European Journal of Clinical Pharmacology

, Volume 70, Issue 12, pp 1453–1463 | Cite as

Population pharmacokinetics of artesunate and dihydroartemisinin during long-term oral administration of artesunate to patients with metastatic breast cancer

  • Therese EricssonEmail author
  • Antje Blank
  • Cornelia von Hagens
  • Michael Ashton
  • Angela Äbelö
Pharmacokinetics and Disposition



The purpose of this study were firstly to characterize the population pharmacokinetics of artesunate (ARS) and its active metabolite dihydroartemisinin (DHA) in patients with metastatic breast cancer during long-term (>3 weeks) daily oral ARS administration and secondly to study the relationship between salivary and plasma concentrations of DHA.


Drug concentration-time data from 23 patients, receiving oral ARS (100, 150, or 200 mg OD), was analyzed using nonlinear mixed effects modeling. A combined drug-metabolite population pharmacokinetic model was developed to describe the plasma pharmacokinetics of ARS and DHA in plasma. Saliva drug concentrations were incorporated as being directly proportional to plasma concentrations.


A first-order absorption model for ARS linked to a combined two-compartment disposition model for ARS and one-compartment disposition model for DHA provided the best fit to the data. No covariates were identified that could explain between-subject variability. A time-dependent increase in apparent elimination clearance of DHA was observed. Salivary DHA concentrations were proportionally correlated with total DHA plasma concentrations, with an estimated slope factor of 0.116.


Population pharmacokinetics of ARS and DHA in patients with breast cancer was well described by a combined drug-metabolite model without any covariates and with an increase in apparent elimination clearance of DHA over time. The estimated DHA saliva/plasma ratio was in good agreement with the reported DHA unbound fraction in human plasma. Saliva ARS concentrations correlated poorly with plasma concentrations. This suggests the use of saliva sampling for therapeutic drug monitoring of DHA. However, further studies are warranted to investigate the robustness of this approach.


Artesunate Breast cancer Dihydroartemisinin Population pharmacokinetics Plasma Saliva 



The authors give their appreciations to the diligent staff at the Medical Clinic at University of Heidelberg. TE thanks Richard Höglund at the Unit for Pharmacokinetics and Drug Metabolism at the University of Gothenburg for his valuable input during the modeling process. Also, appreciations to Dafra Pharma International, Research & Development (Turnhout, Belgium), who supplied the study medication.

Conflicts of interest

The clinical study was supported by H. W. and J. Hector Stiftung, Weinheim, Germany, and Monika-Kutzner-Stiftung, Berlin, Germany. The co-author Antje Blank received personal funding from the Medical Faculty of the University of Heidelberg. The authors further certify that there is no other financial involvement or conflicts of interest regarding the material discussed in the manuscript.

Contribution of authors

T.E.—drug quantitation in plasma and saliva samples, population pharmacokinetic analyses; drafted and finalized manuscript; corresponding author

A.Ä.—senior contribution to the population pharmacokinetic analysis, revised the final manuscript for important intellectual content.

A.B...—contributed to the planning and conduct of study, revised the final manuscript for important intellectual content

C.v.H.—main contributor to the study design and conduct

M.A.—contributed to the study design, interpretation of results, and manuscript development.

Supplementary material

228_2014_1754_Fig6_ESM.gif (104 kb)
Supplement 1

Basic goodness-of-fit plots for artesunate (ARS) in plasma (A, B, C), dihydroartemisinin (DHA) in plasma (D, E, F), and DHA in saliva (G, H, I) using model A and full concentration-time profile data plus sparse sample data. Observed concentrations plotted against population predicted concentrations (A, D, G) and individual predicted concentrations (B, E, H), respectively. Conditional weighted residuals plotted against time after dose (C, F, H). Time axes are truncated to 6.5 h for plasma ARS and salivary DHA, and to 10.5 h for plasma DHA, respectively. Solid lines represent the identity lines and the dashed lines are the locally weighted least square regression lines. All concentrations are represented as transformed values using the natural logarithm. Observed ARS plasma concentrations equal to −5.04 seen in the lower part of plot A and B, represent the BQL data that was imputed as LLOQ/2. (GIF 103 kb)

228_2014_1754_MOESM1_ESM.tif (1.5 mb)
High resolution image (TIFF 1494 kb)
228_2014_1754_Fig7_ESM.gif (82 kb)
Supplement 2

Basic goodness-of-fit plots for plasma artesunate (ARS) (A, B, C) and dihydroartemisinin (DHA) (D, E, F) using model B and only full concentration-time profile data. Observed concentrations plotted against population predicted concentrations (A, D) and individual predicted concentrations (B, E), respectively. Conditional weighted residuals plotted against time after dose (C, F). Time axes are truncated to 6.5 and 8.5 h for ARS and DHA, respectively. Solid lines represent the identity lines and the dashed lines are the locally weighted least square regression lines. All concentrations are represented as transformed values using the natural logarithm. Observed ARS plasma concentrations equal to −5.04 seen in the lower part of plot A and B, represent the BQL data that was imputed as LLOQ/2. (GIF 81 kb)

228_2014_1754_MOESM2_ESM.tif (1.1 mb)
High resolution image (TIFF 1093 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Therese Ericsson
    • 1
    Email author
  • Antje Blank
    • 2
  • Cornelia von Hagens
    • 3
  • Michael Ashton
    • 1
  • Angela Äbelö
    • 1
  1. 1.Unit for Pharmacokinetics and Drug Metabolism, Department of PharmacologySahlgrenska Academy at the University of GothenburgGothenburgSweden
  2. 2.Department of Clinical Pharmacology & PharmacoepidemiologyHeidelberg University HospitalHeidelbergGermany
  3. 3.Department of Gynecological Endocrinology and Reproductive Medicine, Naturopathy and Integrative MedicineUniversity Women’s Hospital HeidelbergHeidelbergGermany

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