Computational identification of potential multi-drug combinations for reduction of microglial inflammation in Alzheimer disease

Multi-dimensional relationships among dementia, depression and prescribed drugs in England and Wales hospitals

Background

 Dementia is a group of symptoms that largely affects older people. The majority of patients face behavioural and psychological symptoms (BPSD) during the course of their illness. Alzheimer’s disease (AD) and vascular dementia (VaD) are two of the most prevalent types of dementia. Available medications provide symptomatic benefits and provide relief from BPSD and associated health issues. However, it is unclear how specific dementia, antidepressant, antipsychotic, antianxiety, and mood stabiliser drugs, used in the treatment of depression and dementia subtypes are prescribed in hospital admission, during hospital stay, and at the time of discharge. To address this, we apply multi-dimensional data analytical approaches to understand drug prescribing practices within hospitals in England and Wales.

Methods

We made use of the UK National Audit of Dementia (NAD) dataset and pre-processed the dataset. We evaluated the pairwise Pearson correlation of the dataset and selected key data features which are highly correlated with dementia subtypes. After that, we selected drug prescribing behaviours (e.g. specific medications at the time of admission, during the hospital stay, and upon discharge), drugs and disorders. Then to shed light on the relations across multiple features or dimensions, we carried out multiple regression analyses, considering the number of dementia, antidepressant, antipsychotic, antianxiety, mood stabiliser, and antiepileptic/anticonvulsant drug prescriptions as dependent variables, and the prescription of other drugs, number of patients with dementia subtypes (AD/VaD), and depression as independent variables.

Results

In terms of antidepressant drugs prescribed in hospital admission, during stay and discharge, the number of sertraline and venlafaxine prescriptions were associated with the number of VaD patients whilst the number of mirtazapine prescriptions was associated with frontotemporal dementia patients. During admission, the number of lamotrigine prescriptions was associated with frontotemporal dementia patients, and with the number of valproate and dosulepin prescriptions. During discharge, the number of mirtazapine prescriptions was associated with the number of donepezil prescriptions in conjunction with frontotemporal dementia patients. Finally, the number of prescriptions of donepezil/memantine at admission, during hospital stay and at discharge exhibited positive association with AD patients.

Conclusion

Our analyses reveal a complex, multifaceted set of interactions among prescribed drug types, dementia subtypes, and depression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01892-9.

Hydrophilic polymer driven crystallization self-assembly: an inflammatory multi-drug combination nanosystem against Alzheimer's disease

The hydrophobic polymer driven crystallization of self-assembled micelles is usually sufficient for their purposes in materials chemistry studies. However, with the state of smart drug delivery research, micelles alone are not enough. The principles of the self assembly driven by hydrophilic dextran brushes together with charged poly(3-acrylamidophenyl boronic acid) (PPBA) are uncovered in this study. A series of poly(ε-caprolactone)-block-poly(3-acrylamidophenyl boronic acid)-dextran (PCL-b-PPBA-Dex) micelles and vesicles are investigated as potential Alzheimer's disease (AD) treatments. Three inflammatory microenvironment responsive micelles, including celecoxib drug-loaded micelles (CEL), ibuprofen drug-loaded micelles (IBU) and telmisartan drug-loaded micelles (TEL), are developed. In vivo, CEL/IBU (mixture of CEL and IBU) and CEL/TEL (mixture of CEL and TEL) suppress the activation of glia and reduce the levels of inflammatory mediators through eliminating cyclooxygenase 2 (COX-2) signals. The CEL/TEL combination nanosystem is better at correcting neuroinflammation and improving the spatial memory ability of a senescence-accelerated mouse prone 8 model (SAMP8). We consider that the inflammation responsive combination nanosystem provides a new potential treatment for AD clinical patients.

Predicting the Potency of Anti-Alzheimer’s Drug Combinations Using Machine Learning

Clinical trials of single drugs intended to slow the progression of Alzheimer’s Disease (AD) have been notoriously unsuccessful. Combinations of repurposed drugs could provide effective treatments for AD. The challenge is to identify potentially effective combinations. To meet this challenge, machine learning (ML) was used to extract the knowledge from two leading AD databases, and then “the machine” predicted which combinations of the drugs in common between the two databases would be the most effective as treatments for AD. Specifically, three-layered artificial neural networks (ANNs) with compound, gated units in their internal layer were trained using ML to predict the cognitive scores of participants, separately in either database, given other data fields including age, demographic variables, comorbidities, and drugs taken. The predictions from the separately trained ANNs were statistically highly significantly correlated. The best drug combinations, jointly determined from both sets of predictions, were high in nonsteroidal anti-inflammatory drugs; anticoagulant, lipid-lowering, and antihypertensive drugs; and female hormones. The results suggest that the neurodegenerative processes that underlie AD and other dementias could be effectively treated using a combination of repurposed drugs. Predicted drug combinations could be evaluated in clinical trials.

