Prospective Current Novel Drug Target for the Identification of Natural Therapeutic Targets for Alzheimer's Disease

Kaman Kumar¹, Pooja Singh¹, Divya Sharma¹, Akanksha Singh¹,

Himanshu Gupta¹, Arjun Singh²*

¹Department of Pharmacology, School of Pharmaceutical Sciences, Bhagwant University,

Sikar Road, Ajmer, Rajasthan 305004, India.

²Department of Medicine, Sidney Kimmel Medical College, Thomas Jefferson University,

Philadelphia, PA 19107, United States.

*Corresponding Author E-mail: arjunphar@gmail.com

ABSTRACT:

In today's societies, Alzheimer's disease (AD) is a significant issue. In the US, more than five million people, most of whom are 65 or older, suffer from Alzheimer's disease. By 2060, there will be fourteen million Americans living with Alzheimer's disease, according to a report by the Alzheimer's Association. To find hits with polypharmacological activities, libraries of compounds can be biologically screened based on these targets. These hits can have their structural properties altered to improve the overall profile, just like molecules created using techniques based on knowledge or medicinal chemistry. Designing multi-target ligands against key targets of interest would undoubtedly benefit from knowledge of the roles played by various targets in the development of AD as well as pharmacophores with related biological activities. Computational tools are used to assist in the design of potential polypharmacological lead molecular scaffolds, in addition to knowledge-based and biological screening-based approaches. It is becoming more common to use pharmacophore modelling, machine learning, and structure-based virtual screening to forecast biological activity and target-ligand interaction for various chemical libraries.

KEYWORDS: Pathogenesis, Alzheimer's disease; medication; multi-target ligands, Pharmacophore modelling.

INTRODUCTION:

The majority of therapeutic drugs on the market are single-target therapies recommended for a range of disorders. Single-target drugs, however, have been shown to be less and less effective in treating diseases like Alzheimer's disease that have a multifactorial pathophysiology¹. The most common therapy options for AD are single-target FDA-approved drugs, as was previously mentioned.

However, these medications only work to treat AD symptoms, not to stop the disease from progressing². Another option for controlling the course of Alzheimer's disease has been suggested involves medications that target a variety of pathogenic pathways or targets. Although two single-target medications are now used in combination therapy to treat Alzheimer's disease, doing so may increase the likelihood of side effects and reduce efficacy³.

The bioavailability and pharmacokinetic properties of each drug component may vary when numerous pharmacological compounds are combined⁴. As a result, the focus has shifted more and more in favor of creating polypharmacological ligands, which are single molecules that modulate two or more different targets of interest simultaneously. The likelihood of experiencing adverse side effects is lower when one ligand is used alone than when two or more ligands are combined⁵. A ligand that only targets one protein is also more vulnerable to resistance because the target's active site can change, which greatly reduces binding affinity and effectiveness⁶. On the other hand, resistance to a drug that targets numerous proteins would require the unusual occurrence of concurrent mutations in the numerous protein targets. Drug-drug interactions are less likely with single-chemical therapy than with combination therapy, and patient medication compliance will rise as a result of the regimen's simplification⁷.

METHODS:

Materials:

Biological screening, virtual screening, and knowledge-based/medicinal chemistry-based approaches are the three methods for creating polypharmacological ligands⁸. The majority of polypharmacological ligands are synthesized via knowledge-based/medicinal chemistry-based methodologies, which rely on biological information about previously approved drugs from scientific literature or industry sources. According to this method, polypharmacological ligands can be classified as conjugate, fused, or merged ligands⁹. The conjugates are made of pharmacophoric structures that are joined by either a metabolically stable linker or a cleavable linker that can be metabolized in vivo, releasing unique active structures that interact differently with each target. Without the use of covalent bonds, the pharmacophoric structures of fused ligands are effectively linked at the junctions¹⁰. The structures' pharmacophores do not overlap; instead, they are connected by the functional groups of the pharmacophores, which react directly with one another. The merged ligands produce smaller, simpler compounds because they share the most pharmacophoric characteristics between the different active components¹¹. Of the three types of ligands, conjugates have the highest molecular weight, followed by fused and then merged ligands¹².

To find hits with polypharmacological activities, libraries of compounds can be biologically screened based on these targets¹³. These hits can have their structural properties altered to improve the overall profile, just like molecules created using techniques based on knowledge or medicinal chemistry¹⁴. Designing multi-target ligands against key targets of interest would undoubtedly benefit from knowledge of the roles played by various targets in the development of AD as well as pharmacophores with related biological activities¹⁵.

