Oluwaseun Emmanuel Agboola

Neglected tropical diseases (NTDs) continue to place a heavy burden on low- and middle-income countries. In these regions, drug development is often slow and expensive, and NTDs are rarely a priority due to limited financial incentives and complex science. This chapter looks at how combining drug repurposing and polypharmacology can offer faster and more affordable ways to discover treatments for NTDs. Using ideas from computational systems biology, we outline both a theoretical and practical approach to exploring lesser-known drug targets through multi-target strategies. We highlight modern computer-based tools, like network pharmacology, omics data analysis, and machine learning, that help identify new links between drugs and diseases in both humans and pathogens. We focus especially on in silico (computer-based) drug repurposing pipelines and how databases like DrugBank, ChEMBL, and SchistoDB can help match existing drugs to neglected diseases. Real-life examples, such as Miltefosine for leishmaniasis, the broad potential of Ivermectin, and using statins to treat schistosomiasis, show how targeting multiple disease pathways can be effective. The chapter also addresses challenges, such as gaps in our understanding of pathogens, drug delivery issues, and regulatory barriers, especially in settings with fewer resources. Looking ahead, we support moving toward more precise tropical medicine by combining genetic and biological data with AI tools, while keeping research ethically and inclusive of local communities. In summary, the chapter shows how drug repurposing can be a powerful and fair way to improve treatment options for neglected diseases.

This chapter looks at the growing importance of the environment in the process of making bioactive compounds useful in medicine. In the past, drug discovery often ignored the environment or saw it as unimportant. However, here, we suggest a new approach that considers both nonliving (abiotic) and living (biotic) environmental factors, such as climate, soil type, microbes, and ecosystems, when planning how to create these compounds. We introduce the idea of environmental fingerprinting, which involves identifying the unique environmental features that influence how and where certain bioactive compounds are produced. These features help scientists decide which compounds to focus on. By using advanced tools such as artificial intelligence (AI), machine learning, and metabolomics, we show how environmental data can improve the search for new medicines by helping predict how well a compound will work and how active it will be in the body. The chapter shares real-life examples, such as desert plants that produce helpful chemicals during droughts and marine bacteria that produce antibiotics in saltwater. These examples show how environmental conditions affect both the variety and power of natural compounds. This new way of thinking could greatly improve how we find new drugs, especially by helping us discover compounds suited to specific environments. The chapter also discusses the challenges of combining environmental data with computer models, such as missing data, ethical concerns, and the need for scientists from different fields to work together. By shifting the paradigm of retrosynthesis toward an environment-driven approach, the chapter paves the way for more efficient and ecologically informed therapeutic innovation.

1. The Tox21 program assembled high-throughput screening data for more than 10,000 compounds across 12 toxicity-relevant assays covering nuclear receptor signaling and cellular stress response path...