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.

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.

Dreaming of a New World Where Alzheimer's Is a Treatable Disorder

Alzheimer’s disease (AD) is the most common form of dementia. It’s a chronic and untreatable neurodegenerative disease with irreversible progression and has important social and economic implications in terms of direct medical and social care costs. Despite prolonged and expensive efforts employed by the scientific community over the last few decades, no effective treatments are still available for patients, and the development of disease-modifying drugs is now a really urgent need. The recent failure of clinical trials based on the immunotherapeutic approach against amyloid-β(Aβ) protein questioned the validity of the “amyloid cascade hypothesis” as the molecular machinery causing the disease. Indeed, most attempts to design effective treatments for AD have been based until now on molecular targets suggested to be implicated in AD pathogenesis by the amyloid cascade hypothesis. However, mounting evidence from scientific literature supports the view of AD as a multifactorial disease that results from the concomitant action of multiple molecular players. This view, together with the lack of success of the disease-modifying single-target approaches, strongly suggests that AD drug design needs to be shifted towards multi-targeted compounds or drug combinations acting synergistically on the main core features of disease pathogenesis. The discovery of drug candidates targeting multiple factors involved in AD would greatly improve drug development. So, it is reasonable that upcoming strategies for the design of preventive and/or therapeutic agents for AD point to a multi-pronged approach including more than one druggable target to definitely defeat the disease.

Network pharmacology study on the active components of Pterocypsela elata and the mechanism of their effect against cerebral ischemia

Objective

The aim of this study was to identify the active anti-ischemic components of Pterocypsela elata (P. elata) using a network pharmacology approach to construct an effective component anti-cerebral ischemic target network and systematically analyze this medicinal material.

Methods

Pharmacological studies have shown that P. elata has an obvious effect against cerebral ischemia. To identify the potential targets, 14 components of P. elata were docked to each structural element of the targets in the DRAR-CPI database by reverse docking technology. We then compared the identified potential targets with FDA-approved targets for stroke/cerebral infarction treatment in the DrugBank database and identified the active components of P. elata and their potential targets for stroke/cerebral infarction treatment. The active component-target networks were constructed using Cytoscape 3.5.1 software. The target protein-protein interactions were analyzed using the STRING database. KEGG pathway analysis and gene ontology (GO) enrichment analysis were performed through the Database for Annotation, Visualization and Integrated Discovery (DAVID).

Results

There were 14 active components identified from P. elata and 21 potential targets identified for cerebral ischemia treatment, including carbonic anhydrase 2, ribosyldihydronicotinamide dehydrogenase, cholinesterase, and glutathione S-transferase P. The main involved pathways include metabolic pathways, complement and coagulation cascades and steroid hormone biosynthesis.

Conclusion

Through a network pharmacology approach, we predicted the active components of P. elata and their potential targets for cerebral ischemia treatment. Our results provide new perspectives and clues for further studies on the anti-cerebral ischemia mechanism of P. elata.

Assessing drug response in engineered brain microenvironments

Tissue engineered systems are important models for the testing and discovery of therapeutics against a number of diseases. The use of these models in vitro can expand both our understanding of the mechanisms behind disease and allow for higher throughput and personalized modeling of therapeutic response. Over the past decade there has been an explosion of models of neurological disorders that can be used in vitro to study new therapies against devastating neurodegenerative, neurodevelopmental, and neuro-oncological disease. These models span several types of engineered microenvironments which are produced using microfluidic devices, microtissue technology and/or the incorporation of biomaterial scaffolds to model neurological conditions such as; Alzheimer's disease, idiopathic autism, Parkinson's disease, Zika-induced microcephaly and neoplasms. Using engineered brain microenvironments, therapeutics can be tested in more physiologically relevant ways leading to new knowledge of the underlying causes and interactions occurring at the tissue level. However, much is still left to learn and model within these systems to make them truly valuable in the discovery and testing of novel therapies. Here we review the current state of the art of engineered brain microenvironments being used specifically to screen and test new therapeutic strategies and discuss the current benefits and limitations that still exist.

