Quantitative structure-activity relationship for antimalarial activity of artemisinin

The increase in resistance to older drugs and the emergence of new types of infection have created an urgent need for discovery and development of new compounds with antimalarial activity. Quantitative-Structure Activity Relationship (QSAR) methodology has been performed to develop models that correlate antimalarial activity of artemisinin analogs and their molecular structures. In this study, the data set consisted of 197 compounds with their activities expressed as log RA (relative activity). These compounds were randomly divided into training set (n=157) and test set (n=40). The initial stage of the study was the generation of a series of descriptors from three-dimensional representations of the compounds in the data set. Several types of descriptors which include topological, connectivity indices, geometrical, physical properties and charge descriptors have been generated. The number of descriptors was then reduced to a set of relevant descriptors by performing a systematic variable selection procedure which includes zero test, pairwise correlation analysis and genetic algorithm (GA). Several models were developed using different combinations of modelling techniques such as multiple linear regression (MLR) and partial least square (PLS) regression. Statistical significance of the final model was characterized by correlation coefficient, r2 and root-mean-square error calibration, RMSEC. The results obtained were comparable to those from previous study on the same data set with r2 values greater than 0.8. Both internal and external validations were carried out to verify that the models have good stability, robustness and predictive ability. The cross-validated regression coefficient (r2 cv) and prediction regression coefficient (r2 test) for the external test set were consistently greater than 0.7. The QSAR models developed in this study should facilitate the search for new compounds with antimalarial activity

Development of structure-activity modelling of carboxamides compounds for Aedes Aegypti repellents

Recently, research has been critically focused on finding new compounds with antirepellent activity due to the rising of new types of mosquito-borne diseases. Mosquito repellents are the safer and cleaner alternative to fight the anthropods from bitten human skins, hence reduce the spread of diseases. This study investigated the relationships between biological activity and structure of carboxamides by using Quantitative Structure-Activity Relationship (QSAR) analysis. The data set used in this study comprised of 40 carboxamide compounds taken from the literature with their activities expressed as log PT (protection time). These compounds were split into training set for model building and test set for external validation using activity-based ranking method. The training set contained approximately 75% of the compounds while the remaining compounds were then used as the validation set to verify the accuracy of the model. DRAGON software was employed to generate molecular descriptors. The important relevant descriptors were further selected and reduced by using Genetic Algorithm (GA) as variable selection method. Two QSAR models were developed by combining GA method with two different modelling techniques that are multiple linear regressions (MLR) and partial least square (PLS). All the models are robust with good correlation coefficient (r2) greater than 0.6 and external validation r2test more than 0.5. Statistics of the GA-MLR model are r2 = 0.779 and r2test = 0.646. Whereas, the second model generated from GA and PLS shows good r2 with value of 0.775 and r2test = 0.563. These results could be useful in finding new, safe, and more effective repellents against Aedes Aegypti in a short time by providing guidance for further laboratory work as well as prediction of external compounds and help to understand the factors affecting their activity

Classification of Malaysian honey using fourier transform infrared spectrroscopy and principal component analysis

Honey is a natural sweetener, which is consumed in a variety of sweet products. It is considered as healthy food because it contains nutrients such as carbohydrate, protein, vitamins and mineral. The presence of adulterated honey in the market is worrying the consumers since it is difficult to distinguish between pure and adulterated honey due to similar appearance and texture of both type honeys. Chemometric analysis combined with spectroscopic data is a powerful technique that has been used to discriminate different type of honey. Samples of pure honey are collected from beekeepers at Ayer Keroh, Melaka and Cameron Highland, Pahang. The adulterants used to prepare adulterated honey are sugar and corn syrup with the concentration of the adulterants added to the pure honey ranging from 10% to 90% by weight of adulterant. All the samples are treated with heat at 40oC to ensure the adulterant and pure honey are mixed well. Fourier transforms infrared spectroscopy (FTIR) is used to generate the spectra of the honey and subsequently subjected to chemometric analysis. The spectra data is then analysed by using Principal Component Analysis (PCA) technique using SOLO+Mia software. In this study, all honeys have been successfully discriminated according to their origins and purity as well as types of adulterants used. Consequently, the developed model can potentially be used as a screening tool to determine the purity of honey in the market

Inventory management improvement suggestion through time-siries forecasting for financial service company

Management is consistently facing fast-flowing and lots of changes in business, including in the inventory management. Especially for fast-moving inventories, the correct stocking, controlling, checking and safety stock calculation is highly needed to have an exquisite inventory management and to reduce the possibility of running out of inventory which leads to unavailability to meet the demand. One of the ways to overcome this is by doing an excellent and appropriate forecasting. Therefore, the objective of this concept paper is to analyse and recommend tools to improve inventory management using the appropriate time-series forecasting method. The firm studied in this study is serving its employees as customers that demand the routine items including stationeries and other routine products to support their job as auditors and consultants for its client. However, there are occasions when there is out-of-stock situation for fast-moving items, especially in the peak season period. Furthermore, the firm is only applying replenishment based on the used inventories from the previous month. Therefore, this study suggests to eliminate out-of-stock items situation by applying precaution initiatives such as time-series forecasting. This study is planned to employ 10 time-series forecasting methods such as moving average, exponential smoothing, regression analysis, Holt-Winters analysis, Seasonal analysis and Autoregressive Integrated Moving Average (ARIMA) using Risk Simulator Software. By simulating those methods, the most appropriate method is selected based on the forecasting accuracy measurement

Predictive model of 2-cyclohexylthiophene for corrosion inhibition in mild steel using computational method

