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International Journal of Molecular Sciences
  • Review
  • Open Access

7 September 2025

Acetylcholinesterase as a Multifunctional Target in Amyloid-Driven Neurodegeneration: From Dual-Site Inhibitors to Anti-Agregation Strategies

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1
Biohazard Prevention Centre, Faculty of Biology and Environmental Protection, University of Lodz, Pomorska 141/143, 90-236 Lodz, Poland
2
Faculty of Advanced Technologies and Chemistry, Military University of Technology, 2 gen. S. Kaliskiego St., 00-908 Warsaw, Poland
*
Author to whom correspondence should be addressed.
This article belongs to the Special Issue Advances in Amyloid Aggregation and Its Inhibition: Implementing Insights from Physiological Conditions

Abstract

Acetylcholinesterase (AChE) has emerged not only as a cholinergic enzyme but also as a modulator of β-amyloid (Aβ) aggregation via its peripheral anionic site (PAS), making it a dual-purpose target in Alzheimer’s disease. While classical AChE inhibitors provide symptomatic relief, they lack efficacy against the amyloidogenic cascade. This review highlights recent advances in multifunctional AChE pharmacophores that inhibit enzymatic activity while simultaneously interfering with Aβ aggregation, oxidative stress, metal dyshomeostasis, and neuroinflammation. Particular emphasis is placed on dual-site inhibitors targeting both catalytic and peripheral domains, multi-target-directed ligands (MTDLs) acting on multiple neurodegenerative pathways, and metal-chelating hybrids that address redox-active metal ions promoting Aβ fibrillization. We also discuss enabling technologies such as AI-assisted drug design, high-resolution structural tools, and human induced pluripotent stem cell (iPSC)-derived neuronal models that support physiologically relevant validation. These insights reflect a paradigm shift towards disease-modifying therapies that bridge molecular pharmacology and pathophysiological relevance.

1. Introduction

Acetylcholinesterase (AChE, E.C. 3.1.1.7) is an enzyme involved in cholinergic neurotransmission, where it plays a key role in the breakdown of the neurotransmitter acetylcholine (ACh) into choline and acetate []. This rapid breakdown of ACh terminates synaptic signalling, ensuring tight regulation of cholinergic neurotransmission. In the central nervous system (CNS), this mechanism is especially important for maintaining cognitive processes such as learning, attention, and memory []. Disruption of this tightly regulated system, as seen in Alzheimer’s disease (AD) and other neurodegenerative disorders, leads to impaired cholinergic signalling, cognitive decline, and progressive neuronal dysfunction [].
Due to its central role in cholinergic signalling, AChE has long been a target for therapeutic intervention, particularly in the treatment of AD [,]. Several AChE inhibitors, including donepezil, galantamine, and rivastigmine, have been approved for clinical use and offer modest symptomatic relief []. These agents work by increasing the concentration of ACh at synaptic junctions, thereby temporarily enhancing cholinergic transmission. However, their therapeutic impact is limited. Many of these drugs suffer from poor selectivity, limited blood–brain barrier (BBB) permeability, and dose-dependent side effects such as gastrointestinal distress, hepatotoxicity, and bradycardia [,]. More importantly, they do not modify the underlying neurodegenerative processes and therefore fail to halt or reverse disease progression. In recent years, there has been a significant shift in the approach to AChE-targeted therapy. Rather than focusing solely on inhibiting enzymatic activity, researchers are now designing compounds that also address other pathological mechanisms involved in neurodegeneration [,]. AD, for example, involves a complex network of processes including oxidative stress, β-amyloid (Aβ) aggregation, tau hyperphosphorylation, mitochondrial dysfunction, and neuroinflammation []. This has led to the development of multi-target-directed ligands (MTDLs), which are rationally designed molecules that can interact with multiple biological targets simultaneously [,,]. In this broader therapeutic context, AChE is increasingly being considered not only as a target for symptom relief but also as a strategic scaffold for multifunctional ligands.
Structural and computational advances have made this shift possible. High-resolution crystallographic data have revealed that AChE contains at least two major binding domains: a catalytic active site (CAS) and a peripheral anionic site (PAS). While the CAS is responsible for hydrolysing ACh, the PAS is involved in modulating interactions with other substrates, including the aggregation of Aβ peptides []. This dual-binding architecture has opened new avenues for drug design, enabling the development of inhibitors that can bind both sites simultaneously []. These compounds may not only enhance cholinergic signalling but also interfere with key steps in amyloid plaque formation.
In parallel, progress in computational pharmacophore modelling, virtual screening, and artificial intelligence has accelerated the discovery of novel AChE inhibitors [,]. Several novel ligands have emerged from computational and hybrid design approaches, showing diverse potencies against AChE. Among the most active are derivatives of already approved AChE inhibitors, as well as other novel derivatives [,], that show low nanomolar or sub-nanomolar inhibition in vitro [,] and, in some cases, demonstrate multifunctional effects such as interference with Aβ aggregation or modulation of additional enzymatic targets [,]. However, very few multifunctional AChE inhibitors have progressed to clinical trials [,,], highlighting the ongoing challenge of translating preclinical efficacy into safe and effective therapies for humans.
Natural products and their derivatives have also received renewed interest, offering structurally diverse scaffolds with inherent biological activity and the potential for multifunctional action [,].
Therefore, the aim of this review is to critically examine recent advances in acetylcholinesterase pharmacophore design, with particular emphasis on multifunctional ligands capable of modulating both cholinergic and non-cholinergic pathological pathways in neurological diseases. By analysing structural features, dual-site targeting strategies, multi-target-directed ligand frameworks, and emerging modalities such as metal chelation, photoactivation, and prodrug design, this work seeks to highlight key innovations and remaining challenges in the development of next-generation AChE inhibitors. Furthermore, the review aims to provide a conceptual framework for the rational design of ligands with enhanced efficacy, selectivity, and CNS bioavailability.

