Biomedical subjects

Orazio Nicolotti

Publications and source records attributed to Orazio Nicolotti.

5 recordsLinked to original sources

PubMed2006

Structural insights into monoamine oxidase inhibitory potency and selectivity of 7-substituted coumarins from ligand- and target-based approaches.

A new series of 3-, 4-, 7-polysubstituted coumarins have been designed and evaluated for their monoamine oxidase A and monoamine oxidase B (MAO-A and MAO-B) inhibitory potency. Substituents at position 7 consisted of a bridge of different physicochemical nature linking a phenyl ring to the coumarin scaffold. Structure-affinity and structure-selectivity relationships, derived through CoMFA-GOLPE and docking studies, revealed the key physicochemical interactions responsible for the observed MAO-B and MAO-A inhibitory potency and suggested the main structural determinants for high selectivity toward one of the two enzymatic isoforms. The predictive power of our models was proved with the design of a new inhibitor demonstrating an outstanding MAO-B affinity (pIC50 = 8.29) and the highest MAO-B selectivity (DeltapIC50 = 3.39) within the entire series of ligands examined herein.

Animals↗

PubMed2004

8-Substituted-9-deazaxanthines as adenosine receptor ligands: design, synthesis and structure-affinity relationships at A2B.

A number of 8-substituted-9-deazaxanthine derivatives (1,3-dialkyl-6-substituted-1H-pyrrolo[3,2-d]pyrimidine-2,4(3H,5H)-diones) were prepared and tested for their antagonistic activity at the recombinant human adenosine receptors, in particular at the A(2B) and A(2A) receptor subtypes. Compounds endowed with micromolar to nanomolar binding affinities, but with poor A(2B)/A(2A) selectivity, were obtained. Preliminary quantitative structure-affinity relationships suggested that the binding potency at the A(2B) receptor is mainly modulated by the electronic and lipophilic properties of the ligands.

Adenosine A2 Receptor Antagonists↗

PubMed2004

Neuronal nicotinic acetylcholine receptor agonists: pharmacophores, evolutionary QSAR and 3D-QSAR models.

Neuronal nicotinic acetylcholine ion channel receptors (nAChRs) exist as several subtypes and are involved in a variety of functions and disorders of the central nervous system (CNS), such as Alzheimer's and Parkinson's diseases. The lack of reliable information on the 3D structure of nAChRs prompted us to focus efforts on pharmacophore and structure-affinity relationships (SAFIRs). The use of DISCO (DIStance COmparison) and Catalyst/HipHop led to the formulation of a pharmacophore that is made of three geometrically unrelated features: (i) an ammonium head involved in coulombic and/or H-bond interactions, (ii) a lone pair of a pyridine nitrogen or a carbonyl oxygen, as H-bond acceptor site, and (iii) a hydrophobic molecular region generally constituted by aliphatic cycles. The quantitative SAFIR (QSAFIR) study was carried out on about three hundred nicotinoid agonists, and coherent results were obtained from classical Hansch-type approach, 3D QSAFIRs, based on Comparative Molecular Field Analysis (CoMFA), and trade-off models generated by Multi-objective Genetic QSAR (MoQSAR), a novel evolutionary software that makes use of Genetic Programming (GP) and multi-objective optimization (MO). Within each congeneric series, Hansch-type equations revealed detrimental steric effects as the major factors modulating the receptor affinity, whereas CoMFA allowed us to merge progressively single-class models in a more global one, whose robustness was supported by crossvalidation, high prediction statistics and satisfactory predictions of the affinity data of a true external ligand set (r(2)(pred) = 0.796). Next, MoQSAR was used to analyze a data set of 58 highly active nicotinoids characterized by 56 descriptors, that are log P, MR and 54 low inter-correlated WHIM (Weighted Holistic Invariant Molecular) indices. Equivalent QSAFIR models, that represent different compromises between structural model complexity, fitting and internal model complexity, were found. Our attention was mostly engaged by a number of nonlinear QSAFIRs, which relate nAChR affinity with the log P and directional WHIM descriptors. The results reviewed herein show as QSAFIRs may helpfully complement the pharmacophores, thus enhancing the applicability of computer-aided methodologies in the field of nAChR agonists.

Hydrogen Bonding↗

PubMed2002

Multiobjective optimization in quantitative structure-activity relationships: deriving accurate and interpretable QSARs.

Deriving quantitative structure-activity relationship (QSAR) models that are accurate, reliable, and easily interpretable is a difficult task. In this study, two new methods have been developed that aim to find useful QSAR models that represent an appropriate balance between model accuracy and complexity. Both methods are based on genetic programming (GP). The first method, referred to as genetic QSAR (or GPQSAR), uses a penalty function to control model complexity. GPQSAR is designed to derive a single linear model that represents an appropriate balance between the variance and the number of descriptors selected for the model. The second method, referred to as multiobjective genetic QSAR (MoQSAR), is based on multiobjective GP and represents a new way of thinking of QSAR. Specifically, QSAR is considered as a multiobjective optimization problem that comprises a number of competitive objectives. Typical objectives include model fitting, the total number of terms, and the occurrence of nonlinear terms. MoQSAR results in a family of equivalent QSAR models where each QSAR represents a different tradeoff in the objectives. A practical consideration often overlooked in QSAR studies is the need for the model to promote an understanding of the biochemical response under investigation. To accomplish this, chemically intuitive descriptors are needed but do not always give rise to statistically robust models. This problem is addressed by the addition of a further objective, called chemical desirability, that aims to reward models that consist of descriptors that are easily interpretable by chemists. GPQSAR and MoQSAR have been tested on various data sets including the Selwood data set and two different solubility data sets. The study demonstrates that the MoQSAR method is able to find models that are at least as good as models derived using standard statistical approaches and also yields models that allow a medicinal chemist to trade statistical robustness for chemical interpretability.

Algorithms↗

PubMedDate not supplied

QSAR and QSPR studies of a highly structured physicochemical domain.

The relevance of terms other than linear when deriving quantitative structure-activity relationship/quantitative structure-property relationship (QSAR/QSPR) models has been rarely considered so far. In this study, the impact of quadratic and interacting terms has been taken into account. The first effect of including such highly structured terms is a significant extension of the parametric domain that moves from the initial N to N(N + 3)/2 parameters. This substantial enlargement over the conventional linear boundaries involves a higher computational cost due to the increased combinatorial number of resulting theoretical QSAR/QSPR models. To face this issue, novel genetic-algorithm-based software, MGZ (multigenetic zooming), was developed and used for both variable selection and model building. To speed up the entire process of domain searching, MGZ was supported with multiple independent evolving populations and genetic storms to further QSAR/QSPR analyses. In addition, a novel fitness function was developed to score models on the basis of their inner predictive capability, assessed on the training set, structure complexity, and presence of nonlinear terms. The models were further validated by monitoring model redundancy and performing intensive randomization runs. The Selwood data set was used as a reference set to derive QSAR models. Furthermore, a QSPR study was conducted on the solubility data set of a large array of organic compounds. The results reported in the present paper demonstrate that our approach is successful in finding linear models, which are at least as good as the models previously derived using standard statistical approaches, and in deriving new nonlinear models with good statistical figures.

Journal Article↗