Q2 Values Research Articles

PB2198 CRUCIAL AGE FOR JAK2 V617F MUTATION: GENDER DIFFERENCES

Background:The prevalence of JAK2 V617F mutation of Ph‐negative myeloproliferative neoplasias (MPN) increases with age. The dependence of the JAK2 V617F mutation prevalence and the rate of increasing its burden from the patients sex was discussion [Patterson‐Fortin J, et al., 2017; Stein BL et al., 2010]. There is monotonically increasing curve of somatic mutations and oncological morbidity as a usual presentation of growth graphs. However, the using of the population threshold model of the age dynamics of oncological diseases makes it possible to set of critical points of inflection (second order phase transition) and to quantify the intensity of the increase in the burden of disease [Landau LD, Lifshitz EM, 2013; Soukhovolsky VG et al., 2015].Aims:Assessment of the intensity of the JAK2 V617F mutation prevalence on the age and sex dependence using the model of the “second order phase transition”.MethodsThe data base of the Krasnoyarsk branch of the National Hematology Research Center from Jan 2012 to Feb 2019 examination has been used. The JAK2V617F allele burden was measured in whole blood samples by an allele‐specific quantitative polymerase chain reaction using of the “Myeloskrin JAK2 kit” (Formula of gene ltd). The relationship between the q (rate of JAK2 V617F detection) and the 1/T indicator (“reverse age”) in each age‐old five‐year cohort on 1154 men and 958 women referred with suspicion of MPN was studied. The values of q2 should be close to zero as according to the model of second order phase transitions for physical systems until reaching a certain critical patients age Tc and the relationship between the q2 and 1/T variables will be described by the equation q2 = a‐b/T with an angular coefficient b characterizing the increase in the intensity of the mutational burden with age after reaching the critical age Tc.Results:Using of our model led to transforms the monotonous growth curve of mutation prevalence with age. The value of q2 is close to zero at high values of 1/T (young patients) and for smaller values of 1/T (more older age) the relationship between q2 and 1/T becomes linear (figure). The hypothesis of the statistical significance differences of regression for male and female was tested by Fisher test for the significance level α=5%. Hypothesis of the absence of differences between the two regressions is not rejected. The critical age of patients transition to the phase with a high risk of mutation development corresponds for male Tc = 48.8 years and for female Tc = 36.0 years. Intensity of further accumulation of the mutation burden in male b = 44.35 ± 12.86, in female b = 27.52 ± 4.57.Summary/Conclusion:The “second order phase transitions model” it possible to introduce in the population analysis the indicators of the “critical age” Tc and the intensity of accumulation of the burden of mutations b. This estimate are not significantly different for male and female for our sample. The calculated critical age turned out to be significantly lower than the median of the age of diagnosis of MPN (53 and 57 years). The proposed model will be useful for analyzing the characteristics of the population of MPN in different cohorts, as well as in different social and environmental conditions of life.image

Plasma Lipidomics Profiles After a Diet Characterized by Whole Grains Compared to a Diet High in Refined Grains and Added Sugars (FS03-07-19)

ObjectivesDietary patterns high in fiber from sources including whole grains, legumes, fruits, vegetables, nuts and seeds, are associated with lower risk of chronic disease, such as cardiovascular disease and cancer. We investigated how plasma lipidomics profiles differed between a diet high in whole grains (WG) versus a diet high in refined grains and added sugars (RG). MethodsUsing a randomized, crossover, controlled feeding study, 80 healthy participants (n = 40 men, n = 40 women, 40 normal weight, 40 overweight/obese), 18–45 y, were randomized to receive either a WG or RG diet for 28 days. After a 28-day washout period where participants resumed their habitual diet, they crossed over to the other diet. Targeted, differential mobility mass spectrometry was performed on fasting plasma samples collected at the baseline and end of each diet period and quantified the concentrations of 863 lipids from 13 classes. Paired t-tests and pairwise partial least squares-discriminant analysis (PLS-DA) were used to evaluate differences in lipid profiles between the two diets. ResultsAt a class level, only ceramides were significantly different when comparing the two diets. After removing lipid species with > 20% missing values or CVs < 25%, 606 were retained for species analysis. Sixty-seven lipid species were significantly different between diets at day 28 (FDR < 0.05): 38 of 414 detected triglycerides, 9 of 59 phosphatidylethanolamines, 9 of 63 phosphatidylcholines, 4 of 22 cholesterol esters, 3 of 11 sphingomyelins, 2 of 13 lysophosphatidylcholines, and 1 of 5 ceramides. The majority of significant lipids were higher in plasma after the WG diet. PLSDA analysis showed the first and second components explaining 49% and 8.4%, respectively. Based on the selected components, lipidomic profiles showed fair separation for the two groups of diet. R2 values were 0.07 and 0.43, and Q2 values were -0.03 and 0.04 for components 1 and 2, respectively. ConclusionsHigher concentrations of some lipid species such as cholesterol ester 12:0, a carrier of high-density lipoprotein, could indicate a favorable shift in lipid profiles. Further investigation using more complex models are being conducted. Funding SourcesNational Cancer Institute - National Institutes of Health.

