Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project.

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

Cervical lordosis after subaxial spinal trauma surgery: relationship with neck pain and stiffness.

BACKGROUND: The objective of this retrospective study is to evaluate how neck pain is influenced by post-operative cervical alignment in patients operated for cervical spinal trauma. PATIENTS AND METHODS: From January 2013 to June 2017, at our department we operated 34 patients with cervical spinal trauma, 22 males and 12 females. Age, sex, level and type of fractures, surgical approach, fixation levels (cervical or cervico-dorsal), preoperative and postoperative CT scan, cervical (C2-C7) Cobb angle (lordotic > +10°, straight 0 /+10°, kyphotic < 0°) at X-rays on sitting position 3 months after surgery, postoperative self-reported neck stiffness scale, preoperative and follow-up ASIA score, pre and postoperative VAS value were evaluated for each patient. Statistical analysis was performed according to the Mann-Whitney and T-test. RESULTS: In this series, 22 patients were operated by anterior approach, 7 patients by posterior approach and 5 by combined approach. Postoperative chronic cervical pain was not correlated with cervical sagittal alignment after surgery, fracture type, surgical approach, fixation level and postoperative ASIA score but is correlated with the presence of neck stiffness (P=0,001). Patients treated with posterior approach (P=0,022) and fracture type C (P=0,026) had higher significantly neck stiffness compared to patients who underwent anterior approach for type B fractures. CONCLUSIONS: The presence of abnormal cervical lordosis after surgery for cervical spinal trauma does not correlate with neck pain. Patients treated with posterior fixation had higher neck stiffness and related chronic pain.

Molecular fingerprint-derived similarity measures for toxicological read-across: Recommendations for optimal use.

Computational approaches are increasingly used to predict toxicity due, in part, to pressures to find alternatives to animal testing. Read-across is the "new paradigm" which aims to predict toxicity by identifying similar, data rich, source compounds. This assumes that similar molecules tend to exhibit similar activities i.e. molecular similarity is integral to read-across. Various of molecular fingerprints and similarity measures may be used to calculate molecular similarity. This study investigated the value and concordance of the Tanimoto similarity values calculated using six widely used fingerprints within six toxicological datasets. There was considerable variability in the similarity values calculated from the various molecular fingerprints for diverse compounds, although they were reasonably concordant for homologous series acting via a common mechanism. The results suggest generic fingerprint-derived similarities are likely to be optimally predictive for local datasets, i.e. following sub-categorisation. Thus, for read-across, generic fingerprint-derived similarities are likely to be most predictive after chemicals are placed into categories (or groups), then similarity is calculated within those categories, rather than for a whole chemically diverse dataset.

Deep spinal infection in instrumented spinal surgery: diagnostic factors and therapy.

AIM: Postoperative surgical site infections (SSI) are complication of spinal surgery. These complications may lead to a poor outcome with neurological deficits, spinal deformity and chronic pain. The purpose of this study is to explore the statistical value of diagnostic parameters and the proper therapy. METHOD: We retrospectively reviewed 550 patients who underwent spinal instrumentation at our department from January 2011 to December 2015. The SSI was present in 16 patients out of 550 operated. Diagnostic criteria of SSI were the positivity of the surgical wound swab or blood culture, the clinical findings, positivity of laboratory tests and radiological elements. All patients had peri-operative antibiotic prophylaxis. Diagnostic laboratory findings were compared with a homogeneous control group of 16 patients and analyzed by univariate statistical analysis with Chi-square test for the discrete variables. P<0,05 was considered statistically significant. RESULTS: Matching the SSI patients with a group of control, fever was not statistically significant for diagnosis as number of leukocytes, neutrophils and lymphocytes. On the contrary values of ESR and CRP were statistically significant with p <0, 01. The hardware was removed only in 3 patients (18%) out of 16 SSI patients. CONCLUSION: In this study the statistically significant parameters to diagnose SSI are ESR and CRP values. The leucocytes count, number of lymphocytes and presence of fever integrates the data of ESR and CRP with no statistical significance. Most patients with SSI reach clinical healing with favorable outcome by means of target antibiotic therapy without hardware removal.

Spontaneous chronic subdural hematoma in young adult: the role of missing coagulation facto.

