Knowledge-driven computational modeling in Alzheimer's disease research: Current state and future trends

Emerging biophysical techniques for probing synaptic transmission in neurodegenerative disorders

Plethora of research has shed light on the critical role of synaptic dysfunction in various neurodegenerative disorders (NDDs), including Alzheimer's disease (AD), Parkinson's disease (PD), Amyotrophic lateral sclerosis (ALS), and Huntington's disease (HD). Synapses, the fundamental units for neural communication in the brain, are highly vulnerable to pathological conditions and are central to the progression of neurological diseases. The presynaptic terminal, a key component of synapses responsible for neurotransmitter release and synaptic communication, undergoes structural and functional alterations in these disorders. Understanding synaptic transmission abnormalities is crucial for unravelling the pathophysiological mechanisms underlying neurodegeneration. In the quest to probe synaptic transmission in NDDs, emerging biophysical techniques play a pivotal role. These advanced methods offer insights into the structural and functional changes occurring at nerve terminals in conditions like AD, PD, HD & ALS. By investigating synaptic plasticity and alterations in neurotransmitter release dynamics, researchers can uncover valuable information about disease progression and potential therapeutic targets. The review articles highlighted provide a comprehensive overview of how synaptic vulnerability and pathology are shared mechanisms across a spectrum of neurological disorders. In major neurodegenerative diseases, synaptic dysfunction is a common thread linking these conditions. The intricate molecular machinery involved in neurotransmitter release, synaptic vesicle dynamics, and presynaptic protein regulation are key areas of focus for understanding synaptic alterations in neurodegenerative diseases.

In-Silico discovery of 17alpha-hydroxywithanolide-D as potential neuroprotective allosteric modulator of NMDA receptor targeting Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by cognitive decline, memory impairment, and behavioral alterations. The N-methyl-D-aspartate (NMDA) receptor has emerged as a promising target for AD pharmacotherapy due to its role in the disease's pathogenesis. This study leverages advanced computational methods to screen 80 active constituents of Withania somnifera (Ashwagandha), a traditional herb known for its neuroprotective effects, against the NMDA receptor, using FDA-approved Ifenprodil as a reference. Our blind virtual screening results demonstrated that all tested compounds could bind to various domains of the NMDA receptor, with binding energies ranging from - 4.1 to -11.9 kcal/mol, compared to Ifenprodil's -7.8 kcal/mol. Binding preference analysis revealed 7 compounds bound to the A-chain, 37 to the B-chain, 7 to the C-chain, and 29 to the D-chain of the receptor. Notable binding was observed predominantly at the Amino Terminal Domain (ATD) core site, some at the ATD-Ligand Binding Domain (LBD) interface, and a few at the Transmembrane Domain (TMD). Particularly, 17alpha-hydroxywithanolide D, with a binding energy of -11.9 kcal/mol, emerged as a prime candidate for further investigation. Molecular dynamics simulations of this compound revealed key interactions, including direct hydrogen bonding with residues ASP165, ARG431, THR433, LYS466, and TYR476 on the D-chain, as well as additional hydrophobic and water-bridging interactions. These simulations highlighted the compound's influence on dynamic conformational states of the GluN1b-GluN2B receptor complex, modulating interactions between GluN1b Lys178 and GluN2B Asn184. Furthermore, the compound affected the distance between LBD heterodimers and the tension within the LBD-M30 linker, demonstrating its potential to modulate NMDA receptor activity. This comprehensive study not only underscores the therapeutic promise of Withania somnifera derivatives for AD but also provides a detailed molecular basis for their efficacy, offering valuable insights for targeted drug development and innovative therapeutic strategies against Alzheimer's disease.

Determinants of approved acetylcholinesterase inhibitor response outcomes in Alzheimer's disease: relevance for precision medicine in neurodegenerative diseases

Acetylcholinesterase inhibitors (ChEI) are the global standard of care for the symptomatic treatment of Alzheimer's disease (AD) and show significant positive effects in neurodegenerative diseases with cognitive and behavioral symptoms. Although experimental and large-scale clinical evidence indicates the potential long-term efficacy of ChEI, primary outcomes are generally heterogeneous across outpatient clinics and regional healthcare systems. Sub-optimal dosing or slow tapering, heterogeneous guidelines about the timing for therapy initiation (prodromal versus dementia stages), healthcare providers' ambivalence to treatment, lack of disease awareness, delayed medical consultation, prescription of ChEI in non-AD cognitive disorders, contribute to the negative outcomes. We present an evidence-based overview of determinants, spanning genetic, molecular, and large-scale networks, involved in the response to ChEI in patients with AD and other neurodegenerative diseases. A comprehensive understanding of cerebral and retinal cholinergic system dysfunctions along with ChEI response predictors in AD is crucial since disease-modifying therapies will frequently be prescribed in combination with ChEI. Therapeutic algorithms tailored to genetic, biological, clinical (endo)phenotypes, and disease stages will help leverage inter-drug synergy and attain optimal combined response outcomes, in line with the precision medicine model.

