Design, synthesis, and in vitro and in vivo characterization of new memantine analogs for Alzheimer's disease

Currently, of the few accessible symptomatic therapies for Alzheimer's disease (AD), memantine is the only N-methyl-d-aspartate receptor (NMDAR) blocker approved by the FDA. This work further explores a series of memantine analogs featuring a benzohomoadamantane scaffold. Most of the newly synthesized compounds block NMDARs in the micromolar range, but with lower potency than previously reported hit IIc, results that were supported by molecular dynamics simulations. Subsequently, electrophysiological studies with the more potent compounds allowed classification of IIc, a low micromolar, uncompetitive, voltage-dependent, NMDAR blocker, as a memantine-like compound. The excellent in vitro DMPK properties of IIc made it a promising candidate for in vivo studies in Caenorhabditis elegans (C. elegans) and in the 5XFAD mouse model of AD. Administration of IIc or memantine improved locomotion and rescues chemotaxis behavior in C. elegans. Furthermore, both compounds enhanced working memory in 5XFAD mice and modified NMDAR and CREB signaling, which may prevent synaptic dysfunction and modulate neurodegenerative progression.

Optimization of 2-Amino-4,6-diarylpyrimidine-5-carbonitriles as Potent and Selective A1 Antagonists

We herein document a large collection of 108 2-amino-4,6-disubstituted-pyrimidine derivatives as potent, structurally simple, and highly selective A₁AR ligands. The most attractive ligands were confirmed as antagonists of the canonical cyclic adenosine monophosphate pathway, and some pharmacokinetic parameters were preliminarilly evaluated. The library, built through a reliable and efficient three-component reaction, comprehensively explored the chemical space allowing the identification of the most prominent features of the structure–activity and structure–selectivity relationships around this scaffold. These included the influence on the selectivity profile of the aromatic residues at positions R⁴ and R⁶ of the pyrimidine core but most importantly the prominent role to the unprecedented A₁AR selectivity profile exerted by the methyl group introduced at the exocyclic amino group. The structure–activity relationship trends on both A₁ and A_2AARs were conveniently interpreted with rigorous free energy perturbation simulations, which started from the receptor-driven docking model that guided the design of these series.

Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML)

This work describes the synthesis and pharmacological evaluation of 2-furoyl-based Melanostatin (MIF-1) peptidomimetics as dopamine D₂ modulating agents. Eight novel peptidomimetics were tested for their ability to enhance the maximal effect of tritiated N-propylapomorphine ([³H]-NPA) at D₂ receptors (D₂R). In this series, 2-furoyl-l-leucylglycinamide (6a) produced a statistically significant increase in the maximal [³H]-NPA response at 10 pM (11 ± 1%), comparable to the effect of MIF-1 (18 ± 9%) at the same concentration. This result supports previous evidence that the replacement of proline residue by heteroaromatic scaffolds are tolerated at the allosteric binding site of MIF-1. Biological assays performed for peptidomimetic 6a using cortex neurons from 19-day-old Wistar-Kyoto rat embryos suggest that 6a displays no neurotoxicity up to 100 μM. Overall, the pharmacological and toxicological profile and the structural simplicity of 6a makes this peptidomimetic a potential lead compound for further development and optimization, paving the way for the development of novel modulating agents of D₂R suitable for the treatment of CNS-related diseases. Additionally, the pharmacological and biological data herein reported, along with >20 000 outcomes of preclinical assays, was used to seek a general model to predict the allosteric modulatory potential of molecular candidates for a myriad of target receptors, organisms, cell lines, and biological activity parameters based on perturbation theory (PT) ideas and machine learning (ML) techniques, abbreviated as ALLOPTML. By doing so, ALLOPTML shows high specificity Sp = 89.2/89.4%, sensitivity Sn = 71.3/72.2%, and accuracy Ac = 86.1%/86.4% in training/validation series, respectively. To the best of our knowledge, ALLOPTML is the first general-purpose chemoinformatic tool using a PTML-based model for the multioutput and multicondition prediction of allosteric compounds, which is expected to save both time and resources during the early drug discovery of allosteric modulators.

