Model-based screening of compounds for the treatment of Chagas disease, a neglected tropical disease.
Salvatore D’Agate (1), Ignacio Cotillo Torrejon (2), Paul Healy (1) and Oscar Della Pasqua (1,3)
(1) Clinical Pharmacology & Therapeutics Group, University College London, London, UK; (2) Kinetoplastid Discovery Performance Unit, GlaxoSmithKline, Tres Cantos, Spain; (3) Clinical Pharmacology Modelling & Simulation, GlaxoSmithKline, Uxbridge, UK.
Objectives: The aim of the investigation was to develop a drug-disease model with suitable parameterisation for the screening and selection of compounds against T. cruzi. A model parameterisation was chosen that allows the description of parasite growth and clearance in a standard in vitro protocol. This screening protocol enables the evaluation of multiple compounds, but empirical metrics of drug effect have shown to be misleading. Our approach provides insight into system- and drug-specific properties as distinct parameters, which in turn allows ranking of compounds considering different mechanisms of action.
Methods: A population modelling approach was applied for the estimation of systemic and drug-related parameters to describe data from an in-vitro experiment performed on H9c2 cells (rat cardiomyocytes) infected with Trypanosoma Cruzi based on previous literature models[1, 2]. Data was obtained from experiments in which different anti-parasitic compounds were tested using high-throughput screening (HTS) assays[3]. The analysis was performed using NONMEM V7.3. Model performance was assessed by diagnostic and GOF criteria. Statistical analysis, dataset handling and graphic visualisation were performed in R. The ranking was based on the estimated efficacy of compounds and was validated by comparison of standard compounds with previous literature data[4].
Results: A four-transit-compartments pharmacodynamic model was found to describe screening data with parallel analysis of multiple compounds per experimental protocol. The model predicts the time course and number of healthy and infected cells along with the number of intracellular amastigotes/trypomastigotes. An exploratory analysis showed the drugs to affect: 1) the duplication rate of intracellular parasites; 2) the premature lysis of H9c2 cells by the parasites, leading to re-infection; 3) the death rate of intracellular amastigotes; and 4) the growth rate of H9c2 healthy cells. It was also possible to show that the use of this model developed in conjunction with PK information allows the prediction of the therapeutic dose in humans.
Conclusion: The use of a drug-disease model allows screening and ranking of novel molecules against T. cruzi. Our analysis shows that the model can be applied prospectively for a more accurate classification of compounds. Despite the limited number of compounds tested so far, model parameterisation seems to provide the basis for the dose rationale in humans.
References
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[4] Soy, D., et al. (2015). "Population pharmacokinetics of benznidazole in adult patients with Chagas disease." Antimicrob Agents Chemother 59(6): 3342-3349.
