Pharmacokinetics/pharmacodynamics model-supported early drug development

Current Pharmaceutical Biotechnology

Volumn 13, Issue 7, 2012, Pages 1360-1375

  Chen, Bin ^a   Dong, Jennifer Q ^c   Pan, Wei Jian ^b   Ruiz, Ana ^c  

^a JOHNSON AND JOHNSON PHARMACEUTICAL RESEARCH AND DEVELOPMENT   (United States)

^b AMGEN INC   (United States)

^c PFIZER INC   (United States)

Author keywords

Biopharmaceutical; Drug discovery; Early drug development; Modeling and simulation; Pharmacokinetics pharmacodynamics

Indexed keywords

ALFENTANIL; ANTICOAGULANT AGENT; ANTIINFLAMMATORY AGENT; ANTINEOPLASTIC AGENT; BETA1A INTERFERON; BIOLOGICAL MARKER; BIOLOGICAL PRODUCT; CALCIUM CHANNEL BLOCKING AGENT; DEXAMETHASONE; DOXORUBICIN; E 7820; FENTANYL; GLYCOGEN PHOSPHORYLASE INHIBITOR; GLYCOSYLTRANSFERASE INHIBITOR; INTERLEUKIN 6; LIPOPOLYSACCHARIDE; MARAVIROC; MITOGEN ACTIVATED PROTEIN KINASE INHIBITOR; OKT 4; OMALIZUMAB; OPIATE; PEGINTERFERON ALPHA2A; PLACEBO; RECOMBINANT ERYTHROPOIETIN; RECOMBINANT GRANULOCYTE COLONY STIMULATING FACTOR; SUSALIMOD; TREFENTANIL; TUMOR NECROSIS FACTOR ALPHA; UNCLASSIFIED DRUG; UNINDEXED DRUG; ZOSUQUIDAR;

ALLOMETRY; ANALYTIC METHOD; ANTIHYPERTENSIVE ACTIVITY; ANTIINFLAMMATORY ACTIVITY; ANTINEOPLASTIC ACTIVITY; AREA UNDER THE CURVE; ARTHRITIS; BAYES THEOREM; CLINICAL PRACTICE; COMPARTMENT MODEL; COST EFFECTIVENESS ANALYSIS; DRUG ABSORPTION; DRUG BINDING; DRUG BLOOD LEVEL; DRUG CLEARANCE; DRUG DESIGN; DRUG DISPOSITION; DRUG DISTRIBUTION; DRUG EFFICACY; DRUG ELIMINATION; DRUG EXCRETION; DRUG FORMULATION; DRUG HALF LIFE; DRUG INDUSTRY; DRUG METABOLISM; DRUG POTENTIATION; DRUG SAFETY; DRUG TOLERABILITY; ELECTROENCEPHALOGRAM; EXPERT SYSTEM; FOOD AND DRUG ADMINISTRATION; HUMAN; HYPERTENSION; IC 50; IN VITRO STUDY; IN VIVO STUDY; KIDNEY FUNCTION TEST; LIVER FUNCTION TEST; MAXIMUM PLASMA CONCENTRATION; MEDICAL DECISION MAKING; NONHUMAN; NONLINEAR MIXED EFFECT METHOD; NONLINEAR REGRESSION ANALYSIS; NONLINEAR SYSTEM; PHARMACODYNAMICS; PHARMACOGENOMICS; PHARMACOLOGICAL PARAMETERS; QT INTERVAL; QT PROLONGATION; REVIEW; RHEUMATOID ARTHRITIS; SIMULATION; SINGLE DRUG DOSE; THROMBOCYTE AGGREGATION; TIME TO MAXIMUM PLASMA CONCENTRATION;

EID: 84861114284     PISSN: 13892010     EISSN: 18734316     Source Type: Journal    
DOI: 10.2174/138920112800624436     Document Type: Review

References (95)

