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약물계량학을 이용한 초기임상1상 시험 용량 예측 방법에 대한 비교연구
Comparative Study of First-in-Human Dose Estimation Approaches using Pharmacometrics 원문보기

한국임상약학회지 = Korean journal of clinical pharmacy, v.26 no.2, 2016년, pp.150 - 162  

백인환 (경성대학교 약학대학 임상약학 연구실)

Abstract AI-Helper 아이콘AI-Helper

Objective: First-in-human dose estimation is an essential approach for successful clinical trials for drug development. In this study, we systematically compared first-in-human dose and human pharmacokinetic parameter estimation approaches. Methods: First-in-human dose estimation approaches divided ...

주제어

질의응답

핵심어 질문 논문에서 추출한 답변
의약품 시장과 수요는 지속적으로 증가하는 이유는? 신약 개발은 성공 시 높은 수익률을 창출하지만 막대한 투자비(high cost)와 많은 시간이 소요되고(long-term investment), 다양한 인력이 필요한 성공률이 매우 낮은 고위험(high risk) 사업이다.1) 그럼에도 불구하고 전세계적인 고령화와 복지사회로의 전환으로 의약품 시장과 수요는 지속적으로 증가하고 있으며, 의약품 산업은 차세대 전략 산업으로 각광받음으로써 신약개발에 대한 관심과 R&D 투자 비용은 증가하고 있다.2)생명공학정책연구센터의 보고서에 따르면 고령화 및 복지사회 진입으로 고성장이 전망되어 세계 의약품 시장은 2020년에 약 1.
신약 개발은 어떤 사업인가? 신약 개발은 성공 시 높은 수익률을 창출하지만 막대한 투자비(high cost)와 많은 시간이 소요되고(long-term investment), 다양한 인력이 필요한 성공률이 매우 낮은 고위험(high risk) 사업이다.1) 그럼에도 불구하고 전세계적인 고령화와 복지사회로의 전환으로 의약품 시장과 수요는 지속적으로 증가하고 있으며, 의약품 산업은 차세대 전략 산업으로 각광받음으로써 신약개발에 대한 관심과 R&D 투자 비용은 증가하고 있다.
1987년 물질특허제도 도입 이후 추진된 신약개발 연구는 어떠한 성과를 거두었는가? 국내의 경우 1987년 물질특허제도 도입 이후 본격적으로 신약개발 연구가 추진되었다. 1999년에 선플라주(SK케미컬)의 KFDA(구MFDS) 허가 이후 2016년 올리타정 (한미약품)까지 27개의 국내 신약이 허가되었다.8) 신약개발 경험이 부족한 실정에서 비교적 짧은 기간 동안 이와 같은 성과 창출은 고무적이나, 국내신약 매출액은 저조하여 상업적 성공을 위한 혁신적 신약개발의 필요성이 대두되고 있다.
질의응답 정보가 도움이 되었나요?

참고문헌 (98)

  1. Adams CP, Brantner VV. Estimating the cost of new drug development: is it really 802 million dollars? Health Aff 2006; 25(2):420-8. 

  2. DiMasi JA, Feldman L, Seckler A, et al. Trends in risks associated with new drug development: success rates for investigational drugs. Clin Pharmacol Ther 2010;87(3):272-7. 

  3. Biotech Policy Research Center. Investigation of current stage and R&D cost for revitalization of domestic drug discovery & development, 2010; 8-24. 

  4. Blume-Kohout ME, Sood N. Market Size and Innovation: Effects of Medicare Part D on Pharmaceutical Research and Development. J Public Econ 2013;97:327-36. 

  5. Morgan SG, Cunningham CM, Law MR. Drug development: Innovation or imitation deficit? Br Med J 2012;345:e5880. 

  6. Chung TD. Collaborative pre-competitive preclinical drug discovery with academics and pharma/biotech partners at Sanford $\mid$ Burnham: infrastructure, capabilities & operational models. Comb Chem High Throughput Screen 2014;17(3):272-89. 

  7. DiMasi JA, Hansen RW, Grabowski HG. The price of innovation: new estimates of drug development costs. J Health Econ 2003;22(2):151-85. 

  8. Korea Drug Research Association. Status of new drug approvals in Korea, May 2016. Available from http://www.kdra.or.kr/website/03web02.php. Accessed May 13, 2016. 

  9. Zou P, Yu Y, Zheng N, et al. Applications of human pharmacokinetic prediction in first-in-human dose estimation. AAPS J 2012;14(2):262-81. 

  10. Reagan-Shaw S, Nihal M, Ahmad N. Dose translation from animal to human studies revisited. Fed Am Soc Exp Biol J 2008;22(3):659-61. 

  11. Morgan P, Van Der Graaf PH, Arrowsmith J, et al. Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug Discov. Today 2012;17(9-10):419-24. 

  12. US Food and Drug Administration (2005). Guidance for industry-Estimating the maximum safe dose in initial clinical trials for therapeutics in adult healthy volunteers. US Food and Drug Administration: Rockville, MD. 

  13. 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. 

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

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

  16. Ring BJ, Chien JY, Adkison KK, et al. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: comparative assessement of prediction methods of human clearance. J Pharm Sci 2011;100(10):4090-110. 

  17. 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. 

  18. 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. 

  19. 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. 

  20. 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. 

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

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

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

  24. 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. 

  25. 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. 

  26. Hakooz N, Ito K, Rawden H, et al. Determination of a human hepatic microsomal scaling factor for predicting in vivo drug clearance. Pharm Res 2006;23(3):533-9. 

  27. 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. 

  28. Niro R, Byers JP, Fournier RL, et al. 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. 

  29. 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. 

