Abstract
The industry-wide biopharmaceutical (i.e., biologic, biotherapeutic) pipeline has been growing at an astonishing rate over the last decade with the proportion of approved new biological entities to new chemical entities on the rise. As biopharmaceuticals appear to be growing in complexity in terms of their structure and mechanism of action, so are interpretation, analysis, and prediction of their quantitative pharmacology. We present here a modeling and simulation (M&S) framework for the successful preclinical development of monoclonal antibodies (as an illustrative example of biopharmaceuticals) and discuss M&S strategies for its implementation. Critical activities during early discovery, lead optimization, and the selection of starting doses for the first-in-human study are discussed in the context of pharmacokinetic–pharmacodynamic (PKPD) and M&S. It was shown that these stages of preclinical development are and should be reliant on M&S activities including systems biology (SB), systems pharmacology (SP), and translational pharmacology (TP). SB, SP, and TP provide an integrated and rationalized framework for decision making during the preclinical development phase. In addition, they provide increased target and systems understanding, describe and interpret data generated in vitro and in vivo, predict human PKPD, and provide a rationalized approach to designing the first-in-human study.
Similar content being viewed by others
REFERENCES
Nagle PC, Nicita CA, Gerdes LA, Schmeichel CJ. Characteristics of and trends in the late-stage biopharmaceutical pipeline. Am J Manag Care. 2008;14(4):226–9.
Sheridan C. Fresh from the biologic pipeline-2009. Nat Biotechnol. 2010;28(4):307–10.
Mullard A. 2010 FDA drug approvals. Nat Rev Drug Discov. 2011;10(2):82–5.
Lalonde RL, Honig P. Clinical pharmacology in the era of biotherapeutics. Clin Pharmacol Ther. 2008;84(5):533–6.
Nagle PC, Lugo TF, Nicita CA. Defining and characterizing the late-stage biopharmaceutical pipeline. Am J Manag Care. 2003;9(6 Suppl):S124–35.
Tibbitts J, Cavagnaro JA, Haller CA, Marafino B, Andrews PA, Sullivan JT. Practical approaches to dose selection for first-in-human clinical trials with novel biopharmaceuticals. Regul Toxicol Pharmacol. 2010;58(2):243–51.
Van Der Graaf PH, Gabrielsson J. Pharmacokinetic–pharmacodynamic reasoning in drug discovery and early development. Future Med Chem. 2009;1(8):1371–4.
Gabrielsson J, Fjellstrom O, Ulander J, Rowley M, Van Der Graaf P. Pharmacodynamic–pharmacokinetic integration as a guide to medicinal chemistry. Curr Top Med Chem. 2011;11:404–18.
Agoram BM, Martin SW, van der Graaf PH. The role of mechanism-based pharmacokinetic–pharmacodynamic (PK-PD) modelling in translational research of biologics. Drug Discov Today. 2007;12(23–24):1018–24.
Mujagic H. Systems biology: potential to improve decision making in pharmaceutical development. Drug News Perspect. 2006;19(9):575–83.
Krishna R, Schaefer HG, Bjerrum OJ. Effective integration of systems biology, biomarkers, biosimulation and modelling in streamlining drug development. Eur J Pharm Sci. 2007;31(1):62–7.
Ahn K, Luo J, Berg A, Keefe D, Wu R. Functional mapping of drug response with pharmacodynamic-pharmacokinetic principles. Trends Pharmacol Sci. 2010;31(7):306–11.
Benson JD, Chen Y-NP, Cornell-Kennon SA, Dorsch M, Kim S, Leszczyniecka M, et al. Validating cancer drug targets. Nature. 2006;441(7092):451–6.
Wangorsch G, Butt E, Mark R, Hubertus K, Geiger J, Dandekar T, et al. Time-resolved in silico modeling of fine-tuned cAMP signaling in platelets: feedback loops, titrated phosphorylations and pharmacological modulation. BMC Syst Biol. 2011;5:178.
Covell DG, Barbet J, Holton OD, Black CD, Parker RJ, Weinstein JN. Pharmacokinetics of monoclonal immunoglobulin G1, F(ab′)2, and Fab′ in mice. Cancer Res. 1986;46(8):3969–78.
Thygesen P, Macheras P, Van Peer A. Physiologically based PK/PD modelling of therapeutic macromolecules. Pharm Res. 2009;26(12):2543–50.
