Skip to main content

Current Approaches for Predicting Human PK for Small Molecule Development Candidates: Findings from the IQ Human PK Prediction Working Group Survey

Abstract

Accurate prediction of human clearance (CL) and volume of distribution at steady state (Vd,ss) for small molecule drug candidates is an essential component of assessing likely efficacious dose and clinical safety margins. In 2021, the IQ Consortium Human PK Prediction Working Group undertook a survey of IQ member companies to understand the current PK prediction methods being used to estimate these parameters across the pharmaceutical industry. The survey revealed a heterogeneity in approaches being used across the industry (e.g., the use of allometric approaches, differing incorporation of binding terms, and inconsistent use of empirical correction factors for in vitro-in vivo extrapolation, IVIVE), which could lead to different PK predictions with the same input data. Member companies expressed an interest in improving human PK predictions by identifying the most appropriate compound-class specific methods, as determined by physiochemical properties and knowledge of CL pathways. Furthermore, there was consensus that increased understanding of the uncertainty inherent to the compound class-dependent prediction would be invaluable in aiding communication of human PK and dose uncertainty at the time of candidate nomination for development. The human PK Prediction Working Group is utilizing these survey findings to help interrogate clinical IV datasets from across the IQ consortium member companies to understand PK prediction accuracy and uncertainty from preclinical datasets.

Graphical abstract

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. Maurer TS, Smith D, Beaumont K, Di L. Dose predictions for drug design. J Med Chem. 2020;63(12):6423–35. https://doi.org/10.1021/acs.jmedchem.9b01365.

    CAS  Article  PubMed  Google Scholar 

  2. Lucas AJ, Sproston JL, Barton P, Riley RJ. Estimating human ADME properties, pharmacokinetic parameters and likely clinical dose in drug discovery. Expert Opin Drug Discov. 2019;14(12):1313–27. https://doi.org/10.1080/17460441.2019.1660642.

    CAS  Article  PubMed  Google Scholar 

  3. Reichel A, Lienau P. Pharmacokinetics in drug discovery: an exposure-centered approach to optimizing and predicting drug efficacy and safety. Handb Exp Pharmacol. 2016;232:235–60. https://doi.org/10.1007/164_2015_26.

    CAS  Article  PubMed  Google Scholar 

  4. Sundqvist M, Lundahl A, Nagard MB, Bredberg U, Gennemark P. Quantifying and communicating uncertainty in preclinical human dose-prediction. CPT Pharmacometrics Syst Pharmacol. 2015;4(4):243–54. https://doi.org/10.1002/psp4.32.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Varma MV, Steyn SJ, Allerton C, El-Kattan AF. Predicting clearance mechanism in drug discovery: extended clearance classification system (ECCS). Pharm Res. 2015;32(12):3785–802. https://doi.org/10.1007/s11095-015-1749-4.

    CAS  Article  PubMed  Google Scholar 

  6. Camenisch G, Riede J, Kunze A, Huwyler J, Poller B, Umehara K. The extended clearance model and its use for the interpretation of hepatobiliary elimination data. ADMET & DMPK. 2015;3(1):1–14. https://doi.org/10.5599/admet.3.1.144.

    Article  Google Scholar 

  7. Chung TDY, Terry DB, Smith LH. In vitro and in vivo assessment of ADME and PK properties during lead selection and lead optimization - guidelines, benchmarks and rules of thumb. In: Markossian S, Grossman A, Brimacombe K, Arkin M, Auld D, Austin CP, et al. editors. Assay Guidance Manual. Bethesda (MD) 2004.

  8. 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.

    CAS  PubMed  Google Scholar 

  9. 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. https://doi.org/10.1007/s11095-004-9015-1.

    CAS  Article  PubMed  Google Scholar 

  10. Boxenbaum H. Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics. J Pharmacokinet Biopharm. 1982;10(2):201–27. https://doi.org/10.1007/BF01062336.