1. U.S. Food and Drug Administration. Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products; Rockville, M.D., 2004.
2. Sheiner, L. B. Learning versus confirming in clinical drug development. Clin. Pharmacol. Ther., 1997, 61(3), 275-291.
3. Dartois, C.; Brendel, K.; Comets, E.; Laffont, C. M.; Laveille, C.; Tranchand, B.; Mentre, F.; Lemenuel-Diot, A.; Girard, P. Overview of model-building strategies in population PK/PD analyses: 2002-2004 literature survey. Br. J. Clin. Pharmacol., 2007, 64(5), 603-612.
4. U.S. Food and Drug Administration. Guidance for Industry: Population Pharmacokinetics; Rockville, M.D., 1999.
5. Pillai, G.C.; Mentre, F.; Steimer, J.L. Non-linear mixed effects modeling-from methodology and software development to driving implementation in drug development science. J. Pharmacokinet. Pharmacodyn., 2005, 32(2), 161-183.
6. Berry, D.A., Ed. Statistical Methodology in the Pharmaceutical Sciences; M. Dekker: New York, 1990.
7. Racine-Poon, A. A Bayesian approach to nonlinear random effects models. Biometrics, 1985, 41(4), 1015-1023.
8. Agoram, B.M.; Martin, S.W.; van der Graaf, P.H. The role of mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modelling in translational research of biologics. Drug Discov. Today, 2007, 12(23-24), 1018-1024.
9. U.S. Food and Drug Administration. Guidance for Industry: Estimating the Maximum Safe Starting Dose in Initial Clinical Trials for Therapeutics in Adult Healthy Volunteers; Rockville, M.D., 2005.
10. European Medicines Agency. Note for Guidance on the Pre-clinical Evaluation of Anticancer Medicinal Products; London, U.K., 1998.
11. European Medicines Agency. Guideline on Strategies to Identify and Mitigate Risks for First-in-Human Clinical Trials with Investigational Medicinal Products; London, U.K., 2007.
12. U.S. Food and Drug Administration. Guidance for Industry: exposure-response relationships-study, design, data analysis, and regulatory applications; Rockville, M.D., 2003.
13. U.S. Food and Drug Administration. Guidance for Industry: E14 Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Non-Antiarrhythmic Drugs; Rockville, M.D., 2005.
14. U.S. Food and Drug Administration. Guidance for Industry: Drug interaction studies-study design data analysis, and implications for dosing and labeling; Rockville, M.D., 2006.
15. European Medicines Agency. Guidance for the Evaluation of the Pharmacokinetics of Medicinal Products in Patients with Impaired Renal Function; London, U.K., 2004.
16. U.S. Food and Drug Administration. Guidance for Industry: Pharmacokinetics in Patients with Impaired Renal Function-Study Design, Data Analysis, and Impact on Dosing Labeling; Rockville, M.D., 2010.
17. Wang, Y.; Bhattaram, A.V.; Jadhav, P.R.; Lesko, L.J.; Madabushi, R.; Powell, J.R.; Qiu, W.; Sun, H.; Yim, D.S.; Zheng, J.J.; Gobburu, J.V. Leveraging prior quantitative knowledge to guide drug development decisions and regulatory science recommendations: impact of FDA pharmacometrics during 2004-2006. J. Clin. Pharmacol., 2008, 48(2), 146-156.
18. U.S. Food and Drug Administration. Guidance for Industry: Endof-Phase 2A Meetings; Rockville, M.D., 2009.
19. U.S. Food and Drug Administration. Guidance for Industry. Adaptive Design Clinical Trials for Drugs and Biologics; Rockville, M.D., 2010.
20. Chien, J. Y.; Friedrich, S.; Heathman, M. A.; de Alwis, D. P.; Sinha, V. Pharmacokinetics/Pharmacodynamics and the stages of drug development: role of modeling and simulation. Aaps. J., 2005, 7(3), E544-559.
