A repository of protein abundance data of drug metabolizing enzymes and transporters for applications in physiologically based pharmacokinetic (PBPK) modelling and simulation

Population factors such as age, gender, ethnicity, genotype and disease state can cause inter-individual variability in pharmacokinetic (PK) profile of drugs. Primarily, this variability arises from differences in abundance of drug metabolizing enzymes and transporters (DMET) among individuals and/or groups. Hence, availability of compiled data on abundance of DMET proteins in different populations can be useful for developing physiologically based pharmacokinetic (PBPK) models. The latter are routinely employed for prediction of PK profiles and drug interactions during drug development and in case of special populations, where clinical studies either are not feasible or have ethical concerns. Therefore, the main aim of this work was to develop a repository of literature-reported DMET abundance data in various human tissues, which included compilation of information on sample size, technique(s) involved, and the demographic factors. The collation of literature reported data revealed high inter-laboratory variability in abundance of DMET proteins. We carried out unbiased meta-analysis to obtain weighted mean and percent coefficient of variation (%CV) values. The obtained %CV values were then integrated into a PBPK model to highlight the variability in drug PK in healthy adults, taking lamotrigine as a model drug. The validated PBPK model was extrapolated to predict PK of lamotrigine in paediatric and hepatic impaired populations. This study thus exemplifies importance of the DMET protein abundance database, and use of determined values of weighted mean and %CV after meta-analysis in PBPK modelling for the prediction of PK of drugs in healthy and special populations.

www.nature.com/scientificreports www.nature.com/scientificreports/ analytical method, relative or absolute quantification method, and references to the data source. The repository has been uploaded online as an open access user-friendly QPrOmics database at http://qpromics.uw.edu/qpromics and it is being updated regularly. The search from database is possible through either protein name, gene name or UniProt ID, which can be further refined by selecting the tissue of interest from various organs, viz., liver, intestine, kidney, brain and lungs. The search output information from website can be downloaded as an Excel spreadsheet (representative example shown in Supplementary Fig. S1).

Assessment of heterogeneity in the abundance of non-CYP enzymes through meta-analyses.
In the case of three non-CYP enzymes, viz., UGT1A4, UGT2B7 and CES1, the heterogeneity constant (H 2 ) value was more than 1.5 in the case of fixed effect (FE) model and the same was below 1.2 in the random effect (RE) model. The H 2 values more than 1.5 mean heterogeneity concern. Also, the FE model showed high to medium heterogeneity based on heterogeneity index (I 2 ) data (Table 1). Considering this, we concluded that the inter-laboratory variability superseded true biological variability for these enzymes (Table 1). In UGT2B10, medium heterogeneity was observed for both FE and RE models. However, the result was shown to be statistically insignificant (P-value > 0.05) based on chi squared (χ 2 ) distribution of the coefficient of heterogeneity (Q). The heterogeneity was low for all other enzymes ( Table 1).

Determination of weighted mean and percent coefficient of variation values. The weighted mean
values of abundance (pmol/mg microsomal protein) of non-CYP enzymes were determined to be in the following order: 1252.93 (CES1) > 75. 21 Supplementary Table S2.
In general, the value of %CV or 95% confidence interval (CI), estimated using method II, was higher in comparison to methods I and III. Other observed advantage of method II was that it remained uninfluenced even when the studies were few in number. Also, it gave 95% CI range, which captured the observed variability. The meta-analyses results, including the weighted mean and %CV values for all non-CYP enzymes considered in the studies, are depicted in Table 2. The forest plots in Figs 2 and 3 provide a visual representation of the inter-laboratory variability across the mean for individual enzymes.
prediction of the pharmacokinetics of lamotrigine in healthy adults and special populations. Figure 4 shows the predicted intravenous (IV) and peroral (PO) PK profiles of lamotrigine in healthy adults. The model was validated across various dosage forms (tablet, solution and capsule) and for an ascending dose of a capsule dosage form, and the resultant overlapping profiles are included in the same figure. The simulated lamotrigine plasma exposure parameters for both IV and PO studies were within the acceptance criteria (Table 3). Further, predicted results of lower and higher 95% CI values around the geometric weighted mean abundance, calculated using method II in adults, reasonably captured the variability in the observed data.
