90Y-CA/SPIONs for dual magnetic hyperthermia-radionuclide nanobrachytherapy of solid tumours.

Radiolabelled superparamagnetic iron oxide nanoparticles (SPIONs) are a promising nanomaterial for the development of dual radiation/hyperthermia cancer therapy. To that purpose, flower-shaped SPIONs with an exceptional heating capability were synthesised and coated with citrate, dextran or (3-aminopropyl)triethoxysilane. Both non-coated and coated SPIONs were nontoxic to CT-26 mouse colon cancer cells up to 1.0 mg ml-1in vitro. In an oscillating magnetic field, citrate-coated SPIONs (CA/SPIONs) displayed the highest heating rate (SAR âˆ¼ 253 W g-1) and the strongest hyperthermia effects against CT-26 cells. Labelling of the CA/SPIONs by the90Y radionuclide, emitting ß-radiation with an average/maximum energy of 0.94/2.23 MeV, and deep tissue penetration generated90Y-CA/SPIONs intended for the therapy of solid tumours. However, intravenous injection of90Y-CA/SPIONs in CT-26 xenograft-bearing mice resulted in low tumour accumulation. On the contrary, intratumoural injection resulted in long-term retention at the injection site. A single intratumoural injection of 0.25 mg CA/SPIONs followed by 30-min courses of magnetic hyperthermia for four consecutive days caused a moderate antitumour effect against CT-26 and 4T1 mouse tumour xenografts. Intratumoural application of 1.85 MBq/0.25 mg90Y-CA/SPIONs, alone or combined with hyperthermia, caused a significant (P ≤ 0.01) antitumour effect without signs of systemic toxicity. The results confirm the suitability of90Y-CA/SPIONs for monotherapy or dual magnetic hyperthermia-radionuclide nanobrachytherapy (NBT) of solid tumours.

Influence of the mobile phase and molecular structure parameters on the retention behavior of protonated basic solutes in chaotropic chromatography.

In this study, we present novel insights into the pH-dependent retention behavior of protonated basic solutes in chaotropic chromatography. To this end, two sets of experiments were performed to distinguish between mobile phase pH and ionic strength effects. In the first set, the ionic strength (I) was varied with the concentration of NaPF6 and additives that adjusted the mobile phase pH, while in the second set, I was kept constant by adding the appropriate amount of NaCl. In each set, the retention behavior of 13 analytes was qualitatively examined in 21 chromatographic systems, which were defined by the NaPF6 concentration in their aqueous phases (1-50mM) and the pH of their mobile phases (2, 3 or 4); the acetonitrile content was fixed at 40%. The addition of NaCl significantly reduced the differences among retention factors at studied pH values due to the effect of the Na+ ions on PF6-adsorption to the stationary phase and the magnitude of the consequential development of the surface potential. A quantitative description of the observed phenomenon was obtained by an extended thermodynamic approach. The contribution of ion-pair formation in the stationary phase to the retention of the solutes was confirmed across models at the studied pH values in the set with varying I. In the systems with a constant I, the shielding effect of the Na+ ions on the surface charge lowered the attractive surface potential and diminished the aforementioned interactions and hence the effect of the mobile phase pH on analyte retention. Eventually, we developed a readily interpretable empirical retention model that simultaneously takes into account analyte molecular structures and the most relevant chromatographic factors. Its coefficients have clear physical meaning, and owing to its good predictive capabilities, the model could be successfully used to clarify the contributions of analyte molecular structures and chromatographic factors to the specific processes underlying separation in chaotropic chromatography.

Theoretical Models and QSRR in Retention Modeling of Eight Aminopyridines.

In this article, retention modeling of eight aminopyridines (synthesized and characterized at the Faculty of Pharmacy) in reversed-phase high performance liquid chromatography (RP-HPLC) was performed. No data related to their retention in the RP-HPLC system were found. Knowing that, it was recognized as very important to describe their retention behavior. The influences of pH of the mobile phase and the organic modifier content on the retention factors were investigated. Two theoretical models for the dependence of retention factor of organic modifier content were tested. Then, the most reliable and accurate prediction of log k was created, testing multiple linear regression model-quantitative structure-retention relationships (MLR-QSRR) and support vector regression machine-quantitative structure-retention relationships (SVM-QSRR). Initially, 400 descriptors were calculated, but four of them (POM, log D, M-SZX/RZX and m-RPCG) were included in the models. SVM-QSRR performed significantly better than the MLR model. Apart from aminopyridines, four structurally similar substances (indapamide, gliclazide, sulfamethoxazole and furosemide) were followed in the same chromatographic system. They were used as external validation set for the QSRR model (it performed well within its applicability domain, which was defined using a bounding box approach). After having described retention of eight aminopyridines with both theoretical and QSRR models, further investigations in this field can be conducted.