21. Jang, G. R.; Harris, R. Z.; Lau, D. T. Pharmacokinetics and its role in small molecule drug discovery research. Med. Res. Rev., 2001, 21(5), 382-96.
22. Van Bree, J.; Nedelman, J.; Steimer, J.-L.; Tse, F.; Robinson, W.; Niederberger, W. Application of sparse sampling approaches in rodent toxicokinetics: A prospective view. Drug Info. J., 1994, 28(17), 263-279.
23. Gething, P. A.; Daley-Yates, P. T. A sparse sampling, mixed effects approach to toxicokinetic analyses. Drug Info. J.,1997, 31, 521-527.
24. Bouzom, F.; Laveille, C.; Merdjan, H.; Jochemsen, R. Use of nonlinear mixed effect modeling for the meta-analysis of preclinical pharmacokinetic data: application to S 20342 in the rat. J. Pharm. Sci., 2000, 89(5), 603-613.
25. Ingwersen, S.H.; Kiehr, B.; Iversen, L.; Andersen, M.P.; Petersen, Y.; Rytved, K.A. Non-linear mixed effects modeling of sparse concentration data from rats: application to a glycogen phosphorylase inhibitor. Eur. J. Drug Metab. Pharmacokinet., 2002, 27(3), 203-212.
26. Amore, B.M.; Gibbs, J.P.; Emery, M.G. Application of in vivo animal models to characterize the pharmacokinetic and pharmacodynamic properties of drug candidates in discovery settings. Comb. Chem. High. Throughput. Screen., 2010, 13(2), 207-218.
27. Ariens, E.J. Affinity and intrinsic activity in the theory of competitive inhibition. I. Problems and theory. Arch. Int. Pharmacodyn. Ther., 1954, 99(1), 32-49.
28. Shimada, S.; Nakajima, Y.; Yamamoto, K.; Sawada, Y.; Iga, T. Comparative pharmacodynamics of eight calcium channel blocking agents in Japanese essential hypertensive patients. Biol. Pharm. Bull., 1996, 19(3), 430-437.
29. Mager, D.E.; Lin, S.X.; Blum, R.A.; Lates, C.D.; Jusko, W.J. Dose equivalency evaluation of major corticosteroids: pharmacokinetics and cell trafficking and cortisol dynamics. J. Clin. Pharmacol., 2003, 43(11), 1216-1227.
30. Kalvass, J.C.; Olson, E.R.; Cassidy, M.P.; Selley, D.E.; Pollack, G. M. Pharmacokinetics and pharmacodynamics of seven opioids in P-glycoprotein-competent mice: assessment of unbound brain EC50, u and correlation of in vitro, preclinical, and clinical data. J. Pharmacol. Exp. Ther., 2007, 323(1), 346-355.
31. Hong, Y.; Mager, D.E.; Blum, R.A.; Jusko, W.J. Population pharmacokinetic/pharmacodynamic modeling of systemic corticosteroid inhibition of whole blood lymphocytes: modeling interoccasion pharmacodynamic variability. Pharm. Res., 2007, 24(6), 1088-1097.
32. Earp, J.C.; Dubois, D.C.; Molano, D.S.; Pyszczynski, N.A.; Keller, C.E.; Almon, R.R.; Jusko, W.J. Modeling corticosteroid effects in a rat model of rheumatoid arthritis I: mechanistic disease progression model for the time course of collagen-induced arthritis in Lewis rats. J. Pharmacol. Exp. Ther., 2008, 326(2), 532-545.
33. Earp, J.C.; Dubois, D.C.; Molano, D.S.; Pyszczynski, N.A.; Almon, R.R.; Jusko, W.J. Modeling corticosteroid effects in a rat model of rheumatoid arthritis II: mechanistic pharmacodynamic model for dexamethasone effects in Lewis rats with collagen-induced arthritis. J. Pharmacol. Exp. Ther., 2008, 326(2), 546-554.
34. Gozzi, P.; Pahlman, I.; Palmer, L.; Gronberg, A.; Persson, S. Pharmacokinetic-pharmacodynamic modeling of the immunomodulating agent susalimod and experimentally induced tumor necrosis factor-alpha levels in the mouse. J. Pharmacol. Exp. Ther., 1999, 291(1), 199-203.
35. Iavarone, L.; Hoke, J.F.; Bottacini, M.; Barnaby, R.; Preston, G.C. First time in human for GV196771: interspecies scaling applied on dose selection. J. Clin. Pharmacol., 1999, 39(6), 560-566.