Quantitative Structure Activity Relationships Study of Soluble Epoxide Hydrolase Inhibitors Using MLR, ANN, CoMFA and CoMSIA Methods

AbstractSoluble epoxide hydrolase (sEH), a member of the α/β hydrolase fold family, catalyzes the hydrolysis of epoxy eicosatrienoic acids to vicinal diol which are involved in the regulation of blood pressure and inflammation. In this study, 2D and 3D‐QSAR analysis of novel N,N′‐disubstituted urea derivatives as sEH inhibitors were performed by stepwise multiple linear regressions (SW‐MLR), stepwise artificial neural networks (SW‐ANN), Comparative Molecular Field Analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). It was found that SW‐MLR model with r2 of 0.954 for the training set and r2 of 0.876 for the test set were more favorable than model established by CoMFA method with r2=0.983, r2pred=0.602 and CoMSIA with r2=0.987, r2pred=0.751 in 3D‐QSAR. In addition, obtaining models were validated by cross validation with cut off value of q2&gt; 0.5. These developed models could be a useful guideline to design and predict the activity of novel compounds with enhanced sEH inhibitory activities.

Osteopenia-osteoporosis discrimination in postmenopausal women by 1H NMR-based metabonomics.

This is a report on how 1H NMR-based metabonomics was employed to discriminate osteopenia from osteoporosis in postmenopausal women, identifying the main metabolites associated to the separation between the groups. The Assays were performed using seventy-eight samples, being twenty-eight healthy volunteers, twenty-six osteopenia patients and twenty-four osteoporosis patients. PCA, LDA, PLS-DA and OPLS-DA formalisms were used. PCA discriminated the samples from healthy volunteers from diseased patient samples. Osteopenia-osteoporosis discrimination was only obtained using Analysis Discriminants formalisms, as LDA, PLS-DA and OPLS-DA. The metabonomics model using LDA formalism presented 88.0% accuracy, 88.5% specificity and 88.0% sensitivity. Cross-Validation, however, presented some problems as the accuracy of modeling decreased. LOOCV resulted in 78.0% accuracy. The OPLS-DA based model was better: R2Y and Q2 values equal to 0.871 (p<0.001) and 0.415 (p<0.001). LDA and OPLS-DA indicated the important spectral regions for discrimination, making possible to assign the metabolites involved in the skeletal system homeostasis, as follows: VLDL, LDL, leucine, isoleucine, allantoin, taurine and unsaturated lipids. These results indicate that 1H NMR-based metabonomics can be used as a diagnosis tool to discriminate osteoporosis from osteopenia using a single serum sample.

Quantitative Structure-Activity Relationship (QSAR) analysis of functionalized triblock copolymers with applications as dehydrating agents of crude oil

QSAR analysis was applied to obtain the water removal efficiency (WRE) values of triblock copolymers functionalized with tertiary amines, employed as demulsifier agents of crude oil. Taking into account the evaluation of F- and G-series reported by Zamora et al., the H-series was synthesized based on a triblock copolymer of 1920 g mol−1, and evaluated in CM1 and CM2 crude oils (7.5 and 18.7 °API). In addition, quantum and QSAR parameters, physicochemical properties of crude oil were included in the QSAR analysis. 66 WRE values were considered in the training set. Q2 value was established to weigh up an equation as acceptable, achieving eight equations, T1 – T8, for the training set. About external validation, F-, G- and H-series were assessed in CM3 heavy crude oil (16.9 °API), considering an overall of 33 WRE values for the validation. For the external validation, the coefficient of the prediction set (R2EXT), the predictive squared correlation coefficient (Q2Fn), the metric value (r2m), the concordance correlation coefficient (CCC), and the regression line slopes (k and k’) criteria were obtained. Only two equations, T1 and T8, complied with threshold established in the validation criteria. About r2m criterion, T8 equation gave a better correlation, 0.62, than that of T1 equation, 0.58. However, both equations could be optimal to calculate the water removal efficiency. Regarding physicochemical parameters of crude oil, number average molecular mass and run-off temperature were the unique parameters showing a clear effect over the calculated water removal efficiency.

