosing occasion with measured tacrolimus concentration, predicted dose, along with graphs of predicted and observed concentrations, will be communicated on the same day to the transplant treatment team via phone, secure chat with confirmation of receipt, or in-person. The predicted dose and the administered dose will be documented in the electronic medical record (EMR). The 4-point profile performed on DD4 will also occur at week 3 and week 8, to provide additional information for dose estimation and as outcome measures. These additional samples on DD4, week 3 and week 8, are the only blood samples taken outside of standard of care, falling within bounds of acceptable sampling volume for clinical research 105 . Target concentration Bayesian dosing uses a ‘target concentration intervention’ 10 approach to maximise time within the acceptable concentration range. The concentration measure used as target in BRUNO-PIC will be to the average steady state concentration (Cssavg) , which is the AUC divided by the dosing interval, thus applicable at any steady-state dose interval. The acceptable range is 80-125% of the target Cssavg . Table 1 details the Ctrough therapeutic ranges for month 1, 2 and 3 after kidney transplantation, and the equivalent AUC and Cssavg targets. The process for deriving Cssavg from Ctrough is expanded upon in the subsequent heading. View this table: View inline View popup Download powerpoint Table 1: Conversion from protocol Ctrough target to Cssavg. Abbreviations: AUC: area under curve; C: concentration; M:month; Tx: transplant. Justification for tacrolimus concentration target The Cssavg target for organ and post-transplant period is derived from the transplant unit-specific therapeutic window, with subsequent conversion. Thus, for a therapeutic window of 8-12 mcg/L, the Csstrough target is 10 mcg/L. This is then converted to a haematocrit-standardized AUC, then to Cssavg (AUC/12 given twice daily dosing). To determine the equivalent exposure (AUC or Cssavg ) at a given target Csstrough , we reviewed tacrolimus pharmacokinetic studies with both Csstrough and full AUC data. This enabled aggregate comparison of the relationship between the central tendency of the two parameters, using a subject number weighted average to determine typical AUC concentrations. This represents the exposure at steady state over the dosing interval (AUCssDI) seen in the average individual when dose is adjusted to target Csstrough, i.e. the AUCss expected with the measured trough concentration when approaching steady state in current practice. We also compared results with published recommendations for AUC targets. Results are summarised in Table 2 below. The table first shows weighted average values (not standardised to HCT-45) indicating that a “raw” AUC 0-12 value of 175.6 mcg/L.h is the typical exposure associated with a “raw” Ctrough of 10 mcg/L. This aligns well with published expert opinion (175 mcg/L.h being the mid-point of proposed window of 150-200 mcg/L.h) 17 ) and published precedence, with Meziyerh et al 84 describing 10-year experience of AUC-guided dosing in 968 adult kidney transplant recipients, with a goal tacrolimus AUC 0-12h goal of 160-180 mcg/L.h (mid-point 170 mcg/L.h). View this table: View inline View popup Download powerpoint Table 2: Literature values and weighted average Cssavg target. Abbreviations: AUC: area under curve; C: concentration; HCT: haematocrit; ss: steady state Subsequently, the calculated AUCss was standardized to a haematocrit of 45%. Tacrolimus target concentrations are typically derived from studies using whole blood tacrolimus concentrations unstandardised for HCT. However, standardizing whole blood tacrolimus concentrations to a haematocrit of 45% (HCT45) account for changes in whole blood concentration with HCT at the same pharmacological active unbound tacrolimus concentration. The use of haematocrit-standardized tacrolimus concentrations has been shown to improve target attainment 61 – 63 . The typical HCT seen in the early post-kidney transplant period of 33% 61 may be used to standardise literature value to a HCT of 45%. Finally, these HCT45 AUC values were divided by 12 to estimate the equivalent HCT45 Cssavg . These steps and analysis, outlined in table below, gives a concentration target of 19.96 mcg/L hence proposing for BRUNO-PIC a target HCT45 Cssavg of 20 mcg/L. 2.4 Comparator The prospective intervention arm will be compared with a retrospective arm from the prior 5 years, using standard of care standard of care dosing. This involves linear weight-based dosing (mg/kg) followed by TWA empiric dose adjustment to achieve Csstrough concentrations within a ‘therapeutic window’ 10 . 