Discovery of Novel Noncovalent KRAS G12D Inhibitors through Structure-Based Virtual Screening and Molecular Dynamics Simulations.

The development of effective inhibitors targeting the Kirsten rat sarcoma viral proto-oncogene (KRASG12D) mutation, a prevalent oncogenic driver in cancer, represents a significant unmet need in precision medicine. In this study, an integrated computational approach combining structure-based virtual screening and molecular dynamics simulation was employed to identify novel noncovalent inhibitors targeting the KRASG12D variant. Through virtual screening of over 1.7 million diverse compounds, potential lead compounds with high binding affinity and specificity were identified using molecular docking and scoring techniques. Subsequently, 200 ns molecular dynamics simulations provided critical insights into the dynamic behavior, stability, and conformational changes of the inhibitor-KRASG12D complexes, facilitating the selection of lead compounds with robust binding profiles. Additionally, in silico absorption, distribution, metabolism, excretion (ADME) profiling, and toxicity predictions were applied to prioritize the lead compounds for further experimental validation. The discovered noncovalent KRASG12D inhibitors exhibit promises as potential candidates for targeted therapy against KRASG12D-driven cancers. This comprehensive computational framework not only expedites the discovery of novel KRASG12D inhibitors but also provides valuable insights for the development of precision treatments tailored to this oncogenic mutation.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics.

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics.

Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics.

Classification for psychiatric disorders including schizophrenia, bipolar disorder, and major depressive disorder using machine learning.

Schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD) are the most common psychiatric disorders. Because there were lots of overlaps among these disorders from genetic epidemiology and molecular genetics, it is hard to realize the diagnoses of these psychiatric disorders. Currently, plenty of studies have been conducted for contributing to the diagnoses of these diseases. However, constructing a classification model with superior performance for differentiating SCZ, BP, and MDD samples is still a great challenge. In this study, the transcriptomic data was applied for discovering key genes and constructing a classification model. In this dataset, there were 268 samples including four groups (67 SCZ patients, 40 BP patients, 57 MDD patients, and 104 healthy controls), which were applied for constructing a classification model. First, 269 probes of differentially expressed genes (DEGs) among four sample groups were identified by the feature selection method. Second, these DEGs were validated by the literature review including disease relevance with the psychiatric disorders of these DEGs, the hub genes in the PPI (protein-protein interaction) network, and GO (gene ontology) terms and pathways. Third, a classification model was constructed using the identified DEGs by machine learning method to classify different groups. The ROC (receiver operator characteristic) curve and AUC (area under the curve) value were used to assess the classification capacity of the model. In summary, this classification model might provide clues for the diagnoses of these psychiatric disorders.

A novel multi-class classification model for schizophrenia, bipolar disorder and healthy controls using comprehensive transcriptomic data.

Two common psychiatric disorders, schizophrenia (SCZ) and bipolar disorder (BP), confer lifelong disability and collectively affect 2% of the world population. Because the diagnosis of psychiatry is based only on symptoms, developing more effective methods for the diagnosis of psychiatric disorders is a major international public health priority. Furthermore, SCZ and BP overlap considerably in terms of symptoms and risk genes. Therefore, the clarity of the underlying etiology and pathology remains lacking for these two disorders. Although many studies have been conducted, a classification model with higher accuracy and consistency was found to still be necessary for accurate diagnoses of SCZ and BP. In this study, a comprehensive dataset was combined from five independent transcriptomic studies. This dataset comprised 120 patients with SCZ, 101 patients with BP, and 149 healthy subjects. The partial least squares discriminant analysis (PLS-DA) method was applied to identify the gene signature among multiple groups, and 341 differentially expressed genes (DEGs) were identified. Then, the disease relevance of these DEGs was systematically performed, including (α) the great disease relevance of the identified signature, (ß) the hub genes of the protein-protein interaction network playing a key role in psychiatric disorders, and (γ) gene ontology terms and enriched pathways playing a key role in psychiatric disorders. Finally, a popular multi-class classifier, support vector machine (SVM), was applied to construct a novel multi-class classification model using the identified signature for SCZ and BP. Using the independent test sets, the classification capacity of this multi-class model was assessed, which showed this model had a strong classification ability.

Construction of the Classification Model Using Key Genes Identified Between Benign and Malignant Thyroid Nodules From Comprehensive Transcriptomic Data.

Thyroid nodules are present in upto 50% of the population worldwide, and thyroid malignancy occurs in only 5-15% of nodules. Until now, fine-needle biopsy with cytologic evaluation remains the diagnostic choice to determine the risk of malignancy, yet it fails to discriminate as benign or malignant in one-third of cases. In order to improve the diagnostic accuracy and reliability, molecular testing based on transcriptomic data has developed rapidly. However, gene signatures of thyroid nodules identified in a plenty of transcriptomic studies are highly inconsistent and extremely difficult to be applied in clinical application. Therefore, it is highly necessary to identify consistent signatures to discriminate benign or malignant thyroid nodules. In this study, five independent transcriptomic studies were combined to discover the gene signature between benign and malignant thyroid nodules. This combined dataset comprises 150 malignant and 93 benign thyroid samples. Then, there were 279 differentially expressed genes (DEGs) discovered by the feature selection method (Student's t test and fold change). And the weighted gene co-expression network analysis (WGCNA) was performed to identify the modules of highly co-expressed genes, and 454 genes in the gray module were discovered as the hub genes. The intersection between DEGs by the feature selection method and hub genes in the WGCNA model was identified as the key genes for thyroid nodules. Finally, four key genes (ST3GAL5, NRCAM, MT1F, and PROS1) participated in the pathogenesis of malignant thyroid nodules were validated using an independent dataset. Moreover, a high-performance classification model for discriminating thyroid nodules was constructed using these key genes. All in all, this study might provide a new insight into the key differentiation of benign and malignant thyroid nodules.

