Emerging paradigms in Alzheimer's therapy

Alzheimer's disease is a neurodegenerative disorder that affects elderly, and its incidence is continuously increasing across the globe. Unfortunately, despite decades of research, a complete cure for Alzheimer's disease continues to elude us. The current medications are mainly symptomatic and slow the disease progression but do not result in reversal of all disease pathologies. The growing body of knowledge on the factors responsible for the onset and progression of the disease has resulted in the identification of new targets that could be targeted for treatment of Alzheimer's disease. This has opened new vistas for treatment of Alzheimer's disease that have moved away from chemotherapeutic agents modulating a single target to biologics and combinations that acted on multiple targets thereby offering better therapeutic outcomes. This review discusses the emerging directions in therapeutic interventions against Alzheimer's disease highlighting their merits that promise to change the treatment paradigm and challenges that limit their clinical translation.

A Machine Learning Algorithm Suggests Repurposing Opportunities for Targeting Selected GPCRs

Repurposing utilizes existing drugs with known safety profiles and discovers new uses by combining experimental and computational approaches. The integration of computational methods has greatly advanced drug repurposing, offering a rational approach and reducing the risk of failure in these efforts. Recognizing the potential for drug repurposing, we employed our Iterative Stochastic Elimination (ISE) algorithm to screen known drugs from the DrugBank database. Repurposing in our hands is based on computer models of the actions of ligands: the ISE algorithm is a machine learning tool that creates ligand-based models by distinguishing between the physicochemical properties of known drugs and those of decoys. The models are large sets of "filters" made out, each, of molecular properties. We screen and score external sets of molecules (in our case- the DrugBank molecules) by our agonism and antagonism models based on published data (i.e., IC50, Ki, or EC50) and pick the top-scoring molecules as candidates for experiments. Such agonist and antagonist models for six G-protein coupled receptors (GPCRs) families facilitated the identification of repurposing opportunities. Our screening revealed 5982 new potential molecular actions (agonists, antagonists), which suggest repurposing candidates for the cannabinoid 2 (CB2), histamine (H1, H3, and H4), and dopamine 3 (D3) receptors, which may be useful to treat conditions such as neuroinflammation, obesity, allergic dermatitis, and drug abuse. These sets of best candidates should now be examined by experimentalists: based on previous such experiments, there is a very high chance of discovering novel highly bioactive molecules.

Uncovering the Oxidative Stress Mechanisms and Targets in Alzheimer's Disease by Integrating Phenotypic Screening Data and Polypharmacology Networks

Background

The oxidative stress hypothesis is challenging the dominant position of amyloid-β (Aβ) in the field of understanding the mechanisms of Alzheimer's disease (AD), a complicated and untreatable neurodegenerative disease.

Objective

The goal of the present study was to uncover the oxidative stress mechanisms causing AD, as well as the potential therapeutic targets and neuroprotective drugs against oxidative stress mechanisms.

Methods

In this study, a systematic workflow combining pharmacological experiments and computational prediction was proposed. 222 drugs and natural products were collected first and then tested on SH-SY5Y cells to obtain phenotypic screening data on neuroprotection. The preliminary screening data were integrated with drug-target interactions (DTIs) and multi-scale biomedical data, which were analyzed with statistical tests and gene set enrichment analysis. A polypharmacology network was further constructed for investigation.

Results

340 DTIs were matched in multiple databases, and 222 cell viability ratios were calculated for experimental compounds. We identified significant potential therapeutic targets based on oxidative stress mechanisms for AD, including NR3C1, SHBG, ESR1, PGR, and AVPR1A, which might be closely related to neuroprotective effects and pathogenesis. 50% of the top 14 enriched pathways were found to correlate with AD, such as arachidonic acid metabolism and neuroactive ligand-receptor interaction. Several approved drugs in this research were also found to exert neuroprotective effects against oxidative stress mechanisms, including beclometasone, methylprednisolone, and conivaptan.

Conclusion

Our results indicated that NR3C1, SHBG, ESR1, PGR, and AVPR1A were promising therapeutic targets and several drugs may be repurposed from the perspective of oxidative stress and AD.

Drug repurposing on Alzheimer's disease through modulation of NRF2 neighborhood

Alzheimer's disease (AD) is an age-dependent neurodegenerative disorder and the most common cause of cognitive decline. The alarming epidemiological features of Alzheimer's disease, combined with the high failure rate of candidate drugs tested in the preclinical phase, impose more intense investigations for new curative treatments. NRF2 (Nuclear factor-erythroid factor 2-related factor 2) plays a critical role in the inflammatory response and in the cellular redox homeostasis and provides cytoprotection in several diseases including those in the neurodegeneration spectrum. These roles suggest that NRF2 and its directly associated proteins may be novel attractive therapeutic targets in the fight against AD. In this study, through a systemics perspective, we propose an in silico drug repurposing approach for AD, based on the NRF2 interactome and regulome, with the aim of highlighting possible repurposed drugs for AD. Using publicly available information based on differential expressions of the NRF2-neighborhood in AD and through a computational drug repurposing pipeline, we derived to a short list of candidate repurposed drugs and small molecules that affect the expression levels of the majority of NRF2-partners. The relevance of these findings was assessed in a four-step computational meta-analysis including i) structural similarity comparisons with currently ongoing NRF2-related drugs in clinical trials ii) evaluation based on the NRF2-diseasome iii) comparison of relevance between targeted pathways of shortlisted drugs and NRF2-related drugs in clinical trials and iv) further comparison with existing knowledge on AD and NRF2-related drugs in clinical trials based on their known modes of action. Overall, our analysis yielded in 5 candidate repurposed drugs for AD. In cell culture, these 5 candidates activated a luciferase reporter for NRF2 activity and in hippocampus derived TH22 cells they increased NRF2 protein levels and the NRF2 transcriptional signatures as determined by increased expression of its downstream target heme oxygenase 1. We expect that our proposed candidate repurposed drugs will be useful for further research and clinical translation for AD.

