Assessing Dose-Exposure-Response Relationships of Miltefosine in Adults and Children using Physiologically-Based Pharmacokinetic Modeling Approach.

Shadrack J Madu, Ke Wang, Siri Kalyan Chirumamilla, David B Turner, Patrick G Steel, Mingzhong Li
Author Information
  1. Shadrack J Madu: School of Pharmacy, De Montfort University, Leicester, LE1 9BH, UK.
  2. Ke Wang: School of Pharmacy, De Montfort University, Leicester, LE1 9BH, UK.
  3. Siri Kalyan Chirumamilla: Certara UK Limited, Simcyp Division, Sheffield, S1 2BJ, UK.
  4. David B Turner: Certara UK Limited, Simcyp Division, Sheffield, S1 2BJ, UK.
  5. Patrick G Steel: Department of Chemistry, Durham University, Durham, DH1 3LE, UK.
  6. Mingzhong Li: School of Pharmacy, De Montfort University, Leicester, LE1 9BH, UK. mli@dmu.ac.uk. ORCID

Abstract

OBJECTIVES: Miltefosine is the first and only oral medication to be successfully utilized as an antileishmanial agent. However, the drug is associated with differences in exposure patterns and cure rates among different population groups e.g. ethnicity and age (i.e., children v adults) in clinical trials. In this work, mechanistic population physiologically-based pharmacokinetic (PBPK) models have been developed to study the dose-exposure-response relationship of miltefosine in in silico clinical trials and evaluate the differences in population groups, particularly children and adults.
METHODS: The Simcyp population pharmacokinetics platform was employed to predict miltefosine exposure in plasma and peripheral blood mononuclear cells (PBMCs) in a virtual population under different dosing regimens. The cure rate of a simulation was based on the percentage of number of the individual virtual subjects with AUC > 535 µg⋅day/mL in the virtual population.
RESULTS: It is shown that both adult and paediatric PBPK models of miltefosine can be developed to predict the PK data of the clinical trials accurately. There was no significant difference in the predicted dose-exposure-response of the miltefosine treatment for different simulated ethnicities under the same dose regime and the dose-selection strategies determined the clinical outcome of the miltefosine treatment. A lower cure rate of the miltefosine treatment in paediatrics was predicted because a lower exposure of miltefosine was simulated in virtual paediatric in comparison with adult virtual populations when they received the same dose of the treatment.
CONCLUSIONS: The mechanistic PBPK model suggested that the higher fraction of unbound miltefosine in plasma was responsible for a higher probability of failure in paediatrics because of the difference in the distribution of plasma proteins between adults and paediatrics. The developed PBPK models could be used to determine an optimal miltefosine dose regime in future clinical trials.

