What Outcomes Digital Twins Can be Used to Predict

One of the most discussed use cases in pharma and biotech for AI is the creation of digital twins. By using massive amounts and data, the technology can create a digital recreation of a real person, which can then be used for various research purposes.

Pharmaceutical Executive spoke with Hamish Brocklebank, founder of Brox.AI, about some of the ways that digital twins are being used within the industry, and where the technology could potentially head in the coming years.

According to him, these twins have a variety of uses. One of the more commonly discussed use cases is for pure research. In this instance, a researcher creates a digital twin and then uses AI to simulate the effect of various molecules and conditions. However, Brocklebank explains that there are other use cases which may provide just as much, if not more, value.

Pharmaceutical Executive: What outcomes can digital twins be used to predict?
Hamish Brocklebank: The use cases span a wide range, so let me walk through them from simple to complex.

Level one is straightforward market research. Imagine a pharmaceutical company is launching a new drug and building an HCP-facing website. They want to A/B test two page layouts to see which one oncologists prefer. That is a quick, targeted research question. What makes this more powerful than it sounds is that digital clones allow you to test thousands of variations simultaneously, run goal-finding research, and get far more precise answers than traditional methods could produce at that speed.

Level two moves into predicting behaviors — things like price sensitivity and treatment recommendations. A good example is GLP-1 therapies: you can model how patients’ price sensitivity changes depending on whether the drug is delivered as a pill or an injectable. Interestingly, even though the injectable is more burdensome — patients typically need to visit a provider to receive it — they may actually show lower price sensitivity for the injectable and in some cases higher compliance, because the clinical touchpoint reinforces the treatment routine. That kind of nuanced prediction is what level two enables.

Level three is macro modeling. A representative question might be: if Drug X receives FDA approval, how quickly will a specific cohort of physicians begin prescribing it, and what interventions would accelerate that adoption? This type of simulation is useful not only for pharmaceutical companies thinking about their own launches but also for financial institutions evaluating M&A targets and investors assessing the commercial potential of pipeline assets. So level three moves from specific behavioral predictions into broad market and world simulations.