The validated model was further extrapolated to predict PK in paediatric and HI populations, considering all the physiological changes including adjusted maximum velocity of the kinetic reaction (V max ) and renal plasma clearance (CL R ) values. The predicted results were within the range of observed clinical data (Table 3 and Fig. 4).  18 . It finds application starting from first in human (FIH) dose selection, clinical study design onto dosing recommendations regarding drug interaction and pharmacogenetic effect in product labeling 4 . In that respect, M&S is targeted to reduction and/or replacement of human/animal studies 2,19 . Other benefit is that it facilitates benefit/risk assessment, whereby it enhances the likelihood of regulatory success. It is for these reasons that PBPK modelling is being currently widely encouraged even by the regulatory agencies, like US FDA 20 , EMA 21 and PMDA 3 . For example, PBPK approaches have been included in regulatory guidance on drug-drug interactions (DDIs) [22][23][24] , paediatrics 25 , HI 26 , renal impairment (RI) 27 and pharmacogenetics 28,29 as a means to guide clinical study design and labeling decisions. Between 2008 and 2017, the FDA's Office of Clinical Pharmacology (OCP) received 130 investigational new drug (IND) applications and 94 new drug applications (NDAs) containing PBPK analyses 30 . The utility of PBPK analyses in these regulatory submissions was primarily to assess enzyme-based DDIs (60%), followed by applications in paediatrics (15%), DDI with transporter (7%), HI (6%), RI (4%), absorption including food effect (4%), and pharmacogenetics (2%) 30 . www.nature.com/scientificreports www.nature.com/scientificreports/ The successful application of PBPK modelling, which meets regulatory expectations, requires integration of drug-specific properties (molecular weight, pKa, logP, pH dependent solubility, apparent permeability, fraction of drug unbound in plasma (fu p ), blood to plasma drug concentration ratio (B:P), etc.) with various physiology parameters (cardiac output, specific organ volume, tissue compositions, DMET abundance, transit times for luminal contents, etc.) 31 . With their help, the drug's PK profile can be predicted and the same can be extrapolated across special populations, like paediatrics, pregnant women, maternal-fetal, HI and RI, etc. Forest plots representing hepatic protein abundance of UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A9, UGT2B4, UGT2B7 and UGT2B10. X-axis denotes protein abundance of enzyme in pmol/mg microsomal protein. Y-axis represents methods I-III and individual studies (first author name, year). Mean protein abundance value for the method I, method II, method III and individual studies are presented as filled circle, filled triangle, filled diamond and filled squares, respectively. The line perpendicular to X-axis denotes weighted mean, whereas lines parallel to X-axis denote 95% CI.
www.nature.com/scientificreports www.nature.com/scientificreports/ Fortunately, drug specific properties such as solubility, permeability, enzyme and transport kinetics are extensively evaluated during early drug development. Further, for existing drugs, databases of drug specific properties are available in plenty, and some of them are freely accessible, like regulatory labels, DrugBank, etc. Good amount of information can also be accessed through material safety data sheets (MSDS).
The compilations and databases outlining body anatomy and physiology (e.g., tissue weights, blood flows to organs, tissue composition, etc.) in healthy and few special populations have been curated in the past several years. While individual physiology databases have been developed for Japanese 32,33 , Chinese 34 , and Indian populations 35 , a better geographically spread compilation is from 5-year effort conducted under the aegis of the International Atomic Energy Agency (IAEA), which accounted for the characteristics of populations in Bangladesh, China, India, Japan, Republic of Korea, Pakistan, Philippines, and Vietnam 36 . Similarly, National Center for Health Statistics (NCHS) designed a program, named National Health and Nutrition Examination Survey (NHANES), in order to assess the health and nutritional status of adults and children in the United States 37,38 . In the same way, Valentin compiled all the information on age-and gender-related differences in the anatomical and physiological characteristics of Western Europeans and North Americans, published earlier by the International Commission on Radiological Protection (ICRP) in 1975 39 . Another such attempt was made by Thompson et al. who compiled data from reported studies, including age-specific and clearance-related parameters in healthy and disease states 40 .
