Version 1
: Received: 23 October 2024 / Approved: 23 October 2024 / Online: 24 October 2024 (03:03:16 CEST)
How to cite:
Montgomery, R. M. Leveraging Artificial Intelligence to Identify Therapeutic Pathways in Trypanosoma cruzi: A Comparative Approach with Trypanosoma brucei. Preprints2024, 2024101782. https://doi.org/10.20944/preprints202410.1782.v1
Montgomery, R. M. Leveraging Artificial Intelligence to Identify Therapeutic Pathways in Trypanosoma cruzi: A Comparative Approach with Trypanosoma brucei. Preprints 2024, 2024101782. https://doi.org/10.20944/preprints202410.1782.v1
Montgomery, R. M. Leveraging Artificial Intelligence to Identify Therapeutic Pathways in Trypanosoma cruzi: A Comparative Approach with Trypanosoma brucei. Preprints2024, 2024101782. https://doi.org/10.20944/preprints202410.1782.v1
APA Style
Montgomery, R. M. (2024). Leveraging Artificial Intelligence to Identify Therapeutic Pathways in <em>Trypanosoma cruzi</em>: A Comparative Approach with <em>Trypanosoma brucei</em>. Preprints. https://doi.org/10.20944/preprints202410.1782.v1
Chicago/Turabian Style
Montgomery, R. M. 2024 "Leveraging Artificial Intelligence to Identify Therapeutic Pathways in <em>Trypanosoma cruzi</em>: A Comparative Approach with <em>Trypanosoma brucei</em>" Preprints. https://doi.org/10.20944/preprints202410.1782.v1
Abstract
Chagas disease, caused by Trypanosoma cruzi, presents a significant challenge in global health due to the parasite's complex life cycle and its persistence in human tissues. In contrast, advances in treating Trypanosoma brucei infections, notably through the use of irreversible enzyme inhibitors like eflornithine, offer valuable insights into therapeutic strategies. This article explores how artificial intelligence (AI) can aid in identifying crucial metabolic and biochemical pathways in T. cruzi that could serve as targets for irreversible enzymatic inhibitors, drawing parallels to the successful inhibition of T. brucei’s ornithine decarboxylase. By employing AI for data mining, drug-target interaction prediction, pathway modelling, and drug repurposing, researchers can accelerate the discovery of novel treatments. We compare the biological and biochemical differences between T. cruzi and T. brucei, highlighting how AI can bridge the gap in drug discovery and offer new possibilities for Chagas disease treatment.
Keywords
Trypanosoma cruzi; Trypanosoma brucei; Chagas disease; African sleeping sickness; AI drug discovery; irreversible enzyme inhibitors; pathway modelling; drug repurposing; eflornithine
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