Toxicity, bio-distribution and metabolism of CO-releasing molecules based on cobalt.

CO-releasing molecules (CORMs) containing [Co2(CO)6] moiety show many bioactivities, such as anti-inflammatory and antitumor cell proliferation. However, so far, no one knows their properties in vivo. So, here, we evaluated some these kind CORMs from drug-like properties including cytotoxicity, toxicity in vivo, distribution and metabolism. The results show all the tested complexes displayed antiproliferative activity to HeLa cell and HepG2 cell lines, and their IC50 values were 36-110µM against HeLa cells and 39-140µM against HepG2 cells. Toxicity tests of mice, we used oral acute toxic class method and got their LD50 values; among them, LD50 of complex 1 and complex 4 were in 2500-5000mgkg(-1) and complex 7 over 5000mgkg(-1). The developmental toxicities of the complexes were investigated in embryonic zebrafish. The mortality, hatch rate, malformation, heart rate, spontaneous movement, and larval behavior were examined, and we found both complexes 4 and 7 have not toxicity at low concentration (<1.0µM) but have higher toxicity at high concentration (>5.0µM). After several consecutive i.p administrations, tested complexes severely damaged rat liver and kidney in both functional and morphological aspects. Through metal ion measurement using ICP-AES, we found the tested complexes were unevenly distributed in tissues and organs; complex 4 has a big prone to collect in liver, whereas complex 7 easily enters to kidney. After administration 480min later, most of complex 7 excreted from kidney and entered urine, while complex 4 needed 9h at least. This results show cobalt did not accumulate, and could excrete with the urine. In vivo, Co(0) in complexes was oxidised to Co(II). In addition, the substituents significantly affected the rate of CO-release, cytotoxicity and their bio-distribution. In the view of these aspects, the CORMs based cobalt has a potential property to be a medicine.

Syntheses, properties and bio-activities of water-soluble CO-releasing molecule based on manganese.

A series of water-soluble CO-releasing molecules, [Mn(CO)3NH2CHRCO2]2 (1-3), [M(CO)3Br[(Py-C = N)(Gly) n CO2] (M = Mn, Re, 4-7), Mn(CO)4[S2CNC m H n CO2] (8-12), were synthesized and characterized by (1)H NMR, IR and ESI-HRMS. The stability of all the complexes in solution was evaluated by means of UV, IR and (1)H NMR. Among all the complexes, complex 4 and complex 8 were stable in H2O, acidic aqueous solution and basic media; complex 1 was stable in acidic aqueous solution and weak basic media (pH < 9.4). The assays showed that each complex has CO-release ability; excess sodium dithionite can enhance CO release. Among them, complexes 8-12 were fast CO-releasers. In the test of the cell proliferation, all the complexes showed anti-proliferative activities for HeLa and HepG2. In particular, complex 8 displayed a 3.5-fold anti-proliferative activity on HeLa cells (IC50 23.13 µM) and fivefold on HepG2 cells (34.00 µM) compared with 5-FU. What is more, the complexes distinctly influenced cell cycle and promoted cell apoptosis; complex 1 arrested HeLa cells in S phase, whereas complex 4 and complex 8 arrested in G2/M phase; all the complexes induced HeLa cells "Early apoptosis". In addition, all complexes 1, 4 and 8 decreased intracellular nitrite levels, and complex 8 was stronger than both of the others. All these data demonstrate that complex 8 has potential to be a drug candidate. Three different categories of water-soluble CORMs 1-12 were synthesized, and their stability were evaluated. The biological activities were preliminarily evaluated. This includes anti-proliferation and anti-inflammatory properties.

[Toxicology and bioactivities of CO-releasing molecules based on cobalt complexes].

Complexes containing cobalt and carbon monoxide ligands, CO releasing molecules(CORMs), have the potential of anti-tumor and anti-inflammatory. In this paper, three hybrid CORMs 1-3 were synthesized and tested for their toxicology in vivo and bioactivities. The results suggest that the complexes have a long half-life in the range of 43-53 min; their oral LD(50) to mouse are between 1 500 mg·kg(-1) and 5 000 mg·kg(-1). After the successive administration, complex 1 exhibited a toxic activity in rats' liver, and induced an injury to liver cells. Complex 1 had a strong growth inhibition activity(IC(50) 36.20 µmol·L(-1) and 39.25 µmol·L(-1)) in both He La cells and Hep G2 cells, complex 2 displayed a lower activity in the inhibition of He La cells proliferation than the control 5-FU(IC(50) 114.19 µmol·L(-1)), but had a higher activity in the inhibition of Hep G2 cells than the control 5-FU(IC(50) 171.34 µmol·L(-1)). The anti-inflammatory study suggests that all of them reduce intracellular nitrite level, complexes 1 and 2 have a stronger activity than complex 3. Their anti-inflammatory activity attributes to the CO molecules of the CORMs, which was confirmed by comparison with the corresponding ligand.