CoMFA, CoMSIA, Topomer CoMFA, HQSAR, molecular docking and molecular dynamics simulations study of triazine morpholino derivatives as mTOR inhibitors for the treatment of breast cancer

mTOR has become a promising target for many types of cancer like breast, lung and renal cell carcinoma. CoMFA, CoMSIA, Topomer CoMFA and HQSAR were performed on the series of 39 triazine morpholino derivatives. CoMFA analysis showed q2 value of 0.735, r2cv value of 0.722 and r2pred value of 0.769. CoMSIA analysis (SEHD) showed q2 value of 0.761, r2cv value of 0.775 and r2pred value of 0.651. Topomer CoMFA analysis showed q2 value of 0.693, r2 (conventional correlation coefficient) value of 0.940 and r2pred value of 0.720. HQSAR analysis showed q2,r2and r2pred values of 0.694, 0.920 and 0.750, respectively. HQSAR analysis with the combination of atomic number (A), bond type (B) and atomic connections showed q2 and r2 values of 0.655 and 0.891, respectively. Contour maps from all studies provided significant insights. Molecular docking studies with molecular dynamics simulations were carried out on the highly potent compound 36. Furthermore, four acridine derivatives were designed and docking results of these designed compounds showed the same interactions as that of the standard PI-103 which proved the efficiency of 3D-QSAR and MD/MS study. In future, this study might be useful prior to synthesis for the designing of novel mTOR inhibitors.

Computational investigations of gram-negative bacteria phosphopantetheine adenylyltransferase inhibitors using 3D-QSAR, molecular docking and molecular dynamic simulations

Phosphopantetheine adenylyltransferase (PPAT) has been recognized as a promising target to develop novel antimicrobial agents, which is a hexameric enzyme that catalyzes the penultimate step in coenzyme A biosynthesis. In this work, molecular modeling study was performed with a series of PPAT inhibitors using molecular docking, three-dimensional qualitative structure-activity relationship (3D-QSAR) and molecular dynamic (MD) simulations to reveal the structural determinants for their bioactivities. Molecular docking study was applied to understand the binding mode of PPAT with its inhibitors. Subsequently, 3D-QSAR model was constructed to find the features required for different substituents on the scaffolds. For the best comparative molecular field analysis (CoMFA) model, the Q2 and R2 values of which were calculated as 0.702 and 0.989, while they were calculated as 0.767 and 0.983 for the best comparative molecular similarity index analysis model. The statistical data verified the significance and accuracy of our 3D-QSAR models. Furthermore, MD simulations were carried out to evaluate the stability of the receptor–ligand contacts in physiological conditions, and the results were consistent with molecular docking studies and 3D-QSAR contour map analysis. Binding free energy was calculated with molecular mechanics generalized born surface area approach, the result of which coincided well with bioactivities and demonstrated that van der Waals accounted for the largest portion. Overall, our study provided a valuable insight for further research work on the recognition of potent PPAT inhibitors.Communicated by Ramaswamy H. Sarma

3D-QSAR CoMFA Studies on Benzenesulfonamides with Benzimidazole Moieties as Inhibitors of Carbonic Anhydrases XII as Antitumor Agents

Introduction: In recent times, a new very significant research area has developed and obtained the potential use of Carbonic Anhydrase inhibitors as Antitumor agents. Methods: A series of sulfonamides and Benzenesulfonamides with benzimidazole moieties to inhibit CSXII was considered in this study. Results and Conclusion: The best model was obtained with a predictive r2 value of 0.678 and cross validated coefficient q2 value as 0.529 in tripos CoMFA region. The paper shows that the electrostatic and steric effects are important features in Carbonic Anhydrase XII inhibition for the investigated compounds. The effort has been undertaken to search for the structural aspects of bioactive molecules.