Biological Mechanism-based Neurology and Psychiatry: A BACE1/2 and Downstream Pathway Model

In oncology, comprehensive omics and functional enrichment studies have led to an extensive profiling of (epi)genetic and neurobiological alterations that can be mapped onto a single tumor's clinical phenotype and divergent clinical phenotypes expressing common pathophysiological pathways. Consequently, molecular pathway-based therapeutic interventions for different cancer typologies, namely tumor type- and site-agnostic treatments, have been developed, encouraging the real-world implementation of a paradigm shift in medicine. Given the breakthrough nature of the new-generation translational research and drug development in oncology, there is an increasing rationale to transfertilize this blueprint to other medical fields, including psychiatry and neurology. In order to illustrate the emerging paradigm shift in neuroscience, we provide a state-of-the-art review of translational studies on the β-site amyloid precursor protein cleaving enzyme (BACE) and its most studied downstream effector, neuregulin, which are molecular orchestrators of distinct biological pathways involved in several neurological and psychiatric diseases. This body of data aligns with the evidence of a shared genetic/biological architecture among Alzheimer's disease, schizoaffective disorder, and autism spectrum disorders. To facilitate a forward-looking discussion about a potential first step towards the adoption of biological pathway-based, clinical symptom-agnostic, categorization models in clinical neurology and psychiatry for precision medicine solutions, we engage in a speculative intellectual exercise gravitating around BACE-related science, which is used as a paradigmatic case here. We draw a perspective whereby pathway-based therapeutic strategies could be catalyzed by highthroughput techniques embedded in systems-scaled biology, neuroscience, and pharmacology approaches that will help overcome the constraints of traditional descriptive clinical symptom and syndrome-focused constructs in neurology and psychiatry.

Interdisciplinary Research in Alzheimer's Disease and the Roles International Societies Can Play

An ever-increasing ageing population has elevated Alzheimer's disease to be one of the biggest challenges in modern medicine. Alzheimer's disease is highly complex, and we are still no closer to understanding the causes, let alone an effective treatment. The lack of good experimental models and lack of critical understanding has led to high failure rates of clinical trials with high associated costs, as well as difficulties in implementing treatments. The multifaceted nature of this disease highlights the need for an interdisciplinary approach to address these concerns. In this essay, we suggest how collaborative work can be useful in addressing some of the above issues. We then propose that international organisations and publishers need to support interdisciplinary research by creating platforms, lobbying funders, and pushing for interdisciplinary publications. We further highlight some of the issues involved in implementing these suggestions and argue that willpower of the research community, together with a re-evaluation of evaluation metrics and incentive systems, are needed in order to foster interdisciplinary research. Overall, we emphasise the need for interdisciplinary research in Alzheimer's disease and suggest that international societies should play a huge role in this endeavour.

Prelimbic Cortical Stimulation Improves Spatial Memory Through Distinct Patterns of Hippocampal Gene Expression in Aged Rats

Dementia poses major health challenges worldwide, yet current treatments are faced with issues of efficacy and toxicity. Deep brain stimulation (DBS) is a promising non-pharmacological treatment for dementia, but most DBS studies use young healthy animals, which may not be aetiologically relevant. In this study, we used an aged rat model in which cognitive decline occurs through a natural ageing process. We used a Morris water maze (MWM) to determine the effects of prelimbic cortex (PrL) DBS on memory in aged rats. To investigate the underlying mechanisms of the effects of DBS, we carried out microarray, quantitative PCR analysis, and mass spectrometry to detect gene expression and neurotransmitter changes in the hippocampus. We showed PrL DBS improved the performance in MWM, with related distinct patterns of gene expression involving G protein-coupled receptor pathways. We further found neurotransmitter changes in the dorsal hippocampus, which corroborated and extended the microarray findings. Our results suggest that non-neurogenesis pathways play roles in the effects of DBS. Further studies are needed to investigate the effects of DBS on memory beyond neurogenesis and to consider the highlighted pathways suggested by our data.