Repurposing of FDA-approved drugs as dual-acting MAO-B and AChE inhibitors against Alzheimer's disease: An in silico and in vitro study

An in silico consensus molecular docking approach and in vitro evaluations were adopted in the present study to explore a dataset of FDA-approved drugs as novel multitarget MAO-B/AChE agents in the treatment of Alzheimer's disease (AD). GOLD 5.3 and Glide were employed in the virtual assessments and consensus superimpositions of the obtained poses were applied to increase the reliability of the docking protocols. Furthermore, the top ranked molecules were subjected to binding free energy calculations using MM/GBSA, Induced fit docking (IFD) simulations, and a literature review. Consequently, the top four multitarget drugs were examined for their in vitro MAO-B and AChE inhibition effects. The consensus molecular docking identified Dolutegravir, Rebamipide, Loracarbef and Diflunisal as potential multitarget drugs. The biological data demonstrated that most of the docking scores were in good correlation with the in vitro experiments, however the theoretical simulations in the active site of MAO-B identified two false-positives - Rebamipide and Diflunisal. Dolutegravir and Loracarbef were accessed as active MAO-B inhibitors, while Dolutegravir, Rebamapide and Diflunisal as potential AChE inhibitors. The antiretroviral agent Dolutegravir exhibited the most potent multitarget activity - 41% inhibition of MAO-B (1 μM) and 68% inhibition of AChE (10 μM). Visualizations of the intermolecular interactions of Dolutegravir in the active sites of MAO-B and AChE revealed the formation of several stable hydrogen bonds. Overall, Dolutegravir was identified as a potential anti-AD drug, however further in vivo evaluations should be considered.

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.

Downregulation of Candidate Gene Expression and Neuroprotection by Piperine in Streptozotocin-Induced Hyperglycemia and Memory Impairment in Rats

There is accumulating evidence showing that hyperglycemia conditions like diabetes possess a greater risk of impairment to the neuronal system because high glucose levels exacerbate oxidative stress, accumulation of amyloid-beta peptides, and mitochondrial dysfunction, and impair cognitive functions and cause neurodegeneration conditions like Alzheimer’s diseases. Due to the extensive focus on pharmacological intervention to prevent neuronal cells’ impairment induced by hyperglycemia, the underlying molecular mechanism that links between Diabetes and Alzheimer’s is still lacking. Given this, the present study aimed to evaluate the protective effect of piperine on streptozotocin (STZ) induced hyperglycemia and candidate gene expression. In the present study, rats were divided into four groups: control (Vehicle only), diabetic control (STZ only), piperine treated (20 mg/kg day, i.p), and sitagliptin (Positive control) treated. The memory function was assessed by Morris water maze and probe test. After treatment, biochemical parameters such as HOMA index and lipid profile were estimated in the serum, whereas histopathology was evaluated in pancreatic and brain tissue samples. Gene expression studies were done by real-time PCR technique. Present data indicated that piperine caused significant memory improvement as compared to diabetic (STZ) control. The assessment of HOMA indices in serum samples showed that piperine and sitagliptin (positive control, PC) caused significant alterations of insulin resistance, β cell function, and insulin sensitivity. Assessment of brain and pancreas histopathology shows significant improvement in tissue architecture in piperine and sitagliptin treated groups compared to diabetic control. The gene expression profile in brain tissue shows significantly reduced BACE1, PSEN1, APAF1, CASPASE3, and CATALASE genes in the piperine and sitagliptin (PC) treated groups compared to Diabetic (STZ) control. The present study demonstrated that piperine not only improves memory in diabetic rats but also reduces the expression of specific AD-related genes that can help design a novel strategy for therapeutic intervention at the molecular level.

Insights into Computational Drug Repurposing for Neurodegenerative Disease

Computational drug repurposing has the ability to remarkably reduce drug development time and cost in an era where these factors are prohibitively high. Several examples of successful repurposed drugs exist in fields such as oncology, diabetes, leprosy, inflammatory bowel disease, among others, however computational drug repurposing in neurodegenerative disease has presented several unique challenges stemming from the lack of validation methods and difficulty in studying heterogenous diseases of aging. Here, we examine existing approaches to computational drug repurposing, including molecular, clinical, and biophysical methods, and propose data sources and methods to advance computational drug repurposing in neurodegenerative disease using Alzheimer's disease as an example.