Indazolylketones as new multitarget cannabinoid drugs

Innovative Therapeutic Strategies in Alzheimer's Disease: A Synergistic Approach to Neurodegenerative Disorders

Alzheimer's disease (AD) remains a significant challenge in the field of neurodegenerative disorders, even nearly a century after its discovery, due to the elusive nature of its causes. The development of drugs that target multiple aspects of the disease has emerged as a promising strategy to address the complexities of AD and related conditions. The immune system's role, particularly in AD, has gained considerable interest, with nanobodies representing a new frontier in biomedical research. Advances in targeting antibodies against amyloid-β (Aβ) and using messenger RNA for genetic translation have revolutionized the production of antibodies and drug development, opening new possibilities for treatment. Despite these advancements, conventional therapies for AD, such as Cognex, Exelon, Razadyne, and Aricept, often have limited long-term effectiveness, underscoring the need for innovative solutions. This necessity has led to the incorporation advanced technologies like artificial intelligence and machine learning into the drug discovery process for neurodegenerative diseases. These technologies help identify therapeutic targets and optimize lead compounds, offering a more effective approach to addressing the challenges of AD and similar conditions.

Alzheimer's Disease and Cancer: Common Targets

There is growing epidemiologic evidence of an inverse association between cancer and AD. In addition, both cell survival and death are regulated by the same signaling pathways, and their abnormal regulation may be implicated in the occurrence and development of cancer and AD. Research shows that there may be a common molecular mechanism between cancer and AD. This review will discuss the role of GSK3, DAPK1, PP2A, P53 and CB2R in the pathogenesis of cancer and AD and describe the current research status of drug development based on these targets.

Development of Potential Multi-Target Inhibitors for Human Cholinesterases and Beta-Secretase 1: A Computational Approach

Alzheimer's disease causes chronic neurodegeneration and is the leading cause of dementia in the world. The causes of this disease are not fully understood but seem to involve two essential cerebral pathways: cholinergic and amyloid. The simultaneous inhibition of AChE, BuChE, and BACE-1, essential enzymes involved in those pathways, is a promising therapeutic approach to treat the symptoms and, hopefully, also halt the disease progression. This study sought to identify triple enzymatic inhibitors based on stereo-electronic requirements deduced from molecular modeling of AChE, BuChE, and BACE-1 active sites. A pharmacophore model was built, displaying four hydrophobic centers, three hydrogen bond acceptors, and one positively charged nitrogen, and used to prioritize molecules found in virtual libraries. Compounds showing adequate overlapping rates with the pharmacophore were subjected to molecular docking against the three enzymes and those with an adequate docking score (n = 12) were evaluated for physicochemical and toxicological parameters and commercial availability. The structure exhibiting the greatest inhibitory potential against all three enzymes was subjected to molecular dynamics simulations (100 ns) to assess the stability of the inhibitor-enzyme systems. The results of this in silico approach indicate ZINC1733 can be a potential multi-target inhibitor of AChE, BuChE, and BACE-1, and future enzymatic assays are planned to validate those results.

Advancements in the development of multi-target directed ligands for the treatment of Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial irreversible neurological disorder which results in cognitive impairment, loss of cholinergic neurons in synapses of the basal forebrain and neuronal death. Exact pathology of the disease is not yet known however, many hypotheses have been proposed for its treatment. The available treatments including monotherapies and combination therapies are not able to combat the disease effectively because of its complex pathological mechanism. A multipotent drug for AD has the potential to bind or inhibit multiple targets responsible for the progression of the disease like aggregated Aβ, hyperphosphorylated tau proteins, cholinergic and adrenergic receptors, MAO enzymes, overactivated N-methyl-d-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor etc. The traditional approach of one disease-one target-one drug has been rationalized to one drug-multi targets for the chronic diseases like AD and cancer. Thus, over the last decade research focus has been shifted towards the development of multi target directed ligands (MTDLs) which can simultaneously inhibit multiple targets and stop or slow the progression of the disease. The MTDLs can be more effective against AD and eliminate any possibility of drug-drug interactions. Many important active pharmacophore units have been fused, merged or incorporated into different scaffolds to synthesize new potent drugs. In the current article, we have described various hypothesis for AD and effectiveness of the MTDLs treatment strategy is discussed in detail. Different chemical scaffolds and their synthetic strategies have been described and important functionalities are identified in the chemical scaffold that have the potential to bind to the multiple targets. The important leads identified in this study with MTDL characteristics have the potential to be developed as drug candidates for the effective treatment of AD.

