A Precision Medicine Model for Targeted NSAID Therapy in Alzheimer's Disease

Biomaterials-based anti-inflammatory treatment strategies for Alzheimer's disease

The current therapeutic drugs for Alzheimer's disease only improve symptoms, they do not delay disease progression. Therefore, there is an urgent need for new effective drugs. The underlying pathogenic factors of Alzheimer's disease are not clear, but neuroinflammation can link various hypotheses of Alzheimer's disease; hence, targeting neuroinflammation may be a new hope for Alzheimer's disease treatment. Inhibiting inflammation can restore neuronal function, promote neuroregeneration, reduce the pathological burden of Alzheimer's disease, and improve or even reverse symptoms of Alzheimer's disease. This review focuses on the relationship between inflammation and various pathological hypotheses of Alzheimer's disease; reports the mechanisms and characteristics of small-molecule drugs (e.g., nonsteroidal anti-inflammatory drugs, neurosteroids, and plant extracts); macromolecule drugs (e.g., peptides, proteins, and gene therapeutics); and nanocarriers (e.g., lipid-based nanoparticles, polymeric nanoparticles, nanoemulsions, and inorganic nanoparticles) in the treatment of Alzheimer's disease. The review also makes recommendations for the prospective development of anti-inflammatory strategies based on nanocarriers for the treatment of Alzheimer's disease.

New insight on microglia activation in neurodegenerative diseases and therapeutics

Microglia are immune cells within the central nervous system (CNS) closely linked to brain health and neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. In response to changes in the surrounding environment, microglia activate and change their state and function. Several factors, example for circadian rhythm disruption and the development of neurodegenerative diseases, influence microglia activation. In this review, we explore microglia's function and the associated neural mechanisms. We elucidate that circadian rhythms are essential factors influencing microglia activation and function. Circadian rhythm disruption affects microglia activation and, consequently, neurodegenerative diseases. In addition, we found that abnormal microglia activation is a common feature of neurodegenerative diseases and an essential factor of disease development. Here we highlight the importance of microglia activation in neurodegenerative diseases. Targeting microglia for neurodegenerative disease treatment is a promising direction. We introduce the progress of methods targeting microglia for the treatment of neurodegenerative diseases and summarize the progress of drugs developed with microglia as targets, hoping to provide new ideas for treating neurodegenerative diseases.

Plasma ATN(I) classification and precision pharmacology in Alzheimer's disease

Evaluating potential therapies for Alzheimer's disease (AD) depends on use of biomarkers for appropriate subject selection and monitoring disease progression. Biomarkers that predict onset of clinical symptoms are particularly important for AD because they enable intervention before irreversible neurodegeneration occurs. The amyloid-β-tau-neurodegeneration (ATN) classification system is currently used as a biological staging model for AD and is based on three classes of biomarkers evaluating amyloid-β (Aβ), tau pathology and neurodegeneration or neuronal injury. Promising blood-based biomarkers for each of these categories have been identified (Aβ42/Aβ40 ratio, phosphorylated tau, neurofilament light chain), and this matrix is now being expanded toward an ATN(I) system, where "I" represents a neuroinflammatory biomarker. The plasma ATN(I) system, together with APOE genotyping, offers a basis for individualized evaluation and a move away from the classic "one size fits all" approach toward a biomarker-driven individualisation of therapy for patients with AD.

Micheliolide attenuates neuroinflammation to improve cognitive impairment of Alzheimer's disease by inhibiting NF-κB and PI3K/Akt signaling pathways

Inflammatory reaction in the brain activates glial cells, and over-activated glial cells secrete inflammatory mediators, which aggravates the inflammatory response in the brain and accelerates the development of Alzheimer's disease (AD) in turn. Numerous natural compounds from herbs can alleviate inflammation, and it is very promising to find anti-neuroinflammatory natural compounds. Micheliolide (MCL) is an asesquiterpene lactone. Studies have proved that MCL showed an obvious anti-inflammatory property. Nevertheless, whether MCL can treat AD has not been determined. In this research, AD model mice were fed with a diet supplemented MCL for 3 months, the cognitive ability and inflammatory state of mice were detected. We found that MCL raised the frequency of touching novel objects, cut down the escape latency, raised the number of crossing platform, inhibited the infiltration of inflammatory cells and the secretion of interleukin-1α (IL-1α), IL-12p40, IL-13, IL-17A, tumor necrosis factor-α (TNF-α), granulocyte colony stimulating factor (G-CSF), macrophage inflammatory protein-1α (MIP-1α) and monocyte chemotactic protein-1 (MCP-1) in peripheral blood samples, inhibited the hyperplasia of glial cells and the production of IL-1α, IL-4, G-CSF, granulocyte-macrophage colony stimulating factor (GM-CSF), MIP-1α and MIP-1β, and reduced the deposition of Aβ peptides in the brain of AD mice. We also concluded that MCL dropped the expression of IL-1β, TNF-α, cyclooxygenase-2 (COX-2), inducible nitric oxide synthase (iNOS), and the phosphorylation of IκB, p65 and Akt in BV-2 cells. In conclusion, MCL alleviates the intensity of systemic inflammatory reaction via inhibiting nuclear transcription factor κ gene binding (NF-κB) and phosphoinositide-3-kinase/serine/threonine kinase (PI3K/Akt) pathways in glial cells, and improves the cognitive impairment of AD mice. Therefore, MCL could be a therapeutic candidate for AD.