Application of Counter-propagation Artificial Neural Networks in Prediction of Topiramate Concentration in Patients with Epilepsy.

PURPOSE: The application of artificial neural networks in the pharmaceutical sciences is broad, ranging from drug discovery to clinical pharmacy. In this study, we explored the applicability of counter-propagation artificial neural networks (CPANNs), combined with genetic algorithm (GA) for prediction of topiramate (TPM) serum levels based on identified factors important for its prediction. METHODS: The study was performed on 118 TPM measurements obtained from 78 adult epileptic patients. Patients were on stable TPM dosing regimen for at least 7 days; therefore, steady-state was assumed. TPM serum concentration was determined by high performance liquid chromatography with fluorescence detection. The influence of demographic, biochemical parameters and therapy characteristics of the patients on TPM levels were tested. Data analysis was performed by CPANNs. GA was used for optimal CPANN parameters, variable selection and adjustment of relative importance. RESULTS: Data for training included 88 measured TPM concentrations, while remaining were used for validation. Among all factors tested, TPM dose, renal function (eGFR) and carbamazepine dose significantly influenced TPM level and their relative importance were 0.7500, 0.2813, 0.0625, respectively. Relative error and root mean squared relative error (%) and their corresponding 95% confidence intervals for training set were 2.14 [(-2.41) - 6.70] and 21.5 [18.5 - 24.1]; and for test set were -6.21 [(-21.2) - 8.77] and 39.9 [31.7 - 46.7], respectively. CONCLUSIONS: Statistical parameters showed acceptable predictive performance. Results indicate the feasibility of CPANNs combined with GA to predict TPM concentrations and to adjust relative importance of identified variability factors in population of adult epileptic patients.

Investigation into the phenomena affecting the retention behavior of basic analytes in chaotropic chromatography: Joint effects of the most relevant chromatographic factors and analytes' molecular properties.

The aim of this study was to systematically investigate the phenomena affecting the retention behavior of structurally diverse basic drugs in ion-interaction chromatographic systems with chaotropic additives. To this end, the influence of three factors was studied: pH value of the aqueous phase, concentration of sodium hexafluorophosphate, and content of acetonitrile in the mobile phase. Mobile phase pH was found to affect the thermodynamic equilibria in the studied system beyond its effects on the analytes' ionization state. Specifically, increasing pH from 2 to 4 led to longer retention times, even with analytes which remain completely protonated. An explanation for this phenomenon was sought by studying the adsorption behavior of acetonitrile and chaotropic additive onto stationary phase. It was shown that the magnitude of the developed surface potential, which significantly affects retention - increases with pH, and that this can be attributed to the larger surface excess of acetonitrile. To study how analytes' structural properties influence their retention, quantitative structure-retention modeling was performed next. A support vector machine regression model was developed, relating mobile phase constituents and structural descriptors with retention data. While the ETA_EtaP_B_RC and XlogP can be considered as molecular descriptors which describe factors affecting retention in any RP-HPLC system, TDB9p and RDF45p are molecular descriptors which account for spatial arrangement of polarizable atoms and they can clearly relate to analytes' behavior on the stationary phase surface, where the electrostatic potential develops. Complementarity of analytes' structure with that of the electric double layer can be seen as a key factor influencing their retention behavior. Structural diversity of analytes and good predictive capabilities over a range of experimental conditions make the established model a useful tool in predicting retention behavior in the studied chromatographic system.

The influence of salt chaotropicity, column hydrophobicity and analytes' molecular properties on the retention of pramipexole and its impurities.

The aim of this study was to examine the interaction of the chaotropic salts of different position in Hofmeister series (CF3COONa, NaClO4, NaPF6) added to the mobile phase with the stationary phases of different hydrophobicity (C8 and C18 XTerra(®) columns), as well as their common influence on the retention behavior of pramipexole and its structurally related impurities. The extended thermodynamic approach enabled the understanding of the underlying separation mechanism. Comparing six different column-salt systems it was observed that general system hydrophobicity presented by salt chaotropicity and column hydrophobicity favors stationary phase ion-pairing over the ion-pair formation in the eluent. Further, an attempt was made to describe the influence of analytes' nature on their retention behavior in such chromatographic systems. An analysis is performed in order to select and elucidate the molecular descriptors (electrostatical, quantum-chemical, geometrical, topological, and constitutional) that best explain the experimental evidence and findings obtained by the thermodynamic approach. The results of this analysis suggest that analytes' charge distribution and its complementarity to the structure of the electric double layer formed on the surface of the stationary phase upon the addition of chaotropic additives can be useful for understanding the differences in retention of structurally related analytes. These findings provide a novel understanding of the interactions between all the components of the chromatographic system containing chaotropic additive and a good basis for further investigations suggesting the development of generally applicable predictors in structure-retention relationship studies in related chromatographic systems.

