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Applications of Human Pharmacokinetic Prediction in First-in-Human Dose Estimation

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Abstract

Quantitative estimations of first-in-human (FIH) doses are critical for phase I clinical trials in drug development. Human pharmacokinetic (PK) prediction methods have been developed to project the human clearance (CL) and bioavailability with reasonable accuracy, which facilitates estimation of a safe yet efficacious FIH dose. However, the FIH dose estimation is still very challenging and complex. The aim of this article is to review the common approaches for FIH dose estimation with an emphasis on PK-guided estimation. We discuss 5 methods for FIH dose estimation, 17 approaches for the prediction of human CL, 6 methods for the prediction of bioavailability, and 3 tools for the prediction of PK profiles. This review may serve as a practical protocol for PK- or pharmacokinetic/pharmacodynamic-guided estimation of the FIH dose.

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REFERENCES

  1. Contrera JF, Matthews EJ, Kruhlak NL, Benz RD. Estimating the safe starting dose in phase I clinical trials and no observed effect level based on QSAR modeling of the human maximum recommended daily dose. Regul Toxicol Pharmacol. 2004;40(3):185–206.

    Article  PubMed  CAS  Google Scholar 

  2. FDA. Guidance for industry-Estimating the maximum safe dose in initial clinical trials for therapeutics in adult healthy volunteers. Rockville. 2005.

  3. Mahmood I. Application of allometric principles for the prediction of pharmacokinetics in human and veterinary drug development. Adv Drug Deliv Rev. 2007;59(11):1177–92.

    Article  PubMed  CAS  Google Scholar 

  4. Reigner BG, Williams PE, Patel IH, Steimer JL, Peck C, van Brummelen P. An evaluation of the integration of pharmacokinetic and pharmacodynamic principles in clinical drug development. Experience within Hoffmann La Roche. Clin Pharmacokinet. 1997;33(2):142–52.

    Article  PubMed  CAS  Google Scholar 

  5. Reigner BG, Blesch KS. Estimating the starting dose for entry into humans: principles and practice. Eur J Clin Pharmacol. 2002;57(12):835–45.

    Article  PubMed  CAS  Google Scholar 

  6. Tang H, Mayersohn M. A global examination of allometric scaling for predicting human drug clearance and the prediction of large vertical allometry. J Pharm Sci. 2006;95(8):1783–99.

    Article  PubMed  CAS  Google Scholar 

  7. Mahmood I. Response to the comments on the commentary ‘Prediction of absolute bioavailability for drugs using oral and renal clearance following a single oral dose: a critical view’. Biopharm Drug Dispos. 1998;19(7):483–4.

    Article  PubMed  CAS  Google Scholar 

  8. Lave T, Dupin S, Schmitt C, Chou RC, Jaeck D, Coassolo P. Integration of in vitro data into allometric scaling to predict hepatic metabolic clearance in man: application to 10 extensively metabolized drugs. J Pharm Sci. 1997;86(5):584–90.

    Article  PubMed  CAS  Google Scholar 

  9. Goteti K, Brassil PJ, Good SS, Garner CE. Estimation of human drug clearance using multiexponential techniques. J Clin Pharmacol. 2008;48(10):1226–36.

    Article  PubMed  CAS  Google Scholar 

  10. Tang H, Mayersohn M. A novel model for prediction of human drug clearance by allometric scaling. Drug Metab Dispos. 2005;33(9):1297–303.

    Article  PubMed  CAS  Google Scholar 

  11. Tang H, Hussain A, Leal M, Mayersohn M, Fluhler E. Interspecies prediction of human drug clearance based on scaling data from one or two animal species. Drug Metab Dispos. 2007;35(10):1886–93.

    Article  PubMed  CAS  Google Scholar 

  12. Obach RS, Baxter JG, Liston TE, Silber BM, Jones BC, MacIntyre F, et al. The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J Pharmacol Exp Ther. 1997;283(1):46–58.