Comorbidities Incorporated to Improve Prediction for Prevalent Mild Cognitive Impairment and Alzheimer's Disease in the HABS-HD Study

BACKGROUND

Blood biomarkers have the potential to transform Alzheimer's disease (AD) diagnosis and monitoring, yet their integration with common medical comorbidities remains insufficiently explored.

OBJECTIVE

This study aims to enhance blood biomarkers' sensitivity, specificity, and predictive performance by incorporating comorbidities. We assess this integration's efficacy in diagnostic classification using machine learning, hypothesizing that it can identify a confident set of predictive features.

METHODS

We analyzed data from 1,705 participants in the Health and Aging Brain Study-Health Disparities, including 116 AD patients, 261 with mild cognitive impairment, and 1,328 cognitively normal controls. Blood samples were assayed using electrochemiluminescence and single molecule array technology, alongside comorbidity data gathered through clinical interviews and medical records. We visually explored blood biomarker and comorbidity characteristics, developed a Feature Importance and SVM-based Leave-One-Out Recursive Feature Elimination (FI-SVM-RFE-LOO) method to optimize feature selection, and compared four models: Biomarker Only, Comorbidity Only, Biomarker and Comorbidity, and Feature-Selected Biomarker and Comorbidity.

RESULTS

The combination model incorporating 17 blood biomarkers and 12 comorbidity variables outperformed single-modal models, with NPV12 at 92.78%, AUC at 67.59%, and Sensitivity at 65.70%. Feature selection led to 22 chosen features, resulting in the highest performance, with NPV12 at 93.76%, AUC at 69.22%, and Sensitivity at 70.69%. Additionally, interpretative machine learning highlighted factors contributing to improved prediction performance.

CONCLUSIONS

In conclusion, combining feature-selected biomarkers and comorbidities enhances prediction performance, while feature selection optimizes their integration. These findings hold promise for understanding AD pathophysiology and advancing preventive treatments.

Precision Medicine for Preventing Alzheimer's Disease: Analysis of the ADAPT Study

BACKGROUND

The Alzheimer's Disease Anti-inflammatory Prevention Trial (ADAPT) was the first-ever large-scale anti-inflammatory prevention trial targeting Alzheimer's disease.

OBJECTIVE

The overall goal of this study was to evaluate predictive blood biomarker profiles that identified individuals most likely to be responders on NSAID treatment or placebo at 12 and 24 months.

METHODS

Baseline (n = 193) and 12-month (n = 562) plasma samples were assayed. The predictive biomarker profile was generated using SVM analyses with response on treatment (yes/no) as the outcome variable.

RESULTS

Baseline (AUC = 0.99) and 12-month (AUC = 0.99) predictive biomarker profiles were highly accurate in predicting response on Celecoxib arm at 12 and 24 months. The baseline (AUC = 0.95) and 12-month (AUC = 0.9) predictive biomarker profile predicting response on Naproxen were also highly accurate at 12 and 24 months. The baseline (AUC = 0.93) and 12-month (AUC = 0.99) predictive biomarker profile was also highly accurate in predicting response on placebo. As with our prior work, the profiles varied by treatment arm.

CONCLUSIONS

The current results provide additional support for a precision medicine model for treating and preventing Alzheimer's disease.

Inflammation context in Alzheimer's disease, a relationship intricate to define

Alzheimer's disease (AD), the most common form of dementia, is characterized by the accumulation of amyloid β (Aβ) and hyperphosphorylated tau protein aggregates. Importantly, Aβ and tau species are able to activate astrocytes and microglia, which release several proinflammatory cytokines, such as tumor necrosis factor α (TNF-α) and interleukin 1β (IL-1β), together with reactive oxygen (ROS) and nitrogen species (RNS), triggering neuroinflammation. However, this inflammatory response has a dual function: it can play a protective role by increasing Aβ degradation and clearance, but it can also contribute to Aβ and tau overproduction and induce neurodegeneration and synaptic loss. Due to the significant role of inflammation in the pathogenesis of AD, several inflammatory mediators have been proposed as AD markers, such as TNF-α, IL-1β, Iba-1, GFAP, NF-κB, TLR2, and MHCII. Importantly, the use of anti-inflammatory drugs such as NSAIDs has emerged as a potential treatment against AD. Moreover, diseases related to systemic or local inflammation, including infections, cerebrovascular accidents, and obesity, have been proposed as risk factors for the development of AD. In the following review, we focus on key inflammatory processes associated with AD pathogenesis.

Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer’s Disease

Alzheimer’s disease (AD) can be predicted either by serum or plasma biomarkers, and a combination may increase predictive power, but due to the high complexity of machine learning, it may also incur overfitting problems. In this paper, we investigated whether combining serum and plasma biomarkers with feature selection could improve prediction performance for AD. 150 D patients and 150 normal controls (NCs) were enrolled for a serum test, and 100 patients and 100 NCs were enrolled for the plasma test. Among these, 79 ADs and 65 NCs had serum and plasma samples in common. A 10 times repeated 5-fold cross-validation model and a feature selection method were used to overcome the overfitting problem when serum and plasma biomarkers were combined. First, we tested to see if simply adding serum and plasma biomarkers improved prediction performance but also caused overfitting. Then we employed a feature selection algorithm we developed to overcome the overfitting problem. Lastly, we tested the prediction performance in a 10 times repeated 5-fold cross validation model for training and testing sets. We found that the combined biomarkers improved AD prediction but also caused overfitting. A further feature selection based on the combination of serum and plasma biomarkers solved the problem and produced an even higher prediction performance than either serum or plasma biomarkers on their own. The combined feature-selected serum–plasma biomarkers may have critical implications for understanding the pathophysiology of AD and for developing preventative treatments.

Advanced therapeutic strategies targeting microglia: beyond neuroinflammation

For a long time, microglia have been recognized as the main culprits of neuroinflammatory responses because they are primary phagocytes present in the parenchyma of the central nervous system (CNS). However, with the evolving concept of microglial biology, advanced and precise approaches, rather than the global inhibition of activated microglia, have been proposed in the management of neurological disorders. Yolk sac-derived resident microglia have heterogeneous composition according to brain region, sex, and diseases. They play a key role in the maintenance of CNS homeostasis and as primary phagocytes. The perturbation of microglia development can induce neurodevelopmental disorders. Microglia aggravate or alleviate neuroinflammation according to microenvironment and their spatiotemporal dynamics. They are long-lived cells and repopulate via their proliferation or external monocyte engraft. Based on this evolving concept, understanding advanced therapeutic strategies targeting microglia can give us an opportunity to discover novel therapies for neurological disorders.

Alzheimer’s Disease and Inflammaging

Alzheimer’s disease is one of the most common age-related neurodegenerative disorders. The main theory of Alzheimer’s disease progress is the amyloid-β cascade hypothesis. However, the initial mechanisms of insoluble forms of amyloid-β formation and hyperphosphorylated tau protein in neurons remain unclear. One of the factors, which might play a key role in senile plaques and tau fibrils generation due to Alzheimer’s disease, is inflammaging, i.e., systemic chronic low-grade age-related inflammation. The activation of the proinflammatory cell phenotype is observed during aging, which might be one of the pivotal mechanisms for the development of chronic inflammatory diseases, e.g., atherosclerosis, metabolic syndrome, type 2 diabetes mellitus, and Alzheimer’s disease. This review discusses the role of the inflammatory processes in developing neurodegeneration, activated during physiological aging and due to various diseases such as atherosclerosis, obesity, type 2 diabetes mellitus, and depressive disorders.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Developing Treatments for Alzheimer's and Related Disorders with Precision Medicine: A Vision

Precision medicine, also known as personalized medicine, is concerned with finding the right treatment for the right patient at the right time. It is a way of thinking focused on parsing heterogeneity ultimately down to the level of the individual. Its main mission is to identify characteristics of heterogeneous clinical conditions so as to target tailored therapies to individuals. Precision Medicine however is not an agnostic collection of all manner of clinical, genetic and other biologic data in select cohorts. This is an important point. Simply collecting as much information as possible on individuals without applying this way of thinking should not be considered Precision Medicine.

A blood screening tool for detecting mild cognitive impairment and Alzheimer's disease among community-dwelling Mexican Americans and non-Hispanic Whites: A method for increasing representation of diverse populations in clinical research

INTRODUCTION

Representation of Mexican Americans in Alzheimer's disease (AD) clinical research has been extremely poor.

METHODS

Data were examined from the ongoing community-based, multi-ethnic Health & Aging Brain among Latino Elders (HABLE) study. Participants underwent functional exams, clinical labs, neuropsychological testing, and 3T magnetic resonance imaging of the brain. Fasting proteomic markers were examined for predicting mild cognitive impairment (MCI) and AD using support vector machine models.