2.5 Outcomes Primary outcome The proportion of participants with tacrolimus concentration within 80-125% of concentration target ( Cssavg ) on post-transplant dosing DD4, Week 3 & Week 8, where Cssavg is calculated by dividing the associated dose by the individual Bayesian estimate of clearance and multiplying by the individual Bayesian estimate of bioavailability Secondary outcomes Median time to acceptable range (80-125% Cssavg ) in the immediate post-transplant period. Time within acceptable range (80-125% of Cssavg ) in the first 8 weeks post-transplant Proportion of tacrolimus concentrations within 80-125% of Csstrough target on DD4 Number of dose adjustments of tacrolimus based on TWA and/or Bayesian. Safety of genotype-informed Bayesian dosing, including description of number of clinical outcomes: rejection, donor-specific antibody formation; tacrolimus toxicities of new onset diabetes after transplantation. Feasibility of genotype-informed Bayesian dosing and barriers to implementation Justification for outcome measures As outlined in background, the excellent short-term outcomes from modern immunosuppression has challenge detecting superiority against hard outcomes with new interventions without very large numbers or longitudinal follow-up 2 3 , with academic and regulatory calls for innovative approaches including use of biomarkers as surrogates 3 70 . Robust data links tacrolimus underexposure and acute rejection, and reduced time within the acceptable range in the first 6 months post-transplant with negative short and long-term outcomes (rejection, de novo DSA formation, graft loss). In addition, these robust associations have a clear and causal mechanistic explanation. Thus, evidence for superior tacrolimus concentration control, with increased time in acceptable range, can reasonably be extrapolated to superior long-term outcomes. 2.6 Participant timeline Eligible participants having undergone consent for BRUNO-PIC will have a blood sample sent for genotyping via a targeted gene panel looking at CYP3A4 and CYP3A5 as described in 3.1. A report is generated and made available at least 24 hours before transplant to be eligible to participate in the study. The assessments and data collection will be taken at set time points over an 8-week period post-transplantation ( Table 3 ). The flow diagram for BRUNO-PIC study is shown in Figure 1 . Download figure Open in new tab Figure 1: BRUNO-PIC study flow diagram. Abbreviations: CYP: cytochrome P450; DD0: Day of first tacrolimus dose where day 0 is the day of the first post-transplant dose; EMR: electronic medical record; SOT: solid-organ transplant. * For Retrospective arm: Tacrolimus doses, adjustments and concentrations will be taken from the EMR. If data is missing on D4, D21 or D56 timepoints, +2day (on D4) or ±7days (from D4 to D56) will be used. Any time points that are missing will be deemed as “lost to follow-up”. # For Prospective arm: Tacrolimus doses, adjustments and concentrations will be recorded from the EMR at set time points and as per transplant team policy. View this table: View inline View popup Table 3: Overview of BRUNO-PIC study visits and timepoints 2.4. Sample size The sample size was calculated based on comparing proportion of participants within each group with a Ctrough concentration within acceptable range on day 4 post transplantation. Prior studies in adult kidney transplant recipients have shown 54% vs 24% 110 , 54.8% vs 20.8% 63 and 58% vs 37.4% 23 , of patients with Ctrough within target range on Day 3-5 when treated with the standard, bodyweight-based dosing vs a dosing algorithm. We assumed 27% of the retrospective control cohort would have a tacrolimus Csstrough within acceptable range on DD4. 165 participants (120 control and 45 intervention participants) would provide 80% power to detect a risk difference of 24% assuming a two-sided alpha of 0.05. 2.5. Recruitment Eligible participants will be screened in the outpatient or inpatient setting or through transplant planning meetings, held by the respective clinical departments, to identify those approaching need for kidney, liver or heart transplantation. Central transplant planning meetings help minimizing eligible participants being missed. For the retrospective cohort, eligible individuals who underwent a SOT – liver, kidney, or heart – over a 5-year period will be extracted from the EMR. 3. METHODS: DATA COLLECTION, MANAGEMENT, AND ANALYSIS 3.1. Data collection Study data from participants and electronic medical records will be entered de-identified into REDcap Case Report Forms. Study data collected for BRUNO-PIC is described in Supplemental File 4 along with databank guidelines. Although stored information will be de-identified it will be re-identifiable to delegated members of the study team for data entry and integrity purposes. For Bayesian estimation, de-identified participant data will be entered into the NextDose server, securely hosted by the University of Otago, New Zealand. 3.2. Data management Participants will be allocated a unique individual identifier prior to their de-identified data being entered into REDCap. All data is protected within Murdoch Children’s Research Institute (MCRI) using the REDCap database by a triple encryption process between the remote user, the server and the REDCap Database. No data can leave the database in an identifiable format. 