AIM: Chronic subdural hematoma (CSDH) is typically in elderly and rarely in young people. To prevent complications and re-bleeding after surgical treatment of CSDH it is important to assess the risk factors as coagulation disorders especially in young patients (below 65 years) with no history of head trauma, alcohol abuse or anticoagulant therapy. PATIENTS AND METHODS: This study consists of 16 patients (12 males, 4 females) with age ranging from 27 to 59 years (median 48,25 years) operated for CSDH. All patients are submitted to routine coagulation parameters pre-operatively and complete screening for unknown coagulation deficit in the follow-up. RESULTS: Factor VII was altered in 6 out of 16 patients and one patient had the alteration of the Von Willebrand factor. Recurrence occurred in 4 out of 16 patients and all of these patients were positive for factor VII deficiency. Three pts were in therapy with ASA. No patients were alcoholists or suffered from hematological disease. CONCLUSION: In this study we documented that the decreased activity of VII factor may play a role in the pathophysiology and recurrence of spontaneous CSDH in young adults. We suggest that for young patients aged under 65 y.o. suffered from CSDH the screening of coagulation factors is useful to planning a safely and correct surgical therapy.

Predictive models for carcinogenicity and mutagenicity: frameworks, state-of-the-art, and perspectives.

Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include Vitotox, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-throughput assays combined with innovative data-mining and in silico methods. Various initiatives in this regard have begun, including CAESAR, OSIRIS, CHEMOMENTUM, CHEMPREDICT, OpenTox, EPAA, and ToxCast. In silico methods can be used for priority setting, mechanistic studies, and to estimate potency. Ultimately, such efforts should lead to improvements in application of in silico methods for predicting carcinogenicity to assist industry and regulators and to enhance protection of public health.

Understanding genetic toxicity through data mining: the process of building knowledge by integrating multiple genetic toxicity databases.

ABSTRACT Genetic toxicity data from various sources were integrated into a rigorously designed database using the ToxML schema. The public database sources include the U.S. Food and Drug Administration (FDA) submission data from approved new drug applications, food contact notifications, generally recognized as safe food ingredients, and chemicals from the NTP and CCRIS databases. The data from public sources were then combined with data from private industry according to ToxML criteria. The resulting "integrated" database, enriched in pharmaceuticals, was used for data mining analysis. Structural features describing the database were used to differentiate the chemical spaces of drugs/candidates, food ingredients, and industrial chemicals. In general, structures for drugs/candidates and food ingredients are associated with lower frequencies of mutagenicity and clastogenicity, whereas industrial chemicals as a group contain a much higher proportion of positives. Structural features were selected to analyze endpoint outcomes of the genetic toxicity studies. Although most of the well-known genotoxic carcinogenic alerts were identified, some discrepancies from the classic Ashby-Tennant alerts were observed. Using these influential features as the independent variables, the results of four types of genotoxicity studies were correlated. High Pearson correlations were found between the results of Salmonella mutagenicity and mouse lymphoma assay testing as well as those from in vitro chromosome aberration studies. This paper demonstrates the usefulness of representing a chemical by its structural features and the use of these features to profile a battery of tests rather than relying on a single toxicity test of a given chemical. This paper presents data mining/profiling methods applied in a weight-of-evidence approach to assess potential for genetic toxicity, and to guide the development of intelligent testing strategies.

Chemical structure of mutagens and carcinogens and the relationship with biological activity.

Chemical carcinogenicity has been the target of numerous attempts to create predictive models alternative to the animal ones, ranging from short-term biological assays (e.g. mutagenicity tests) to theoretical models. Among the theoretical models, the application of the science of Structure-Activity Relationships (SAR) has earned special prominence. SAR has been applied both in a qualitative way (for example as simple recognition of suspected sub-structures or Structural Alerts), and in a quantitative way (Quantitative SAR, QSAR) to build mathematical models linking the physical chemical or structural properties of the molecules to the toxicological endpoints. This paper summarizes the contribution that the two approaches can provide in different situations. It concludes that the study of the structure of the chemicals generates predictions with limited reliability for the individual chemicals, however it has been demonstrated to be an extremely powerful tool for priority setting relative to large samples of chemicals.

