Evaluation of a multiphasic parasite clearance profile after treatment of experimental human infection with the investigational anti-malarial M5717 using segmented mixed effect models

Malaria Journal

Research

Xiaoyan Yin¹  Ying Li¹  Özkan Yalkinoglu²  Aliona Tappert²  Louise Marquart³  James McCarthy⁴  Claude Oeuvray⁵  Wilhelmina Bagchus⁶  Deon Bezuidenhout⁶ 

[1] EMD Serono Research & Development Institute, Inc. (an Affiliate of Merck KGaA), 45 Middlesex Turnpike, 01821, Billerica, MA, USA;Merck KGaA, Darmstadt, Germany;QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia;School of Public Health, University of Queensland, Brisbane, Australia;QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia;The Peter Doherty Institute for Infection and Immunity, The University of Melbourne and the Royal Melbourne Hospital, Melbourne, VIC, Australia;The Global Health Institute of Merck (an Affiliate of Merck KGaA), Eysins, Switzerland;WMB: Merck Serono S.A (an Affiliate of Merck KGaA), Lausanne, Switzerland;

关键词: Parasite clearance;    Segmented mixed model;    Malaria;   

DOI  :  10.1186/s12936-023-04627-x

 received in 2023-01-13, accepted in 2023-06-16,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundEvaluation of parasite clearance patterns in experimental human infection trials helps increase understanding of drug action. In a previously reported phase Ib trial of a new investigational anti-malarial drug M5717, parasite clearance showed a biphasic linear pattern: slow removal phase with a near flat clearance rate followed by a fast clearance phase with a steep slope. In this study three statistical approaches were implemented and compared to estimate the parasite clearance rate for each phase and the time point corresponding to the change of clearance rates (changepoint between the two phases).MethodsData using three M5717 doses 150 mg (n = 6), 400 mg (n = 8), 800 mg (n = 8) were used to estimate biphasic clearance rates. Three models were investigated: firstly, segmented mixed models with estimated changepoint—models with/without random effects in various parameters were compared. Secondly, a segmented mixed model using grid search—this method is similar to the first except that changepoints were not estimated, instead they were selected based on model fit from given candidate values. Thirdly, a two-stage approach whereby a segmented regression model fit to each participant followed by a meta-analysis method. Hourly rate of parasite clearance (HRPC) interpreted as the percentage of parasites removed each hour was calculated.ResultsThe three models generated similar results. Using segmented mixed models, the estimated changepoints after treatment in hours (95% CI) were: 150 mg: 33.9 (28.7, 39.1); 400 mg: 57.4 (52.5, 62.4); and 800 mg: 52.8 (47.4, 58.1). For all three treatment groups, there was nearly no clearance before the changepoints, but rapid clearance in the second phase (HRPC [95% CI]): 150 mg: 16.8% (14.3, 19.1%); 400 mg: 18.6% (16.0, 21.1%); and 800 mg: 11.7% (9.3, 14.1%).ConclusionsAll three statistical approaches are effective tools to characterize the bi-phasic clearance of M5717 in the phase 1b experimental Plasmodium falciparum malaria human infection study. The statistical approaches produced similar results to estimate the two-phase clearance rates and the changepoint for each treatment dose of M5717. However, the segmented mixed model with random changepoints has several advantages: it is computationally efficient, provides precision for changepoint estimates and is robust concerning outlying datapoints or individuals.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 1

42004_2023_911_Article_IEq48.gif

Fig. 5

Fig. 4

Fig. 3

Fig. 2

Fig. 2

Fig. 1

Fig. 5

42004_2023_911_Article_IEq37.gif

42004_2023_911_Article_IEq45.gif

Fig. 1

42004_2023_911_Article_IEq042.gif

42004_2023_911_Article_IEq041.gif

Fig. 1

42004_2023_911_Article_IEq36.gif

42004_2023_911_Article_IEq33.gif

42004_2023_911_Article_IEq32.gif

Fig. 2

Fig. 1

Fig. 5

42004_2023_911_Article_IEq031.gif

42004_2023_911_Article_IEq30.gif

Fig. 5

42004_2023_911_Article_IEq28.gif

Fig. 8

Fig. 1

42004_2023_911_Article_IEq25.gif

Fig. 4

42004_2023_911_Article_IEq23.gif

Fig. 3

42004_2023_911_Article_IEq20.gif

42004_2023_911_Article_IEq19.gif

42004_2023_911_Article_IEq18.gif

42004_2023_911_Article_IEq17.gif

42004_2023_911_Article_IEq16.gif

42004_2023_911_Article_IEq15.gif

Fig. 1

Fig. 3