By using foundation models to analyze vast datasets, companies can create a synthetic 'standard of care' arm for single-arm Phase 1 trials. The AI matches patients based on deep clinical and genomic parameters, providing insights comparable to a much larger Phase 3 trial.

General large language models trained on internet data lack life sciences patterns, resulting in low accuracy when forecasting clinical trial success. Post-training models on millions of longitudinal patient records and historical trials enables accurate predictions of medical outcomes and adverse events.

A major cause of clinical trial failure is unforeseen toxicity. By creating AI-powered models based on single-cell atlases, researchers can predict which unintended cells express a drug's target receptor. This allows them to anticipate side effects, like kidney toxicity, in silico, saving billions in failed drug development.

By modeling a complete patient trajectory using multi-omics data, AI could predict how a patient would fare on a placebo or standard care. This would allow for "synthetic" control arms, reducing the ethical and practical challenges of enrolling patients in non-treatment groups.

Digital twins and virtual trials are not currently used to replace human clinical trials. Instead, they serve as powerful simulation tools to 'test drive' protocols with virtual patient data. This helps create synthetic control arms, anticipate challenges, and optimize trial designs before launching a costly real-world study.

The highest-value application of AI in clinical development is in the design phase. By simulating trial outcomes with historical data and virtual patient cohorts, companies can identify and resolve potential bottlenecks, like flawed protocols or recruitment issues, before committing massive resources. This

It's impossible to generate human data at the scale of in silico experiments. The key is to create highly accurate simulations of human physiology (digital twins) and then validate their predictions with limited, strategic human data. If the model proves reliable, it could drastically accelerate R&D.

While AI is on the verge of cracking preclinical challenges, the biggest problem is the high drug failure rate in human trials. The next wave of innovation will use AI to design molecules for properties that predict human efficacy, addressing the fundamental reason drugs fail late-stage.

Conquer's Farsight Twin can predict a patient's response to a novel drug, standard of care, and the combination therapy separately. This allows pharma companies to determine if a positive response in an early-phase trial is truly driven by their new asset or just the background therapy, providing crucial efficacy data.