Opportunities for multiscale computational modelling of serotonergic drug effects in Alzheimer's disease

Alzheimer's disease (AD) is an age-specific neurodegenerative disease that compromises cognitive functioning and impacts the quality of life of an individual. Pathologically, AD is characterised by abnormal accumulation of beta-amyloid (Aβ) and hyperphosphorylated tau protein. Despite research advances over the last few decades, there is currently still no cure for AD. Although, medications are available to control some behavioural symptoms and slow the disease's progression, most prescribed medications are based on cholinesterase inhibitors. Over the last decade, there has been increased attention towards novel drugs, targeting alternative neurotransmitter pathways, particularly those targeting serotonergic (5-HT) system. In this review, we focused on 5-HT receptor (5-HTR) mediated signalling and drugs that target these receptors. These pathways regulate key proteins and kinases such as GSK-3 that are associated with abnormal levels of Aβ and tau in AD. We then review computational studies related to 5-HT signalling pathways with the potential for providing deeper understanding of AD pathologies. In particular, we suggest that multiscale and multilevel modelling approaches could potentially provide new insights into AD mechanisms, and towards discovering novel 5-HTR based therapeutic targets.

Alzheimer's Disease, and Breast and Prostate Cancer Research: Translational Failures and the Importance to Monitor Outputs and Impact of Funded Research

Dementia and cancer are becoming increasingly prevalent in Western countries. In the last two decades, research focused on Alzheimer's disease (AD) and cancer, in particular, breast cancer (BC) and prostate cancer (PC), has been substantially funded both in Europe and worldwide. While scientific research outcomes have contributed to increase our understanding of the disease etiopathology, still the prevalence of these chronic degenerative conditions remains very high across the globe. By definition, no model is perfect. In particular, animal models of AD, BC, and PC have been and still are traditionally used in basic/fundamental, translational, and preclinical research to study human disease mechanisms, identify new therapeutic targets, and develop new drugs. However, animals do not adequately model some essential features of human disease; therefore, they are often unable to pave the way to the development of drugs effective in human patients. The rise of new technological tools and models in life science, and the increasing need for multidisciplinary approaches have encouraged many interdisciplinary research initiatives. With considerable funds being invested in biomedical research, it is becoming pivotal to define and apply indicators to monitor the contribution to innovation and impact of funded research. Here, we discuss some of the issues underlying translational failure in AD, BC, and PC research, and describe how indicators could be applied to retrospectively measure outputs and impact of funded biomedical research.

Binding site matching in rational drug design: algorithms and applications

Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Neuronal Circuit-Based Computer Modeling as a Phenotypic Strategy for CNS R&D

With the success rate of drugs for CNS indications at an all-time low, new approaches are needed to turn the tide of failed clinical trials. This paper reviews the history of CNS drug Discovery over the last 60 years and proposes a new paradigm based on the lessons learned. The initial wave of successful therapeutics discovered using careful clinical observations was followed by an emphasis on a phenotypic target-agnostic approach, often leading to successful drugs with a rich pharmacology. The subsequent introduction of molecular biology and the focus on a target-driven strategy has largely dominated drug discovery efforts over the last 30 years, but has not increased the probability of success, because these highly selective molecules are unlikely to address the complex pathological phenotypes of most CNS disorders. In many cases, reliance on preclinical animal models has lacked robust translational power. We argue that Quantitative Systems Pharmacology (QSP), a mechanism-based computer model of biological processes informed by preclinical knowledge and enhanced by neuroimaging and clinical data could be a new powerful knowledge generator engine and paradigm for rational polypharmacy. Progress in the academic discipline of computational neurosciences, allows one to model the effect of pathology and therapeutic interventions on neuronal circuit firing activity that can relate to clinical phenotypes, driven by complex properties of specific brain region activation states. The model is validated by optimizing the correlation between relevant emergent properties of these neuronal circuits and historical clinical and imaging datasets. A rationally designed polypharmacy target profile will be discovered using reverse engineering and sensitivity analysis. Small molecules will be identified using a combination of Artificial Intelligence methods and computational modeling, tested subsequently in heterologous cellular systems with human targets. Animal models will be used to establish target engagement and for ADME-Tox, with the QSP approach complemented by in vivo preclinical models that can be further refined to increase predictive validity. The QSP platform can also mitigate the variability in clinical trials with the concept of virtual patients. Because the QSP platform integrates knowledge from a wide variety of sources in an actionable simulation, it offers the possibility of substantially improving the success rate of CNS R&D programs while, at the same time, reducing both cost and the number of animals.