Keywords

References

  1. J Pharm Sci. 2006 Jun;95(6):1238-57 [PMID: 16639716]
  2. Clin Infect Dis. 2019 Apr 24;68(9):1530-1538 [PMID: 30188978]
  3. PLoS One. 2014 Jun 18;9(6):e100220 [PMID: 24941345]
  4. Int J Antimicrob Agents. 2022 Jan;59(1):106459 [PMID: 34695563]
  5. Pharmaceutics. 2020 Sep 30;12(10): [PMID: 33008144]
  6. Br J Clin Pharmacol. 2022 Feb;88(4):1913-1924 [PMID: 34705297]
  7. Eur J Pharm Sci. 2010 Mar 18;39(5):298-309 [PMID: 20025966]
  8. Molecules. 2018 Nov 30;23(12): [PMID: 30513673]
  9. Antimicrob Agents Chemother. 2017 Feb 23;61(3): [PMID: 27956421]
  10. AAPS J. 2013 Apr;15(2):455-64 [PMID: 23344790]
  11. RSC Med Chem. 2021 Jan 7;12(4):472-482 [PMID: 34041488]
  12. Expert Opin Drug Metab Toxicol. 2007 Apr;3(2):235-49 [PMID: 17428153]
  13. PLoS Negl Trop Dis. 2016 Sep 14;10(9):e0004880 [PMID: 27627654]
  14. Clin Infect Dis. 2004 Jan 15;38(2):217-21 [PMID: 14699453]
  15. J Pharm Sci. 2005 Jun;94(6):1259-76 [PMID: 15858854]
  16. AAPS J. 2013 Oct;15(4):1109-18 [PMID: 23943382]
  17. Mol Pharm. 2018 Mar 5;15(3):821-830 [PMID: 29337578]
  18. Biochim Biophys Acta. 2016 Jun;1858(6):1160-4 [PMID: 26947181]
  19. J Antimicrob Chemother. 2020 Nov 1;75(11):3260-3268 [PMID: 32780098]
  20. Drug Metab Pharmacokinet. 2009;24(1):53-75 [PMID: 19252336]
  21. Drug Metab Pharmacokinet. 2012;27(5):466-77 [PMID: 22813719]
  22. J Infect Chemother. 2004 Dec;10(6):307-15 [PMID: 15614453]
  23. Int J Infect Dis. 2011 Aug;15(8):e525-32 [PMID: 21605997]
  24. Mol Pharm. 2021 Dec 6;18(12):4272-4289 [PMID: 34748332]
  25. Cryst Growth Des. 2022 Oct 5;22(10):6262-6266 [PMID: 36217416]
  26. J Antimicrob Chemother. 2012 Nov;67(11):2576-97 [PMID: 22833634]
  27. Antimicrob Agents Chemother. 2008 Aug;52(8):2855-60 [PMID: 18519729]
  28. Mol Pharm. 2017 Dec 4;14(12):4305-4320 [PMID: 28771009]
  29. Toxicol Pathol. 2009 Oct;37(6):770-5 [PMID: 19690151]
  30. Pharm Res. 2022 Aug;39(8):1701-1731 [PMID: 35552967]
  31. J Infect Dis. 2014 Jul 1;210(1):146-53 [PMID: 24443541]
  32. Clin Infect Dis. 2010 Jan 1;50(1):80-3 [PMID: 19951107]
  33. Expert Rev Anti Infect Ther. 2006 Apr;4(2):177-85 [PMID: 16597200]
  34. J Antimicrob Chemother. 2018 Aug 1;73(8):2104-2111 [PMID: 29757380]
  35. CPT Pharmacometrics Syst Pharmacol. 2022 Jul;11(7):805-821 [PMID: 35344639]
  36. Eur J Pharm Sci. 2017 May 1;102:284-298 [PMID: 28286289]
  37. Acta Trop. 2007 Jul;103(1):33-40 [PMID: 17586452]
  38. J Infect Dis. 2007 Aug 15;196(4):591-8 [PMID: 17624846]
  39. Clin Microbiol Infect. 2011 Oct;17(10):1478-83 [PMID: 21933306]
  40. PLoS One. 2016 Jun 14;11(6):e0157607 [PMID: 27299737]
  41. J Antimicrob Chemother. 2017 Nov 01;72(11):3131-3140 [PMID: 28961737]
  42. Mol Pharm. 2008 Sep-Oct;5(5):760-75 [PMID: 18547054]
  43. N Engl J Med. 2002 Nov 28;347(22):1739-46 [PMID: 12456849]
  44. Pharm Res. 2007 May;24(5):918-33 [PMID: 17372687]
  45. Pharmaceutics. 2021 Jul 29;13(8): [PMID: 34452130]
  46. Clin Pharmacokinet. 2018 May;57(5):577-589 [PMID: 28779462]
  47. Mol Pharm. 2018 Mar 5;15(3):831-839 [PMID: 29337562]
  48. Eur J Pharm Sci. 2014 Oct 15;63:103-12 [PMID: 25008118]
  49. CPT Pharmacometrics Syst Pharmacol. 2016 Sep;5(9):455-65 [PMID: 27393710]
  50. Pediatr Infect Dis J. 2003 May;22(5):434-8 [PMID: 12792385]
  51. Antimicrob Agents Chemother. 2012 Jul;56(7):3864-72 [PMID: 22585212]

Grants

  1. EP/T020490/1/Engineering and Physical Sciences Research Council

MeSH Term

Adult
Humans
Child
Leukocytes, Mononuclear
Phosphorylcholine
Computer Simulation
Antiprotozoal Agents
Models, Biological

Chemicals

miltefosine
Phosphorylcholine
Antiprotozoal Agents

Word Cloud

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