Tahereh Momeni Isfahani

Quinoline alkaloid camptothecin (CPT) derivatives are compounds with a wide range of inhibitory activities and they are the basis of several groups of drugs. CPT is one of the prominent lead compounds in anticancer drug development. It inhibits DNA topoisomerase I (topo I) enzyme and has shown remarkable anticancer activity against lung, colon, rectum, ovarian, breast, and stomach cancers. Quantitative structure–activity relationship (QSAR) is basically a statistical approach correlating the response activity data with descriptors encoding chemical information. In the current research, a QSAR study was performed for modeling and predicting the anticancer activity (pIC₅₀) of 76 CPT derivatives as an inhibitor of DNA topo I using the genetic algorithm multiple linear regression (GA‐MLR) and back propagation artificial neural network (BP‐ANN) methods. The results of this study indicate that the use of constitutional and geometrical descriptors provide good estimate for pIC₅₀. The obtained results of statistical criteria, internal and external validation show the superiority of BP‐ANN model than GA‐MLR to predict the pIC₅₀ values of the investigated compounds. QSAR model developed in this study can be used for design and development of novel potent CPT derivatives and for predicting their anticancer activity.

Thermodynamic properties of molecules provide fundamental information for binding in fragment‐based drug discovery programs and for understanding complex interactions. Experimental measurements of some thermodynamic properties are not always feasible. Quantum mechanical methods and group contribution methods are the major toolboxes to obtain some theoretical quantities. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational methods have been widely used to optimize promising new ligands by estimating their binding affinity to proteins using the thermodynamic ligand binding parameters (Gibbs energy, enthalpy, entropy, and heat capacity). In this article, the relationship between molecular descriptors and quantum chemical properties, such as the Gibbs free energy (ΔG kJ mol⁻¹) and enthalpy of formation (ΔH_f kJ mol⁻¹) of 2‐phenylindole derivatives as anticancer drugs is studied. These properties were calculated at the DFT‐B3LYP/6‐311G (d,p) level of quantum chemistry by Gaussian 09 software. Molecular descriptors were calculated by Dragon 5.4 software, and the stepwise multiple linear regression (MLR) and the Genetic algorithm (GA) techniques were used to select the best descriptors and build QSPR models. To evaluate the predictive ability of developed quantitative structure‐property relationship (QSPR) models, different internal and external validation methods were adopted. The predictive powers of the models were found to be satisfactory. The models revealed that 3D matrix‐based descriptors (H3D, SEig) are useful to predict the Gibbs free energy and enthalpy of formation of the investigated compounds respectively.

Pyridine and its derivatives have been applied clinically for the treatment of a wide range of diseases and in the synthesis of novel drugs. In the present work, imidazo[4,5‐b]pyridine derivatives as anticancer drugs were exhibited to select the important descriptor for quantum chemical properties. Two of the fundamental thermodynamic properties are heat capacity (Cv) and entropy (S), which are important in the field of chemical kinetics and are key in the understanding and design of chemical processes involving chemical reactions. A Quantitative Structure–Property Relationship (QSPR) study was used to predict the quantum chemical properties like Cv and S of 105 imidazole derivatives using molecular descriptors and the genetic algorithm–multiple linear regression (GA–MLR). The best QSPR models were selected using criteria coefficients such as R², R²_adj, RMSE and Fisher ratio. Different internal and external validation metrics were adopted to evaluate the stability, fit and predictive power of the QSPR models. The validation results and statistical analysis show that the models possess good prediction power and robustness, and the total size (TS) and Sanderson electronegativity(RDF060e) and total information content index(TIC1) of imidazo[4,5‐b]pyridine derivatives are increasingly related to the studied properties.

In the world of medicine, the discovery of acyclovir, an antiviral medication often used to treat herpes infections, is very important. Accurate and sensitive detection are essential for patient safety since acyclovir is recognized for its possible adverse effects and toxicity at high dosages. A Cu metal-organic framework (MOF) doping with Fe₃O₄/SiO₂ was prepared by direct Co-precipitation method. This binary Fe₃O₄-SiO₂/Cu-MOF was analysis by scanning electron microscopy (SEM) and X-ray diffraction spectroscopy (XRD), and this MOF was used to modify the glassy carbon electrode (GCE) surface. Modified GCE was used for the electrochemical monitoring of Acyclovir in the plasma samples. Acyclovir’s electro-oxidation behavior was assessed using cyclic and differential pulse voltammetric techniques. A redox mechanism was postulated based on the effect of the potential scanning rate and solution pH on the voltammetric response of Acyclovir oxidation. A 0.03 μM limit of detection was acquired for Acyclovir analysis with a linear response in the range of 1–60 μM. Finally, acyclovir quantification in the blood serum samples was successfully performed.

