Discovery of new acetylcholinesterase inhibitors with small core structures through shape-based virtual screening

Ultrasound-Assisted Synthesis of Piperidinyl-Quinoline Acylhydrazones as New Anti-Alzheimer's Agents: Assessment of Cholinesterase Inhibitory Profile, Molecular Docking Analysis, and Drug-like Properties

Alzheimer's disease (AD) is one of the progressive neurological disorders and the main cause of dementia all over the world. The multifactorial nature of Alzheimer's disease is a reason for the lack of effective drugs as well as a basis for the development of new structural leads. In addition, the appalling side effects such as nausea, vomiting, loss of appetite, muscle cramps, and headaches associated with the marketed treatment modalities and many failed clinical trials significantly limit the use of drugs and alarm for a detailed understanding of disease heterogeneity and the development of preventive and multifaceted remedial approach desperately. With this motivation, we herein report a diverse series of piperidinyl-quinoline acylhydrazone therapeutics as selective as well as potent inhibitors of cholinesterase enzymes. Ultrasound-assisted conjugation of 6/8-methyl-2-(piperidin-1-yl)quinoline-3-carbaldehydes (4a,b) and (un)substituted aromatic acid hydrazides (7a-m) provided facile access to target compounds (8a-m and 9a-j) in 4-6 min in excellent yields. The structures were fully established using spectroscopic techniques such as FTIR, ¹H- and ¹³C NMR, and purity was estimated using elemental analysis. The synthesized compounds were investigated for their cholinesterase inhibitory potential. In vitro enzymatic studies revealed potent and selective inhibitors of AChE and BuChE. Compound 8c showed remarkable results and emerged as a lead candidate for the inhibition of AChE with an IC₅₀ value of 5.3 ± 0.51 µM. The inhibitory strength of the optimal compound was 3-fold higher compared to neostigmine (IC₅₀ = 16.3 ± 1.12 µM). Compound 8g exhibited the highest potency and inhibited the BuChE selectively with an IC₅₀ value of 1.31 ± 0.05 µM. Several compounds, such as 8a-c, also displayed dual inhibitory strength, and acquired data were superior to the standard drugs. In vitro results were further supported by molecular docking analysis, where potent compounds revealed various important interactions with the key amino acid residues in the active site of both enzymes. Molecular dynamics simulation data, as well as physicochemical properties of the lead compounds, supported the identified class of hybrid compounds as a promising avenue for the discovery and development of new molecules for multifactorial diseases, such as Alzheimer's disease (AD).

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

UPLC-MS/MS Profiling, Antioxidant, α-Glucosidase Inhibitory, Cholinesterase Inhibitory, and Cardiovascular Protection Potentials of Jialing 20 (Morus multicaulis Perr.) Mulberry Branch Extract

As a by-product in the sericulture industry, mulberry branches are not currently utilized effectively. Jialing 20 is an artificial triploids mulberry that widely cultivated in southwest China. In this study, the chemical composition of the Jialing 20 mulberry branch extract (MBE) was first analyzed by UPLC-MS/MS, and 42 components, including alkaloids, flavonoids, and coumarins, were obtained. Then, the antioxidant activities, hypoglycemic effect, Alzheimer’s disease inhibition, and cardiovascular protection of MBE were also evaluated in vitro. The IC₅₀ values for the scavenging DPPH and ABTS radicals were, respectively, 31.23 ± 0.57 μg/mL and 8.88 ± 0.36 μg/mL (IC₅₀ values of positive Vc were, respectively, 4.41 ± 0.19 μg/mL and 8.79 ± 0.41 μg/mL). The IC₅₀ value for inhibiting α-glucosidase was 1.90 ± 0.05 μg/mL (IC₅₀ value of positive acarbose was 0.03 μg/mL). The IC₅₀ values for inhibiting acetylcholinesterase and butyrylcholinesterase were, respectively, 179.47 ± 0.38 μg/mL and 101.82 ± 3.37 μg/mL (IC₅₀ values of positive berberine were, respectively, 1.27 ± 0.03 μg/mL and 57.41 ± 0.21 μg/mL). MBE (10 μg/mL and 40 μg/mL) significantly increased the survival rate of oxidized low-density lipoprotein- (ox-LDL) induced human umbilical vein endothelial cells (HUVECs) and significantly decreased the intracellular reactive oxygen species. These results suggest that the extracts of Jialing 20 mulberry branches could be used as a functional food additive.

