AI-Driven Pharmacovigilance and Molecular Profiling of Fluoroquinolone-Associated Cardiotoxicity in the UAE: A Geospatial and Machine Learning Analysis with Structural Modification Strategies (2018-2023)

Background: Fluoroquinolones, while clinically indispensable, carry underappreciated cardiovascular risks, particularly QT prolongation and life-threatening arrhythmias. Emerging evidence suggests geographic and genetic variations in susceptibility, yet Middle Eastern populations remain underrepresented in global pharmacovigilance datasets.Objective: This study investigates the prescribing trends and awareness of fluoroquinolone-related adverse effects among healthcare providers in the UAE using a multimodal combination of artificial intelligence (AI) integrating pharmacovigilance data, environmental exposure mapping, predictive ECG analytics and natural language (NLP) of electronic health records (EHRs)Methods: We conducted a retrospective cohort study (2018–2023) combining structured ADR reports from UAE MOHAP, WHO-VigiAccess, FAERS, and EMA with unstructured clinical narratives. A hybrid NLP pipeline (BioBERT-based NER, sentiment analysis, and relationship extraction) identified unreported risk patterns. Machine learning (Random Forest, SVM, BioBERT-NLP) stratified high-risk cases, validated against MIMIC-IV ECG waveforms. Geospatial modeling correlated wastewater fluoroquinolone levels with regional arrhythmia incidence.Results: Among 1,522 adjudicated ADRs, moxifloxacin demonstrated the strongest cardiotoxicity signal (OR=1.45, 95% CI 1.2–1.8, *p*<0.001), with AI-ECG models detecting subclinical torsades de pointes at 96% sensitivity (AUC 0.97). NLP revealed significant ECG monitoring disparities in Northern Emirates (under documentation rate: 43%). Environmental analyses identified a dose-dependent relationship between moxifloxacin water contamination and arrhythmia hospitalizations (+22% in high-exposure regions, *p*=0.01). Molecular dynamics simulations implicated C7 substituent modification as a viable strategy to reduce hERG channel binding.Conclusion: We integrated multi-omics analysis with pharmacovigilance mining to stratify cardiotoxic risk among fluoroquinolone users in the UAE bridging pharmacovigilance, environmental epidemiology, and structural pharmacology. Our framework enables precision monitoring through AI-ECG integration, policy interventions targeting high-risk prescribing, and drug redesigning to mitigate hERG liability.