3.3. Governance for Data Management and reporting The Trial Monitoring Group consists of the Principal Investigators (RC and DM), academic Pharmacist/study coordinator (DK) and associate investigators (TS, JK, JM, EB, NH). The TMG will contribute to the organisation of Pharmacogenomic Steering Committee (PSC) meetings and will assess adverse events, serious adverse events and serious unexpected adverse events (SUSAR) with reporting in line with NHMRC standards. The TMG is also responsible for maintenance of the database, budget administration, HREC updates and amendments. 3.4. Statistics methods Outcomes analysis Comparison between the prospective intervention arm and the control arm (retrospective historical comparator) will be utilised for analyses where comparable data is available. A stabilised inverse probability weighting (IPW) approach will be used with robust standard errors to control for potential bias between “exposure” groups (i.e. NextDose (intervention) vs retrospective control cohort). IPW is an extension of the propensity score method used to summarise the conditional probability of assignment to an exposure. The weights are the inverse probability of assigning an exposure derived from a logistic model with group as the dependent variable and observed patient-level characteristics as the independent variables. These will include factors that may influence tacrolimus concentrations, including demographic variables (age, sex, height, weight), time after transplant, use of drugs known to interact with tacrolimus, presence of comorbidities known to influence transplant outcomes, presence of liver dysfunction, prednisolone daily dose in mg, and HCT levels (all measured on day of transplant D0). We will stratify by type of solid organ transplant (heart, liver or kidney). Stabilisation is accomplished by multiplying the “exposure” weights (separately) by a constant, equal to the expected value of being in the intervention or control groups. Each participant is weighted by the inverse of the estimated probability of the exposure received. We will then use IPW regression models weighted with exposure to estimate the adjusted associations between exposure and each outcome. The primary outcome analysis will compare the proportion of participants with a Cssavg concentration within acceptable range at DD4, week 3 and week 8. This will be using a risk difference estimated using a marginalised IPW mixed-effects logistic regression model including a random effect for the intercept (to allow for clustering of repeated measures within participants), and a fixed effect for dosing type (control vs intervention), time-period (DD4, week3, week8), SOT type (stratification factor) and other factors known to influence tacrolimus concentrations. Cssavg will be calculated using a maximum a posteriori (MAP) approach, using drug concentrations and doses on days DD4, week3 and week8, along with participant clinical covariate information and the published popPK model within NextDose. For secondary outcome, time to an acceptable Cssavg target, we will compare groups using a hazard ratio and 95% confidence interval estimated similarly to primary outcomes via a IPW adjusted Cox proportional hazards model. Between-group difference in mean percentage time within acceptable range over the first 8-weeks will be compared using IPW adjusted linear regression. For secondary outcomes where Cssavg is used, in the control group Cssavg will be calculated using measured concentrations and MAP estimation as above. 4. METHODS: MONITORING 4.1. Trial monitoring committee Safety monitoring will be coordinated by the Trial Steering Committee, chaired by PIs and the study research assistant. Serious adverse events or drug reactions, and significant safety issues, are as defined in the National Health and Medical Research Council (NHMRC) Guidance: Safety monitoring and reporting in clinical trials involving therapeutic goods (2016). The TSC and broader research group meet fortnightly to oversee trial progress and review any adverse events. Reported adverse events will be assessed and reported as per NHMRC guidance using the trial Expedited Safety Report Form, Supplementary File 2. 5. ETHICS AND DISSEMINATION 5.1. Research ethics approval BRUNO-PIC has been approved by the Sydney Children’s Ethics Committee (2023/ETH02699), and letter of authorisation from The Royal Children’s Hospital Research Governance Office (SSA/105019/RCHM-2024). 5.2. Protocol amendments The study will be conducted in accordance with currently approved study protocol, with amendments to the protocol requiring HREC approval prior to implementation. 5.3. Consent Eligible participants and their parents/caregivers will be introduced to the trial and if interested provided with the Participant Information Sheet and Consent Form (Supplementary Files 3). At subsequent hospital visit, a member of the trial team will discuss the trial in detail and provide sufficient time for questions prior to informed consent being attained. Waiver of consent has been granted for the retrospective cohort by the approving ethics committee. 