The DPTO Framework: A Unified, Quantitative, and Ethically Governed Systems-Medicine Model for Predictive, Personalized, and Sustainable Healthcare

The prevention, diagnosis, and treatment of complex human diseases require an integrative, quantitatively rigorous framework that unifies molecular biology, systems physiology, nutrition science, pharmacology, immunology, regenerative medicine, medical imaging, and clinical decision science. Traditional reductionist approaches—while successful for isolated mechanisms—often fail to capture nonlinear interactions among biological systems, environmental exposures, therapeutic interventions, and temporal disease evolution.

This work presents a fully formalized, mathematically defined, and clinically grounded systems-medicine framework and aligned with contemporary standards of evidence-based medicine and validated models from pathology, microbiology, virology, nutrition epidemiology, pharmacokinetics–pharmacodynamics (PK/PD), transplantation biology, tissue engineering, and artificial-intelligence–assisted clinical analytics.

At the core of the framework is the Disease–Patient–Therapy–Outcome (DPTO) model, a multivariate, time-dependent system linking measurable clinical biomarkers, immune competence, genetic and epigenetic variation, nutritional status, environmental risk factors, and therapeutic interventions to clinically relevant outcomes such as survival probability, disease remission, functional recovery, quality-adjusted life years (QALYs), and adverse-event risk. The model is explicitly parameterized using real-world clinical data and supports probabilistic inference, uncertainty quantification, and personalized treatment optimization.

The framework integrates:

• nutrition-based disease prevention and risk modification,
• pharmacological and radiotherapeutic treatment modeling,
• medical imaging and biomarker-driven diagnostics,
• regenerative medicine including stem-cell therapy and tissue engineering,
• organ transplantation and emerging xenotransplantation research under strict biosafety and ethical constraints,
• and artificial-intelligence–based clinical decision support systems.

Ethical governance, regulatory compliance, biosafety limits, and human-subject protection are embedded as formal constraints within the model. The resulting formulation establishes a globally acceptable, reproducible, and testable foundation for precision medicine, clinical decision support, and public-health planning across diverse disease domains.

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