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Application and Impact of Human Dose Projection from Discovery to Early Drug Development

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Abstract

The application and impact of human dose projection (HDP) has been well recognized in the late drug development phase, with increasing appreciation earlier during discovery and early development. This commentary describes the perspective of pharmaceutical scientists on the evolving application and impact of HDP at various phases from discovery to early development, including lead generation, lead optimization, lead up to candidate nomination, and early drug development. The underlying fundamental concepts and key input parameters for HDP are briefly discussed. A broad overview of phase-specific tools and approaches commonly utilized for human dose projection in the pharmaceutical industry is provided. A discussion of phase-appropriate implementation strategies, associated limitations/assumptions and continuous refinement for HDP from discovery to early development is presented. The authors describe the phase-specific applications of human dose projection to facilitate key assessments and relative impact on decision points.

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References

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

    CAS  PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  3. Waring MJ, Arrowsmith J, Leach AR, Leeson PD, Mandrell S, Owen RM, et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat Rev Drug Discov. 2015;14:475.

    CAS  PubMed  Google Scholar 

  4. Reigner BG, Williams PEO, Patel IH, Steimer J-L, Peck C, van Brummelen P. An evaluation of the integration of pharmacokinetic and pharmacodynamic principles in clinical drug development. Clin Pharmacokinet. 1997;33(2):142–52.

    CAS  PubMed  Google Scholar 

  5. 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.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol. 2011;162(6):1239–49.

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  8. 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.

    CAS  PubMed  Google Scholar 

  9. Peach ML, Zakharov AV, Liu R, Pugliese A, Tawa G, Wallqvist A, et al. Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software. Future Med Chem. 2012;4(15):1907–32.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717.

    PubMed  PubMed Central  Google Scholar 

  11. Madden JC. In silico approaches for predicting ADME properties. In: Puzyn T, Leszczynski J, Cronin MT, editors. Recent advances in QSAR studies: methods and applications. Dordrecht: Springer Netherlands; 2010. p. 283–304.

    Google Scholar 

  12. Hallifax D, Foster JA, Houston JB. Prediction of human metabolic clearance from in vitro systems: retrospective analysis and prospective view. Pharm Res. 2010;27(10):2150–61.

    CAS  PubMed  Google Scholar 

  13. Obach RS. Predicting clearance in humans from in vitro data. Curr Top Med Chem. 2011;11(4):334–9.

    CAS  PubMed  Google Scholar 

  14. Patel D, Dierks E. Single-species allometric scaling: a strategic approach to support drug discovery. J Pharm Res Int. 2018;22(3):7.

    CAS  Google Scholar 

  15. Alex A. Physicochemical profiling (solubility, permeability and charge state). Curr Top Med Chem. 2001;1(4):277–351.

    Google Scholar 

  16. 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):3–25.

    CAS  Google Scholar 

  17. Po-Chang C, Yiding H. Simultaneous determination of LogD, LogP, and pKa of drugs by using a reverse phase HPLC coupled with a 96-well plate auto injector. Comb Chem High Throughput Screen. 2009;12(3):250–7.

    Google Scholar 

  18. Jones RD, Jones HM, Rowland M, Gibson CR, Yates JWT, Chien JY, 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.

    CAS  PubMed  Google Scholar 

  19. Howard ML, Hill JJ, Galluppi GR, McLean MA. Plasma protein binding in drug discovery and development. Comb Chem High Throughput Screen. 2010;13(2):170–87.

    CAS  PubMed  Google Scholar 

  20. Jules H, Stephan S, Hartmut D. When is protein binding important? J Pharm Sci. 2013;102(9):3458–67.

    Google Scholar 

  21. Varma MVS, Obach RS, Rotter C, Miller HR, Chang G, Steyn SJ, et al. Physicochemical space for optimum oral bioavailability: contribution of human intestinal absorption and first-pass elimination. J Med Chem. 2010;53(3):1098–108.

    CAS  PubMed  Google Scholar 

  22. Pidgeon C, Pitlick WH. Unique approach to calculation of first-order absorption rate constants from blood or urine data. J Pharmacokinet Biopharm. 1980;8(2):203–14.

