Synthesis, Complexation Properties, and Evaluation of New Aminodiphosphonic Acids as Vector Molecules for 68Ga Radiopharmaceuticals.

Two new aminodiphosphonic acids derived from salicylic acid and its phosphonic analogue were prepared through a simple and efficient synthesis. 2-[(2-Amino-2,2-diphosphono)ethyloxy]-benzoic acid 8 and 2-[(2-amino-2,2-diphosphono)ethyloxy]-5-ethyl-phenylphosphonic acid 9 were evaluated for their applicability as 68Ga binding bone-seeking agents. Protonation constants of 8 and 9 and stability constants of the Ga3+ complexes with 8 and 9 in water were determined. The stability constant of Ga3+ complex with fully phosphorylated acid 9 (logKGaL = 31.92 ± 0.32) significantly exceeds stability constant of Ga3+ complex with 8 (logKGaL = 26.63 ± 0.24). Ligands 8 and 9 are as effective for Ga3+ cation binding as ethylenediamine-N,N'-diacetic-N,N'-bis(methy1enephosphonic) acid and ethylenediamine-N,N,N',N'-tetrakis(methylenephosphonic) acid, respectively. The labelling process and stability of [68Ga]Ga-8 and [68Ga]Ga-9 were studied. Both 8 and 9 readily form 68Ga-complexes stable to ten-fold dilution with saline. However, in fetal bovine serum, only [68Ga]Ga-9 was stable enough to be subject to biological evaluation. It was injected into rats with bone pathology and aseptic inflammation of soft tissues. For [68Ga]Ga-9 in animals with a bone pathology model in 60 and 120 min after injection, a slight accumulation in the pathology site, stable blood percentage level, and moderate accumulation in the liver were observed. For animals with an aseptic inflammation, the accumulation of [68Ga]Ga-9 in the pathology site was higher than that in animals with bone pathology. Moreover, the accumulation of [68Ga]Ga-9 in inflammation sites was more stable than that for [68Ga]Ga-citrate. The percentage of [68Ga]Ga-9 in the blood decreased from 3.1% ID/g (60 min) to 1.5% ID/g (120 min). Accumulation in the liver was comparable to that obtained for [68Ga]Ga-citrate.

The impact of alicyclic substituents on the extraction ability of new family of 1,10-phenanthroline-2,9-diamides.

Development of efficient extractants for the separation of actinides and lanthanides in the technologies of nuclear fuel cycle is one of the most urgent and complex tasks in modern nuclear energetics. New family of 4,7-dichloro-1,10-phenanthroline-2,9-dicarboxylic acid diamides based on cyclic amines was synthesized and shown to exhibit high selectivity in the La/Am pair separation (SF (Am/La ≈ 10)) and in the Am/Eu pair separation (SF (Am/Eu ≈ 12)). It was shown that pyrrolidine derived diamide is more efficient extractant for americium, curium and lanthanides from highly acidic HNO3 solution than its non-cyclic N,N,N',N'-tetraalkyl analogues. The structures of synthesized compounds were studied in details by IR, NMR spectroscopy, and single crystal X-ray diffraction. According to spectroscopy data, incorporation of aromatic rings to the amide fragment of ligand leads to complex dynamic behavior in solutions what is believed to strongly affect the extraction ability of synthesized ligands.

Predictive Models for HOMO and LUMO Energies of N-Donor Heterocycles as Ligands for Lanthanides Separation.