    PubMed  CAS  Google Scholar 

  13. Ito K, Houston JB. Prediction of human drug clearance from in vitro and preclinical data using physiologically based and empirical approaches. Pharm Res. 2005;22(1):103–12.

    Article  PubMed  CAS  Google Scholar 

  14. Lave T, Coassolo P, Reigner B. Prediction of hepatic metabolic clearance based on interspecies allometric scaling techniques and in vitro-in vivo correlations. Clin Pharmacokinet. 1999;36(3):211–31.

    Article  PubMed  CAS  Google Scholar 

  15. Hosea NA, Collard WT, Cole S, Maurer TS, Fang RX, Jones H, et al. Prediction of human pharmacokinetics from preclinical information: comparative accuracy of quantitative prediction approaches. J Clin Pharmacol. 2009;49(5):513–33.

    Article  PubMed  CAS  Google Scholar 

  16. Lowe PJ, Hijazi Y, Luttringer O, Yin H, Sarangapani R, Howard D. On the anticipation of the human dose in first-in-man trials from preclinical and prior clinical information in early drug development. Xenobiotica. 2007;37(10–11):1331–54.

    Article  PubMed  CAS  Google Scholar 

  17. Fagerholm U. Prediction of human pharmacokinetics—evaluation of methods for prediction of volume of distribution. J Pharm Pharmacol. 2007;59(9):1181–90.

    Article  PubMed  CAS  Google Scholar 

  18. Fagerholm U. Prediction of human pharmacokinetics—evaluation of methods for prediction of hepatic metabolic clearance. J Pharm Pharmacol. 2007;59(6):803–28.

    Article  PubMed  CAS  Google Scholar 

  19. Fagerholm U. Prediction of human pharmacokinetics-biliary and intestinal clearance and enterohepatic circulation. J Pharm Pharmacol. 2008;60(5):535–42.

    Article  PubMed  CAS  Google Scholar 

  20. Fagerholm U. Prediction of human pharmacokinetics—renal metabolic and excretion clearance. J Pharm Pharmacol. 2007;59(11):1463–71.

    Article  PubMed  Google Scholar 

  21. Ghibellini G, Leslie EM, Brouwer KL. Methods to evaluate biliary excretion of drugs in humans: an updated review. Mol Pharm. 2006;3(3):198–211.

    Article  PubMed  CAS  Google Scholar 

  22. Houston JB, Galetin A. Progress towards prediction of human pharmacokinetic parameters from in vitro technologies. Drug Metab Rev. 2003;35(4):393–415.

    Article  PubMed  CAS  Google Scholar 

  23. Chiba M, Ishii Y, Sugiyama Y. Prediction of hepatic clearance in human from in vitro data for successful drug development. AAPS J. 2009;11(2):262–76.

    Article  PubMed  CAS  Google Scholar 

  24. Sharma V, McNeill JH. To scale or not to scale: the principles of dose extrapolation. Br J Pharmacol. 2009;157(6):907–21.

    Article  PubMed  CAS  Google Scholar 

  25. European Medicines Agency. Guideline on strategies to identify and mitigate risks for first-in-human clinical trials with investigational medicinal products. 2007.

  26. Mahmood I, Green MD, Fisher JE. Selection of the first-time dose in humans: comparison of different approaches based on interspecies scaling of clearance. J Clin Pharmacol. 2003;43(7):692–7.

    PubMed  CAS  Google Scholar 

  27. Iavarone L, Hoke JF, Bottacini M, Barnaby R, Preston GC. First time in human for GV196771: interspecies scaling applied on dose selection. J Clin Pharmacol. 1999;39(6):560–6.

    Article  PubMed  CAS  Google Scholar 

  28. Agoram BM. Use of pharmacokinetic/pharmacodynamic modelling for starting dose selection in first-in-human trials of high-risk biologics. Br J Clin Pharmacol. 2009;67(2):153–60.

    Article  PubMed  CAS  Google Scholar 

  29. Heimbach T, Lakshminarayana SB, Hu W, He H. Practical anticipation of human efficacious doses and pharmacokinetics using in vitro and preclinical in vivo data. AAPS J. 2009;11(3):602–14.