The clearance of drugs is primarily governed by DMET proteins, and hence the abundance of the latter has direct bearing on prediction of drug's PK profiles and extrapolation to special populations. This is because abundance of DMET protein varies with demographic, biological and genetic factors, such as age, sex, ethnicity, disease . Forest plots representing hepatic protein abundance of UGT2B15, UGT2B17, CES1, FMO3 and FMO5. X-axis denotes protein abundance of enzyme in pmol/mg microsomal protein. Y-axis represents methods I-III and individual studies (first author name, year). Mean protein abundance value for the method I, method II, method III and individual studies are presented as filled circle, filled triangle, filled diamond and filled squares, respectively. The line perpendicular to X-axis denotes weighted mean, whereas lines parallel to X-axis denote 95% CI.
www.nature.com/scientificreports www.nature.com/scientificreports/ condition and genotype 1 . This necessitates the availability of a repository containing quantitative information of DMET proteins. Therefore, the primary goal of the present study was to develop an online public repository that compiled the literature reported data on DMET proteins in various human tissues. Another target was to collate the information on the effect of associated covariates.
During the process of compiling the DMET abundance data, we observed vast inter-laboratory variability, which was higher than the anticipated biological variability 41 . This highlighted the need to derive more robust conclusions by performing meta-analyses, which provides good assessment of heterogeneity, and the calculated values of weighted mean and %CV, which can be integrated into PBPK modelling to predict the variability in PK [13][14][15][16]42 .
To assess heterogeneity as a part of meta-analyses, both FE and RE models were applied in the present study. Our results for UGT enzymes based on FE model (Table 1) were consistent with the previously published meta-analysis studies on same set of enzymes 14 . However, we observed that for two enzymes, where heterogeneity was evident through high I 2 in the FE model, the RE model displayed none or low statistical heterogeneity. This indicated that FE model was perhaps a simpler model in describing statistical heterogeneity in this set of reported DME abundance data. A critical analysis of individual DME abundance studies showed major role of methodological heterogeneity, in the terms of sample source; its procurement and storage (frozen versus fresh www.nature.com/scientificreports www.nature.com/scientificreports/ tissue); sectioning procedure; sample preparation, and the technique of analysis. Among the latter, conventional immunoquantification-based methods like Western blotting, enzyme-linked immunosorbent assay (ELISA) and microarray can be considered less selective and low throughput than LC-MS/MS based quantification. However, inter-laboratory variability is also observed with LC-MS/MS proteomics, which can be attributed to the use of different peptides, differential protein extraction recovery, digestion efficiency and other methodological factors 5,41,43 . Therefore, harmonization of protein abundance determination protocol across laboratories is warranted.
The meta-analysis of UGT1A4 was in congruence with the large inter-individual variability in observed clinical PK of lamotrigine, which was successfully captured in the model by making use of %CV values of UGT1A4 protein abundance, obtained through method II ( Fig. 4 and Table 3). The high inter-individual variability of this particular DME has been held responsible for adverse effects of lamotrigine, such as benign rashes, gastrointestinal disturbances and multi-organ failure associated with Steven Johnson syndrome 44,45 . In other reports, the reason for the observed variability has been attributed to underlying factors, such as age, gender, weight, co-medication, and state of renal and hepatic function [46][47][48][49][50] , highlighting the possibility of population effects.  www.nature.com/scientificreports www.nature.com/scientificreports/ Also, in the case of lamotrigine, therapeutic drug monitoring (TDM) is resorted for dose adjustment. Its serum concentration of 2.5-15 µg/mL is considered to be efficient and safe [51][52][53] . The drug is metabolized mainly by glucuronidation pathway, whose contribution is 86% and around 4% unidentified metabolites are formed 54 . The remaining 10% of drug dose is excreted unchanged in the urine 54 . The DMEs reported to be involved in lamotrigine metabolism are UGT1A3 46 , UGT1A4 46 and UGT2B7 55 . However, more recent reports observed that UGT2B7 had no role in lamotrigine glucuronide formation 46 . Amongst UGT1A3 and UGT1A4, the latter is involved in ten-fold higher intrinsic clearance of the drug as compared to the former 46 . The neonatal level of UGT1A4 is ~50-fold lower than adult level 56 . Further, UGT1A4 abundance in alcoholic and hepatitis C virus (HCV) cirrhotic liver samples has been reported to be 12-and 4-fold lower than healthy liver samples, respectively 9 .
