P.S.V College of Pharmaceutical Science and Research, Krishnagiri - 635108.
Digital Twin (DT) technology, introduced by Michael Grieves in 2002, creates dynamic virtual models of patients by integrating data from electronic health records, medical imaging, wearable sensors, genomics, and real-time physiological monitoring. Using artificial intelligence, machine learning, physics-based modeling, and big data analytics, DTs simulate disease progression, predict treatment responses, and support clinical decision-making. In healthcare, they enable personalized medicine, remote patient monitoring, and improved treatment planning for conditions such as cardiovascular diseases, cancer, orthopedic disorders, and type 1 diabetes. In drug development, DTs support clinical trial design, enable in-silico trials, and reduce costs and recruitment challenges. However, issues like data privacy, validation, ethical concerns, technological limitations, and regulatory uncertainty remain challenges. Overall, digital twin technology has strong potential to advance predictive, preventive, and personalized healthcare.
Patients frequently recall their medical history, allergies, current and previous medications, and other details to several persons during their interactions with healthcare providers. For individuals with several chronic illnesses, as well as for their families and caregivers, repetition can be draining. The implementation of electronic health records (EHRs) intends to improve patient happiness, safety, and continuity of care by better capturing this data.A VT is a physiologically based (PB) human model that is created by "virtualizing" a base model with the distinct genetic, molecular, physiological, and pathological features of each individual. It serves as the centre of VTHM and lets the user see the body and its health information. It is utilized in the pharmaceutical industry to simulate drug pharmacokinetics (±pharmacodynamics)It is mainly used in MIPD tool in clinical practice;
MIPD tool in clinical practiceMIPD tools in clinical practice are essential for optimizing drug dosing based on individual patient characteristics. These tools utilize pharmacometric models and computer software to determine the best dosage regimen for a patient, aiming for maximum efficacy and minimal toxicity. MIPD is particularly beneficial for drugs with narrow therapeutic indices and for patients with unique characteristics, such as children, the elderly, or those with comorbidities[1]
Methods of Digital Twin Technology in Healthcare
Digital Twin Technology in healthcare encompasses a range of methods and applications, including:
3. ROLES
4. OPTIMIZATION
Dose Optimization in Cancer:
Digital twins help optimize immunotherapy dosing in cancer by simulating patient-specific tumor–immune interactions using AI and computational models. Unlike fixed dosing regimens, DTs consider patient heterogeneity, tumor dynamics, and treatment resistance to predict responses and design personalized dosing schedules that maximize immune activation while minimizing toxicity.
Dose Optimization in Type 1 Diabetes:
In type 1 diabetes, digital twins model an individual patient’s glucose–insulin dynamics using wearable data and mathematical models. These virtual models allow personalized insulin dosing and automatic adjustments based on changing physiology. Studies, such as the 2025 randomized clinical trial by Carlos E. Builes-Montaño et al. in Scientific Reports, show that digital twin–based decision support systems improve glucose time-in-range.
Digital Clinical Trials and Drug Development:
Digital twins create virtual patient populations representing different ages, genetics, comorbidities, and sexes. These models allow researchers to simulate treatment responses, evaluate drug safety and efficacy, and optimize clinical trial design (e.g., recruitment, endpoints, and sample size). This approach can reduce costs, shorten development time, and improve the reliability of trial outcomes in personalized medicine.[3]
5. CLINICAL TRAILS
Digital twin (DT) technology is transforming clinical trials in pharmacotherapy by improving trial design, efficiency, and predictive accuracy. DTs use patient-specific data to simulate treatment responses, allowing more precise trials with fewer participants and better prediction of drug efficacy and safety.
Key benefits include:
Enhanced trial precision: Patient-level predictions improve trial accuracy and reduce the number of participants required.
Reduced control groups: DTs simulate outcomes under standard care, lowering the need for large control groups.
Personalized treatment plans: Patient-specific models help design safer and more effective therapies.
Predictive insights: Simulations of drug interactions and responses optimize trial design and development strategies.
