Digital twins for personalized management of opioid use disorder: A future outlook

The opioid crisis, affecting over 16 million individuals globally and causing approximately 450,000 deaths annually, underscores the urgent need for personalized treatment approaches for opioid use disorder (OUD). Current treatments, such as medication-assisted therapy, suffer from low retention rates and variable responses due to individual differences.

This study proposes a digital twin framework, integrating genetic profiles, eg, OPRM1 variants, electronic health records, ecological momentary assessments, and real-time physiological data from wearables. Machine learning and pharmacokinetic modeling are employed to develop patient-specific models, with validation planned across short-term (2 years), medium-term (5 years), and long-term (10 years) phases. Proposed framework: The framework enables personalized dosing, relapse risk prediction, and tailored behavioral interventions by synthesizing multimodal data into dynamic virtual models. It leverages cloud-based platforms for scalability and complies with Health Insurance Portability and Accountability Act and General Data Protection Regulation for data privacy.

The framework addresses OUD treatment gaps by improving retention and reducing overdose risks, eg, 87,000 overdose deaths in the United States (US) in 2024. It supports public health by optimizing resource allocation for the 2 million prescription-related and 600,000 heroin-related OUD cases in the US. Challenges include data integration, clinical validation, and equitable access.

Digital twins offer a transformative approach to OUD management, with future research needed to validate and integrate this technology into healthcare systems.