Diego Oyarzún, d.oyarzun@ed.ac.uk
Shay Cohen, scohen@inf.ed.ac.uk
Project Description
Neglected Tropical Diseases (NTDs) are a diverse group of infectious diseases that primarily affect populations in low-income regions, particularly in tropical and subtropical climates. These diseases disproportionately affect poor and marginalized communities with limited access to adequate healthcare, sanitation, and clean water. Despite their significant impact on global health, these diseases often receive little attention from the pharmaceutical industry due to their unprofitability and lack of financial incentives for research and development for new therapies.
This project aims to address the urgent need for affordable therapies against NTDs, focusing specifically on Chagas disease and in collaboration with the Drugs for Neglected Diseases initiative (DNDi). Chagas is a potentially life-threatening illness caused by the protozoan parasite Trypanosoma cruzi. It primarily affects people in poor areas of Latin America, and increased population migration have carried Chagas disease to new regions such as the United States and European countries. The acute phase occurs shortly after infection and may exhibit mild symptoms or go unnoticed. However, if left untreated, the infection progresses to the chronic phase, which can last for years or even decades. During the chronic phase, the parasite can cause severe damage to the heart, digestive system, and other organs, leading to complications such as heart failure, arrhythmias, and other serious abnormalities.
The impact of Chagas disease on global health is significant, with an estimated 6-7 million people infected worldwide and approximately 10,000 deaths annually. There are only two drugs, benznidazole and nifurtimox, currently approved for use, but these have significant limitations, including high cost, lengthy treatment durations, and potential side effects.
The project aims at building a machine learning pipeline to identify chemical scaffolds with therapeutic potential and use this information to design compound libraries for further experimental screening by DNDi. The approach involves training binary classifiers of drug action using ensemble models and graph neural networks, as well as chemical large language models (LLMs) to screen compound libraries. We will utilize in-house screening data from DNDi, incorporating nearly 900,000 chemical structures with various readouts of drug effect. The size and coverage of the dataset makes it particularly suited for trialling cutting-edge deep learning and LLM tools on real-world data aimed at solving significant health challenges in low-income regions of the planet.
The project is part of an exciting partnership with DNDi, a leading not-for-profit research organization developing new treatments for neglected patients. DNDis mission is to discover, develop, and deliver new treatments for neglected patients around the world that are affordable and patient-friendly. Work by DNDi has already saved millions of lives through the development of affordable therapies for malaria, sleeping sickness and other tropical diseases of unmet need.