Quantum chemical calculations combined with QSPR methodology reveal challenging perspectives for the solution of a number of fundamental and applied problems. In this work, we performed the PM7 and DFT calculations and QSPR modeling of HOMO and LUMO energies for polydentate N-heterocyclic ligands promising for the extraction separation of lanthanides because these values are related to the ligands selectivity in the respect to the target cations. Data for QSPR modeling comprised the PM7 calculated HOMO and LUMO energies of N-donor heterocycles, including several types of both known and virtual undescribed polydentate ligands. Ensemble modeling included various molecular fragments as descriptors and different variable selection techniques to build consensus models (CMs) on a training set of 388 ligands using external cross-validation. CMs were then verified to make predictions for two external test sets: 45 ligands (T1) that were similar to the ligands of the training set, and 1546 structures (T2), which were substantially different from the ligands of the training set. The consensus models predict well in 5-fold cross-validation (RMSEHOMO =0.097 eV, RMSELUMO =0.064 eV), and on the external test sets (T1: RMSEHOMO =0.26 eV, RMSELUMO =0.24 eV; T2: RMSEHOMO =0.26 eV, RMSELUMO =0.17 eV). An analysis of the results reveals that substituents in heteroaromatic rings of the ligands and at the amide nitrogens can deeply influence their metal binding properties.

Predictive cartography of metal binders using generative topographic mapping.

Generative topographic mapping (GTM) approach is used to visualize the chemical space of organic molecules (L) with respect to binding a wide range of 41 different metal cations (M) and also to build predictive models for stability constants (logK) of 1:1 (M:L) complexes using "density maps," "activity landscapes," and "selectivity landscapes" techniques. A two-dimensional map describing the entire set of 2962 metal binders reveals the selectivity and promiscuity zones with respect to individual metals or groups of metals with similar chemical properties (lanthanides, transition metals, etc). The GTM-based global (for entire set) and local (for selected subsets) models demonstrate a good predictive performance in the cross-validation procedure. It is also shown that the data likelihood could be used as a definition of the applicability domain of GTM-based models. Thus, the GTM approach represents an efficient tool for the predictive cartography of metal binders, which can both visualize their chemical space and predict the affinity profile of metals for new ligands.

Exhaustive QSPR studies of a large diverse set of ionic liquids: how accurately can we predict melting points?

Several popular machine learning methods--Associative Neural Networks (ANN), Support Vector Machines (SVM), k Nearest Neighbors (kNN), modified version of the partial least-squares analysis (PLSM), backpropagation neural network (BPNN), and Multiple Linear Regression Analysis (MLR)--implemented in ISIDA, NASAWIN, and VCCLAB software have been used to perform QSPR modeling of melting point of structurally diverse data set of 717 bromides of nitrogen-containing organic cations (FULL) including 126 pyridinium bromides (PYR), 384 imidazolium and benzoimidazolium bromides (IMZ), and 207 quaternary ammonium bromides (QUAT). Several types of descriptors were tested: E-state indices, counts of atoms determined for E-state atom types, molecular descriptors generated by the DRAGON program, and different types of substructural molecular fragments. Predictive ability of the models was analyzed using a 5-fold external cross-validation procedure in which every compound in the parent set was included in one of five test sets. Among the 16 types of developed structure--melting point models, nonlinear SVM, ASNN, and BPNN techniques demonstrate slightly better performance over other methods. For the full set, the accuracy of predictions does not significantly change as a function of the type of descriptors. For other sets, the performance of descriptors varies as a function of method and data set used. The root-mean squared error (RMSE) of prediction calculated on independent test sets is in the range of 37.5-46.4 degrees C (FULL), 26.2-34.8 degrees C (PYR), 38.8-45.9 degrees C (IMZ), and 34.2-49.3 degrees C (QUAT). The moderate accuracy of predictions can be related to the quality of the experimental data used for obtaining the models as well as to difficulties to take into account the structural features of ionic liquids in the solid state (polymorphic effects, eutectics, glass formation).

Correlation of blood-brain penetration using structural descriptors.

Experimental blood-brain partition coefficients (logBB) for a diverse set of 113 drug molecules are correlated with computed structural descriptors using CODESSA-PRO and ISIDA programs to give statistically significant QSAR models based respectively, on molecular and on fragment descriptors. The linear correlation CODESSA-PRO five-descriptor model has correlation coefficient R2=0.781 and standard deviation s2=0.123. The 'consensus model' of ISIDA gave R2=0.872 and s2=0.047. The developed models were successfully validated using the central nervous system activity data of an external test set of 40 drug molecules.

