AI-Driven miRNA Target Identification Combined with Nanocarrier-Based Therapy Enhances Amastigote Clearance in Canine Visceral Leishmaniasis: An Integrated In Silico and Ex Vivo Study

Study rationale and knowledge gap: Canine visceral leishmaniasis is a major zoonotic disease in which intracellular Leishmania infantum persists within macrophages, while treatment limitations and emerging resistance highlight the need for improved therapeutic strategies. MicroRNA-mediated regulation of host immune responses and nanoparticle-based drug delivery offer complementary approaches, but their integration with artificial intelligence (AI)-assisted target identification has not been adequately investigated in canine visceral leishmaniasis. This study addressed this knowledge gap by integrating computational miRNA–gene prioritization with nanoparticle-mediated drug delivery and ex vivo validation in primary canine macrophages. Methods Publicly available transcriptomic datasets and experimentally supported miRNA–gene interactions were analysed using machine-learning approaches to prioritize candidate regulatory miRNAs. The selected candidates were evaluated in primary macrophages obtained from 16 dogs using miltefosine-loaded PLGA nanoparticles, miRNA modulation and combination treatment. Parasite burden, cell viability, cytokine responses and expression of selected immune-related genes were assessed during the experimental period. Results The computational framework identified miR-155 and miR-146a among the principal candidates for experimental investigation. The selected machine-learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.89 during cross-validation and 0.84 during transfer to the canine dataset. Experimentally, the combination of PLGA-miltefosine nanoparticles with miR-155 modulation produced the greatest reduction in intracellular parasite burden, reaching a 78% reduction in infection index at 48 h compared with infected untreated controls. The combination was also associated with increased TNF-α, IL-12 and iNOS expression and reduced IL-10, together with a favourable cytotoxicity profile under the conditions tested. Key findings: These findings provide ex vivo proof-of-concept evidence that AI-assisted miRNA prioritization can be integrated with nanoparticle-based miltefosine delivery and host-directed miRNA modulation to enhance intracellular L. infantum suppression in primary canine macrophages. The computationally predicted miRNA–gene interactions remain hypothesis-generating and require direct functional validation. Further studies incorporating target-level validation, nanoparticle uptake, broader immune profiling and in vivo evaluation are needed to determine the therapeutic relevance of this combined approach.

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