Publications

Abstract Low range mass spectra (MS) characterization of serum proteome offers the best chance of discovering proteome-(early drug-induced cardiac toxicity) relationships, called here Pro-EDICToRs. However, due to the thousands of proteins involved, finding the single disease-related protein could be a hard task. The search for a model based on general MS patterns becomes a more realistic choice. In our previous work (Gonzalez-Diaz, H., et al. Chem. Res. Toxicol. 2003, 16, 1318-1327), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs). In this previous work, quantitative structure-toxicity relationship (QSTR) techniques allowed us to link 3D-MEDNEs with blood toxicological properties of drugs. In this second part, we extend 3D-MEDNEs to numerically encode biologically relevant information present in MS of the serum proteome for the first time. Using the same idea behind QSTR techniques, we can seek now by analogy a quantitative proteome-toxicity relationship (QPTR). The new QPTR models link MS 3D-MEDNEs with drug-induced toxicological properties from blood proteome information. We first generalized Randic's spiral graph and lattice networks of protein sequences to represent the MS of 62 serum proteome samples with more than 370 100 intensity (I(i)) signals with m/z bandwidth above 700-12000 each. Next, we calculated the 3D-MEDNEs for each MS using the software MARCA-INSIDE. After that, we developed several QPTR models using different machine learning and MS representation algorithms to classify samples as control or positive Pro-EDICToRs samples. The best QPTR proposed showed accuracy values ranging from 83.8% to 87.1% and leave-one-out (LOO) predictive ability of 77.4-85.5%. This work demonstrated that the idea behind classic drug QSTR models may be extended to construct QPTRs with proteome MS data.

Abstract Up to now, very few applications of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies have been reported in the literature. However, none of them report the optimization of objectives related directly to the final pharmaceutical profile of a drug. In this paper, a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies, simultaneously considering the potency, bioavailability, and safety of a set of drug candidates, is introduced. The results of the desirability-based MOOP (the levels of the predictor variables concurrently producing the best possible compromise between the properties determining an optimal drug candidate) are used for the implementation of a ranking method that is also based on the application of desirability functions. This method allows ranking drug candidates with unknown pharmaceutical properties from combinatorial libraries according to the degree of similarity with the previously determined optimal candidate. Application of this method will make it possible to filter the most promising drug candidates of a library (the best-ranked candidates), which should have the best pharmaceutical profile (the best compromise between potency, safety and bioavailability). In addition, a validation method of the ranking process, as well as a quantitative measure of the quality of a ranking, the ranking quality index (W), is proposed. The usefulness of the desirability-based methods of MOOP and ranking is demonstrated by its application to a library of 95 fluoroquinolones, reporting their gram-negative antibacterial activity and mammalian cell cytotoxicity. Finally, the combined use of the desirability-based methods of MOOP and ranking proposed here seems to be a valuable tool for rational drug discovery and development.

Abstract The Quantitative Structure-Property Relationships (QSPRs) based on Graph OF Network Theory are important for predicting the properties of polymeric systems. In the three previous papers of this series (Polymer 45 (2004) 3845-3853; Polymer 46 (2005) 2791-2798: and Polymer 46 (2005) 6461-6473) we focused on the uses of molecular graph parameters called topological indices (TIs) to link the structure of polymers with their biological properties. However, there has been little effort to extend these TIs to the Study of complex mixtures of artificial polymers OF biopolymers such as nucleic acids and proteins. In this sense, Blood Proteome (BP) is one of the most important and complex mixtures containing protein polymers. For instance, outcomes obtained by Mass Spectrometry (MS) analysis of BP are very useful for the early detection of diseases and drug-induced toxicities. Here, we use two Spiral and Star Network representations of the MS outcomes and defined a new type of TIs. The new TIs introduced here are the spectral moments (pi(k)) of the stochastic matrix associated to the Spiral graph and describe non-linear relationships between the different regions of the MS characteristic of BR We used the MARCH-INSIDE approach to calculate the pi(k)(SN) of different BP samples and S2SNet to determine several Star graph TIs. In the second step, we develop the corresponding Quantitative Proteome-Property Relationship (QPPR) models using the Linear Discriminant Analysis (LDA). QPPRs are the analogues of QSPRs in the case of complex biopolymer mixtures. Specifically, the new QPPRs derived here may be used to detect drug-induced cardiac toxicities from BP samples. Different Machine Learning classification algorithms were used to fit the QPPRs based on pi(k)(SN), showing J48 decision tree classifier to have the best performance. These results suggest that the present approach Captures important features of the complex biopolymers mixtures and opens new opportunities to the application of the idea supporting classic QSPRs in polymer sciences.