36. Hosea, N.A.; Collard, W.T.; Cole, S.; Maurer, T.S.; Fang, R.X.; Jones, H.; Kakar, S.M.; Nakai, Y.; Smith, B.J.; Webster, R.; Beaumont, K. Prediction of Human Pharmacokinetics From Preclinical Information: Comparative Accuracy of Quantitative Prediction Approaches. J. Clin. Pharmacol., 2009, 49, 513-533.
37. Dong, J.Q.; Salinger, D.H.; Endres, C.J.; Gibbs, J.P.; Hsu, C.P.; Stouch, B.J.; Hurh, E.; Gibbs, M. A. Quantitative prediction of human pharmacokinetics for monoclonal antibodies: retrospective analysis of monkey as a single species for first-in-human prediction. Clin. Pharmacokinet., 2011, 50(2), 131-142.
38. Kagan, L.; Abraham, A.K.; Harrold, J.M.; Mager, D.E. Interspecies scaling of receptor-mediated pharmacokinetics and pharmacodynamics of type I interferons. Pharm. Res., 2010, 27(5), 920-932.
39. Chaikin, P.; Rhodes, G.R.; Bruno, R.; Rohatagi, S.; Natarajan, C. Pharmacokinetics/pharmacodynamics in drug development: an industrial perspective. J. Clin. Pharmacol., 2000, 40(12 Pt 2), 1428-1438.
40. Mager, D. E.; Woo, S.; Jusko, W. J. Scaling pharmacodynamics from in vitro and preclinical animal studies to humans. Drug Metab. Pharmacokinet., 2009, 24(1), 16-24.
41. Agoram, B. M. 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), 153-160.
42. Ploeger, B. A.; van der Graaf, P. H.; Danhof, M. Incorporating receptor theory in mechanism-based pharmacokineticpharmacodynamic (PK-PD) modeling. Drug Metab. Pharmacokinet., 2009, 24(1), 3-15.
43. Keizer, R.J.; Zamacona, M. K.; Jansen, M.; Critchley, D.; Wanders, J.; Beijnen, J.H.; Schellens, J.H.; Huitema, A.D. Application of population pharmacokinetic modeling in early clinical development of the anticancer agent E7820. Invest. New Drugs, 2009, 27(2), 140-152.
44. Weatherley, B.; McFadyen, L. Maraviroc modelling strategy: use of early phase 1 data to support a semi-mechanistic population pharmacokinetic model. Br. J. Clin. Pharmacol., 2009, 68(3), 355-369.
45. Ramakrishnan, R.; Cheung, W.K.; Wacholtz, M.C.; Minton, N.; Jusko, W.J. Pharmacokinetic and pharmacodynamic modeling of recombinant human erythropoietin after single and multiple doses in healthy volunteers. J. Clin. Pharmacol., 2004, 44(9), 991-1002.
46. Lee, L.; Niu, H.; Rueger, R.; Igawa, Y.; Deutsch, J.; Ishii, N.; Mu, S.; Sakamoto, Y.; Busse-Reid, R.; Gimmi, C.; Goelzer, P.; De Schepper, S.; Yoshimura, Y.; Barrett, J.; Ishikawa, Y.; Weissgerber, G.; Peck, R. The safety, tolerability, pharmacokinetics, and pharmacodynamics of single oral doses of CH4987655 in healthy volunteers: target suppression using a biomarker. Clin. Cancer Res., 2009, 15(23), 7368-7374.
47. Samtani, M.N.; Perez-Ruixo, J.J.; Brown, K.H.; Cerneus, D.; Molloy, C.J. Pharmacokinetic and pharmacodynamic modeling of pegylated thrombopoietin mimetic peptide (PEG-TPOm) after single intravenous dose administration in healthy subjects. J. Clin. Pharmacol., 2009, 49(3), 336-350.
48. Lemmens, H.J.; Dyck, J.B.; Shafer, S.L.; Stanski, D.R. Pharmacokinetic-pharmacodynamic modeling in drug development: application to the investigational opioid trefentanil. Clin. Pharmacol. Ther., 1994, 56(3), 261-271.
49. Nieforth, K.A.; Nadeau, R.; Patel, I.H.; Mould, D. Use of an indirect pharmacodynamic stimulation model of MX protein induction to compare in vivo activity of interferon alfa-2a and a polyethylene glycol-modified derivative in healthy subjects. Clin. Pharmacol. Ther., 1996, 59(6), 636-646.