Exploring the Correlation between the Cognitive Benefits of Drug Combinations in a Clinical Database and the Efficacies of the Same Drug Combinations Predicted from a Computational Model

Identification of drug combinations that could be effective in Alzheimer’s disease treatment is made difficult by the sheer number of possible combinations. This analysis identifies as potentially therapeutic those drug combinations that rank highest when their efficacy is determined jointly from two independent data sources. Estimates of the efficacy of the same drug combinations were derived from a clinical dataset on cognitively impaired elderly participants and from pre-clinical data, in the form of a computational model of neuroinflammation. Linear regression was used to show that the two sets of estimates were correlated, and to rule out confounds. The ten highest ranking, jointly determined drug combinations most frequently consisted of COX2 inhibitors and aspirin, along with various antihypertensive medications. Ten combinations of from five to nine drugs, and the three-drug combination of a COX2 inhibitor, aspirin, and a calcium-channel blocker, are discussed as candidates for consideration in future pre-clinical and clinical studies.

Administration of Momordica charantia Enhances the Neuroprotection and Reduces the Side Effects of LiCl in the Treatment of Alzheimer's Disease

Recently, the use of natural food supplements to reduce the side effects of chemical compounds used for the treatment of various diseases has become popular. Lithium chloride (LiCl) has some protective effects in neurological diseases, including Alzheimer’s disease (AD). However, its toxic effects on various systems and some relevant interactions with other drugs limit its broader use in clinical practice. In this study, we investigated the in vitro and in vivo pharmacological functions of LiCl combined with Momordica charantia (MC) in the treatment of AD. The in vitro results show that the order of the neuroprotective effect is MC5, MC3, MC2, and MC5523 under hyperglycemia or tau hyperphosphorylation. Therefore, MC5523 (80 mg/kg; oral gavage) and/or LiCl (141.3 mg/kg; intraperitoneal injection) were applied to ovariectomized (OVX) 3×Tg-AD female and C57BL/6J (B6) male mice that received intracerebroventricular injections of streptozotocin (icv-STZ, 3 mg/kg) for 28 days. We found that the combined treatment not only increased the survival rate by reducing hepatotoxicity but also increased neuroprotection associated with anti-gliosis in the icv-STZ OVX 3×Tg-AD mice. Furthermore, the cotreatment with MC5523 and LiCl prevented memory deficits associated with reduced neuronal loss, gliosis, oligomeric Aβ level, and tau hyperphosphorylation and increased the expression levels of synaptic-related protein and pS9-GSK3β (inactive form) in the icv-STZ B6 mice. Therefore, MC5523 combined with LiCl could be a potential strategy for the treatment of AD.

Network pharmacology-based prediction of the active ingredients, potential targets, and signaling pathways in compound Lian-Ge granules for treatment of diabetes

AIMS

Compound Lian-Ge granules (CLGGs) is a traditional Chinese medicine preparation with good hypoglycemic effect and health function. This study was to predict its active ingredients, potential targets, signaling pathways, and investigate its mechanism of "ingredient-targets-pathways."

METHODS

Pharmacodynamics studies on diabetic rats showed that CLGGs had an obvious hypoglycemic effect. On this basis, 27 hypoglycemic active ingredients were screened out. Their targets were confirmed by comparing with these hypoglycemic targets in PharmMapper and DrugBank databases via reversed pharmacophore matching approach. The relationships between ingredients and targets were revealed by comparing data in the String database. A network of "ingredient-target-passageway" was constructed.

RESULTS

Studies showed that CLGGs had 24 active ingredients, ie, berberine, puerarin, danshinolic acid A, and sinigrin, etc. These ingredients involved nine targets, ie, insulin-like growth factor 1 receptor, insulin-degrading enzyme, ɑ-amylase, and so on, and 111 metabolic pathways, eg, hypoxia-inducible factor 1 signaling pathway, PI3K-Akt signaling pathway, mammalian target of rapamycin signaling pathway, and FoxO signaling pathway.

CONCLUSION

Using network pharmacology methods, this study predicted the hypoglycemic active ingredients in CLGGs and revealed their targets, and provided a clue for further exploration of the hypoglycemic mechanism of CLGGs.

A Low-Cost Morris Water Maze for Undergraduate Research: Construction and Demonstration in a Rat Model of Obesity-Induced Diabetes

The Morris Water Maze (MWM) is a standard task for assessing hippocampal-dependent learning and memory, but the cost of commercial versions of the task may be prohibitive for some undergraduate research projects. We describe the construction of a low-cost MWM for use with rodents and demonstrate the effectiveness of the MWM in a study of the effect of diet-induced obesity on cognitive function in rats. Previous studies have described an impairment in MWM performance in rats fed a high-fat diet combined with streptozotocin injection (to model Type 2 diabetes). We attempted to replicate this finding with our water maze design, and to test the ability of a novel anti-inflammatory treatment to reduce cognitive deficits in the diabetic model. Across five days of hidden-platform training, rats in all groups (normal pellet diet vs. high-fat diet, vehicle vs. treatment) improved on the water maze at similar rates. On a 30-second probe trial, each group showed a preference for the target quadrant used during training. These results did not replicate previous findings that a high-fat diet combined with streptozotocin injections produces deficits in the water maze. However, the results validate the effectiveness of a low-cost water maze ($400 USD) constructed from commonly available materials for hidden platform water maze training. When combined with a low-cost video tracking solution (less than $1,000), we expect this apparatus will be of use to other undergraduate researchers interested in learning and memory.