In addition to knowledge-based and biological screening-based approaches, computational tools are used to aid the design of possible polypharmacological lead molecular scaffolds¹⁶. Pharmacophore modeling, machine learning, and structure-based virtual screening are increasingly being utilized to predict biological activity and target-ligand interaction for diverse chemical libraries¹⁷. To obtain the expected activity spectrum of small compounds based on molecular similarities and patterns, both pharmacophore modeling and machine learning use vast bioactivity datasets. Structure-based virtual screening involves computationally screening libraries of compounds against targets with known 3D structures in order to predict the molecular interactions between the target and each chemical component¹⁸. These computational tools can help you prioritize molecular fragments for the rational design of novel lead molecules. In fact, these computational techniques are beneficial for prioritizing molecular fragments for the rational creation of new lead compounds having polypharmacological effects¹⁹.The following section lists multi-target ligands for Alzheimer's disease identified using knowledge/medicinal chemistry, biological screening, and virtual screening-based techniques²⁰.

Table 1. Examples of compounds, their corresponding biological activities for the design of lead compounds for AD

Compound	Biological Activities	Chemical Moiety
Flavonoid	Antioxidant Anti-inflammatory Inhibition of AChE Anti-aggregation Inhibition of monoamine oxidase (MAO) Metal chelating agent	Polyphenol with chroman-4-one or chromone core system
Coumarin	Inhibition of AChE Inhibition of MAO	2H-chromen-2-one heterocycle
Tacrine	Inhibition of AChE Inhibition of BuChE	9-amino-1,2,3,4-tetrahydroacridine (THA)
Donepezil	Inhibition of AChE Reduction of neural toxicity of β-amyloid peptide Affinity for nicotinic receptor Neuroprotective action against oxidative stress	Indanone and N-benzylpiperidine
Clioquinol	Metal-chelating agent (Metal-protein-attenuating compound (MPAC)) Disaggregation of β-amyloid peptide; promote its solubilization and clearance	5-chloro-7-iodoquinoline-8-ol
Rasagiline and Selegiline	Irreversible MAO inhibitor with selective inhibition against MAO-B	Propargylamine Selegiline Rasagiline
Serotonin and Dopamine	Stimulation of serotonergic receptor (5-HT1A or 5-HT4) Activation of α-secretase; promotion of non-amyloidogenic cleavage of APP	Indolamine and phenethylamine fragments Serotonin Dopamine
Lipoic acid	Antioxidant with high capacity to scavenge free radicals Increase in acetylcholine level Metal chelating agent Anti-inflammatory	Alpha-lipoic acid (ALA)
Resveratrol	Anti-inflammatory Decrease in matrix metallopeptidase 9 (MMP-9) Antioxidant Metabolic regulation of AMP-activated protein kinase (AMPK), sirtuin 1 (SIRT1), peroxisome proliferator-activated receptor gamma coactivator-1-alpha (PGC-1α)	Polyphenolic phytoalexin
Ferulic acid and caffeic acid	Antioxidant Anti-inflammatory	3,4-dihydroxycinnamic acid Ferulic acid, R = CH₃ Caffeic acid, R = H

CONCLUSION:

Given the intricate network of AD pathogenic processes, the development of multi-target medications as an alternative to currently available single-target therapies and combination therapy has been suggested²¹. Despite the potential of multi-target medications to outperform single target therapy, there are also other factors to consider when creating multi-target ligands. As Alzheimer's disease advances from the primary to the secondary stage, and then to the symptomatic stage, the multi-target ligands' therapeutic effects should be tailored to the stage of the disease²². The mechanisms of action of target combinations for concurrent modulation with multi-target ligands must be compatible; nevertheless, it is also critical to avoid promiscuous effects caused by interactions with detrimental off-target sites^23-27. To do this, a thorough understanding of pathway-target-drug-disease linkages, as well as adverse event profiling, is required. Furthermore, it is critical to ensure that the multi-target ligands have balanced activity toward the targets of interest at the given dose. To find hits with polypharmacological activities, libraries of compounds can be biologically screened based on these targets. These hits can have their structural properties altered to improve the overall profile, just like molecules created using techniques based on knowledge or medicinal chemistry. Designing multi-target ligands against key targets of interest would undoubtedly benefit from knowledge of the roles played by various targets in the development of AD as well as pharmacophores with related biological activities.

CONFLICT OF INTEREST:

The author has no conflicts of interest.

ACKNOWLEDGMENTS:

The author would like to thank NCBI, PubMed and Web of Science for the free database services for their kind support during this study.

REFERENCES:

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Received on 29.12.2022 Modified on 03.04.2023

Asian J. Pharm. Tech. 2023; 13(3):171-174.

DOI: 10.52711/2231-5713.2023.00030