50. Marshall, S.; Macintyre, F.; James, I.; Krams, M.; Jonsson, N.E. Role of mechanistically-based pharmacokinetic/pharmacodynamic models in drug development: a case study of a therapeutic protein. Clin. Pharmacokinet., 2006, 45(2), 177-197.
51. Mager, D.E.; Jusko, W.J. Receptor-mediated pharmacokinetic/ pharmacodynamic model of interferon-beta 1a in humans. Pharm. Res., 2002, 19(10), 1537-1543.
52. Roskos, L.K.; Lum, P.; Lockbaum, P.; Schwab, G.; Yang, B. B. Pharmacokinetic/pharmacodynamic modeling of pegfilgrastim in healthy subjects. J. Clin. Pharmacol., 2006, 46(7), 747-757.
53. Ng, C.M.; Stefanich, E.; Anand, B.S.; Fielder, P.J.; Vaickus, L. Pharmacokinetics/pharmacodynamics of nondepleting anti-CD4 monoclonal antibody (TRX1) in healthy human volunteers. Pharm. Res., 2006, 23(1), 95-103.
54. Krzyzanski, W.; Jusko, W.J.; Wacholtz, M.C.; Minton, N.; Cheung, W.K. Pharmacokinetic and pharmacodynamic modeling of recombinant human erythropoietin after multiple subcutaneous doses in healthy subjects. Eur. J. Pharm. Sci., 2005, 26(3-4), 295-306.
55. Hayashi, N.; Tsukamoto, Y.; Sallas, W.M.; Lowe, P. J. A mechanism-based binding model for the population pharmacokinetics and pharmacodynamics of omalizumab. Br. J. Clin. Pharmacol., 2007, 63(5), 548-561.
56. Espie, P.; Tytgat, D.; Sargentini-Maier, M.L.; Poggesi, I.; Watelet, J.B. Physiologically based pharmacokinetics (PBPK). Drug Metab. Rev., 2009, 41(3), 391-407.
57. Jones, H.M.; Gardner, I.B.; Watson, K.J. Modelling and PBPK simulation in drug discovery. Aaps. J., 2009, 11(1), 155-166.
58. Peters, S.A.; Ungell, A.L.; Dolgos, H. Physiologically based pharmacokinetic (PBPK) modeling and simulation: applications in lead optimization. Curr. Opin. Drug Discov. Devel., 2009, 12(4), 509-518.
59. Parrott, N.; Jones, H.; Paquereau, N.; Lave, T. Application of full physiological models for pharmaceutical drug candidate selection and extrapolation of pharmacokinetics to man. Basic. Clin. Pharmacol. Toxicol., 2005, 96(3), 193-199.
60. Parrott, N.; Lave, T. Applications of physiologically based absorption models in drug discovery and development. Mol. Pharm., 2008, 5(5), 760-775.
61. Davda, J.P.; Jain, M.; Batra, S.K.; Gwilt, P.R.; Robinson, D. H. A physiologically based pharmacokinetic (PBPK) model to characterize and predict the disposition of monoclonal antibody CC49 and its single chain Fv constructs. Int. Immunopharmacol., 2008, 8(3), 401-413.
62. Zhao, P.; Ragueneau-Majlessi, I.; Zhang, L.; Strong, J.M.; Reynolds, K.S.; Levy, R.H.; Thummel, K.E.; Huang, S. M. Quantitative evaluation of pharmacokinetic inhibition of CYP3A substrates by ketoconazole: a simulation study. J. Clin. Pharmacol., 2009, 49(3), 351-359.
63. Youdim, K.A.; Zayed, A.; Dickins, M.; Phipps, A.; Griffiths, M.; Darekar, A.; Hyland, R.; Fahmi, O.; Hurst, S.; Plowchalk, D.R.; Cook, J.; Guo, F.; Obach, R. S. Application of CYP3A4 in vitro data to predict clinical drug-drug interactions; predictions of compounds as objects of interaction. Br. J. Clin. Pharmacol., 2008, 65(5), 680-692.
64. Wang, Y. H. Confidence assessment of the Simcyp time-based approach and a static mathematical model in predicting clinical drug-drug interactions for mechanism-based CYP3A inhibitors. Drug Metab. Dispos., 2010, 38(7), 1094-1104.
65. Fenneteau, F.; Poulin, P.; Nekka, F. Physiologically based predictions of the impact of inhibition of intestinal and hepatic metabolism on human pharmacokinetics of CYP3A substrates. J. Pharm. Sci., 1999, 1, 486-514.