Clinical trials often face challenges such as high costs, recruitment difficulties, and delays, especially with personalized medicine targeting smaller populations. Digital twins can help address these issues by enabling smaller trials with greater statistical power and supporting studies affected by low recruitment or high dropout rates.
DT technology is already being explored by companies like Unlearn AI to accelerate research in multiple sclerosis and Alzheimer’s disease. Additionally, in-silico trials—fully digital trials using simulated patients—can evaluate treatments without relying entirely on real participants. For example, the Virtual Imaging Clinical Trial for Regulatory Evaluation used 2,986 virtual patients to study breast lesion detection and produced results consistent with a clinical trial involving 400 women, demonstrating the reliability of this approach.[4]
6.APPLICATIONS
Application in Disease Management (Short Form):
Digital twin (DT) technology is increasingly used in disease management by creating virtual models of organs or patients to support diagnosis, treatment planning, and outcome prediction.
• Cardiovascular System:
DT heart models help in the accurate management of cardiovascular diseases. For example, Philips HeartNavigator integrates CT images to create a 3D model of a patient’s heart, providing real-time guidance during cardiac procedures and helping surgeons select appropriate devices and plan surgeries. Technologies such as HeartFlow Analysis also simulate blood flow and coronary disease to support diagnosis and improve patient outcomes.
• Surgery:
In surgical practice, DTs create patient-specific anatomical models that allow multidisciplinary teams to plan procedures and avoid structural damage. Simulations have been tested in neurosurgery, vascular surgery, and interventional radiology. Studies have also used DT models to assess carotid stenosis severity and predict rupture risk in intracranial aneurysms using CT imaging, computational fluid dynamics, and clinical data.
• Orthopaedics:
In orthopaedics, DTs combine physics-based and data-driven models to study bone and joint biomechanics. Applications include trauma management using agent-based DT systems and digital models of the lumbar spine that monitor motion and biomechanical properties in real time. AI- and CT-based DT models also improve anatomical landmark identification and joint alignment assessment, increasing surgical accuracy.[5]
Role of Digital Twins in Personalized Medicine (Short Form):
• Enhanced Understanding of Patient Health:
Digital twins provide a detailed and comprehensive view of a patient’s health by integrating data from multiple sources such as physiological measurements, lifestyle factors, and genetic information. This integrated analysis helps healthcare professionals design personalized treatment plans tailored to each patient’s specific needs, ultimately improving clinical outcomes.
• Predictive Modeling and Risk Assessment:
Using advanced algorithms and machine learning, digital twins enable predictive modeling and risk assessment. By combining real-time patient data with data from similar cases, digital twins can predict how patients may respond to different treatments. This supports informed clinical decision-making, improves treatment success rates, and reduces trial-and-error approaches.
• Digital Clinical Trials:
Digital trials using digital twins reduce the need for physical infrastructure, lower administrative workload, and support remote patient monitoring, making clinical research more efficient and accessible.[6]
CONCLUSION
Digital twin technology is a transformative innovation in pharmacotherapy that creates dynamic, data-driven virtual models of individual patients. These models integrate clinical data, genetic information, lifestyle factors, and real-time physiological data to simulate drug responses, optimize dosing, and predict treatment outcomes before therapy begins.
In pharmacotherapy, digital twins support precision medicine by enabling personalized drug selection and dosage based on each patient’s biological profile. They also improve safety by predicting adverse drug reactions, reducing trial-and-error prescribing, and aiding decision-making in complex or chronic conditions.
Additionally, digital twins accelerate drug development by simulating clinical trials, identifying suitable patient populations, and reducing the cost and time required for traditional studies. Overall, this technology has the potential to enhance treatment effectiveness, minimize side effects, and advance personalized healthcare, shifting pharmacotherapy toward a predictive, preventive, and personalized approach.
REFERENCES
A. Mumtaj Begam, D. Aruna, S. Kokila, J. Maheswari, K. Maadevan, V. Tamilselvan, To Assessment the Digital Twin Technology in Pharmacotherapy, Int. J. of Pharm. Sci., 2026, Vol 4, Issue 3, 3182-3187, https://doi.org/10.5281/zenodo.19228928
10.5281/zenodo.19228928