Abstract Up to now, very few reports have been published concerning the application of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies. However, none reports the optimization of objectives related directly to the desired pharmaceutical profile of the drug. In this work, for the first time, it is proposed a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies considering simultaneously the pharmacological, pharmacokinetic and toxicological profile of a set of molecule candidates. The usefulness of the method is demonstrated by applying it to the simultaneous optimization of the analgesic, anti inflammatory, and ulcerogenic properties of a library of fifteen 3-(3-methylplienyl)-2-substituted amino-3H-quinazolin-4-one compounds. The levels of the predictor variables producing concurrently the best possible compromise between these properties is found and used to design a set of new optimized drug candidates. Our results also suggest the relevant role of the bulkiness of alkyl substituents on the C-2 position of the quinazoline ring over the Ulcerogenic properties for this family of compounds. Finally, and most importantly, the desirability-based MOOP method proposed is a valuable toot and shall aid in the future rational design of novel successful drugs. (C) 2008 Wiley Periodicals, Inc.

Abstract Idiosyncratic drug toxicity (IDT), considered as a toxic host-dependent event, with an apparent lack of dose response relationship, is usually not predictable from early phases of clinical trials, representing a particularly confounding complication in drug development. Albeit a rare event (usually <1/5000), IDT is often life threatening and is one of the major reasons new drugs never reach the market or are withdrawn post marketing. Computational methodologies, like the computer-based approach proposed in the present study, can play an important role in addressing IDT in early drug discovery. We report for the first time a systematic evaluation of classification models to predict idiosyncratic hepatotoxicity based on linear discriminant analysis (LDA), artificial neural networks (ANN), and machine learning algorithms (OneR) in conjunction with a 3D molecular structure representation and feature selection methods. These modeling techniques (LDA, feature selection to prevent over-fitting and multicollinearity, ANN to capture nonlinear relationships in the data, as well as the simple OneR classifier) were found to produce QSTR models with satisfactory internal cross-validation statistics and predictivity on an external subset of chemicals. More specifically, the models reached values of accuracy/sensitivity/specificity over 84%/78%/90%, respectively in the training series along with predictivity values ranging from ca. 78 to 86% of correctly classified drugs. An LDA-based desirability analysis was carried out in order to select the levels of the predictor variables needed to trigger the more desirable drug, i.e. the drug with lower potential for idiosyncratic hepatotoxicity. Finally, two external test sets were used to evaluate the ability of the models in discriminating toxic from nontoxic structurally and phannacologically related drugs and the ability of the best model (LDA) in detecting potential idiosyncratic hepatotoxic drugs, respectively. The computational approach proposed here can be considered as a useful tool in early IDT prognosis. (c) 2007 Wiley Periodicals, Inc.

Abstract The standard (p degrees = 0.1 MPa) molar enthalpies of formation in the crystalline phases of ortho, meta and para-terphenyl isomers, at T = 298.15 K, were derived from the standard molar energies of combustion, measured by mini-bomb combustion calorimetry. The Knudsen mass-loss effusion technique was used to measure the dependence of the vapour pressure of the crystals with the temperature, thus deriving their standard molar enthalpies of sublimation by means of the Clausius-Clapeyron equation. Combining the standard molar enthalpies of formation and sublimation of the crystalline terphenyls, the standard molar enthalpies of formation in the gaseous state, at T = 298.15 K, were derived for the three isomers. [GRAPHICS] The results show small but detectable isomerization enthalpies between the terphenyls, indicating the following relative enthalpic stabilities: m- > p- approximate to o-terphenyl.