AI and SAR approaches for predicting chemical carcinogenicity: survey and status report.

A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR) approaches have been applied to tackling the general problem of predicting rodent chemical carcinogenicity. Given the diversity of chemical structures and mechanisms relative to this endpoint, the shared challenge of these approaches is to accurately delineate classes of active chemicals representing distinct biological and chemical mechanism domains, and within those classes determine the structural features and properties responsible for modulating activity. In the following discussion, we present a survey of AI and SAR approaches that have been applied to the prediction of rodent carcinogenicity, and discuss these in general terms and in the context of the results of two organized prediction exercises (PTE-1 and PTE-2) sponsored by the US National Cancer Institute/National Toxicology Program. Most models participating in these exercises were successful in identifying major structural-alerting classes of active carcinogens, but failed in modeling the more subtle modifiers to activity within those classes. In addition, methods that incorporated mechanism-based reasoning or biological data along with structural information outperformed models limited to structural information exclusively. Finally, a few recent carcinogenicity-modeling efforts are presented illustrating progress in tackling some aspects of the carcinogenicity prediction problem. The first example, a QSAR model for predicting carcinogenic potency of aromatic amines, illustrates that success is possible within well-represented classes of carcinogens. From the second example, a newly developed FDA/OTR MultiCASE model for predicting the carcinogenicity of pharmaceuticals, we conclude that the definitions of biological activity and nature of chemicals in the training set are important determinants of the predictive success and specificity/sensitivity characteristics of a derived model.

Prediction of rodent carcinogenicity of aromatic amines: a quantitative structure-activity relationships model.

The aromatic amines are widely used industrial chemicals and can be found in tobacco smoke as well as in products generated during cooking. In a previous study, we established quantitative structure-activity relationship (QSAR) models linking the carcinogenic potency of non-heterocyclic carcinogenic aromatic amines to a series of molecular determinants. We also found that QSAR models for carcinogenic potency were inadequate in describing the difference between carcinogenic and non-carcinogenic amines [Benigni,R., Giuliani,A., Franke,R. and Gruska,A. (2000) CHEM: Rev., 100, 3697-3714]. In this paper, we derived specific QSAR models for separating active from inactive amines. It appeared that hydrophobicity (as measured by the octanol/water partition coefficient, logP) played a major role in modulating the potency of the carcinogens, whereas mainly electronic (reactivity) and steric characteristics separated the carcinogens from the non-carcinogens. Interestingly, a similar pattern was previously demonstrated by us regarding their mutagenic activity [Benigni,R., Passerini,L., Gallo,G., Giorgi,F. and Cotta-Ramusino,M. (1998) ENVIRON: Mol. Mutagen., 32, 75-83]. Based on the QSAR models found, the molecular determinants of the mechanisms of action of aromatic amines are discussed in detail. The QSAR models obtained can be used directly for estimating the carcinogenicity of other non-heterocyclic aromatic amines for which experimental data are not available. With the QSARs in Benigni et al. (2000) and the present results, a two-step prediction of carcinogenicity of aromatic amines is possible: (i) step 1, yes/no activity from the discriminant functions; and (ii) step 2, if the answer from step 1 is yes then prediction of the degree of potency from the equations in Benigni et al. (2000). Thus, QSAR models can contribute to the following: the direct synthesis of safer chemicals; the estimation of the risk posed by amines present in the environment; setting priorities for further experimentation, thus also reducing the use of experimental animals. Whereas the quality of in vivo experimental data is often questioned, the robustness and interpretability of the present results strongly support the reliability of the rodent carcinogenicity assay.

The sex difference in tumor incidence is related to the female condition: models for Europe and Italy.