3D-QSAR (CoMFA, CoMSIA, HQSAR and topomer CoMFA), MD simulations and molecular docking studies on purinylpyridine derivatives as B-Raf inhibitors for the treatment of melanoma cancer

As per the World Health Organization (WHO), cancer is the second most leading cause of death after cardiovascular diseases in worldwide with around 9.88 million total new cases and 1.08 million were observed due to skin cancer in 2018. Amongst two types of skin cancer, progression of melanoma cancer is increasing day by day due to the environmental changes than non-melanoma cancer. Most of B-Raf mutation, specifically B-RafV600E, is responsible for the progression of the melanoma cancer. Here, various 3D-QSAR techniques like comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), molecular hologram QSAR (HQSAR) and topomer CoMFA were used to design novel B-Raf inhibitors by using 28 synthetic B-Raf inhibitors. Except for topomer CoMFA model, remaining models were generated by three different alignment methods in which distil-based alignment method was found best and gave prominent statistical values. After performing N-fold statistical validation, in CoMFA, q2, r2 and r2pred values were found to be 0.638, 0.969 and 0.848, respectively. Similarly, q2, r2 and r2pred values were found to be 0.796, 0.978 and 0.891 in CoMSIA (SHD) and 0.761, 0.973 and 0.852 in CoMSIA (SH) by N-fold statistical validation. In HQSAR analysis, statistical values were found for q2 as 0.984, r2 as 0.999 and r2pred as 0.634 with 97 as best hologram length (BHL). The results of topomer CoMFA showed the q2 value of 0.663 and the r2 value of 0.967. Important features of purinylpyridine were identified by contour map analysis of all 3D-QSAR techniques, which could be useful to design the novel molecules as B-Raf inhibitors for the treatment of melanoma cancer.

Investigation of Empirical and Semi‐Empirical Charges to Study the Effects of Partial Charges on Quality and Prediction Accuracy in 3D‐QSAR

AbstractIn Quantitative Structure‐Activity Relationship (QSAR) studies, selection of an appropriate method to assign the partial charge is crucial to derive a reliable model. The prediction accuracy of 3D‐QSAR models depends mainly on the method by which the partial charges were calculated. Therefore, we are interested in examining the effects of empirical and semi‐empirical partial charges on 3D‐QSAR methods, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). The feasibility and performance of these charges have been tested on Janus Kinase 2 inhibitors. Both empirical (Gasteiger‐Huckel, Del‐Re, Gasteiger Marsili, Huckel, Merck Molecular Force Field 94 (MMFF94), Pullman) and semi‐empirical (Austin Model 1 (AM1), Parameterized Model 3 (PM3), Recife Model 1 (RM1), and Modified Neglect of Diatomic Overlap (MNDO)) charges exhibited statistically significant quality. Among them, MMFF94, MNDO and AM1 charges yielded higher cross‐validation correlation coefficient (q2) values whereas Del‐Re and MMFF94 charges produced topmost predictive ability (r2pred). Overall, MMFF94 in CoMFA and MMFF94 and MNDO in CoMSIA models was found to be the best charges when both q2 and r2pred values were used as an evaluation criterion. Our results could provide an idea of selection of appropriate charge rather than using default charge to enhance the quality of 3D‐QSAR when prediction accuracy and predictive ability were employed.

Application of 1H-NMR based metabolite fingerprinting and chemometrics for authentication of Curcuma longa adulterated with C. heyneana

Curcuma longa is known to have many pharmacological activities. This rhizome has the rich content in curcuminoids which curcumin is the main active component. It is important to ensure the authenticity of C. longa to avoid adulteration because it is related to the quality, efficacy, and safety of the products. Other Curcuma species such as C. heyneana have the potential to be used as adulterant because of its lower price, its strong yellow color, and its wide availability. In this study, thin layer chromatography (TLC) method was used to determine curcumin content in pure and adulterated powder of C. longa with C. heyneana. Spectroscopy proton-nuclear magnetic resonance (1H NMR) based metabolite fingerprinting and chemometrics were employed to distinguish pure and adulterated powder of C. longa with C. heyneana. Curcumin content measured by TLC in pure C. longa and C. heyneana was found 5.63% and 0.77%, respectively. Curcumin content in adulterated C. longa with C. heyneana using adulterant concentration of 5–75 % was in the range of 5.40−1.44 %. Representative 1H NMR spectra showed a clear difference in aromatic region between C. longa and C. heyneana. 2D NMR J-resolved confirmed curcuminoid compounds in C. longa. Principal component analysis (PCA) using 8 principal components (PC) provided a clear separation of C. longa, adulterated C. longa, and C. heyneana. The high R2 value (0.992) of PCA indicated the good of fit of the model. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) using 1 predictive and 5 orthogonal components provided a clear separation between C. longa and adulterated C. longa. The model has a good of fit and good of predictivity indicated by the value of R2X (0.85), R2Y (0.992), and Q2 (0.899). The results exhibited that 1H NMR based metabolite fingerprinting and chemometrics of multivariate analysis can be applied to distinguish pure and adulterated C. longa with C. heyneana. This developed study may be applied to monitor the authenticity of medicinal plants.

