Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach

Finding the best predictive model for hypertensive depression in older adults based on machine learning and metabolomics research

Objective

Depression is a common comorbidity in hypertensive older adults, yet depression is more difficult to diagnose correctly. Our goal is to find predictive models of depression in hypertensive patients using a combination of various machine learning (ML) methods and metabolomics.

Methods

Methods We recruited 379 elderly people aged ≥65 years from the Chinese community. Plasma samples were collected and assayed by gas chromatography/liquid chromatography-mass spectrometry (GC/LC-MS). Orthogonal partial least squares discriminant analysis (OPLS-DA), volcano diagrams and thermograms were used to distinguish metabolites. The attribute discriminators CfsSubsetEval combined with search method BestFirst in WEKA software was used to find the best predicted metabolite combinations, and then 24 classification methods with 10-fold cross-validation were used for prediction.

Results

34 individuals were considered hypertensive combined with depression according to our criteria, and 34 subjects with hypertension only were matched according to age and sex. 19 metabolites by GC-MS and 65 metabolites by LC-MS contributed significantly to the differentiation between the depressed and non-depressed cohorts, with a VIP value of more than 1 and a P value of less than 0.05. There were multiple metabolic pathway alterations. The metabolite combinations screened with WEKA for optimal diagnostic value included 12 metabolites. The machine learning methods with AUC values greater than 0.9 were bayesNet and random forests, and their other evaluation measures are also better.

Conclusion

Altered metabolites and metabolic pathways are present in older adults with hypertension combined with depression. Methods using metabolomics and machine learning performed quite well in predicting depression in hypertensive older adults, contributing to further clinical research.

Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors from Hericium erinaceus

Alzheimer's disease (AD) is the most common type of dementia, affecting over 50 million people worldwide. Currently, most approved medications for AD inhibit the activity of acetylcholinesterase (AChE), but these treatments often come with harmful side effects. There is growing interest in the use of natural compounds for disease prevention, alleviation, and treatment. This trend is driven by the anticipation that these substances may incur fewer side effects than existing medications. This research presents a computational approach combining machine learning with structural modeling to discover compounds from medicinal mushrooms with a high potential to inhibit the activity of AChE. First, we developed a deep neural network capable of rapidly screening a vast number of compounds to indicate their potential to inhibit AChE activity. Subsequently, we applied deep learning models to screen the compounds in the BACMUSHBASE database, which catalogs the bioactive compounds from cultivated and wild mushroom varieties local to Thailand, resulting in the identification of five promising compounds. Next, the five identified compounds underwent molecular docking techniques to calculate the binding energy between the compounds and AChE. This allowed us to refine the selection to two compounds, erinacerin A and hericenone B. Further analysis of the binding energy patterns between these compounds and the target protein revealed that both compounds displayed binding energy profiles similar to the combined characteristics of donepezil and galanthamine, the prescription drugs for AD. We propose that these two compounds, derived from Hericium erinaceus (also known as lion's mane mushroom), are suitable candidates for further research and development into symptom-alleviating AD medications.

QSAR analysis of VEGFR-2 inhibitors based on machine learning, Topomer CoMFA and molecule docking

VEGFR-2 kinase inhibitors are clinically approved drugs that can effectively target cancer angiogenesis. However, such inhibitors have adverse effects such as skin toxicity, gastrointestinal reactions and hepatic impairment. In this study, machine learning and Topomer CoMFA, which is an alignment-dependent, descriptor-based method, were employed to build structural activity relationship models of potentially new VEGFR-2 inhibitors. The prediction ac-curacy of the training and test sets of the 2D-SAR model were 82.4 and 80.1%, respectively, with KNN. Topomer CoMFA approach was then used for 3D-QSAR modeling of VEGFR-2 inhibitors. The coefficient of q2 for cross-validation of the model 1 was greater than 0.5, suggesting that a stable drug activity-prediction model was obtained. Molecular docking was further performed to simulate the interactions between the five most promising compounds and VEGFR-2 target protein and the Total Scores were all greater than 6, indicating that they had a strong hydrogen bond interactions were present. This study successfully used machine learning to obtain five potentially novel VEGFR-2 inhibitors to increase our arsenal of drugs to combat cancer.

Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma

Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis and treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital for advancing diagnostics and therapies. This study combines bioinformatics and machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such as Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, and survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with HCC's pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, and ESR1 were significantly positively correlated with overall patient survival, while AKR1B10 and NQO1 displayed a negative correlation. Moreover, the Adaboost prediction model yielded an 86.8 % accuracy, showcasing machine learning's potential in deciphering complex dataset patterns for clinically relevant predictions. These findings promise to contribute valuable insights into the elusive mechanisms driving liver cancer (HCC). They hold the potential to guide the development of more precise diagnostic methods and treatment strategies in the future. In the fight against this global health challenge, unraveling HCC's intricacies is of paramount importance.

Machine learning in Alzheimer's disease drug discovery and target identification

Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.

Artificial intelligence for dementia drug discovery and trials optimization

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation.

A Combined Feature Screening Approach of Random Forest and Filter-based Methods for Ultra-high Dimensional Data

Background:

Various feature (variable) screening approaches have been proposed in the past decade to mitigate the impact of ultra-high dimensionality in classification and regression problems, including filter based methods such as sure indepen¬dence screening, and wrapper based methods such random forest. However, the former type of methods rely heavily on strong modelling assumptions while the latter ones requires an adequate sample size to make the data speak for themselves. These require¬ments can seldom be met in biochemical studies in cases where we have only access to ultra-high dimensional data with a complex structure and a small number of observations.

Objective:

In this research, we want to investigate the possibility of combing both filter based screening methods and random forest based screening methods in the regression context.

Method:

We have combined four state-of-art filter approaches, namely, sure independence screening (SIS) , robust rank corre¬lation based screening (RRCS), high dimensional ordinary least squares projection (HOLP) and a model free sure independence screening procedure based on the distance correlation (DCSIS) from the statistical community with a random forest based Boruta screening method from the machine learning community for regression problems.

Result:

Among all combined methods, RF-DCSIS performs better than the other methods in terms of screening accuracy and prediction capability on the simulated scenarios and real benchmark datasets.

Conclusion:

By empirical study from both extensive simulation and real data, we have shown that both filter based screening and random forest based screening have their pros and cons while a combination of both may lead to a better feature screening result and prediction capability

Keywords:

feature screening, filter-based method, ultra-high dimensional data, variable selection, random forest,RF-DCSIS

Tailored Modeling of Rivastigmine Derivatives as Dual Acetylcholinesterase and Butyrylcholinesterase Inhibitors for Alzheimer's Disease Treatment

Rational modification of known drug candidates to design more potent ones using computational methods has found application in drug design, development, and discovery. Herein, we integrate computational and theoretical methodologies to unveil rivastigmine derivatives as dual inhibitors of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) for Alzheimer's disease (AD) management. The investigation entails pharmacokinetics screening, density functional theory (DFT) mechanistic study, molecular docking, and molecular dynamics (MD) simulation. We designed over 20 rivastigmine substituents, subject them to some analyses, and identified RL2 with an appreciable blood-brain barrier score and no permeability glycoprotein binding. The compound shows higher acylation energy and a favored binding affinity to the cholinesterase enzymes. RL2 interacts with the AChE and BuChE active sites showing values of -41.1/-39.5 kcal mol^-1 while rivastigmine binds with -32.7/-30.7 kcal mol^-1 for these enzymes. The study revealed RL2 (4-fluorophenyl rivastigmine) as a potential dual inhibitor for AChE and BuChE towards Alzheimer's disorder management.

Alzheimer's Disease and β-Secretase Inhibition: An Update With a Focus on Computer-Aided Inhibitor Design

INTRODUCTION

Alzheimer's disease (AD) is an intensifying neurodegenerative illness due to its irreversible nature. Identification of β-site amyloid precursor protein (APP) cleaving enzyme1 (BACE1) has been a significant medicinal focus towards AD treatment, and this has opened ground for several investigations. Despite the numerous works in this direction, no BACE1 inhibitor has made it to the final approval stage as an anti-AD drug.

METHOD

We provide an introductory background of the subject with a general overview of the pathogenesis of AD. The review features BACE1 inhibitor design and development with a focus on some clinical trials and discontinued drugs. Using the topical keywords BACE1, inhibitor design, and computational/theoretical study in the Web of Science and Scopus database, we retrieved over 49 relevant articles. The search years are from 2010 and 2020, with analysis conducted from May 2020 to March 2021.

