Conversational No-code, Multi-agentic Disease Module Identification and Drug Repurposing Prediction with ChatDRex
📝 Abstract
Repurposing approved drugs offers a time-efficient and cost-effective alternative to traditional drug development. However, in silico prediction of repurposing candidates is challenging and requires the effective collaboration of specialists in various fields, including pharmacology, medicine, biology, and bioinformatics. Fragmented, specialized algorithms and tools often address only narrow aspects of the overall problem. Heterogeneous, unstructured data landscapes require the expertise of specialized users. Hence, these data services do not integrate smoothly across workflows. With ChatDRex, we present a conversation-based, multi-agent system that facilitates the execution of complex bioinformatic analyses aiming for network-based drug repurposing prediction. It builds on the integrated systems medicine knowledge graph (NeDRex KG). ChatDRex provides natural language access to its extensive biomedical knowledge base. It integrates bioinformatics agents for network analysis, literature mining, and drug repurposing. These are complemented by agents that evaluate functional coherence for in silico validation. Its flexible multi-agent design assigns specific tasks to specialized agents, including query routing, data retrieval, algorithm execution, and result visualization. A dedicated reasoning module keeps the user in the loop and allows for hallucination detection. By enabling physicians and researchers without computer science expertise to control complex analyses with natural language, ChatDRex democratizes access to bioinformatics as an important resource for drug repurposing. It enables clinical experts to generate hypotheses and explore drug repurposing opportunities, ultimately accelerating the discovery of novel therapies and advancing personalized medicine and translational research. ChatDRex is publicly available at apps.cosy.bio/chatdrex.
💡 Analysis
Repurposing approved drugs offers a time-efficient and cost-effective alternative to traditional drug development. However, in silico prediction of repurposing candidates is challenging and requires the effective collaboration of specialists in various fields, including pharmacology, medicine, biology, and bioinformatics. Fragmented, specialized algorithms and tools often address only narrow aspects of the overall problem. Heterogeneous, unstructured data landscapes require the expertise of specialized users. Hence, these data services do not integrate smoothly across workflows. With ChatDRex, we present a conversation-based, multi-agent system that facilitates the execution of complex bioinformatic analyses aiming for network-based drug repurposing prediction. It builds on the integrated systems medicine knowledge graph (NeDRex KG). ChatDRex provides natural language access to its extensive biomedical knowledge base. It integrates bioinformatics agents for network analysis, literature mining, and drug repurposing. These are complemented by agents that evaluate functional coherence for in silico validation. Its flexible multi-agent design assigns specific tasks to specialized agents, including query routing, data retrieval, algorithm execution, and result visualization. A dedicated reasoning module keeps the user in the loop and allows for hallucination detection. By enabling physicians and researchers without computer science expertise to control complex analyses with natural language, ChatDRex democratizes access to bioinformatics as an important resource for drug repurposing. It enables clinical experts to generate hypotheses and explore drug repurposing opportunities, ultimately accelerating the discovery of novel therapies and advancing personalized medicine and translational research. ChatDRex is publicly available at apps.cosy.bio/chatdrex.
📄 Content
The development of novel pharmaceuticals requires substantial time and financial investments. Additionally, it is often accompanied by a high risk of failure in the late stages of clinical trials [1][2][3]. To address these challenges, there is an increasing emphasis on repurposing already formulated drugs for novel therapeutic applications. This approach utilizes comprehensive pharmacological, clinical, and genomic data to identify potential new indications for existing drugs [1,2]. This approach not only significantly accelerates the development process but also reduces the risk of clinical failures, as the safety and tolerability of the substances are already established [1,2].
In particular, contemporary computational methodologies, exemplified by systems medicine, have emerged as instrumental in identifying promising candidates, thereby expediting the transition from discovery to clinical application [4]. Networks facilitate the analysis of the interrelationships between genes, drugs, proteins, and diseases, enabling the identification of novel indications for existing pharmaceutical agents [5]. This has already been demonstrated in Alzheimer’s disease and cancer [6,7].
A generic workflow for network-driven drug repurposing was established within the NeDRex project [8]. Central to this methodology are concepts such as seed genes, which serve as the starting point for drug repurposing queries, typically genes known to be implicated in a specific disease. Disease modules are clusters of interconnected genes contributing to the manifestation of a disease phenotype. For example, in Huntington’s disease, the HTT gene can be regarded as the main disease-causing gene, and the associated network consisting of genes involved in neuronal damage forms the Huntington’s disease module. Disease module identification algorithms, such as DIAMOnD (DIseAse MOdule Detection), are used to extract these modules from complex biological networks [9]. Once a disease module is identified, drug prioritization algorithms rank drugs that may potentially target the identified module, thereby allowing researchers to focus on the most promising candidates for repurposing [10]. These algorithms utilize network topology, often through proximity measures, to determine the association between a drug target and the disease module, providing insights into the drug’s potential efficacy [10].
Nevertheless, the field faces considerable challenges due to the substantial and intricate nature of biomedical information that must be examined to identify drug candidates with potential for novel indications. The NeDRex (Network-based Drug Repurposing and Exploration) platform, primarily composed of the NeDRex KG and the NeDRexAPI, was developed to address the identified challenges by providing a comprehensive suite of tools for network-based drug repurposing [8]. Utilizing these advanced tools requires a substantial degree of bioinformatics and programming expertise, which poses a significant hurdle for many clinicians despite their expertise [11]. Consequently, there is a pressing need for more accessible and user-friendly interfaces to bridge the gap between complex bioinformatics tools and the researchers and clinicians who could benefit most from their insights, ultimately accelerating the development of new treatments and personalized medicine approaches [12,13].
Large language models (LLMs) represent a transformative advance in this regard, offering unprecedented capabilities in natural language processing, including question answering and even medical reasoning [14,15]. The success of these models can be attributed to their extensive training on vast quantities of text data, which has enabled them to comprehend and generate text that closely resembles human language [15]. These models have revolutionized biomedical data analysis by providing powerful text-understanding capabilities [16][17][18]. However, their generalized nature often limits their effectiveness in specialized biomedical domains [19,20]. Additionally, it has been observed that LLMs tend to generate hallucinations, producing plausible yet erroneous or fallacious information [21,22]. This phenomenon poses a significant challenge in high-risk domains such as biomedicine. The unreliability of LLMs underscores the necessity for domain-specific adaptations and rigorous validation when employing LLMs in biomedical research and clinical decision-making processes [21,22]. To overcome this limitation, Retrieval-Augmented Generation (RAG) and in-context learning (ICL) techniques promise to enhance LLMs by incorporating external knowledge retrieval, thereby improving the accuracy and relevance of responses in specialized fields [15,23]. LLMs can further be organized as agents, which are autonomous entities designed to perform specific tasks. Each agent operates without supervision, allowing them to pursue individual objectives and implement different strategies, including analyzing e
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