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Liver microsomal stability, a crucial aspect of metabolic stability, significantly impacts practical drug discovery. However, current models for predicting liver microsomal stability are based on limited molecular information from a single species. To address this limitation, we constructed the largest public database of compounds from three common species: human, rat, and mouse. Subsequently, we developed a series of classification models using both traditional descriptor-based and classic graph-based machine learning (ML) algorithms. Remarkably, the best-performing models for the three species achieved Matthews correlation coefficients (MCCs) of 0.616, 0.603, and 0.574, respectively, on the test set. Furthermore, through the construction of consensus models based on these individual models, we have demonstrated their superior predictive performance in comparison with the existing models of the same type. To explore the similarities and differences in the properties of liver microsomal stability among multispecies molecules, we conducted preliminary interpretative explorations using the Shapley additive explanations (SHAP) and atom heatmap approaches for the models and misclassified molecules. Additionally, we further investigated representative structural modifications and substructures that decrease the liver microsomal stability in different species using the matched molecule pair analysis (MMPA) method and substructure extraction techniques. The established prediction models, along with insightful interpretation information regarding liver microsomal stability, will significantly contribute to enhancing the efficiency of exploring practical drugs for development.

The n -octanol/buffer solution distribution coefficient at pH = 7.4 (log  D 7.4 ) is an indicator of lipophilicity, and it influences a wide variety of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and druggability of compounds. In log  D 7.4 prediction, graph neural networks (GNNs) can uncover subtle structure-property relationships (SPRs) by automatically extracting features from molecular graphs that facilitate the learning of SPRs, but their performances are often limited by the small size of available datasets. Herein, we present a transfer learning strategy called pretraining on computational data and then fine-tuning on experimental data (PCFE) to fully exploit the predictive potential of GNNs. PCFE works by pretraining a GNN model on 1.71 million computational log  D data (low-fidelity data) and then fine-tuning it on 19,155 experimental log  D 7.4 data (high-fidelity data). The experiments for three GNN architectures (graph convolutional network (GCN), graph attention network (GAT), and Attentive FP) demonstrated the effectiveness of PCFE in improving GNNs for log  D 7.4 predictions. Moreover, the optimal PCFE-trained GNN model (cx-Attentive FP, R test 2 = 0.909) outperformed four excellent descriptor-based models (random forest (RF), gradient boosting (GB), support vector machine (SVM), and extreme gradient boosting (XGBoost)). The robustness of the cx-Attentive FP model was also confirmed by evaluating the models with different training data sizes and dataset splitting strategies. Therefore, we developed a webserver and defined the applicability domain for this model. The webserver (http://tools.scbdd.com/chemlogd/) provides free log  D 7.4 prediction services. In addition, the important descriptors for log  D 7.4 were detected by the Shapley additive explanations (SHAP) method, and the most relevant substructures of log  D 7.4 were identified by the attention mechanism. Finally, the matched molecular pair analysis (MMPA) was performed to summarize the contributions of common chemical substituents to log  D 7.4 , including a variety of hydrocarbon groups, halogen groups, heteroatoms, and polar groups. In conclusion, we believe that the cx-Attentive FP model can serve as a reliable tool to predict log  D 7.4 and hope that pretraining on low-fidelity data can help GNNs make accurate predictions of other endpoints in drug discovery.

Hematotoxicity has been becoming a serious but overlooked toxicity in drug discovery. However, only a few in silico models have been reported for the prediction of hematotoxicity. In this study, we constructed a high-quality dataset comprising 759 hematotoxic compounds and 1623 nonhematotoxic compounds and then established a series of classification models based on a combination of seven machine learning (ML) algorithms and nine molecular representations. The results based on two data partitioning strategies and applicability domain (AD) analysis illustrate that the best prediction model based on Attentive FP yielded a balanced accuracy (BA) of 72.6%, an area under the receiver operating characteristic curve (AUC) value of 76.8% for the validation set, and a BA of 69.2%, an AUC of 75.9% for the test set. In addition, compared with existing filtering rules and models, our model achieved the highest BA value of 67.5% for the external validation set. Additionally, the shapley additive explanation (SHAP) and atom heatmap approaches were utilized to discover the important features and structural fragments related to hematotoxicity, which could offer helpful tips to detect undesired positive substances. Furthermore, matched molecular pair analysis (MMPA) and representative substructure derivation technique were employed to further characterize and investigate the transformation principles and distinctive structural features of hematotoxic chemicals. We believe that the novel graph-based deep learning algorithms and insightful interpretation presented in this study can be used as a trustworthy and effective tool to assess hematotoxicity in the development of new drugs.

Objective: To develop a way for tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) receptors quantification in cancer via its' thermostability., Methods: Endougenous alkaline phosphatase (AP) activity and denaturation temperature of pancreatic cancer cell lines AsPC-1 and Capan-2 were detected. Boiling treated recombinant protein death receptor 5 (DR5), named DR5 AP, as well as pancreatic cancer cells lines AsPC-1 and Capan-2 were incubated with AP-tagged TRAIL (AP-TRAIL), and then reacted with Reagent A and Reagent S, the substrate of AP, to quantitive and in site detection of the receptor., Results: The endougenous AP activity of pancreatic cancer cells lines AsPC-1 and Capan-2 could not be totally inactivated by incubated at 65 °C, thus inhibited the detection of TRAIL receptor, but the activity was dramatically decreased after treated with boiling water, whereas the DR5-AP was thermal stable. The surface receptor of AsPC-1 and Capan-2 could be recognized and bound by AP-TRAIL after treated at 100 °C, the readings were 2. 210±0. 393 and 2. 027±0. 019., Conclusion: The TRAIL receptors are thermostable and this may provide a better diagnosis and prognosis of cancer as well as personalize cancer therapy.