    Article  PubMed  CAS  Google Scholar 

  30. Suntharalingam G, Perry MR, Ward S, Brett SJ, Castello-Cortes A, Brunner MD, et al. Cytokine storm in a phase 1 trial of the anti-CD28 monoclonal antibody TGN1412. N Engl J Med. 2006;355(10):1018–28.

    Article  PubMed  CAS  Google Scholar 

  31. Artursson P, Karlsson J. Correlation between oral drug absorption in humans and apparent drug permeability coefficients in human intestinal epithelial (Caco-2) cells. Biochem Biophys Res Commun. 1991;175(3):880–5.

    Article  PubMed  CAS  Google Scholar 

  32. Rubas W, Cromwell ME, Shahrokh Z, Villagran J, Nguyen TN, Wellton M, et al. Flux measurements across Caco-2 monolayers may predict transport in human large intestinal tissue. J Pharm Sci. 1996;85(2):165–9.

    Article  PubMed  CAS  Google Scholar 

  33. Muller PY, Brennan FR. Safety assessment and dose selection for first-in-human clinical trials with immunomodulatory monoclonal antibodies. Clin Pharmacol Ther. 2009;85(3):247–58.

    Article  PubMed  CAS  Google Scholar 

  34. Fagerholm U. The role of permeability in drug ADME/PK, interactions and toxicity—presentation of a permeability-based classification system (PCS) for prediction of ADME/PK in humans. Pharm Res. 2008;25(3):625–38.

    Article  PubMed  CAS  Google Scholar 

  35. Dokoumetzidis A, Kosmidis K, Argyrakis P, Macheras P. Modeling and Monte Carlo simulations in oral drug absorption. Basic Clin Pharmacol Toxicol. 2005;96(3):200–5.

    Article  PubMed  CAS  Google Scholar 

  36. Tang H, Mayersohn M. Utility of the coefficient of determination (r2) in assessing the accuracy of interspecies allometric predictions: illumination or illusion? Drug Metab Dispos. 2007;35(12):2139–42.

    Article  PubMed  CAS  Google Scholar 

  37. Sinha VK, De Buck SS, Fenu LA, Smit JW, Nijsen M, Gilissen RA, et al. Predicting oral clearance in humans: how close can we get with allometry? Clin Pharmacokinet. 2008;47(1):35–45.

    Article  PubMed  CAS  Google Scholar 

  38. Mahmood I. Interspecies scaling: role of protein binding in the prediction of clearance from animals to humans. J Clin Pharmacol. 2000;40(12 Pt 2):1439–46.

    PubMed  CAS  Google Scholar 

  39. Boxenbaum H, Fertig JB. Scaling of antipyrine intrinsic clearance of unbound drug in 15 mammalian species. Eur J Drug Metab Pharmacokinet. 1984;9(2):177–83.

    Article  PubMed  CAS  Google Scholar 

  40. Mahmood I. Prediction of human drug clearance from animal data: application of the rule of exponents and ‘fu Corrected Intercept Method’ (FCIM). J Pharm Sci. 2006;95(8):1810–21.

    Article  PubMed  CAS  Google Scholar 

  41. Stoner CL, Cleton A, Johnson K, Oh DM, Hallak H, Brodfuehrer J, et al. Integrated oral bioavailability projection using in vitro screening data as a selection tool in drug discovery. Int J Pharm. 2004;269(1):241–9.

    Article  PubMed  CAS  Google Scholar 

  42. Nagilla R, Ward KW. A comprehensive analysis of the role of correction factors in the allometric predictivity of clearance from rat, dog, and monkey to humans. J Pharm Sci. 2004;93(10):2522–34.

    Article  PubMed  CAS  Google Scholar 

  43. Houston JB. Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance. Biochem Pharmacol. 1994;47(9):1469–79.

    Article  PubMed  CAS  Google Scholar 

  44. Mahmood I. Interspecies scaling of biliary excreted drugs: a comparison of several methods. J Pharm Sci. 2005;94(4):883–92.

