Alessio Coi

Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals

A dataset comprising 55 chemicals with hepatocarcinogenic potency indices was collected from the Carcinogenic Potency Database with the aim of developing QSAR models enabling prediction of the above unwanted property for New Chemical Entities. The dataset was rationally split into training and test sets by means of a sphere-exclusion type algorithm. Among the many algorithms explored to search regression models, only a Support Vector Machine (SVM) method led to a QSAR model, which was proved to pass rigorous validation criteria, in accordance with the OECD guidelines. The proposed model is capable to explain the hepatocarcinogenic toxicity and could be exploited for predicting this property for chemicals at the early stage of their development, so optimizing resources and reducing animal testing.

Predictive models, based on classification algorithms, for compounds potentially active as mitochondrial ATP-sensitive potassium channel openers

Heart mitochondrial ATP-sensitive potassium channel (mito-K(ATP) channels) are deeply implicated in the self-defense mechanism of ischemic preconditioning. Therefore, exogenous molecules activating these channels are considered as a promising pharmacological tool to reduce the myocardial injury deriving from ischemia/reperfusion events. In our laboratory, a series of 4-spiro-substituted benzopyran derivatives were earlier synthesized and some of them exhibited anti-ischemic properties. In this study, the above compounds are exploited in order to develop QSAR models, based on classification approaches, capable of discriminating between the ones acting as cardioprotective agents and those that are unable to elicit such a property. Molecules belonging to the whole dataset were subjected to CODESSA and E-Dragon calculations in order to compute a large number of molecular descriptors enabling the construction of classification models. Based on the two program packages used, two different experiments were carried out, with the aim of identify batteries of models to be exploited for designing new cardioprotective agents from libraries of new chemical entities. Both model batteries satisfy the rigorous criteria adopted for the validation, either when tested on the training and test set, according to the most straightforward protocol, and when tested on an additional prediction set. They were proven to ensure successful applications in the field of cardioprotective agent design.

QSAR studies on BK channel activators

QSAR studies were developed on the basis of a dataset comprising BK channel activators previously synthesized and biologically assayed in our laboratory, in order to obtain highly accurate models enabling prediction of affinity toward the channel for New Chemical Entities (NCEs). Many molecular descriptors were computed by the CODESSA software. They were initially exploited in order to rationally split the available dataset into training and test set pairs, which supplied the basis for the development of QSAR models. Models were subjected to rigorous validation analysis based on the estimate of several statistical parameters, for the seek of the most accurate and simplest model enabling prediction of BK channel affinity.

Combining structure- and ligand-based approaches for studies of interactions between different conformations of the hERG K+ channel pore and known ligands

Drug-induced insurgence of cardiotoxic effects signaled by the prolongation of the QT interval in the electrocardiogram, has the potential to evolve into a characteristic arrhythmic event named Torsade de Pointes (TdP). Although several different mechanisms can theoretically lead to prolonged QT interval, most of drugs showing this side effect, prolong the cardiac repolarization time through the inhibition of the rapid component of the delayed repolarizing current (IKr) which in humans is carried by a K(+) channel protein encoded by hERG. In this study, four 3D-models, representing different conformational states of hERG K(+) channel, were built by a homology-based technique. A dataset of 59 compounds was collected from the literature and rationally selected according to the availability of IC50 values derived from whole-cell patch clamp performed at 37 °C on HEK cells. Molecular docking was carried out on each one of the four conformations of the channel, hundreds of docking-based molecular descriptors were obtained and used, together with other 2D and 3D molecular descriptors, to develop QSAR models. The statistical parameters describing the accordance between predicted and experimental data and the interpretation of the QSAR models enabled us to assess the reliability of the four 3D-models of the channel pore, thus allowing to look in more depth at binding modes and key features of the interactions occurring between the hERG K(+) channel and ligands endowed of blocking activity.

Development of classification models for identifying "true" P-glycoprotein (P-gp) inhibitors through inhibition, ATPase activation and monolayer efflux assays.

P-glycoprotein (P-gp) is an efflux pump involved in the protection of tissues of several organs by influencing xenobiotic disposition. P-gp plays a key role in multidrug resistance and in the progression of many neurodegenerative diseases. The development of new and more effective therapeutics targeting P-gp thus represents an intriguing challenge in drug discovery. P-gp inhibition may be considered as a valid approach to improve drug bioavailability as well as to overcome drug resistance to many kinds of tumours characterized by the over-expression of this protein. This study aims to develop classification models from a unique dataset of 59 compounds for which there were homogeneous experimental data on P-gp inhibition, ATPase activation and monolayer efflux. For each experiment, the dataset was split into a training and a test set comprising 39 and 20 molecules, respectively. Rational splitting was accomplished using a sphere-exclusion type algorithm. After a two-step (internal/external) validation, the best-performing classification models were used in a consensus predicting task for the identification of compounds named as “true” P-gp inhibitors, i.e., molecules able to inhibit P-gp without being effluxed by P-gp itself and simultaneously unable to activate the ATPase function.

