External Validation of a prediction method for the individual serum concentration and therapeutic effect for optimizing adalimumab therapy in Crohn’s Disease

Crohn’s disease (CD) is a long-term condition where parts of the digestive system become swollen and irritated. It can cause symptoms such as abdominal pain, diarrhea, weight loss, and fatigue. CD affects hundreds of thousands of people worldwide and often requires ongoing treatment to control inflammation (the body’s response to injury or infection, causing redness, swelling, and pain) and prevent complications like fistulas (abnormal connections between organs).

Adalimumab is a medication used to treat moderate-to-severe CD. It is a type of biologic drug, which means it is made from living cells. Adalimumab works by blocking a substance in the body called tumor necrosis factor alpha (TNF-α), which plays a key role in causing inflammation. While this treatment has helped many people, it can be difficult to predict who will respond well to it. Doctors often use a strategy called therapeutic drug monitoring (TDM), which involves measuring the level of medication in the blood and adjusting the dose accordingly. However, it is still unclear what the best drug levels are, or when to measure them, especially early in treatment.

To help improve this process, I have developed a new approach called model-informed precision dosing (MIPD). This method uses early measurements of the drug level in the blood and a score of disease activity (called the Crohn’s Disease Activity Index or CDAI) taken during the second week of treatment. With these early data, individual patient characteristics can be calculated using a statistical approach called the empirical Bayesian method. Each patient’s likely response during the longer-term maintenance phase of treatment (starting from week 4 onward) can then be simulated.

This method has worked well in a small test group, but before it can be used more widely, it needs to be validated in a larger, real-world setting. For this reason, I am requesting access to patient data from the SERENE-CD clinical trial (ClinicalTrials.gov ID: NCT02065570), which followed over 500 people with CD who started treatment with adalimumab. This study collected both drug levels and CDAI scores during the early and later phases of treatment.

Using this dataset, I will:
1. Evaluate how accurately the model predicts drug levels and disease activity during maintenance treatment.
2. Compare the model’s ability to predict remission (when symptoms are under control) with the traditional TDM method.
3. Identify patients who may need a higher dose or may not benefit from continuing treatment.

If successful, this approach could help doctors better personalize treatment for people with CD and could also be adapted for other drugs and diseases where precise dosing is important.