Prediction of cytochrome P450-mediated bioactivation using machine learning models and in vitro validation.

Cytochrome P450 (P450)-mediated bioactivation, which can lead to the hepatotoxicity through the formation of reactive metabolites (RMs), has been regarded as the major problem of drug failures. Herein, we purposed to establish machine learning models to predict the bioactivation of P450. On the basis of the literature-derived bioactivation dataset, models for Benzene ring, Nitrogen heterocycle and Sulfur heterocycle were developed with machine learning methods, i.e., Random Forest, Random Subspace, SVM and Naïve Bayes. The models were assessed by metrics like "Precision", "Recall", "F-Measure", "AUC" (Area Under the Curve), etc. Random Forest algorithms illustrated the best predictability, with nice AUC values of 0.949, 0.973 and 0.958 for the test sets of Benzene ring, Nitrogen heterocycle and Sulfur heterocycle models, respectively. 2D descriptors like topological indices, 2D autocorrelations and Burden eigenvalues, etc. contributed most to the models. Furthermore, the models were applied to predict the occurrence of bioactivation of an external verification set. Drugs like selpercatinib, glafenine, encorafenib, etc. were predicted to undergo bioactivation into toxic RMs. In vitro, IC50 shift experiment was performed to assess the potential of bioactivation to validate the prediction. Encorafenib and tirbanibulin were observed of bioactivation potential with shifts of 3-6 folds or so. Overall, this study provided a reliable and robust strategy to predict the P450-mediated bioactivation, which will be helpful to the assessment of adverse drug reactions (ADRs) in clinic and the design of new candidates with lower toxicities.

Super-Resolution Imaging of Autophagy by a Preferred Pair of Self-Labeling Protein Tags and Fluorescent Ligands.

Autophagy is a core recycling process for homeostasis, with its dysfunction associated with tumorigenesis and various diseases. Yet, its subtle intracellular details are covered due to the limited resolution of conventional microscopies. The major challenge for modern super-resolution microscopy deployment is the lack of a practical labeling system, which could provide robust fluorescence with fidelity in the context of the dynamic autophagy microenvironment. Herein, a representative autophagy marker LC3 protein is selected to develop two hybrid self-labeling systems with tetramethylrhodamine (TMR) fluorophores through SNAP/Halo-tag technologies. A systematic investigation indicated that the match of the LC3-Halo and TMR ligand remarkably outperforms that of LC3-SNAP, as the former Halo system exhibited more robust single-molecule brightness (440 vs 247), total photon numbers (45600 vs 13500), and dwell time of the initial bright state (0.82 vs 0.40 s) than the latter. With the aid of this desirable Halo system, for the first time, live-cell ferritinophagy is monitored with a spatial resolution of ∼50 nm, which disclosed reduced sizes of autophagosomes (∼650 nm, ferritinophagy) than those in nonselective (∼840 nm, mammalian target of rapamycin (mTOR)) and selective autophagy (∼900 nm, mitophagy).