2-Oxaadamant-1-yl Ureas as Soluble Epoxide Hydrolase Inhibitors: In Vivo Evaluation in a Murine Model of Acute Pancreatitis

In vivo pharmacological inhibition of soluble epoxide hydrolase (sEH) reduces inflammatory diseases, including acute pancreatitis (AP). Adamantyl ureas are very potent sEH inhibitors, but the lipophilicity and metabolism of the adamantane group compromise their overall usefulness. Herein, we report that the replacement of a methylene unit of the adamantane group by an oxygen atom increases the solubility, permeability, and stability of three series of urea-based sEH inhibitors. Most of these oxa-analogues are nanomolar inhibitors of both the human and murine sEH. Molecular dynamics simulations rationalize the molecular basis for their activity and suggest that the presence of the oxygen atom on the adamantane scaffold results in active site rearrangements to establish a weak hydrogen bond. The 2-oxaadamantane 22, which has a good solubility, microsomal stability, and selectivity for sEH, was selected for further in vitro and in vivo studies in models of cerulein-induced AP. Both in prophylactic and treatment studies, 22 diminished the overexpression of inflammatory and endoplasmic reticulum stress markers induced by cerulein and reduced the pancreatic damage.

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics

Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70-91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC₅₀, EC₅₀, K_i, etc.) for compounds in assays involving different cell lines used, organisms of the protein target, or organism of assay for proteins in the dopamine pathway.

Development of Fluorescent Probes that Target Serotonin 5-HT2B Receptors

Some 5-HT2B fluorescent probes were obtained by tagging 1-(2,5-dimethoxy-4-iodophenyl)-propan-2-amine (DOI) with a subset of fluorescent amines. Some of the resulting fluorescent ligands showed excellent affinity and selectivity profiles at the 5-HT2B receptors (e.g. 12b), while retain the agonistic functional behaviour of the model ligand (DOI). The study highlighted the most salient features of the structure-activity relationship in this series and these were substantiated by a molecular modelling study based on a receptor-driven docking model constructed on the basis of the crystal structure of the human 5-HT2B receptor. One of the fluorescent ligands developed in this work, compound 12i, specifically labelled CHO-K1 cells expressing 5-HT2B receptors and not parental CHO-K1 cells in a concentration-dependent manner. 12i enables imaging and quantification of specific 5-HT2B receptor labelling in live cells by automated fluorescence microscopy as well as quantification by measurements of fluorescence intensity using a fluorescence plate reader.

Computer-aided structure-based design of multitarget leads for Alzheimer's disease

Alzheimer's disease is a neurodegenerative pathology with unmet clinical needs. A highly desirable approach to this syndrome would be to find a single lead that could bind to some or all of the selected biomolecules that participate in the amyloid cascade, the most accepted route for Alzheimer disease genesis. In order to circumvent the challenge posed by the sizable differences in the binding sites of the molecular targets, we propose a computer-assisted protocol based on a pharmacophore and a set of required interactions with the targets that allows for the automated screening of candidates. We used a combination of docking and molecular dynamics protocols in order to discard nonbinders, optimize the best candidates, and provide a rationale for their potential as inhibitors. To provide a proof of concept, we proceeded to screen the literature and databases, a task that allowed us to identify a set of carbazole-containing compounds that initially showed affinity only for the cholinergic targets in our experimental assays. Two cycles of design based on our protocol led to a new set of analogues that were synthesized and assayed. The assay results revealed that the designed inhibitors had improved affinities for BACE-1 by more than 3 orders of magnitude and also displayed amyloid aggregation inhibition and affinity for AChE and BuChE, a result that led us to a group of multitarget amyloid cascade inhibitors that also could have a positive effect at the cholinergic level.