AI : Causal Modeling

Supporting targeted clinical trials

In 2023 Simbec-Orion announced a strategic partnership with to bring AI-powered insights and predictive models to support data-driven predictions to support optimum clinical trial design. By leveraging causal AI models we can better predict trial outcomes and use these insights to inform clinical trial design.

What is ‘AI-enabled causal modeling’ and how is it different?

Machine learning and AI are based on statistical modeling. Most of statistics is concerned with correlations – there is a familiar phrase which states ‘correlation does not mean causation’. Causal inference is the type of statistics that goes after causation, proving that A causes B. The answers provided by causal modeling are more precise and more actionable than those based on correlations, and this is important in the field of drug development, where the lives and wellbeing of patients are at stake.

Can early utilisation of AI-enabled causal modeling de-risk and accelerate clinical development?

We invited CEO of Simbec-Orion, Fabrice Chartier, and CEO of, Joern Klinger, to discuss how early utilisation of AI could help de-risk and accelerate clinical development, and where drug developers should start when considering adding AI to their clinical development strategy. You can watch the fireside chat on demand.

Support decision-making & provide additional data to demonstrate value to investors from the nonclinical stage


If your investors have data supporting clinical efficacy potential, it is  much easier to raise funds, even at a pre-clinical stage.

Causal modeling can also build investor confidence in planned phase II & III studies by accurately predicting outcomes.

Supporting animal models’s models are closer to humans than animal models, and can also offer accurate predictions for efficacy in phase II.

Extract maximum value from your early-stage trials

Biomarker intelligence

Identify all biomarkers that are causal for the disease, ensuring those biomarkers are included in phase I

Efficacy data early

Using biomarker intelligence and predicted performance can support gaining efficacy data early

Indication screening & selection

Screening for indications which will show efficacy can be done in the beginning of the drug development process (non-clinical or phase 1)

In addition, depending on the outcome, causal modeling can support confirming an indication or pivoting to an alternative.

Provide intelligence to support efficient, highly targeted clinical trial design

Patient selection

Determine the optimum sample size and cohort to support efficient clinical trial design with maximum statistical power.

Trial design’s causal modeling supports analysis of target, disease and mode of action to provide information crucial to trial design.

Clinical endpoints

Causal modeling can ensure only the most meaningful clinical endpoints are included and measured in the study.

Got a question?
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