Skin permeation rate as a function of chemical structure.

Multilinear and nonlinear QSAR models were built for the skin permeation rate (Log K(p)) of a set of 143 diverse compounds. Satisfactory models were obtained by three approaches applied: (i) CODESSA PRO, (ii) Neural Network modeling using large pools of theoretical molecular descriptors, and (iii) ISIDA modeling based on fragment descriptors. The predictive abilities of the models were assessed by internal and external validations. The descriptors involved in the equations are discussed from the physicochemical point of view to illuminate the factors that influence skin permeation.

Benchmarking of linear and nonlinear approaches for quantitative structure-property relationship studies of metal complexation with ionophores.

A benchmark of several popular methods, Associative Neural Networks (ANN), Support Vector Machines (SVM), k Nearest Neighbors (kNN), Maximal Margin Linear Programming (MMLP), Radial Basis Function Neural Network (RBFNN), and Multiple Linear Regression (MLR), is reported for quantitative-structure property relationships (QSPR) of stability constants logK1 for the 1:1 (M:L) and logbeta2 for 1:2 complexes of metal cations Ag+ and Eu3+ with diverse sets of organic molecules in water at 298 K and ionic strength 0.1 M. The methods were tested on three types of descriptors: molecular descriptors including E-state values, counts of atoms determined for E-state atom types, and substructural molecular fragments (SMF). Comparison of the models was performed using a 5-fold external cross-validation procedure. Robust statistical tests (bootstrap and Kolmogorov-Smirnov statistics) were employed to evaluate the significance of calculated models. The Wilcoxon signed-rank test was used to compare the performance of methods. Individual structure-complexation property models obtained with nonlinear methods demonstrated a significantly better performance than the models built using multilinear regression analysis (MLRA). However, the averaging of several MLRA models based on SMF descriptors provided as good of a prediction as the most efficient nonlinear techniques. Support Vector Machines and Associative Neural Networks contributed in the largest number of significant models. Models based on fragments (SMF descriptors and E-state counts) had higher prediction ability than those based on E-state indices. The use of SMF descriptors and E-state counts provided similar results, whereas E-state indices lead to less significant models. The current study illustrates the difficulties of quantitative comparison of different methods: conclusions based only on one data set without appropriate statistical tests could be wrong.

QSAR modeling of blood:air and tissue:air partition coefficients using theoretical descriptors.

Human blood:air, human and rat tissue (fat, brain, liver, muscle, and kidney):air partition coefficients of a diverse set of organic compounds were correlated and predicted using structural descriptors by employing CODESSA-PRO and ISIDA programs. Four and five descriptor regression models developed using CODESSA-PRO were validated on three different test sets. Overall, these models have reasonable values of correlation coefficients (R(2)) and leave-one-out correlation coefficients (R(cv)(2)): R(2) = 0.881-0.983; R(cv)(2) = 0.826-0.962. Calculations with ISIDA resulted in models based on atom/bond sequences involving two to three atoms with statistical parameters that were similar to those of models obtained with CODESSA-PRO (R(2) = 0.911-0.974; R(cv)(2) = 0.831-0.936). A mixed pool of molecular and fragment descriptors did not lead to significant improvement of the models.

Quantitative structure-property relationship modeling of beta-cyclodextrin complexation free energies.

CODESSA-PRO was used to model binding energies for 1:1 complexation systems between 218 organic guest molecules and beta-cyclodextrin, using a seven-parameter equation with R2 = 0.796 and Rcv2 = 0.779. Fragment-based TRAIL calculations gave a better fit with R2 = 0.943 and Rcv2 = 0.848 for 195 data points in the database. The advantages and disadvantages of each approach are discussed, and it is concluded that a combination of the two approaches has much promise from a practical viewpoint.