    Article  PubMed  CAS  Google Scholar 

  45. Lave T, Coassolo P, Ubeaud G, Brandt R, Schmitt C, Dupin S, et al. Interspecies scaling of bosentan, a new endothelin receptor antagonist and integration of in vitro data into allometric scaling. Pharm Res. 1996;13(1):97–101.

    Article  PubMed  CAS  Google Scholar 

  46. Obach RS. The prediction of human clearance from hepatic microsomal metabolism data. Curr Opin Drug Discov Devel. 2001;4(1):36–44.

    PubMed  CAS  Google Scholar 

  47. Shibata Y, Takahashi H, Chiba M, Ishii Y. Prediction of hepatic clearance and availability by cryopreserved human hepatocytes: an application of serum incubation method. Drug Metab Dispos. 2002;30(8):892–6.

    Article  PubMed  CAS  Google Scholar 

  48. Goteti K, Garner CE, Mahmood I. Prediction of human drug clearance from two species: a comparison of several allometric methods. J Pharm Sci. 2010;99(3):1601–13.

    Article  PubMed  CAS  Google Scholar 

  49. Ward KW, Smith BR. A comprehensive quantitative and qualitative evaluation of extrapolation of intravenous pharmacokinetic parameters from rat, dog, and monkey to humans. II. Volume of distribution and mean residence time. Drug Metab Dispos. 2004;32(6):612–9.

    Article  PubMed  CAS  Google Scholar 

  50. Mahmood I. Role of fixed coefficients and exponents in the prediction of human drug clearance: how accurate are the predictions from one or two species? J Pharm Sci. 2009;98(7):2472–93.

    Article  PubMed  CAS  Google Scholar 

  51. Charman WN, Porter CJ, Mithani S, Dressman JB. Physiochemical and physiological mechanisms for the effects of food on drug absorption: the role of lipids and pH. J Pharm Sci. 1997;86(3):269–82.

    Article  PubMed  CAS  Google Scholar 

  52. Tang H, Mayersohn M. On the observed large interspecies overprediction of human clearance (“vertical allometry”) of UCN-01: further support for a proposed model based on plasma protein binding. J Clin Pharmacol. 2006;46(4):398–400.

    Article  PubMed  CAS  Google Scholar 

  53. Hakooz N, Ito K, Rawden H, Gill H, Lemmers L, Boobis AR, et al. Determination of a human hepatic microsomal scaling factor for predicting in vivo drug clearance. Pharm Res. 2006;23(3):533–9.

    Article  PubMed  CAS  Google Scholar 

  54. Ito K, Houston JB. Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Pharm Res. 2004;21(5):785–92.

    Article  PubMed  CAS  Google Scholar 

  55. Niro R, Byers JP, Fournier RL, Bachmann K. Application of a convective-dispersion model to predict in vivo hepatic clearance from in vitro measurements utilizing cryopreserved human hepatocytes. Curr Drug Metab. 2003;4(5):357–69.

    Article  PubMed  CAS  Google Scholar 

  56. Ashforth EI, Carlile DJ, Chenery R, Houston JB. Prediction of in vivo disposition from in vitro systems: clearance of phenytoin and tolbutamide using rat hepatic microsomal and hepatocyte data. J Pharmacol Exp Ther. 1995;274(2):761–6.

    PubMed  CAS  Google Scholar 

  57. Soars MG, Burchell B, Riley RJ. In vitro analysis of human drug glucuronidation and prediction of in vivo metabolic clearance. J Pharmacol Exp Ther. 2002;301(1):382–90.

    Article  PubMed  CAS  Google Scholar 

  58. Brown HS, Griffin M, Houston JB. Evaluation of cryopreserved human hepatocytes as an alternative in vitro system to microsomes for the prediction of metabolic clearance. Drug Metab Dispos. 2007;35(2):293–301.