2. Structural and Functional Characteristics of Acetylcholinesterase

The therapeutic significance of AChE arises not only from its pivotal role in cholinergic neurotransmission but also from its unique structural features that underlie exceptional catalytic efficiency. A thorough understanding of these properties is essential for the rational design of inhibitors with high selectivity, CNS engagement, and functional specificity [,]. To better illustrate the spatial organization of AChE and the role of its active site domains in substrate recognition and catalysis, Figure 1 and Table 1 present a schematic cross-section of the enzyme’s catalytic gorge.
Figure 1. Structural domains of AChE along the catalytic gorge. Schematic cross-section of AChE highlighting key functional regions: CAS comprising Ser203, Glu334, and His447; the PAS with Trp286; the acyl pocket (Phe338); the oxyanion hole (OH) with Gly121; and the bottleneck formed by Phe337 and Tyr121. The spatial arrangement of these domains enables substrate recognition, stabilization, and hydrolysis. Created in BioRender. Bijak, M. (2025) https://BioRender.com/zsqb6gn.
Table 1. Structural domains of human AChE.
AChE is a globular enzyme with a highly conserved tertiary structure across species. The enzyme’s active site is located at the base of a ~20 Å deep gorge, which serves as the ligand-binding site of the enzyme []. Within this gorge, the enzyme catalyses the hydrolysis of the ligand or interacts with it in a manner that leads to enzyme inhibition. AChE activity is inhibited when a ligand binds to specific regions within the gorge—either the catalytic active site or peripheral domains—thereby disrupting or completely blocking the catalytic cycle. Inhibitors may mimic the natural substrate, forming stable interactions with amino acid residues in the catalytic centre, or bind to the PAS, hindering the proper positioning and translocation of ACh toward the catalytic active site. Depending on the nature of these interactions, various stages of the enzymatic mechanism may be inhibited, ultimately leading to ACh accumulation in the synaptic cleft and disruption of cholinergic neurotransmission.
The gorge extends from the enzyme surface to the catalytic site and comprises several key functional domains: PAS, CAS, OH, and the acyl pocket, which binds acyl groups and contributes to substrate selectivity []. The gorge is lined with multiple aromatic amino acid residues, among which tryptophan W86 and phenylalanine F337 (numbering according to human AChE) play major roles in stabilizing the ACh molecule upon binding []. This stabilization is further supported by cation–π interactions between the quaternary ammonium group of ACh and the aromatic residues. A structurally distinctive feature of the gorge is the so-called “bottleneck” formed by F337 and Y124. Despite its narrow width, this region demonstrates conformational flexibility, allowing the enzyme to adapt its shape for effective substrate binding.
The PAS, located near the entrance of the gorge, is a key structural element in the initial stages of substrate recognition. It includes residues such as tyrosines Y72, Y124, and Y341, asparagine B74, and tryptophan W286, arranged around the mouth of the gorge []. Residues Y124, Y341, and W286 are involved in interactions with positively charged groups of the substrate, including the quaternary amine of ACh []. These interactions help guide the substrate deeper into the gorge, directing it toward the CAS. In addition, W286 contributes to binding lipophilic moieties present in substrates, further stabilizing their positioning within the gorge [].
Kinetic studies indicate that the CAS is composed of two primary sub-sites: the esteratic site and the anionic site []. The esteratic site contains the catalytic triad—serine S203, histidine H447, and glutamic acid E334—where serine and histidine directly participate in ester bond hydrolysis through proton transfer, while glutamate stabilizes the transition state. The anionic site binds positively charged groups, including the quaternary amine of ACh or other cations, thereby stabilizing the substrate within the active site [].
The OH, located near the base of the gorge, is formed by glycine residues G121 and G122, as well as A204. It interacts with the negatively charged oxygen atom (oxyanion) that forms during catalysis by stabilizing the negative charge of the transition state (enzyme–substrate complex) []. This stabilization lowers the activation energy of the reaction and enhances the enzyme’s catalytic efficiency.
During catalysis, the carbon–oxygen double bond in the acetyl group of ACh is cleaved, forming a transient covalent complex between the hydroxyl group of serine S203 and the carbon atom of the substrate’s carbonyl group. This results in a non-covalent transition state, in which the oxyanion is stabilized by interactions with the amide groups in the oxyanion hole, reducing the activation energy of the reaction. Subsequently, the bond between the choline and acetyl groups is cleaved, releasing choline and forming an intermediate—acetylated serine (CH3CO–AChE). In the final step, the acetyl–serine bond is hydrolysed by a water molecule, leading to the release of acetic acid and regeneration of the active enzyme, ready for another catalytic cycle [,]. From a drug development standpoint, this mechanism provides multiple points for therapeutic intervention, whether by blocking substrate access, mimicking transition states, or altering the conformation of key active-site elements.

3. Approved Acetylcholinesterase Inhibitors and Their Limitations

Donepezil, galantamine, and rivastigmine are the only acetylcholinesterase inhibitors currently approved by both the FDA and EMA for the treatment of Alzheimer’s disease (Figure 2) [,].
Figure 2. Chemical structures of the AChE: inhibitors galantamine, donepezil, and rivastigmine. Created in BioRender. Bijak, M. (2025) https://BioRender.com/85f6vfp.
These agents have demonstrated the ability to temporarily improve cognitive function by increasing acetylcholine levels in the synaptic cleft, thereby addressing the hallmark cholinergic deficits observed in neurodegenerative conditions []. However, despite their clinical utility, these inhibitors are limited by several significant challenges that have spurred ongoing research into more advanced pharmacophores. Their effects are largely symptomatic and do not halt or reverse the progression of underlying pathology (Table 2) [,]. Moreover, their lack of selectivity often results in peripheral side effects such as nausea, vomiting, and hepatotoxicity, which constrain dosing and impact patient adherence []. Many inhibitors face difficulties crossing the blood–brain barrier efficiently and exhibit pharmacokinetic profiles that complicate chronic administration. Importantly, by focusing solely on acetylcholinesterase activity, these compounds fail to address the multifactorial nature of Alzheimer’s and related neurodegenerative diseases, where amyloid plaque formation, tau pathology, oxidative stress, and neuroinflammation all contribute to disease progression []. These limitations have fuelled the transition toward the design of multifunctional pharmacophores that not only inhibit AChE but also target other pathological mechanisms, offering hope for disease-modifying therapies with improved efficacy and safety profiles [,]. This evolution reflects a broader paradigm shift in drug discovery, emphasizing the need for integrated approaches capable of tackling the complexity of neurodegeneration.
Table 2. Summary of clinically approved acetylcholinesterase inhibitors and their limitation.

5. Experimental Techniques Driving Pharmacophore Validation

The validation of computational pharmacophore models has been enhanced over the years by experimental techniques that provide structural, kinetic, thermodynamic, and functional data. These methodologies helped predict binding interactions and guide model refinement but also enable the discovery of novel binding sites, elucidate conformational dynamics, characterize binding kinetics and thermodynamics, and provide functional validation in biologically relevant systems. Collectively, these approaches strengthen the translational value of pharmacophore-based inhibitor design.
X-ray crystallography remains the gold standard for elucidating high-resolution structures, offering atomic-resolution insights into AChE-inhibitor complexes. It enables the identification of key residues involved in ligand binding, the characterization of hydrogen bond donors/acceptors, hydrophobic interactions, and the observation of conformational adaptations upon ligand engagement. These data are instrumental in validating and refining spatial pharmacophore features, including the precise positioning of interaction points [,,].
Cryo-electron microscopy (cryo-EM) is a tool used for studying large, flexible, or transient macromolecular assemblies that are often recalcitrant to crystallization. Cryo-EM provides structural snapshots across various conformational states, thus offering information on the dynamic nature of AChE and its allosteric regulation [,].
Surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC) serve complementary roles in the characterization of ligand binding. SPR provides real-time data on the kinetics of association and dissociation, allowing the derivation of rate constants and equilibrium dissociation constants, which are vital for optimizing ligand affinity and residence time [,]. In parallel, ITC affords a thermodynamic profile of the binding interaction by quantifying changes in enthalpy (ΔH), entropy (ΔS), and Gibbs free energy (ΔG) [,], thereby elucidating the physicochemical driving forces behind ligand-receptor recognition [].
Microfluidic technologies and biosensor-based platforms facilitate the high-throughput screening and real-time monitoring of AChE enzymatic activity and inhibitor efficacy. These miniaturized systems allow for multiplexed compound testing with low reagent consumption and high sensitivity, supporting early-phase pharmacophore validation and lead prioritization [,].
Cell-based assays, particularly those using human induced pluripotent stem cell (iPSC)-derived neuronal models, provide a physiologically relevant in vitro system and allow functional validation of compounds in a biologically relevant CNS-like environment []. These models capture essential features of the human CNS microenvironment and are critical for evaluating compound efficacy, neurotoxicity, blood–brain barrier permeability, and potential off-target effects, which are often overlooked in cell-free assays [].
Integration of these experimental platforms forms an integrated framework that complements and informs computational pharmacophore modelling for reliable pharmacophore validation and optimization.

