Discovery of novel MIF inhibitors that attenuate microglial inflammatory activation by structures-based virtual screening and in vitro bioassays.

Macrophage migration inhibitory factor (MIF) is a pluripotent pro-inflammatory cytokine and is related to acute and chronic inflammatory responses, immune disorders, tumors, and other diseases. In this study, an integrated virtual screening strategy and bioassays were used to search for potent MIF inhibitors. Twelve compounds with better bioactivity than the prototypical MIF-inhibitor ISO-1 (IC50 = 14.41 µM) were identified by an in vitro enzymatic activity assay. Structural analysis revealed that these inhibitors have novel structural scaffolds. Compound 11 was then chosen for further characterization in vitro, and it exhibited marked anti-inflammatory efficacy in LPS-activated BV-2 microglial cells by suppressing the activation of nuclear factor kappa B (NF-κB) and mitogen-activated protein kinases (MAPKs). Our findings suggest that MIF may be involved in the regulation of microglial inflammatory activation and that small-molecule MIF inhibitors may serve as promising therapeutic agents for neuroinflammatory diseases.

A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ.

Nowadays, more and more attention has been attracted to develop selective PI3Kγ inhibitors, but the unique structural features of PI3Kγ protein make it a very big challenge. In the present study, a virtual screening strategy based on machine learning with multiple PI3Kγ protein structures was developed to screen novel PI3Kγ inhibitors. First, six mainstream docking programs were chosen to evaluate their scoring power and screening power; CDOCKER and Glide show satisfactory reliability and accuracy against the PI3Kγ system. Next, virtual screening integrating multiple PI3Kγ protein structures was demonstrated to significantly improve the screening enrichment rate comparing to that with an individual protein structure. Last, a multi-conformational Naïve Bayesian Classification model with the optimal docking programs was constructed, and it performed a true capability in the screening of PI3Kγ inhibitors. Taken together, the current study could provide some guidance for the docking-based virtual screening to discover novel PI3Kγ inhibitors.

ADMET evaluation in drug discovery. 13. Development of in silico prediction models for P-glycoprotein substrates.

P-glycoprotein (P-gp) actively transports a wide variety of chemically diverse compounds out of cells. It is highly associated with the ADMET properties of drugs and drug candidates and, moreover, plays a major role in the multidrug resistance (MDR) phenomenon, which leads to the failure of chemotherapy in cancer treatments. Therefore, the recognition of potential P-gp substrates at the early stages of the drug discovery process is quite important. Here, we compiled an extensive data set containing 423 P-gp substrates and 399 nonsubstrates, which is the largest P-gp substrate/nonsubstrate data set yet published. Comparison of the distributions of eight important physicochemical properties for the substrates and nonsubstrates reveals that molecular weight and molecular solubility are the informative attributes differentiating P-gp substrates from nonsubstrates. Examination of the distributions of eight physicochemical properties for 735 P-gp inhibitors and 423 substrates gives the fact that inhibitors are significantly more hydrophobic than substrates while substrates tend to have more H-bond donors than inhibitors. Then, the classification models based on simple molecular properties, topological descriptors, and molecular fingerprints were developed using the naive Bayesian classification technique. The best naive Bayesian classifier yields a Matthews correlation coefficient of 0.824 and a prediction accuracy of 91.2% for the training set from a 5-fold cross-validation procedure, and a Matthews correlation coefficient of 0.667 and a prediction accuracy of 83.5% for the test set containing 200 molecules. Analysis of the important structural fragments given by the Bayesian classifier shows that the essential H-bond acceptors arranged in distinct spatial patterns and flexibility are quite essential for P-gp substrate-likeness, which affords a deeper understanding on the molecular basis of substrate/P-gp interaction. Finally, the reasons for mispredictions were discussed. It turns out that the presented classifier could be used as a reliable virtual screening tool for identifying potential substrates of P-gp.

Optimization of virtual screening against phosphoinositide 3-kinase delta: Integration of common feature pharmacophore and multicomplex-based molecular docking.