RESULTS

Data were examined from n = 1649 participants (Mexican American n = 866; non-Hispanic White n = 783). Proteomic profiles were highly accurate in detecting MCI (area under the curve [AUC] = 0.91) and dementia (AUC = 0.95). The proteomic profiles varied significantly between ethnic groups and disease state. Negative predictive value was excellent for ruling out MCI and dementia across ethnic groups.

DISCUSSION

A blood-based screening tool can serve as a method for increasing access to state-of-the-art AD clinical research by bridging between community-based and clinic-based settings.

Proteomic Profiles of Neurodegeneration Among Mexican Americans and Non-Hispanic Whites in the HABS-HD Study

BACKGROUND

Hispanics are expected to experience the largest increase in Alzheimer's disease (AD) and AD related dementias over the next several decades. However, few studies have examined biomarkers of AD among Mexican Americans, the largest segment of the U.S. Hispanic population.

OBJECTIVE

We sought to examine proteomic profiles of an MRI-based marker of neurodegeneration from the AT(N) framework among a multi-ethnic, community-dwelling cohort.

METHODS

Community-dwelling Mexican Americans and non-Hispanic white adults and elders were recruited. All participants underwent comprehensive assessments including an interview, functional exam, clinical labs, informant interview, neuropsychological testing, and 3T MRI of the brain. A neurodegeneration MRI meta-ROI biomarker for the AT(N) framework was calculated.

RESULTS

Data was examined from n = 1,291 participants. Proteomic profiles were highly accurate for detecting neurodegeneration (i.e., N+) among both Mexican Americans (AUC = 1.0) and non-Hispanic whites (AUC = 0.98). The proteomic profile of N + was different between ethnic groups. Further analyses revealed that the proteomic profiles of N + varied by diagnostic status (control, MCI, dementia) and ethnicity (Mexican American versus non-Hispanic whites) though diagnostic accuracy was high for all classifications.

CONCLUSION

A proteomic profile of neurodegeneration has tremendous value and point towards novel diagnostic and intervention opportunities. The current findings demonstrate that the underlying biological factors associated with neurodegeneration are different between Mexican Americans versus non-Hispanic whites as well as at different levels of disease progression.

Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer's Disease With High Performance Computing

Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased. Machine learning (ML) methods have delivered remarkable prediction for the early diagnosis of Alzheimer's disease (AD). Although ML has improved accuracy of AD prediction, the requirement for the complexity of algorithms in ML increases, for example, hyperparameters tuning, which in turn, increases its computational complexity. Thus, accelerating high performance ML for AD is an important research challenge facing these fields. This work reports a multicore high performance support vector machine (SVM) hyperparameter tuning workflow with 100 times repeated 5-fold cross-validation for speeding up ML for AD. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public MRI data for AD and included demographic factors such as age, sex and education. Results showed that computational efficiency increased by 96%, which helped to shed light on future diagnostic AD biomarker applications. The high performance hyperparameter tuning model can also be applied to other ML algorithms such as random forest, logistic regression, xgboost, etc.

Analysis of a precision medicine approach to treating Parkinson's disease: Analysis of the DATATOP study

INTRODUCTION

The aim of this study was to examine the potential application of a targeted proteomic predictive biomarker comprised predominantly of inflammatory proteins in distinguishing those who responded to a previously conducted clinical trial for Parkinson's disease (PD).

METHODS

Plasma samples obtained from a biorepository were assayed from a total of n = 520 DATATOP (Deprenyl And Tocopherol Antioxidative Therapy Of Parkinsonism) clinical trial participants across treatment arms. Support vector machine analyses were conducted to distinguish responder status on primary (need for Levodopa) and secondary trial endpoints (UPDRS Motor and Total Scores).

RESULTS

For the α-tocopherol and deprenyl placebo treatment arm (TOC), the targeted proteomic biomarker was able to distinguish responder status with an accuracy (area under the curve [AUC]) of 91% for the primary endpoint while it was 100% across secondary endpoints. For the deprenyl and α-tocopherol placebo treatment arm (DEP), the AUC was 93% for the primary endpoint and 99-100% for the secondary endpoints. For the combined treatment arm, AUC was 87% for the primary and 94-96% for the secondary endpoints.

DISCUSSION

The targeted proteomic predictive biomarker was highly accurate in distinguishing responder status across treatment arms thereby supporting the application of a precision medicine approach to treating PD.