66. Zhang, X.; Quinney, S.K.; Gorski, J.C.; Jones, D.R.; Hall, S.D. Semiphysiologically based pharmacokinetic models for the inhibition of midazolam clearance by diltiazem and its major metabolite. Drug Metab. Dispos., 2009, 37(8), 1587-1597.
67. Xu, L.; Chen, Y.; Pan, Y.; Skiles, G.L.; Shou, M. Prediction of human drug-drug interactions from time-dependent inactivation of CYP3A4 in primary hepatocytes using a population-based simulator. Drug Metab. Dispos., 2009, 37(12), 2330-2339.
68. Watanabe, T.; Kusuhara, H.; Maeda, K.; Shitara, Y.; Sugiyama, Y. Physiologically based pharmacokinetic modeling to predict transporter-mediated clearance and distribution of pravastatin in humans. J. Pharmacol. Exp. Ther., 2009, 328(2), 652-662.
69. U.S. Food and Drug Administration. Guidance for Industry: Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlations; Rockville, M.D., 1997.
70. Sirisuth, N.; Augsburger, L.L.; Eddington, N. D. Development and validation of a non-linear IVIVC model for a diltiazem extended release formulation. Biopharm. Drug Dispos., 2002, 23(1), 1-8.
71. Wagner, J. G.; Nelson, E. Kinetic Analysis of Blood Levels and Urinary Excretion in the Absorptive Phase after Single Doses of Drug. J. Pharm. Sci., 1964, 53, 1392-1403.
72. Gillespie, W. R. Convolution-based approaches for in vivo-in vitro correlation modeling. Adv. Exp. Med. Biol., 1997, 423, 53-65.
73. Veng-Pedersen, P.; Gobburu, J. V.; Meyer, M. C.; Straughn, A. B. Carbamazepine level-A in vivo-in vitro correlation (IVIVC): a scaled convolution based predictive approach. Biopharm. Drug Dispos., 2000, 21(1), 1-6.
74. O'Hara, T.; Hayes, S.; Davis, J.; Devane, J.; Smart, T.; Dunne, A. In vivo-in vitro correlation (IVIVC) modeling incorporating a convolution step. J. Pharmacokinet. Pharmacodyn., 2001, 28(3), 277-298.
75. Gaynor, C.; Dunne, A.; Davis, J. A comparison of the prediction accuracy of two IVIVC modelling techniques. J. Pharm. Sci., 2008, 97(8), 3422-3432.
76. Kortejarvi, H.; Malkki, J.; Marvola, M.; Urtti, A.; Yliperttula, M.; Pajunen, P. Level A in vitro-in vivo correlation (IVIVC) model with Bayesian approach to formulation series. J. Pharm. Sci., 2006, 95(7), 1595-1605.
77. Jones, D. Of Medicine and Meta-Analysis. Nat. Rev. Drug Discov., 2008, 7, 376-377.
78. Senn, S. Statistical Issues in Drug Development, 2nd Ed.; Wiley-Interscience: 2008.
79. Cohen, R.D.; Woseth, D.M.; Thisted, R.A.; Hanauer, S.B. A metaanalysis and overview of the literature on treatment options for leftsided ulcerative colitis and ulcerative proctitis. Am. J. Gastroenterol., 2000, 95(5), 1263-1276.
80. Bonate, P. L.; Floret, S.; Bentzen, C. Population pharmacokinetics of APOMINE: a meta-analysis in cancer patients and healthy males. Br. J. Clin. Pharmacol., 2004, 58(2), 142-155.
81. van Hest, R.M.; Mathot, R.A.; Pescovitz, M.D.; Gordon, R.; Mamelok, R.D.; van Gelder, T. Explaining variability in mycophenolic acid exposure to optimize mycophenolate mofetil dosing: a population pharmacokinetic meta-analysis of mycophenolic acid in renal transplant recipients. J. Am. Soc. Nephrol., 2006, 17(3), 871-880.
82. Yu, M.; Kim, S.; Wang, Z.; Hall, S.; Li, L. A Bayesian metaanalysis on published sample mean and variance pharmacokinetic data with application to drug-drug interaction prediction. J. Biopharm. Stat., 2008, 18(6), 1063-1083.
83. Houk, B. E.; Bello, C. L.; Kang, D.; Amantea, M. A population pharmacokinetic meta-analysis of sunitinib malate (SU11248) and its primary metabolite (SU12662) in healthy volunteers and oncology patients. Clin. Cancer. Res., 2009, 15(7), 2497-2506.