    CAS  PubMed  Google Scholar 

  23. Lin L, Wong H. Predicting oral drug absorption: mini review on physiologically-based pharmacokinetic models. Pharmaceutics. 2017;9(4).

  24. Tong W-Q. Molecular and physicochemical properties impacting oral absorption of drugs. In: Krishna R, Yu L, editors. Biopharmaceutics applications in drug development. Boston: Springer US; 2008. p. 26–46.

    Google Scholar 

  25. Adveef A. Permeability—PAMPA. In: Adveef A, editor. Absorption and drug development; 2012. p. 319–498.

    Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Jones HM, Gardner IB, Watson KJ. Modelling and PBPK simulation in drug discovery. AAPS J. 2009;11(1):155–66.

    PubMed  PubMed Central  Google Scholar 

  28. Rizk ML, Zou L, Savic RM, Dooley KE. Importance of drug pharmacokinetics at the site of action. Clin Transl Sci. 2017;10(3):133–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Gabrielsson J, Fjellstrom O, Ulander J, Rowley M, Van Der Graaf PH. Pharmacodynamic-pharmacokinetic integration as a guide to medicinal chemistry. Curr Top Med Chem. 2011;11(4):404–18.

    CAS  PubMed  Google Scholar 

  30. Visser SAG, de Alwis DP, Kerbusch T, Stone JA, Allerheiligen SRB. Implementation of quantitative and systems pharmacology in large pharma. CPT Pharmacometrics Syst Pharmacol. 2014;3(10):e142–e.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Jones HM, Gardner IB, Collard WT, Stanley P, Oxley P, Hosea NA, et al. Simulation of human intravenous and oral pharmacokinetics of 21 diverse compounds using physiologically based pharmacokinetic modelling. Clin Pharmacokinet. 2011;50(5):331–47.

    CAS  PubMed  Google Scholar 

  32. Theil F-P, Guentert TW, Haddad S, Poulin P. Utility of physiologically based pharmacokinetic models to drug development and rational drug discovery candidate selection. Toxicol Lett. 2003;138(1):29–49.

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  34. Toshihiro W, Yoshitaka Y, Kazuya F, Takayoshi O. Prediction of human pharmacokinetic profile in animal scale up based on normalizing time course profiles. J Pharm Sci. 2004;93(7):1890–900.

    Google Scholar 

  35. Boxenbaum H, Ronfeld R. Interspecies pharmacokinetic scaling and the Dedrick plots. Am J Physiol. 1983; 245(6): R768–R775.

  36. Page KM. Validation of early human dose prediction: a key metric for compound progression in drug discovery. Mol Pharm. 2016;13(2):609–20.

    CAS  PubMed  Google Scholar 

  37. Riegelman S, Loo JCK, Rowland M. Shortcomings in pharmacokinetic analysis by conceiving the body to exhibit properties of a single compartment. J Pharm Sci. 1968;57(1):117–23.

    CAS  PubMed  Google Scholar 

  38. Levy G. Kinetics of drug action: an overview. J Allergy Clin Immunol. 1986;78(4, Part 2):754–61.

    CAS  PubMed  Google Scholar 

  39. Derendorf H, Meibohm B. Modeling of pharmacokinetic/pharmacodynamic (PK/PD) relationships: concepts and perspectives. Pharm Res. 1999;16(2):176–85.

    CAS  PubMed  Google Scholar 

  40. Guo Y, Chu X, Parrott NJ, Brouwer KLR, Hsu V, Nagar S, et al. Advancing predictions of tissue and intracellular drug concentrations using in vitro, imaging and physiologically based pharmacokinetic modeling approaches. Clin Pharmacol Ther. 2018;104(5):865–89.

    PubMed  PubMed Central  Google Scholar 

  41. Jones HM, Rowland-Yeo K. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacometrics Syst Pharmacol. 2013;2(8):e63.

    PubMed  PubMed Central  Google Scholar 

  42. Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, et al. Applied concepts in PBPK modeling: how to build a PBPK/PD model. CPT Pharmacometrics Syst Pharmacol. 2016;5(10):516–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Jones HM, Chen Y, Gibson C, Heimbach T, Parrott N, Peters SA, et al. Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective. Clin Pharmacol Ther. 2015;97(3):247–62.