Astragaloside IV Ameliorates Cognitive Impairment and Neuroinflammation in an Oligomeric Aβ Induced Alzheimer's Disease Mouse Model via Inhibition of Microglial Activation and NADPH Oxidase Expression

Microglial activation and neuroinflammation induced by amyloid β (Aβ) play pivotal roles in Alzheimer's disease (AD) pathogenesis. Astragaloside IV (AS-IV) is one of the major active compounds of the traditional Chinese medicine Astmgali Radix. It has been reported that AS-IV could protect against Aβ-induced neuroinflammation and cognitive impairment, but the underlying mechanisms need to be further clarified. In this study, the therapeutic effects of AS-IV were investigated in an oligomeric Aβ (oAβ) induced AD mice model. The effects of AS-IV on microglial activation, neuronal damage and reduced nicotinamide adenine dinucleotide phosphate (NADPH) oxidase expression were further studied. Different doses of AS-IV were administered intragastrically once a day after intracerebroventricularly oAβ injection. Results of behavioral experiments including novel object recognition (NOR) test and Morris water maze (MWM) test revealed that AS-IV administration could significantly ameliorate oAβ-induced cognitive impairment in a dose dependent manner. Enzyme linked immunosorbent assay (ELISA) results showed that increased levels of reactive oxygen species (ROS), tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β) and IL-6 in hippocampal tissues induced by oAβ injection were remarkably inhibited after AS-IV treatment. OAβ induced microglial activation and neuronal damage was significantly suppressed in AS-IV-treated mice brain, observed in immunohistochemistry results. Furthermore, oAβ upregulated protein expression of NADPH oxidase subunits gp91phox, p47phox, p22phox and p67phox were remarkably reduced by AS-IV in Western blotting assay. These results revealed that AS-IV could ameliorate oAβ-induced cognitive impairment, neuroinflammation and neuronal damage, which were possibly mediated by inhibition of microglial activation and down-regulation of NADPH oxidase protein expression. Our findings provide new insights of AS-IV for the treatment of neuroinflammation related diseases such as AD.

Dimeric Tacrine(10)-hupyridone as a Multitarget-Directed Ligand To Treat Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder with multiple pathological features. Therefore, a multitarget-directed ligands (MTDLs) strategy has been developed to treat AD. We have previously designed and synthesized dimeric tacrine(10)-hupyridone (A10E), a novel tacrine derivative with acetylcholinesterase (AChE) inhibition and brain-derived neurotrophic factor (BDNF) activation activity, by linking tacrine and a fragment of huperzine A. However, it was largely unknown whether A10E could act on other AD targets and produce cognitive-enhancing ability in AD animal models. In this study, A10E could prevent cognitive impairments in APP/PS1 transgenic mice and β-amyloid (Aβ) oligomers-treated mice, with higher potency than tacrine and huperzine A. Moreover, A10E could effectively inhibit Aβ production and deposition, alleviate neuroinflammation, enhance BDNF expression, and elevate cholinergic neurotransmission in vivo. At nanomolar concentrations, A10E could inhibit Aβ oligomers-induced neurotoxicity via the activation of tyrosine kinase receptor B (TrkB)/Akt pathway in SH-SY5Y cells. Furthermore, Aβ oligomerization and fibrillization could be directly disrupted by A10E. Importantly, A10E at high concentrations did not produce obvious hepatotoxicity. Our results indicated that A10E could produce anti-AD neuroprotective effects via the inhibition of Aβ aggregation, the activation of the BDNF/TrkB pathway, the alleviation of neuroinflammation, and the decrease of AChE activity. As MTDLs could produce additional benefits, such as overcoming the deficits of drug combination and enhancing the compliance of AD patients, our results also suggested that A10E might be developed as a promising MTDL lead for the treatment of AD.

Novel Applications of NSAIDs: Insight and Future Perspectives in Cardiovascular, Neurodegenerative, Diabetes and Cancer Disease Therapy

Once it became clear that inflammation takes place in the modulation of different degenerative disease including neurodegenerative, cardiovascular, diabetes and cancer the researchers has started intensive programs evaluating potential role of non-steroidal anti-inflammatory drugs (NSAIDs) in the prevention or therapy of these diseases. This review discusses the novel mechanism of action of NSAIDs and its potential use in the pharmacotherapy of neurodegenerative, cardiovascular, diabetes and cancer diseases. Many different molecular and cellular factors which are not yet fully understood play an important role in the pathogenesis of inflammation, axonal damage, demyelination, atherosclerosis, carcinogenesis thus further NSAID studies for a new potential indications based on precise pharmacotherapy model are warranted since NSAIDs are a heterogeneous group of medicines with relative different pharmacokinetics and pharmacodynamics profiles. Hopefully the new data from studies will fill in the gap between experimental and clinical results and translate our knowledge into successful disease therapy.

