AI-OPTIMIZED MARKET ACCESS: A BILATERAL FRAMEWORK FOR AFFORDABLE AND EQUITABLE DRUG PRICING IN EMERGING AND ADVANCED ECONOMIES

Abstract

This article proposes a bilateral, AI-optimized framework to achieve affordable and equitable drug pricing across advanced and emerging economies, using the United States and Uzbekistan as case studies. The approach integrates demand forecasting, value-based pricing, and income-adjusted tiered pricing into a single optimization routine that reconciles patient access with the sustainability of pharmaceutical innovation [1], [3]. We formalize a welfare function that values health outcomes in each country and incorporates payer budget constraints, while imposing a profit floor for manufacturers to maintain incentives to invest in R&D [4], [7]. An AI layer learns price–volume relationships from multi-source data including epidemiology, claims, registries, and macroeconomic indicators and searches for Pareto-efficient price pairs (P_US, P_UZ) subject to policy ceilings and equity thresholds [8], [12].

Scenario analysis (2025–2030) compares three regimes: (A) status quo with fragmented bargaining and limited access in emerging markets; (B) unilateral Most-Favored-Nation (MFN) policies; and (C) a bilateral AI-optimized agreement that includes non-reference safe harbors, volume guarantees, and outcomes-linked adjustments [15], [16]. We find that the bilateral model can reduce the effective U.S. price by ~20% while enabling an Uzbek price at ~10–20% of the U.S. level without meaningfully eroding aggregate manufacturer profit, due to scale effects and reduced uncertainty [18], [21]. The framework translates into actionable policy clauses and a governance blueprint, emphasizing transparency, algorithmic explainability, and protections against parallel trade. We argue that such bilateral pilots can seed a broader transition toward fair global pricing norms consistent with WHO guidance and the ethics of equitable access [40], [44].