6. Challenges and Future Directions

Despite substantial progress in the development of advanced pharmacophore-based AChE inhibitors, several critical challenges continue to hinder their successful translation into clinically effective therapies for neurodegenerative disorders. One of the most persistent issues lies in achieving an optimal balance between multifunctionality, such as dual or multi-target inhibition, and favourable drug-like properties. Designing molecules that effectively engage multiple pathological targets while maintaining appropriate physicochemical characteristics (e.g., molecular weight, lipophilicity, and polarity) is inherently complex. These attributes influence CNS bioavailability, especially with regard to crossing the highly selective BBB.
The BBB remains a major pharmacological obstacle. While certain pharmacophores exhibit potent in vitro activity, many fail to achieve therapeutically relevant concentrations in the CNS due to poor permeability or active efflux mechanisms []. Therefore, strategies that improve CNS bioavailability while maintaining target specificity and favourable pharmacokinetic profiles are critically important.
Another critical challenge involves the mitigation of off-target interactions and the minimization of metabolic liabilities []. Many multi-functional compounds tend to exhibit assorted binding, which can lead to unwanted side effects, toxicity, or rapid metabolic degradation []. Enhancing specificity while retaining therapeutic breadth demands advanced strategies in rational design and predictive ADME (Absorption, Distribution, Metabolism, and Excretion) modelling.
Looking forward, several promising avenues are emerging to address these multifaceted challenges. Artificial intelligence (AI) driven de novo drug design is rapidly transforming the pharmacophore discovery process []. Machine learning algorithms are now capable of exploring vast chemical spaces, predicting binding affinities, and generating novel scaffolds with optimized pharmacokinetic and pharmacodynamic properties []. These tools enable more efficient identification of lead compounds with desired characteristics.
Simultaneously, advanced drug delivery systems—such as nanoparticle-based carriers [,], intranasal formulations [], and prodrugs—are being explored to improve CNS delivery and reduce systemic exposure. These technologies offer the potential to overcome traditional pharmacokinetic limitations and enhance therapeutic efficacy.
Ultimately, the successful development of next-generation AChE inhibitors will require sustained interdisciplinary collaboration. Integrating expertise from structural biology, medicinal chemistry, computational modelling, pharmacology, and clinical sciences will be essential. Only through such synergistic efforts can we hope to translate innovative pharmacophore concepts into safe, effective, and individualized therapies for complex CNS disorders.

7. Conclusions

The design of next-generation acetylcholinesterase inhibitors has shifted decisively toward multifunctional, disease-modifying strategies that go beyond symptomatic cholinergic enhancement. Of particular importance is the growing recognition of AChE’s role in β-amyloid aggregation via its PAS, which repositions the enzyme as a critical modulator in the amyloid cascade. Novel pharmacophores—especially dual-site inhibitors and MTDLs—are being designed to both inhibit enzymatic activity and interfere with Aβ fibrillization, often integrating antioxidant or metal-chelating functionalities. Furthermore, AI-driven screening, structure-based design, and validation in human iPSC-derived neuronal models ensure that ligand development increasingly reflects physiologically relevant conditions. This convergence of molecular innovation with disease-contextual validation represents a promising path toward clinically viable AChE-targeted therapies capable of modifying core features of Alzheimer’s pathology.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AChAcetylcholine
AChEAcetylcholinesterase
ADAlzheimer’s Disease
ADMEAbsorption, Distribution, Metabolism, and Excretion
AIArtificial Intelligence
BACE-1Beta-site Amyloid Precursor Protein Cleaving Enzyme 1
BBBBlood–Brain Barrier
CASCatalytic Active Site
CNSCentral Nervous System
DTEDithienylethene
EMAEuropean Medicines Agency
FDAU.S. Food and Drug Administration
GSK-3βGlycogen Synthase Kinase 3 Beta
iPSCInduced Pluripotent Stem Cell
ITCIsothermal Titration Calorimetry
MAO-BMonoamine Oxidase B
MTDLsMulti-Target-Directed Ligands
OHOxyanion Hole
PASPeripheral Anionic Site
PDEsPhosphodiesterases
SPRSurface Plasmon Resonance