Reassessment of Pioglitazone for Alzheimer's Disease

Alzheimer's disease is a quintessential 'unmet medical need', accounting for ∼65% of progressive cognitive impairment among the elderly, and 700,000 deaths in the United States in 2020. In 2019, the cost of caring for Alzheimer's sufferers was $244B, not including the emotional and physical toll on caregivers. In spite of this dismal reality, no treatments are available that reduce the risk of developing AD or that offer prolonged mitiagation of its most devestating symptoms. This review summarizes key aspects of the biology and genetics of Alzheimer's disease, and we describe how pioglitazone improves many of the patholophysiological determinants of AD. We also summarize the results of pre-clinical experiments, longitudinal observational studies, and clinical trials. The results of animal testing suggest that pioglitazone can be corrective as well as protective, and that its efficacy is enhanced in a time- and dose-dependent manner, but the dose-effect relations are not monotonic or sigmoid. Longitudinal cohort studies suggests that it delays the onset of dementia in individuals with pre-existing type 2 diabetes mellitus, which small scale, unblinded pilot studies seem to confirm. However, the results of placebo-controlled, blinded clinical trials have not borne this out, and we discuss possible explanations for these discrepancies.

Recursive Support Vector Machine Biomarker Selection for Alzheimer's Disease

BACKGROUND

There is a need for more reliable diagnostic tools for the early detection of Alzheimer's disease (AD). This can be a challenge due to a number of factors and logistics making machine learning a viable option.

OBJECTIVE

In this paper, we present on a Support Vector Machine Leave-One-Out Recursive Feature Elimination and Cross Validation (SVM-RFE-LOO) algorithm for use in the early detection of AD and show how the SVM-RFE-LOO method can be used for both classification and prediction of AD.

METHODS

Data were analyzed on n = 300 participants (n = 150 AD; n = 150 cognitively normal controls). Serum samples were assayed via a multi-plex biomarker assay platform using electrochemiluminescence (ECL).

RESULTS

The SVM-RFE-LOO method reduced the number of features in the model from 21 to 16 biomarkers and achieved an area under the curve (AUC) of 0.980 with a sensitivity of 94.0% and a specificity of 93.3%. When the classification and prediction performance of SVM-RFE-LOO was compared to that of SVM and SVM-RFE, we found similar performance across the models; however, the SVM-RFE-LOO method utilized fewer markers.

CONCLUSION

We found that 1) the SVM-RFE-LOO is suitable for analyzing noisy high-throughput proteomic data, 2) it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features, and 3) it can improve the prediction performance. Our recursive feature elimination model can serve as a general model for biomarker discovery in other diseases.

A Precision Medicine Approach to Treating Alzheimer's Disease Using Rosiglitazone Therapy: A Biomarker Analysis of the REFLECT Trials

BACKGROUND

The REFLECT trials were conducted to examine the treatment of mild-to-moderate Alzheimer's disease utilizing a peroxisome proliferator-activated receptor gamma agonist.

OBJECTIVE

To generate a predictive biomarker indicative of positive treatment response using samples from the previously conducted REFLECT trials.

METHODS

Data were analyzed on 360 participants spanning multiple negative REFLECT trials, which included treatment with rosiglitazone and rosiglitazone XR. Support vector machine analyses were conducted to generate a predictive biomarker profile.

RESULTS

A pre-defined 6-protein predictive biomarker (IL6, IL10, CRP, TNFα, FABP-3, and PPY) correctly classified treatment response with 100%accuracy across study arms for REFLECT Phase II trial (AVA100193) and multiple Phase III trials (AVA105640, AV102672, and AVA102670). When the data was combined across all rosiglitazone trial arms, a global RSG-predictive biomarker with the same 6-protein predictive biomarker was able to accurately classify 98%of treatment responders.

CONCLUSION

A predictive biomarker comprising of metabolic and inflammatory markers was highly accurate in identifying those patients most likely to experience positive treatment response across the REFLECT trials. This study provides additional proof-of-concept that a predictive biomarker can be utilized to help with screening and predicting treatment response, which holds tremendous benefit for clinical trials.

Are Heat Shock Proteins an Important Link between Type 2 Diabetes and Alzheimer Disease?

Type 2 diabetes (T2D) and Alzheimer’s disease (AD) are growing in prevalence worldwide. The development of T2D increases the risk of AD disease, while AD patients can show glucose imbalance due to an increased insulin resistance. T2D and AD share similar pathological features and underlying mechanisms, including the deposition of amyloidogenic peptides in pancreatic islets (i.e., islet amyloid polypeptide; IAPP) and brain (β-Amyloid; Aβ). Both IAPP and Aβ can undergo misfolding and aggregation and accumulate in the extracellular space of their respective tissues of origin. As a main response to protein misfolding, there is evidence of the role of heat shock proteins (HSPs) in moderating T2D and AD. HSPs play a pivotal role in cell homeostasis by providing cytoprotection during acute and chronic metabolic stresses. In T2D and AD, intracellular HSP (iHSP) levels are reduced, potentially due to the ability of the cell to export HSPs to the extracellular space (eHSP). The increase in eHSPs can contribute to oxidative damage and is associated with various pro-inflammatory pathways in T2D and AD. Here, we review the role of HSP in moderating T2D and AD, as well as propose that these chaperone proteins are an important link in the relationship between T2D and AD.