To describe UGT1A4 mediated variability in lamotrigine PK, we developed and validated a whole-body PBPK model of the drug using GastroPlus software. The predicted results were successfully able to capture, in particular, the elimination phase (Fig. 4), which is directly affected by the variability in UGT1A4. However, the absorption phase was not well captured. A high variability was also observed in lamotrigine absorption in clinic. The primary cause of the same remains to be ascertained.
For prediction of PK profile of lamotrigine in special population, age-and disease-dependent UGT1A4 abundance was integrated into the PBPK model, which well predicted the drug's PK, even in the selected population. The differential protein abundance of UGT1A3 was not considered here, because of its small contribution to the metabolic clearance of lamotrigine and the lack of data. The extrapolated model well captured the PK parameters in children, including early and middle childhood and the obtained results were comparable to clinically reported PK values. In the case of HI population, while the PK parameters (AUC) were reasonably predicted, the simulated profile of lamotrigine was visually different than the observed clinical data 48 . This was perhaps due to the unknown etiology of the liver disease and its influence on the observed PK data reported in the clinical study. Moreover, the protein abundance of UGT1A4 is known to vary between alcoholic and HCV cirrhotic liver tissues 9 .
To summarize, a comprehensive repository of DMET protein abundance data was developed. Meta-analysis was successfully carried out on the compiled information to estimate overall variability (%CV) of protein abundance, and the influence of the latter on variability in PK profiles was established, taking lamotrigine as a model drug. The developed model was extrapolated to predict PK of lamotrigine in paediatric and HI populations.

In silico tools. Numerical values of abundance were extracted from the reported figures using GetData Graph
Digitizer version 2.25 (http://getdata-graph-digitizer.com/). MySQL open source relational database management system (Cupertino, CA, USA) was used as a platform for the QPrOmics database. All the simulations for PBPK model development of lamotrigine were carried out in GastroPlus version 9.6 (Simulations Plus, Inc., Lancaster, California, USA). Figures for visual representation of statistical and simulation data were created using Excel 2016 (Microsoft, Redmond, WA); the same software was used for meta-analyses, and its in-build statistical function 'CHIDIST' was used to calculate P-values.
Compilation of published DMET protein abundance data. Human DMET protein abundance data in different tissues with demographic details were from published literature that was searched through online search engines like PubMed, Google Scholar, Microsoft Academic, ScienceDirect, etc. For relevant search, the keyword combinations used were: drug metabolizing enzyme/transporter + abundance/expression + words like quantification/quantitation, LC-MS, LC-MS/MS, liquid chromatography-high resolution mass spectrometry (LC-HRMS), proteomics, or Western blotting/immunoblotting. Also the terms, such as quantity, concentration, content, quantification or measurement, were used as substitutes for the term "abundance/expression" to widen the search scope. The cross-references of individual articles were critically reviewed for any additional reported data. All available information till January 2019 for tissue distribution, donor demographics (including age, gender, ethnicity, genotype, disease, smoking, alcohol consumption and medication) and analytical methods employed were collated.

Meta-analyses of protein abundance data of non-CYP enzymes.
To demonstrate the utility of the database, a systematic meta-analysis was performed as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline 57 to establish the overall abundance of non-CYP enzymes. The inclusion of reported data in meta-analyses was based on the following pre-defined criteria: i) considering only individual microsomal samples, excluding data from pooled donor samples; ii) taking absolute protein abundance values that were quantified by LC-MS/MS or Western blotting (immunoblotting), and excluding LC-MS global proteomics, mRNA expression levels and enzyme activity data, iii) including studies reporting data in picomole (pmol) per mg protein unit, but excluding studies with abundance data in arbitrary, relative or non-standard units; and iv) adding only those proteins where there were more than one reports from different laboratories.
