Modeling Drug-Induced Anorexia by Molecular Topology

Sarcopenia and polypharmacy among older adults: A scoping review of the literature

BACKGROUND

Sarcopenia and polypharmacy are both prevalent conditions in the geriatric population, leading to poor quality of life and adverse outcomes.

OBJECTIVE

To explore the evidence on the relationship between sarcopenia and polypharmacy and to summarize the findings and the gaps from the existing literature.

METHOD

A systematic scoping review was conducted between March and May 2021, with no restriction on publication date, using the Arksey and O'Malley framework and reported according to PRISMA-ScR. Four bibliographic databases, PubMed, Web of Science, Scopus, Proquest One Academic, and four sources of gray literature were searched for studies written in English or Greek. Data were extracted quantitatively and using thematic analysis.

RESULTS

Of the 397 initially retrieved records, 22 studies were finally included in this review, 20 published articles and 2 posters-presentations. Most of the studies used cross-sectional data. The relationship between sarcopenia and polypharmacy should be interpreted on the basis of the definition of polypharmacy, the diagnostic criteria of sarcopenia used, and the population setting. Sarcopenia or risk for sarcopenia are associated with polypharmacy or the number of medications in community-dwelling older adults, regardless of diagnostic criteria used for sarcopenia.

CONCLUSION

There is an association between sarcopenia or risk for sarcopenia and polypharmacy or the number of medications in community-dwelling older adults but not among residents of nursing homes or inpatients. Specific widely accepted definitions of polypharmacy and sarcopenia, a consensus on the method of sarcopenia assessment, and prospective studies are needed to identify polypharmacy as a potential risk factor for sarcopenia.

Quantitative structure-activity relationship models for compounds with anticonvulsant activity

Introduction: Third-generation antiepileptic drugs have seemingly failed to improve the global figures of seizure control and can still be regarded as symptomatic treatments. Quantitative structure-activity relationships (QSAR) can be used to guide hit-to-lead and lead optimization projects and applied to the large-scale virtual screening of chemical libraries. Areas covered: In this review, the authors cover reports on QSAR models related to antiepileptic drugs and drug targets in epilepsy, analyzing whether they refer to classic or non-classic QSAR and if they apply QSAR as a descriptive or predictive approach, among other considerations. The article finally focuses on a more detailed discussion of those predictive studies which include some sort of experimental validation, i.e. papers in which the reported models have been used to identify novel active compounds which have been tested in vitro and/or in vivo. Expert opinion: There are significant opportunities to apply the QSAR methodology to assist the discovery of more efficacious antiepileptic drugs. Considering the intrinsic complexity of the disorder, such applications should focus on state-of-the-art approximations (e.g. systemic, multi-target and multi-scale QSAR as well as ensemble and deep learning) and modeling the effects on novel drug targets and modern screening tools.

Molecular topology applied to the discovery of 1-benzyl-2-(3-fluorophenyl)-4-hydroxy-3-(3-phenylpropanoyl)-2H-pyrrole-5-one as a non-ligand-binding-pocket antiandrogen

We report the discovery of 1-benzyl-2-(3-fluorophenyl)-4-hydroxy-3-(3-phenylpropanoyl)-2H-pyrrole-5-one as a novel non-ligand binding pocket (non-LBP) antagonist of the androgen receptor (AR) through the application of molecular topology techniques. This compound, validated through time-resolved fluorescence resonance energy transfer and fluorescence polarization biological assays, provides the basis for lead optimization and structure-activity relationship analysis of a new series of non-LBP AR antagonists. Induced-fit docking and molecular dynamics studies have been performed to establish a consistent hypothesis for the interaction of the new active molecule on the AR surface.

QSAR methods for the discovery of new inflammatory bowel disease drugs

INTRODUCTION

Inflammatory bowel disease (IBD) represents an important class of chronic gastrointestinal tract disease. And although there are already several useful treatments to reduce and control the symptoms, there is still no cure. One drug discovery technique used is the computer-aided (in silico) discovery approach which has largely demonstrated efficacy. Computational techniques, when used in combination with traditional drug discovery methodology, greatly increase the chance of drug discovery in a sustainable and economical fashion.

AREAS COVERED

This review aims to provide the most recent and important advances of in silico IBD drug discovery. While this review is mainly focused on QSAR methods, especially those based on molecular topology (MT), additional topics, such as docking or comparative field analysis are also addressed.

EXPERT OPINION

IBD is a worldwide growing health concern that can only be currently treated in symptomatic and palliative way; thus, the search for new drugs is imperative. Computer-aided methods, which focus on the drug-receptor interaction, are essential tool in this regard. It is noted, however that a major problem is that although there are many known receptors associated with IBD, none of these have yet been found essential. The use of other approaches, including QSAR methodology, is certainly a complementary and attractive alternative; especially QSAR methods based on MT, which has proven successful in other drug discovery.

Predicting Dyspnea Inducers by Molecular Topology

QSAR based on molecular topology (MT) is an excellent methodology used in predicting physicochemical and biological properties of compounds. This approach is applied here for the development of a mathematical model capable to recognize drugs showing dyspnea as a side effect. Using linear discriminant analysis, it was found a four-variable regression equations enabling a predictive rate of about 81% and 73% in the training and test sets of compounds, respectively. These results demonstrate that QSAR-MT is an efficient tool to predict the appearance of dyspnea associated with drug consumption.