New option for clinical trials: Patient in Silico

Patient-in-Silico (PiS) refers to the use of AI to create a virtual or digital representation of an individual patient or a population of patients. These digital patients integrate clinical data, physiology, anatomy, genomics, and treatment variables to simulate disease progression and predict responses to therapies. Now these digital patients receive treatment before interventions are applied in the real world.

PiS is a combination of patient data from labs, imaging studies, genomics, and vitals. The data combines biological and physiological models, including organ systems and disease pathways. This data is interpreted by AI and machine learning. The result is a testable virtual patient that can receive virtual treatment under different scenarios.

Benefits of in-silico trials

In silico has the potential to reduce the cost of clinical trials, which are currently estimated at $1.3 billion to $2.6 billion using conventional trials. Also, there is considerable time to take a new drug from the test tube to FDA approval, which is 10 to 15 years. This explains the daunting statistic that roughly 1 in 5000 new compounds are approved as pharmaceutical drugs by the Food and Drug Administration (FDA). Using an in silico model, the time and costs are considerably reduced.

Another benefit of PiS drug development is the opportunity to tailor therapy for an individual patient.

PiS can be applied to medical device development. The model creates virtual testing of new devices under varied patient anatomies.

At present, the FDA is increasing its recognition of in silico evidence.

PiS is a risk-free clinical decision simulation.

With more sensitivity to animal testing in phase 1 clinical trials, in-silico trials reduce the reliance on animals in early trials.

Finally, PiS trials enable the identification of treatments for rare diseases, where funding is difficult to obtain.

Impact of PiS trials on physicians

PiS represents a shift from population-based averages to predictive, personalized decision-making. The benefit is that the model is intended to supplement, rather than replace, clinical judgment.

PiS is an emerging approach that uses advanced computerized models to create virtual representations of individual patients or patient populations. By integrating clinical data, imaging, physiology, genomics, and treatment variables, PiS enables simulation of disease progression and therapeutic response before real-world intervention. These models combine artificial intelligence and machine learning to test multiple clinical scenarios safely and efficiently.

PiS virtual trials reduce development costs, shorten timelines, and limit patient exposure to risk while allowing evaluation of rare or extreme clinical scenarios. For clinicians, PiS offers the potential to support personalized treatment selection, risk stratification, and shared decision-making.

Despite its promise, PiS challenges remain, including model validation, data quality, transparency, and regulatory acceptance. As standards mature and clinical integration improves, PiS is poised to become a powerful complement to traditional clinical trials, enhancing drug development and streamlining the time from drug creation to clinical application. This is just one more example of AI supporting physicians rather than replacing physician judgment.

Neil Baum, M.D., is a professor of clinical urology at Tulane University in New Orleans. Dr. Baum is the author of several books, including the bestselling book “Marketing Your Medical Practice: Ethically, Effectively, and Economically,” which has sold over 225,000 copies and has been translated into Spanish.