    CAS  Article  PubMed  Google Scholar 

  11. Mahmood I, Martinez M, Hunter RP. Interspecies allometric scaling. Part I: prediction of clearance in large animals. J Vet Pharmacol Ther. 2006;29(5):415–23. https://doi.org/10.1111/j.1365-2885.2006.00786.x.

    CAS  Article  PubMed  Google Scholar 

  12. 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. https://doi.org/10.1124/dmd.107.016188.

    CAS  Article  PubMed  Google Scholar 

  13. Liu D, Song H, Song L, Liu Y, Cao Y, Jiang J, et al. A unified strategy in selection of the best allometric scaling methods to predict human clearance based on drug disposition pathway. Xenobiotica. 2016;46(12):1105–11. https://doi.org/10.1080/00498254.2016.1205761.

    CAS  Article  PubMed  Google Scholar 

  14. Wood FL, Houston JB, Hallifax D. Clearance prediction methodology needs fundamental improvement: trends common to rat and human hepatocytes/microsomes and implications for experimental methodology. Drug Metab Dispos. 2017;45(11):1178–88. https://doi.org/10.1124/dmd.117.077040.

    CAS  Article  PubMed  Google Scholar 

  15. Sodhi JK, Benet LZ. Successful and unsuccessful prediction of human hepatic clearance for lead optimization. J Med Chem. 2021;64(7):3546–59. https://doi.org/10.1021/acs.jmedchem.0c01930.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. Rowland M, Pang KS. Hepatic clearance models and IVIVE predictions. Clin Pharmacol Ther. 2022. https://doi.org/10.1002/cpt.2525.

  17. Tess DA, Eng H, Kalgutkar AS, Litchfield J, Edmonds DJ, Griffith DA, et al. Predicting the human hepatic clearance of acidic and zwitterionic drugs. J Med Chem. 2020;63(20):11831–44. https://doi.org/10.1021/acs.jmedchem.0c01033.

    CAS  Article  PubMed  Google Scholar 

  18. Berry LM, Li C, Zhao Z. Species differences in distribution and prediction of human V(ss) from preclinical data. Drug Metab Dispos. 2011;39(11):2103–16. https://doi.org/10.1124/dmd.111.040766.

    CAS  Article  PubMed  Google Scholar 

  19. Oie S, Tozer TN. Effect of altered plasma protein binding on apparent volume of distribution. J Pharm Sci. 1979;68(9):1203–5. https://doi.org/10.1002/jps.2600680948.

    CAS  Article  PubMed  Google Scholar 

  20. Wajima T, Fukumura K, Yano Y, Oguma T. Prediction of human pharmacokinetics from animal data and molecular structural parameters using multivariate regression analysis: oral clearance. J Pharm Sci. 2003;92(12):2427–40. https://doi.org/10.1002/jps.10510.

    CAS  Article  PubMed  Google Scholar 

  21. Lombardo F, Obach RS, Shalaeva MY, Gao F. Prediction of volume of distribution values in humans for neutral and basic drugs using physicochemical measurements and plasma protein binding data. J Med Chem. 2002;45(13):2867–76. https://doi.org/10.1021/jm0200409.

    CAS  Article  PubMed  Google Scholar 

  22. Arundel PA. A Multi-compartmental model generally applicable to physiologically-based pharmacokinetics. IFAC Proceedings. 1997;30(2):129–33. https://doi.org/10.1016/S1474-6670(17)44557-5.

    Article  Google Scholar 

  23. Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J Pharm Sci. 2002;91(1):129–56. https://doi.org/10.1002/jps.10005.

    CAS  Article  PubMed  Google Scholar 

  24. Rodgers T, Rowland M. Mechanistic approaches to volume of distribution predictions: understanding the processes. Pharm Res. 2007;24(5):918–33. https://doi.org/10.1007/s11095-006-9210-3.

