Elisa Tacconi(1), Salvatore D’Agate (1), Maria-Jose La Fuente Monasterio (2) and Oscar Della Pasqua (1,3)
(1) Clinical Pharmacology & Therapeutics Group, University College London, London, UK; (2) Malaria Group, DDW Discovery Performance Unit, GlaxoSmithKline, Tres Cantos, Spain; (3) Clinical Pharmacology Modelling & Simulation, GlaxoSmithKline, Uxbridge, UK.
Objectives: Malaria is a serious tropical disease caused by infection with Plasmodium protozoa that are transmitted by Anopheles mosquito bite. Five different species of Plasmodium infect humans with severe disease, but human malaria is primarily caused by Plasmodium Falciparum. When the mosquito bites humans, the parasite is released into human bloodstream [1]. The role of malaria on the developing world is huge, and a fully protective vaccine is still missing. One of the biggest challenges for the development of new antimalarial drugs and vaccines is the lack of accessible animal models to study P. Falciparum infection because the parasite is restricted human erythrocytes hosts. In this project, I developed a pharmacodynamics “humanized” mice model that describes the infection cycle of disease and the drug effect to select antimalarial compound with higher efficacy [2]. The main aim of the study was to develop a drug-disease model for the screening and ranking of compounds against Plasmodium Falciparum based on efficacy parameters.
Methods: Preclinical data was obtained from in vivo experiment done on “humanized” mice (mice engrafted with human erythrocytes) infected by P. Falciparum. Data was obtained from experiments in which four different anti-parasitic compounds were tested according a specific experimental protocol. All animal studies were ethically reviewed and carried out in accordance with European Directive 2010/63/EEC and the GSK Policy on the Care, Welfare and Treatment of Animals. For the estimation of drug-effect parameters was used a population modelling approach, the analysis was carried out in NONMEM V7.3 [3]. The data was first analysed and rearranged to allow the readability in NONMEM, data manipulation was performed using R V3.0.1 and R Studio user interface. The estimation methods used were FOCE with interaction and LAPLACIAN [4]. The model prediction ability was verified using goodness of fit and validation criteria. The ranking compounds was based on estimated efficacy parameters.
Results: It was used a compartmental pharmacodynamics model to describe data. The use of a compartmental model-based approach proved to be appropriate to mechanistically describe the life cycle of parasite. The model predicts the number of parasites inside the humanized mice blood for each tested compound until 30 days after infection to predict the recrudescence (recurrence of symptoms after a quiescent stage). The EC50KDEATH was selected as the efficacy parameter to base the compound ranking for the precision in describing the level of parasitemia (number of parasites) in mice blood.
Conclusions: The pharmacodynamics mice model allows the screening and ranking of compounds against Plasmodium Falciparum. The compound ranking is based on direct killing parasites drug action. The model could also predict the recrudescence of parasite and could be applied in the future for a more accurate and extended classification of compounds and could provide the basis for a possible dose in humans.
References:
[1] Nicholas J. White, May Ho. 1992. The Pathophysiology of Malaria. Advances in Parasitology. Volume 31, Pages 83-173.
[2] Jimenez-Diaz MB, Mulet T, Gomez V, Garuti H, Ibanez J, Alvarez-Doval A, Shultz LD, Martinez A, Gargallo-Viola D, Angulo-Barturen. 2009. Improved murine model of malaria using plasmodium falciparum competent strains and non myelodepleted NOD-scid LI2Rgammanull mice engrafted with human erythtocytes. Antimicrob Agents Chemoter.
[3] Beal S., Sheiner LB, Boeckmann A., Bauer R.J. 2009. NONMEM User’s Guides.: Icon Development Solutions, Elicott city, MD, USA.
[4] Hooker A.C., Staazt CE, Karlsson MO. 2007. Conditional weighted residuals (CWRES): a model diagnostic for the FOCE method. Pharmaceutical research 2007.
Reference: PAGE 27 (2018) Abstr 8733 [www.page-meeting.org/?abstract=8733]
Poster: Drug/Disease Modelling - Infection