008: Is There a Need for a Dedicated Pharmacokinetic Trial for a Drug in Obese Populations? A Drug Prioritization Decision Tree Framework

Statement of Purpose: Obesity is a significant public health concern, with nearly 1 billion people globally affected and a three-fold increase in the obesity rate since 1975, highlighting the need for urgent action to address this growing epidemic[1–3]. The rising rates of obesity have led to an increase in the use of medications in obese patients, despite their under-representation in clinical trials[4–7]. Unfortunately, this has led to a paucity of proper dosing information for obese subjects, posing significant risks to both efficacy and safety. Thus, this work aims to propose a decision tree framework to aid in the prioritization of drugs to be studied in a dedicated pharmacokinetic trial in obese subjects.

Description of Methods & Materials: We first identified key clinical pharmacology considerations to develop the decision tree framework, such as the therapeutic window and drug-related factors, including physicochemical properties, elimination pathway, and weight considerations on dosing. Based on these considerations, we developed the Expected Exposure Change decision tree (Figure 1A), which guides where changes in pharmacokinetics can be expected to lead to changes in exposures in obese subjects. The Drug Prioritization Tree (Figure 1B) then leverages the information from the first decision tree to prioritize the need for a dedicated pharmacokinetic trial, specifically in the obese population. To test the performance of the Expected Exposure Change decision tree, we randomly selected 30 drugs from the literature across different therapeutic areas and evaluated their expected pharmacokinetic changes using the decision tree. We then compared the results with available empirical evidence from the literature for the chosen drugs.

Data & Results: Our decision tree framework successfully identified drugs that are expected to have changes in pharmacokinetics in obese patients. The proposed decision tree (Figure 1A) accurately predicted the anticipated direction of exposure change in 73% of the 30 drugs considered, with a 3% error rate for both false positives and false negatives and 20% of drugs unable to be predicted (Table 1). The Drug Prioritization Tree (Figure 1B) then uses this information to prioritize the need for dedicated pharmacokinetic trials in obese subjects by considering additional prior knowledge of the drug and dosing.

Interpretation, Conclusion or Significance: Our decision tree framework provides a systematic and practical approach to assess or prioritize drugs that would necessitate a dedicated pharmacokinetic trial to be conducted in obese subjects during drug development. This framework can help to identify potential risks associated with dosing medications in obese patients and improve health outcomes for this population. Additionally, we have incorporated the decision tree framework into an easy-to-use web-based application https://blangev1.github.io/Obesity-prioritization-app/application.html to guide users through the prioritization process. This application has the potential to reduce the regulatory burden on pharmaceutical companies by providing a standardized approach to the assessment and prioritization of pharmacokinetic studies in obese subjects during drug development. Ultimately, our decision tree framework can contribute to the development of evidence-based dosing recommendations specific to obese subjects and improve the safety and efficacy of medications for this population.

Disclosures: Dr Gobburu is co-founder of Vivpro Corp (www.vivpro.ai) and Pumas-AI Inc (www.pumas.ai).