    Article  PubMed  CAS  Google Scholar 

  59. Sun D, Yu LX, Hussain MA, Wall DA, Smith RL, Amidon GL. In vitro testing of drug absorption for drug ‘developability’ assessment: forming an interface between in vitro preclinical data and clinical outcome. Curr Opin Drug Discov Devel. 2004;7(1):75–85.

    PubMed  CAS  Google Scholar 

  60. Naritomi Y, Terashita S, Kimura S, Suzuki A, Kagayama A, Sugiyama Y. Prediction of human hepatic clearance from in vivo animal experiments and in vitro metabolic studies with liver microsomes from animals and humans. Drug Metab Dispos. 2001;29(10):1316–24.

    PubMed  CAS  Google Scholar 

  61. Naritomi Y, Terashita S, Kagayama A, Sugiyama Y. Utility of hepatocytes in predicting drug metabolism: comparison of hepatic intrinsic clearance in rats and humans in vivo and in vitro. Drug Metab Dispos. 2003;31(5):580–8.

    Article  PubMed  CAS  Google Scholar 

  62. Lave T, Dupin S, Schmitt C, Valles B, Ubeaud G, Chou RC, et al. The use of human hepatocytes to select compounds based on their expected hepatic extraction ratios in humans. Pharm Res. 1997;14(2):152–5.

    Article  PubMed  CAS  Google Scholar 

  63. Zuegge J, Schneider G, Coassolo P, Lave T. Prediction of hepatic metabolic clearance: comparison and assessment of prediction models. Clin Pharmacokinet. 2001;40(7):553–63.

    Article  PubMed  CAS  Google Scholar 

  64. Fagerholm U. Prediction of human pharmacokinetics—improving microsome-based predictions of hepatic metabolic clearance. J Pharm Pharmacol. 2007;59(10):1427–31.

    Article  PubMed  CAS  Google Scholar 

  65. Obach RS. Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: an examination of in vitro half-life approach and nonspecific binding to microsomes. Drug Metab Dispos. 1999;27(11):1350–9.

    PubMed  CAS  Google Scholar 

  66. Stringer R, Nicklin PL, Houston JB. Reliability of human cryopreserved hepatocytes and liver microsomes as in vitro systems to predict metabolic clearance. Xenobiotica. 2008;38(10):1313–29.

    Article  PubMed  CAS  Google Scholar 

  67. Skaggs SM, Foti RS, Fisher MB. A streamlined method to predict hepatic clearance using human liver microsomes in the presence of human plasma. J Pharmacol Toxicol Methods. 2006;53(3):284–90.

    Article  PubMed  CAS  Google Scholar 

  68. Stringer RA, Strain-Damerell C, Nicklin P, Houston JB. Evaluation of recombinant cytochrome P450 enzymes as an in vitro system for metabolic clearance predictions. Drug Metab Dispos. 2009;37(5):1025–34.

    Article  PubMed  CAS  Google Scholar 

  69. Galetin A, Brown C, Hallifax D, Ito K, Houston JB. Utility of recombinant enzyme kinetics in prediction of human clearance: impact of variability, CYP3A5, and CYP2C19 on CYP3A4 probe substrates. Drug Metab Dispos. 2004;32(12):1411–20.

    Article  PubMed  CAS  Google Scholar 

  70. Kilford PJ, Stringer R, Sohal B, Houston JB, Galetin A. Prediction of drug clearance by glucuronidation from in vitro data: use of combined cytochrome P450 and UDP-glucuronosyltransferase cofactors in alamethicin-activated human liver microsomes. Drug Metab Dispos. 2009;37(1):82–9.

    Article  PubMed  CAS  Google Scholar 

  71. Yang J, Jamei M, Yeo KR, Tucker GT, Rostami-Hodjegan A. Prediction of intestinal first-pass drug metabolism. Curr Drug Metab. 2007;8(7):676–84.

    Article  PubMed  CAS  Google Scholar 

  72. Wajima T, Fukumura K, Yano Y, Oguma T. Prediction of human clearance from animal data and molecular structural parameters using multivariate regression analysis. J Pharm Sci. 2002;91(12):2489–99.