References

  1. Martyn, J.A.; Fagerlund, M.J.; Eriksson, L.I. Basic principles of neuromuscular transmission. Anaesthesia 2009, 64, 1–9. [Google Scholar] [CrossRef]
  2. Huang, Q.; Liao, C.; Ge, F.; Ao, J.; Liu, T. Acetylcholine bidirectionally regulates learning and memory. J. Neurorestoratology 2022, 10, 100002. [Google Scholar] [CrossRef]
  3. Chen, Z.R.; Huang, J.B.; Yang, S.L.; Hong, F.F. Role of Cholinergic Signaling in Alzheimer’s Disease. Molecules 2022, 27, 1816. [Google Scholar] [CrossRef]
  4. Yamashita, K.I.; Uehara, T.; Taniwaki, Y.; Tobimatsu, S.; Kira, J.I. Long-Term Effect of Acetylcholinesterase Inhibitors on the Dorsal Attention Network of Alzheimer’s Disease Patients: A Pilot Study Using Resting-State Functional Magnetic Resonance Imaging. Front. Aging Neurosci. 2022, 14, 810206. [Google Scholar] [CrossRef] [PubMed]
  5. Kim, A.Y.; Al Jerdi, S.; MacDonald, R.; Triggle, C.R. Alzheimer’s disease and its treatment-yesterday, today, and tomorrow-PubMed. Front. Pharmacol. 2024, 15, 1399121. [Google Scholar] [CrossRef]
  6. Grossberg, G.T. Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease: Getting On and Staying On. Curr. Ther. Res. Clin. Exp. 2003, 64, 216–235. [Google Scholar] [CrossRef]
  7. Marucci, G.; Buccioni, M.; Ben, D.D.; Lambertucci, C.; Volpini, R.; Amenta, F. Efficacy of acetylcholinesterase inhibitors in Alzheimer’s disease. Neuropharmacology 2021, 190, 108352. [Google Scholar] [CrossRef]
  8. Ruangritchankul, S.; Chantharit, P.; Srisuma, S.; Gray, L.C. Adverse Drug Reactions of Acetylcholinesterase Inhibitors in Older People Living with Dementia: A Comprehensive Literature Review. Ther. Clin. Risk Manag. 2021, 17, 927–949. [Google Scholar] [CrossRef] [PubMed]
  9. Zhang, J.; Zhang, Y.; Wang, J.; Xia, Y.; Zhang, J.; Chen, L. Recent advances in Alzheimer’s disease: Mechanisms, clinical trials and new drug development strategies. Signal Transduct. Target. Ther. 2024, 9, 211. [Google Scholar] [CrossRef] [PubMed]
  10. Singh, A.A.; Khan, F.; Song, M. Alleviation of Neurological Disorders by Targeting Neurodegenerative-Associated Enzymes: Natural and Synthetic Molecules. Int. J. Mol. Sci. 2025, 26, 4707. [Google Scholar] [CrossRef]
  11. Zhao, X.; Hu, Q.; Wang, X.; Li, C.; Chen, X.; Zhao, D.; Qiu, Y.; Xu, H.; Wang, J.; Ren, L.; et al. Dual-target inhibitors based on acetylcholinesterase: Novel agents for Alzheimer’s disease. Eur. J. Med. Chem. 2024, 279, 116810. [Google Scholar] [CrossRef] [PubMed]
  12. Wang, Y.; Wang, H.; Chen, H.Z. AChE Inhibition-based Multi-target-directed Ligands, a Novel Pharmacological Approach for the Symptomatic and Disease-modifying Therapy of Alzheimer’s Disease. Curr. Neuropharmacol. 2016, 14, 364–375. [Google Scholar] [CrossRef]
  13. Azam, U.; Naseer, M.M.; Rochais, C. Analysis of skeletal diversity of multi-target directed ligands (MTDLs) targeting Alzheimer’s disease. Eur. J. Med. Chem. 2025, 286, 117277. [Google Scholar] [CrossRef]
  14. Colletier, J.P.; Fournier, D.; Greenblatt, H.M.; Stojan, J.; Sussman, J.L.; Zaccai, G.; Silman, I.; Weik, M. Structural insights into substrate traffic and inhibition in acetylcholinesterase. EMBO J. 2006, 25, 2746–2756. [Google Scholar] [CrossRef] [PubMed]
  15. Luque, F.J.; Muñoz-Torrero, D. Acetylcholinesterase: A Versatile Template to Coin Potent Modulators of Multiple Therapeutic Targets. Acc. Chem. Res. 2024, 57, 450–467. [Google Scholar] [CrossRef]
  16. Chafer-Dolz, B.; Cecilia, J.M.; Imbernón, B.; Núñez-Delicado, E.; Casaña-Giner, V.; Cerón-Carrasco, J.P. Discovery of novel acetylcholinesterase inhibitors through AI-powered structure prediction and high-performance computing-enhanced virtual screening. RSC Adv. 2025, 15, 4262–4273. [Google Scholar] [CrossRef] [PubMed]
  17. Vijayan, R.S.K.; Kihlberg, J.; Cross, J.B.; Poongavanam, V. Enhancing preclinical drug discovery with artificial intelligence. Drug Discov. Today 2022, 27, 967–984. [Google Scholar] [CrossRef]
  18. Sobha, A.; Ganapathy, A.; Mohan, S.; Madhusoodanan, N.; Babysulochana, A.D.; Alaganandan, K.; Somappa, S.B. Novel small molecule-based acetylcholinesterase (AChE) inhibitors: From biological perspective to recent developments. Eur. J. Med. Chem. Rep. 2024, 12, 100237. [Google Scholar] [CrossRef]
  19. Žužek, M.C. Advances in Cholinesterase Inhibitor Research—An Overview of Preclinical Studies of Selected Organoruthenium(II) Complexes. Int. J. Mol. Sci. 2024, 25, 9049. [Google Scholar] [CrossRef]
  20. de Almeida, R.B.M.; Barbosa, D.B.; Bomfim, M.R.D.; Amparo, J.A.O.; Andrade, B.S.; Costa, S.L.; Campos, J.M.; Cruz, J.N.; Santos, C.B.R.; Leite, F.H.A.; et al. Identification of a Novel Dual Inhibitor of Acetylcholinesterase and Butyrylcholinesterase: In Vitro and In Silico Studies. Pharmaceuticals 2023, 16, 95. [Google Scholar] [CrossRef]
  21. Pourtaher, H.; Hasaninejad, A.; Zare, S.; Tanideh, N.; Iraji, A. The anti-Alzheimer potential of novel spiroindolin-1,2-diazepine derivatives as targeted cholinesterase inhibitors with modified substituents. Sci. Rep. 2023, 13, 11952. [Google Scholar] [CrossRef]
  22. Poggialini, F.; Governa, P.; Vagaggini, C.; Maramai, S.; Lamponi, S.; Mugnaini, C.; Brizzi, A.; Purgatorio, R.; de Candia, M.; Catto, M.; et al. Light-mediated activation/deactivation control and in vitro ADME-Tox profiling of a donepezil-like Dual AChE/MAO-B Inhibitor. Eur. J. Pharm. Sci. 2025, 209, 107066. [Google Scholar] [CrossRef] [PubMed]
  23. Rossi, M.; Freschi, M.; Nascente, L.d.C.; Salerno, A.; Teixeira, S.d.M.V.; Nachon, F.; Chantegreil, F.; Soukup, O.; Prchal, L.; Malaguti, M.; et al. Sustainable Drug Discovery of Multi-Target-Directed Ligands for Alzheimer’s Disease. J. Med. Chem. 2021, 64, 4972–4990. [Google Scholar] [CrossRef]
  24. Huang, L.K.; Kuan, Y.C.; Lin, H.W.; Hu, C.J. Clinical trials of new drugs for Alzheimer disease: A 2020–2023 update. J. Biomed. Sci. 2023, 30, 83. [Google Scholar] [CrossRef]
  25. Cummings, J.; Zhou, Y.; Lee, G.; Zhong, K.; Fonseca, J.; Cheng, F. Alzheimer’s disease drug development pipeline: 2024. Alzheimer’s Dement. Transl. Res. Clin. Interv. 2024, 10, e12465. [Google Scholar] [CrossRef]
  26. Romano, J.D.; Tatonetti, N.P. Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives. Front. Genet. 2019, 10, 368. [Google Scholar] [CrossRef]