Candidate Therapeutics by Screening for Multitargeting Ligands: Combining the CB2 Receptor With CB1, PPARγ and 5-HT4 Receptors

In recent years, the cannabinoid type 2 receptor (CB2R) has become a major target for treating many disease conditions. The old therapeutic paradigm of “one disease-one target-one drug” is being transformed to “complex disease-many targets-one drug.” Multitargeting, therefore, attracts much attention as a promising approach. We thus focus on designing single multitargeting agents (MTAs), which have many advantages over combined therapies. Using our ligand-based approach, the “Iterative Stochastic Elimination” (ISE) algorithm, we produce activity models of agonists and antagonists for desired therapeutic targets and anti-targets. These models are used for sequential virtual screening and scoring large libraries of molecules in order to pick top-scored candidates for testing in vitro and in vivo. In this study, we built activity models for CB2R and other targets for combinations that could be used for several indications. Those additional targets are the cannabinoid 1 receptor (CB1R), peroxisome proliferator-activated receptor gamma (PPARγ), and 5-Hydroxytryptamine receptor 4 (5-HT4R). All these models have high statistical parameters and are reliable. Many more CB2R/CBIR agonists were found than combined CB2R agonists with CB1R antagonist activity (by 200 fold). CB2R agonism combined with PPARγ or 5-HT4R agonist activity may be used for treating Inflammatory Bowel Disease (IBD). Combining CB2R agonism with 5-HT4R generates more candidates (14,008) than combining CB2R agonism with agonists for the nuclear receptor PPARγ (374 candidates) from an initial set of ∼2.1 million molecules. Improved enrichment of true vs. false positives may be achieved by requiring a better ISE score cutoff or by performing docking. Those candidates can be purchased and tested experimentally to validate their activity. Further, we performed docking to CB2R structures and found lower statistical performance of the docking (“structure-based”) compared to ISE modeling (“ligand-based”). Therefore, ISE modeling may be a better starting point for molecular discovery than docking.

Cannabinoid receptor type 2 ligands: an analysis of granted patents since 2010

The G-protein-coupled cannabinoid receptor type 2 (CB2R) is a key element of the endocannabinoid (EC) system. EC/CB2R signaling has significant therapeutic potential in major pathologies affecting humans such as allergies, neurodegenerative disorders, inflammation or ocular diseases. CB2R agonism exerts anti-inflammatory and tissue protective effects in preclinical animal models of cardiovascular, gastrointestinal, liver, kidney, lung and neurodegenerative disorders. Existing ligands can be subdivided into endocannabinoids, cannabinoid-like and synthetic CB2R ligands that possess various degrees of potency on and selectivity against the cannabinoid receptor type 1. This review is an account of granted CB2R ligand patents from 2010 up to the present, which were surveyed using Derwent Innovation®.

Recent Advances in the Potential of Cannabinoids for Neuroprotection in Alzheimer's, Parkinson's, and Huntington's Diseases