Quantitative Structure–Activity Relationship Models for Predicting Biological Properties, Developed by Combining Structure- and Ligand-Based Approaches: An Application to the Human Ether-a-go-go-Related Gene Potassium Channel Inhibition

A strategy for developing accurate quantitative structure–activity relationship models enabling predictions of biological properties, when suitable knowledge concerning both ligands and biological target is available, was tested on a data set where molecules are characterized by high structural diversity. Such a strategy was applied to human ether‐a‐go‐go‐related gene K+ channel inhibition and consists of a combination of ligand‐ and structure‐based approaches, which can be carried out whenever the three‐dimensional structure of the target macromolecule is known or may be modeled with good accuracy. Molecular conformations of ligands were obtained by means of molecular docking, performed in a previously built theoretical model of the channel pore, so that descriptors depending upon the three‐dimensional molecular structure were properly computed. A modification of the directed sphere‐exclusion algorithm was developed and exploited to properly splitting the whole dataset into Training/Test set pairs. Molecular descriptors, computed by means of the codessa program, were used for the search of reliable quantitative structure–activity relationship models that were subsequently identified through a rigorous validation analysis. Finally, pIC50 values of a prediction set, external to the initial dataset, were predicted and the results confirmed the high predictive power of the model within a quite wide chemical space.

Annoyance Judgment and Measurements of Environmental Noise: A Focus on Italian Secondary Schools

The effects of noise on students’ health, well-being, and learning are of growing concern among both the general public and policy-makers in Europe. Several studies have highlighted the consequences of noise on children’s learning and performance at school. This study investigates the relationship between noise judgment in school goers aged 11–18 and noise measurements aimed at evaluating their exposure at school. For this purpose, a questionnaire was administered to 521 individuals in 28 classrooms in eight schools of four cities in Italy, with different environmental characteristics. Using a Likert-type scale, a selected set of responses related to noise generated an Annoyance Index (AI) score for each student and a classroom median score (MAI). From the noise data acquired, a global noise score (GNS) was assigned to each classroom. A higher AI was found in industrialized areas and among younger students. No significant differences in noise judgment were found by gender. A significant inverse correlation was described between MAI and GNS, thus the better the acoustic quality of the classrooms, the less the perceived noise and annoyance. The results show that noise perception and consequent disturbance are highly correlated with classroom acoustics, and confirm that annoyance represents the most widespread subjective response to noise.

Participatory health impact assessment used to support decision-making in waste management planning: A replicable experience from Italy

The lack of participatory tools in Health Impact Assessment (HIA) to support decision-makers is a critical factor that negatively affects the impacts of waste policies. This study describes the participatory HIA used in deciding on the possible doubling of the municipal solid waste incinerating plant located near the city of Arezzo, Italy. Within the framework of the new waste management plan, a methodology for the democratic participation of stakeholders was designed adopting the Local Agenda 21 methodology. Communication and participation events with the stakeholders were set up from the plans development to its implementation. Eleven different categories of stakeholders including individual citizens were involved in 21 local events, reaching over 500 participants in three years. Actions were performed to build the commitment and ownership of the local administrators. Then, together with the environment and health agencies and a representative from the local committees, the local administrators collaborated with scientists and technicians in the knowledge-building and scoping stages. Focus groups of voluntary citizens worked together with the researchers to provide qualitative and quantitative evidence in the assessment stage. Periodic public forums were held to discuss processes, methods and findings. The local government authority considered the HIA results in the final decision and a new waste strategy was adopted both in the short term (increased curbside collection, waste sustainability program) and in the long term (limited repowering of the incinerator, new targets for separate collection). In conclusion, an effective participatory HIA was carried out at the municipal level to support decision makers in the waste management plan. The HIA21 study contributed to evidence-based decisions and to make a broadly participatory experience. The authors are confident that these achievements may improve the governance of the waste cycle and the trust in the public administration.

The Quality of Rare Disease Registries: Evaluation and Characterization

Background: The focus on the quality of the procedures for data collection, storing, and analysis in the definition and implementation of a rare disease registry (RDR) is the basis for developing a valid and long-term sustainable tool. The aim of this study was to provide useful information for characterizing a quality profile for RDRs using an analytical approach applied to RDRs participating in the European Platform for Rare Disease Registries 2011-2014 (EPIRARE) survey. Methods: An indicator of quality was defined by choosing a small set of quality-related variables derived from the survey. The random forest method was used to identify the variables best defining a quality profile for RDRs. Fishers exact test was employed to assess the association with the indicator of quality, and the Cochran-Armitage test was used to check the presence of a linear trend along different levels of quality. Results: The set of variables found to characterize high-quality RDRs focused on ethical and legal issues, governance, communication of activities and results, established procedures to regulate access to data and security, and established plans to ensure long-term sustainability. Conclusions: The quality of RDRs is usually associated with a good oversight and governance mechanism and with durable funding. The results suggest that RDRs would benefit from support in management, information technology, epidemiology, and statistics.

Molecular modelling of human CYP2D6 and molecular docking of a series of ajmalicine- and quinidine-like inhibitors

3D-models were created and refined for CYP2D6 and for its complexes with ajmalicine and quinidine. The influence of the conformation of the enzyme active site on its interaction with ligands was evaluated by performing three series of molecular docking on selected ajmalicine- and quinidine-like inhibitors. The results suggested that the experimental binding values of ajmalicine- and quinidine-like inhibitors better fit with the energetic terms derived from their interaction with structures of CYP2D6 obtained by, respectively, optimizing the ajmalicine/CYP2D6 and the quinidine/CYP2D6 complexes, rather than exploiting the 3D-strucure of the enzyme not subjected to a ligand-induced conformational change. It suggests the relevance of induced-fit phenomena in the biological system of interest.