  30. 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. 

  31. Naritomi Y, Terashita S, Kimura S, et al. 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. 

  32. Naritomi Y, Terashita S, Kagayama A, et al. 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. 

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

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

  35. 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. 

  36. 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. 

  37. 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. 

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

  39. Galetin A, Brown C, Hallifax D, et al. 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. 

  40. Kilford PJ, Stringer R, Sohal B, et al. 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. 

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

  42. 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. 

  43. 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. 

  44. Jones RD, Jones HM, Rowland M, et al. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: comparative assessment of prediction methods of human volume of distribution. J Pharm Sci 2011;100(10):4074-89. 

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

  46. 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. 

  47. Mahmood I. Prediction of absolute bioavailability for drugs using oral and renal clearance following a single oral dose: a critical view. Biopharm Drug Dispos 1997;18(6):465-73. 

  48. Stoner CL, Cleton A, Johnson K, 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. 

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

  50. Cao X, Gibbs ST, Fang L, 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. 

  51. 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. 

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

  53. Poulin P, Jones RD, Jones HM, et al. PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: prediction of plasma concentration-time profiles in human by using the physiologically-based pharmacokinetic modeling approach. J Pharm Sci 2011;100(10):4127-57. 

  54. Obach RS, Baxter JG, Liston TE, et al. The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J Pharmacol Exp Ther 1997;283(1):46-58. 

  55. Hosea NA, Collard WT, Cole S, et al. Prediction of human pharmacokinetics from preclinical information: comparative accuracy of quantitative prediction approaches. J Clin Pharmacol 2009;49(5):513-33. 

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

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

  58. Heimbach T, Lakshminarayana SB, Hu W, et al. Practical anticipation of human efficacious doses and pharmacokinetics using in vitro and preclinical in vivo data. 2009;11(3):602-14. 

  59. 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. 

  60. 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. 

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

  62. Fura A, Vyas V, Humphreys W, et al. Prediction of human oral pharmacokinetics using nonclinical data: examples involving four proprietary compounds. Biopharm Drug Dispos 2008;29(8):455-68. 

  63. Gibson CR, Bergman A, Lu P, et al. Prediction of phase I single-dose pharmacokinetics using recombinant cytochromes P450 and physiologically based modelling. 2009;39(9):637-48. 

  64. Lowe PJ, Tannenbaum S, Wu K, et al. 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. 

  65. De Buck SS, Sinha VK, Fenu LA, et al. Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs. Drug Metab Dispos 2007;35(10):1766-80. 

  66. Jones HM, Parrott N, Jorga K, et al. A novel strategy for physiologically based predictions of human pharmacokinetics. Clin Pharmacokinet 2006;45(5):511-42. 

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

  68. 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. 

  69. Badhan R, Penny J, Galetin A, et al. 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. 

  70. Reigner BG, Williams PE, Patel IH, et al. 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. 

  71. Blackwell B, Martz BL. For the first time in man. Clin Pharmacol Ther 1972;13(5):812-26. 

  72. US Food and Drug Administration (2005). Guidance for Industry: Estimating the Maximum Safe Starting Dose in Adult Healthy Volunteer. US Food and Drug Administration: Rockville, MD. 

  73. Contrera JF, Matthews EJ, Kruhlak NL, et al. 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. 

  74. Rennen MAJ, Hakkert BC, Stevenson H, et al. Data-based derived values for the inter-species extrapolation, a quantitative analysis of historical toxicity data. Comments Toxicol 2001;7:423-36. 

  75. West GB, Brown JH. The origin of allometric scaling laws in biology from genomes to ecosystems: towards a quantitative unifying theory of biological structure and organization. J Exp Biol 2005;208:1575-92. 

  76. Bokkers BG, Slob W. Deriving a data-based interspecies assessment factor using the NOAEL and the benchmark dose approach. Crit Rev Toxicol 2007;37: 355-73. 

  77. Suntharalingam G, Perry MR, Ward S, 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. 

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

  79. Lowe PJ, Hijazi Y, Luttringer O, et al. 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. 

  80. 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. 

  81. Iavarone L, Hoke JF, Bottacini M, et al. First time in human for GV196771: interspecies scaling applied on dose selection. J Clin Pharmacol 1999;39(6):560-6. 

  82. 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. 

  83. Sheiner LB, Steimer JL. Pharmacokinetic/pharmacodynamic modeling in drug development. Annu Rev Pharmacol Toxicol 2000;40:67-95. 

  84. Yun HY, Baek IH, Seo JW, et al. The role of PK/PD modeling and simulation in model-based new drug development. Kor J Clin Pharm 2008;18(2):84-96. 

  85. 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. 

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

  87. 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. 

  88. Lave T, Dupin S, Schmitt C, 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. 

  89. 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. 

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

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

  92. Lave T, Dupin S, Schmitt C, et al. 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. 

  93. Jones RD, Jones HM, Rowland M, et al. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: comparative assessment of prediction methods of human volume of distribution. J Pharm Sci 2011;100(10):4074-89 

  94. Sawada Y, Hanano M, Sugiyama Y, et al. Prediction of the volumes of distribution of basic drugs in humans based on data from animals. J Pharmacokinet Biopharm 1984;12(6):587-96. 

  95. 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. 

  96. 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. 

  97. Sugano K, Hamada H, Machida M, et al. Optimized conditions of biomimetic artificial membrane permeation assay. Int J Pharm 2001;228(1-2):181-8. 

  98. Pidgeon C, Ong S, Liu H, et al. IAM chromatography: an in vitro screen for predicting drug membrane permeability. J Med Chem 1995;38(4):590-4. 

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