Principal Component Analysis Differentiates the Receptor Binding Profiles of Three Antipsychotic Drug Candidates from Current Antipsychotic Drugs

In-silico approaches to assessing multiple high-level drug-drug and drug-disease adverse drug effects

INTRODUCTION

Pharmacovigilance plays a pivotal role in monitoring adverse events (AEs) related to chemical substances in human/animal populations. With increasing spontaneous-reporting systems, researchers turned to in-silico approaches to efficiently analyze drug safety profiles. Here, we review in-silico methods employed for assessing multiple drug-drug/drug-disease AEs covered by comparative analyses and visualization strategies.

AREAS COVERED

Disproportionality, involving multi-stage statistical methodologies and data processing, identifies safety signals among drug-AE pairs. By stratifying data based on disease indications/demographics, researchers address confounders and assess drug safety. Comparative analyses, including clustering techniques and visualization techniques, assess drug similarities, patterns, and trends, calculate correlations, and identify distinct toxicities. Furthermore, we conducted a thorough Scopus search on 'pharmacovigilance,' yielding 5,836 publications spanning 2003 to 2023.

EXPERT OPINION

Pharmacovigilance relies on diverse data sources, presenting challenges in the integration of in-silico approaches and requiring compliance with regulations and AI adoption. Systematic use of statistical analyses enables identifications of potential risks with drugs. Frequentist and Bayesian methods are used in disproportionalities, each with its strengths and weaknesses. Integration of pharmacogenomics with pharmacovigilance enables personalized medicine, with AI further enhancing patient engagement. This multidisciplinary approach holds promise, improving drug efficacy and safety, and should be a core mission of One-Health studies.

Discovery of new chemotypes of dual 5-HT2A/D2 receptor antagonists with a strategy of drug design methodologies

Aim: Through the application of structure- and ligand-based methods, the authors aimed to create an integrative approach to developing a computational protocol for the rational drug design of potent dual 5-HT_2A/D₂ receptor antagonists without off-target activities on H₁ receptors. Materials & methods: Molecular dynamics and virtual docking methods were used to identify key interactions of the structurally diverse antagonists in the binding sites of the studied targets, and to generate their bioactive conformations for further 3D-quantitative structure-activity relationship modeling. Results & conclusion: Toward the goal of finding multi-potent drugs with a more effective and safer profile, the obtained results led to the design of a new set of dual antagonists and opened a new perspective on the therapy for complex brain diseases.

Dopamine and Dopamine-Related Ligands Can Bind Not Only to Dopamine Receptors

The dopaminergic system is one of the most important neurotransmitter systems in the central nervous system (CNS). It acts mainly by activation of the D₁-like receptor family at the target cell. Additionally, fine-tuning of the signal is achieved via pre-synaptic modulation by the D₂-like receptor family. Some dopamine drugs (both agonists and antagonists) bind in addition to DRs also to α₂-ARs and 5-HT receptors. Unfortunately, these compounds are often considered subtype(s) specific. Thus, it is important to consider the presence of these receptor subtypes in specific CNS areas as the function virtually elicited by one receptor type could be an effect of other—or the co-effect of multiple receptors. However, there are enough molecules with adequate specificity. In this review, we want to give an overview of the most common off-targets for established dopamine receptor ligands. To give an overall picture, we included a discussion on subtype selectivity. Molecules used as antipsychotic drugs are reviewed too. Therefore, we will summarize reported affinities and give an outline of molecules sufficiently specific for one or more subtypes (i.e., for subfamily), the presence of DR, α₂-ARs, and 5-HT receptors in CNS areas, which could help avoid ambiguous results.

Design, synthesis, and evaluation of bitopic arylpiperazine-phthalimides as selective dopamine D3 receptor agonists

The dopamine D3 receptor (D3R) is a proven therapeutic target for the treatment of neurological and neuropsychiatric disorders. In particular, D3R-selective ligands that can eliminate side effects associated with dopamine D2 receptor (D2R) therapeutics have been validated. However, the high homology in signaling pathways and the sequence similarity between D2R and D3R have rendered the development of D3R-selective ligands challenging. Herein, we designed and synthesized a series of piperazine-phthalimide bitopic ligands based on a fragment-based and molecular docking inspired design. Compound 9i was identified as the most selective D3R ligand among these bitopic ligands. Its selectivity was improved compared to reference compounds 1 and 2 by 9- and 2-fold, respectively, and it was 21-fold more potent than compound 2. Molecular docking demonstrated that the orientation of Leu2.64 and Phe7.39 and the packing at the junction of helices may affect the specificity for D3R over D2R. Functional evaluation revealed that D3R-selective ligand 9i displayed a subpicomolar agonist activity at D3R with a 199-fold increase in potency compared to quinpirole. These results may be useful for the fragment-based design of bitopic compounds as selective D3R ligands.

