Background: Silicone gel breast implants are associated with long-term adverse events, including capsular contracture, with reported incidence rates as high as 50 percent. However, it is not clear how long the follow-up period should be and whether there is any association with estrogen or menopausal status. In addition, the placement of Baker grade II subjects in the majority of reports has been in data sets of controls instead of capsular contracture. Methods: A retrospective medical study (1998 to 2004) was performed in women (n = 157) who received textured silicone breast implants for aesthetic or reconstructive procedures at the Hospital of S. Joao (Portugal). Medical data were collected that included the following: patient demographics, history, lifestyle factors, surgical procedures, and postoperative complications. Statistical analyses included Pearson chi-square testing, logistic regression modeling, and chi-squared automatic interaction detection (CHAID) methods. Results: The reconstructive cohort had a great incidence of capsular contracture compared with the cosmetic cohort. If one considered no capsular contracture versus capsular contracture, the follow-up period should be longer than 42 months. However, if considering no capsular contracture and grade II subjects versus grade III or IV subjects, a longer follow-up period of 64 months was determined. There was no association between capsular contracture and menopause/estrogen status. Conclusions: Increased frequencies of capsular contracture were recorded in breast reconstruction that were not attributable to estrogen or menopausal status. On the basis of these results, the authors propose a follow-up period longer than 42 months and the inclusion of Baker grade II subjects. (Plast. Reconstr. Surg. 126: 769, 2010.)

Desirability theory (DT) is a well-known multi-criteria decision-making approach. In this work, DT is employed as a prediction model (PM) interpretation tool to extract useful information on the desired trade-offs between binding and relative efficacy of N6-substituted-4'-thioadenosines A(3) adenosine receptor (A(3)AR) agonists. At the same time, it was shown the usefulness of a parallel but independent approach providing a feedback on the reliability of the combination of properties predicted as a unique desirability value. The appliance of belief theory allowed the quantification of the reliability of the predicted desirability of a compound according to two inverse and independent but complementary prediction approaches. This information is proven to be useful as a ranking criterion in a ligand-based virtual screening study. The development of a linear PM of the A(3)AR agonists overall desirability allows finding significant clues based on simple molecular descriptors. The model suggests a relevant role of the type of substituent on the N6 position of the adenine ring that in general contribute to reduce the flexibility and hydrophobicity of the lead compound. The mapping of the desirability function derived of the PM offers specific information such as the shape and optimal size of the N6 substituent. The model herein developed allows a simultaneous analysis of both binding and relative efficacy profiles of A(3)AR agonists. The information retrieved guides the theoretical design and assembling of a combinatorial library suitable for filtering new N6-substituted-4'-thioadenosines A(3)AR agonist candidates with simultaneously improved binding and relative efficacy profiles. The utility of the desirability/belief-based proposed virtual screening strategy was deduced from our training set. Based on the overall results, it is possible to assert that the combined use of desirability and belief theories in computational medicinal chemistry research can aid the discovery of A(3)AR agonist candidates with favorable balance between binding and relative efficacy profiles.

Thermodynamic stability of metal-aminoacid complexes in water is discussed in terms of the Gibbs free energy of water-ligand exchange processes, and the electronic stabilizing factors thoroughly investigated by means of 1-electron and 2-electron density properties. Hexacoordinated complexes formed between iron cations and glycine molecules acting as monodentate or bidentate ligands have been chosen as targets for the current study. Results agree with experimental findings, and complexes formed with bidentate ligands are found to be more stable than those formed with monodentate ones. The larger the number of the coordinated glycine molecules the more stable is the complex. Fe(III) complexes are more stable than Fe(II) ones, but differences are small and the Fe(3+)/Fe(2+) exchange process appears to be energetically feasible for these complexes. Formation of the second glycine-iron interaction involving the amino nitrogen in the bidentate ligands is enthalpycally unfavorable but takes place due to the large entropy rise of the process. The larger stability of Fe(III) complexes is due however to the balance between energetic and solvation terms, which is favorable to these complexes. Electron density properties account satisfactorily for the electronic energy changes along the complex formation in terms of ligand-metal electron transfer and covalent bond orders. (C) 2010 Wiley Periodicals, Inc. J Comput Chem 31: 2735-2745, 2010