    Article  PubMed  CAS  Google Scholar 

  73. Nikolic K, Agababa D. Prediction of hepatic microsomal intrinsic clearance and human clearance values for drugs. J Mol Graph Model. 2009;28(3):245–52.

    Article  PubMed  CAS  Google Scholar 

  74. Dokoumetzidis A, Kalantzi L, Fotaki N. Predictive models for oral drug absorption: from in silico methods to integrated dynamical models. Expert Opin Drug Metab Toxicol. 2007;3(4):491–505.

    Article  PubMed  CAS  Google Scholar 

  75. Obach RS, Lombardo F, Waters NJ. Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metab Dispos. 2008;36(7):1385–405.

    Article  PubMed  CAS  Google Scholar 

  76. Badhan R, Penny J, Galetin A, Houston JB. Methodology for development of a physiological model incorporating CYP3A and P-glycoprotein for the prediction of intestinal drug absorption. J Pharm Sci. 2009;98(6):2180–97.

    Article  PubMed  CAS  Google Scholar 

  77. Kesisoglou F, Wu Y. Understanding the effect of API properties on bioavailability through absorption modeling. AAPS J. 2008;10(4):516–25.

    Article  PubMed  CAS  Google Scholar 

  78. Yee S. In vitro permeability across Caco-2 cells (colonic) can predict in vivo (small intestinal) absorption in man—fact or myth. Pharm Res. 1997;14(6):763–6.

    Article  PubMed  CAS  Google Scholar 

  79. Sugano K, Hamada H, Machida M, Ushio H, Saitoh K, Terada K. Optimized conditions of bio-mimetic artificial membrane permeation assay. Int J Pharm. 2001;228(1–2):181–8.

    Article  PubMed  CAS  Google Scholar 

  80. Pidgeon C, Ong S, Liu H, Qiu X, Pidgeon M, Dantzig AH, et al. IAM chromatography: an in vitro screen for predicting drug membrane permeability. J Med Chem. 1995;38(4):590–4.

    Article  PubMed  CAS  Google Scholar 

  81. Kansy M, Senner F, Gubernator K. Physicochemical high throughput screening: parallel artificial membrane permeation assay in the description of passive absorption processes. J Med Chem. 1998;41(7):1007–10.

    Article  PubMed  CAS  Google Scholar 

  82. Zhu C, Jiang L, Chen TM, Hwang KK. A comparative study of artificial membrane permeability assay for high throughput profiling of drug absorption potential. Eur J Med Chem. 2002;37(5):399–407.

    Article  PubMed  CAS  Google Scholar 

  83. Kotecha J, Shah S, Rathod I, Subbaiah G. Relationship between immobilized artificial membrane chromatographic retention and human oral absorption of structurally diverse drugs. Int J Pharm. 2007;333(1–2):127–35.

    Article  PubMed  CAS  Google Scholar 

  84. Cao X, Gibbs ST, Fang L, Miller HA, Landowski CP, Shin HC, et al. Why is it challenging to predict intestinal drug absorption and oral bioavailability in human using rat model. Pharm Res. 2006;23(8):1675–86.

    Article  PubMed  CAS  Google Scholar 

  85. Akabane T, Tabata K, Kadono K, Sakuda S, Terashita S, Teramura T. A comparison of pharmacokinetics between humans and monkeys. Drug Metab Dispos. 2010;38(2):308–16.

    Article  PubMed  CAS  Google Scholar 

  86. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 1997;23(1–3):3–25.

    Article  CAS  Google Scholar 

  87. Andrews CW, Bennett L, Yu LX. Predicting human oral bioavailability of a compound: development of a novel quantitative structure-bioavailability relationship. Pharm Res. 2000;17(6):639–44.

    Article  PubMed  CAS  Google Scholar 

  88. Yoshida F, Topliss JG. QSAR model for drug human oral bioavailability. J Med Chem. 2000;43(13):2575–85.

    Article  PubMed  CAS  Google Scholar 

  89. Dedrick R, Bischoff KB, Zaharko DS. Interspecies correlation of plasma concentration history of methotrexate (NSC-740). Cancer Chemother Rep. 1970;54(2):95–101.

