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Benchmarking of Human Dose Prediction for Inhaled Medicines from Preclinical In Vivo Data

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Abstract

Purpose

A scientifically robust prediction of human dose is important in determining whether to progress a candidate drug into clinical development. A particular challenge for inhaled medicines is that unbound drug concentrations at the pharmacological target site cannot be easily measured or predicted. In the absence of such data, alternative empirical methods can be useful. This work is a post hoc analysis based on preclinical in vivo pharmacokinetic/pharmacodynamic (PK/PD) data with the aim to evaluate such approaches and provide guidance on clinically effective dose prediction for inhaled medicines.

Methods

Five empirically based methodologies were applied on a diverse set of marketed inhaled therapeutics (inhaled corticosteroids and bronchodilators). The approaches include scaling of dose based on body weight or body surface area and variants of PK/PD approaches aiming to predict the therapeutic dose based on having efficacious concentrations of drug in the lung over the dosing interval.

Results

The most robust predictions of dose were made by body weight adjustment (90% within 3-fold) and by a specific PK/PD approach aiming for an average predicted 75% effect level during the dosing interval (80% within 3-fold). Scaling of dose based on body surface area consistently under predicted the therapeutic dose.

Conclusions

Preclinical in vivo data and empirical scaling to man can be used as a baseline method for clinical dose predictions of inhaled medicines. The development of more sophisticated translational models utilizing free drug concentration and target engagement data is a desirable build.

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Abbreviations

BID:

Twice daily

BSA-CF:

Body surface area conversion factor

BUD:

Budesonide

Cavg,ED50 :

Average lung concentration over the whole challenge period

CI:

Confidence interval

Clung :

Total lung concentration

COPD:

Chronic obstructive pulmonary disease

DPI:

Dry powder inhalation

DtM:

Dose-to-man

FDA:

U S Food and Drug administration

FF:

Fluticasone furoate

FOR:

Formoterol

FP:

Fluticasone propionate

GP:

Glycopyrronium

HED:

Human equivalent dose

IC50 :

Total lung concentration associated with 50% inhibition of challenge induced lung inflammation or bronchoconstriction

ICS:

Inhaled corticosteroids

Imax :

Maximum inhibitory effect of drug on challenge induced lung inflammation or bronchoconstriction

IND:

Indacaterol

IPRA:

Ipratropium

IT:

Intratracheal

IV:

Intravenous

LABA:

Long acting β2 adrenoceptor agonists

LAMA:

Long acting muscarinic antagonists

LDD:

Lung deposited dose

PD:

Pharmacodynamics

PK:

Pharmacokinetics

PBPK:

Physiologically based pharmacokinetic

PBPK/PD:

Physiologically based pharmacokinetic/pharmacodynamic

PK/PD:

Pharmacokinetic/pharmacodynamic

QD:

Once daily

QID:

Four times a day

SABA:

Short acting β2 adrenoceptor agonists

SALB:

Salbutamol

SALM:

Salmeterol

TID:

Three times a day

TIO:

Tiotropium

References

  1. Backman P, Adelmann H, Petersson G, Jones CB. Advances in inhaled technologies: understanding the therapeutic challenge, predicting clinical performance, and designing the optimal inhaled product. Clin Pharmacol Ther. 2014;95(5):509–20.

    Article  CAS  Google Scholar 

  2. Cooper AE, Ferguson D, Grime K. Optimisation of DMPK by the inhaled route: challenges and approaches. Curr Drug Metab. 2012;13(4):457–73.

    Article  CAS  Google Scholar 

  3. 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.

    Article  CAS  Google Scholar 

  4. Danhof M, Alvan G, Dahl SG, Kuhlmann J, Paintaud G. Mechanism-based pharmacokinetic-pharmacodynamic modeling-a new classification of biomarkers. Pharm Res. 2005;22(9):1432–7.

    Article  CAS  Google Scholar 

  5. Smith DA, Di L, Kerns EH. The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery. Nat Rev Drug Discov. 2010;9(12):929–39.

    Article  CAS  Google Scholar 

  6. McGinnity DF, Collington J, Austin RP, Riley RJ. Evaluation of human pharmacokinetics, therapeutic dose and exposure predictions using marketed oral drugs. Curr Drug Metab. 2007;8(5):463–79.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  9. Fernandes CA, Vanbever R. Preclinical models for pulmonary drug delivery. Expert Opin Drug Deliv. 2009;6(11):1231–45.

    Article  CAS  Google Scholar 

  10. Rohatagi S, Rhodes GR, Chaikin P. Absolute oral versus inhaled bioavailability: significance for inhaled drugs with special reference to inhaled glucocorticoids. J Clin Pharmacol. 1999;39(7):661–3.

    Article  CAS  Google Scholar 

  11. Haddad E-B, Underwood SL, Dabrowski D, Birrel MA, McCluskie K, Battram CH, et al. Critical role for T cells in sephadex-induced airway inflammation: pharmacological and immunological characterization and molecular biomarker identification. J Immunol. 2002;168(6):3004–16.

    Article  CAS  Google Scholar 

  12. Raab OG, Yeh HC, Newton GJ, Phalen RF, Velasquez DJ. Deposition of inhaled monodisperse aerosols in small rodents. Inhaled Part. 1975;4(Pt 1):3–21.

