Dual Inhibitors of AChE and BACE-1 for Reducing Aβ in Alzheimer's Disease: From In Silico to In Vivo

The link between amyloid β and ferroptosis pathway in Alzheimer's disease progression

Alzheimer's disease (AD) affects millions of people worldwide and represents the most prevalent form of dementia. Treatment strategies aiming to interfere with the formation of amyloid β (Aβ) plaques and neurofibrillary tangles (NFTs), the two major AD hallmarks, have shown modest or no effect. Recent evidence suggests that ferroptosis, a type of programmed cell death caused by iron accumulation and lipid peroxidation, contributes to AD pathogenesis. The existing link between ferroptosis and AD has been largely based on cell culture and animal studies, while evidence from human brain tissue is limited. Here we evaluate if Aβ is associated with ferroptosis pathways in post-mortem human brain tissue and whether ferroptosis inhibition could attenuate Aβ-related effects in human brain organoids. Performing positive pixel density scoring on immunohistochemically stained post-mortem Brodmann Area 17 sections revealed that the progression of AD pathology was accompanied by decreased expression of nuclear receptor co-activator 4 and glutathione peroxidase 4 in the grey matter. Differentiating between white and grey matter, allowed for a more precise understanding of the disease's impact on different brain regions. In addition, ferroptosis inhibition prevented Aβ pathology, decreased lipid peroxidation and restored iron storage in human AD iPSCs-derived brain cortical organoids at day 50 of differentiation. Differential gene expression analysis of RNAseq of AD organoids compared to isogenic controls indicated activation of the ferroptotic pathway. This was also supported by results from untargeted proteomic analysis revealing significant changes between AD and isogenic brain organoids. Determining the causality between the development of Aβ plaques and the deregulation of molecular pathways involved in ferroptosis is crucial for developing potential therapeutic interventions.

Secondary metabolites in Cordyceps javanica with insecticidal potential

Cordyceps javanica has been registered as a fungal insecticide in several countries. However, little is known about whether metabolic toxins are involved in the insecticidal process. In this research, we assessed the insecticidal activity of the fermentation broth of C. javanica. Myzus persicae mortality differed when exposed to the metabolized C. javanica broths at 3 days post fermentation (DPF) and 5 DPF. Comparison of the metabolic fluid at 3 DPF and 5 DPF revealed a key alkaloid, heteratisine, which was found to have insecticidal activity and acetylcholinesterase (AChE) inhibitory activity. Heteratisine has high insecticidal activity against adult M. persicae, the absolute 50% lethal concentration (LC50) was only 0.2272 mg/L. Heteratisine showed high inhibitory activity on AChE with the 50% maximal inhibitory concentration (IC50) of 76.69 μM. Molecular docking and dynamic simulations showed that heteratisine conjugation occurs at the peripheral anionic site (PAS) of the AChE of M. persicae, leading to suppression of enzyme activity. Heteratisine was rarely found in fungal metabolites, which helps us to understand the complex and elaborate insecticidal mechanism of C. javanica.

Recent advances in Alzheimer's disease: Mechanisms, clinical trials and new drug development strategies

Alzheimer's disease (AD) stands as the predominant form of dementia, presenting significant and escalating global challenges. Its etiology is intricate and diverse, stemming from a combination of factors such as aging, genetics, and environment. Our current understanding of AD pathologies involves various hypotheses, such as the cholinergic, amyloid, tau protein, inflammatory, oxidative stress, metal ion, glutamate excitotoxicity, microbiota-gut-brain axis, and abnormal autophagy. Nonetheless, unraveling the interplay among these pathological aspects and pinpointing the primary initiators of AD require further elucidation and validation. In the past decades, most clinical drugs have been discontinued due to limited effectiveness or adverse effects. Presently, available drugs primarily offer symptomatic relief and often accompanied by undesirable side effects. However, recent approvals of aducanumab (1) and lecanemab (2) by the Food and Drug Administration (FDA) present the potential in disrease-modifying effects. Nevertheless, the long-term efficacy and safety of these drugs need further validation. Consequently, the quest for safer and more effective AD drugs persists as a formidable and pressing task. This review discusses the current understanding of AD pathogenesis, advances in diagnostic biomarkers, the latest updates of clinical trials, and emerging technologies for AD drug development. We highlight recent progress in the discovery of selective inhibitors, dual-target inhibitors, allosteric modulators, covalent inhibitors, proteolysis-targeting chimeras (PROTACs), and protein-protein interaction (PPI) modulators. Our goal is to provide insights into the prospective development and clinical application of novel AD drugs.