Structural insights into binding of small molecule inhibitors to Enhancer of Zeste Homolog 2.

Enhancer of Zeste Homolog 2 (EZH2) is a SET domain protein lysine methyltransferase (PKMT) which has recently emerged as a chemically tractable and therapeutically promising epigenetic target, evidenced by the discovery and characterization of potent and highly selective EZH2 inhibitors. However, no experimental structures of the inhibitors co-crystallized to EZH2 have been resolved, and the structural basis for their activity and selectivity remains unknown. Considering the need to minimize cross-reactivity between prospective PKMT inhibitors, much can be learned from understanding the molecular basis for selective inhibition of EZH2. Thus, to elucidate the binding of small-molecule inhibitors to EZH2, we have developed a model of its fully-formed cofactor binding site and used it to carry out molecular dynamics simulations of protein-ligand complexes, followed by molecular mechanics/generalized born surface area calculations. The obtained results are in good agreement with biochemical inhibition data and reflect the structure-activity relationships of known ligands. Our findings suggest that the variable and flexible post-SET domain plays an important role in inhibitor binding, allowing possibly distinct binding modes of inhibitors with only small variations in their structure. Insights from this study present a good basis for design of novel and optimization of existing compounds targeting the cofactor binding site of EZH2.

In silico design of small molecule inhibitors of CDK9/cyclin T1 interaction.

In order to design a small molecule which potentially may interfere with CDK9/cyclin T1 complex formation and therefore influence its physiological role, a computational study of dynamics and druggability of CDK9 binding surface was conducted. Druggability estimates and pocket opening analyses indicated binding regions of cyclin T1 residues, Phe 146 and Lys 6, as starting points for the design of small molecules with the potential to inhibit the CDK9/cyclin T1 association. A pharmacophore model was created, based on these two residues and used to select potential inhibitor structures. Binding energies of the inhibitors were estimated with MM-GBSA. A good correlation of MM-GBSA energies and FTMap druggability predictions was observed. Amongst studied compounds a derivative of 2-amino-8-hydroxyquinoline was identified as the best potential candidate to inhibit CDK9/cyclin T1 interactions.

Computational study and peptide inhibitors design for the CDK9 - cyclin T1 complex.

Cyclin dependent kinase 9 (CDK9) is a protein that belongs to the cyclin-dependent kinases family, and its main role is in the regulation of the cell transcription processes. Since the increased activity of CDK9 is connected with the development of pathological processes such as tumor growth and survival and HIV-1 replication, inhibition of the CDK9 could be of particular interest for treating such diseases. The activation of CDK9 is initiated by the formation of CDK9/cyclin T1 complex, therefore disruption of its formation could be a promising strategy for the design of CDK9 inhibitors. In order to assist in the design of potential inhibitors of CDK9/cyclin T1 complex formation, a computational study of the CDK9/cyclin T1 interface was conducted. Ten peptides were designed using the information from the analysis of the complex, hot spot residues and fragment based design. The designed peptides were docked to CDK9 structures obtained by molecular dynamics simulations of CDK9/cyclin T1 complex and the CDK9 alone and their binding affinities were evaluated using molecular mechanics Poisson Boltzman surface area (MM-PBSA) method and steered molecular dynamics (SMD). Designed peptide sequences LQTLGF and ESIILQ, both derived from the surface of cyclin T1, as well as the peptide sequence PRWPE, derived from fragment based design, showed the most favorable binding properties and were selected for our further studies.

Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks.

In this work, we present a novel approach for the development of models for prediction of aqueous solubility, based on the implementation of an algorithm for the automatic adjustment of descriptor's relative importance (AARI) in counter-propagation artificial neural networks (CPANN). Using this approach, the interpretability of the models based on artificial neural networks, which are traditionally considered as "black box" models, was significantly improved. For the development of the model, a data set consisting of 374 diverse drug-like molecules, divided into training (n=280) and test (n=94) sets using self-organizing maps, was used. Heuristic method was applied in preselecting a small number of the most significant descriptors to serve as inputs for CPANN training. The performances of the final model based on 7 descriptors for prediction of solubility were satisfactory for both training (RMSEP(train)=0.668) and test set (RMSEP(test)=0.679). The model was found to be a highly interpretable in terms of solubility, as well as rationalizing structural features that could have an impact on the solubility of the compounds investigated. Therefore, the proposed approach can significantly enhance model usability by giving guidance for structural modifications of compounds with the aim of improving solubility in the early phase of drug discovery.

Target fishing and docking studies of the novel derivatives of aryl-aminopyridines with potential anticancer activity.