Baxter LT, Zhu H, Mackensen DG, Butler WF, Jain RK. Biodistribution of monoclonal antibodies: scale-up from mouse to human using a physiologically based pharmacokinetic model. Cancer Res. 1995;55(20):4611–22.
Baxter LT, Zhu H, Mackensen DG, Jain RK. Physiologically based pharmacokinetic model for specific and nonspecific monoclonal antibodies and fragments in normal tissues and human tumor xenografts in nude mice. Cancer Res. 1994;54(6):1517–28.
Garg A, Balthasar J. Physiologically based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn. 2007;34(5):687–709.
Urva SR, Yang VC, Balthasar JP. Physiologically based pharmacokinetic model for T84.66: a monoclonal anti-CEA antibody. J Pharm Sci. 2010;99(3):1582–600.
Davda JP, Jain M, Batra SK, Gwilt PR, Robinson DH. 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):401–13.
Hussain I. APP transgenic mouse models and their use in drug discovery to evaluate amyloid-lowering therapeutics. CNS Neurol Disord Drug Targets. 2010;9(4):395–402.
Firestone B. The challenge of selecting the ‘right’ in vivo oncology pharmacology model. Curr Opin Pharmacol. 2010;10(4):391–6.
Wendler A, Wehling M. The translatability of animal models for clinical development: biomarkers and disease models. Curr Opin Pharmacol. 2010;10(5):601–6.
Liu D, Lon HK, Dubois DC, Almon RR, Jusko WJ. Population pharmacokinetic–pharmacodynamic-disease progression model for effects of anakinra in Lewis rats with collagen-induced arthritis. J Pharmacokinet Pharmacodyn. 2011;38(6):769–86.
Clarke J, Leach W, Pippig S, Joshi A, Wu B, House R, et al. Evaluation of a surrogate antibody for preclinical safety testing of an anti-CD11a monoclonal antibody. Regul Toxicol Pharmacol. 2004;40(3):219–26.
Lobo ED, Hansen RJ, Balthasar JP. Antibody pharmacokinetics and pharmacodynamics. J Pharm Sci. 2004;93(11):2645–68.
Wang W, Wang EQ, Balthasar JP. Monoclonal antibody pharmacokinetics and pharmacodynamics. Clin Pharmacol Ther. 2008;84(5):548–58.
Roskos LK, Davis CG, Schwab GM. The clinical pharmacology of therapeutic monoclonal antibodies. Drug Dev Res. 2004;61:108–20.
Mahmood I, Green MD. Pharmacokinetic and pharmacodynamic considerations in the development of therapeutic proteins. Clin Pharmacokinet. 2005;44(4):331–47.
Mager DE, Jusko WJ. General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. J Pharmacokinet Pharmacodyn. 2001;28(6):507–32.
Mager DE, Neuteboom B, Efthymiopoulos C, Munafo A, Jusko WJ. Receptor-mediated pharmacokinetics and pharmacodynamics of interferon-beta1a in monkeys. J Pharmacol Exp Ther. 2003;306(1):262–70.
Mager DE, Krzyzanski W. Quasi-equilibrium pharmacokinetic model for drugs exhibiting target-mediated drug disposition. Pharm Res. 2005;22(10):1589–96.
Ng CM, Stefanich E, Anand BS, Fielder PJ, Vaickus L. Pharmacokinetics/pharmacodynamics of nondepleting anti-CD4 monoclonal antibody (TRX1) in healthy human volunteers. Pharm Res. 2006;23(1):95–103.
Gibiansky L, Gibiansky E, Kakkar T, Ma P. Approximations of the target-mediated drug disposition model and identifiability of model parameters. J Pharmacokinet Pharmacodyn. 2008;35(5):573–91.
Shankar G, Shores E, Wagner C, Mire-Sluis A. Scientific and regulatory considerations on the immunogenicity of biologics. Trends Biotechnol. 2006;24(6):274–80.
Luu KT, Bergqvist S, Chen E, Hu-Lowe D, Kraynov E. A model-based approach to predicting the human pharmacokinetics of a monoclonal antibody exhibiting target-mediated drug disposition. J Pharmacol Exp Ther. 2012;341(3):702–8.
Ponce R, Abad L, Amaravadi L, Gelzleichter T, Gore E, Green J, et al. Immunogenicity of biologically-derived therapeutics: assessment and interpretation of nonclinical safety studies. Regul Toxicol Pharmacol. 2009;54(2):164–82.
