SAR and QSAR models of cyclooxygenase-1 (COX-1) inhibitors

Cyclooxygenase-1 (COX-1) is one isoform of COX, and it is a main target of nonsteroidal anti-inflammatory drugs (NSAIDs). It is important to develop efficient and selective COX-1 inhibitors. In this work, 12 classification models for 1530 cyclooxygenase-1 (COX-1) inhibitors were built by support vector machine (SVM), decision tree (DT) and random forest (RF) methods. The best classification model (model 1A) was built by SVM with MACCS fingerprints. The classification accuracies for the training and test sets were 99.67% and 97.39%, respectively. The Matthews correlation coefficient (MCC) of the test set was 0.94. We also divided the 1530 COX-1 inhibitors into nine subsets according to their different scaffolds using Kohonen's self-organizing map (SOM). In addition, six quantitative structure-activity relationship (QSAR) models for 181 COX-1 inhibitors whose IC₅₀ were measured by enzyme immunoassay were built by multiple linear regression (MLR) and SVM. The best QSAR model (model 5A) was built by SVM with CORINA Symphony descriptors. The correlation coefficients of the training and test sets are 0.93 and 0.84, respectively. The models built in this study can be obtained from the authors.

Multiregion whole-exome sequencing of matched primary and metastatic tumors revealed genomic heterogeneity and suggested polyclonal seeding in colorectal cancer metastasis

Background

Distant metastasis accounts for 90% of deaths from colorectal cancer (CRC). Genomic heterogeneity has been reported in various solid malignancies, but remains largely under-explored in metastatic CRC tumors, especially in primary to metastatic tumor evolution.

Patients and methods

We conducted high-depth whole-exome sequencing in multiple regions of matched primary and metastatic CRC tumors. Using a total of 28 tumor, normal, and lymph node tissues, we analyzed inter- and intra-individual heterogeneity, inferred the tumor subclonal architectures, and depicted the subclonal evolutionary routes from primary to metastatic tumors.

Results

CRC has significant inter-individual but relatively limited intra-individual heterogeneity. Genomic landscapes were more similar within primary, metastatic, or lymph node tumors than across these types. Metastatic tumors exhibited less intratumor heterogeneity than primary tumors, indicating that single-region sequencing may be adequate to identify important metastasis mutations to guide treatment. Remarkably, all metastatic tumors inherited multiple genetically distinct subclones from primary tumors, supporting a possible polyclonal seeding mechanism for metastasis. Analysis of one patient with the trio samples of primary, metastatic, and lymph node tumors supported a mechanism of synchronous parallel dissemination from the primary to metastatic tumors that was not mediated through lymph nodes.

Conclusions

In CRC, metastatic tumors have different but less heterogeneous genomic landscapes than primary tumors. It is possible that CRC metastasis is, at least partly, mediated through a polyclonal seeding mechanism. These findings demonstrated the rationale and feasibility for identifying and targeting primary tumor-derived metastasis-potent subclones for the prediction, prevention, and treatment of CRC metastasis.

SAR and QSAR study on the bioactivities of human epidermal growth factor receptor-2 (HER2) inhibitors

In this paper, structure-activity relationship (SAR, classification) and quantitative structure-activity relationship (QSAR) models have been established to predict the bioactivity of human epidermal growth factor receptor-2 (HER2) inhibitors. For the SAR study, we established six SAR (or classification) models to distinguish highly and weakly active HER2 inhibitors. The dataset contained 868 HER2 inhibitors, which was split into a training set including 580 inhibitors and a test set including 288 inhibitors by a Kohonen's self-organizing map (SOM), or a random method. The SAR models were performed using support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP) methods. Among the six models, SVM models obtained superior results compared with other models. The prediction accuracy of the best model (model 1A) was 90.27% and the Matthews correlation coefficient (MCC) was 0.80 on the test set. For the QSAR study, we chose 286 HER2 inhibitors to establish six quantitative prediction models using MLR, SVM and MLP methods. The correlation coefficient (r) of the best model (model 4B) was 0.92 on the test set. The descriptors analysis showed that HAccN, lone pair electronegativity and π electronegativity were closely related to the bioactivity of HER2 inhibitors.

