122: A Literature Review on the Performance of Tiny Tract for In Vitro Modeling to Predict Food-Drug & pH-Dependent Drug-Drug Interaction Risks

Statement of Purpose, Innovation or Hypothesis: Tiny-TIM is an intricate and dynamic in vitro model designed to replicate the conditions in the stomach and small intestine of human. Widely utilized for investigating food digestion, tiny-TIM also provides insights into the bioavailability of various oral dosage forms. The objective of this project was to conduct a literature review on the tiny-TIM and its current predictive performance on the effect of food and elevated pH on the absorption of poorly soluble active pharmaceutical ingredients (APIs). This work may support the utilization of tiny-TIM for regulatory decisions including those concerning generic drugs.

Description of Methods & Materials: Keywords used for the literature search included: TIM, tiny-TIM, simulation. The following information was collected from the articles retrieved: APIs, food and pH effect from published clinical data, and food and pH effect predicted by tiny-TIM. The predictive performance of tiny-TIM was evaluated by comparing the food and pH effects based on the bio-accessibility data obtained from TIM with those based on the bioavailability data obtained from clinical data.

Data & Results: A total of 22 articles (published between 2006 and 2023) were retrieved and reviewed. The literature provided performance of the tiny-TIM to predict drug absorption under the influence of food for 12 drug products and elevated pH for 13 drug products (references available upon request). The collected information from the articles was used to assess the performance of tiny-TIM to predict the food effect on 12 drug products. When food had no effect on drug absorption (n = 6) or had negative effect on drug absorption (n = 1), the TIM prediction deviated < 20% from that based on the in vivo clinical data. For drug products with positive food effect on drug absorption (n=5), the TIM prediction deviated from 25% to 203% from that of the in vivo clinical data. The collected information was also utilized to examine the performance of tiny-TIM to predict the pH effect on 13 drug products. When elevated pH had no effect on drug absorption (n = 4), the tiny-TIM prediction of the pH effect deviated < 20% from that based on the in vivo clinical data. When elevated pH had negative effect on drug absorption, the tiny-TIM prediction had variable performance: deviated < 20% (n = 4), deviated ≥ 20% but < 100% (n = 2), or deviated ≥ 100% (n = 3) from that of the in vivo clinical data.

Interpretation, Conclusion or Significance: The tiny-TIM system may reliably predict the effect of food and elevated pH on drug absorption in some cases. However, the tiny-TIM prediction may not always be accurate, as it might over-estimate the food effect and under-estimate the pH effect on drug absorption. Therefore, further work is needed to understand the possible reasons behind, and additional considerations should be taken for model improvement. This work highlights that, after improvement and validation, the tiny-TIM system could be applied to investigate the effect of food intake and gastric pH modification on the bio-accessibility of drug products, providing valuable information for regulatory decision making including those concerning generic drugs.

Disclosures: The abstract reflects the views of the authors and should not be construed to represent FDA’s views or policies.

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Additional Information/Authors: None