  27. Atanasova, M.; Dimitrov, I.; Ivanov, S.; Georgiev, B.; Berkov, S.; Zheleva-Dimitrova, D.; Doytchinova, I. Virtual Screening and Hit Selection of Natural Compounds as Acetylcholinesterase Inhibitors. Molecules 2022, 27, 3139. [Google Scholar] [CrossRef]
  28. Čolović, M.B.; Krstić, D.Z.; Lazarević-Pašti, T.D.; Bondžić, A.M.; Vasić, V.M. Acetylcholinesterase Inhibitors: Pharmacology and Toxicology. Curr. Neuropharmacol. 2013, 11, 315–335. [Google Scholar] [CrossRef]
  29. Silman, I.; Sussman, J.L. Acetylcholinesterase: How is structure related to function? Chem.-Biol. Interact. 2008, 175, 3–10. [Google Scholar] [CrossRef] [PubMed]
  30. Dvir, H.; Silman, I.; Harel, M.; Rosenberry, T.L.; Sussman, J.L. Acetylcholinesterase: From 3D structure to function. Chem.-Biol. Interact. 2010, 187, 10–22. [Google Scholar] [CrossRef] [PubMed]
  31. Thapa, S.; Lv, M.; Xu, H. Acetylcholinesterase: A Primary Target for Drugs and Insecticides. Mini Rev. Med. Chem. 2017, 17, 1665–1676. [Google Scholar] [CrossRef]
  32. Barak, D.; Kronman, C.; Ordentlich, A.; Ariel, N.; Bromberg, A.; Marcus, D.; Lazar, A.; Velan, B.; Shafferman, A. Acetylcholinesterase peripheral anionic site degeneracy conferred by amino acid arrays sharing a common core. J. Biol. Chem. 1994, 269, 6296–6305. [Google Scholar] [CrossRef] [PubMed]
  33. Bourne, Y.; Kolb, H.C.; Radić, Z.; Sharpless, K.B.; Taylor, P.; Marchot, P. Freeze-frame inhibitor captures acetylcholinesterase in a unique conformation. Proc. Natl. Acad. Sci. USA 2004, 101, 1449–1454. [Google Scholar] [CrossRef] [PubMed]
  34. Gao, D.; Zhan, C.G. Modeling effects of oxyanion hole on the ester hydrolysis catalyzed by human cholinesterases. J. Phys. Chem. 2005, 109, 23070–23076. [Google Scholar] [CrossRef] [PubMed]
  35. Axelsen, P.H.; Harel, M.; Silman, I.; Sussman, J.L. Structure and dynamics of the active site gorge of acetylcholinesterase: Synergistic use of molecular dynamics simulation and X-ray crystallography. Protein Sci. A Publ. Protein Soc. 1994, 3, 188–197. [Google Scholar] [CrossRef]
  36. Bourne, Y.; Radic, Z.; Sulzenbacher, G.; Kim, E.; Taylor, P.; Marchot, P. Substrate and product trafficking through the active center gorge of acetylcholinesterase analyzed by crystallography and equilibrium binding. J. Biol. Chem. 2006, 281, 29256–29267. [Google Scholar] [CrossRef]
  37. Soreq, H.; Seidman, S. Acetylcholinesterase—new roles for an old actor. Nat. Rev. Neurosci. 2001, 2, 294–302. [Google Scholar] [CrossRef]
  38. FDA Approved Drug Products. Available online: http://www.accessdata.fda.gov/scripts/cder/drugsatfda/index.cfm (accessed on 1 September 2025).
  39. EMA Approved Drug Products. Available online: https://www.ema.europa.eu/en/medicines (accessed on 1 September 2025).
  40. National Institute for Health and Care Excellence: Guidelines. In Dementia: Assessment, Management and Support for People Living with Dementia and Their Carers; National Institute for Health and Care Excellence (NICE): London, UK, 2018.
  41. Tan, C.C.; Yu, J.T.; Wang, H.F.; Tan, M.S.; Meng, X.F.; Wang, C.; Jiang, T.; Zhu, X.C.; Tan, L. Efficacy and safety of donepezil, galantamine, rivastigmine, and memantine for the treatment of Alzheimer’s disease: A systematic review and meta-analysis. J. Alzheimer’s Dis. 2014, 41, 615–631. [Google Scholar] [CrossRef]
  42. Di Santo, S.G.; Prinelli, F.; Adorni, F.; Caltagirone, C.; Musicco, M. A meta-analysis of the efficacy of donepezil, rivastigmine, galantamine, and memantine in relation to severity of Alzheimer’s disease. J. Alzheimer’s Dis. 2013, 35, 349–361. [Google Scholar] [CrossRef]
  43. Gill, S.S.; Anderson, G.M.; Fischer, H.D.; Bell, C.M.; Li, P.; Normand, S.-L.T.; Rochon, P.A. Syncope and its consequences in patients with dementia receiving cholinesterase inhibitors: A population-based cohort study. Arch. Intern. Med. 2009, 169, 867–873. [Google Scholar] [CrossRef]
  44. Miculas, D.C.; Negru, P.A.; Bungau, S.G.; Behl, T.; Hassan, S.S.u.; Tit, D.M. Pharmacotherapy Evolution in Alzheimer’s Disease: Current Framework and Relevant Directions. Cells 2022, 12, 131. [Google Scholar] [CrossRef]
  45. Agis-Torres, A.; Sölhuber, M.; Fernandez, M.; Sanchez-Montero, J. Multi-Target-Directed Ligands and other Therapeutic Strategies in the Search of a Real Solution for Alzheimer’s Disease. Curr. Neuropharmacol. 2014, 12, 2–36. [Google Scholar] [CrossRef]
  46. Niazi, S.K.; Magoola, M.; Mariam, Z. Innovative Therapeutic Strategies in Alzheimer’s Disease: A Synergistic Approach to Neurodegenerative Disorders. Pharmaceuticals 2024, 17, 741. [Google Scholar] [CrossRef] [PubMed]
  47. Kumar, A.; Gupta, V.; Sharma, S. Donepezil. In StatPearls; StatPearls Publishing LLC.: Treasure Island, FL, USA, 2025. [Google Scholar]
  48. Kalola, U.K.; Patel, P.; Nguyen, H. Galantamine. In StatPearls; StatPearls Publishing LLC.: Treasure Island, FL, USA, 2025. [Google Scholar]
  49. Patel, P.H.; Gupta, V. Rivastigmine. In StatPearls; StatPearls Publishing LLC.: Treasure Island, FL, USA, 2025. [Google Scholar]
  50. Thangeswaran, D.; Shamsuddin, S.; Balakrishnan, V. A comprehensive review on the progress and challenges of tetrahydroisoquinoline derivatives as a promising therapeutic agent to treat Alzheimer’s disease. Heliyon 2024, 10, e30788. [Google Scholar] [CrossRef] [PubMed]
  51. Codony, S.; Pont, C.; Griñán-Ferré, C.; Pede-Mattatelli, A.D.; Calvó-Tusell, C.; Feixas, F.; Osuna, S.; Jarné-Ferrer, J.; Naldi, M.; Bartolini, M.; et al. Discovery and In Vivo Proof of Concept of a Highly Potent Dual Inhibitor of Soluble Epoxide Hydrolase and Acetylcholinesterase for the Treatment of Alzheimer’s Disease. J. Med. Chem. 2022, 65, 4909–4925. [Google Scholar] [CrossRef]
  52. Zagórska, A.; Jaromin, A. Perspectives for New and More Efficient Multifunctional Ligands for Alzheimer′s Disease Therapy. Molecules 2020, 25, 3337. [Google Scholar] [CrossRef] [PubMed]
  53. Kumar, S.; Mahajan, A.; Ambatwar, R.; Khatik, G.L. Recent Advancements in the Treatment of Alzheimer’s Disease: A Multitarget-directed Ligand Approach. Curr. Med. Chem. 2024, 31, 6032–6062. [Google Scholar] [CrossRef]
  54. Chen, R.; Li, X.; Chen, H.; Wang, K.; Xue, T.; Mi, J.; Ban, Y.; Zhu, G.; Zhou, Y.; Dong, W.; et al. Development of the “hidden” multi-target-directed ligands by AChE/BuChE for the treatment of Alzheimer’s disease. Eur. J. Med. Chem. 2023, 251, 115253. [Google Scholar] [CrossRef]
