Consideration of vendor-related differences in hepatic metabolic stability data to optimize early ADME screening in drug discovery.

Hepatic metabolic stability is a crucial determinant of oral bioavailability and plasma concentrations of a compound, and its measurement is important in early drug discovery. Preliminary metabolic stability estimations are commonly performed in liver microsomal fractions. At the National Center for Advancing Translational Sciences, a single-point assay in rat liver microsomes (RLM) is employed for initial stability assessment (Tier I) and a multi-point detailed stability assay is employed as a Tier II assay for promising compounds. Although the in vitro and in vivo metabolic stability of compounds typically exhibit good correlation, conflicting results may arise in certain cases. While investigating one such instance, we serendipitously found vendor-related RLM differences in metabolic stability and metabolite formation, which had implications for in vitro and in vivo correlations. In this study, we highlight the importance of considering vendor differences in hepatic metabolic stability data and discuss strategies to avoid these pitfalls.

Dynamics and observations of long-term orthodontic tooth movement and subsequent relapse in C57BL/6 mice.

The risk of relapse associated with orthodontic treatment is a major problem. Despite extensive research and discussion regarding the risk of orthodontic relapse, the underlying mechanisms remain to be elucidated. This study aimed to evaluate relapse following orthodontic treatment in mice (C57BL/6) tested via the coil spring method based on tooth movement at 21 days and mechanical retention at 7 days after completion of the procedure. During the experiment, relapse was observed and evaluated over 7 days. At the end of orthodontic tooth movement, the average distance was 259.6 (± 10.9) µm, and tooth movement was observed in all mice. No significant differences in distance were observed at the end of the experimental treatment period or after 7 days of mechanical retention. The distance at the start of observation was 258.6 (± 10.4) µm, whereas that at the end was 155.4 (± 12.4) µm, indicating that the distance had decreased significantly. Relative to the total relapse distance over the 7-day period, 45.7 (± 4.3)% of the relapse was observed on Day 0-1. The mouse model established in the current study provides an effective and reproducible method for the optimal evaluation of relapse. Our findings clarified that most of the relapse occurs within 7 days during the initial observation stage.

Development and validation of PAMPA-BBB QSAR model to predict brain penetration potential of novel drug candidates.

Efficiently circumventing the blood-brain barrier (BBB) poses a major hurdle in the development of drugs that target the central nervous system. Although there are several methods to determine BBB permeability of small molecules, the Parallel Artificial Membrane Permeability Assay (PAMPA) is one of the most common assays in drug discovery due to its robust and high-throughput nature. Drug discovery is a long and costly venture, thus, any advances to streamline this process are beneficial. In this study, ∼2,000 compounds from over 60 NCATS projects were screened in the PAMPA-BBB assay to develop a quantitative structure-activity relationship model to predict BBB permeability of small molecules. After analyzing both state-of-the-art and latest machine learning methods, we found that random forest based on RDKit descriptors as additional features provided the best training balanced accuracy (0.70 ± 0.015) and a message-passing variant of graph convolutional neural network that uses RDKit descriptors provided the highest balanced accuracy (0.72) on a prospective validation set. Finally, we correlated in vitro PAMPA-BBB data with in vivo brain permeation data in rodents to observe a categorical correlation of 77%, suggesting that models developed using data from PAMPA-BBB can forecast in vivo brain permeability. Given that majority of prior research has relied on in vitro or in vivo data for assessing BBB permeability, our model, developed using the largest PAMPA-BBB dataset to date, offers an orthogonal means to estimate BBB permeability of small molecules. We deposited a subset of our data into PubChem bioassay database (AID: 1845228) and deployed the best performing model on the NCATS Open Data ADME portal (https://opendata.ncats.nih.gov/adme/). These initiatives were undertaken with the aim of providing valuable resources for the drug discovery community.