Personalised Transdermal Therapy for Chronic Pain with Digital Twin Technology

Area of Science:

• Biomedical Engineering
• Pharmacology
• Computational Medicine

Background:

• Chronic pain affects millions globally, necessitating advanced treatment strategies.
• Current transdermal drug delivery systems (TDDS) often lack personalization, leading to suboptimal efficacy and side effects.
• Digital twin technology offers a novel paradigm for tailored therapeutic interventions.

Purpose of the Study:

• To explore the application of digital twin technology in enhancing transdermal drug delivery for chronic pain management.
• To investigate how patient-specific computational models can optimize drug absorption, patch application, and dosage.
• To assess the potential of digital twins in improving therapeutic outcomes and minimizing risks associated with chronic pain treatment.

Main Methods:

• Development of patient-specific computational models integrating physiological data.
• Simulation of drug absorption, distribution, metabolism, and excretion (ADME) using digital twins.
• Real-time monitoring and adjustment of transdermal drug delivery parameters based on simulation outputs.

Main Results:

• Digital twins enable precise, individualized transdermal drug delivery, optimizing fentanyl patch use and site-specific delivery.
• Simulations facilitate real-time adjustments to dosage and patch application, enhancing therapeutic efficacy.
• Thermally controlled systems integrated with digital twins show promise for improved drug release profiles.

Conclusions:

• Digital twin technology presents a significant advancement for personalized transdermal drug delivery in chronic pain management.
• Addressing challenges like data security, ethical concerns, and standardization is crucial for widespread adoption.
• Future integration with AI and IoT will further refine digital twin applications in precision medicine for pain relief.