Personalised healthcare revolution: The rise of digital twins in medicine
The trajectory of medicine is being redefined by pioneering research into computational models, advancing towards a future where medical treatments are tailored not to the average patient, but to each individual. Envision possessing a ‘digital twin’—a virtual counterpart that can undergo trials and treatments, sparing you the need for direct medication or surgical intervention. Scientists project that within the next decade, we could witness the routine use of ‘in silico’ trials, utilising virtual organs to evaluate drug safety and effectiveness, while bespoke organ models might be employed to customise patient care and avert medical complications.
Digital twins represent sophisticated computer-generated replicas of physical entities or processes, continuously refined with data from their actual counterparts. In the medical realm, this entails the fusion of extensive biological data—including genetic, proteomic, cellular, and systemic information—with individual patient data to craft detailed virtual models of their organs, and potentially, in time, their entire body.
Professor Peter Coveney, Director of the Centre for Computational Science at University College London and co-author of ‘Virtual You’, suggests that much of current medical practice lacks a scientific underpinning. He compares it to navigating by looking in the rear-view mirror—basing treatment for the patient at hand on historical cases. “A digital twin utilises your own data within a model that encapsulates your unique physiology and pathology. It’s a move away from decisions based on potentially unrepresentative population data to truly personalised medicine,” explains Prof. Coveney.
Cardiology is at the forefront of this cutting-edge model. Companies are already harnessing patient-specific heart models to aid in the design of medical devices. Meanwhile, the Barcelona-based enterprise ELEM BioTech is at the forefront, granting companies the capability to test drugs and devices on simulated human hearts. “We’ve conducted numerous virtual human trials on several compounds and are on the cusp of launching a new phase, with our cloud-ready product accessible to pharmaceutical clients,” shares Chris Morton, co-founder and CEO of ELEM.
At the recent Digital Twins conference hosted by the Royal Society of Medicine in London, Dr. Caroline Roney from Queen Mary University of London detailed the development of tailored heart models which could significantly aid surgeons in planning interventions for atrial fibrillation patients. “Surgeons typically resort to average-based approaches, but crafting patient-specific predictions that forecast long-term outcomes remains a formidable challenge,” Dr. Roney stated. She foresees widespread application of this technology in cardiovascular treatments, including decisions on valve selection and placement during replacements.
The field of oncology is also poised to benefit from digital twins. Teams from GSK and King’s College London are joining forces to construct virtual duplicates of patient tumours, amalgamating imaging, genetic, and molecular data with 3D cultures of cancer cells, and observing their drug responses. Leveraging machine learning, researchers can foresee how individual patients may react to various treatments, drug combinations, and dosages. “Conducting repetitive trials on a real patient with multiple treatments isn’t viable. Our aim is to devise a strategy while the patient is still with us, preparing us for any recurrence of cancer,” said Professor Tony Ng from King’s College.
The advent of digital twins extends even to the realm of pregnancy, offering the potential to develop treatments for conditions such as placental insufficiency or pre-eclampsia, and deepening our grasp of pregnancy and labour physiology. Professor Michelle Oyen, Director of the Center for Women’s Health Engineering at Washington University in St Louis, is crafting placenta models from ultrasound scans and post-birth high-resolution imagery to predict complications during pregnancy. “We’re striving to identify measures in a live person that could forewarn us of placental issues, aiming to preempt adverse outcomes like stillbirth,” Prof. Oyen elucidates.
In collaboration, Professor Kristin Myers from Columbia University is modelling the cervix, uterus, and foetal membranes, with the overarching goal to merge these into a comprehensive individual model to predict pregnancy outcomes. “We hope to analyse a simple ultrasound scan to understand how the uterus will adapt and when labour might occur,” Prof. Myers aspires, potentially guiding decisions on interventions like caesarean sections.
Moreover, the concept of digital twins is being expanded to model entire hospitals to enhance patient flow and healthcare system efficiency. Dr. Jacob Koris, a trauma and orthopaedic surgeon and digital lead at Getting It Right First Time, describes how tracking digital footprints left by patient interactions—from X-rays to outpatient appointments—can provide a granular, real-time view of patient treatment pathways. “Such insights could pinpoint areas for improvement and exemplary practices that could revolutionise patient care,” Dr. Koris believes.
This ambitious step forward in computational medicine promises a leap from the traditional, one-size-fits-all model to a future where every treatment is as unique as the patient it serves.