Extensive research has accumulated which suggests that phosphatidylinositol 3-kinase delta (PI3Kδ) is closely related to the occurrence and development of various human diseases, making PI3Kδ a highly promising drug target. However, PI3Kδ exhibits high homology with other members of the PI3K family, which poses significant challenges to the development of PI3Kδ inhibitors. Therefore, in the present study, a hybrid virtual screening (VS) approach based on a ligand-based pharmacophore model and multicomplex-based molecular docking was developed to find novel PI3Kδ inhibitors. 13 crystal structures of the human PI3Kδ-inhibitor complex were collected to establish models. The inhibitors were extracted from the crystal structures to generate the common feature pharmacophore. The crystallographic protein structures were used to construct a naïve Bayesian classification model that integrates molecular docking based on multiple PI3Kδ conformations. Subsequently, three VS protocols involving sequential or parallel molecular docking and pharmacophore approaches were employed. External predictions demonstrated that the protocol combining molecular docking and pharmacophore resulted in a significant improvement in the enrichment of active PI3Kδ inhibitors. Finally, the optimal VS method was utilized for virtual screening against a large chemical database, and some potential hit compounds were identified. We hope that the developed VS strategy will provide valuable guidance for the discovery of novel PI3Kδ inhibitors.

Developing a Naïve Bayesian Classification Model with PI3Kγ structural features for virtual screening against PI3Kγ: Combining molecular docking and pharmacophore based on multiple PI3Kγ conformations.

Phosphatidylinositol 3-kinase gamma (PI3Kγ) plays a critical role in immune signaling, thus identifying PI3Kγ as a potential therapeutic target. However, developing selective PI3Kγ inhibitors is hampered by the highly conserved structure of the ATP-binding pocket. Focused effort would be needed to improve upon the γ-subtype selectivity of the inhibitors; therefore, in the present study, a naïve Bayesian classification (NBC) model with PI3Kγ structural features that integrates molecular docking and pharmacophore based on multiple PI3Kγ conformations was developed for virtual screening against PI3Kγ to find novel selective PI3Kγ inhibitors. First, the active PI3Kγ inhibitors/decoy dataset was used to prove whether molecular docking or pharmacophore, integrating multiple PI3Kγ conformations always has higher prediction accuracy than that of any single conformation. Second, both internal cross-validation and external prediction revealed that the NBC model combining molecular docking and pharmacophore could significantly improve the enrichment of active PI3Kγ inhibitors. Then, an analog dataset based on JN-PK1 (a reference compound) was constructed and submitted to virtual screening using the optimal NBC model. Finally, a novel inhibitor with higher PI3Kγ inhibitory activity than JN-PK1 was identified through a series of biological assays, showing both good accuracy and significant reliability of the NBC model with the PI3Kγ structural features. We hope that the developed virtual screening strategy will provide valuable guidance for the discovery of novel selective PI3Kγ inhibitors.

Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3ß Inhibitors.

Glycogen synthase kinase-3ß (GSK3ß) is associated with various key biological processes, and it has been considered as a critical therapeutic target for the treatment of many diseases. However, it is a big challenge to develop ATP-competition GSK3ß inhibitors because of the high sequence homology with other kinases. In this work, a novel parallel virtual screening strategy based on multiple GSK3ß protein structures, integrating molecular docking, complex-based pharmacophore, and naive Bayesian classification, was developed to screen a large chemical database, the 50 compounds with top-scores then underwent a luminescent kinase assay, which led to the discovery of two GSK3ß inhibitor hits. The high screening enrichment rate indicates the reliability and practicability of the integrated protocol. Finally, molecular docking and molecular dynamics simulation were employed to investigate the binding modes of the GSK3ß inhibitors, and some "hot residues" critical to GSK3ß affinity were highlighted. The present study may provide some valuable guidance for the development of novel GSK3ß inhibitors.

Detecting oropharyngeal carcinoma using multispectral, narrow-band imaging and machine learning.

