Imagine the worst has happened: you've contracted a rare yet deadly disease, the treatment for which is high-risk, and harrowing. But what if you could clone yourself, and let the doctors work out the guesswork on a non-conscious carbon copy of your body? It turns out a new field is doing something much like this, but instead of a physical clone, the other you exists on a computer.
Researchers are using computer and data sciences to develop a "digital twin" of individual patients, on which sensitive therapies involving trial-and-error dosages could be tested, before the flesh-and-blood patient is subjected to the real deal, according to a recent blog post from Empa, a research institute of the ETH Domain.
A digital twin can help predict side effects in patients from drugs
Substantial advances in modern medicine have enabled cutting-edge medical facilities to provide patients with an enhanced quality of life, even when severe illnesses strike. For example, synthetic opiates can help control severe pain experienced from cancer. But the exact dosage remains a challenge. Painkillers like fentanyl can seriously take the edge off of acute pain, but if the dosage isn't extremely precise, it can harm patients with life-threatening side effects. Today, such painkillers can be applied to patients through the skin via a drug patch, which is a way of giving patients' bodies a soft introduction to the drug without interfering with daily life.
However, it can take time to find the right dosage for each patient, forcing medical professionals to practice trial-and-error on individual cases, which sometimes risks under- or overdoses no one can observe until reactions are already underway, long after the drug has been fully administered. But, at least at Empa, this is about to change forever. To guarantee patients receive the correct drug dosage specific to individual needs, Empa researchers have joined with a team from the University of Bern to develop a digital twin of a human body, with which doctors and scientists may test potential treatments to see how each patient's simulated body will react.
Mathematical models serve as the basis for a digital twin, and researchers include a wide spectrum of variables from real people like lifestyle and age into the virtual patient. These and many others are necessary to accurately predict a drug's effect on a person, which forms a diagnostic pathway affected by many physical parameters that vary from one to the next patient. "When creating an avatar, we take into account, for example, how the drug is metabolized in the body during treatment and how much ultimately gets to the pain center in the person's brain," said Thijs Defraeye, the team lead of Empa's "Biomimetic Membranes and Textiles" lab, in St. Gallen.
Patients could offer 'feedback parameters' to the digital twin, enhancing treatment
The digital twin can also be updated with psychological and physiological feedback from real patients, to reverse-engineer emergent complications after dosages are given to real patients. For example, humans can offer ongoing information on whether and to what extent their pain or other side effects persist, following a drug dose. Crucial to this capability is the length of pain-free periods. For people on painkillers, some days are good days, others not so much, brimming with painful and ostensibly random events. "With this feedback from humans, the avatar can dynamically adjust the therapy and even predict the course," said Flora Bahrami, another Empa researcher, in the blog post.
Future sensors could also measure additional physiological parameters like a patient's respiration or heartbeat rates in real-time, to update the digital twin. Obviously, this is an extremely exciting technology, especially at a time when many people in first-world countries lack proper access to effective healthcare. Self-reporting systems to a diagnostic AI updating your digital twin could rapidly expand accessibility for literally millions. And, most importantly, enabling doctors to kill your digital twin again and again might advance experimental treatments or drugs to stages safe enough for patients to enjoy more options, potentially saving lives.