Three prevalent neurodegenerative diseases, Parkinson's, Alzheimer's, and Huntington's are in need of symptomatic relief of slowing disease progression or both. This chapter focuses on the potential of cannabinoids to afford neuroprotection, i.e. avoid or retard neuronal death. The neuroprotective potential of cannabinoids is known from the work in animal models and is mediated by the two cannabinoid receptors (CB₁/CB₂) and eventually, by their heteromers, GPR55, orphan receptors (GPR3/GPR6/GPR12/GPR18), or PPARγ. Now, there is the time to translate the findings into patients. The chapter takes primarily into account advances since 2016 and addresses the issue of proving neuroprotection in humans. One recent discovery is the existence of activated microglia with neuroprotective phenotype; cannabinoids are good candidates to skew phenotype, especially via glial CB₂ receptors (CB₂R), whose targeting has, a priori, less side effects those targeting the CBs₁ receptor (CB₁R), which are expressed in both neurons and glia. The fact that a cannabis extract (SativexTM) is approved for human therapy, such that cannabis use will likely be legalized in many countries and different possibilities that cannabinoid pharmacology suggests a successful route of cannabinoids (natural or synthetic) all the way to be approved and used in the treatment of neurodegeneration.

Cannabinoid Receptor Subtype 2 (CB2R) in a Multitarget Approach: Perspective of an Innovative Strategy in Cancer and Neurodegeneration

The cannabinoid receptor subtype 2 (CB2R) represents an interesting and new therapeutic target for its involvement in the first steps of neurodegeneration as well as in cancer onset and progression. Several studies, focused on different types of tumors, report a promising anticancer activity induced by CB2R agonists due to their ability to reduce inflammation and cell proliferation. Moreover, in neuroinflammation, the stimulation of CB2R, overexpressed in microglial cells, exerts beneficial effects in neurodegenerative disorders. With the aim to overcome current treatment limitations, new drugs can be developed by specifically modulating, together with CB2R, other targets involved in such multifactorial disorders. Building on successful case studies of already developed multitarget strategies involving CB2R, in this Perspective we aim at prompting the scientific community to consider new promising target associations involving HDACs (histone deacetylases) and σ receptors by employing modern approaches based on molecular hybridization, computational polypharmacology, and machine learning algorithms.

New cannabinoid receptor antagonists as pharmacological tool

Synthesis and pharmacological evaluation of a new series of cannabinoid receptor antagonists of indazole ether derivatives have been performed. Pharmacological evaluation includes radioligand binding assays with [³H]-CP55940 for CB1 and CB2 receptors and functional activity for cannabinoid receptors on isolated tissue. In addition, functional activity of the two synthetic cannabinoids antagonists 18 (PGN36) and 17 (PGN38) were carried out in the osteoblastic cell line MC3T3-E1 that is able to express CB2R upon osteogenic conditions. Both antagonists abolished the increase in collagen type I gene expression by the well-known inducer of bone activity, the HU308 agonist. The results of pharmacological tests have revealed that four of these derivatives behave as CB2R cannabinoid antagonists. In particular, the compounds 17 (PGN38) and 18 (PGN36) highlight as promising candidates as pharmacological tools.

Novel approaches and current challenges with targeting the endocannabinoid system

INTRODUCTION

The pathophysiological relevance of the endocannabinoid system has been widely demonstrated in a variety of diseases including cancer, neurological disorders, and metabolic issues. Therefore, targeting the receptors and the endogenous machinery involved in this system can provide a successful therapeutic outcome. Ligands targeting the canonical cannabinoid receptors, CB1 and CB2, along with inhibitors of the endocannabinoid enzymes have been thoroughly studied in diverse disease models. In fact, phytocannabinoids such as cannabidiol or Δ9-tetrahydrocannabinol are currently on the market for the management of neuropathic pain due to spasticity in multiple sclerosis or seizures in children epilepsy amongst others.

AREAS COVERED

Challenges in the pharmacology of cannabinoids arise from its pharmacokinetics, off-target effects, and psychoactive effects. In this context, the current review outlines the novel molecular approaches emerging in the field discussing their clinical potential.

EXPERT OPINION

Even if orthosteric CB1 and CB2 ligands are on the forefront in cannabinoid clinical research, emerging strategies such as allosteric or biased modulation of these receptors along with controlled off-targets effects may increase the therapeutic potential of cannabinoids.