5.4. Confidentiality Participant identity and clinical information, along with trial-related documents and data, will remian strictly confidential and accessible to delegated trial staff. No information concerning the trial, or the data will be released to any unauthorized third party, without prior written approval of the sponsoring institution. MCRI may inspect participant medical and pharmacy records with hospital consent. All evaluation forms will use Participant IDs for anonymity. 5.5. Declaration of interests There are no financial or other competing interest for any investigators participating in this trial. 5.6. Dissemination policy At the conclusion of the trial, participants will receive trial results and an overview of findings. Data Availability All data produced in the present study are available upon reasonable request to the authors CONTRIBUTUTORS DK, DM and RC wrote the main manuscript text; DK prepared the figures and DK and DM prepared the tables. All authors contributed to refinement of the study protocol and approved the final manuscript. FUNDING BRUNO-PIC is funded by the 2023 Medical Research Future Fund – Genomics Health Future Mission (MARVEL-PIC (MRF/2024900). This funding source had no role in the design of this study and will not have any role during its execution, analyses, interpretation of the data, or decision to submit results. DISCLAIMER The funding source had no role in the design of this study and will not have any role during its execution, analysis, interpretation of the data, or decision to submit results. COMPETING INTEREST None declared. PATIENT CONSENT FOR PUBLICATION Not applicable. 6.0 SUPPLEMENTARY Supplementary 1: SPIRIT checklist Supplementary 2: Protocol Supplementary 3: Informed Consent Forms (PICFS)-Participant and Parent/Guardian Supplementary 4: Data Endpoint and Databank Guidelines ACKNOWLEDGEMENTS RC is supported by the Kids Cancer Project, The Royal Children’s Hospital Foundation, Victorian Paediatric Cancer Consortium, Medical Research Future Fund and holds a Murdoch Children’s Research Institute (MCRI) Clinician Scientist Tier 2 Fellowship and VESKI FAIR Fellowship. DK is supported by Medical Research Future Fund (MRF/2024900). CM is supported by a Melbourne University Research Training Program Strategic Scholarship. RSK is supported by a Fellowship from the National Health and Medical Research Council of Australia (APP2033152). The Chair in Genomic Medicine awarded to JC is generously supported by The Royal Children’s Hospital Foundation. 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You are going to email the following Protocol for Bayesian combined multi-genotype and concentration informed tacrolimus dosing in paediatric solid organ transplantation (BRUNO-PIC) Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Protocol for Bayesian combined multi-genotype and concentration informed tacrolimus dosing in paediatric solid organ transplantation (BRUNO-PIC) Dhrita Khatri , Andreas Halman , Joshua Kausman , Jacob Mathew , Elizabeth Bannister , Tayla Stenta , Claire Moore , Elizabeth Williams , Roxanne Dyas , Julian Stolper , Cecilia Moore , Mark Pinese , Rishi S. Kotecha , Christopher Gyngell , Sebastian Lunke , John Christodoulou , Amanda Gwee , Rachel Conyers , David Metz medRxiv 2025.05.06.25327120; doi: https://doi.org/10.1101/2025.05.06.25327120 Share This Article: Copy Citation Tools Protocol for Bayesian combined multi-genotype and concentration informed tacrolimus dosing in paediatric solid organ transplantation (BRUNO-PIC) Dhrita Khatri , Andreas Halman , Joshua Kausman , Jacob Mathew , Elizabeth Bannister , Tayla Stenta , Claire Moore , Elizabeth Williams , Roxanne Dyas , Julian Stolper , Cecilia Moore , Mark Pinese , Rishi S. Kotecha , Christopher Gyngell , Sebastian Lunke , John Christodoulou , Amanda Gwee , Rachel Conyers , David Metz medRxiv 2025.05.06.25327120; doi: https://doi.org/10.1101/2025.05.06.25327120 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Pediatrics Subject Areas All Articles Addiction Medicine (567) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4411) Dentistry and Oral Medicine (443) Dermatology (380) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1505) Epidemiology (15205) Forensic Medicine (30) Gastroenterology (1119) Genetic and Genomic Medicine (6574) Geriatric Medicine (666) Health Economics (994) Health Informatics (4511) Health Policy (1365) Health Systems and Quality Improvement (1608) Hematology (537) HIV/AIDS (1263) Infectious Diseases (except HIV/AIDS) (15903) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (666) Neurology (6573) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1139) Occupational and Environmental Health (954) Oncology (3319) Ophthalmology (968) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (662) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5422) Public and Global Health (9205) Radiology and Imaging (2191) Rehabilitation Medicine and Physical Therapy (1367) Respiratory Medicine (1191) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9feb10867be71640',t:'MTc3OTI3NzIwNQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();