Supplementray_MaterialFinal SUPPLEMENTARY MATERIAL S. Grandoni et al. “Building in-house PBPK modelling tools for oral drug administration from literature information” System-specific parameters values Values of the physiological parameters used in the PBPK model described in the paper are here summarized. These values refer to a typical subject of 250 g for rats, 10 kg for dogs and 70 kg for man. Rat parameters Fluxes [ml/min], [21,22] Volumes [ml], [21,22,23] Qbrain 1.79 Vbrain 1.43 Qgut 11.92 Vgut 6.75 Qspleen 0.8 Vspleen 0.5 Qliver 14.6 Vliver 9.15 Qmuscle 24.91 Vmuscle 101.8 Qadipose 6.27 Vadipose 16.6 Qheart 4.39 Vheart 0.83 Qkidney 12.64 Vkidney 1.83 QRestofthebody 23.1 VRestofthebody 72.36 Cardiac Output 89.6 Vlung 1.25 Vven 10.12 Vart 3.38 Gastrointestinal absorption model parameters Volumes of intestinal segments [ml], [25] Vstomach 3 V1 0.6 V2 0.66 V3 0.66 V4 0.41 V5 0.41 V6 0.41 V7 0.41 Vcolon 3 pH of intestinal segments, [25] pHstomach 3 pH1 7.1 pH2 7.3 pH3 7.5 pH4 7.7 pH5 7.9 pH6 8 pH7 7.4 pHcolon 7.6 MRT values Stomach 10 min Small intestine 88 min Colon 228 min Rat tissue composition to apply the Poulin’s methods, [35] Rat tissue composition, to apply the method of Rodgers, [37] Rat tissues Volume fraction of Phospholipids, Vph Volume fraction of Neutral lipids, Vnl Volume fraction of Water, Vw Volume fraction of Interstitial space Adipose 0.002 0.853 0.12 0.715 Bone 0.0027 0.0273 0.446 0.42 Brain 0.0533 0.0392 0.788 0.162 Gut 0.0138 0.0292 0.749 0.39 Heart 0.0118 0.014 0.779 0.156 Kidney 0.0284 0.0123 0.771 0.346 Liver 0.0303 0.0138 0.705 0.159 Lung 0.014 0.0219 0.79 0.484 Muscle 0.009 0.01 0.756 0.115 Skin 0.018 0.0239 0.651 0.462 Spleen 0.0136 0.0077 0.771 0.264 Plasma 0.00083 0.00147 0.96 1 Erythrocytes - - - - Rat tissues Neutral phospholipids Neutral lipids Extracellular Water Intracellular water Tissue Concentration of Acidic Phospholipids (mg/g) Adipose 0.853 0.0016 0.135 0.017 0.40 Bone 0.017 0.0017 0.100 0.346 0.67 Brain 0.039 0.0015 0.162 0.620 0.40 Gut 0.038 0.0125 0.282 0.475 2.41 Heart 0.014 0.0111 0.320 0.475 2.25 Kidney 0.012 0.0242 0.273 0.483 5.03 Liver 0.014 0.0240 0.161 0.573 4.56 Lung 0.022 0.0128 0.336 0.446 3.91 Muscle 0.010 0.0072 0.118 0.630 1.53 Pancreas 0.041 0.0093 0.120 0.664 1.67 Skin 0.060 0.0044 0.382 0.291 1.32 Tracheobronchial surface, STB: 81.75 cm 2 [14]. Hematocrit to compute the distribution: 0.46 [22]. Conversion factor to obtain the in vivo estimates of the hepatic clearance: - MPPGL: 45 mg/g [25], - HPGL: 125*10^6 cells/g [25], - Liver Weight: 9.15 g [21]. Filtration parameter to model the renal clearance: - GFR: 1.31 ml/min [22]. Spleen 0.0077 0.0113 0.207 0.579 3.18 Thymus 0.017 0.0092 0.150 0.626 2.30 Dog parameters Fluxes [ml/min], [21,22] Volumes [ml], [21,22,23] Qbrain 21 Vbrain 78 Qgut 216 Vgut 368 Qspleen 24 Vspleen 27 Qliver 288 Vliver 329 Qmuscle 227.9 Vmuscle 456.5 Qadipose 34 Vadipose 1380 Qheart 48.3 Vheart 78 Qkidney 181.65 Vkidney 55 QRestofthebody 246.8 VRestofthebody 1538 Cardiac Output 21 Vlung 82 Vven 675 Vart 225 Gastrointestinal absorption model parameters Volumes of intestinal segments [ml], [25] Vstomach 14.54 V1 30.54 V2 32 V3 32 V4 20.1 V5 20.1 V6 20.1 V7 20.1 Vcolon 290.9 pH of intestinal segments, [25] pHstomach 1.5 pH1 6 pH2 6 pH3 6 pH4 6.2 pH5 6.2 pH6 6.2 pH7 7.4 pHcolon 6.5 MRT values Stomach 30 min Small intestine 109 min Colon 9.4 h Tracheobronchial surface, STB =1176 cm 2, estimated with linear regression from the rat and man BW-STB data [14]. Haematocrit: 0.42 [22]. Conversion factor to obtain the in vivo estimates of the hepatic clearance: - MPPGL 43 mg/g [25], - HPGL 120*10^6 cells/g [25], - Liver Weight 329 g [21]. Filtration parameter to model the renal clearance: - GFR 61.3 ml/min [22]. Human parameters Fluxes [ml/min], [21,22] Volumes [ml], [21,22,23] Qbrain 745 Vbrain 1400 Qgut 1046 Vgut 1155 Qspleen 160 Vspleen 182 Qliver 1578 Vliver 1799 Qmuscle 1055 Vmuscle 28000 Qadipose 310 Vadipose 14994 Qheart 248 Vheart 329 Qkidney 1179 Vkidney 308 QRestofthebody 1308 VRestofthebody 10801 Cardiac Output 6204 Vlung 532 Vven 3900 Vart 1300 Gastrointestinal absorption model parameters Volumes of intestinal segments [ml], [25] Vstomach 50 V1 105 V2 110 V3 110 V4 69 V5 69 V6 69 V7 69 Vcolon 1000 pH of intestinal segments pHstomach 2 pH1 6 pH2 6.2 pH3 6.6 pH4 6.8 pH5 7 pH6 7.2 pH7 7.4 pHcolon 7 MRT values Stomach 30 min Small intestine 199.2 min Colon 11 h Information available on human tissue composition, [35] Tracheobronchial surface, STB: 8990 cm 2 [14]. Haematocrit: 0.44 [22]. Conversion factor to obtain the in vivo estimates of the hepatic clearance: - MGPPGL: 32 mg/g [S1], - HPGL: 99*10^6 cells/g [S1], - Liver Weight: 1799 g [21]. Filtration parameter to model the renal clearance: - GFR 125 ml/min [22]. Human tissues Volume fraction of Phospholipids, Vph Volume fraction of Neutral lipids, Vnl Volume fraction of Water, Vw Adipose 0.002 0.79 0.18 Bone 0.0011 0.074 0.439 Brain 0.0565 0.051 0.77 Gut 0.0163 0.0487 0.718 Heart 0.0166 0.0115 0.758 Kidney 0.0162 0.0207 0.783 Liver 0.0252 0.0348 0.751 Lung 0.009 0.003 0.811 Muscle 0.0072 0.0238 0.76 Skin 0.0111 0.0284 0.718 Spleen 0.0198 0.0201 0.788 Plasma 0.00225 0.0035 0.945 Erythrocytes - - - Drug-related parameters relationships In this section the equations to calculate the drug-specific parameters are reported. Absorption The Henderson-Hasselbalch equations to calculate the solubility at a certain pH are here reported Monoprotic acids CspH=Sint(1+10 (pH-pKa1)) (s1) Monoprotic bases CspH=Sint(1+10 (-pH+pKa1)) (s2) Diprotic acids CspH=Sint (1+10 (-pH+pKa1)+10(2pH-pKa1-pKa2)) (s3) Diprotic bases CspH=Sint (1+10 (-pH+pKa1)+10(-2pH+pKa1+pKa2)) (s4) Neutals CspH=Sint (s5) Zwitterions CspH=Sint(1+10 (-pH+pKaA)+10(pH-pKaB)) (s6) where pKaA is the acidic pKa and pKaB is the basic pKa. Partition coefficients This subsection contains the equations needed to calculate PT:B values with the method of Poulin [35,36] and of Rodgers [37,38]. For the latter the equations for each chemical species are reported. Poulin’s Method The fractional volumes of phospholipids (Vph), neutral lipids (Vnl) and water (Vw), required to apply the method, are reported in the species-specific parameters section. In the following P indicates plasma and T tissue. Pow=10logP Dow=10logD fuT=1/(1+(1-fuP)/fuP0.5) For non-adipose tissues PT:p=[(Pow(VnlT+0.3VphT)+(VwT+0.7VphT)]/[Pow(Vnlp+0.3Vphp)+(Vwp+0.7Vphp)](fup/fut) (s7) For adipose tissues PT:p=[(Dow(VnlT+0.3VphT)+(VwT+0.7VphT)]/[Dow(Vnlp+0.3Vphp)+(Vwp+0.7Vphp)]fup (s8) To obtain the values of PT:B from the PT:P, the tissue to plasma partition coefficient, can be applied the following equation: PT:B= PT:p/BP (s9) Distribution Rodger’s Method The volumes related to tissues composition in terms of neutral lipids (nl), neutral phospholipids (nph), extracellular