In silico-based screen synergistic drug combinations from herb medicines: a case using Cistanche tubulosa

Neuroinflammation is characterized by the elaborated inflammatory response repertoire of central nervous system tissue. The limitations of the current treatments for neuroinflammation are well-known side effects in the clinical trials of monotherapy. Drug combination therapies are promising strategies to overcome the compensatory mechanisms and off-target effects. However, discovery of synergistic drug combinations from herb medicines is rare. Encouraged by the successfully applied cases we move on to investigate the effective drug combinations based on system pharmacology among compounds from Cistanche tubulosa (SCHENK) R. WIGHT. Firstly, 63 potential bioactive compounds, the related 133 direct and indirect targets are screened out by Drug-likeness evaluation combined with drug targeting process. Secondly, Compound-Target network is built to acquire the data set for predicting drug combinations. We list the top 10 drug combinations which are employed by the algorithm Probability Ensemble Approach (PEA), and Compound-Target-Pathway network is then constructed by the 12 compounds of the combinations, targets, and pathways to unearth the corresponding pharmacological actions. Finally, an integrating pathway approach is developed to elucidate the therapeutic effects of the herb in different pathological features-relevant biological processes. Overall, the method may provide a productive avenue for developing drug combination therapeutics.

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

Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to be required for further progress in understanding and treating AD.

Towards the virtual human patient. Quantitative Systems Pharmacology in Alzheimer's disease

Development of successful therapeutic interventions in Central Nervous Systems (CNS) disorders is a daunting challenge with a low success rate. Probable reasons include the lack of translation from preclinical animal models, the individual variability of many pathological processes converging upon the same clinical phenotype, the pharmacodynamical interaction of various comedications and last but not least the complexity of the human brain. This paper argues for a re-engineering of the pharmaceutical CNS Research & Development strategy using ideas focused on advanced computer modeling and simulation from adjacent engineering-based industries. We provide examples that such a Quantitative Systems Pharmacology approach based on computer simulation of biological processes and that combines the best of preclinical research with actual clinical outcomes can enhance translation to the clinical situation. We will expand upon (1) the need to go from Big Data to Smart Data and develop predictive and quantitative algorithms that are actionable for the pharma industry, (2) using this platform as a "knowledge machine" that captures community-wide expertise in an active hypothesis-testing approach, (3) learning from failed clinical trials and (4) the need to go beyond simple linear hypotheses and embrace complex non-linear hypotheses. We will propose a strategy for applying these concepts to the substantial individual variability of AD patient subgroups and the treatment of neuropsychiatric problems in AD. Quantitative Systems Pharmacology is a new 'humanized' tool for supporting drug discovery and development in general and CNS disorders in particular.

Computational modeling of cytokine signaling in microglia

Neuroinflammation due to glial activation has been linked to many CNS diseases. We developed a computational model of a microglial cytokine interaction network to study the regulatory mechanisms of microglia-mediated neuroinflammation. We established a literature-based cytokine network, including TNFα, TGFβ, and IL-10, and fitted a mathematical model to published data from LPS-treated microglia. The addition of a previously unreported TGFβ autoregulation loop to our model was required to account for experimental data. Global sensitivity analysis revealed that TGFβ- and IL-10-mediated inhibition of TNFα was critical for regulating network behavior. We assessed the sensitivity of the LPS-induced TNFα response profile to the initial TGFβ and IL-10 levels. The analysis showed two relatively shifted TNFα response profiles within separate domains of initial condition space. Further analysis revealed that TNFα exhibited adaptation to sustained LPS stimulation. We simulated the effects of functionally inhibiting TGFβ and IL-10 on TNFα adaptation. Our analysis showed that TGFβ and IL-10 knockouts (TGFβ KO and IL-10 KO) exert divergent effects on adaptation. TFGβ KO attenuated TNFα adaptation whereas IL-10 KO enhanced TNFα adaptation. We experimentally tested the hypothesis that IL-10 KO enhances TNFα adaptation in murine macrophages and found supporting evidence. These opposing effects could be explained by differential kinetics of negative feedback. Inhibition of IL-10 reduced early negative feedback that results in enhanced TNFα-mediated TGFβ expression. We propose that differential kinetics in parallel negative feedback loops constitute a novel mechanism underlying the complex and non-intuitive pro- versus anti-inflammatory effects of individual cytokine perturbations.