Background: The microenvironment within solid tumors has often been shown to exhibit an acidic extracellular pH. Although the morphologic and functional differences in natural killer (NK) cells of the liver and spleen have been reported previously under physiological conditions, the difference under acidic conditions is still unclear. This study was to investigate the differences in the morphological and functional characteristics between rat liver and spleen NK cells under normal and acidic conditions in vitro., Methods: Liver and spleen NK cells were isolated and purified from Sprague-Dawley rats by density gradient centrifugation and the Dynabeads(®) FlowComp(TM) Flexi system, and stimulated for 4 days with or without IL-2 or treated with low pH or control for different times. Morphology was examined by scanning electron microscopy (SEM) and transmission electron microscopy (TEM), cell death and proliferation assays were performed by flow cytometry, IFN-gamma production was tested by ELISA, and cytotoxic activity was evaluated by lactate dehydrogenase (LDH) release assay., Results: Liver NK cells had significantly higher levels of cytotoxic activity than spleen NK cells under normal and acidic conditions, and the maximum difference was observed at pH 5.6. Further analysis revealed that the cytotoxic activity of NK cells was correlated with morphology, cell death, proliferative activity and IFN-gamma production. By TEM, liver NK cells contained a greater number of electron-dense granules per cell at pH 5.6. Moreover, a modest elevation of cell death and reduction of proliferation of liver NK cells occurred within a range of 5.6-7.2. Interestingly, an acidic extracellular pH only marginally, and not significantly, suppressed IFN-gamma production by liver NK cells., Conclusion: The sharp morphological and functional differences shown by the two types of NK cells in vitro indicate that liver NK cells are unexpectedly resistant to pH shock.

Background: Liver enriched natural killer (NK) cells are of high immune activity. However, the function of donor liver NK cells in allogeneic liver transplantation (LTx) remains unclear., Methods: Ten Gy of whole body gamma-irradiation (WBI) from a 60Co source at 0.6 Gy/min was used for depleting donor-derived leukocytes, and transfusion of purified liver NK cells isolated from the same type rat as donor (donor type liver NK cells, dtlNKs) through portal vein was performed immediately after grafting the irradiated liver. Post-transplant survival observation on recipients and histopathological detection of liver grafts were adoptive to evaluate the biological impact of donor liver NK cells on recipients' survival in rat LTx., Results: Transfusion of dtlNKs did not shorten the survival time among the recipients of spontaneous tolerance model (BN to LEW rat) after rat LTx, but prolonged the liver graft survival among the recipients depleted of donor-derived leukocytes in the acute rejection model (LEW to BN rat). Compared to the recipients in the groups which received the graft depleted of donor-derived leukocytes, better survival and less damage in the allografts were also found among the recipients in the two different strain combinations of liver allograft due to transfusion of dtlNKs., Conclusions: Donor liver NK cells alone do not exacerbate liver allograft acute rejection. Conversely, they can alleviate it, and improve the recipients' survival.

We report a rare case of duodenal pseudolymphoma without any symptoms. The lesion located in front of the head of the pancreas was found accidentally during a medical examination. The findings of computed tomography and positron emission tomography-computed tomography suggested a stromal tumor or malignant lymphoma. Surgical resection was performed. The lesions were pathologically diagnosed as duodenal pseudolymphoma.

Aim: To investigate how to reduce the incidence of biliary complications in rat orthotopic liver transplantation., Methods: A total of 165 male Wistar rats were randomly divided into three groups: Group A, orthotropic liver transplantation with modified "two-cuff" technique; Group B, bile duct was cut and reconstructed without transplantation; and Group C, only laparotomy was performed. Based on the approaches used for biliary reconstruction, Group A was divided into two sub-groups:A1 (n = 30), duct-duct reconstruction, and A2 (n = 30), duct-duodenum reconstruction. To study the influence of artery reconstruction on bile duct complication, Group B was divided into four sub-groups: B1 (n = 10), duct-duct reconstruction with hepatic artery ligation, B2 (n = 10), duct-duct reconstruction without hepatic artery ligation, B3 (n = 10), duct-duodenum reconstruction with hepatic artery ligation, and B4 (n = 10), duct-duodenum reconstruction without hepatic artery ligation. The samples were harvested 14 d after operation or at the time when significant biliary complication was found., Results: In Group A, the anhepatic phase was 13.7 ± 1.06 min, and cold ischemia time was 50.5 ± 8.6 min. There was no significant difference between A1 and A2 in the operation duration. The time for biliary reconstruction was almost the same among all groups. The success rate for transplantation was 98.3% (59/60). Significant differences were found in the incidence of biliary complications in Groups A (41.7%), B (27.5%) and C (0%). A2 was more likely to have biliary complications than A1 (50% vs 33.3%). B3 had the highest incidence of biliary complications in Group B., Conclusion: Biliary complications are almost inevitable using the classical "two cuff" techniques, and duct-duodenum reconstruction is not an ideal option in rat orthotopic liver transplantation.