water (ew), intracellular water (iw), the ratios such as the lipoprotein ratio (lr), the albumin ratio (ar) and the tissue concentration of acidic phospholipids (ap) are reported in the species-specific parameters section. In the notation, T indicates the tissue and B the blood. The values of pHp, pHiw and pHbc are fixed, as reported by the authors, to 7.4, 7 and 7.22 respectively. The values for fNLp and fNPp are fixed as 0.0023 and 0.0013 respectively, as reported in the paper. For all tissues, except adipose ones, the value P in the subsequent equations is the n-octanol:water partition coefficient (here reported as P1); for the adipose tissues the vegetable oil:water partition coefficient was deemed more appropriate (here indicated as P2). To obtain the value of PT:B from the Kpu (tissue to plasma unbound partition coefficient) the following equation can be applied: PT:B=Kpu fuP/BP (s10) P1=10logP (s11) logPveg=1.115 logP-1.35 (s12) P2=10logPveg (s13) Acids X=1+10(pHiw-pKa) Y=1+10(pHp-pKa) KpuT=ewT+X iwT/Y+((P nlT+(0.3 P+0.7) nphT)/Y)+(1/fup-1-(P fNLp+(0.3 P+0.7) fNPp)/Y) arT (s14) Diprotic acids In this equations pKa17 X=1+10(pKaB-pHiw)+10(pHiw-pKaA) Y=1+10(pKaB-pHp)+10(pHp-pKaA) X1=1+10(pKaB-pHbc)+10(pHbc-pKaA) Y1=1+10(pKaB-pHp)+10(pHp-pKaA) X2=10(pKaB-pHbc)+10(pHbc-pKaA) KpuBC=(BP-1+haematocrit)/haematocrit/fup; KaAP=(KpuBC-(X1/Y1 iwb)-(P nlb+(0.3 P+0.7) nphb)/Y1) (Y1/apb/X2) KpuT=ewT+X iwT/Y+(P nlT+(0.3 P+0.7) nphT)/Y+((KaAP apT 10 (pKaB-pHiw))+10(pHiw-pKaA))/Y (s21) All other zwitterions X=1+10(pKaB-pHiw)+10(pHiw-pKaA) Y=1+10(pKaB-pHp)+10(pHp-pKaA) KpuT=ewT+X iwT/Y+((P nlT+(0.3 P+0.7) nphT)/Y)+(1/fup-1-(P fNLp+(0.3 P+0.7) fNPp)/Y) arT (s22) Metabolism and Elimination The equations to apply the “Qgut” model [33], with the related scaling factors, to obtain FGUT in humans from measurement of in vitro intrinsic clearance from HLM, for CYP3A metabolizers are here reported. The fraction of drug escaping the first pass metabolism can be calculated as follows: FGut=Qvilli/(Qvilli+fuGUTCLuint,GUT(1+Qvilli/CLperm)) (s23) where Qvilli is the intestinal villi blood flow that for humans is 300 ml/min; fuGUT is the unbounded drug fraction in gut, if not available can be supposed equal to 1; CLuint,GUT is the net metabolic intrinsic clearance based on the unbound drug concentration, this last term can be obtained from the HLM as follows: Cluint,GUT =(Cluint/PEMP)NEWI (s24) where CLuint is the unbound hepatic intrinsic clearance obtained from HLM and expressed in microliter/minute/milligram of protein, PEMP is the Picomol of CYP3A Enzymes for Milligram of Protein that is 155 picomol/milligram of protein, NEWI is the value of Nanomol of Enzyme for the Whole Intestine that is 70.5 nanomol [33]. The value of CLperm, can be obtained as: CLperm=Peffhuman A (s25) where A is the area of the intestine, for humans 6600 cm2 obtained supposing a radius of 1.75 cm and a length of 6 m [33]. Additonal References [s1] Zoe E. Barter, Martin K. Bayliss, Philip H. Beaune, Alan R. Boobis, David J. Carlile, Robert J. Edwards, J. Brian Houston, Brian G. Lake, John C. Lipscomb, Olavi R. Pelkonen, Geoffrey T. Tucker1 and Amin Rostami-Hodjegan. Scaling Factors for the Extrapolation of In Vivo Metabolic Drug Clearance From In Vitro Data: Reaching a Consensus on Values of Human Micro- somal Protein and Hepatocellularity Per Gram of Liver. Current Drug Metabolism 8 (2007) 33-45.