Accordingly, for the purpose of this publication, only 18 out of 220 studies were included in the meta-analyses (Supplementary Fig. S2). The selected studies covered protein abundance data of the following non-CYP enzymes: UGT1A1, UGT1A3, UGTA4, UGT1A6, UGT1A9, UGT2B4, UGT2B7, UGT2B10, UGT2B15, UGT2B17, CES1, FMO3 and FMO5. The data were subjected to heterogeneity tests (weighting by inverse variance) to investigate intra-and inter-laboratory variability, which included determination of H 2 , I 2 , P-value and heterogeneity class 13,14,17,58 . Thereafter, the data were subjected to calculation of weighted mean (weighting by sample size) 13,14,17 , determination of %CV using three methods I-III. Eventually, 95% geometric CI, calculated using %CV (method II), was incorporated into the adult PBPK model of lamotrigine to explain the variability in its PK. (2019) 9:9709 | https://doi.org/10.1038/s41598-019-45778-9 www.nature.com/scientificreports www.nature.com/scientificreports/ Assessment of heterogeneity across studies. Summary estimates of studies and coefficient of heterogeneity were determined by FE and RE meta-analysis, to assess heterogeneity in data of different studies. The basic assumption for FE model is that the studies conducted are virtually identical (e.g., same study design, experimental conditions, etc.). On the other hand, the RE model assumes that the observed results may vary from study to study and follow certain distribution pattern 59 .
where, w j is the FE weight of the study j, calculated as inverse of variance [w j = 1/(SD j ) 2 ]; and X j represents the mean abundance of a particular non-CYP enzyme for individual study j. Further, Equation 2 was used to determine heterogeneity by FE model 58 .
where, ⁎ w j is the RE weight of the study j, calculated by formula = +τ −⁎ w 1/(w ) j j 1 2 . Herein τ 2 is the between-study heterogeneity estimator, which was obtained using Q F and degrees of freedom (df; calculated as k-1, where k is the number of studies) by Equation 4 61 .
The coefficient of heterogeneity of RE meta-analysis (Q R ) 58 was estimated when Q F > df (Equation 5). Otherwise τ 2 value was considered as zero, thus implying that RE meta-analysis would lead to same results as those obtained in FE meta-analysis.
Further, heterogeneity was determined through H 2 and I 2 indices for both FE and RE models using Equations 6 and 7, respectively.
where, Q is coefficient of heterogeneity, defined as Q F and Q R for FE and RE models, respectively. As mentioned in discussion section, H 2 values more than 1.5 generally cause considerable heterogeneity concern, while values below 1.2 are of little concern for heterogeneity 49 . The I 2 index provides a percentage of overall variability between individual studies, with values 0%, ~25%, ~50% and ~75% classified as none, low, medium and high heterogeneity, respectively 13,14 . When I 2 is negative, it is set to zero. Further, P-values for determining the statistical significance of analysis were calculated using chi-squared (χ 2 ) distribution of the Q and df values 13,14 . Calculation of weighted means and coefficient of variation. The meta-analysis was carried out considering weighting by sample size 13,17,62 . The weighted mean (WM) was calculated using Equation 8: In method II, %CV was determined through overall SD and WM using Equation 11 63 .
Overall SD WM 100 Overall sum of sqaures/N WM 100 (11) where, the overall sum of squares was calculated considering standard deviation (SD j ), X j and n j of individual study j, and WM employing Equation 12.
where, σ is the standard deviation of the data on the natural log scale.

Development and validation of lamotrigine pBpK model and extension to special populations. Model development for healthy adult population.
In the first step of PBPK model development for lamotrigine in GastroPlus, drug-specific physicochemical properties and system-specific input parameters were compiled, which are listed in Table 4. The next step involved development of a whole-body PBPK model for a healthy adult of 30 years age and 70 kg weight. The adult physiology was created using Population Estimates for Age Related (PEAR) physiology module within the simulator. In particular, intravenous plasma clearance (CL IV , L/h) and steady-state volume of distribution (V ss , L) values were taken from reported PBPK model of lamotrigine, which was primarily focused on optimal profiling of lamotrigine formulations, drug disposition and drug-drug interactions 65 . All tissues were assumed to be perfusion-limited compartments. Only liver tissue was considered for metabolic clearance of the drug.
In vitro-in vivo extrapolation (IVIVE). The third step was development of a mechanistic model for which CL IV and fraction of unchanged drug cleared through renal route (f CL,renal ) data were used to estimate hepatic plasma clearance (CL H , 1.8 L/h) and CL R (CL R = f CL,renal × CL IV , 0.2 L/h). The net unbound intrinsic hepatic clearance (CLu int,H , L/h) was back-calculated from CL H by taking into account fu p (0.45), and B:P (1) using "well-stirred" model 66  www.nature.com/scientificreports www.nature.com/scientificreports/ where, MPPGL (mg of microsomal protein per g of liver; default GastroPlus value 38), liver weight (default GastroPlus value 1637.7 g) and a factor of 60 × 10 −6 was used for unit conversion.