A remarkable aspect of cancer distribution in Europe is the large spatial variability of the male-female incidence ratio, from no difference up to 50%. Given the evidence of the predominantly environmental origin of cancer, we studied the ability of a set of socioeconomic indicators of the female condition to model the spatial variability of the sex difference in tumor incidence at two different scales: between countries (Europe) and between provinces (Italy). The sex difference in tumor incidence correlated with female socioeconomic condition indicators at the same extent (r = 0.73) in both situations, but in opposite directions. In the European study the higher the sexual social equality the lower the differential tumor incidence, whereas the opposite result was shown by the between-provinces Italian study. We also investigated the relation of the female condition indicator with other social and cultural descriptors of the same populations, and we suggest explanatory models linking female condition and pathology at the continental and local scales. Overall, our analysis supports the predominantly environmental origin of cancer and stresses the importance of relating cancer patterns to societal determinants. Our analysis also suggests that the sex difference in tumor incidence is a very useful probe for exploring the social-economic cultural correlates of cancer in human populations. We emphasize the need for a thorough analysis of the empirical correlations highlighted in ecologic studies.

Infrared spectra as chemical descriptors for QSAR models.

Infrared spectra (IR) were used as regressors for a number of QSARs and compared with both mechanistically oriented descriptors and heuristic "chemically neutral" descriptors (modified adjacency matrices eigenvalues). IR spectra usually gave results inferior to those obtained with the mechanistically driven descriptors, with one notable exception, and comparable to those obtained by adjacency matrices eigenvalues. So the IR spectra cannot be considered as an "a-priori" optimal description of molecules for QSAR. However the relation of IR with the chemicophysical bases of drug-receptor interaction suggests the use of IR spectra for elucidating mechanistic details.

Mapping protein sequence spaces by recurrence quantification analysis: a case study on chimeric structures.

Recurrence quantification analysis (RQA) was used to characterize the folding properties of 22 chimeric sequences derived from two parent proteins of similar length but different three-dimensional arrangement. A non-linear relation between sequence data and their RQA representation was revealed, which points to new information carried by this method as compared with classical best-alignment methods. This new information is significantly correlated with the folding properties of the hybrid polypeptide chains, as substantiated by careful statistical analysis of the recurrence plots' numerical descriptors, thus encouraging their systematic use to complement sequence data in both proteomics and protein engineering tasks. Even the direct visual screening of the qualitative graphical features of recurrence plots is shown to provide useful hints to discriminate between different recurrence structures of protein sequences.

Deriving a quantitative chirality measure from molecular similarity indices.

A versatile new method has been developed as a continuous symmetry measure for chiral compounds. The application of principal component analysis (PCA) to the complete N x N pairwise similarity matrices (electrostatic potential and shape indices) of a series of dihydropyridine calcium channel antagonists allowed to single out a chirality component and to compute a chirality score in terms of the between-enantiomers difference on the component value. The possibility to have chirality defined continuously at the series level could be of importance in eudismic analyses where the relative potency of two enantiomers is studied as well as in QSAR studies dealing with chiral molecules in order to improve the power of the generated models.

The cultural heritage shapes the pattern of tumour profiles in Europe: a correlation study.

STUDY OBJECTIVE: This study investigates the spatial pattern of tumours in Europe to check the feasibility of a large scale ecological epidemiology approach to cancer in Europe. SETTING: The tumour types relative frequencies and cancer incidence (for men and women) reported in the European cancer registries were investigated by exploratory data analysis techniques. Socioeconomical descriptors of the female condition were considered as well. MAIN RESULTS: The classification of the European regional areas covered by the cancer registries followed almost exactly the boundaries set by the long and intermingled European history in terms of life styles and cultural heritage. This result supports the notion of a predominant role of environmental factors in cancer induction. Further support to the above result was given by the finding of a correlation between differential male-female cancer incidence, and socioeconomic descriptors of the female condition. CONCLUSIONS: From a methodological point of view, the consistency of these results pointed to the feasibility of an ecological approach to tumour epidemiology.

Nonlinear methods in the analysis of protein sequences: a case study in rubredoxins.

Two computational methods widely used in time series analysis were applied to protein sequences, and their ability to derive structural information not directly accessible through classical sequence comparisons methods was assessed. The primary structures of 19 rubredoxins of both mesophilic and thermophilic bacteria, coded with hydrophobicity values of amino acid residues, were considered as time series and were analyzed by 1) recurrence quantification analysis and 2) spectral analysis of the sequence major eigenfunctions. The results of the two methods agreed to a large extent and generated a classification consistent with known 3D structural characteristics of the studied proteins. This classification separated in a clearcut manner a thermophilic protein from mesophilic proteins. The classification of primary structures given by the two dynamical methods was demonstrated to be basically different from classification stemming from classical sequence homology metrics. Moreover, on a more detailed scale, the method was able to discriminate between thermophilic and mesophilic proteins from a set of chimeric sequences generated from the mixing of a mesophilic (Rubr Clopa) and a thermophilic (Rubr Pyrfu) protein. Overall, our results point to a new way of looking at protein sequence comparisons.

