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A combined ligand and target-based virtual screening strategy to repurpose drugs as putrescine uptake inhibitors with trypanocidal activity

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Abstract

Chagas disease, also known as American trypanosomiasis, is a neglected tropical disease caused by the protozoa Trypanosoma cruzi, affecting nearly 7 million people only in the Americas. Polyamines are essential compounds for parasite growth, survival, and differentiation. However, because trypanosomatids are auxotrophic for polyamines, they must be obtained from the host by specific transporters. In this investigation, an ensemble of QSAR classifiers able to identify polyamine analogs with trypanocidal activity was developed. Then, a multi-template homology model of the dimeric polyamine transporter of T. cruzi, TcPAT12, was created with Rosetta, and then refined by enhanced sampling molecular dynamics simulations. Using representative snapshots extracted from the trajectory, a docking model able to discriminate between active and inactive compounds was developed and validated. Both models were applied in a parallel virtual screening campaign to repurpose known drugs as anti-trypanosomal compounds inhibiting polyamine transport in T. cruzi. Montelukast, Quinestrol, Danazol, and Dutasteride were selected for in vitro testing, and all of them inhibited putrescine uptake in biochemical assays, confirming the predictive ability of the computational models. Furthermore, all the confirmed hits proved to inhibit epimastigote proliferation, and Quinestrol and Danazol were able to inhibit, in the low micromolar range, the viability of trypomastigotes and the intracellular growth of amastigotes.

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Acknowledgements

L. Alberca, M. L. Sbaraglini, L. Fraccarolli, C. Alba Soto, C. Carrillo, L. Gavernet, and A. Talevi are members of Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina (CONICET). M. A. Llanos, M. D. Ruiz, C. Miranda, and A. Pino-Martinez are fellowship holders of CONICET.

Funding

We thank Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación (PICT 2017-0643 and PICT 2018-01124), Universidad Nacional de La Plata (Incentivos UNLP X785 and PPID program X043) and Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina PIP2015-0733.

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Authors and Affiliations

  1. Departamento de Ciencias Biológicas and Laboratorio de Investigación y Desarrollo de Bioactivos (LIDeB), Facultad de Ciencias Exactas, Universidad Nacional de La Plata. La Plata (B1900ADU), Buenos Aires, Argentina

    Manuel A. Llanos, Lucas N. Alberca, María L. Sbaraglini, Luciana Gavernet & Alan Talevi

  2. Laboratorio de Biología Molecular y Bioquímica en Trypanosoma cruzi y otros agentes infecciosos, Instituto de Ciencia y Tecnología (ICT) Milstein - Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Buenos Aires, Argentina

    María D. Ruiz, Laura Fraccaroli & Carolina Carrillo

  3. Departamento de Microbiología, Parasitología e Inmunología, Facultad de Medicina, Instituto de Investigaciones en Microbiología y Parasitología Médica (IMPaM, UBA-CONICET), Universidad de Buenos Aires., Buenos Aires, Argentina

    Cristian Miranda, Agustina Pino-Martinez & Catalina D. Alba Soto

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  1. Manuel A. Llanos
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  2. Lucas N. Alberca
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  4. María L. Sbaraglini
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  8. Carolina Carrillo
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  9. Catalina D. Alba Soto
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  10. Luciana Gavernet
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  11. Alan Talevi
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Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by MAL, LNA, MDR, MLS, CM, APM and LF under the supervision of LG, CC, CDAS, and AT. The first draft of the manuscript was written by LG, CC, CDAS, AT, and MAL and then all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Luciana Gavernet.

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Llanos, M.A., Alberca, L.N., Ruiz, M.D. et al. A combined ligand and target-based virtual screening strategy to repurpose drugs as putrescine uptake inhibitors with trypanocidal activity. J Comput Aided Mol Des 37, 75–90 (2023). https://doi.org/10.1007/s10822-022-00491-0

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