    CAS  PubMed  Google Scholar 

  44. Caldwell GW. In silico tools used for compound selection during target-based drug discovery and development. Expert Opin Drug Discovery. 2015;10(8):901–23.

    CAS  Google Scholar 

  45. Li AP. Preclinical in vitro screening assays for drug-like properties. Drug Discov Today Technol. 2005;2(2):179–85.

    CAS  PubMed  Google Scholar 

  46. Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: applications to targets and beyond. Br J Pharmacol. 2007;152(1):21–37.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Rezaee R, Abdollahi M. The importance of translatability in drug discovery. Expert Opin Drug Discovery. 2017;12(3):237–9.

    Google Scholar 

  48. Van den Bergh A, Sinha V, Gilissen R, Straetemans R, Wuyts K, Morrison D, et al. Prediction of human oral plasma concentration-time profiles using preclinical data. Clin Pharmacokinet. 2011;50(8):505–17.

    PubMed  Google Scholar 

  49. Quinn K, Gullapalli RP, Merisko-liversidge E, Goldbach E, Wong A, Liversidge GG, et al. A formulation strategy for gamma secretase inhibitor ELND006, a BCS class II compound: development of a nanosuspension formulation with improved oral bioavailability and reduced food effects in dogs. J Pharm Sci. 2012;101(4):1462–74.

    CAS  PubMed  Google Scholar 

  50. Fancher RM, Zhang H, Sleczka B, Derbin G, Rockar R, Marathe P. Development of a canine model to enable the preclinical assessment of ph-dependent absorption of test compounds. J Pharm Sci. 2011;100(7):2979–88.

    CAS  PubMed  Google Scholar 

  51. Lentz KA, Quitko M, Morgan DG, Grace JE Jr, Gleason C, Marathe PH. Development and validation of a preclinical food effect model. J Pharm Sci. 2007;96(2):459–72.

    CAS  PubMed  Google Scholar 

  52. Sundqvist M, Lundahl A, Någård MB, Bredberg U, Gennemark P. Quantifying and communicating uncertainty in preclinical human dose-prediction. CPT Pharmacometrics Syst Pharmacol. 2015;4(4):243–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Di L, Feng B, Goosen TC, Lai Y, Steyn SJ, Varma MV, et al. A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development. Drug Metab Dispos. 2013;41(12):1975–93.

    CAS  PubMed  Google Scholar 

  54. Pritchard JF, Jurima-Romet M, Reimer ML, Mortimer E, Rolfe B, Cayen MN. Making better drugs: decision gates in non-clinical drug development. Nat Rev Drug Discov. 2003;2(7):542–53.

    CAS  PubMed  Google Scholar 

  55. Shen J, Swift B, Mamelok R, Pine S, Sinclair J, Attar M. Design and conduct considerations for first-in-human trials. Clin Transl Sci. 2019;12(1):6–19.

    PubMed  Google Scholar 

  56. Huang LF, Tong WQ. Impact of solid state properties on developability assessment of drug candidates. Adv Drug Deliv Rev. 2004;56(3):321–34.

    CAS  PubMed  Google Scholar 

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Acknowledgments

The authors are grateful to Shobha Bhattachar (Eli Lilly and Company) for the inception of the idea and valuable guidance for foundation of this manuscript. The authors also thank Elizabeth Dierks (BMS) for insightful suggestions in the development of this manuscript.

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Authors and Affiliations

  1. Department of Metabolism and Pharmacokinetics, Bristol-Myers Squibb Research and Development, Princeton, New Jersey, 08543, USA

    Dipal Patel

  2. Early Product Development, Pharmaceutical Sciences, R&D, AstraZeneca, Boston, Massachusetts, 02451, USA

    Wenzhan Yang

  3. MRL, Merck & Co., Inc., Kenilworth, New Jersey, 07033, USA

    Maya Lipert

  4. Drug Product Technologies Amgen Inc., Thousand Oaks, California, 91320, USA

    Tian Wu

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  1. Dipal Patel
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Correspondence to Dipal Patel.

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Patel, D., Yang, W., Lipert, M. et al. Application and Impact of Human Dose Projection from Discovery to Early Drug Development. AAPS PharmSciTech 21, 44 (2020). https://doi.org/10.1208/s12249-019-1598-2

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