Abstract Two substituted N-acylthioureas and the respective Ni(II) and Cu(II) complexes were synthesized, namely: N,N-di-n-butyl-N'-thenoylthiourea (Hnbtu); N,N-di-iso-butyl-N'-tlienoylthiourea (Hibtu); bis[N,N-di-n-butyl-N'-thenoylthioureato]nickel(II), [Ni(nbtu)(2)]; bis[N,N-di-n-butyl-N'-thenoylthioureato]copper(II), [Cu(nbtu)(2)]; bis[N,N-di-iso-butyl-N'-thenoylthioureato]nickel(II), [Ni(ibtu)2]; bis[N,N-di-iso-butyl-N'-thenoylthioureato]copper(II), [Cu(ibtu)(2)]. The standard (p degrees = 0.1 MPa) molar enthalpies of formation and sublimation of the two N-acylthioureas were measured, at T = 298.15 K, by rotating-bomb combustion calorimetry and Calvet microcalorimetry, respectively. The standard (p = 0.1 MPa) molar enthalpies of formation of the Ni(H) and Cu(H) complexes were determined, at T= 298.15 K, by high precision solution-reaction calorimetry. From the results obtained, the enthalpies of hypothetical metal-ligand and metal-metal exchange reactions, in the gaseous phase, were derived, thus allowing a discussion of the gaseous phase energetic difference between the complexation of Ni(II) and Cu(II) to 1,3-ligand systems with (S,O) ligator atoms.

Abstract The spontaneous formation of vesicles by the salt-free surfactant hexadecyltrimethylammonium octylsulfonate (TASo) and the features of an unusual vesicle-micelle transition are investigated in this work. In a previous work, we have shown that this highly asymmetric catanionic surfactant displays a rare lamellar miscibility gap in the concentrated regime. Here, we analyze in detail the aggregation behavior in the dilute regime (less than 3 wt % surfactant) as a function of both concentration and temperature. The phase diagram is dominated by a two-phase region consisting of a dispersion of a swollen lamellar phase (L alpha') in the excess solvent phase (L-1). Stable vesicles form in this two-phase region, and upon temperature increase, a transition to a single solution phase containing only elongated micelles occurs. The structural characterization of the aggregates and the investigation of their equilibrium properties have been carried out by light microscopy, cryo-TEM, water self-diffusion NMR, and SANS. Similarly to the lamellar-lamellar coexistence, the changes in microstructure at high dilution and high temperature can be understood from solubility differences, electrostatic interactions, and preferred aggregate curvature. Surface charge in the aggregates stems from the higher solubility of the octylsulfonate (So(-)) ion as compared to that of the hexadecyltrimethylammonium ion (TA(+)). Upon temperature increase, the ratio of free So(-) relative to the neutral TASo increases. Consequently, the surface charge density of the aggregates increases, and this ultimately induces a transition to a higher-curvature morphology (elongated micelles). Vesicles can also be spontaneously formed by cooling solutions from the micellar region, and the mean size obtained is practically independent of cooling rate, suggesting that dissociation/charge effects also control this process.

Abstract The mixed didodecyldimethylammonium bromide (DDAB)-sodium taurodeoxycholate (STDC)-(H2O)-H-2 catanionic system forms a large isotropic (L-1) phase at 25 degrees C. The evolution of microstructure along different dilution lines has been followed by means of rheology and NMR diffusometry. In general, the L, phase is characterised by a weak viscoelasticity and Newtonian response. In the STDC-rich regime (W-s = [DDAB]/[STDC] = 0.2), 5 wt% is an overlapping concentration at which the discrete-to-rodlike micellar transition occurs as indicated from the total surfactant concentration (C-s) dependency of both zero-shear viscosity (eta(0) similar to C-s(3.7)) and surfactant self-diffusion (D-s similar to C-s(-3.0)). As the surfactant molar ratio (W-s >= 1) increases, i.e., DDAB concentration increases, and at constant C-s, eta(0) decreases and D-s increases, indicating the formation of a multiconnected micellar network. (c) 2008 Elsevier Inc. All Fights reserved.