Classification of beta-site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods

Immunotherapeutic approaches for Alzheimer's disease: Exploring active and passive vaccine progress

Alzheimer's disease (AD) is the most common neurodegeneration having non-effective treatments. Vaccines or monoclonal antibodies are two typical immunotherapies for AD. Due to Aβ neurotoxicity, most of the treatments target its generation and deposition. However, therapies that specifically target tau protein are also being investigated. UB311 vaccine generates N-terminal anti-Aβ antibodies, that neutralize Aβ toxicity and promote plaque clearance. It is designed to elicit specific B-cell and wide T-cell responses. ACC001 or PF05236806 vaccine has the same Aβ fragment and QS21 as an adjuvant. CAD106 stimulates response against Aβ1-6. However, Nasopharyngitis and injection site erythema are its side effects. AN1792, the first-generation vaccine was formulated in proinflammatory QS21 adjuvant. However, T-cell epitopes are omitted from the developed epitope AD vaccine with Aβ1-42B-cell epitopes. The first-generation vaccine immune response was immensely successful in clearing Aβ, but it was also sufficient to provoke meningoencephalitis. Immunotherapies have been at the forefront of these initiatives in recent years. The review covers the recent updates on active and passive immunotherapy for AD.

Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease

Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and β-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.

Artificial neural network models driven novel virtual screening workflow for the identification and biological evaluation of BACE1 inhibitors

Beta-site amyloid-β precursor protein-cleaving enzyme 1 (BACE1) is a transmembrane aspartic protease and has shown potential as a possible therapeutic target for Alzheimer's disease. This aggravating disease involves the aberrant production of β amyloid plaques by BACE1 which catalyzes the rate-limiting step by cleaving the amyloid precursor protein (APP), generating the neurotoxic amyloid β protein that aggregates to form plaques leading to neurodegeneration. Therefore, it is indispensable to inhibit BACE1, thus modulating the APP processing. In this study, we present a workflow that utilizes a multi-stage virtual screening protocol for identifying potential BACE1 inhibitors by employing multiple artificial neural network-based models. Collectively, all the hyperparameter tuned models were assigned a task to virtually screen Maybridge library, thus yielding a consensus of 41 hits. The majority of these hits exhibited optimal pharmacokinetic properties confirmed by high central nervous system multiparameter optimization (CNS-MPO) scores. Further shortlisting of 8 compounds by molecular docking into the active site of BACE1 and their subsequent in-vitro evaluation identified 4 compounds as potent BACE1 inhibitors with IC50 values falling in the range 0.028-0.052 μM and can be further optimized with medicinal chemistry efforts to improve their activity.

Machine Learning for the Prediction of Antiviral Compounds Targeting Avian Influenza A/H9N2 Viral Proteins

Avian influenza subtype A/H9N2—which infects chickens, reducing egg production by up to 80%—may be transmissible to humans. In humans, this virus is very harmful since it attacks the respiratory system and reproductive tract, replicating in both. Previous attempts to find antiviral candidates capable of inhibiting influenza A/H9N2 transmission were unsuccessful. This study aims to better characterize A/H9N2 to facilitate the discovery of antiviral compounds capable of inhibiting its transmission. The Symmetry of this study is to apply several machine learning methods to perform virtual screening to identify H9N2 antivirus candidates. The parameters used to measure the machine learning model’s quality included accuracy, sensitivity, specificity, balanced accuracy, and receiver operating characteristic score. We found that the extreme gradient boosting method yielded better results in classifying compounds predicted to be suitable antiviral compounds than six other machine learning methods, including logistic regression, k-nearest neighbor analysis, support vector machine, multilayer perceptron, random forest, and gradient boosting. Using this algorithm, we identified 10 candidate synthetic compounds with the highest scores. These high scores predicted that the molecular fingerprint may involve strong bonding characteristics. Thus, we were able to find significant candidates for synthetic H9N2 antivirus compounds and identify the best machine learning method to perform virtual screenings.

News about Therapies of Alzheimer’s Disease: Extracellular Vesicles from Stem Cells Exhibit Advantages Compared to Other Treatments

Upon its discovery, Alzheimer’s, the neurodegenerative disease that affects many millions of patients in the world, remained without an effective therapy. The first drugs, made available near the end of last century, induced some effects, which remained only marginal. More promising effects are now present, induced by two approaches. Blockers of the enzyme BACE-1 induce, in neurons and glial cells, decreased levels of Aβ, the key peptide of the Alzheimer’s disease. If administered at early AD steps, the BACE-1 blockers preclude further development of the disease. However, they have no effect on established, irreversible lesions. The extracellular vesicles secreted by mesenchymal stem cells induce therapy effects analogous, but more convenient, than the effects of their original cells. After their specific fusion to target cells, the action of these vesicles depends on their ensuing release of cargo molecules, such as proteins and many miRNAs, active primarily on the cell cytoplasm. Operationally, these vesicles exhibit numerous advantages: they exclude, by their accurate selection, the heterogeneity of the original cells; exhibit molecular specificity due to their engineering and drug accumulation; and induce effective actions, mediated by variable concentrations of factors and molecules and by activation of signaling cascades. Their strength is reinforced by their combination with various factors and processes. The recent molecular and operations changes, induced especially by the stem cell target cells, result in encouraging and important improvement of the disease. Their further development is expected in the near future.