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Using an Artificial Intelligence Platform to Accurately Predict Pharmacokinetic Sampling Outcomes in Clinical Trials

 

In this poster you will learn:

  • Remote patient monitoring is critical in ensuring optimal drug exposure and accurately measuring drug intake
  • It is estimated that 30-50% of CNS studies fail because subjects are not taking the study drug 
  • High rates of non-adherence in clinical research and the lack of a continuous and accurate measure of adherence interfere with hypothesis testing and the interpretation of trial results 
  • Current measures of adherence such as pill counts are inaccurate and underestimate non-adherence 
  • PK (pharmacokinetic) sampling, while accurate, is used sparsely in ambulatory settings and does not capture adherence in the placebo group 
  • An artificial intelligence (AI) platform that uses computer vision and neural networks to visually confirm ingestion and capture ingestion-related behaviors was evaluated for accuracy in predicting PK outcomes 
  • The AI platform is the only known modality to be able to distinguish between non-adherence and fraud 
  • A preliminary analysis using 6 CNS studies and 278 subjects with available PK data was conducted to assess the ability of adherence to predict PK levels 
  • A subsequent analysis on 256 of those subjects with available PK data was conducted to assess the ability of adherence and fraud to predict PK levels