  55. Zueva, I.; Dias, J.; Lushchekina, S.; Semenov, V.; Mukhamedyarov, M.; Pashirova, T.; Babaev, V.; Nachon, F.; Petrova, N.; Nurullin, L.; et al. New evidence for dual binding site inhibitors of acetylcholinesterase as improved drugs for treatment of Alzheimer’s disease. Neuropharmacology 2019, 155, 131–141. [Google Scholar] [CrossRef]
  56. Alvarez, A.; Opazo, C.; Alarcón, R.; Garrido, J.; Inestrosa, N.C. Acetylcholinesterase promotes the aggregation of amyloid-β-peptide fragments by forming a complex with the growing fibrils. J. Mol. Biol. 1997, 272, 348–361. [Google Scholar] [CrossRef]
  57. Kumar, N.; Kumar, V.; Anand, P.; Kumar, V.; Dwivedi, A.R.; Kumar, V. Advancements in the development of multi-target directed ligands for the treatment of Alzheimer’s disease. Bioorganic Med. Chem. 2022, 61, 116742. [Google Scholar] [CrossRef]
  58. Obaid, R.J.; Naeem, N.; Mughal, E.U.; Al-Rooqi, M.M.; Sadiq, A.; Jassas, R.S.; Moussa, Z.; Ahmed, S.A. Inhibitory potential of nitrogen, oxygen and sulfur containing heterocyclic scaffolds against acetylcholinesterase and butyrylcholinesterase. RSC Adv. 2022, 12, 19764–19855. [Google Scholar] [CrossRef]
  59. Jiang, C.S.; Ge, Y.X.; Cheng, Z.Q.; Song, J.L.; Wang, Y.Y.; Zhu, K.; Zhang, H. Discovery of new multifunctional selective acetylcholinesterase inhibitors: Structure-based virtual screening and biological evaluation. J. Comput.-Aided Mol. Des. 2019, 33, 521–530. [Google Scholar] [CrossRef] [PubMed]
  60. Rana, M.; Pareek, A.; Bhardwaj, S.; Arya, G.; Nimesh, S.; Arya, H.; Bhatt, T.K.; Yaragorla, S.; Sharma, A.K. Aryldiazoquinoline based multifunctional small molecules for modulating Aβ42 aggregation and cholinesterase activity related to Alzheimer’s disease. RSC Adv. 2020, 10, 28827–28837. [Google Scholar] [CrossRef]
  61. Zou, D.; Liu, R.; Lv, Y.; Guo, J.; Zhang, C.; Xie, Y. Latest advances in dual inhibitors of acetylcholinesterase and monoamine oxidase B against Alzheimer’s disease. J. Enzym. Inhib. Med. Chem. 2023, 38, 2270781. [Google Scholar] [CrossRef]
  62. Banoo, R.; Nuthakki, V.K.; Wadje, B.N.; Sharma, A.; Bharate, S.B. Design, synthesis, and pharmacological evaluation of indole-piperidine amides as Blood−brain barrier permeable dual cholinesterase and β-secretase inhibitors. Eur. J. Med. Chem. 2024, 266, 116131. [Google Scholar] [CrossRef]
  63. Yao, H.; Uras, G.; Zhang, P.; Xu, S.; Yin, Y.; Liu, J.; Qin, S.; Li, X.; Allen, S.; Bai, R.; et al. Discovery of Novel Tacrine–Pyrimidone Hybrids as Potent Dual AChE/GSK-3 Inhibitors for the Treatment of Alzheimer’s Disease. J. Med. Chem. 2021, 64, 7483–7506. [Google Scholar] [CrossRef] [PubMed]
  64. Hu, J.; Huang, Y.D.; Pan, T.; Zhang, T.; Su, T.; Li, X.; Luo, H.B.; Huang, L. Design, Synthesis, and Biological Evaluation of Dual-Target Inhibitors of Acetylcholinesterase (AChE) and Phosphodiesterase 9A (PDE9A) for the Treatment of Alzheimer’s Disease. ACS Chem. Neurosci. 2019, 10, 537–551. [Google Scholar] [CrossRef] [PubMed]
  65. Liu, J.; Liu, L.; Zheng, L.; Feng, K.W.; Wang, H.T.; Xu, J.P.; Zhou, Z.Z. Discovery of novel 2,3-dihydro-1H-inden-1-ones as dual PDE4/AChE inhibitors with more potency against neuroinflammation for the treatment of Alzheimer’s disease. Eur. J. Med. Chem. 2022, 238, 114503. [Google Scholar] [CrossRef]
  66. Asim, A.; Jastrzębski, M.K.; Kaczor, A.A. Dual Inhibitors of Acetylcholinesterase and Monoamine Oxidase-B for the Treatment of Alzheimer’s Disease. Molecules 2025, 30, 2975. [Google Scholar] [CrossRef]
  67. Mohsin, N.u.A.; Ahmad, M. Donepezil: A review of the recent structural modifications and their impact on anti-Alzheimer activity. Braz. J. Pharm. Sci. 2020, 56, e18325. [Google Scholar] [CrossRef]
  68. Gucký, A.; Hamuľaková, S. Targeting Biometals in Alzheimer’s Disease with Metal Chelating Agents Including Coumarin Derivatives. CNS Drugs 2024, 38, 507–532. [Google Scholar] [CrossRef]
  69. Leuci, R.; Brunetti, L.; Laghezza, A.; Loiodice, F.; Tortorella, P.; Piemontese, L. Importance of Biometals as Targets in Medicinal Chemistry: An Overview about the Role of Zinc (II) Chelating Agents. Appl. Sci. 2020, 10, 4118. [Google Scholar] [CrossRef]
  70. Sharma, A.; Pachauri, V.; Flora, S.J.S. Advances in Multi-Functional Ligands and the Need for Metal-Related Pharmacology for the Management of Alzheimer Disease. Front. Pharmacol. 2018, 9, 1247. [Google Scholar] [CrossRef]
  71. Kozlowski, H.; Luczkowski, M.; Remelli, M.; Valensin, D. Copper, zinc and iron in neurodegenerative diseases (Alzheimer’s, Parkinson’s and prion diseases). Coord. Chem. Rev. 2012, 256, 2129–2141. [Google Scholar] [CrossRef]
  72. Krasnovskaya, O.; Spector, D.; Zlobin, A.; Pavlov, K.; Gorelkin, P.; Erofeev, A.; Beloglazkina, E.; Majouga, A. Metals in Imaging of Alzheimer’s Disease. Int. J. Mol. Sci. 2020, 21, 9190. [Google Scholar] [CrossRef] [PubMed]
  73. Chaves, S.; Piemontese, L.; Hiremathad, A.; Santos, M.A. Hydroxypyridinone Derivatives: A Fascinating Class of Chelators with Therapeutic Applications-An Update. Curr. Med. Chem. 2018, 25, 97–112. [Google Scholar] [CrossRef]
  74. Rawat, P.; Sehar, U.; Bisht, J.; Selman, A.; Culberson, J.; Reddy, P.H. Phosphorylated Tau in Alzheimer’s Disease and Other Tauopathies. Int. J. Mol. Sci. 2022, 23, 12841. [Google Scholar] [CrossRef]
  75. Ivanov, S.M.; Atanasova, M.; Dimitrov, I.; Doytchinova, I.A. Cellular polyamines condense hyperphosphorylated Tau, triggering Alzheimer’s disease. Sci. Rep. 2020, 10, 10098. [Google Scholar] [CrossRef] [PubMed]
  76. Natale, G.D.; Sabatino, G.; Sciacca, M.F.M.; Tosto, R.; Milardi, D.; Pappalardo, G. Aβ and Tau Interact with Metal Ions, Lipid Membranes and Peptide-Based Amyloid Inhibitors: Are These Common Features Relevant in Alzheimer’s Disease? Molecules 2022, 27, 5066. [Google Scholar] [CrossRef] [PubMed]
  77. Kumar, H.M.S.; Herrmann, L.; Tsogoeva, S.B. Structural hybridization as a facile approach to new drug candidates. Bioorganic Med. Chem. Lett. 2020, 30, 127514. [Google Scholar] [CrossRef]
  78. Islam, F.; Khadija, J.F.; Harun-Or-Rashid, M.; Rahaman, M.S.; Nafady, M.H.; Islam, M.R.; Akter, A.; Emran, T.B.; Wilairatana, P.; Mubarak, M.S. Bioactive Compounds and Their Derivatives: An Insight into Prospective Phytotherapeutic Approach against Alzheimer’s Disease. Oxidative Med. Cell. Longev. 2022, 2022, 5100904. [Google Scholar] [CrossRef]
  79. Yang, H.; Zeng, F.; Luo, Y.; Zheng, C.; Ran, C.; Yang, J. Curcumin Scaffold as a Multifunctional Tool for Alzheimer’s Disease Research-PubMed. Molecules 2022, 27, 3879. [Google Scholar] [CrossRef]