Chemoinformatic analysis of alkaloids isolated from Peganum genus

Peganum genus is rich with its high phytochemical and botanical variability. Peganum species have been used as a sedative, antitumor, analgesic and antidepressant. This paper aims to study the molecular diversity of Peganum genus and to shed more light on the structure-activity relationship of the alkaloids isolated from Peganum genus. All Peganum alkaloids were grouped according to their structural properties. A chemoinformatic approach (SwissTargetPrediction) was used to determine the molecular targets of these alkaloids. To analyze and visualize the results, R software was used to generate hierarchical clustering heatmaps. The results of this study can help researchers to better understand the structure-activity relationship of Peganum alkaloids and how substitution can affect the biological activity of those alkaloids.

A review of the mechanisms underlying selected comorbidities in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the central nervous system (CNS) leading to mental deterioration and devastation, and eventually a fatal outcome. AD affects mostly the elderly. AD is frequently accompanied by hypercholesterolemia, hypertension, atherosclerosis, and diabetes mellitus, and these are significant risk factors of AD. Other conditions triggered by the progression of AD include psychosis, sleep disorders, epilepsy, and depression. One important comorbidity is Down’s syndrome, which directly contributes to the severity and rapid progression of AD. The development of new therapeutic strategies for AD includes the repurposing of drugs currently used for the treatment of comorbidities. A better understanding of the influence of comorbidities on the pathogenesis of AD, and the medications used in its treatment, might allow better control of disease progression, and more effective pharmacotherapy.

Recent Advances in Virtual Screening for Cholinesterase Inhibitors

Alzheimer's disease (AD) is a significant health crisis, and current treatments provide only limited benefits to cognition at the cost of serious side effects. Recently, virtual screening techniques such as ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) have emerged as powerful drug discovery tools for identifying potential ligands of a biological target from a large database of chemical structures. The cholinesterases are an AD target particularly well suited for drug discovery using virtual screening due to their well-characterized active sites and comprehensive understanding of the structure-activity relationships of existing inhibitors. Over the last 5 years (2015-2020), at least 15 studies have used virtual screening techniques to discover potent new cholinesterase inhibitors. Herein we review how LBVS and SBVS have been applied individually or in tandem to discover novel acetylcholinesterase and butyrylcholinesterase inhibitors for AD, and highlight the need to confirm in vitro activity of screening compounds.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Discovery of Selective Butyrylcholinesterase (BChE) Inhibitors through a Combination of Computational Studies and Biological Evaluations

As there are increased levels and activity of butyrylcholiesterase (BChE) in the late stage of Alzheimer’s disease (AD), development of selective BChE inhibitors is of vital importance. In this study, a workflow combining computational technologies and biological assays were implemented to identify selective BChE inhibitors with new chemical scaffolds. In particular, a pharmacophore model served as a 3D search query to screen three compound collections containing 3.0 million compounds. Molecular docking and cluster analysis were performed to increase the efficiency and accuracy of virtual screening. Finally, 15 compounds were retained for biological investigation. Results revealed that compounds 8 and 18 could potently and highly selectively inhibit BChE activities (IC₅₀ values < 10 μM on human BChE, selectivity index BChE > 30). These active compounds with novel scaffolds provided us with a good starting point to further design potent and selective BChE inhibitors, which may be beneficial for the treatment of AD.

Natural Peptides in Drug Discovery Targeting Acetylcholinesterase

Acetylcholinesterase-inhibitory peptide has gained much importance since it can inhibit acetylcholinesterase (AChE) and increase the availability of acetylcholine in cholinergic synapses, enhancing cholinergic transmission in pharmacological treatment of Alzheimer’s disease (AD). Natural peptides have received considerable attention as biologically important substances as a source of AChE inhibitors. These natural peptides have high potential pharmaceutical and medicinal values due to their bioactivities as neuroprotective and neurodegenerative treatment activities. These peptides have attracted great interest in the pharmaceutical industries, in order to design potential peptides for use in the prophylactic and therapy purposes. Some natural peptides and their derivatives have high commercial values and have succeeded in reaching the pharmaceutical market. A large number of peptides are already in preclinical and clinical pipelines for treatment of various diseases. This review highlights the recent researches on the various natural peptides and future prospects for AD management.

Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs

Chinese herbal medicine has recently gained worldwide attention. The curative mechanism of Chinese herbal medicine is compared with that of western medicine at the molecular level. The treatment mechanism of most Chinese herbal medicines is still not clear. How do we integrate Chinese herbal medicine compounds with modern medicine? Chinese herbal medicine drug-like prediction method is particularly important. A growing number of Chinese herbal source compounds are now widely used as drug-like compound candidates. An important way for pharmaceutical companies to develop drugs is to discover potentially active compounds from related herbs in Chinese herbs. The methods for predicting the drug-like properties of Chinese herbal compounds include the virtual screening method, pharmacophore model method and machine learning method. In this paper, we focus on the prediction methods for the medicinal properties of Chinese herbal medicines. We analyze the advantages and disadvantages of the above three methods, and then introduce the specific steps of the virtual screening method. Finally, we present the prospect of the joint application of various methods.

Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review

BACKGROUND

Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increasing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered training set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity.

OBJECTIVE

The study aims to review ML-based methods used for VS and applications to Alzheimer's Disease (AD) drug discovery.

METHODS

To update the current knowledge on ML for VS, we review thorough backgrounds, explanations, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN).

RESULTS

All techniques have found success in VS, but the future of VS is likely to lean more largely toward the use of neural networks - and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for conducting ML-based VS for potential therapeutics for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation.

CONCLUSION

Different ML techniques are powerful tools for VS, and they have advantages and disadvantages albeit. ML-based VS can be applied to AD drug development.

New (benz)imidazolopyridino tacrines as nonhepatotoxic, cholinesterase inhibitors for Alzheimer disease

Aim: Due to the multifactorial nature of Alzheimer’s disease, there is an urgent search for new more efficient, multitarget-directed drugs. Results: This paper describes the synthesis, antioxidant and in vitro biological evaluation of ten (benz)imidazopyridino tacrines (7–16), showing less toxicity than tacrine at high doses, and potent cholinesterase inhibitory capacity, in the low micromolar range. Among them, compound 10 is a nonhepatotoxic tacrine at 1000 mM, showing moderate, but totally selective electric eel acetylcholinesterase inhibition, whereas molecule 16 is twofold less toxic than tacrine at 1000 μM, showing moderate and almost equipotent inhibition for electric eel acetylcholinesterase and equine butyrylcholinesterase. Conclusion: (Benz)imidazopyridino tacrines (7–16) have been identified as a new and promising type of tacrines for the potential treatment of Alzheimer’s disease.

Discovery of new acetylcholinesterase and butyrylcholinesterase inhibitors through structure-based virtual screening

Small molecule cholinesterase (ChE) inhibitors represent one of the most effective therapeutic strategies for the treatment of Alzheimer's disease (AD).

Identification of 4-aminoquinoline core for the design of new cholinesterase inhibitors

Inhibition of acetylcholinesterase (AChE) using small molecules is still one of the most successful therapeutic strategies in the treatment of Alzheimer's disease (AD). Previously we reported compound T5369186 with a core of quinolone as a new cholinesterase inhibitor. In the present study, in order to identify new cores for the designing of AChE inhibitors, we screened different derivatives of this core with the aim to identify the best core as the starting point for further optimization. Based on the results, we confirmed that only 4-aminoquinoline (compound 04 and 07) had cholinesterase inhibitory effects. Considering the simple structure and high inhibitory potency against AChE, 4-aminoquinoline provides a good starting core for further designing novel multifunctional AChEIs.

Betulinic acid acetate, an antiproliferative natural product, suppresses client proteins of heat shock protein pathways through a CDC37-binding mechanism

CDC37 has emerged as a promising target in antitumor chemotherapy because of its significant role in oncogenic signaling networks.

Discovery of new scaffolds from approved drugs as acetylcholinesterase inhibitors

Six approved drugs show acetylcholinesterase inhibition and can be the starting point in designing new acetylcholinesterase inhibitors (AChEIs).