Recent advancements in vet informatics have underscored the value of integrating diverse data types—from genomic and proteomic to clinical and environmental data—to accelerate drug discovery processes. These integrated datasets provide a comprehensive view of disease mechanisms, enabling the identification of novel drug targets with greater efficiency than traditional methods. Machine learning (ML) and artificial intelligence (AI) are particularly powerful for analyzing complex datasets, uncovering patterns, and predicting outcomes (1). For example, a study using ML models showed improved accuracy in predicting drug efficacy across different animal species, reducing the time and cost of preclinical trials (2).

However, data integration faces challenges. Lack of standardized data collection and sharing practices across veterinary institutions results in inconsistencies, lowering the reliability and reproducibility of findings (3). Additionally, integrating disparate datasets requires significant computational resources and expertise, which are often unavailable in smaller or resource-limited veterinary practices. Ensuring data security is also paramount, as the use of electronic health records (EHRs) raises concerns about data breaches and the misuse of sensitive information. To address these challenges, establishing standardized data protocols and ethical guidelines is essential for broad adoption (4).

Personalized veterinary medicine, where treatment plans are tailored based on an individual animal's genetic makeup, lifestyle, and environment, has made significant strides with the rise of vet informatics. This approach has shown promise in improving therapeutic outcomes and minimizing adverse reactions, similar to trends in human precision medicine. For example, incorporating genetic data into treatment strategies for canine cancer has optimized dosing regimens and reduced side effects (5). Furthermore, personalized approaches are proving beneficial in managing chronic diseases in livestock, such as metabolic disorders in dairy cows.

Despite these advancements, challenges remain. A key obstacle is the limited availability of comprehensive genetic and genomic databases for many veterinary species. While projects like the Canine Genome Project have improved our understanding of dogs, efforts to map the genomes of other species, such as felines, livestock, and exotic animals, are still in their infancy. Economic constraints also hinder the widespread adoption of personalized veterinary medicine (6). Genetic testing, bioinformatics analysis, and individualized treatment plans can be prohibitively expensive, particularly for small or underserved practices. Expanding public-private partnerships and reducing the cost of genetic testing could make personalized veterinary medicine more accessible (7).

Vet informatics offers significant promise in drug repurposing, where existing medications are used for new veterinary applications. This approach is especially valuable given the high cost and regulatory barriers of developing new veterinary drugs. AI-based models, for instance, have been used to analyze existing pharmacological databases, identifying human drugs that could potentially treat diseases in animals (8). A notable example is the use of computational tools to repurpose antiviral drugs for treating respiratory conditions in livestock.

However, drug repurposing in veterinary medicine is constrained by several factors. The availability of pharmacological data for veterinary species is limited, and most existing databases focus on human medicine (9). Differences in drug metabolism between species mean that drugs effective in humans may not yield the same results in animals, necessitating rigorous preclinical and clinical trials. Collaborative efforts among researchers, regulators, and the pharmaceutical industry are essential for building comprehensive datasets and regulatory frameworks to accelerate drug repurposing (10).