[Correlative study between X-ray type after healing of congenital pseudarthrosis of the tibia in children and postoperative refracture]

Objective: To investigate the relationship between postoperative X-ray type in 2 years after healing of congenital pseudarthrosis of the tibia(CPT) and refracture of CPT in children. Methods: A retrospective study was performed on 67 children patients with Crawford type Ⅳ CPT who were treated with combined surgeries from December 2007 to August 2012.There were 46 male and 21 female patients with 37 cases with left CPT and 30 cases with right CPT. There were 12 cases with proximal tibia dysplasia, 56 cases with neurofibromatosis type 1. The median age when operation was 2.8 years(from 0.6 to 11.2 years). The patients were divided into three groups, CPT with hypertrophic group, CPT with mediate group and CPT with atrophic group, on the basis of ratio of healing cross-sectional area and transition zone in pseudarthrosis.The incidence of refracture in the three groups were investigated. Results: The refracture rates of three groups were 13%(5/38), 14%(3/21), 5/8, respectively.The refracture rate difference between CPT with hypertrophic group and CPT with mediate group was not statistically significant(P=0.590). The refracture rate of CPT with atrophic group was statistically significant lower than that of CPT with hypertrophic group and CPT with mediate group(P=0.007, 0.019). In addition, the refracture-free cumulative survival rate of CPT with hypertrophic group or CPT with mediate group was higher than that of CPT with atrophic group with the statistically significant difference(both P<0.05). And the refracture-free cumulative survival rate in CPT with hypertrophic group was lower than that in CPT with mediate group, the difference was not significant(P>0.05). Conclusion: After the union of CPT, patients with hypertrophic, mediate type X ray characteristic showed lower incidence of refracture than those with atrophic type.

QSAR study on the antimalarial activity of Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) inhibitors

Plasmodium falciparum, the most fatal parasite that causes malaria, is responsible for over one million deaths per year. P. falciparum dihydroorotate dehydrogenase (PfDHODH) has been validated as a promising drug development target for antimalarial therapy since it catalyzes the rate-limiting step for DNA and RNA biosynthesis. In this study, we investigated the quantitative structure-activity relationships (QSAR) of the antimalarial activity of PfDHODH inhibitors by generating four computational models using a multilinear regression (MLR) and a support vector machine (SVM) based on a dataset of 255 PfDHODH inhibitors. All the models display good prediction quality with a leave-one-out q(2) >0.66, a correlation coefficient (r) >0.85 on both training sets and test sets, and a mean square error (MSE) <0.32 on training sets and <0.37 on test sets, respectively. The study indicated that the hydrogen bonding ability, atom polarizabilities and ring complexity are predominant factors for inhibitors' antimalarial activity. The models are capable of predicting inhibitors' antimalarial activity and the molecular descriptors for building the models could be helpful in the development of new antimalarial drugs.

Effect of stoichiometry on the magnetocrystalline anisotropy of Fe-Pt and Co-Pt from first-principles calculation

The effect of stoichiometry on magnetocrystalline anisotropy energy (MAE) of Fe1+xPt1-x and Co1+xPt1-x (-0.5 < x < 0.5) is studied by use of first-principles method. The calculated MAEs show maxima at x = 0 for both fully L10-ordered systems. Compared with that, the MAEs of partially L10-ordered systems reduce but their composition dependences do not change, without shift of the maximum MAE to Fe/Co-rich alloy as found in experiment at room temperature. In the off-stoichiometric alloys, the misoccupied Fe/Co and Pt show large MAEs, which is explained by the enhanced in-plane hybridization between Fe/Co and Pt. The composition dependence of the atom-resolved MAE is governed by the varying number of heterogeneous ligands around the atom. The MAE(T)/MAE(0) is discussed based on spontaneous magnetization and Curie temperature, which suggests that the temperature effect may contribute to the discrepancy between calculation and experiment in the composition dependence of MAE.