Walsh G. Post-translational modifications of protein biopharmaceuticals. Drug Discov Today. 2010;15(17–18):773–80.
Shankar G, Devanarayan V, Amaravadi L, Barrett YC, Bowsher R, Finco-Kent D, et al. Recommendations for the validation of immunoassays used for detection of host antibodies against biotechnology products. J Pharm Biomed Anal. 2008;48(5):1267–81.
Johansson A, Erlandsson A, Eriksson D, Ullén A, Holm P, Sundström BE, et al. Idiotypic–anti-idiotypic complexes and their in vivo metabolism. Cancer. 2002;94(S4):1306–13.
Rojas JR, Taylor RP, Cunningham MR, Rutkoski TJ, Vennarini J, Jang H, et al. Formation, distribution, and elimination of infliximab and anti-infliximab immune complexes in cynomolgus monkeys. J Pharmacol Exp Ther. 2005;313(2):578–85.
Kuang B, King L, Wang HF. Therapeutic monoclonal antibody concentration monitoring: free or total? Bioanalysis. 2010;2(6):1125–40.
Lee JW, Kelley M, King LE, Yang J, Salimi-Moosavi H, Tang MT, et al. Bioanalytical approaches to quantify “total” and “free” therapeutic antibodies and their targets: technical challenges and PK/PD applications over the course of drug development. AAPS J. 2011;13(1):99–110.
Zhang Y, Pastan I. High shed antigen levels within tumors: an additional barrier to immunoconjugate therapy. Clin Cancer Res. 2008;14(24):7981–6.
Salimi-Moosavi H, Lee J, DeSilva B, Doellgast G. Novel approaches using alkaline or acid/guanidine treatment to eliminate therapeutic antibody interference in the measurement of total target ligand. J Pharm Biomed Anal. 2010;51(5):1128–33.
Lachmann HJ, Lowe P, Felix SD, Rordorf C, Leslie K, Madhoo S, et al. In vivo regulation of interleukin 1{beta} in patients with cryopyrin-associated periodic syndromes. J Exp Med. 2009;206(5):1029–36.
Albitar M, Do K-A, Johnson MM, Giles FJ, Jilani I, O’Brien S, et al. Free circulating soluble CD52 as a tumor marker in chronic lymphocytic leukemia and its implication in therapy with anti-CD52 antibodies. Cancer. 2004;101(5):999–1008.
Vugmeyster Y, Tian X, Szklut P, Kasaian M, Xu X. Pharmacokinetic and pharmacodynamic modeling of a humanized anti-IL-13 antibody in naive and Ascaris-challenged cynomolgus monkeys. Pharm Res. 2009;26(2):306–15.
Wang W, Prueksaritanont T. Prediction of human clearance of therapeutic proteins: simple allometric scaling method revisited. Biopharm Drug Dispos. 2010;31(4):253–63. Epub 2010/05/04.
Mordenti J, Chen SA, Moore JA, Ferraiolo BL, Green JD. Interspecies scaling of clearance and volume of distribution data for five therapeutic proteins. Pharm Res. 1991;8(11):1351–9.
Mahmood I. Pharmacokinetic allometric scaling of antibodies: application to the first-in-human dose estimation. J Pharm Sci. 2009;98(10):3850–61.
Deng R, Iyer S, Theil FP, Mortensen DL, Fielder PJ, Prabhu S. Projecting human pharmacokinetics of therapeutic antibodies from nonclinical data: what have we learned? MAbs. 2011;3(1):61–6.
Muller PY, Milton M, Lloyd P, Sims J, Brennan FR. The minimum anticipated biological effect level (MABEL) for selection of first human dose in clinical trials with monoclonal antibodies. Curr Opin Biotechnol. 2009;20(6):722–9.
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.
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.
ACKNOWLEDGMENTS
The authors wish to acknowledge Dr. Scott Fountain for reviewing, editing, as well as supporting efforts leading to the completion of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Guest Editors: Cheryl Li, Pratap Singh, and Anjaneya Chimalakonda
Rights and permissions
About this article
Cite this article
Luu, K.T., Kraynov, E., Kuang, B. et al. Modeling, Simulation, and Translation Framework for the Preclinical Development of Monoclonal Antibodies. AAPS J 15, 551–558 (2013). https://doi.org/10.1208/s12248-013-9464-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1208/s12248-013-9464-8