Multitarget Approach to Drug Candidates against Alzheimer's Disease Related to AChE, SERT, BACE1 and GSK3β Protein Targets

Alzheimer’s disease is a neurodegenerative condition for which currently there are no drugs that can cure its devastating impact on human brain function. Although there are therapeutics that are being used in contemporary medicine for treatment against Alzheimer’s disease, new and more effective drugs are in great demand. In this work, we proposed three potential drug candidates which may act as multifunctional compounds simultaneously toward AChE, SERT, BACE1 and GSK3β protein targets. These candidates were discovered by using state-of-the-art methods as molecular calculations (molecular docking and molecular dynamics), artificial neural networks and multilinear regression models. These methods were used for virtual screening of the publicly available library containing more than twenty thousand compounds. The experimental testing enabled us to confirm a multitarget drug candidate active at low micromolar concentrations against two targets, e.g., AChE and BACE1.

Targeting Cannabinoid Receptor Activation and BACE-1 Activity Counteracts TgAPP Mice Memory Impairment and Alzheimer's Disease Lymphoblast Alterations

Alzheimer's disease (AD), the leading cause of dementia in the elderly, is a neurodegenerative disorder marked by progressive impairment of cognitive ability. Patients with AD display neuropathological lesions including senile plaques, neurofibrillary tangles, and neuronal loss. There are no disease-modifying drugs currently available. With the number of affected individuals increasing dramatically throughout the world, there is obvious urgent need for effective treatment strategy for AD. The multifactorial nature of AD encouraged the development of multifunctional compounds, able to interact with several putative targets. Here, we have evaluated the effects of two in-house designed cannabinoid receptors (CB) agonists showing inhibitory actions on β-secretase-1 (BACE-1) (NP137) and BACE-1/butyrylcholinesterase (BuChE) (NP148), on cellular models of AD, including immortalized lymphocytes from late-onset AD patients. Furthermore, the performance of TgAPP mice in a spatial navigation task was investigated following chronic administration of NP137 and NP148. We report here that NP137 and NP148 showed neuroprotective effects in amyloid-β-treated primary cortical neurons, and NP137 in particular rescued the cognitive deficit of TgAPP mice. The latter compound was able to blunt the abnormal cell response to serum addition or withdrawal of lymphoblasts derived from AD patients. It is suggested that NP137 could be a good drug candidate for future treatment of AD.

Multi-target-directed-ligands acting as enzyme inhibitors and receptor ligands

In this review, we present the latest advances in the field of multi-target-directed ligand (MTDL) design for the treatment of various complex pathologies of multifactorial origin. In particular, latest findings in the field of MTDL design targeting both an enzyme and a receptor are presented for different diseases such as Alzheimer's disease (AD), depression, addiction, glaucoma, non-alcoholic steatohepatitis and pain and inflammation. The ethology of the diseases is briefly described, with special emphasis on how the MTDL can evolve into novel therapies that replace the classic pharmacological dogma "one target one disease". Considering the current needs for therapy adherence improvement, it is exposed as from the medicinal chemistry, different molecular scaffolds are studied. With the use of structure activity relationship studies and molecular optimization, new hybrid molecules are generated with improved biological properties acting at two biologically very distinct targets.

QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer's Disease

Alzheimer’s disease is one of the most common neurodegenerative disorders in elder population. The β-site amyloid cleavage enzyme 1 (BACE1) is the major constituent of amyloid plaques and plays a central role in this brain pathogenesis, thus it constitutes an auspicious pharmacological target for its treatment. In this paper, a QSAR model for identification of potential inhibitors of BACE1 protein is designed by using classification methods. For building this model, a database with 215 molecules collected from different sources has been assembled. This dataset contains diverse compounds with different scaffolds and physical-chemical properties, covering a wide chemical space in the drug-like range. The most distinctive aspect of the applied QSAR strategy is the combination of hybridization with backward elimination of models, which contributes to improve the quality of the final QSAR model. Another relevant step is the visual analysis of the molecular descriptors that allows guaranteeing the absence of information redundancy in the model. The QSAR model performances have been assessed by traditional metrics, and the final proposed model has low cardinality, and reaches a high percentage of chemical compounds correctly classified.