Urine-NMR metabolomics for screening of advanced colorectal adenoma and early stage colorectal cancer

Although colorectal cancer (CRC) is considered one of the most preventable cancers, no non-invasive, accurate diagnostic tool to screen CRC exists. We explored the potential of urine nuclear magnetic resonance (NMR) metabolomics as a diagnostic tool for early detection of CRC, focusing on advanced adenoma and stage 0 CRC. Urine metabolomics profiles from patients with colorectal neoplasia (CRN; 36 advanced adenomas and 56 CRCs at various stages, n = 92) and healthy controls (normal, n = 156) were analyzed by NMR spectroscopy. Healthy and CRN groups were statistically discriminated using orthogonal projections to latent structure discriminant analysis (OPLS-DA). The class prediction model was validated by three-fold cross-validation. The advanced adenoma and stage 0 CRC were grouped together as pre-invasive CRN. The OPLS-DA score plot showed statistically significant discrimination between pre-invasive CRN as well as advanced CRC and healthy controls with a Q2 value of 0.746. In the prediction validation study, the sensitivity and specificity for diagnosing pre-invasive CRN were 96.2% and 95%, respectively. The grades predicted by the OPLS-DA model showed that the areas under the curve were 0.823 for taurine, 0.783 for alanine, and 0.842 for 3-aminoisobutyrate. In multiple receiver operating characteristics curve analyses, taurine, alanine, and 3-aminoisobutyrate were good discriminators for CRC patients. NMR-based urine metabolomics profiles significantly and accurately discriminate patients with pre-invasive CRN as well as advanced CRC from healthy individuals. Urine-NMR metabolomics has potential as a screening tool for accurate diagnosis of pre-invasive CRN.

Two- and three-dimensional QSAR studies on hURAT1 inhibitors with flexible linkers: topomer CoMFA and HQSAR.

hURAT1 (human urate transporter 1) is a successful target for hyperuricemia. Recently, the modification work on hURAT1 inhibitors showed that the flexible linkers would benefit biological activity. The study aimed to investigate the contribution of the linkers and give modification strategies on this kind of structures based on QSAR models (HQSAR and topomer CoMFA). The most effective HQSAR and topomer CoMFA models were generated by applying the training set containing 63 compounds, with the cross-validated q2 values of 0.869/0.818 and the non-cross-validated correlation coefficients r2 of 0.951/0.978, respectively. The Y-randomization test was applied to ensure the robustness of the models. The external predictive correlation coefficient (rpred2) grounded on the external test set (21 compounds) of two models was 0.910 and 0.907, respectively. In addition, the models were validated by Golbraikh-Tropsha and Roy methods, as well as other statistical metrics. The results showed that both models were reliable. Topomer CoMFA steric/electrostatic contours and HQSAR atomic contribution maps illustrated the structural features which governed their inhibitory potency. The dependable results could provide important insights to guide the designing of more potential hURAT1 inhibitors.

QSAR Studies on Thiazole Derivatives as HCV NS5A Inhibitors via CoMFA and CoMSIA Methods

Background: Hepatitis C Virus (HCV) infection is the major cause of hepatitis after transfusion. And HCV Nonstructural Protein 5A (NS5A) inhibitors have become a new hotspot in the study of HCV inhibitors due to their strong antiviral activity, rapid speed of viral removing and broad antiviral spectrum. Methods: Forty-five NS5A inhibitors were chosen to process three-dimensional quantitative structure- activity relationship (3D-QSAR) by using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. A training set consisting of 30 compounds was applied to establish the models and a test set consisting of 15 compounds was applied to do the external validation. Results: The CoMFA model predicted a q2 value of 0.607 and an r2 value of 0.934. And the CoMSIA model predicted a q2 value of 0.516 and an r2 value of 0.960 established on the effects of steric, electrostatic, hydrophobic and hydrogen-bond acceptor. 0.713 and 0.939 were the predictive correlation co-efficients (r2pred) of CoMFA and CoMSIA models, respectively. Conclusion: These conclusions provide a theoretical basis for drug design and screening of HCV NS5A complex inhibitors.