84. Nalda-Molina, R.; Valenzuela, B.; Ramon-Lopez, A.; Miguel-Lillo, B.; Soto-Matos, A.; Perez-Ruixo, J. J. Population pharmacokinetics meta-analysis of plitidepsin (Aplidin) in cancer subjects. Cancer Chemother. Pharmacol., 2009, 64(1), 97-108.
85. Gibbs, J.P.; Fredrickson, J.; Barbee, T.; Correa, I.; Somith, B.; Lin, S.-L.; Gibbs, M. A. Quantitative Model of the Relationship Between DPP-4 Inhibition and Response: Meta-Analysis of Alogliptin, Saxagliptin, Sitagliptin, and Vildagliptin Efficacy Results. In: American Society of Clinical Pharmacology & Therapeutics (ASCPT) 2010 Annual Meeting, Atlanta, G.A.,17-20 March, 2010.
86. Mandema, J. W.; Salinger, D. H.; Dong, J. Q.; Baumgartner, S. W.; Martin, D. A.; Gibbs, M. A. Model-based comparison of ACR dose response for biologics in rheumatoid and psoriatic arthritis: a meta analysis of publicly available data. In: American Society of Clinical Pharmacology & Therapeutics (ASCPT) 2010 Annual Meeting, Atlanta, G.A.,17-20 March, 2010.
87. Jacqmin, P.; Snoeck, E.; van Schaick, E.A.; Gieschke, R.; Pillai, P.; Steimer, J.L.; Girard, P. Modelling response time profiles in the absence of drug concentrations: definition and performance evaluation of the K-PD model. J. Pharmacokinet. Pharmacodyn., 2007, 34(1), 57-85.
88. Frank, R.; Hargreaves, R. Clinical biomarkers in drug discovery and development. Nat. Rev. Drug Discov., 2003, 2(7), 566-580.
89. Lesko, L.J.; Salerno, R.A.; Spear, B.B.; Anderson, D.C.; Anderson, T.; Brazell, C.; Collins, J.; Dorner, A.; Essayan, D.; Gomez-Mancilla, B.; Hackett, J.; Huang, S.M.; Ide, S.; Killinger, J.; Leighton, J.; Mansfield, E.; Meyer, R.; Ryan, S.G.; Schmith, V.; Shaw, P.; Sistare, F.; Watson, M.; Worobec, A. Pharmacogenetics and pharmacogenomics in drug development and regulatory decision making: report of the first FDA-PWG-PhRMA-DruSafe Workshop. J. Clin. Pharmacol., 2003, 43(4), 342-358.
90. Yengi, L. G.; Xiang, Q.; Shen, L.; Chandrasekaran, A.; Kao, J.; Scatina, J. Application of pharmacogenomics in drug discovery and development: correlations between transcriptional modulation and preclinical safety observations. Drug Metab. Lett., 2007, 1(1), 41-8.
91. Peeters, M.; Oliner, K.S.; Parker, A.; Huang, J.; Siena, S.; Humblet, Y.; Laethem, J.-L. V.; André, T.; Wiezorek, J.; Reese, D.; Patterson, S.D.; Cutsem, E. V. In: Use of massively parallel, nextgeneration sequencing to identify gene mutations beyond KRAS that predict response to panitumumab in a randomized, phase 3, monotherapy study of metastatic colorectal cancer (mCRC). In: American Association for Cancer Research (AACR) 101st Annual Meeting, Washington, D.C., 2010.
92. European Medicines Agency. Reflection Paper on the Use of Pharmacogenetics in the Pharmacokinetic Evaluation of Medicinal Products. London, U.K., 2007.
93. Guo, Y.; Shafer, S.; Weller, P.; Usuka, J.; Peltz, G. Pharmacogenomics and drug development. Pharmacogenomics, 2005, 6(8), 857-864.
94. Hamberg, A.K.; Dahl, M.L.; Barban, M.; Scordo, M.G.; Wadelius, M.; Pengo, V.; Padrini, R.; Jonsson, E. N. A PK-PD model for predicting the impact of age, CYP2C9, and VKORC1 genotype on individualization of warfarin therapy. Clin. Pharmacol. Ther., 2007, 81(4), 529-538.
95. Salinger, D.H.; Shen, D.D.; Thummel, K.; Wittkowsky, A.K.; Vicini, P.; Veenstra, D.L. Pharmacogenomic trial design: use of a PK/PD model to explore warfarin dosing interventions through clinical trial simulation. Pharmacogenet. Genomics, 2009, 19(12), 965-971.