Computational Heterogeneous-Response Pharmacology (CHRP): A Formalized Framework for Embodied Pathology-Guided Individualized Drug Verification 计算异质性响应药理学验证理论(CHRP):基于具身病理学思想的个体用药显性验证形式化框架
Contemporary pharmacology remains anchored to a population-centric epistemology, wherein the "average patient" derived from randomized controlled trials (RCTs) serves as the gold standard for dosing and therapeutic decision-making. This framework systematically marginalizes individual heterogeneity in drug response, treating deviation from the mean as statistical noise rather than ontologically significant signal. This manuscript presents the **Computational Heterogeneous-Response Pharmacology (CHRP)** framework, a formalized theoretical architecture for individualized drug verification grounded in the philosophy of **embodied pathology**. CHRP advances three core axioms: (1) individual pharmacological heterogeneity constitutes an ontological reality, not merely measurement error; (2) patient-reported outcomes (PROs) possess epistemic parity with objective biomarkers; and (3) individual drug responses are computationally predictable through multi-scale modeling. From these axioms, CHRP derives a **three-level verification closure**: micro-level digital twin calibration with micro-dosing; meso-level N-of-1 adaptive trials with Bayesian response algorithms; and macro-level federated learning regulatory networks that preserve data sovereignty. The framework distinguishes contextual implementation pathways: developed economies may leverage full digital twin infrastructure and electronic health record integration, while developing regions can deploy lightweight federated learning nodes without raw data leakage. CHRP thus claims **global explanatory universality** at the axiomatic level while demanding **contextual adaptation** at the implementation level. **Current Status Declaration:** This manuscript presents a completed theoretical framework with formalized axioms, mechanisms, and falsifiable propositions. Large-scale multi-center N-of-1 clinical trials to empirically validate its core hypotheses are pending. CHRP is offered as a candidate paradigm for global academic discussion, falsification, and iterative refinement. **Disclaimer:** This theoretical framework does not constitute clinical guidance. All therapeutic decisions must comply with local regulatory requirements and established clinical standards. 当代药理学仍深植于一种以群体为中心的认识论:随机对照试验(RCT)所推导出的"平均患者",是剂量确定与治疗决策的黄金标准。这一框架系统性地边缘化了药物反应的个体异质性,将偏离均值的现象视为统计噪声,而非具有本体论意义的信号。 本文提出**计算异质性响应药理学验证理论(CHRP)**,一个基于**具身病理学**哲学思想的、用于个体用药验证的形式化理论架构。CHRP 提出三条核心公理:(1)个体药理学异质性构成本体论层面的实在,而非单纯的测量误差;(2)患者报告结局(PROs)与客观生物标志物具有同等认识论地位;(3)个体药物反应可通过多尺度建模进行计算预测。基于这三条公理,CHRP 推导出**三级验证闭合机制**:微观层面的数字孪生校准与微剂量探测;中观层面的 N-of-1 自适应试验与贝叶斯响应算法;以及宏观层面的联邦学习监管网络,在保护数据主权的前提下实现跨机构知识聚合。 该框架区分了情境化的实施路径:发达经济体可利用完整的数字孪生基础设施与电子健康档案整合;发展中地区则可部署轻量级联邦学习节点,无需原始数据泄露即可参与全球网络。因此,CHRP 在公理层面主张**全球普适解释力**,在实施层面要求**情境化适配**。 **当前状态声明:** 本文呈现的是一个理论框架已完成的文稿,包含形式化公理、机制设计与可证伪命题。验证其核心假设的大规模多中心 N-of-1 临床试验尚待开展。CHRP 作为候选范式,供全球学术界讨论、证伪与迭代完善。 **免责声明:** 本理论框架不构成临床指导。所有治疗决策须遵循当地监管要求与既定临床标准。