INSILICO STUDIES OF SULFUR-CONTAINING SHIKONIN OXIME DERIVATIVES AS INHIBITORS OF MGC 803 GASTRIC CANCER CELL LINE

Quantitative structural activity relationship and molecular docking studies were performed to predict the anticancer activity of 41 sulfur-containing shikonin oxime derivatives. Quantum chemical calculations method (Density Functional Theory with B3LYP/6-31G* basis set) were used in optimising the studied molecules to find the lowest geometry. Genetic function algorithm was employed in selecting five relevent molecular descriptors that derived a quantitative relationship between the anticancer activity and the structural properties of the studied compounds. Based on the value of coefficient of correlation R2 0.833, R2ext value of 0.64 R2adj value of 0.799 and Q2 value of 0.737, the model was found to be robust, highly satisfactory and predictive. Docking study between the derivatives of sulfur- containing shikonin oxime and fibroblast growth factor receptor (5A46) revealed that compound number 24 has the highest binding energy of -9.3 kcal/mol and all the other compounds has favourable binding affinity toward the target receptor.

Process monitoring of virus-like particle reassembly by diafiltration with UV/Vis spectroscopy and light scattering.

Virus‐like particles (VLPs) have shown great potential as biopharmaceuticals in the market and in clinics. Nonenveloped, in vivo assembled VLPs are typically disassembled and reassembled in vitro to improve particle stability, homogeneity, and immunogenicity. At the industrial scale, cross‐flow filtration (CFF) is the method of choice for performing reassembly by diafiltration. Here, we developed an experimental CFF setup with an on‐line measurement loop for the implementation of process analytical technology (PAT). The measurement loop included an ultraviolet and visible (UV/Vis) spectrometer as well as a light scattering photometer. These sensors allowed for monitoring protein concentration, protein tertiary structure, and protein quaternary structure. The experimental setup was tested with three Hepatitis B core Antigen (HBcAg) variants. With each variant, three reassembly processes were performed at different transmembrane pressures (TMPs). While light scattering provided information on the assembly progress, UV/Vis allowed for monitoring the protein concentration and the rate of VLP assembly based on the microenvironment of Tyrosine‐132. VLP formation was verified by off‐line dynamic light scattering (DLS) and transmission electron microscopy (TEM). Furthermore, the experimental results provided evidence of aggregate‐related assembly inhibition and showed that off‐line size‐exclusion chromatography does not provide a complete picture of the particle content. Finally, a Partial‐Least Squares (PLS) model was calibrated to predict VLP concentrations in the process solution. Q2 values of 0.947–0.984 were reached for the three HBcAg variants. In summary, the proposed experimental setup provides a powerful platform for developing and monitoring VLP reassembly steps by CFF.

Good and bad molecular fingerprints for human rhinovirus 3C protease inhibition: identification, validation, and application in designing of new inhibitors through Monte Carlo-based QSAR study

HRV 3 C protease (HRV 3Cpro) is an important target for common cold and upper respiratory tract infection. Keeping in view of the non-availability of drug for the treatment, newer computer-based modelling strategies should be applied to rationalize the process of antiviral drug discovery in order to decrease the valuable time and huge expenditure of the process. The present work demonstrates a structure wise optimization using Monte Carlo-based QSAR method that decomposes ligand compounds (in SMILES format) into several molecular fingerprints/descriptors. The current state-of-the-art in QSAR study involves the balance of correlation approach using four different sets: training, invisible training, calibration, and validation. The final models were also validated through mean absolute error, index of ideality of correlation, Y-randomization and applicability domain analysis. R2 and Q2 values for the best model were 0.8602, 0.8507 (training); 0.8435, 0.8331 (invisible training); 0.7424, 0.7020 (calibration); 0.5993, 0.5216 (validation), respectively. The process identified some molecular substructures as good and bad fingerprints depending on their effect to increase or decrease the HRV 3Cpro inhibition. Finally, new inhibitors were designed based on the fundamental concept to replace the bad fragments with the good fragments as well as including more good fragments into the structure. The study points out the importance of the fingerprint based drug design strategy through Monte Carlo optimization method in the modelling of HRV 3Cpro inhibitors.