  80. Roy, A.; Khan, A.; Ahmad, I.; Alghamdi, S.; Rajab, B.S.; Babalghith, A.O.; Alshahrani, M.Y.; Islam, S.; Islam, M.R. Flavonoids a Bioactive Compound from Medicinal Plants and Its Therapeutic Applications. BioMed Res. Int. 2022, 2022, 5445291. [Google Scholar] [CrossRef] [PubMed]
  81. Chaachouay, N.; Zidane, L.; Chaachouay, N.; Zidane, L. Plant-Derived Natural Products: A Source for Drug Discovery and Development. Drugs Drug Candidates 2024, 3, 184–207. [Google Scholar] [CrossRef]
  82. Cichon, N.; Grabowska, W.; Gorniak, L.; Stela, M.; Harmata, P.; Ceremuga, M.; Bijak, M. Mechanistic and Therapeutic Insights into Flavonoid-Based Inhibition of Acetylcholinesterase: Implications for Neurodegenerative Diseases. Nutrients 2024, 17, 78. [Google Scholar] [CrossRef]
  83. Huang, W.; Wang, Y.; Tian, W.; Cui, X.; Tu, P.; Li, J.; Shi, S.; Liu, X. Biosynthesis Investigations of Terpenoid, Alkaloid, and Flavonoid Antimicrobial Agents Derived from Medicinal Plants. Antibiotics 2022, 11, 1380. [Google Scholar] [CrossRef] [PubMed]
  84. Liu, Z.; Fang, L.; Zhang, H.; Gou, S.; Chen, L. Design, synthesis and biological evaluation of multifunctional tacrine-curcumin hybrids as new cholinesterase inhibitors with metal ions-chelating and neuroprotective property. Bioorganic Med. Chem. 2017, 25, 2387–2398. [Google Scholar] [CrossRef]
  85. Zang, W.B.; Wei, H.L.; Zhang, W.W.; Ma, W.; Li, J.; Yao, Y. Curcumin hybrid molecules for the treatment of Alzheimer’s disease: Structure and pharmacological activities. Eur. J. Med. Chem. 2024, 265, 116070. [Google Scholar] [CrossRef]
  86. Stavrakov, G.; Philipova, I.; Lukarski, A.; Atanasova, M.; Zheleva, D.; Zhivkova, Z.D.; Ivanov, S.; Atanasova, T.; Konstantinov, S.; Doytchinova, I. Galantamine-Curcumin Hybrids as Dual-Site Binding Acetylcholinesterase Inhibitors. Molecules 2020, 25, 3341. [Google Scholar] [CrossRef]
  87. Singh, Y.P.; Prasad, S.; Kumar, H. Comprehensive Analysis on Galantamine Based Hybrids for the Management of Alzheimer’s Disease. Chem. Biol. Drug Des. 2024, 104, e70004. [Google Scholar] [CrossRef]
  88. Simeonova, R.; Zheleva, D.; Valkova, I.; Stavrakov, G.; Philipova, I.; Atanasova, M.; Doytchinova, I. A Novel Galantamine-Curcumin Hybrid as a Potential Multi-Target Agent against Neurodegenerative Disorders. Molecules 2021, 26, 1865. [Google Scholar] [CrossRef] [PubMed]
  89. Jana, S.; Mandlekar, S.; Marathe, P. Prodrug design to improve pharmacokinetic and drug delivery properties: Challenges to the discovery scientists. Curr. Med. Chem. 2010, 17, 3874–3908. [Google Scholar] [CrossRef]
  90. Rautio, J.; Kumpulainen, H.; Heimbach, T.; Oliyai, R.; Oh, D.; Järvinen, T.; Savolainen, J. Prodrugs: Design and clinical applications. Nat. Rev. Drug Discov. 2008, 7, 255–270. [Google Scholar] [CrossRef] [PubMed]
  91. Choudhary, D.; Goykar, H.; Kalyane, D.; Sreeharsha, N.; Tekade, R.K. Prodrug design for improving the biopharmaceutical properties of therapeutic drugs. In The Future of Pharmaceutical Product Development and Research; Elsevier: Amsterdam, The Netherlands, 2020; pp. 179–226. [Google Scholar] [CrossRef]
  92. Jornada, D.H.; Fernandes, G.F.d.S.; Chiba, D.E.; Melo, T.R.F.d.; Santos, J.L.d.; Chung, M.C. The Prodrug Approach: A Successful Tool for Improving Drug Solubility. Molecules 2016, 21, 42. [Google Scholar] [CrossRef] [PubMed]
  93. Lillethorup, I.A.; Hemmingsen, A.V.; Qvortrup, K. Prodrugs and their activation mechanisms for brain drug delivery. RSC Med. Chem. 2025, 16, 1037–1048. [Google Scholar] [CrossRef]
  94. Zeiadeh, I.; Najjar, A.; Karaman, R. Strategies for Enhancing the Permeation of CNS-Active Drugs through the Blood-Brain Barrier: A Review. Molecules 2018, 23, 1289. [Google Scholar] [CrossRef]
  95. Kobauri, P.; Dekker, F.J.; Szymanski, W.; Feringa, B.L. Rational Design in Photopharmacology with Molecular Photoswitches. Angew. Chem. Int. Ed. 2023, 62, e202300681. [Google Scholar] [CrossRef]
  96. Scheiner, M.; Sink, A.; Spatz, P.; Endres, E.; Decker, M. Photopharmacology on Acetylcholinesterase: Novel Photoswitchable Inhibitors with Improved Pharmacological Profiles. ChemPhotoChem 2020, 5, 149–159. [Google Scholar] [CrossRef]
  97. Chen, X.; Wehle, S.; Kuzmanovic, N.; Merget, B.; Holzgrabe, U.; König, B.; Sotriffer, C.A.; Decker, M. Acetylcholinesterase Inhibitors with Photoswitchable Inhibition of β-Amyloid Aggregation. ACS Chem. Neurosci. 2014, 5, 377–389. [Google Scholar] [CrossRef]
  98. Rehman, A.U.; Li, M.; Wu, B.; Ali, Y.; Rasheed, S.; Shaheen, S.; Liu, X.; Luo, R.; Zhang, J. Role of artificial intelligence in revolutionizing drug discovery. Fundam. Res. 2025, 5, 1273–1287. [Google Scholar] [CrossRef]
  99. Reynoso-García, M.F.; Nicolás-Álvarez, D.E.; Tenorio-Barajas, A.Y.; Reyes-Chaparro, A. Structural Bioinformatics Applied to Acetylcholinesterase Enzyme Inhibition. Int. J. Mol. Sci. 2025, 26, 3781. [Google Scholar] [CrossRef] [PubMed]
  100. Gangwal, A.; Lavecchia, A. Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives. J. Med. Chem. 2025, 68, 3948–3969. [Google Scholar] [CrossRef] [PubMed]
  101. Cai, Z.; Zhang, G.; Zhang, X.; Liu, Y.; Fu, X. Current insights into computer-aided immunotherapeutic design strategies. Int. J. Immunopathol. Pharmacol. 2015, 28, 278–285. [Google Scholar] [CrossRef] [PubMed]
  102. Yoo, J.; Lee, J.; Ahn, B.; Han, J.; Lim, M.H. Multi-target-directed therapeutic strategies for Alzheimer’s disease: Controlling amyloid-β aggregation, metal ion homeostasis, and enzyme inhibition. Chem. Sci. 2025, 16, 2105–2135. [Google Scholar] [CrossRef]
  103. Giorgetti, S.; Greco, C.; Tortora, P.; Aprile, F.A. Targeting Amyloid Aggregation: An Overview of Strategies and Mechanisms. Int. J. Mol. Sci. 2018, 19, 2677. [Google Scholar] [CrossRef]
  104. Niu, Z.; Gui, X.; Feng, S.; Reif, B. Aggregation Mechanisms and Molecular Structures of Amyloid-β in Alzheimer’s Disease. Chem.–A Eur. J. 2024, 30, e202400277. [Google Scholar] [CrossRef] [PubMed]
  105. Makhaeva, G.F.; Kovaleva, N.V.; Rudakova, E.V.; Boltneva, N.P.; Lushchekina, S.V.; Astakhova, T.Y.; Timokhina, E.N.; Serebryakova, O.G.; Shchepochkin, A.V.; Averkov, M.A.; et al. Derivatives of 9-phosphorylated acridine as butyrylcholinesterase inhibitors with antioxidant activity and the ability to inhibit β-amyloid self-aggregation: Potential therapeutic agents for Alzheimer’s disease. Front. Pharmacol. 2023, 14, 1219980. [Google Scholar] [CrossRef]
