(T-125) Beyond the Michaelis-Menten: Evaluation of Novel IVIVE Approach for Predicting In Vivo Intrinsic Clearance from Hepatocyte Assays

Yun Min Song, n/a – Ph.D. Candidate, Biomedical Mathematics Group, Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea.; Khanh Linh Duong, n/a – Ph.D. Candidate, College of Pharmacy, Chungnam National University, Daejeon 34134, South Korea; Jung-woo Chae, n/a – Professor, College of Pharmacy, College of Pharmacy, Chungnam National University, Daejeon 34134, South Korea; Jae Kyoung Kim, n/a – Professor, Biomedical Mathematics Group, Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea.; Sang Kyum Kim, n/a – Professor, College of Pharmacy, College of Pharmacy, Chungnam National University, Daejeon 34134, South Korea; Hwi-yeol Yun, n/a – Professor, College of Pharmacy, College of Pharmacy, Chungnam National University, Daejeon 34134, South Korea

Objectives: A comparative evaluation analysis was conducted to assess the efficacy of a novel approach that incorporates the saturation of drug metabolism rate at high hepatocyte enzyme levels to extrapolate in vivo intrinsic clearance from in vitro hepatocyte assays. The objective was to evaluate the accuracy of predicting in vivo intrinsic clearance using this new formula, with a focus on selecting the most appropriate hepatocyte model to ensure precision.

Methods: The new approach was developed based on previous research, which suggested that significant errors in predicting hepatic intrinsic clearance occur when the drug's Michaelis-Menten constant (KM) is low and not sufficiently higher than the hepatic concentration of their major metabolic enzyme (ET). The new approach developed to address this limitation by considering metabolism enzyme saturation effects (~ET/KM+ET) and incorporate this value to equation.

To evaluate the predictability of the new approach in extrapolating in vivo intrinsic clearance from in vitro data obtained from hepatocyte assays, 76 values of intrinsic clearance from in vitro and in vivo studies were collected. Among them, 15 drug candidates additionally collected hepatocyte Km values from literature. In vivo intrinsic clearance values were reverse-calculated from human clearance and served as observed values, while in vitro intrinsic clearance values were obtained from various open-source database, including High-Throughput Toxicokinetics, Astrazeneca Bioassay, and the ChEMBL Database. In addition, the total concentration of each enzyme (ET) in human liver was collected with previous reported literature.

Results: In our research, the new method showed better prediction performance without knowledge of the unbound fraction in blood (Fup) and microsome (Fu-mic). Specifically, when we ignored these concepts and applied the new method with the different value ranges of Km of most drug substances to calculate intrinsic clearance, and compared to using the canonical formula with considering of fup and fu-mic through validation parameters including RMSE, AFE, AAFE, and 2-fold difference, the impression is that the 2-fold difference value increased by 1.5 times. This result is observed with collected 76 values of intrinsic clearance from in vitro and in vivo studies. Besides, when applying known Km values from hepatocytes to extrapolate in vivo intrinsic clearance for 15 drug candidates, the new approach also demonstrated better prediction performance. RMSE decreased by 0.0484, approaching 0, while both AFE and AAFE changed from 2.19 and 3.865 to 1.76 and 3.521, respectively, closer to 1. Additionally, the 1.5-fold difference value increased by 1.25 folds.

Conclusions: Based on the predicted results obtained, we affirm the accuracy of predicting in vivo intrinsic clearance using the new In Vitro to In Vivo Extrapolation (IVIVE) approach with consideration of metabolism enzyme saturation effects (~ET/KM+ET).

Citations: [1] Back, H., Yun, H., Kim, S. K., & Kim, J. K. (2020). Beyond the Michaelis‐Menten: Accurate prediction of in vivo hepatic clearance for drugs with low K M. Clinical and Translational Science.