Lydia Burgert (1, 2), Sophie Zaloumis (3), Nathalie Gobeau (4), Mohammed Cherkaoui (4), James McCarthy (5), Jörg J. Möhrle (2, 4), Melissa A. Penny (1, 2)
(1) Swiss Tropical and Public Health Institute, Basel, Switzerland, (2) University of Basel, Basel, Switzerland, (3) Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia, (4) Medicines for Malaria Venture, Geneva, Switzerland, (5) QIMR Berghofer Medical Research Institute, Brisbane, Australia
Objectives: In the context of aspired malaria elimination being threatened by emerging resistance against current first-line treatments, health system weaknesses, and sub-optimal adherence to existing treatment schedules, the development of new antimalarial compounds and regimens is more urgent than ever [1]. Antimalarial development is employing murine malarial infection in P. berghei – NMRI mice [2] and P. falciparum – NOD scidIL-2R’ c-/- mice [3] to assess drug efficacy before volunteer infection studies with P. falciparum provide the opportunity to evaluate activity in asymptomatic humans [4]. Quantification of indices for efficacy, such as proportional parasite clearance (PRR) [5] and subsequent prediction of impact throughout the next clinical stages is a central measure to accelerate drug development efforts. A thorough understanding of parasite host dynamics along the clinical development pathway of new antimalarials is essential to understand drug efficacy patterns seen and make optimal use of insights gained in every clinical stage.
Methods: We analyzed untreated parasite growth data from 43 experiments for murine P. berghei – NMRI mouse infection, 32 experiments for P. falciparum – SCID mouse infection, and from 177 human subjects in 27 treatment cohorts for P. falciparum-human infection. Parasite growth was captured in mechanistic models with different level of detail depending on the data available and potential parasite-host dynamics. In murine infection, the investigated dynamics mainly include adaptions of the host-system to high and increasing parasite burden. In human infection, we captured the synchronicity of parasite growth and investigated levels of parameter variability to distinguish between parasite and host dependent parameters. We estimated drug efficacy using information on parasite clearance after dosing with the two antimalarials MMV048 [6] and OZ439 [7]. Subsequently, we investigated sensitivity of the experimental endpoint parasite clearance towards host-parasite dynamics throughout the clinical development pathway.
Results: Analysis of parasite behavior after treatment with MMV048 revealed patterns of delayed parasite clearance in both murine hosts. In contrast, we identified a delayed drug effect through a turnover mechanism for OZ439. We found evidence for delayed parasite maturation in human infection after treatment with both compounds. Experimental background and/or adaptive host dynamics influence sensitivity of parasite clearance depending on experimental system, w.g. in SCID mice, erythrocyte clearance plays a key role, whereas for P. berghei – NMRI infection and P. falciparum-human infection parameters of drug action and their interactions are more influential. Endpoints linked to cure and recrudescence are missing mechanistic insights for a conclusive mechanistic translation.
Conclusions: General comparison of parasite-host combinations between clinical testing systems shows differing scales of infection in terms of inoculum size and the resulting parasite burden. Additionally, a contradictory trend becomes obvious when comparing data richness and potential parasite-host dynamics in the analyzed (pre-) clinical stages. Our sensitivity analysis of parasite clearance towards host-parasite dynamics throughout the clinical stages revealed a continuously high sensitivity towards parameters of drug action in higher dose ranges. In contrast, parameters of parasite growth show influence in lower dose ranges in murine and human P. falciparum infection. Overall, a disconnect in experimental endpoints and their dependencies between the clinical testing stages becomes apparent which leads to an aggravation of translatability of parasite-host-drug dynamics between the different clinical stages moving towards clinical field studies.
References:
[1] World Health, O. World malaria report 2019. 166 p. (World Health Organization, 2019).
[2] Jiménez-Díaz, M. B. et al. A new in vivo screening paradigm to accelerate antimalarial drug discovery. PLOS ONE 8, e66967, doi:10.1371/journal.pone.0066967 (2013).
[3] Angulo-Barturen, I. et al. A murine model of falciparum-malaria by in vivo selection of competent strains in non-myelodepleted mice engrafted with human erythrocytes. PLoS One 3, e2252, doi:10.1371/journal.pone.0002252 (2008).
[4] McCarthy, J. S. et al. A pilot randomised trial of induced blood-stage Plasmodium falciparum infections in healthy volunteers for testing efficacy of new antimalarial drugs. PLoS One 6, doi:10.1371/journal.pone.0021914 (2011).
[5] White, N. J. The parasite clearance curve. Malaria journal 10, 278, doi:10.1186/1475-2875-10-278 (2011).
[6] Paquet, T. et al. Antimalarial efficacy of MMV390048, an inhibitor of Plasmodium phosphatidylinositol 4-kinase. Science translational medicine 9, eaad9735, doi:10.1126/scitranslmed.aad9735 (2017).
[7] McCarthy, J. S. et al. Efficacy of OZ439 (artefenomel) against early Plasmodium falciparum blood-stage malaria infection in healthy volunteers. The Journal of antimicrobial chemotherapy 71, doi:10.1093/jac/dkw174 (2016).
Reference: PAGE () Abstr 9493 [www.page-meeting.org/?abstract=9493]
Poster: Oral: Drug/Disease Modelling