3D-QSAR Studies on Barbituric Acid Derivatives as Urease Inhibitors and the Effect of Charges on the Quality of a Model

Urease enzyme (EC 3.5.1.5) has been determined as a virulence factor in pathogenic microorganisms that are accountable for the development of different diseases in humans and animals. In continuance of our earlier study on the helicobacter pylori urease inhibition by barbituric acid derivatives, 3D-QSAR (three dimensional quantitative structural activity relationship) advance studies were performed by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. Different partial charges were calculated to examine their consequences on the predictive ability of the developed models. The finest developed model for CoMFA and CoMSIA were achieved by using MMFF94 charges. The developed CoMFA model gives significant results with cross-validation (q²) value of 0.597 and correlation coefficients (r²) of 0.897. Moreover, five different fields i.e., steric, electrostatic, and hydrophobic, H-bond acceptor and H-bond donors were used to produce a CoMSIA model, with q² and r² of 0.602 and 0.98, respectively. The generated models were further validated by using an external test set. Both models display good predictive power with r²pred ≥ 0.8. The analysis of obtained CoMFA and CoMSIA contour maps provided detailed insight for the promising modification of the barbituric acid derivatives with an enhanced biological activity.

Multidimensional de novo design reveals 5-HT2B receptor-selective ligands

We report a multi-objective de novo design study driven by synthetic tractability and aimed at the prioritization of computer-generated 5-HT2B receptor ligands with accurately predicted target-binding affinities. Relying on quantitative bioactivity models we designed and synthesized structurally novel, selective, nanomolar, and ligand-efficient 5-HT2B modulators with sustained cell-based effects. Our results suggest that seamless amalgamation of computational activity prediction and molecular design with microfluidics-assisted synthesis enables the swift generation of small molecules with the desired polypharmacology.

Effects of amisulpride on human resting cerebral perfusion

RATIONALE

Quantitative neuroimaging studies show that different neuroleptics have similar effects on resting metabolism/perfusion in the basal ganglia, but vary in their effect on the cortex, especially in the prefrontal and temporal lobes. These differences may represent signatures of the action of medication on distinctive receptor combinations.

OBJECTIVES

This study seeks to determine the effect on cerebral perfusion at rest of low-dose amisulpride, a neuroleptic with a receptor profile relatively selective to dopaminergic D2-receptors and both antidepressant and antipsychotic efficacy.

METHODS

Continuous arterial spin labelling in a placebo-controlled, double blind, crossover study at steady state of N = 20 healthy male adults.

RESULTS

Relative to placebo, amisulpride was associated with extensive and significant cortical decrements in resting perfusion levels, particularly in the prefrontal lobes (p = 0.01, corrected). Decrements spared the basal ganglia, where perfusion was slightly increased.

CONCLUSIONS

In contrast to earlier reports on other neuroleptics, amisulpride was associated with intense cortical perfusion decrements at rest. These results are consistent with an existing model in which dopaminergic blockade is associated not only with metabolism/perfusion increases in the basal ganglia, but also with decreases in the cerebral cortex that in most neuroleptics are compensated by action on other receptor systems. The selective receptor profile of amisulpride may explain the extensive cortical decrements.

A novel multilevel statistical method for the study of the relationships between multireceptorial binding affinity profiles and in vivo endpoints

The present work introduces a novel method for drug research based on the sequential building of linked multivariate statistical models, each one introducing a different level of drug description. The use of multivariate methods allows us to overcome the traditional one-target assumption and to link in vivo endpoints with drug binding profiles, involving multiple receptors. The method starts with a set of drugs, for which in vivo or clinical observations and binding affinities for potentially relevant receptors are known, and allows obtaining predictions of the in vivo endpoints highlighting the most influential receptors. Moreover, provided that the structure of the receptor binding sites is known (experimentally or by homology modeling), the proposed method also highlights receptor regions and ligand-receptor interactions that are more likely to be linked to the in vivo endpoints, which is information of high interest for the design of novel compounds. The method is illustrated by a practical application dealing with the study of the metabolic side effects of antipsychotic drugs. Herein, the method detects related receptors confirmed by experimental results. Moreover, the use of structural models of the receptor binding sites allows identifying regions and ligand-receptor interactions that are involved in the discrimination between antipsychotic drugs that show metabolic side effects and those that do not. The structural results suggest that the topology of a hydrophobic sandwich involving residues in transmembrane helices (TM) 3, 5, and 6, as well as the assembling of polar residues in TM5, are important discriminators between target/antitarget receptors. Ultimately, this will provide useful information for the design of safer compounds inducing fewer side effects.