Allylestrenol

CYP2B6 is an important enzyme in the phase 1 metabolism of key pharmaceuticals, and inhibition of this enzyme can lead to adverse drug events. Machine learning models can potentially predict interactions with CYP2B6; however, there is limited data with which to train these models in the public domain. We proposed enhancing the applicability domain and improving the predictive capability of our CYP2B6 inhibition model by selecting a small, diverse set of compounds to test in vitro and adding the results to our model training set. We used a distance-based approach to define the applicability domain of the model and then measured the chemical diversity by creating t-distributed stochastic neighbor embedding plots to represent the chemical space of our model. After comparing this chemical space with a 49-plate drug-repurposing library, we were able to identify a plate with the highest average minimum Euclidean distance from the model training set. We then performed in vitro testing of this plate for CYP2B6 inhibition activity at 10 μM and added this new data to our machine learning model. A one-class classification approach was used to evaluate the efficacy of our applicability domain-expansion technique. The results showed that this method did not appreciably increase the performance of the model or the applicability domain, but we did increase the diversity of the training set. Additionally, the in vitro experiments identified vilanterol and allylestrenol as inhibitors of CYP2B6 with IC₅₀ values in the sub to low micromolar range. SIGNIFICANCE STATEMENT: CYP2B6 inhibition can affect the metabolism of important drugs, like methadone and propofol, and result in variability that can lead to adverse events. Machine learning models can help uncover new molecules with inhibitory potential against CYP2B6, but only if predictions of these models are reliable. This study illustrates how the intentional expansion of a machine learning model's applicability domain is neither a simple nor straightforward task, but even a conservative effort can reveal new molecules with CYP2B6 inhibition activity.

Camellia japonica radix (CJR), derived from the root of Camellia japonica L., has the potential to function as an herbal tea substitute for the prevention and intervention of metabolic dysfunction-associated steatotic liver disease (MASLD). It can provide systemic therapeutic benefits, boast a favorable safety profile, facilitate convenient consumption, and support long-term applicability. Despite its potential, research on CJR remains limited.

The aim of this study aims is to elucidate the therapeutic mechanisms of CJR in MASLD, thereby providing evidence to support its clinical application.

The therapeutic effects of CJR were evaluated using a water-supplementation model in MASLD mice. Integrated microbiome, transcriptome, proteome, and metabolome analyses were employed to comprehensively explore the mechanisms involved. A drug-target pull-down assay was performed to identify specific protein targets of small molecule metabolites in vitro. Fecal microbiota transplantation in antibiotic-treated ABX mice was conducted to confirm the critical role of gut microbiota and its metabolites. Furthermore, customized medicated feed supplemented with linoleic acid was used to explore the intervention effect of its metabolite, 9(S)-HpODE, as well as to evaluate its dietary intervention potential.

This present study explicitly elucidates the efficacy of CJR extract in alleviating hepatic inflammation and steatosis in a MASLD model mice, with its pharmacological mechanism associated with gut microbiota, linoleic acid metabolism, and GPX4-mediated ferroptosis. Notably, 9(S)-HpODE was discovered to be a key metabolite of linoleic acid, which could target both KEAP1 and SLC7A11, bidirectionally regulating GPX4-mediated ferroptosis, while acting as a signaling molecule at low doses to induce redox adaptation via oxidative preconditioning, thus ameliorating oxidative stress in MASLD.

Our findings indicate that both CJR and linoleic acid exhibit significant potential as dietary interventions for the management of MASLD, offering promising avenues for future research and clinical application.