| Literature DB >> 35338410 |
Carl Amilon1,2, Mikael Boberg1, Joel Tarning3,4, Angela Äbelö1, Michael Ashton1, Rasmus Jansson-Löfmark5,6.
Abstract
Eflornithine is a recommended treatment against late-stage gambiense human African trypanosomiasis, a neglected tropical disease. Standard dosing of eflornithine consists of repeated intravenous infusions of a racemic mixture of L- and D-eflornithine. Data from three clinical studies, (i) eflornithine intravenous monotherapy, (ii) nifurtimox-eflornithine combination therapy, and (iii) eflornithine oral monotherapy, were pooled and analyzed using a time-to-event pharmacodynamic modeling approach, supported by in vitro activity data of the individual enantiomers. Our aim was to assess (i) the efficacy of the eflornithine regimens in a time-to-event analysis and (ii) the feasibility of an L-eflornithine-based therapy integrating clinical and preclinical data. A pharmacodynamic time-to-event model was used to estimate the total dose of eflornithine, associated with 50% reduction in baseline hazard, when administered as monotherapy or in the nifurtimox-eflornithine combination therapy. The estimated total doses were 159, 60 and 291 g for intravenous eflornithine monotherapy, nifurtimox-eflornithine combination therapy and oral eflornithine monotherapy, respectively. Simulations suggested that L-eflornithine achieves a higher predicted median survival, compared to when racemate is administered, as treatment against late-stage gambiense human African trypanosomiasis. Our findings showed that oral L-eflornithine-based monotherapy would not result in adequate efficacy, even at high dose, and warrants further investigations to assess the potential of oral L-eflornithine-based treatment in combination with other treatments such as nifurtimox. An all-oral eflornithine-based regimen would provide easier access to treatment and reduce burden on patients and healthcare systems in gambiense human African trypanosomiasis endemic areas. Graphical abstract.Entities:
Keywords: enantiomers; neglected tropical diseases; nonlinear mixed-effects modeling; sleeping sickness; time-to-event analysis
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Year: 2022 PMID: 35338410 DOI: 10.1208/s12248-022-00693-2
Source DB: PubMed Journal: AAPS J ISSN: 1550-7416 Impact factor: 4.009