Discontinued disease-modifying therapies for Alzheimer's disease: status and future perspectives

INTRODUCTION

Alzheimer's disease (AD) is the main cause of dementia and represents a huge burden for patients, carers, and healthcare systems. Extensive efforts for over 20 years have failed to find effective disease-modifying drugs. Although amyloid-β (Aβ) accumulation in the brain predicts cognitive decline, effective reduction of plaque load by numerous drug candidates has not yielded significant clinical benefits. A similar pattern is now emerging for drugs which target hyperphosphorylated tau, and trials with anti-inflammatory drugs have been negative despite neuroinflammation appearing to have a crucial role in AD pathogenesis.

AREAS COVERED

This article reviews key drugs that have been discontinued while in development for AD and delineates the future landscape for present and alternative approaches.

EXPERT OPINION

Anti-Aβ drugs have failed to validate the Aβ cascade hypothesis of AD. Early findings suggest that the same is happening with therapeutics targeting tau and focussing future research solely on anti-tau drugs is inappropriate. Alternative targets should be pursued, including apolipoprotein E, immunomodulation, plasma exchange, protein autophagy and clearance, mitochondrial dysfunction, abnormal glucose metabolism, neurovascular unit support, epigenetic dysregulation, synaptic loss and dysfunction, microbiota dysbiosis, and combination therapies. Meanwhile, repurposing of drugs approved for other indications is justified where scientific rationale and robust preclinical evidence exist.

Proteomic profiles of incident mild cognitive impairment and Alzheimer's disease among adults with Down syndrome

INTRODUCTION

We sought to determine if proteomic profiles could predict risk for incident mild cognitive impairment (MCI) and Alzheimer's disease (AD) among adults with Down syndrome (DS).

METHODS

In a cohort of 398 adults with DS, a total of n = 186 participants were determined to be non-demented and without MCI or AD at baseline and throughout follow-up; n = 103 had incident MCI and n = 81 had incident AD. Proteomics were conducted on banked plasma samples from a previously generated algorithm.

RESULTS

The proteomic profile was highly accurate in predicting incident MCI (area under the curve [AUC] = 0.92) and incident AD (AUC = 0.88). For MCI risk, the support vector machine (SVM)-based high/low cut-point yielded an adjusted hazard ratio (HR) = 6.46 (P < .001). For AD risk, the SVM-based high/low cut-point score yielded an adjusted HR = 8.4 (P < .001).

DISCUSSION

The current results provide support for our blood-based proteomic profile for predicting risk for MCI and AD among adults with DS.

Proteomic profiles of prevalent mild cognitive impairment and Alzheimer's disease among adults with Down syndrome

INTRODUCTION

We sought to determine if a proteomic profile approach developed to detect Alzheimer's disease (AD) in the general population would apply to adults with Down syndrome (DS).

METHODS

Plasma samples were obtained from 398 members of a community-based cohort of adults with DS. A total of n = 186 participants were determined to be non-demented and without mild cognitive impairment (MCI) at baseline and throughout follow-up; n = 50 had prevalent MCI; n = 42 had prevalent AD.

RESULTS

The proteomic profile yielded an area under the curve (AUC) of 0.92, sensitivity (SN) = 0.80, and specificity (SP) = 0.98 detecting prevalent MCI. For detecting prevalent AD, the proteomic profile yielded an AUC of 0.89, SN = 0.81, and SP = 0.97. The overall profile closely resembled our previously published profile of AD in the general population.

DISCUSSION

These data provide evidence of the applicability of our blood-based algorithm for detecting MCI/AD among adults with DS.

A Path Toward Precision Medicine for Neuroinflammatory Mechanisms in Alzheimer's Disease