Thereafter, V max,DME j , which is maximum velocity of the kinetic reaction for individual DME isoforms (pmol/ min/pmol isoform) was calculated using Equation 19.
In vitro CL K fu DME abundance ISEF (19) max,DME int,DME m ,DME mic j D ME j j j j where, K m,DME j is the in vitro Michaelis-Menten constant (µM), fu mic is the unbound fraction in microsomes (default GastroPlus value 1.0), DME j abundance is the abundance of individual DME isoforms in liver (default GastroPlus value 7.6 and 7.9 pmol/mg protein for UGT1A3 and UGT1A4, respectively), ISEF DME j is the inter-system extrapolation factor for individual DME isoforms (default GastroPlus value 1.0), as mentioned in Table 4.

Parameters Values/models
Physiochemical and blood binding properties  www.nature.com/scientificreports www.nature.com/scientificreports/ To address inter-laboratory variability in specific protein abundance, we adjusted the V max,DME j values for individual enzymes using Equation 20 and then the same were input in the Enzymes and Transporter module of GastroPlus. (20) max,DME m ax,DME CI j j j where, SF is the scale factor for inter-laboratory variability of specific DME, which was calculated using Equation 21.
Weighted lower or higher 95% CI DME abundance in adults Weighted mean DME abundance in adults (21) CI j j j The oral absorption model was established considering absorption parameters involving intestinal permeability, solubility, diffusion coefficient and particle size in "Human-Fasted" gut physiology model (default GastroPlus value except for permeability and solubility) with the disposition parameters as optimized above. Intestinal metabolism was assumed negligible as the oral bioavailability of lamotrigine is 98%.
Model evaluation. In a further step, the predictive performance of the developed models was evaluated by comparing the simulated exposure parameters with literature-based observed clinical exposure parameters (C max and AUC), in accordance with the acceptance criteria suggested in the literature 64 . The lower and higher 99.998% CIs were calculated taking z value as 4.26 using Equations 14 and 15, and considering individual clinical PK data given in Supplementary Table S3.
Extrapolation of model to paediatric and HI population. The last step involved extrapolation of the validated adult PBPK model to predict PK in different paediatric populations, which was done using the PEAR Physiology module of GastroPlus. The models were developed for three age groups (representing average age and weight in each group), viz., early childhood (4 years, 17.34 kg), children (7 years, 26.54 kg) and middle childhood (9 years, 34.45 kg).
Although the protein abundance in adult, paediatric and HI population was considered similar in the software, to address protein abundance alteration, we adjusted the V max,DME j values for individual enzymes using Equation 22 and then the same were input in the Enzymes and Transporter module of GastroPlus.
max,DME m ax,DME DME M PPGL j j j where, SF is the scale factor, which was calculated as altered abundance of enzyme and MPPGL due to effect of age and disease (Supplementary Table S4), by using Equations 23 and 24, respectively: Mean or 95% CI DME abundance in paediatric/HI population Mean DME abundance in healthy adults (23) DME j j j = SF Mean MPPGL in paediatric/HI population Mean MPPGL in healthy adults (24) MPPGL The determinations were hence made for different age groups and HI population, especially for the abundance of UGT1A4 9,56 , and the results were incorporated into the model in order to capture differential PK of lamotrigine in these populations. Our published protein abundance data of UGT1A4 enzyme in alcoholic and HCV cirrhotic livers 9 were used to develop two separate SFs for each of these populations. Further, scaled CLu int,DME j value was obtained through IVIVE using Equation 25: Scaled CLu Adjusted V D ME abundance ISEF K f u MPPGL Liver weight 60 10 int,DME max,DME j DME m,DME m ic 6 j j j j Default GastroPlus liver weight values for each age group were input in the above mentioned Equation 25, which accounts for age-dependent change, viz., early childhood (592.12 g at 4 years), children (726.23 g at 7 years), middle childhood (906.24 g at 9 years). For Child-Pugh C class of HI population, liver weight taken was 867.97 g for 30 years age.
The renal plasma clearance in paediatric and HI group (CL R,paediatric/HI ) were calculated 67  where, fu paediatric/HI and GFR paediatric/HI values were obtained from GastroPlus.