Alternatives of Animal Models for Biomedical Research: a Comprehensive Review of Modern Approaches

Biomedical research has long relied on animal models to unravel the intricacies of human physiology and pathology. However, concerns surrounding ethics, expenses, and inherent species differences have catalyzed the exploration of alternative avenues. The contemporary alternatives to traditional animal models in biomedical research delve into three main categories of alternative approaches: in vitro models, in vertebrate models, and in silico models. This unique approach to artificial intelligence and machine learning has been a keen interest to be used in different biomedical research. The main goal of this review is to serve as a guide to researchers seeking novel avenues for their investigations and underscores the importance of considering alternative models in the pursuit of scientific knowledge and medical breakthroughs, including showcasing the broad spectrum of modern approaches that are revolutionizing biomedical research and leading the way toward a more ethical, efficient, and innovative future. Models can insight into cellular processes, developmental biology, drug interaction, assessing toxicology, and understanding molecular mechanisms.

In-silico lead identification of the pan-mutant IDH1 and IDH2 inhibitors to target glioblastoma

Glioblastoma (GBM) is an aggressive malignant type of brain tumor. Targeting one single intracellular pathway might not alleviate the disease, rather it activates the other molecular pathways that lead to the worsening of the disease condition. Therefore, in this study, we attempted to target both isocitrate dehydrogenase 1 (IDH1) and IDH2, which are one of the most commonly mutated proteins in GBM and other cancer types. Here, standard precision and extra precision docking, IFD, MM-GBSA, QikProp, and molecular dynamics (MD) simulation were performed to identify the potential dual inhibitor for IDH1 and IDH2 from the enamine database containing 59,161 ligands. Upon docking the ligands with IDH1 (PDB: 6VEI) and IDH2 (PDB: 6VFZ), the top eight ligands were selected, based on the XP Glide score. These ligands produced favourable MMGBSA scores and ADME characteristics. Finally, the top four ligands 12953, 44825, 51295, and 53210 were subjected to MD analysis. Interestingly, 53210 showed maximum interaction with Gln 277 for 99% in IDH1 and Gln 316 for 100% in IDH2, which are the crucial amino acids for the inhibitory function of IDH1 and IDH2 to target GBM. Therefore, the present study attempts to identify the novel molecules which could possess a pan-inhibitory action on both IDH1 and IDH that could be crucial in the management of GBM. Yet further evaluation involving in vitro and in vivo studies is warranted to support the data in our current study.Communicated by Ramaswamy H. Sarma.

Explaining Multiclass Compound Activity Predictions Using Counterfactuals and Shapley Values

Most machine learning (ML) models produce black box predictions that are difficult, if not impossible, to understand. In pharmaceutical research, black box predictions work against the acceptance of ML models for guiding experimental work. Hence, there is increasing interest in approaches for explainable ML, which is a part of explainable artificial intelligence (XAI), to better understand prediction outcomes. Herein, we have devised a test system for the rationalization of multiclass compound activity prediction models that combines two approaches from XAI for feature relevance or importance analysis, including counterfactuals (CFs) and Shapley additive explanations (SHAP). For compounds with different single- and dual-target activities, we identified small compound modifications that induce feature changes inverting class label predictions. In combination with feature mapping, CFs and SHAP value calculations provide chemically intuitive explanations for model decisions.

Activity Models of Key GPCR Families in the Central Nervous System: A Tool for Many Purposes

G protein-coupled receptors (GPCRs) are targets of many drugs, of which ∼25% are indicated for central nervous system (CNS) disorders. Drug promiscuity affects their efficacy and safety profiles. Predicting the polypharmacology profile of compounds against GPCRs can thus provide a basis for producing more precise therapeutics by considering the targets and the anti-targets in that family of closely related proteins. We provide a tool for predicting the polypharmacology of compounds within prominent GPCR families in the CNS: serotonin, dopamine, histamine, muscarinic, opioid, and cannabinoid receptors. Our in-house algorithm, "iterative stochastic elimination" (ISE), produces high-quality ligand-based models for agonism and antagonism at 31 GPCRs. The ISE models correctly predict 68% of CNS drug-GPCR interactions, while the "similarity ensemble approach" predicts only 33%. The activity models correctly predict 56% of reported activities of DrugBank molecules for these CNS receptors. We conclude that the combination of interactions and activity profiles generated by screening through our models form the basis for subsequent designing and discovering novel therapeutics, either single, multitargeting, or repurposed.

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.