    PubMed  Google Scholar 

  13. US Food and Drug Administration. Guidance for industry estimating the maximum safe starting dose in initial clinical trials for therapeutics in adult healthy volunteers. 2005. Access December 2016. Available from: http://www.fda.gov/downloads/Drugs/.../Guidances/UCM078932.pdf.

  14. Altman DG, Bland JM. Measurement in medicine: the analysis of method comparison studies. J R Stat Soc Ser D (The Statistician). 1983;32:307–17.

    Google Scholar 

  15. Villetti G, Bergamaschi M, Bassani F, Bolzoni PT, Harrison S, Gigli PM, et al. Pharmacological assessment of the duration of action of glycopyrrolate vs tiotropium and ipratropium in guinea-pig and human airways. Br J Pharmacol. 2006;148(3):291–8.

    Article  CAS  Google Scholar 

  16. Molimard M, Naline E, Zhang Y, Le Gros V, Begaud B, Advenier C. Long- and short-acting beta2 adrenoceptor agonists: interactions in human contracted bronchi. Eur Respir J. 1998;11(3):583–8.

    CAS  PubMed  Google Scholar 

  17. Stocks MJ, Alcaraz L, Bailey A, Bonnert R, Cadogan E, Christie J, et al. Discovery of AZD3199, an inhaled ultralong acting beta2 receptor agonist with rapid onset of action. ACS Med Chem Lett. 2014;5(4):416–21.

    Article  CAS  Google Scholar 

  18. Stoeck M, Riedel R, Hochhaus G, Hafner D, Masso JM, Schmidt B, et al. In vitro and in vivo anti-inflammatory activity of the new glucocorticoid ciclesonide. J Pharmacol Exp Ther. 2004;309(1):249–58.

    Article  CAS  Google Scholar 

  19. Rohrschneider M, Bhagwat S, Krampe R, Michler V, Breitkreutz J, Hochhaus G. Evaluation of the transwell system for characterization of dissolution behavior of inhalation drugs: effects of membrane and surfactant. Mol Pharm. 2015;12(8):2618–24.

    Article  CAS  Google Scholar 

  20. Ramakrishnan R, DuBois DC, Almon RR, Pyszczynski NA, Jusko WJ. Fifth-generation model for corticosteroid pharmacodynamics: application to steady-state receptor down-regulation and enzyme induction patterns during seven-day continuous infusion of methylprednisolone in rats. J Pharmacokinet Pharmacodyn. 2002;29(1):1–24.

    Article  CAS  Google Scholar 

  21. Clewell HJ, Reddy MB, Lave T, Andersen ME. Physiologically based pharmacokinetic modelling. In: Gad SC, editor. Pre-clinical development handbook: ADME and biopharmaceutical properties. Hoboken: John Wiley & Sons, Inc.; 2008. p. 1167–227.

    Google Scholar 

  22. Caniga M, Cabal A, Mehta K, Ross DS, Gil MA, Woodhouse JD, et al. Preclinical experimental and mathematical approaches for assessing effective doses of inhaled drugs, using mometasone to support human dose predictions. J Aerosol Med Pulm Drug Deliv. 2016;29(4):362–77.

    Article  CAS  Google Scholar 

  23. Boger E, Evans N, Chappell M, Lundqvist A, Ewing P, Wigenborg A, et al. Systems pharmacology approach for prediction of pulmonary and systemic pharmacokinetics and receptor occupancy of inhaled drugs. CPT Pharmacometrics Syst Pharmacol. 2016;5(4):201–10.

    Article  CAS  Google Scholar 

  24. Mullane K, Williams M. Animal models of asthma: reprise or reboot? Biochem Pharmacol. 2014;87(1):131–9.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Drug Metabolism and Pharmacokinetics (DMPK), Respiratory, Inflammation, and Autoimmunity Innovative Medicines (RIA IMED), AstraZeneca R&D, Gothenburg, Sweden

    Therese Ericsson, Markus Fridén & Ken Grime

  2. Translational PKPD Group, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden

    Markus Fridén

  3. AstraZeneca AB, Pepparedsleden 1, 431 50, Molndal, Sweden

    Markus Fridén

  4. Department of Translational Biology, RIA IMED AstraZeneca R&D, Gothenburg, Sweden

    Carina Kärrman-Mårdh

  5. Biotech Unit, RIA IMED AstraZeneca R&D, Gothenburg, Sweden

    Ian Dainty

Authors
  1. Therese Ericsson
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  2. Markus Fridén
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  3. Carina Kärrman-Mårdh
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  4. Ian Dainty
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  5. Ken Grime
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Corresponding author

Correspondence to Markus Fridén.

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The authors declare that they have no conflicts of interest.

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Ericsson, T., Fridén, M., Kärrman-Mårdh, C. et al. Benchmarking of Human Dose Prediction for Inhaled Medicines from Preclinical In Vivo Data. Pharm Res 34, 2557–2567 (2017). https://doi.org/10.1007/s11095-017-2218-z

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  • DOI: https://doi.org/10.1007/s11095-017-2218-z

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