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Nature's Toolbox for Alzheimer's Disease: A Review on the Potential of Natural Products as Alzheimer's Disease Drugs

Mohamed Yusof NIS, Mohd Fauzi F

Neurochem Int, 2024 Jun;176:105738.
PMID: 38616012 DOI: 10.1016/j.neuint.2024.105738

Numerous clinical trials involving natural products have been conducted to observe cognitive performances and biomarkers in Alzheimer's Disease (AD) patients. However, to date, no natural-based drugs have been approved by the FDA as treatments for AD. In this review, natural product-based compounds that were tested in clinical trials from 2011 to 2023, registered at www.clinicaltrials.gov were reviewed. Thirteen compounds, encompassing 7 different mechanisms of action were covered. Several observations were deduced, which are: i) several compounds showed cognitive improvement, but these improvements may not extend to AD, ii) compounds that are endogenous to the human body showed better outcomes, and iii) Docosahexaenoic acid (DHA) and cerebrolysin had the most potential as AD drugs among the 13 compounds. Based on the current findings, natural products may be more suitable as a supplement than AD drugs in most cases. However, the studies covered here were conducted in a relatively short amount of time, where compounds acting on AD pathways may take time to show any effect. Given the diverse pathways that these natural products are involved in, they may potentially produce synergistic effects that would be beneficial in treating AD. Additionally, natural products benefit from both physicochemical properties being in more favorable ranges and active transport playing a more significant role than it does for synthetic compounds.
Insight into the Structure and Physicochemical Properties of Potent Chemokine Receptor 5 Inhibitors for the Discovery of Novel Alzheimer's Disease Drugs

Mohamed Yusof NIS, Awaluddin NA, Fauzi FM

Cent Nerv Syst Agents Med Chem, 2023;23(2):95-108.
PMID: 37496242 DOI: 10.2174/1871524923666230726102846

BACKGROUND: In Alzheimer's Disease (AD), chemokines recruit pro-inflammatory mediators and increase the aggregation of both Aβ (amyloid-β) plaque and neurofibrillary tangles (NFTs). Chemokine receptor 5 (CCR5) has been demonstrated to be involved in neuroinflammation and neuroimmunology, where its inhibition was shown to enhance memory, plasticity and learning.
OBJECTIVE: In this study, compounds that inhibit CCR5 obtained from the ChEMBL database were analysed, specifically for whether specific substructures and physicochemical properties are correlated to biological activity.
METHODS: Clustering was first performed to group 1,237 compounds into 10 clusters based on the similarities of their structure. Then, molecular docking was performed on 10 compounds representative of each cluster. Lastly, the Spearman correlation was computed between physicochemical properties and biological activity.
RESULTS: Results showed that potent CCR5 inhibitors tend to: (i) be larger in size (molecular weight of more than 500 g/mol), (ii) bind at the deep hydrophobic pocket, mostly through π-π stacking and (iii) have more than 1 aromatic ring. The larger size may aid in reaching the deep hydrophobic pocket. However, these requirements may lead to the violation of more than 1 Lipinski's Rule of 5.
CONCLUSION: Future studies should include analyses of the analogues or derivatives of the representative compounds to further expand on the findings here and establish the structure-activity relationship for CCR5 inhibition. This would aid in the development of new AD drugs since drug discovery and development of AD drugs are suffering from high attrition.
Fulltext Maternal Cognitive Impairment Associated with Gestational Diabetes Mellitus-A Review of Potential Contributing Mechanisms

John CM, Mohamed Yusof NIS, Abdul Aziz SH, Mohd Fauzi F

Int J Mol Sci, 2018 Dec 05;19(12).
PMID: 30563117 DOI: 10.3390/ijms19123894

Gestational diabetes mellitus (GDM) carries many risks, where high blood pressure, preeclampsia and future type II diabetes are widely acknowledged, but less focus has been placed on its effect on cognitive function. Although the multifactorial pathogenesis of maternal cognitive impairment is not completely understood, it shares several features with type 2 diabetes mellitus (T2DM). In this review, we discuss some key pathophysiologies of GDM that may lead to cognitive impairment, specifically hyperglycemia, insulin resistance, oxidative stress, and neuroinflammation. We explain how these incidents: (i) impair the insulin-signaling pathway and/or (ii) lead to cognitive impairment through hyperphosphorylation of τ protein, overexpression of amyloid-β and/or activation of microglia. The aforementioned pathologies impair the insulin-signaling pathway primarily through serine phosphorylation of insulin receptor substances (IRS). This then leads to the inactivation of the phosphatidylinositol 3-kinase/Protein kinase B (PI3K/AKT) signaling cascade, which is responsible for maintaining brain homeostasis and normal cognitive functioning. PI3K/AKT is crucial in maintaining normal cognitive function through the inactivation of glycogen synthase kinase 3β (GSκ3β), which hyperphosphorylates τ protein and releases pro-inflammatory cytokines that are neurotoxic. Several biomarkers were also highlighted as potential biomarkers of GDM-related cognitive impairment such as AGEs, serine-phosphorylated IRS-1 and inflammatory markers such as tumor necrosis factor α (TNF-α), high-sensitivity C-reactive protein (hs-CRP), leptin, interleukin 1β (IL-1β), and IL-6. Although GDM is a transient disease, its complications may be long-term, and hence increased mechanistic knowledge of the molecular changes contributing to cognitive impairment may provide important clues for interventional strategies.
In silico toxicity studies of traditional Chinese herbal medicine: A mini review

Zulkifli MH, Abdullah ZL, Mohamed Yusof NIS, Mohd Fauzi F

Curr Opin Struct Biol, 2023 Jun;80:102588.
PMID: 37028096 DOI: 10.1016/j.sbi.2023.102588

With the availability of public databases that store compound-target/compound-toxicity information, and Traditional Chinese medicine (TCM) databases, in silico approaches are used in toxicity studies of TCM herbal medicine. Here, three in silico approaches for toxicity studies were reviewed, which include machine learning, network toxicology and molecular docking. For each method, its application and implementation e.g., single classifier vs. multiple classifier, single compound vs. multiple compounds, validation vs. screening, were explored. While these methods provide data-driven toxicity prediction that is validated in vitro and/or in vivo, it is still limited to single compound analysis. In addition, these methods are limited to several types of toxicity, with hepatotoxicity being the most dominant. Future studies involving the testing of combination of compounds on the front end i.e., to generate data for in silico modeling, and back end i.e., validate findings from prediction models will advance the in silico toxicity modeling of TCM compounds.