Challenges in Alzheimer's Disease Drug Discovery and Development: The Role of Modeling, Simulation, and Open Data

Screening Techniques for Drug Discovery in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive and irreversible impairment of memory and other cognitive functions of the aging brain. Pathways such as amyloid beta neurotoxicity, tau pathogenesis and neuroinflammatory have been used to understand AD, despite not knowing the definite molecular mechanism which causes this progressive disease. This review attempts to summarize the small molecules that target these pathways using various techniques involving high-throughput screening, molecular modeling, custom bioassays, and spectroscopic detection tools. Novel and evolving screening methods developed to advance drug discovery initiatives in AD research are also highlighted.

Ketogenic Diet Alleviates Brain Iron Deposition and Cognitive Dysfunction via Nrf2-mediated Ferroptosis pathway in APP/PS1 Mouse

Progressive cognitive decline and increased brain iron deposition with age are important features of Alzheimer's disease. Previous studies have found that the short-term ketogenic diet has neuroprotective effects in a variety of neurodegenerative diseases, but the effects of an early and long-term ketogenic diet on brain iron content and cognition of Alzheimer's disease have not been reported. In our study, 8-week-old APP/PS1 mice were given a 12-month ketogenic or standard diet, while C57BL/6 mice matched with the age and genetic background of APP/PS1 mice were used as normal controls to be given a standard diet for the same length of time. We found that 12 months of an early ketogenic diet improved the impaired learning and memory ability of APP/PS1 mice. The improvement of cognitive function may be related to the reduction of amyloid-beta deposition and neuronal ferroptosis. The mechanism was achieved by the regulation of ferroptosis-related pathways after activation of nuclear factor erythroid 2-related factor 2 by ketogenic diet-induced elevated β-hydroxybutyrate. In addition, blood biochemical results showed that compared with the standard diet group of the disease, although the early and long-term ketogenic diet increased blood lipids to some extent, it seemed to reduce liver, renal, and myocardial damage caused by genetic differences. This will provide a piece of positive evidence for the early and long-term use of ketogenic diets in people at risk of Alzheimer's disease.

Deep Reinforcement Learning-Based Retinal Imaging in Alzheimer's Disease: Potential and Perspectives

Alzheimer's disease (AD) remains a global health challenge in the 21st century due to its increasing prevalence as the major cause of dementia. State-of-the-art artificial intelligence (AI)-based tests could potentially improve population-based strategies to detect and manage AD. Current retinal imaging demonstrates immense potential as a non-invasive screening measure for AD, by studying qualitative and quantitative changes in the neuronal and vascular structures of the retina that are often associated with degenerative changes in the brain. On the other hand, the tremendous success of AI, especially deep learning, in recent years has encouraged its incorporation with retinal imaging for predicting systemic diseases. Further development in deep reinforcement learning (DRL), defined as a subfield of machine learning that combines deep learning and reinforcement learning, also prompts the question of how it can work hand in hand with retinal imaging as a viable tool for automated prediction of AD. This review aims to discuss potential applications of DRL in using retinal imaging to study AD, and their synergistic application to unlock other possibilities, such as AD detection and prediction of AD progression. Challenges and future directions, such as the use of inverse DRL in defining reward function, lack of standardization in retinal imaging, and data availability, will also be addressed to bridge gaps for its transition into clinical use.

Two statins and cromolyn as possible drugs against the cytotoxicity of Aβ(31-35) and Aβ(25-35) peptides: a comparative study by advanced computer simulation methods

In this work, possible effective mechanisms of cromolyn, atorvastatin and lovastatin on the cytotoxicity of Aβ(31-35) and Aβ(25-35) peptides were investigated by classical molecular dynamics and well-tempered metadynamics simulations. The results demonstrate that all the drugs affect the behavior of the peptides, such as their ability to aggregate, and alter their secondary structures and their affinity to a particular drug. Our findings from the computed properties suggest that the best drug candidate is lovastatin. This medicine inhibits peptide aggregation, adsorbs the peptides on the surface of the drug clusters, changes the secondary structure and binds to MET₃₅, which has been seen as the reason for the toxicity of the studied peptide sequences. Moreover, lovastatin is the drug which previously has demonstrated the strongest ability to penetrate the blood-brain barrier and makes lovastatin the most promising medicine among the three investigated drugs. Atorvastatin is also seen as a potential candidate if its penetration through the blood-brain barrier could be improved. Otherwise, its properties are even better than the ones demonstrated by lovastatin. Cromolyn appears to be less interesting as an anti-aggregant from the computational data, in comparison to the two statins.

Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry

Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of drug response variability and enables precision dosing. Reinforcement learning (RL)-a set of computational methods addressing optimization problems as a continuous learning process-shows relevance for precision dosing with high flexibility for dosing rule adaptation and for coping with high dimensional efficacy and/or safety markers, constituting a relevant approach to take advantage of data from digital health technologies. RL can also support contributions to the successful development of digital health applications, recognized as key players of the future healthcare systems, in particular for reducing the burden of non-communicable diseases to society. RL is also pivotal in computational psychiatry-a way to characterize mental dysfunctions in terms of aberrant brain computations-and represents an innovative modeling approach forpsychiatric indications such as depression or substance abuse disorders for which digital therapeutics are foreseen as promising modalities.

Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer's disease

Multi-target directed ligand-based 2D-QSAR models were developed using different N-benzyl piperidine derivatives showing inhibitory activity toward acetylcholinesterase (AChE) and β-Site amyloid precursor protein cleaving enzyme (BACE1). Five different classes of molecular descriptors belonging to spatial, structural, thermodynamics, electro-topological and E-state indices were used for machine learning by linear method, genetic function approximation (GFA) and nonlinear method, support vector machine (SVM) and artificial neural network (ANN). Dataset used for QSAR model development includes 57 AChE and 53 BACE1 inhibitors. Statistically significant models were developed for AChE (R² = 0.8688, q² = 0.8600) and BACE1 (R² = 0.8177, q² = 0.7888) enzyme inhibitors. Each model was generated with an optimum five significant molecular descriptors such as electro-topological (ES_Count_aaCH and ES_Count_dssC), structural (QED_HBD, Num_TerminalRotomers), spatial (JURS_FNSA_1) for AChE and structural (Cl_Count, Num_Terminal Rotomers), electro-topological (ES_Count_dO), electronic (Dipole_Z) and spatial (Shadow_nu) for BACE1 enzyme, determining the key role in its enzyme inhibitory activity. The predictive ability of the generated machine learning models was validated using the leave-one-out, Fischer (F) statistics and predictions based on the test set of 11 AChE (r² = 0.8469, r²_pred = 0.8138) and BACE1 (r² = 0.7805, r²_pred = 0.7128) inhibitors. Further, nonlinear machine learning methods such as ANN and SVM predicted better than the linear method GFA. These molecular descriptors are very important in describing the inhibitory activity of AChE and BACE1 enzymes and should be used further for the rational design of multi-targeted anti-Alzheimer's lead molecules.

How Society 5.0 and Industry 4.0 Ideas Shape the Open Data Performance Expectancy

The open data (OD) performance expectancy is a critical factor for the user technology acceptance models for future implementation OD in Industry 4.0, and to have an impact in area of Society 5.0. The purpose of this article is identifying trends and key words (leading terms) in promoting ODs for their use in Industry 4.0 and Society 5.0. We are also looking for leaders in Europe in promoting the use of OD in the context of Industry 4.0 and Society 5.0. The research methodology includes methods such as: analyses based on text mining, visualization techniques, and multidimensional cluster analyses with correlation analyses. The dataset covered 288 digital products and services based on OD. The timeframe covers the period January 2018–January 2020, and the research focuses on European issues. The research is focused on texts promoting the digital OD products and services, with the most popular being applications, websites and platforms. The main direction in presenting the benefits of their use is related to promoting them as tools to provide real time information on public issues, primarily in areas such as transport, education, culture and sport, economics and finance and health. The main types of OD are geodata and those specified as national and local. Additionally, the geographical area in Europe-dominating countries, and the key terms promoting product and services in context of OD performance expectancy in Western Europe, Northern Europe, Southern Europe and Eastern Europe, were found.

Effective Data Sharing as a Conduit for Advancing Medical Product Development

INTRODUCTION

Patient-level data sharing has the potential to significantly impact the lives of patients by optimizing and improving the medical product development process. In the product development setting, successful data sharing is defined as data sharing that is actionable and facilitates decision making during the development and review of medical products. This often occurs through the creation of new product development tools or methodologies, such as novel clinical trial design and enrichment strategies, predictive pre-clinical and clinical models, clinical trial simulation tools, biomarkers, and clinical outcomes assessments, and more.

METHODS

To be successful, extensive partnerships must be established between all relevant stakeholders, including industry, academia, research institutes and societies, patient-advocacy groups, and governmental agencies, and a neutral third-party convening organization that can provide a pre-competitive space for data sharing to occur.

CONCLUSIONS

Data sharing focused on identified regulatory deliverables that improve the medical product development process encounters significant challenges that are not seen with data sharing aimed at advancing clinical decision making and requires the commitment of all stakeholders. Regulatory data sharing challenges and solutions, as well as multiple examples of previous successful data sharing initiatives are presented and discussed in the context of medical product development.

Human Induced Pluripotent Stem Cell-Derived Neural Cells from Alzheimer's Disease Patients Exhibited Different Susceptibility to Oxidative Stress

The cell-type-specific response of neural cells to oxidative stress, a crucial mechanism for accelerating aging and cognitive dysfunction in Alzheimer's disease (AD), is still far from understood. Here, we employed human-induced pluripotent stem cells (hiPSCs)-derived neural stem cells (hiPSC-NSCs), neurons (hiPSC-Neurons), and microglia-like cells (hiPSC-MGLs) from sporadic AD (sAD) patients, age-matched cognitive normal controls (CNCs), and young subjects to observe human neural cell-type response to H₂O₂ stimulation. Without H₂O₂ exposure, reactive oxygen species (ROS) cannot be detected in hiPSC-NSCs from all three groups, but the viability of hiPSC-NSCs from AD patients was significantly lower than those of CNCs and young subjects. There were no significant differences in ROS, viabilities, neurite length, and neurite branch points in hiPSC-Neurons among three groups. No significant differences in viabilities, phagocytosis, and secretion of cytokines were observed in hiPSC-MGLs among three groups, but higher ROS levels in sAD hiPSC-MGLs. Under H₂O₂ exposure, the viability, neurite length, and neurite branch points of hiPSC-Neurons from AD patients reduced more significantly accompanied by more ROS release. H₂O₂ exposure caused hiPSC-MGLs from AD patients to release more ROS, cytokines, and stronger phagocytosis. Nevertheless, H₂O₂ exposure had no effect on viability of hiPSC-NSCs. Our results showed hiPSC-Neurons and hiPSC-MGLs were more sensitive to H₂O₂ than hiPSC-NSCs, which indicated the different response styles of hiPSC-NSCs, hiPSC-Neurons, and hiPSC-MGLs to oxidative stress. HiPSC-derived neural cells from AD patients suffered more severe injury from H₂O₂ than those of CNCs and young subjects, indicating that the vulnerability to oxidative stress of AD patients can be recapitulated in hiPSCs.