Patient digital twins & in-silico trials

Patient digital twins and in-silico trials use computational patient models and virtual simulation to reduce, accelerate or de-risk clinical drug development, rather than treating patients directly. Unlearn’s AI-generated digital twins are increasingly used to power synthetic control arms in place of a portion of a trial’s control group, and the company published a roadmap on causal inference and digital twins for the future of clinical trials in npj Digital Medicine in June 2026; it also announced a collaboration with Acumen Pharmaceuticals in July 2026 to explore digital-twin analyses of Alzheimer’s disease clinical programs. Certara’s Simcyp Simulator, a physiologically-based pharmacokinetic (PBPK) platform built over 25 years with a consortium of more than 35 pharmaceutical companies, released Version 25 in early 2026 building on EMA qualification, and Certara reported that Simcyp simulation results replaced ten human trials for the chronic myeloid leukemia therapy asciminib. VeriSIM Life is building what it describes as “full-stack predictive infrastructure” to help pharma companies foresee translation challenges from laboratory discovery to human therapy, formalizing a research collaboration with the FDA’s National Center for Toxicological Research in June 2026 under the agency’s Roadmap to Reducing Animal Testing in Preclinical Safety Studies. In France, Novadiscovery (Nova In Silico) prospectively predicted the LDL-C outcomes of Merck’s Phase 3 CORALreef Lipids trial using its jinkō causal-AI in-silico clinical trial platform, announced in November 2025.

The key directions of patient digital twins & in-silico trials are:

1. AI-generated synthetic control arms (AI-Generated Synthetic Control Arms): digital twins that replace or supplement a portion of a trial’s control group, reducing the number of patients needed to receive placebo — Unlearn.AI’s TwinRCT methodology.
2. Physiologically-based pharmacokinetic modeling (Physiologically-Based Pharmacokinetic Modeling): mechanistic simulation of a drug’s absorption, distribution, metabolism and excretion, regulator-qualified to substitute for some human trials — Certara’s Simcyp Simulator.
3. Preclinical-to-clinical translation AI (Preclinical-to-Clinical Translation AI): predictive infrastructure that forecasts how a molecule will behave moving from lab to human trials, aligned with regulatory efforts to reduce animal testing — VeriSIM Life.
4. Causal-AI virtual patient simulation (Causal-AI Virtual Patient Simulation): virtual patient “twins” simulating real-world physiology, disease progression and treatment response for trial design and prediction — Novadiscovery’s jinkō platform.

Sectoral value chain#

[patient/physiology model development] ──> [model validation against real trial data] ──> [regulatory qualification (FDA/EMA/NMPA)]
                                                    │
                                       (trial design/simulation deployment)
                                                    │
                                                    ▼
[expanded regulatory acceptance] <─── [prospective prediction accuracy tracking] <─────┘

Value chain levels#

Cross-cutting technologies of the sector:

• Causal AI digital twin generation (AI-Generated Synthetic Control Arms, Causal-AI Virtual Patient Simulation): machine-learning methods that generate an individualized predicted patient trajectory, usable as a synthetic control or trial-design input.
• Mechanistic PBPK simulation (Physiologically-Based Pharmacokinetic Modeling): whole-body physiological modeling of drug absorption, distribution, metabolism and excretion, built and refined over decades with pharma-industry consortium input.
• Cross-species translation modeling (Preclinical-to-Clinical Translation AI): predictive models bridging animal/preclinical data to human clinical outcomes, supporting regulatory efforts to reduce animal testing.

The stack applies computational patient modeling and simulation to reduce, accelerate or de-risk clinical drug development ahead of or alongside human trials.

1. AI-generated synthetic control arms (AI-Generated Synthetic Control Arms):
   • Unlearn.AI’s TwinRCT methodology generates individualized digital twins usable as synthetic control-arm data, reducing reliance on placebo-arm patients, with its approach now the subject of a peer-reviewed roadmap and a named Alzheimer’s-disease pharma collaboration.
2. Physiologically-based pharmacokinetic modeling (Physiologically-Based Pharmacokinetic Modeling):
   • Certara’s Simcyp Simulator mechanistically models drug absorption, distribution, metabolism and excretion, with EMA-qualified results that have replaced ten human trials for a leukemia therapy.
3. Preclinical-to-clinical translation AI (Preclinical-to-Clinical Translation AI):
   • VeriSIM Life’s predictive infrastructure forecasts translation risk from lab to human therapy, formalized through a direct FDA National Center for Toxicological Research research collaboration.
4. Causal-AI virtual patient simulation (Causal-AI Virtual Patient Simulation):
   • Novadiscovery’s jinkō platform builds virtual patient twins simulating physiology and disease progression, validated by a prospective, named prediction of a Merck Phase 3 trial’s outcome.