A translational screening platform with AI integration for clinically predictive discovery of safe and effective macropinocytosis-dependent cancer therapeutics

Background: Macropinocytosis-dependent cancers rely on nutrient-scavenging pathways to sustain tumor growth under stress. Current therapies rarely provide durable control, underscoring the need for strategies that stabilize cancer progression as a chronic condition. Although extensive clinical data exist for approved drugs-linking mechanisms of action (MoAs), safety, and efficacy-clinically translatable, macropinocytosis-relevant cell-based screens remain limited. We developed an AI-driven translational discovery platform integrating phenotypic screening with clinical correlation to identify safe, mechanistically meaningful, and clinically predictive therapeutic candidates. Methods: An automated image-based cytometry assay was optimized to quantify macropinocytosis in cancer cells with minimal human bias. FDA-approved drugs and proprietary compounds were screened with this medium throughput method. AI algorithms correlated in vitro phenotypes with retrospective clinical data, integrating MoA, toxicity, and efficacy metrics. Mechanistic clustering identified shared therapeutic pathways among clinically effective agents. AI modeling also predicted clinical performance and generated rational combination hypotheses emphasizing safety and translational potential. Results: AI validation confirmed strong concordance between phenotypic results and clinical safety/efficacy profiles from 892 FDA-approved cancer and non-cancer drugs, demonstrating high translational fidelity. Mechanistic clustering revealed a convergent signaling axis associated with durable benefit and low toxicity. Several novel in-house compounds exhibited comparable inhibition profiles and AI-predicted clinical potential. AI modeling proposed synergistic combinations between approved and proprietary agents with underexplored mechanisms, prioritizing safe, durable therapeutic responses. Conclusions: This platform unites bias-free phenotypic assays with AI-guided clinical validation, enabling early identification of therapeutically meaningful and translatable drug candidates. Discovery of a shared mechanistic target and AI-predicted novel compounds demonstrates a clinically grounded, data-driven model for developing safe, effective, and chronic disease-oriented cancer therapies. Keywords: Phenotypic screening; macropinocytosis; AI drug discovery; translational oncology; clinical validation; mechanistic clustering; combination therapy; drug repurposing; safety profiling