Identifying Potential Therapeutic Agents for Alzheimer’s Disease through Machine Learning: Exploring the Role of Extra Virgin Olive Oil Phytochemicals

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by memory loss, cognitive decline, and behavioral changes.

With the aging population growing worldwide, AD has become a significant public health concern. Despite decades of research, effective treatments for AD remain elusive.

In recent years, advancements in machine learning techniques have opened new avenues for drug discovery and target identification. Leveraging these tools, the present study aimed to identify FDA-approved drugs and phytochemicals present in Extra Virgin Olive Oil (EVOO) that might interact with genes and proteins associated with AD pathophysiology.

EVOO Phytochemicals and their Neuroprotective Effects
Quercetin
Quercetin, a flavonoid abundant in EVOO, has been extensively studied for its neuroprotective properties. It possesses antioxidant and anti-inflammatory activities, which may help combat oxidative stress and neuroinflammation, both implicated in AD pathogenesis. Preclinical studies have demonstrated quercetin’s ability to inhibit amyloid beta aggregation and promote the clearance of toxic protein aggregates. Clinical investigations have shown promising results, warranting further exploration of quercetin as a potential AD therapeutic.

Genistein
Genistein, an isoflavone found in EVOO, has been investigated for its neuroprotective effects against AD. Studies suggest that genistein can modulate several molecular pathways involved in AD pathogenesis, including amyloid beta metabolism, tau phosphorylation, and neuroinflammation. Furthermore, genistein’s estrogenic properties may contribute to its neuroprotective potential in AD. Preclinical evidence has demonstrated its ability to enhance cognitive function and reduce amyloid beta burden in AD animal models.

Apigenin
Apigenin, a flavone present in EVOO, has attracted attention for its neuroprotective effects against AD. It exerts antioxidant, anti-inflammatory, and anti-apoptotic activities, which collectively contribute to its potential in countering AD-related neurodegeneration. Apigenin has been shown to inhibit amyloid beta aggregation and reduce neurotoxicity in experimental studies. However, further investigations are needed to establish its efficacy and safety in clinical settings.

Catechin
Catechin, a flavanol abundant in EVOO, has been studied for its neuroprotective properties in AD. Its antioxidant and anti-inflammatory actions have been linked to improved cognitive function and reduced AD pathology in animal models. Catechin’s ability to modulate tau hyperphosphorylation and synaptic dysfunction suggests a potential role in halting AD progression. Clinical studies exploring catechin’s effects on AD patients are limited but warrant consideration.

Kaempferol
Kaempferol, a flavonol present in EVOO, has shown promise as a neuroprotective agent against AD. Its antioxidant and anti-inflammatory activities contribute to its potential in mitigating AD-related neurodegeneration. Kaempferol has been reported to inhibit amyloid beta aggregation and enhance cognitive function in preclinical studies. Clinical investigations are essential to validate its neuroprotective effects in human subjects.

Mechanisms of Action and Molecular Targets
The neuroprotective effects of these EVOO phytochemicals are attributed to their interactions with various molecular targets involved in AD pathophysiology. These include the modulation of oxidative stress, inflammation, amyloid beta and tau metabolism, synaptic function, and apoptosis. Quercetin, genistein, apigenin, catechin, and kaempferol have been found to influence multiple pathways critical for AD development and progression.

Preclinical and Clinical Studies
While preclinical studies have provided promising evidence of the neuroprotective effects of these EVOO phytochemicals, clinical trials are scarce and limited in sample size. The translation of preclinical findings into clinical practice remains a significant challenge. Further research is needed to explore the safety, efficacy, and optimal dosages of these phytochemicals in human trials.

The Machine Learning Approach

The study employed a machine learning model initially trained to differentiate between FDA-approved drugs in phases 3 or 4 for treating AD (positive class) and those approved for other diseases (negative class). This model was then used to predict EVOO phytochemicals that could potentially impact the development and progression of AD.

The correlation probability scores obtained for the model-predicted EVOO phytochemicals were found to be comparable to those for FDA-approved phase 3 or 4 drugs, suggesting that both interact with similar pathways associated with AD.

The Validity of the Approach

Using network propagation (random walk with restarts), the study validated its approach, which involved leveraging existing databases for drug target identification. While acknowledging that this field is still in its infancy, the study demonstrated the accuracy of its predictions based on the quality of the databases used.

Several EVOO phytochemicals identified by the model, including quercetin, genistein, apigenin, catechin, and kaempferol, have been previously studied for their potential neuroprotective effects against AD. Quercetin, in particular, was highlighted as having a strong correlation probability with AD due to its ability to reduce oxidative stress, modulate cytokines, inhibit amyloid beta aggregation, and decrease tau phosphorylation.

Pathways Targeted by EVOO Phytochemicals

The study identified eleven different KEGG pathways that were targeted by the ten most likely EVOO phytochemicals to interact with AD.

Notably, pathways such as Olfactory Transduction, Alzheimer’s disease, Insulin Signaling, Phosphatidylinositol Signaling System, and Vascular Smooth Muscle Contraction were among the most prominent.

While Olfactory Transduction may not be directly related to AD, changes in olfactory function have been associated with the disease, and amyloid beta accumulation has been implicated in disrupting the olfactory transduction pathway. The Insulin Signaling Pathway, Phosphatidylinositol Signaling System, and Vascular Smooth Muscle Contraction pathways play essential roles in AD pathogenesis, influencing various aspects such as amyloid beta and tau metabolism, neuroinflammation, and synaptic dysfunction.

Limitations and Future Directions

The analytical approach used in the study comes with certain limitations, mostly stemming from the nature of the underlying datasets. As the exact pathogenesis of AD remains unknown, undiscovered proteins and genes with significant impacts on AD development might not be accounted for in the study

. Furthermore, the completeness of databases like STITCH and STRING, which provide information on protein interactions, phytochemical-protein interactions, and protein-protein interactions, may affect the model’s accuracy. Additionally, the study only analyzed a subset of expert-curated genes associated with AD, which might not fully characterize the disease process.

Conclusion

Despite its limitations, the present in silico study demonstrates the potential of machine learning in identifying drug targets and exploring the effects of EVOO phytochemicals on AD-associated pathways.

The findings highlight the promise of EVOO-derived compounds, particularly quercetin, genistein, apigenin, catechin, and kaempferol, as potential therapeutic agents for AD.

The study also sheds light on various pathways targeted by these phytochemicals, offering valuable insights into their potential mechanisms of action. Moving forward, experimental studies are required to validate these predictions fully and assess the role of EVOO in preventing and treating AD.

With advancements in data availability and improvements in machine learning algorithms, this approach holds great promise for accelerating drug discovery and development for neurodegenerative diseases like AD.


reference link : https://humgenomics.biomedcentral.com/articles/10.1186/s40246-023-00503-6#Sec9

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