OBJECTIVE: To determine if multispectral narrow-band imaging (mNBI) can be used for automated, quantitative detection of oropharyngeal carcinoma (OPC). STUDY DESIGN: Prospective cohort study. METHODS: Multispectral narrow-band imaging and white light endoscopy (WLE) were used to examine the lymphoepithelial tissues of the oropharynx in a preliminary cohort of 30 patients (20 with biopsy-proven OPC, 10 healthy). Low-level image features from five patients were then extracted to train naïve Bayesian classifiers for healthy and malignant tissue. RESULTS: Tumors were classified by color features with 65.9% accuracy, 66.8% sensitivity, and 64.9% specificity under mNBI. In contrast, tumors were classified with 52.3% accuracy (P = 0.0108), 44.8% sensitivity (P = 0.0793), and 59.9% specificity (P = 0.312) under WLE. Receiver operating characteristic analysis yielded areas under the curve (AUC) of 72.3% and 54.6% for classification under mNBI and WLE, respectively (P = 0.00168). For classification by both color and texture features, AUC under mNBI increased (80.1%, P = 0.00230) but did not improve under WLE (below 55% for both models, P = 0.180). Cross-validation with five folds yielded an AUC above 80% for both mNBI models and below 55% for both WLE models (P = 0.0000410 and 0.000116). CONCLUSION: Compared to WLE, mNBI significantly enhanced the performance of a naïve Bayesian classifier trained on low-level image features of oropharyngeal mucosa. These findings suggest that automated clinical detection of OPC might be used to enhance surgical vision, improve early diagnosis, and allow for high-throughput screening. LEVEL OF EVIDENCE: NA. Laryngoscope, 2514-2520, 2018.

Using Naïve Bayesian Analysis to Determine Imaging Characteristics of KRAS Mutations in Metastatic Colon Cancer.

Genotype, particularly Ras status, greatly affects prognosis and treatment of liver metastasis in colon cancer patients. This pilot aimed to apply word frequency analysis and a naive Bayes classifier on radiology reports to extract distinguishing imaging descriptors of wild-type colon cancer patients and those with v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations. In this institutional-review-board-approved study, we compiled a SNaPshot mutation analysis dataset from 457 colon adenocarcinoma patients. From this cohort of patients, we analyzed radiology reports of 299 patients (> 32,000 reports) who either were wild-type (147 patients) or had a KRAS (152 patients) mutation. Our algorithm determined word frequency within the wild-type and mutant radiology reports and used a naive Bayes classifier to determine the probability of a given word belonging to either group. The classifier determined that words with a greater than 50% chance of being in the KRAS mutation group and which had the highest absolute probability difference compared to the wild-type group included: "several", "innumerable", "confluent", and "numerous" (p < 0.01). In contrast, words with a greater than 50% chance of being in the wild type group and with the highest absolute probability difference included: "few", "discrete", and "[no] recurrent" (p = 0.03). Words used in radiology reports, which have direct implications on disease course, tumor burden, and therapy, appear with differing frequency in patients with KRAS mutations versus wild-type colon adenocarcinoma. Moreover, likely characteristic imaging traits of mutant tumors make probabilistic word analysis useful in identifying unique characteristics and disease course, with applications ranging from radiology and pathology reports to clinical notes.

Identification of potent inhibitors of DNA methyltransferase 1 (DNMT1) through a pharmacophore-based virtual screening approach.

DNA methylation is an epigenetic change that results in the addition of a methyl group at the carbon-5 position of cytosine residues. DNA methyltransferase (DNMT) inhibitors can suppress tumour growth and have significant therapeutic value. However, the established inhibitors are limited in their application due to their substantial cytotoxicity. Additionally, the standard drugs for DNMT inhibition are non-selective cytosine analogues with considerable cytotoxic side-effects. In the present study, we have designed a workflow by integrating various ligand-based and structure-based approaches to discover new agents active against DNMT1. We have derived a pharmacophore model with the help of available DNMT1 inhibitors. Utilising this model, we performed the virtual screening of Maybridge chemical library and the identified hits were then subsequently filtered based on the Naïve Bayesian classification model. The molecules that have returned from this classification model were subjected to ensemble based docking. We have selected 10 molecules for the biological assay by inspecting the interactions portrayed by these molecules. Three out of the ten tested compounds have shown DNMT1 inhibitory activity. These compounds were also found to demonstrate potential inhibition of cellular proliferation in human breast cancer MDA-MB-231 cells. In the present study, we have utilized a multi-step virtual screening protocol to identify inhibitors of DNMT1, which offers a starting point to develop more potent DNMT1 inhibitors as anti-cancer agents.