Potential Therapeutic Approaches to Alzheimer's Disease By Bioinformatics, Cheminformatics And Predicted Adme-Tox Tools

Bioinformatics and Deep Learning Approach to Discover Food-Derived Active Ingredients for Alzheimer's Disease Therapy

Alzheimer's disease (AD) prevention is a critical challenge for aging societies, necessitating the exploration of food ingredients and whole foods as potential therapeutic agents. This study aimed to identify natural compounds (NCs) with therapeutic potential in AD using an innovative bioinformatics-integrated deep neural analysis approach, combining computational predictions with molecular docking and in vitro experiments for comprehensive evaluation. We employed the bioinformatics-integrated deep neural analysis of NCs for Disease Discovery (BioDeepNat) application in the data collected from chemical databases. Random forest regression models were utilized to predict the IC50 (pIC50) values of ligands interacting with AD-related target proteins, including acetylcholinesterase (AChE), amyloid precursor protein (APP), beta-secretase 1 (BACE1), microtubule-associated protein tau (MAPT), presenilin-1 (PSEN1), tumor necrosis factor (TNF)-α, and valosin-containing protein (VCP). Their activities were then validated through a molecular docking analysis using Autodock Vina. Predictions by the deep neural analysis identified 166 NCs with potential effects on AD across seven proteins, demonstrating outstanding recall performance. The top five food sources of these predicted compounds were black walnut, safflower, ginger, fig, corn, and pepper. Statistical clustering methodologies segregated the NCs into six well-defined groups, each characterized by convergent structural and chemical signatures. The systematic examination of structure-activity relationships uncovered differential molecular patterns among clusters, illuminating the sophisticated correlation between molecular properties and biological activity. Notably, NCs with high activity, such as astragalin, dihydromyricetin, and coumarin, and medium activity, such as luteolin, showed promising effects in improving cell survival and reducing lipid peroxidation and TNF-α expression levels in PC12 cells treated with lipopolysaccharide. In conclusion, our findings demonstrate the efficacy of combining bioinformatics with deep neural networks to expedite the discovery of previously unidentified food-derived active ingredients (NCs) for AD intervention.

In silico analysis of the use of solanine derivatives as a treatment for Alzheimer's disease

Alzheimer's disease (AD) is a brain illness that causes cognitive impairment in the elderly, especially females, as a result of genetics, hormones, and life experiences. It becomes more severe with age and is associated with cardiovascular disease, hypertension, and diabetes. Beta-amyloid plaques and hyper phosphorylated Tau protein buildup are common clinical findings. Misfiling of amyloid precursor protein (APP) and Amyloid beta peptide (Aβ) proteins contributes to Alzheimer's disease. Enzyme Acetylcholinesterase enzyme interacts with amyloid-beta, enhancing its accumulation in insoluble plaques, leading to successful treatment for Alzheimer's disease primarily based on lowering this enzyme. Treatments include using the Rivastigmine for mild, moderate, or severe Alzheimer's disease, which inhibits acetylcholinesterase, but may cause side effects; Solanine derivatives, nightshade toxin, it is cholinesterase inhibitory, may mitigate Alzheimer's illness is progressing. In this research utilized a molecular docking program, which is a computer's computational ability to determine the optimal position for a specific compound to bind to a protein or target, forming a target-ligand complex and displaying biological activity and aiding in the development of effective anti-AD treatments and understanding AD pathological mechanisms. The study examined complexes of 3LII (Acetylcholinesterase receptor) in the A and B chain with Solanine and Rivastigmine derivatives, using an in-silico approach. PyRx default sorter was used to improve docking accuracy. Four compounds were selected based on their higher binding affinities in chain A and B. The results showed that Solanine derivatives (alpha-Solanine, Beta1-Solanine and Beta2-Solanine) have higher binding strength (-9.0,-9.3 and -8.6) than Rivastigmine (-7.2) in chain A, and also the binding strength was high for the Solanine derivatives (alpha-Solanine, Beta1-Solanine, and Beta2-Solanine) (-9.0,-8.8 and -8.9) is higher than Rivastigmine (-6.0) in the chain B. Solanine derivatives showed higher binding strength with acetylcholinesterase, potentially for to reduce the progression of the disease.

Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products

In recent years, the FDA has approved numerous anti-cancer drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborates the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation-aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarize chemical drugs, such as lorlatinib, osimertinib, and other natural products, that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.

A Chiral Nanocomplex for Multitarget Therapy to Alleviate Neuropathology and Rescue Alzheimer's Cognitive Deficits

Alzheimer's disease (AD) is a severe neurodegenerative condition characterized by inflammation, beta-amyloid (Aβ) plaques, and neurodegeneration, which currently lack effective treatments. Chiral nanomaterials have emerged as a promising option for treating neurodegenerative disorders due to their high biocompatibility, strong sustained release ability, and specific enantiomer selectivity. The development of a stimulus-responsive chiral nanomaterial, UiO-66-NH2 @l-MoS2 QDs@PA-Ni (MSP-U), for the treatment of AD is reported. MSP-U is found to stimulate neural stem cell (NSCs) differentiation, promote in situ hydrogen (H2 ) production, and clear Aβ plaques. l-MoS2 QDs modified with l-Cysteine (l-Cys) effectively enhance the differentiation of NSCs into neurons through circularly polarized near-infrared radiation. Doped-phytic acid nickel (PA-Ni) improves the activity of l-MoS2 QDs in scavenging reactive oxygen species at the lesion site via photocatalytic H2 production. Loading l-MoS2 QDs with UiO-66 type metal oxide suppresses electron-hole recombination effect, thereby achieving rapid charge separation and improving transport of photogenerated electrons, leading to significantly improved H2 production efficiency. The photothermal effect of MSP-U also clears the generated Aβ plaques. In vivo evaluations show that MSP-U improves spatial cognition and memory, suggesting a promising potential candidate for the treatment of AD using chiral nanomaterials.