Nonnucleoside reverse transcriptase (RT) inhibitors (NNRTIs) constitute a promising therapeutic option for AIDS. However, the emergence of virus-NNRTIs resistance was found to be a major problem in the field. Toward that goal, a "knock-out" strategy stands out between the several options to circumvent the problem. However the high drug or drug-drug concentrations often used generate additional safety concerns. The need for approaches able to early integrate drug- or lead-likeness, toxicity and bioavailability criteria in the drug discovery phase is an emergent issue. Given that, we propose a combined strategy based on desirability-based multiobjective optimization (MOOP) and ranking for the prioritization of HIV-1 NNRTIs hits with appropriate trade-offs between inhibitory efficacy over the HIV-1 RI and toxic effects over MT4 blood cells. Through the MOOP process, the theoretical levels of the predictive variables required to reach a desirable RI inhibitor candidate with the best possible compromise between efficacy and safety were found. This information is used as a pattern to rank the library of compounds according to a similarity-based structural criterion, providing a ranking quality of 64%/71 %/73% in training/validation/test set. A comparative study between the sequential, parallel and multiobjective virtual screening revealed that the multiobjective approach can outperform the other approaches. These results suggest that the identification of NNRTIs hits with appropriate trade-offs between potency and safety, rather than fully optimized hits solely based on potency, can facilitate the hit to lead transition and increase the likelihood of the candidate to evolve into a successful antiretroviral drug.

Objective. The purpose of this study is to develop a quantitative structure-activity relationship (QSAR) model that can distinguish mutagenic from non-mutagenic species with alpha,beta-unsaturated carbonyl moiety using two endpoints for this activity - Ames test and mammalian cell genemutation test - and also to gather information about the molecular features that most contribute to eliminate the mutagenic effects of these chemicals. Methods. Two data sets were used for modeling the two mutagenicity endpoints: ( 1) Ames test and ( 2) mammalian cells mutagenesis. The first one comprised 220 molecules, while the second one 48 substances, ranging from acrylates, methacrylates to alpha,beta-unsaturated carbonyl compounds. The QSAR models were developed by applying linear discriminant analysis (LDA) along with different sets of descriptors computed using the DRAGON software. Results. For both endpoints, there was a concordance of 89% in the prediction and 97% confidentiality by combining the three models for the Ames test mutagenicity. We have also identified several structural alerts to assist the design of new monomers. Significance. These individual models and especially their combination are attractive from the point of view of molecular modeling and could be used for the prediction and design of new monomers that do not pose a human health risk.

Worldwide, legislative and governmental efforts are focusing on establishing simple screening tools for identifying those chemicals most likely to cause adverse effects without experimentally testing all chemicals of regulatory concern. This is because even the most basic biological testing of compounds of concern, apart from requiring a huge number of test animals, would be neither resource nor time effective. Thus, alternative approaches such as the one proposed here, quantitative structure-activity relationship (QSAR) modelling, are increasingly being used for identifying the potential health hazards and subsequent regulation of new industrial chemicals. This paper follows up on our earlier work that demonstrated the use of the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach to QSAR modelling for predictions of the carcinogenic potency of nitroso compounds. The data set comprises 56 nitroso compounds which have been bio-assayed in female rats and administered by the oral water route. The QSAR model was able to account for about 81% of the variance in the experimental activity and exhibited good cross-validation statistics. A reasonable interpretation of the TOPS-MODE descriptors was achieved by means of bond contributions, which in turn afforded the recognition of structural alerts (SAs) regarding carcinogenicity. A comparison of the SAs obtained from different data sets showed that experimental factors, such as the sex and the oral administration route, exert a major influence on the carcinogenicity of nitroso compounds. The present and previous QSAR models combined together provide a reliable tool for estimating the carcinogenic potency of yet untested nitroso compounds and they should allow the identification of SAs, which can be used as the basis of prediction systems for the rodent carcinogenicity of these compounds.

MLR is a classical approach to regression problems in QSARs. In this study, the behaviour of SEEB descriptors was analysed with a MLR model. For this purpose a SEEB/MLR 3D-QSAR model was developed to evaluate the efficiency of benzamide trypsin inhibitors. The development of inhibitors of trypsin-like serine proteases has been an active area of research. They are involved in many bio-logical processes like protein digestion and blood coagulation and also serve as a useful model system to study protein-ligand interaction. The regression coefficients, obtained by this procedure, have an intuitively simple and therefore appealing meaning for the relative influence of each amino acid residue to the predictive model. The predictive capability of SEEB is shown to be comparable to those of other QSAR methods.