Integrated Ligand and Structure-Based Investigation of Structural Requirements for Silent Information Regulator 1 (SIRT1) Activation.

A series of imidazothiazole and oxazolopyridine derivatives as human Silent Information Regulator 1 (SIRT1) activators were subjected to the integrated 2D and 3D QSAR approaches. The derived 3D QSAR models yielded high cross-validated q2 values of 0.682 and 0.628 for CoMFA and CoMSIA, respectively. The non-cross validated values of r2 training = 0.89; predictive r2 test = 0.69 for CoMFA and r2=0.87; predictive r2 test =0.67 for CoMSIA reflected the statistical significance of the developed model. The steric, electrostatic, hydrophobic and hydrogen bond acceptor interactions have been found important in describing the variation in human SIRT1 activation. Further, 2D QSAR model for the same dataset yielded high statistical significance and derived 2D model's parameters corroborated with the 3D model in terms of features. Derived model was also validated by the crystal structure of active conformation of SIRT1. Developed models may be useful for the identification of potential novel human SIRT1 activators as a therapeutic agent.

Application of quantitative structure‐activity relationship analysis on an antibody and alternariol‐like compounds interaction study

The antigen-antibody interaction determines the sensitivity and specificity of competitive immunoassay for hapten detection. In this paper, the specificity of a monoclonal antibody against alternariol-like compounds was evaluated through indirect competitive ELISA. The results showed that the antibody had cross-reactivity with 33 compounds with the binding affinity (expressed by IC50 ) ranging from 9.4ng/mL to 12.0μg/mL. All the 33 compounds contained a common moiety and similar substituents. To understand how this common moiety and substituents affected the recognition ability of the antibody, a three-dimensional quantitative structure-activity relationship (3D-QSAR) between the antibody and the 33 alternariol-like compounds was constructed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The q2 values of the CoMFA and CoMSIA models were 0.785 and 0.782, respectively, and the r2 values were 0.911 and 0.988, respectively, indicating that the models had good predictive ability. The results of 3D-QSAR showed that the most important factor affecting antibody recognition was the hydrogen bond mainly formed by the hydroxyl group of alternariol, followed by the hydrophobic force mainly formed by the methyl group. This study provides a reference for the design of new hapten and the mechanisms for antibody recognition.

Reliable structural information for rational design of benzoxazole type potential cholesteryl ester transfer protein (CETP) inhibitors through multiple validated modeling techniques

The drug design and discovery of lipid modulators is very demanding as no new molecule has entered into the market in the last 35 years. Cholesteryl ester transfer protein (CETP) is a promising target as lipid modulators. Inhibition of the CETP enzyme reduces the risk of cardiovascular events. The first CETP inhibitor torcetrapib and related drug candidates failed in the clinical trial due to the off-target effects leading to high toxicity. Thus, newer CETP inhibitors have now paramount importance to accelerate the drug discovery efforts in the field of cardiovascular disease (CVD). In the present study, 140 benzoxazole compounds were studied by using different chemometric techniques, for example, pharmacophore mapping, molecular docking, three-dimensional quantitative structure–activity relationship comparative molecular field analysis (3D-QSAR CoMFA), topomer CoMFA and Bayesian classification, in order to generate complete and reliable information regarding the structural requirements for the CETP inhibition. The best pharmacophore hypothesis was statistically significant (regression coefficient of 0.957 and a lower root mean square of 0.890). Molecular docking study revealed that cyano-substituted compounds form hydrogen bond with targeted macromolecule. The 3D-QSAR CoMFA model also produced a leave-one-out (LOO) cross-validated Q2 of 0.527, an R2 of 0.853 and an R2Pred of 0.603. Similarly, two topomer CoMFA models were also statistically significant and reliable in terms of their Q2, R2 and R2Pred values. The Bayesian classification study also provided the excellent ROC values of 0.919 and 0.939 for training and test sets, respectively. Overall, this study may help in the rational design of newer benzoxazole type compounds with higher CETP inhibition.Communicated by Ramaswamy H. Sarma