Corrosion inhibition activity of 2-cyclohexylthiophene (2CHT) for mild steel in acidic media was predicted using QSAR tool. The model used two descriptors namely; Moran autocorrelation of lag4 weighted by mass (MATS4M) which explained the linearity and branching of the compounds and largest eigen values n3 of burden matrix weighted by mass (SPMAX3-Bh(m)) describes the nature and size of the neighboring atom. The modeling results revealed the potential of the compounds as a good corrosion inhibitor with percentage inhibition efficiency (%IE) of 76.5%. Quantum chemical calculation using DFT with 6-311G++(d,p) basis was used to evaluate the performance of the predicted compound as corrosion inhibitor by quantum chemical parameters such as EHUMO, ELUMO, Energy gap (Egap), hardness (?), softness (S), dipole moment (µ), electronegativity (X), electron affinity (A), ionization energy (I) and total energy (TE). The results obtained from quantum chemical parameters were found to be consistent with predicted result

Theoretical and experimental studies of corrosion inhibition of thiohene-2-ethylamine on mild steel in acid media

Corrosion inhibition of mild steel in 0.5M H2SO4 at 30°C with thiophene-2- ethylamine (TEA) as inhibitor has been assess by quantitative structure activity relation (QSAR) model and quantum chemical calculations. The results were evaluated using weight loss and electrochemical methods such as potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS). The results showed good performance of TEA in corrosion protection which behaves as mixed inhibitor from PDP. The micrograph from FESEM and EDX dot mapping showed that the inhibitor adsorbed onto the metal surface with different distribution for S, C and N atoms which indicate less damage on the metal surface in the presence of TEA

Chemometrics and multiblock methods for quantitative structure-activity studies of artemisinin analogues and polychlorinated diphenylethers

Three major aspects of chemometrics have been investigated in this study namely Quantitative Structure-Activity Relationship (QSAR) and database mining, classification and multiblock methods. In the first analysis, 197 artemisinin compounds were divided into training set and test set together with structural descriptors generated by DRAGON 6.0 software had been used to develop three QSAR models. Statistics of the models were (r2/ rtest 2) 0.790/0.853 for Forward Stepwise-Multiple Linear Regression (MLR), 0.807/0.789 for Genetic Algorithm (GA)-MLR and 0.795/0.811 for GA-Partial Least Square (PLS). The rigorously validated QSAR models were then applied to mine a chemical database which resulted in four potential new anti-malarial agents. The same artemisinin data set was then classified into active and less active compounds to develop reliable predictive classification models and to investigate the consequences of using various data splitting and data pre-processing methods on classification. Principal Component Analysis (PCA) and boundary plot had been utilized to visualize the four classifiers namely Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Linear Vector Quantization (LVQ) and Quadratic Discriminant Analysis (QDA). Kennard-Stone data splitting and standardization had produced better results in terms of percent correctly classified (% CC) compared to Duplex data-splitting and mean-centering. Moreover, LDA was found to be superior as compared to the other three classifiers with lower risk of over-fitting. Lastly, multiblock analysis methods such as Multiblock PLS and Consensus PCA have been implemented on polychlorinated diphenyl ethers (PCDEs) data set together with their respective descriptors blocked into three groups labelled as X1D, X2D, X3D and a property block, Y which consists of log PL (Pa, 25°C), log KOW (25°C) and log SWL (mol/L, 25°C). Their performance were then compared to single block methods that is PLS and PCA. The PLS models of each descriptor block with respect to each property were statistically best-fitted and well predicted with rtrain 2 values greater than 0.96 while the rtest 2 values range from 0.86 to 0.98. It is interesting to note that the combination of the three descriptor blocks into a single block to produce Multiblock PLS superscores (MBSS) model which was superior than Multiblock PLS block-scores (MBBS) yielded slightly better rtrain 2 value and significantly better prediction with higher rtest 2 as compared to PLS model of individual descriptor block. In addition, three measures of block similarity such as Mantel Test, Rv coefficient and Procrustes analysis were used to investigate similarity and correlation between the blocks along with Monte Carlo simulations to determine their significance. Based on the similarity index between two blocks, X1D descriptors resembled Y block better while X2D was more correlated to X1D block. In short, the chemometric methods had been applied successfully on both data sets using various descriptors generated by DRAGON software and yielded promising results beneficial not only in chemometrics area but also in drug design

Density functional theory calculations of structure-antioxidant activity of selected phenolic acids and flavonoids found in Malaysian honey

Phenolic acids and flavonoids exist naturally in Malaysian honey and contribute significantly to antioxidant contents. Antioxidants plays an important role in scavenging free radicals and prevent health deterioration. Total antioxidant content is measured using DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging activity. The phenolic acids such as gallic, caffeic, syringic and hydroxybenzoic acids and flavonoids like naringenin, apigenin, kaempferol, catechin and luteolin previously have been identified in Malaysian honey of tualang, gelam and borneo type using high-performance liquid chromatography (HPLC). In order to investigate the structure-antioxidant activity relationships of these phenolic compounds using hydrogen atom transfer (HAT) mechanism, density functional theory (DFT) calculation at B3LYP/6-311++G(d,p) levels of theory was performed. In this work, optimization of the compounds chemical structure and radical forms in gas-phase has been calculated with computation of bond dissociation enthalpy (BDE) as antioxidant descriptor. The finding showed that abstraction of H at different OH groups in the structure of the compound, led to a different scavenging free radical activity thus contribute to the overall variation in the antioxidant properties. Besides that, B ring of flavonoids and unsaturated bond in pyran ring are proposed factors that could lower the BDE values and consequently influence the antioxidant properties of the antioxidant compounds. Hence, DFT calculation with BDE descriptor had been successfully applied to investigate the relationship between structure of phenolic acids and antioxidant activity of Malaysian honey and the interesting results could contribute in future development of antioxidant compound