    CAS  Article  PubMed  Google Scholar 

  25. Schmitt MV, Reichel A, Liu X, Fricker G, Lienau P. Extension of the mechanistic tissue distribution model of rodgers and rowland by systematic incorporation of lysosomal trapping: impact on unbound partition coefficient and volume of distribution predictions in the rat. Drug Metab Dispos. 2021;49(1):53–61. https://doi.org/10.1124/dmd.120.000161.

    CAS  Article  PubMed  Google Scholar 

  26. Poulin P, Jones HM, Jones RD, Yates JW, Gibson CR, Chien JY, et al. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 1: goals, properties of the PhRMA dataset, and comparison with literature datasets. J Pharm Sci. 2011;100(10):4050–73. https://doi.org/10.1002/jps.22554.

    CAS  Article  PubMed  Google Scholar 

  27. Ring BJ, Chien JY, Adkison KK, Jones HM, Rowland M, Jones RD, 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. https://doi.org/10.1002/jps.22552.

    CAS  Article  PubMed  Google Scholar 

  28. Jones RD, Jones HM, Rowland M, Gibson CR, Yates JW, 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. https://doi.org/10.1002/jps.22553.

    CAS  Article  PubMed  Google Scholar 

  29. 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. https://doi.org/10.1177/0091270009333209.

  30. Choi GW, Lee YB, Cho HY. Interpretation of non-clinical data for prediction of human pharmacokinetic parameters: in vitro-in vivo extrapolation and allometric scaling. Pharmaceutics. 2019;11(4). https://doi.org/10.3390/pharmaceutics11040168.

  31. Pang KS, Rowland M. Hepatic clearance of drugs. I. Theoretical considerations of a “well-stirred” model and a “parallel tube” model. Influence of hepatic blood flow, plasma and blood cell binding, and the hepatocellular enzymatic activity on hepatic drug clearance. J Pharmacokinet Biopharm. 1977;5(6):625–53. https://doi.org/10.1007/BF01059688.

    CAS  Article  PubMed  Google Scholar 

  32. Chen Y, Liu L, Nguyen K, Fretland AJ. Utility of intersystem extrapolation factors in early reaction phenotyping and the quantitative extrapolation of human liver microsomal intrinsic clearance using recombinant cytochromes P450. Drug Metab Dispos. 2011;39(3):373–82. https://doi.org/10.1124/dmd.110.035147.

    CAS  Article  PubMed  Google Scholar 

  33. Riley RJ, McGinnity DF, Austin RP. A unified model for predicting human hepatic, metabolic clearance from in vitro intrinsic clearance data in hepatocytes and microsomes. Drug Metab Dispos. 2005;33(9):1304–11. https://doi.org/10.1124/dmd.105.004259.

    CAS  Article  PubMed  Google Scholar 

  34. Sohlenius-Sternbeck AK, Jones C, Ferguson D, Middleton BJ, Projean D, Floby E, et al. Practical use of the regression offset approach for the prediction of in vivo intrinsic clearance from hepatocytes. Xenobiotica. 2012;42(9):841–53. https://doi.org/10.3109/00498254.2012.669080.

  35. Poulin P, Kenny JR, Hop CE, Haddad S. In vitro-in vivo extrapolation of clearance: modeling hepatic metabolic clearance of highly bound drugs and comparative assessment with existing calculation methods. J Pharm Sci. 2012;101(2):838–51. https://doi.org/10.1002/jps.22792.

    CAS  Article  PubMed  Google Scholar 

  36. Hultman I, Vedin C, Abrahamsson A, Winiwarter S, Darnell M. Use of HmuREL human coculture system for prediction of intrinsic clearance and metabolite formation for slowly metabolized compounds. Mol Pharm. 2016;13(8):2796–807. https://doi.org/10.1021/acs.molpharmaceut.6b00396.

