Molecules to market: A probabilistic artificial intelligence framework for predicting managed care adoption at the level of drug discovery.

Abstract

Artificial intelligence (AI) is rapidly compressing the timeline from molecular discovery to regulatory submission, meaning pipeline therapies will reach formulary committees faster and in greater numbers than ever before. For managed care organizations (MCOs), coverage and contracting decisions must be made earlier, especially for drug classes where the clinical and economic profile remains deeply uncertain. Yet the pipeline monitoring frameworks MCOs rely on, that is, static cost-effectiveness models, manufacturer dossiers, and binary pipeline assumptions, may not keep pace, leaving teams to evaluate therapies with limited visibility into the upstream uncertainty that shapes a drug's real-world value. No current framework links early AI-generated predictions of molecular performance to factors like formulary placement, rebate dynamics, and payer decision-making, leaving MCOs reactive at precisely the moment when anticipatory intelligence would be most valuable. This article proposes a probabilistic AI framework that carries uncertainty from molecular discovery through human trials, health economics, and managed care decision-making. The framework preserves and updates uncertainty across each stage rather than simplifying it into deterministic outcomes. By integrating molecular modeling, Bayesian clinical forecasting, dynamic health economic modeling, and negotiation simulations, the framework can generate early forecasts of formulary placement, cost-effectiveness profile, and more. This article uses lesinurad as an illustrative case study of how drugs can clear every preclinical and regulatory hurdle yet still fail at the formulary-precisely the kind of late-stage surprise anticipatory intelligence is designed to prevent. The 4-layer framework can give MCOs earlier visibility into a therapy's likely clinical profile, cost-effectiveness distribution, and formulary placement probability before a manufacturer's dossier arrives. Such a framework allows budget forecasting and contracting strategy to keep pace with growing AI-accelerated pipelines.