Infiuss Health

We are building computational models that simulate how patients will respond to treatments before they ever enroll in a clinical trial. We exist because clinical trials are slow and expensive. Our technology lets researchers predict patient outcomes upfront, cutting trial timelines and costs while opening the door to more diverse participant pools. Our mission is to make drug development faster, smarter, and actually representative of the people these drugs are meant to help.

Clinical trials in the US are broken. The average drug takes 10 to 15 years to go from lab to patient, costs upwards of $2.6 billion, and has a failure rate north of 90%. Most of that failure happens not because the science is bad, but because we're guessing. We enroll patients, cross our fingers, and hope the outcomes match our hypotheses. When they don't, we burn through years of work and billions of dollars with nothing to show for it. Meanwhile, patients who desperately need new treatments keep waiting. As a nursing student, I see Doctors and researchers cycle through protocols that look great on paper but fall apart in execution because the patient population doesn't match the assumptions baked into the study design especially in places like rural Alabama. The disconnect between how trials are planned and how patients actually respond is massive, and it's costing lives, not just money. That's why we decided to build computational models that simulate individual patient responses to specific treatments before a single dose is administered in a trial. Think of it like a flight simulator, but for drug development. Instead of putting a pilot in the air untested, you run them through thousands of scenarios first. We do the same thing with patients and treatments. Before enrollment begins, researchers can see which patients are most likely to respond, which dosing strategies hold the most promise, and which trial designs will actually yield meaningful results. This matters for several important reasons. First, it saves time. Pharmaceutical companies spend years recruiting patients and running trials that ultimately fail. Modeling patient outcomes before enrollment can shave years off the development timeline by catching bad assumptions early and allowing researchers to iterate on study designs before spending a dime on actual patient recruitment. Second, it saves money. Failed trials aren't free. Every Phase III failure represents hundreds of millions in sunk costs. By catching potential failures earlier or by optimizing trial design so that failures happen less often, we directly reduce the financial risk that makes drug development so prohibitively expensive. Third, it produces better data. Current trial methodology generates results based on broad population averages, which masks how individual patients actually respond. Digital Patient Twins flip that model. They give researchers granular, patient-level insight before a trial launches, which leads to tighter study designs, cleaner datasets, and stronger regulatory submissions. Better data means faster approvals and fewer costly late-stage surprises.