Background: Prediction of toxicity of imidazo[4,5-b]pyridine derivatives is carried out using GA-MLR and BPANN methods. Objective: A quantitative structure-property relationship (QSPR) was determined based on methods, including genetic algorithm-multiple linear regression (GA-MLR) and backpropagation artificial neural network (BP-ANN). These methods were employed for modeling and predicting the anticancer potency of imidazo[4,5-b]pyridine derivatives. Materials and Methods: A dataset of imidazo[4,5-b]pyridine derivatives was randomly divided into two groups, training and test sets consisting of 75% and 25% of data points, respectively. The optimized conformation of compounds was obtained using the DFT-B3LYP method and 6-31G* basis sets level with Gaussian 09 software. A large number of molecular descriptors were calculated using Dragon software. The QSAR models were optimized using multiple linear regressions (MLR). Results: The most relevant molecular descriptors were obtained using the genetic algorithm (GA) and backward stepwise regression. The predictive powers of the GA-MLR models were studied using leaveone- out (LOO) cross-validation and an external test set. Conclusion: The obtained results of statistical parameters showed the BP-ANN model to have better performance compared to the GA-MLR model. To assess the predictive ability of QSAR models, many statistical terms, such as correlation coefficient (R2), leave-one-out cross-validation (LOOCV), root mean squared error (RMSE), and external and internal validation were used. The results of validation methods demonstrate the QSAR model to be robust and with high predictivity.

In this research, the Cu-MOF (metal-organic framework, HKUST-1) was synthesized via co-precipitation method and it was into the carbon paste electrode and has been investigated in the measurement of codeine. The electrochemical performance of the modified electrode was evaluated by cyclic voltammetry and differential pulse voltammetry. The effective parameters in the sensitivity of the method were optimized. Quantitative measurements and determination of codeine at the surface of the modified electrode were performed by using differential pulse voltammetry. Finally, the ability of the developed method to measure codeine in real plasma samples was investigated. Under the optimal conditions, the linear range was obtained from 2 to 100 μM with a limit of detection of 0.66 μM. The high efficiency of the developed electrode in plasma samples was proved by using high and acceptable accuracy and satisfactory relative recovery percentage. The results in which the recoveries values with RSD% for three repeated measurements were in the range of 97–109 (%RSD = 3.75 to 4).

A facile and efficient method was described for oxidation of some 3,5‐diacyl or 3,5‐diester 1,4‐dihydropyridines using H₂O₂ in the presence of superparamagnetic core‐shell metal–organic framework Fe₃O₄@Ni‐MOF. The Fe₃O₄@Ni‐MOF has been obtained by Step‐by‐Step method in which magnetic Fe₃O₄ magnetic nanoparticles were coated with Ni‐MOF using a mercaptoacetic acid linker. The synthesized catalyst was characterized using thermogravimetric analysis, FT‐IR spectroscopy, powder X‐ray diffraction, field emission scanning electron microscopy and energy‐dispersive X‐ray analysis. The novel superparamagnetic core‐shell metal–organic framework Fe₃O₄@Ni‐MOF revealed high efficiency for oxidation of various 1,4‐dihydropyridines using hydrogen peroxide. The Box–Behnken design matrix and the response surface method were applied to investigate the optimization of the reaction conditions. The conditions for optimal reaction yield and time were: amount of catalyst ≈17 mmol, temperature ≈78°C and amount of hydrogen peroxide ≈ 1 ml. A variety of 3,5‐diacyl or 3,5‐diester 1,4‐dihydropyridines with different substituted functional groups have been converted to corresponding pyridines with good to excellent isolated yields using H₂O₂ and Fe₃O₄@Ni‐MOF. The catalyst was reused up to five times for the oxidation of 1,4‐dihydropyridines without a significant loss in catalytic activity. The short reaction times, simplicity of method, good to excellent yields and reusability of catalyst were some advantages of the proposed procedure.

In the present study, quantitative structure-property relationship (QSPR) models were used for modeling and predicting retention index (RI) values of chemical composition in Polygonum minus essential oil. In gas chromatography, RIs are used to convert retention times into system-independent constants. The dataset of 40 molecules was divided into training and external validation sets. A set of molecular descriptors were calculated from the optimized structures of the molecules using Gaussian 09 and Dragon software. The genetic algorithm (GA), backward stepwise multiple linear regression (BW-MLR), and back-propagation artificial neural networks (BPANN) were used to obtain suitable QSPR models. The predictive power of the QSPR model was discussed using a coefficient of determination (R²), average absolute deviation (AAD), mean squared error (MSE), and leave-one-out cross-validation (Q²cv). According to our findings, it was concluded that the QSPR model with three descriptors (ATS5m, BEHm4, and G1) established by the GA-BPANN method could be efficiently used for predicting RI of chemical components in P. minus essential oil and may be helpful for modeling and designing of new molecules of the essential oils.