A set of 16 previously synthesized aryl-aminopyridine and aryl-aminoquinoline derivatives have been evaluated for cytotoxic activity against three cancer cell lines (human cervical cancer-HeLa; human chronic myeloid leukemia-K562; human melanoma-Fem-x) and two types of normal peripheral blood mononuclear cells, with and without phytohemaglutinin (PBMC-PHA; PBMC+PHA). Twelve of the studied compounds showed moderate cytotoxicity, with selectivity against K562 but not the remaining two cancer cell lines. Four compounds were not active in cytotoxicity assays, presumably due to high predicted lipophilicity and low solubility. To rationalize the observed cytotoxic effects, structure-based virtual screening was carried out against a pool of potential targets constructed using the inverse docking program Tarfisdock and bibliographical references. The putative targets were identified on the basis of the best correlation between docking scores and in vitro cytotoxicity. It is proposed that the mechanism of action of the studied aminopyridines involves the disruption of signaling pathways and cancer cell cycle through the inhibition of cyclin-dependent kinases and several tyrosine kinases, namely Bcr-Abl kinase and KIT receptor kinase. The obtained results can guide further structural modifications of the studied compounds aimed at developing selective agents targeting proteins involved in cancer cell survival and proliferation.

Antiproliferative activity and QSAR studies of a series of new 4-aminomethylidene derivatives of some pyrazol-5-ones.

Twenty five 4-aminomethylidene derivatives obtained from 3-phenyl-2-pyrazolin-5-one and 1,3-diphenyl-2-pyrazolin-5-one were synthesized and tested for their antiproliferative activity against human breast cancer MDA-MB-361 and MDA-MB-453 cell lines. The compounds derived from 1,3-diphenyl-2-pyrazolin-5-one exhibited the most remarkable activity in the treatment of both cell lines. In vitro antiproliferative activities were accompanied by an important apoptotic fraction of both cell lines; also, compounds inhibited key endothelial cell functions implicated in invasion and angiogenesis. QSAR methods were performed in order to analyze the influence of structural features of the compounds investigated on the antiproliferative potential on MDA-MB-361 and MDA-MB-453 cancer cells. One-parameter heuristic analysis was performed and different whole molecule and fragmental descriptors were considered for rationalization of mechanism of interaction of these compounds with active place of hypothetical target included in tumorigenesis.

Synthesis, antitumor activity and QSAR studies of some 4-aminomethylidene derivatives of edaravone.

A series of aminomethylidene derivatives obtained from 4-formyledaravone were synthesized and characterized by IR, NMR and elemental analysis. All the compounds were screened for their antitumor activity. The compound containing 5-phenylpyrazole moiety (3q) exhibited remarkable antitumor activity in in vitro assays, especially against human breast cancer MDA-MB-361 and MDA-MB-453 cell lines. The most important whole-molecule descriptors for antitumor activity on MDA-MB-453 cells belong to the group of quantum-chemical descriptors.

Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks.

In this work, a novel algorithm for optimization of counter-propagation artificial neural networks has been used for development of quantitative structure-activity relationships model for prediction of the estrogenic activity of endocrine-disrupting chemicals. The search for the best model was performed using genetic algorithms. Genetic algorithms were used not only for selection of the most suitable descriptors for modeling, but also for automatic adjustment of their relative importance. Using our recently developed algorithm for automatic adjustment of the relative importance of the input variables, we have developed simple models with very good generalization performances using only few interpretable descriptors. One of the developed models is in details discussed in this article. The simplicity of the chosen descriptors and their relative importance for this model helped us in performing a detailed data exploratory analysis which gave us an insight in the structural features required for the activity of the estrogenic endocrine-disrupting chemicals.

Role of complexes formation between drugs and penetration enhancers in transdermal delivery.

The use of chemical penetration enhancers (CPE) is growing due to their ability to improve drug delivery through the skin. A possible mechanism of penetration enhancement could involve the complex formation between drug and components in the pharmaceutical formulation, thus altering the physicochemical properties of the active substance. Here, modelling studies indicate that hydrocarbon and oxygen-containing terpenes (penetration enhancers) could form complexes with drugs. Satisfactory correlations have been obtained between the predicted molecular properties of enhancers and their enhancement effects.

Quantitative structure-activity relationships of alpha1 adrenergic antagonists.

A quantitative structure-activity relationship study with respect to selectivity for alpha1 adrenoreceptor subtypes (alpha1a, alpha1b and alpha1d) of a wide series of structurally heterogeneous alpha1 adrenoreceptor antagonists has been performed. A large variety of molecular descriptors have been calculated and then analyzed by a heuristic method. The orthogonalization of the descriptors has been applied to build the QSAR equations. Ad hoc defined shape descriptors calculated by the Connolly algorithm with respect to reference supermolecules have also been considered in the rationalization of the mechanism of the activity of the ligands acting as antagonists on all three subtypes of alpha1 adrenoreceptors.