    PubMed  CAS  Google Scholar 

  90. Mahmood I, Yuan R. A comparative study of allometric scaling with plasma concentrations predicted by species-invariant time methods. Biopharm Drug Dispos. 1999;20(3):137–44.

    Article  PubMed  CAS  Google Scholar 

  91. Wajima T, Yano Y, Fukumura K, Oguma T. Prediction of human pharmacokinetic profile in animal scale up based on normalizing time course profiles. J Pharm Sci. 2004;93(7):1890–900.

    Article  PubMed  CAS  Google Scholar 

  92. Fura A, Vyas V, Humphreys W, Chimalokonda A, Rodrigues D. Prediction of human oral pharmacokinetics using nonclinical data: examples involving four proprietary compounds. Biopharm Drug Dispos. 2008;29(8):455–68.

    Article  PubMed  CAS  Google Scholar 

  93. Gibson CR, Bergman A, Lu P, Kesisoglou F, Denney WS, Mulrooney E. Prediction of phase I single-dose pharmacokinetics using recombinant cytochromes P450 and physiologically based modelling. Xenobiotica. 2009;39(9):637–48.

    Article  PubMed  CAS  Google Scholar 

  94. Lowe PJ, Tannenbaum S, Wu K, Lloyd P, Sims J. On setting the first dose in man: quantitating biotherapeutic drug-target binding through pharmacokinetic and pharmacodynamic models. Basic Clin Pharmacol Toxicol. 2010;106(3):195–209.

    Article  PubMed  CAS  Google Scholar 

  95. De Buck SS, Sinha VK, Fenu LA, Nijsen MJ, Mackie CE, Gilissen RA. Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs. Drug Metab Dispos. 2007;35(10):1766–80.

    Article  PubMed  Google Scholar 

  96. Jones HM, Parrott N, Jorga K, Lave T. A novel strategy for physiologically based predictions of human pharmacokinetics. Clin Pharmacokinet. 2006;45(5):511–42.

    Article  PubMed  CAS  Google Scholar 

  97. Schneider G, Coassolo P, Lave T. Combining in vitro and in vivo pharmacokinetic data for prediction of hepatic drug clearance in humans by artificial neural networks and multivariate statistical techniques. J Med Chem. 1999;42(25):5072–6.

    Article  PubMed  CAS  Google Scholar 

  98. Mohutsky MA, Chien JY, Ring BJ, Wrighton SA. Predictions of the in vivo clearance of drugs from rate of loss using human liver microsomes for phase I and phase II biotransformations. Pharm Res. 2006;23(4):654–62.

    Article  PubMed  CAS  Google Scholar 

  99. Huang C, Zheng M, Yang Z, Rodrigues AD, Marathe P. Projection of exposure and efficacious dose prior to first-in-human studies: how successful have we been? Pharm Res. 2008;25(4):713–26.

    Article  PubMed  CAS  Google Scholar 

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ACKNOWLEDGMENTS

This work was partially supported by the National Institutes of Health (RO1 CA120023); University of Michigan Cancer Center Research Grant (Munn); and University of Michigan Cancer Center Core Grant to DS.

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  1. Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, 428 Church Street, Ann Arbor, Michigan, 48109, USA

    Peng Zou, Yanke Yu, Nan Zheng, Hayley J. Paholak & Duxin Sun

  2. Office of Testing and Research, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, 20993, USA

    Yongsheng Yang

  3. Office of Generic Drugs, Center for Drug Evaluation and Research, Food and Drug Administration, Rockville, Maryland, 20857, USA

    Lawrence X. Yu

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  1. Peng Zou
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Zou, P., Yu, Y., Zheng, N. et al. Applications of Human Pharmacokinetic Prediction in First-in-Human Dose Estimation. AAPS J 14, 262–281 (2012). https://doi.org/10.1208/s12248-012-9332-y

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