Profiles of chemically-induced tumors in rodents: quantitative relationships.

The rodent carcinogenicity bioassay has been used for several decades for evaluating hundreds of chemicals, with the two aims of better understanding the etiologies of cancer, and of assessing the hazard posed by environmental and industrial chemicals. This has generated an enormous wealth of data and information on the phenomenon of chemical carcinogenicity. However, this information cannot be appreciated easily, since too many details may obscure the general trends present in the data; on the contrary, the use of computerized data analysis techniques suitable for the exploration of large databases makes its investigation much more fruitful, and its results more reliable. For this work, we collected a database of 536 rodent carcinogens, and we investigated the profiles of tumors (target organs) induced in the four experimental systems which are usually employed (rat and mouse, male and female). The analysis was performed with an Artificial Neural Network called Kohonen Self-Organizing Map, which is a computer-intensive method aimed at making the relevant information emerge automatically from the data itself. The analysis generated a global view, as well as a quantitative measure of the associations among the individual tumor types, and among the tumor profiles induced by the chemicals. In the complex interplay between the organ and species specificity of tumor induction, the species specificity generally overcame organ specificity, except for a few tumors (namely Lymphatic System, Brain, Forestomach, Stomach and Thyroid Gland). Moreover, the species specificity was remarkably stronger than the trans-species sex specificity. For three chemical classes (Aromatic Amines, Electrophilic/Alkylating Agents, Nitroarenes) most represented in the database, we investigated the hypothesis that a single mechanism of interaction with DNA would produce one, or a few very similar tumor profiles. Our analysis pointed out that no obvious association exists between chemical/mode of action class, and tumor profile. On the contrary, none of these classes induces a single tumor or pattern of tumors, but rather it appears that each class produces tumors at a wide range of sites. This suggests that an important determinant of the differences in tumor profile are the events that surround the ultimate mechanism of interaction with DNA.

QSAR models for discriminating between mutagenic and nonmutagenic aromatic and heteroaromatic amines.

In a previous article, we demonstrated that the structure-activity relationship model for the mutagenic potency of aromatic amines is different from that for discriminating between mutagens and nonmutagens. In this work, we present further analyses on the molecular determinants of the mutagenicity of aromatic amines. Based on the use of various methodological approaches, our results indicate that mutagenic activity is influenced by different molecular characteristics in different subclasses of aromatic amines. Thus, the general lesson of this article is that 1) in genetic toxicology, it is necessary to separately investigate the structure-activity relationships for discrimination between positive and negative chemicals, and the structure-activity relationships for the potency of the positive chemicals; 2) in structure-activity studies, it is necessary to investigate the degree of homogeneity (congenericity) of apparently similar chemicals in order to assess and describe the various mechanisms of action that may be elicited by the chemicals.

Quantitative structure-based modeling applied to characterization and prediction of chemical toxicity.

Quantitative modeling methods, relating aspects of chemical structure to biological activity, have long been applied to the prediction and characterization of chemical toxicity. The early linear free-energy approaches of Hansch and Free Wilson provided a fundamental scientific framework for the quantitative correlation of chemical structure with biological activity and spurred many developments in the field of quantitative structure-activity relationships (QSARs). In addition to modeling of chemical toxicity, these methods have been extensively applied to modeling of medicinal properties of chemicals. However, there are important differences in the nature and objectives of these two applications, which have led to the evolution of different modeling approaches (namely, the need for treating sets of noncongeneric toxic compounds). In this paper are discussed those approaches to chemical toxicity that have taken a more "personalized" configuration and have undergone implementation into software programs able to perform the various steps of the assessment of the hazard posed by the chemicals. These models focus both on a variety of toxicological endpoints and on key elements of toxicity mechanisms, such as metabolism.