    CAS  Article  PubMed  Google Scholar 

  37. Chan TS, Yu H, Moore A, Khetani SR, Tweedie D. Meeting the challenge of predicting hepatic clearance of compounds slowly metabolized by cytochrome P450 using a novel hepatocyte model. HepatoPac. Drug Metab Dispos. 2013;41(12):2024–32. https://doi.org/10.1124/dmd.113.053397.

    Article  PubMed  Google Scholar 

  38. Di L, Trapa P, Obach RS, Atkinson K, Bi YA, Wolford AC, et al. A novel relay method for determining low-clearance values. Drug Metab Dispos. 2012;40(9):1860–5. https://doi.org/10.1124/dmd.112.046425.

  39. Jones HM, Barton HA, Lai Y, Bi YA, Kimoto E, Kempshall S, et al. Mechanistic pharmacokinetic modeling for the prediction of transporter-mediated disposition in humans from sandwich culture human hepatocyte data. Drug Metab Dispos. 2012;40(5):1007–17. https://doi.org/10.1124/dmd.111.042994.

  40. Naritomi Y, Sanoh S, Ohta S. Utility of chimeric mice with humanized liver for predicting human pharmacokinetics in drug discovery: comparison with in vitro-in vivo extrapolation and allometric scaling. Biol Pharm Bull. 2019;42(3):327–36. https://doi.org/10.1248/bpb.b18-00754.

    CAS  Article  PubMed  Google Scholar 

  41. Zanelli U, Michna T, Petersson C. Determination of low intrinsic clearance in vitro: the benefit of a novel internal standard in human hepatocyte incubations. Xenobiotica. 2019;49(4):381–7. https://doi.org/10.1080/00498254.2018.1451010.

    CAS  Article  PubMed  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. https://doi.org/10.1002/jps.20169.

    CAS  Article  PubMed  Google Scholar 

  43. Sayama H, Komura H, Kogayu M. Application of hybrid approach based on empirical and physiological concept for predicting pharmacokinetics in humans--usefulness of exponent on prospective evaluation of predictability. Drug Metab Dispos. 2013;41(2):498–507. https://doi.org/10.1124/dmd.112.048819.

    CAS  Article  PubMed  Google Scholar 

  44. Tang H, Mayersohn M. A novel model for prediction of human drug clearance by allometric scaling. Drug Metab Dispos. 2005;33(9):1297–303. https://doi.org/10.1124/dmd.105.004143.

    CAS  Article  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  46. 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. https://doi.org/10.1002/jps.20099.

    CAS  Article  PubMed  Google Scholar 

  47. Li Z, Litchfield J, Tess DA, Carlo AA, Eng H, Keefer C, et al. A physiologically based in silico tool to assess the risk of drug-related crystalluria. J Med Chem. 2020;63(12):6489–98. https://doi.org/10.1021/acs.jmedchem.9b01995.

  48. 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. https://doi.org/10.1124/dmd.113.054031.

  49. Kunze A, Huwyler J, Camenisch G, Poller B. Prediction of organic anion-transporting polypeptide 1B1- and 1B3-mediated hepatic uptake of statins based on transporter protein expression and activity data. Drug Metab Dispos. 2014;42(9):1514–21. https://doi.org/10.1124/dmd.114.058412.49.

    Article  PubMed  Google Scholar 

  50. Sato M, Toshimoto K, Tomaru A, Yoshikado T, Tanaka Y, Hisaka A, et al. Physiologically based pharmacokinetic modeling of bosentan identifies the saturable hepatic uptake as a major contributor to its nonlinear pharmacokinetics. Drug Metab Dispos. 2018;46(5):740–8. https://doi.org/10.1124/dmd.117.078972.meeting.org/?abstract=9036].

  51. Davies B, Morris T. Physiological parameters in laboratory animals and humans. Pharm Res. 1993;10(7):1093–5. https://doi.org/10.1023/a:1018943613122.

    CAS  Article  PubMed  Google Scholar 

  52. Lin JH. Applications and limitations of interspecies scaling and in vitro extrapolation in pharmacokinetics. Drug Metab Dispos. 1998;26(12):1202–12.