RESULTS AND DISCUSSION

Researchers have employed computational methodologies to unravel potential BACE1 inhibitors with a significant outcome. The most used computer-aided approach in BACE1 inhibitor design and binding/interaction studies are pharmacophore development, quantitative structure-activity relationship (QSAR), virtual screening, docking, and molecular dynamics (MD) simulations. These methods, plus more advanced ones including quantum mechanics/molecular mechanics (QM/MM) and QM, have proven substantial in the computational framework for BACE1 inhibitor design. Computational chemists have embraced the incorporation of in vitro assay to provide insight into the inhibition performance of identified molecules with potential inhibition towards BACE1. Significant IC₅₀ values up to 50 nM, better than clinical trial compounds, are available in the literature.

CONCLUSION

The continuous failure of potent BACE1 inhibitors at clinical trials is attracting many queries prompting researchers to investigate newer concepts necessary for effective inhibitor design. The considered properties for efficient BACE1 inhibitor design seem enormous and require thorough scrutiny. Lately, researchers noticed that besides appreciable binding affinity and blood-brain barrier (BBB) permeation, BACE1 inhibitor must show low or no affinity for permeability-glycoprotein. Computational modeling methods have profound applications in drug discovery strategy. With the volume of recent in silico studies on BACE1 inhibition, the prospect of identifying potent molecules that would reach the approved level is feasible. Investigators should try pushing many of the identified BACE1 compounds with significant anti-AD properties to preclinical and clinical trial stages. We also advise computational research on allosteric inhibitor design, exosite modeling, and multisite inhibition of BACE1. These alternatives might be a solution to BACE1 drug discovery in AD therapy.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

A Comprehensive Analysis Identified Hub Genes and Associated Drugs in Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disease among the elderly and has become a growing global health problem causing great concern. However, the pathogenesis of AD is unclear and no specific therapeutics are available to provide the sustained remission of the disease. In this study, we used comprehensive bioinformatics to determine 158 potential genes, whose expression levels changed between the entorhinal and temporal lobe cortex samples from cognitively normal individuals and patients with AD. Then, we clustered these genes in the protein-protein interaction analysis and identified six significant genes that had more biological functions. Besides, we conducted a drug-gene interaction analysis of module genes in the drug-gene interaction database and obtained 26 existing drugs that might be applied for the prevention and treatment of AD. In addition, a predictive model was built based on the selected genes using different machine learning algorithms to identify individuals with AD. These findings may provide new insights into AD therapy.

Neuroprotective Effect of β-secretase Inhibitory Peptide from Pacific Hake (Merluccius productus) Fish Protein Hydrolysate

BACKGROUND

Various methodologies have been employed for the therapeutic interpolation of the progressive brain disorder Alzheimer's disease. Thus, β-secretase inhibition is significant to prevent disease progression in the early stages.

OBJECTIVE

This study seeks to purify and characterize a novel β-secretase inhibitory peptide from Pacific hake enzymatic hydrolysate.

METHODS

A potent β-secretase inhibitory peptide was isolated by sequential purifications using Sephadex G-25 column chromatography and octadecylsilane (ODS) C18 reversed-phase HPLC. A total of seven peptides were synthesized using the isolated peptide sequences. SH-SY5Y cells stably transfected with the human ''Swedish'' amyloid precursor protein (APP) mutation APP695 (SH-SY5YAPP695swe) were used as an in-vitro model system to investigate the effect of Leu-Asn peptide on APP processing.

RESULTS

The β-secretase inhibitory activity (IC50) of the purified peptide (Ser-Leu-Ala-Phe-Val-Asp- Asp-Val-Leu-Asn) from fish protein hydrolysate was 18.65 μM and dipeptide Leu-Asn was the most potent β-secretase inhibitor (IC50 value = 8.82 µM). When comparing all the seven peptides, the inhibition pattern of Leu-Asn dipeptide was found to be competitive by Lineweaver-Burk plot and Dixon plot (Ki value = 4.24 µM). The 24 h treatment with Leu-Asn peptide in SH-SY5Y cells resulted in reducing the β-amyloid (Aβ) production in a dose-dependent manner.

CONCLUSION

Therefore, the results of this study suggest that β-secretase inhibitory peptides derived from marine organisms could be potential candidates to develop nutraceuticals or pharmaceuticals as antidementia agents.