A new class of histamine H3 receptor antagonists derived from ligand based design

Artificial Intelligence Effecting a Paradigm Shift in Drug Development

The inverse relationship between the cost of drug development and the successful integration of drugs into the market has resulted in the need for innovative solutions to overcome this burgeoning problem. This problem could be attributed to several factors, including the premature termination of clinical trials, regulatory factors, or decisions made in the earlier drug development processes. The introduction of artificial intelligence (AI) to accelerate and assist drug development has resulted in cheaper and more efficient processes, ultimately improving the success rates of clinical trials. This review aims to showcase and compare the different applications of AI technology that aid automation and improve success in drug development, particularly in novel drug target identification and design, drug repositioning, biomarker identification, and effective patient stratification, through exploration of different disease landscapes. In addition, it will also highlight how these technologies are translated into the clinic. This paradigm shift will lead to even greater advancements in the integration of AI in automating processes within drug development and discovery, enabling the probability and reality of attaining future precision and personalized medicine.

Accessing an azaborine building block: synthesis and substitution reactions of 2-chloromethyl-2,1-borazaronaphthalene

One major synthetic route to the synthesis of benzyl amines, ethers, and esters is the nucleophilic substitution of a benzylic halide. To develop a method for the facile synthesis and functionalization of the isosteric azaborines, 2-chloromethyl-2,1-borazaronaphthalene has been synthesized in one step to afford a similar common precursor to a benzylic halide. This B–N isostere has been shown to be an effective building block by serving as an electrophile in substitution reactions with a large variety of nucleophiles.

Synthesis and characterization of new bivalent agents as melatonin- and histamine H3-ligands

Melatonin is an endogenous molecule involved in many pathophysiological processes. In addition to the control of circadian rhythms, its antioxidant and neuroprotective properties have been widely described. Thus far, different bivalent compounds composed by a melatonin molecule linked to another neuroprotective agent were synthesized and tested for their ability to block neurodegenerative processes in vitro and in vivo. To identify a novel class of potential neuroprotective compounds, we prepared a series of bivalent ligands, in which a prototypic melatonergic ligand is connected to an imidazole-based H₃ receptor antagonist through a flexible linker. Four imidazolyl-alkyloxy-anilinoethylamide derivatives, characterized by linkers of different length, were synthesized and their binding affinity for human MT₁, MT₂ and H₃ receptor subtypes was evaluated. Among the tested compounds, 14c and 14d, bearing a pentyl and a hexyl linker, respectively, were able to bind to all receptor subtypes at micromolar concentrations and represent the first bivalent melatonergic/histaminergic ligands reported so far. These preliminary results, based on binding affinity evaluation, pave the way for the future development of new dual-acting compounds targeting both melatonin and histamine receptors, which could represent promising therapeutic agents for the treatment of neurodegenerative pathologies.

Synthesis, biological evaluation, and computational studies of Tri- and tetracyclic nitrogen-bridgehead compounds as potent dual-acting AChE inhibitors and hH3 receptor antagonists

Combination of AChE inhibiting and histamine H3 receptor antagonizing properties in a single molecule might show synergistic effects to improve cognitive deficits in Alzheimer's disease, since both pharmacological actions are able to enhance cholinergic neurotransmission in the cortex. However, whereas AChE inhibitors prevent hydrolysis of acetylcholine also peripherally, histamine H3 antagonists will raise acetylcholine levels mostly in the brain due to predominant occurrence of the receptor in the central nervous system. In this work, we designed and synthesized two novel classes of tri- and tetracyclic nitrogen-bridgehead compounds acting as dual AChE inhibitors and histamine H3 antagonists by combining the nitrogen-bridgehead moiety of novel AChE inhibitors with a second N-basic fragment based on the piperidinylpropoxy pharmacophore with different spacer lengths. Intensive structure-activity relationships (SARs) with regard to both biological targets led to compound 41 which showed balanced affinities as hAChE inhibitor with IC50 = 33.9 nM, and hH3R antagonism with Ki = 76.2 nM with greater than 200-fold selectivity over the other histamine receptor subtypes. Molecular docking studies were performed to explain the potent AChE inhibition of the target compounds and molecular dynamics studies to explain high affinity at the hH3R.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Automated design of ligands to polypharmacological profiles

The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand-target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.

Fragment-based approaches and computer-aided drug discovery

Fragment-based design has significantly modified drug discovery strategies and paradigms in the last decade. Besides technological advances and novel therapeutic avenues, one of the most significant changes brought by this new discipline has occurred in the minds of drug designers. Fragment-based approaches have markedly impacted rational computer-aided design both in method development and in applications. The present review illustrates the importance of molecular fragments in many aspects of rational ligand design, and discusses how thinking in "fragment space" has boosted computational biology and chemistry.

Microwave-assisted synthesis of quinoline, isoquinoline, quinoxaline and quinazoline derivatives as CB2 receptor agonists

Quinoline, isoquinoline, quinoxaline, and quinazoline derivatives were synthesized using microwave-assisted synthesis and their CB1/CB2 receptor activities were determined using the [³⁵S]GTPγS binding assay. Most of the prepared quinoline, isoquinoline, and quinoxalinyl phenyl amines showed low-potency partial CB2 receptor agonists activity. The most potent CB2 ligand was the 4-morpholinylmethanone derivative (compound 40e) (-log EC₅₀ = 7.8; E(max) = 75%). The isoquinolin-1-yl(3-trifluoromethyl-phenyl)amine (compound 26c) was a high efficacy CB2 agonist (-log EC₅₀ = 5.8; E(max) = 128%). No significant CB1 receptor activation or inactivation was shown in these studies, except 40e, which showed weak CB1 agonist activity (CB1 -log EC₅₀ = 5.0). These ligands serve as novel templates for the development of selective CB2 receptor agonist.

De novo design: balancing novelty and confined chemical space

IMPORTANCE OF THE FIELD

De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

AREAS COVERED IN THIS REVIEW

This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

WHAT THE READER WILL GAIN

The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

TAKE HOME MESSAGE

De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.