Bioinformatics Tools for the Analysis of Active Compounds Identified in Ranunculaceae Species

The chemical compounds from extracts of three Ranunculaceae species, Aconitum toxicum Rchb., Anemone nemorosa L. and Helleborus odorus Waldst. & Kit. ex Willd., respectively, were isolated using the HPLC purification technique and analyzed from a bioinformatics point of view. The classes of compounds identified based on the proportion in the rhizomes/leaves/flowers used for microwave-assisted extraction and ultrasound-assisted extraction were alkaloids and phenols. Here, the quantifying of pharmacokinetics, pharmacogenomics and pharmacodynamics helps us to identify the actual biologically active compounds. Our results showed that (i) pharmacokinetically, the compounds show good absorption at the intestinal level and high permeability at the level of the central nervous system for alkaloids; (ii) regarding pharmacogenomics, alkaloids can influence tumor sensitivity and the effectiveness of some treatments; (iii) and pharmacodynamically, the compounds of these Ranunculaceae species bind to carbonic anhydrase and aldose reductase. The results obtained showed a high affinity of the compounds in the binding solution at the level of carbonic anhydrases. Carbonic anhydrase inhibitors extracted from natural sources can represent the path to new drugs useful both in the treatment of glaucoma, but also of some renal, neurological and even neoplastic diseases. The identification of natural compounds with the role of inhibitors can have a role in different types of pathologies, both associated with studied and known receptors such as carbonic anhydrase and aldose reductase, as well as new pathologies not yet addressed.

Highly Accessible Computational Prediction and In Vivo/In Vitro Experimental Validation: Novel Synthetic Phenyl Ketone Derivatives as Promising Agents against NAFLD via Modulating Oxidoreductase Activity

Nonalcoholic fatty liver disease (NAFLD) has reached epidemic proportions with no pharmacological treatment approved. Several highly accessible computational tools were employed to predict the activities of twelve novel compounds prior to actual chemical synthesis. We began our work by designing two or three hydroxyl groups appended to the phenyl ketone core, followed by prediction of drug-likeness and targets. Most predicted targets for each compound overlapped with NAFLD targets (≥80%). Enrichment analysis showed that these compounds might regulate oxidoreductase activity. Then, these compounds were synthesized and confirmed by IR, MS, ¹H, and ¹³C NMR. Their cell viability demonstrated that twelve compounds exhibited appreciable potencies against NAFLD (EC₅₀ values ≤ 13.5 μM). Furthermore, the most potent compound 5f effectively prevented NAFLD progression as evidenced by the change in histological features. 5f significantly reduced total cholesterol and triglyceride levels in vitro/in vivo, and the effects of 5f were significantly stronger than those of the control drug. The proteomic data showed that oxidoreductase activity was the most significantly enriched, and this finding was consistent with docking results. In summary, this validated presynthesis prediction approach was cost-saving and worthy of popularization. The novel synthetic phenyl ketone derivative 5f holds great therapeutic potential by modulating oxidoreductase activity to counter NAFLD.

Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications

Computational approaches to drug development are rapidly growing in popularity and have been used to produce significant results. Recent developments in information science have expanded databases and chemical informatics knowledge relating to natural products. Natural products have long been well-studied, and a large number of unique structures and remarkable active substances have been reported. Analyzing accumulated natural product knowledge using emerging computational science techniques is expected to yield more new discoveries. In this article, we discuss the current state of natural product research using machine learning. The basic concepts and frameworks of machine learning are summarized. Natural product research that utilizes machine learning is described in terms of the exploration of active compounds, automatic compound design, and application to spectral data. In addition, efforts to develop drugs for intractable diseases will be addressed. Lastly, we discuss key considerations for applying machine learning in this field. This paper aims to promote progress in natural product research by presenting the current state of computational science and chemoinformatics approaches in terms of its applications, strengths, limitations, and implications for the field.

Targeting the liver in dementia and cognitive impairment: Dietary macronutrients and diabetic therapeutics

Many people living with dementia and cognitive impairment have dysfunctional mitochondrial and insulin-glucose metabolism resembling type 2 diabetes mellitus and old age. Evidence from human trials shows that nutritional interventions and anti-diabetic medicines that target nutrient-sensing pathways overcome these deficits in glucose and energy metabolism and can improve cognition and/or reduce symptoms of dementia. The liver is the main organ that mediates the systemic effects of diets and many diabetic medicines; therefore, it is an intermediate target for such dementia interventions. A challenge is the efficacy of these treatments in older age. Solutions include the targeted hepatic delivery of diabetic medicines using nanotechnologies and titration of macronutrients to optimize hepatic energy metabolism.

Therapeutic potential of quinazoline derivatives for Alzheimer's disease: A comprehensive review

Quinazolines are considered as a promising class of bioactive heterocyclic compounds with broad properties. Particularly, the quinazoline scaffold has an impressive role in the design and synthesis of new CNS-active drugs. The drug-like properties and pharmacological characteristics of quinazoline could lead to different drugs with various targets. Among CNS disorders, Alzheimer's disease (AD) is a progressive neurodegenerative disorder with memory loss, cognitive decline and language dysfunction. AD is a complex and multifactorial disease therefore, the need for finding multi-target drugs against this devastative disease is urgent. A literature survey revealed that quinazoline derivatives have diverse therapeutic potential for AD as modulators/inhibitors of β-amyloid, tau protein, cholinesterases, monoamine oxidases, and phosphodiesterases as well as other protective effects. Thus, we describe here the most relevant and recent studies about anti-AD agents with quinazoline structure which can further aid the development and discovery of new anti-AD agents.