    CAS  PubMed  Google Scholar 

  53. Luttringer O, Theil FP, Poulin P, Schmitt-Hoffmann AH, Guentert TW, Lave T. Physiologically based pharmacokinetic (PBPK) modeling of disposition of epiroprim in humans. J Pharm Sci. 2003;92(10):1990–2007. https://doi.org/10.1002/jps.10461.

    CAS  Article  PubMed  Google Scholar 

  54. Gibson CR, Gleason A, Messina E. Measurement of total liver blood flow in intact anesthetized rats using ultrasound imaging. Pharmacol Res Perspect. 2021;9(2):e00731. https://doi.org/10.1002/prp2.731.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. Sahin S, Benet LZ. The operational multiple dosing half-life: a key to defining drug accumulation in patients and to designing extended release dosage forms. Pharm Res. 2008;25(12):2869–77. https://doi.org/10.1007/s11095-008-9787-9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. Yang J, Jamei M, Yeo KR, Rostami-Hodjegan A, Tucker GT. Misuse of the well-stirred model of hepatic drug clearance. Drug Metab Dispos. 2007;35(3):501–2. https://doi.org/10.1124/dmd.106.013359.

    CAS  Article  PubMed  Google Scholar 

  57. Bonn B, Svanberg P, Janefelt A, Hultman I, Grime K. Determination of human hepatocyte intrinsic clearance for slowly metabolized compounds: comparison of a primary hepatocyte/stromal cell co-culture with plated primary hepatocytes and HepaRG. Drug Metab Dispos. 2016;44(4):527–33. https://doi.org/10.1124/dmd.115.067769.

    CAS  Article  PubMed  Google Scholar 

  58. Kratochwil N, Meille C, Fowler S, Klammers F, Ekiciler A, Molitor B, Simon S, Walter I, McGinnis C, Walther J, Leonard B, Triyatni M, Javanbakht H, Funk C, Schuler F, Lavé, Parrott NJ. Metabolic profiling of human long-term liver models and hepatic clearance predictions from in vitro data using nonlinear mixed-effects modeling. AAPS J. 2017;19:534–50. https://doi.org/10.1208/s12248-016-0019-7.

    CAS  Article  PubMed  Google Scholar 

  59. Schaefer M, Schänzle G, Bischoff D, Süssmuth RD. Upcyte human hepatocytes: a potent in vitro tool for the prediction of hepatic clearance of metabolically stable compounds. Drug Metab Dispos. 2016;44(3):435–44. https://doi.org/10.1124/dmd.115.067348.

    Article  PubMed  Google Scholar 

  60. Hallifax D, Houston JB. Use of segregated hepatocyte scaling factors and cross-species relationships to resolve clearance dependence in the prediction of human hepatic clearance. Drug Metab Dispos. 2019;47(3):320–7. https://doi.org/10.1124/dmd.118.085191.

    CAS  Article  PubMed  Google Scholar 

  61. Umehara K, Cantrill C, Wittwer M.B., Lenarda E.D., Klammers F, Ekiciler A, Parrott N, Fowler S, Mohammed U. Application of the extended clearance classification system (ECCS) in drug discovery and development: selection of appropriate in vitro tools and clearance prediction. Drug Metab Dispos. 2020;48(10): 849–860. doi: https://doi.org/10.1124/dmd.120.000133.

  62. Grime K, Paine SW. Species differences in biliary clearance and possible relevance of hepatic uptake and efflux transporters involvement. Drug Metab Dispos. 2013;41(2):372–8. https://doi.org/10.1124/dmd.112.049312.

    CAS  Article  PubMed  Google Scholar 

  63. Jansen K, Pou Casellas C, Groenink L, Wever KE, Masereeuw R. Humans are animals, but are animals human enough? A systematic review and meta-analysis on interspecies differences in renal drug clearance. Drug Discov Today. 2020;25(4):706–17. https://doi.org/10.1016/j.drudis.2020.01.018.