Computational models on quantitative prediction of bioactivity of HIV-1 integrase 3' processing inhibitors

In this study, four computational quantitative structure-activity relationship (QSAR) models were built to predict the bioactivity of 3' processing (3'P) inhibitors of HIV-1 integrase. Some 453 inhibitors whose bioactivity values were detected by the radiolabelling method were collected. The molecular structures were represented with MOE descriptors. In total, 21 descriptors were selected for modelling. All inhibitors were divided into a training set and a test set with two methods: (1) by a Kohonen's self-organizing map (SOM); (2) by a random selection. For every training set and test set, a multilinear regression (MLR) analysis and a support vector machine (SVM) were used to establish models, respectively. For the training/test set divided by SOM, the correlation coefficients (r) were over 0.84, and for the training/test set split randomly, the r values were over 0.86. Some molecular properties such as hydrogen bond donor capacity, atomic partial charge properties, molecular refractivity, the number of aromatic bonds and molecular surface area, volume and shape properties played important roles for inhibiting 3' processing step of HIV-1 integrase.

Quantitative structure and bioactivity relationship study on HCV NS5B polymerase inhibitors

Several QSAR (quantitative structure-activity relationship) models for predicting the inhibitory activity of 333 hepatitis C virus (HCV) NS5B polymerase inhibitors were developed. All the inhibitors are HCV polymerase non-nucleoside analogue inhibitors (NNIs) fitting into the pocket of the NNI III binding site. For each molecule, global descriptors and 2D property autocorrelation descriptors were calculated from the program ADRIANA.Code. Pearson correlation analysis was used to select the significant descriptors for building models. The whole dataset was split into a training set and a test set randomly or using a Kohonen's self-organizing map (SOM). Then, the inhibitory activity of 333 HCV NS5B polymerase inhibitors was predicted using multilinear regression (MLR) analysis and support vector machine (SVM) method, respectively. For the test set of the best model (Model 2B), correlation coefficient of 0.91 was achieved. Some molecular descriptors, such as molecular complexity (Complexity), the number of hydrogen bonding donors (HDon) and the solubility of the molecule in water (log S) were found to be very important factors which determined the bioactivity of the HCV NS5B inhibitors. Some other molecular properties such as electrostatic and charge properties also played important roles in the interaction between the ligand and the protein. The selected molecular descriptors were further confirmed by analysing the interaction between two representative inhibitors and the polymerase in their crystal structures.

A robust general phase retrieval method for medical applications

From medical imaging perspective the robustness of a phase retrieval method is of critical importance. In this presentation we compare the robustness of two general phase retrieval methods, namely the transport of intensity equation inversion (TIE-inversion) method and the attenuation partition based (AP-based) method. We showed that the TIE-inversion method, regardless if being assisted with the Tikhonov regularization, failed to retrieve the phase maps in two experimental studies. The failure exposes this method's weakness as being unstable against the noise. In contrast, the sample phase maps are retrieved successfully by using the AP-based method. The stark performance differences of the two methods are rooted in their different techniques dealing with the singularity problem. This comparison shows that the robust AP-based phase retrieval method will be superior to the TIE-inversion method for medical imaging applications where radiation doses are stringently limited.

In-silico prediction of blood-brain barrier permeability

The ability of penetration of the blood-brain barrier is one of the significant properties of a drug or drug-like compound for the central nervous system (CNS), which is commonly expressed by log BB (log BB = log (C (brain)/C (blood))). In this work, a dataset of 320 compounds with log BB values was split into a training set including 198 compounds and a test set including 122 compounds according to their structure properties by a Kohonen's self-organizing map (SOM). Each molecule was represented by global and shape descriptors, 2D autocorrelation descriptors and RDF descriptors calculated by ADRIANA.Code. Several quantitative models for prediction of log BB were built by a multilinear regression (MLR), a support vector machine (SVM) and an artificial neural network (ANN) analysis. The models show good prediction performance on the test set compounds.