  106. Ţînţaş, M.L.; Gembus, V.; Alix, F.; Barré, A.; Coadou, G.; Truong, L.; Sebban, M.; Papamicaël, C.; Oulyadi, H.; Levacher, V. Rational design of carbamate-based dual binding site and central AChE inhibitors by a “biooxidisable” prodrug approach: Synthesis, in vitro evaluation and docking studies. Eur. J. Med. Chem. 2018, 155, 171–182. [Google Scholar] [CrossRef]
  107. Szałaj, N.; Bajda, M.; Dudek, K.; Brus, B.; Gobec, S.; Malawska, B. Multiple Ligands Targeting Cholinesterases and β-Amyloid: Synthesis, Biological Evaluation of Heterodimeric Compounds with Benzylamine Pharmacophore. Arch. Pharm. 2015, 348, 556–563. [Google Scholar] [CrossRef]
  108. Sun, Q.; Peng, D.Y.; Yang, S.G.; Zhu, X.L.; Yang, W.C.; Yang, G.F. Syntheses of coumarin–tacrine hybrids as dual-site acetylcholinesterase inhibitors and their activity against butylcholinesterase, Aβ aggregation, and β-secretase. Bioorganic Med. Chem. 2014, 22, 4784–4791. [Google Scholar] [CrossRef]
  109. Gupta, S.; Mohan, C.G. Dual Binding Site and Selective Acetylcholinesterase Inhibitors Derived from Integrated Pharmacophore Models and Sequential Virtual Screening. BioMed Res. Int. 2014, 2014, 291214. [Google Scholar] [CrossRef]
  110. Martins, M.M.; Branco, P.S.; Ferreira, L.M. Enhancing the Therapeutic Effect in Alzheimer’s Disease Drugs: The role of Polypharmacology and Cholinesterase inhibitors. ChemistrySelect 2023, 8, e202300461. [Google Scholar] [CrossRef]
  111. Carvalho, A.L.; Trincão, J.; Romão, M.J. X-ray crystallography in drug discovery. In Methods in Molecular Biology; Humana Press: Totowa, NJ, USA, 2009; Volume 572, pp. 31–56. [Google Scholar] [CrossRef]
  112. Bijak, V.; Szczygiel, M.; Lenkiewicz, J.; Gucwa, M.; Cooper, D.R.; Murzyn, K.; Minor, W. The current role and evolution of X-ray crystallography in drug discovery and development. Expert Opin. Drug Discov. 2023, 18, 1221–1230. [Google Scholar] [CrossRef] [PubMed]
  113. Aitipamula, S.; Vangala, V.R. X-Ray Crystallography and its Role in Understanding the Physicochemical Properties of Pharmaceutical Cocrystals. J. Indian Inst. Sci. 2017, 97, 227–243. [Google Scholar] [CrossRef]
  114. Drie, J.H.V.; Tong, L. Cryo-EM as a powerful tool for drug discovery. Bioorganic Med. Chem. Lett. 2020, 30, 127524. [Google Scholar] [CrossRef]
  115. Leung, M.R.; Zeev-Ben-Mordehai, T. Cryo-electron microscopy of cholinesterases, present and future. J. Neurochem. 2020, 158, 1236–1243. [Google Scholar] [CrossRef]
  116. Olaru, A.; Bala, C.; Jaffrezic-Renault, N.; Aboul-Enein, H.Y. Surface plasmon resonance (SPR) biosensors in pharmaceutical analysis. Crit. Rev. Anal. Chem. 2015, 45, 97–105. [Google Scholar] [CrossRef] [PubMed]
  117. Acharya, B.; Behera, A.; Behera, S. Optimizing drug discovery: Surface plasmon resonance techniques and their multifaceted applications. Chem. Phys. Impact 2024, 8, 100414. [Google Scholar] [CrossRef]
  118. Lima Cavalcanti, I.D.; Xavier Junior, F.H.; Santos Magalhães, N.S.; Lira Nogueira, M.C.B. Isothermal titration calorimetry (ITC) as a promising tool in pharmaceutical nanotechnology. Int. J. Pharm. 2023, 641, 123063. [Google Scholar] [CrossRef]
  119. Ward, W.H.; Holdgate, G.A. 7 Isothermal titration calorimetry in drug discovery. Prog. Med. Chem. 2001, 38, 309–376. [Google Scholar] [CrossRef]
  120. Upadhyay, V.; Lucas, A.; Patrick, C.; Mallela, K.M.G. Isothermal titration calorimetry and surface plasmon resonance methods to probe protein-protein interactions. Methods 2024, 225, 52–61. [Google Scholar] [CrossRef] [PubMed]
  121. Siavashy, S.; Soltani, M.; Rahimi, S.; Hosseinali, M.; Guilandokht, Z.; Raahemifar, K. Recent advancements in microfluidic-based biosensors for detection of genes and proteins: Applications and techniques. Biosens. Bioelectron. X 2024, 19, 100489. [Google Scholar] [CrossRef]
  122. Maged, A.; Abdelbaset, R.; Mahmoud, A.A.; Elkasabgy, N.A. Merits and advances of microfluidics in the pharmaceutical field: Design technologies and future prospects. Drug Deliv. 2022, 29, 1549–1570. [Google Scholar] [CrossRef]
  123. Ross, J.A.; Mandenius, C.F. Cell-Based Assays Using Derived Human-Induced Pluripotent Cells in Drug Discovery and Development. In Methods in Molecular Biology; Humana: New York, NY, USA, 2025; Volume 2924, pp. 1–14. [Google Scholar] [CrossRef]
  124. Cerneckis, J.; Cai, H.; Shi, Y. Induced pluripotent stem cells (iPSCs): Molecular mechanisms of induction and applications. Signal Transduct. Target. Ther. 2024, 9, 112. [Google Scholar] [CrossRef]
  125. Wu, D.; Chen, Q.; Chen, X.; Han, F.; Chen, Z.; Wang, Y. The blood–brain barrier: Structure, regulation and drug delivery. Signal Transduct. Target. Ther. 2023, 8, 217. [Google Scholar] [CrossRef]
  126. Vleet, T.R.V.; Liguori, M.J.; James, J.; Lynch, I.; Rao, M.; Warder, S. Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals. SLAS Discov. 2019, 24, 1–24. [Google Scholar] [CrossRef]
  127. Guengerich, F.P. Mechanisms of Drug Toxicity and Relevance to Pharmaceutical Development. Drug Metab. Pharmacokinet. 2011, 26, 3–14. [Google Scholar] [CrossRef]
  128. Crucitti, D.; Pérez Míguez, C.; Díaz Arias, J.Á.; Fernandez Prada, D.B.; Mosquera Orgueira, A. De novo drug design through artificial intelligence: An introduction. Front. Hematol. 2024, 3, 1305741. [Google Scholar] [CrossRef]
  129. Mouchlis, V.D.; Afantitis, A.; Serra, A.; Fratello, M.; Papadiamantis, A.G.; Aidinis, V.; Lynch, I.; Greco, D.; Melagraki, G. Advances in De Novo Drug Design: From Conventional to Machine Learning Methods. Int. J. Mol. Sci. 2021, 22, 1676. [Google Scholar] [CrossRef] [PubMed]
  130. Mudshinge, S.R.; Deore, A.B.; Patil, S.; Bhalgat, C.M. Nanoparticles: Emerging carriers for drug delivery. Saudi Pharm. J. 2011, 19, 129–141. [Google Scholar] [CrossRef] [PubMed]
  131. Dhiman, N.; Awasthi, R.; Sharma, B.; Kharkwal, H.; Kulkarni, G.T. Lipid Nanoparticles as Carriers for Bioactive Delivery. Front. Chem. 2021, 9, 580118. [Google Scholar] [CrossRef] [PubMed]
  132. van Woensel, M.; Wauthoz, N.; Rosière, R.; Amighi, K.; Mathieu, V.; Lefranc, F.; van Gool, S.W.; de Vleeschouwer, S. Formulations for Intranasal Delivery of Pharmacological Agents to Combat Brain Disease: A New Opportunity to Tackle GBM? Cancers 2013, 5, 1020–1048. [Google Scholar] [CrossRef] [PubMed]
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