    CAS  Article  PubMed  Google Scholar 

  64. Paine SW, Menochet K, Denton R, McGinnity DF, Riley RJ. Prediction of human renal clearance from preclinical species for a diverse set of drugs that exhibit both active secretion and net reabsorption. Drug Metab Dispos. 2011;39(6):1008–13. https://doi.org/10.1124/dmd.110.037267.

    CAS  Article  PubMed  Google Scholar 

  65. Mathew S, Tess D, Burchett W, Chang G, Woody N, Keefer C, et al. Evaluation of prediction accuracy for volume of distribution in rat and human using in vitro, in vivo, PBPK and QSAR methods. J Pharm Sci. 2021;110(4):1799–823. https://doi.org/10.1016/j.xphs.2020.12.005.

    CAS  Article  PubMed  Google Scholar 

  66. Davies M, Jones RDO, Grime K, Jansson-Lofmark R, Fretland AJ, Winiwarter S, et al. Improving the accuracy of predicted human pharmacokinetics: lessons learned from the AstraZeneca drug pipeline over two decades. Trends Pharmacol Sci. 2020;41(6):390–408. https://doi.org/10.1016/j.tips.2020.03.004.

  67. Hsu F, Chen YC, Broccatelli F. Evaluation of tissue binding in three tissues across five species and prediction of volume of distribution from plasma protein and tissue binding with an existing model. Drug Metab Dispos. 2021;49(4):330–6. https://doi.org/10.1124/dmd.120.000337.

    CAS  Article  PubMed  Google Scholar 

  68. Beaumont K, Smith DA. Does human pharmacokinetic prediction add significant value to compound selection in drug discovery research? Curr Opin Drug Discov Devel. 2009;12(1):61–71.

    CAS  PubMed  Google Scholar 

  69. van Nuland M, Rosing H, Huitema ADR, Beijnen JH. Predictive value of microdose pharmacokinetics. Clin. Pharmacokinet. 2019;58:1221–36. https://doi.org/10.1007/s40262-019-00769-x.

    Article  PubMed  Google Scholar 

  70. Jansson-Lofmark R, Hjorth S, Gabrielsson J. Does in vitro potency predict clinically efficacious concentrations? Clin Pharmacol Ther. 2020;108(2):298–305. https://doi.org/10.1002/cpt.1846.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  71. Morgan P, Van Der Graaf PH, Arrowsmith J, Feltner DE, Drummond KS, Wegner CD, 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. https://doi.org/10.1016/j.drudis.2011.12.020.

  72. Cook D, Brown D, Alexander R, March R, Morgan P, Satterthwaite G, et al. Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework. Nat Rev Drug Discov. 2014;13(6):419–31. https://doi.org/10.1038/nrd4309.

Download references

Acknowledgements

The authors thank the IQ Secretariat and the Translational ADME Leadership Group for their support of this work. Specifically, we thank Svetlana Lyapustina, Maja Leah Marshall, and Jamie Vergis for their work in designing, conducting, and reporting the survey. Additionally, we would like to recognize Dr Chris Gibson for his scientific insights and enthusiastic sponsorship of the Human PK Prediction Working Group.

Author information

Authors and Affiliations

Authors

Contributions

All authors meet the following four criteria: substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; drafting the work or revising it critically for important intellectual content; final approval of the version to be published; and agreement to be account able for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to Carl Petersson.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Petersson, C., Zhou, X., Berghausen, J. et al. Current Approaches for Predicting Human PK for Small Molecule Development Candidates: Findings from the IQ Human PK Prediction Working Group Survey. AAPS J 24, 85 (2022). https://doi.org/10.1208/s12248-022-00735-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1208/s12248-022-00735-9

Keywords

  • Human PK prediction
  • Clearance
  • Volume of distribution
  • Uncertainty
  • IQ Consortium