Neuroinflammation commences decades before Alzheimer's disease (AD) clinical onset and represents one of the earliest pathomechanistic alterations throughout the AD continuum. Large-scale genome-wide association studies point out several genetic variants—TREM2, CD33, PILRA, CR1, MS4A, CLU, ABCA7, EPHA1, and HLA-DRB5-HLA-DRB1—potentially linked to neuroinflammation. Most of these genes are involved in proinflammatory intracellular signaling, cytokines/interleukins/cell turnover, synaptic activity, lipid metabolism, and vesicle trafficking. Proteomic studies indicate that a plethora of interconnected aberrant molecular pathways, set off and perpetuated by TNF-α, TGF-β, IL-1β, and the receptor protein TREM2, are involved in neuroinflammation. Microglia and astrocytes are key cellular drivers and regulators of neuroinflammation. Under physiological conditions, they are important for neurotransmission and synaptic homeostasis. In AD, there is a turning point throughout its pathophysiological evolution where glial cells sustain an overexpressed inflammatory response that synergizes with amyloid-β and tau accumulation, and drives synaptotoxicity and neurodegeneration in a self-reinforcing manner. Despite a strong therapeutic rationale, previous clinical trials investigating compounds with anti-inflammatory properties, including non-steroidal anti-inflammatory drugs (NSAIDs), did not achieve primary efficacy endpoints. It is conceivable that study design issues, including the lack of diagnostic accuracy and biomarkers for target population identification and proof of mechanism, may partially explain the negative outcomes. However, a recent meta-analysis indicates a potential biological effect of NSAIDs. In this regard, candidate fluid biomarkers of neuroinflammation are under analytical/clinical validation, i.e., TREM2, IL-1β, MCP-1, IL-6, TNF-α receptor complexes, TGF-β, and YKL-40. PET radio-ligands are investigated to accomplish in vivo and longitudinal regional exploration of neuroinflammation. Biomarkers tracking different molecular pathways (body fluid matrixes) along with brain neuroinflammatory endophenotypes (neuroimaging markers), can untangle temporal–spatial dynamics between neuroinflammation and other AD pathophysiological mechanisms. Robust biomarker–drug codevelopment pipelines are expected to enrich large-scale clinical trials testing new-generation compounds active, directly or indirectly, on neuroinflammatory targets and displaying putative disease-modifying effects: novel NSAIDs, AL002 (anti-TREM2 antibody), anti-Aβ protofibrils (BAN2401), and AL003 (anti-CD33 antibody). As a next step, taking advantage of breakthrough and multimodal techniques coupled with a systems biology approach is the path to pursue for developing individualized therapeutic strategies targeting neuroinflammation under the framework of precision medicine.

Pharmacotherapy of Alzheimer's Disease: Seeking Clarity in a Time of Uncertainty

Alzheimer's disease (AD) is recognized as a major health hazard that mostly affects people older than 60 years. AD is one of the biggest medical, economic, and social concerns to patients and their caregivers. AD was ranked as the 5th leading cause of global deaths in 2016 by the World Health Organization (WHO). Many drugs targeting the production, aggregation, and clearance of Aβ plaques failed to give any conclusive clinical outcomes. This mainly stems from the fact that AD is not a disease attributed to a single-gene mutation. Two hallmarks of AD, Aβ plaques and neurofibrillary tangles (NFTs), can simultaneously induce other AD etiologies where every pathway is a loop of consequential events. Therefore, the focus of recent AD research has shifted to exploring other etiologies, such as neuroinflammation and central hyperexcitability. Neuroinflammation results from the hyperactivation of microglia and astrocytes that release pro-inflammatory cytokines due to the neurological insults caused by Aβ plaques and NFTs, eventually leading to synaptic dysfunction and neuronal death. This review will report the failures and side effects of many anti-Aβ drugs. In addition, emerging treatments targeting neuroinflammation in AD, such as nonsteroidal anti-inflammatory drugs (NSAIDs) and receptor-interacting serine/threonine protein kinase 1 (RIPK1), that restore calcium dyshomeostasis and microglia physiological function in clearing Aβ plaques, respectively, will be deliberately discussed. Other novel pharmacotherapy strategies in treating AD, including disease-modifying agents (DMTs), repurposing of medications used to treat non-AD illnesses, and multi target-directed ligands (MTDLs) are also reviewed. These approaches open new doors to the development of AD therapy, especially combination therapy that can cater for several targets simultaneously, hence effectively slowing or stopping AD.

Update on targeted cancer therapies, single or in combination, and their fine tuning for precision medicine

BACKGROUND

Until recently, patients who have the same type and stage of cancer all receive the same treatment. It has been established, however, that individuals with the same disease respond differently to the same therapy. Further, each tumor undergoes genetic changes that cause cancer to grow and metastasize. The changes that occur in one person's cancer may not occur in others with the same cancer type. These differences also lead to different responses to treatment. Precision medicine, also known as personalized medicine, is a strategy that allows the selection of a treatment based on the patient's genetic makeup. In the case of cancer, the treatment is tailored to take into account the genetic changes that may occur in an individual's tumor. Precision medicine, therefore, could be defined in terms of the targets involved in targeted therapy.

METHODS

A literature search in electronic data bases using keywords "cancer targeted therapy, personalized medicine and cancer combination therapies" was conducted to include papers from 2010 to June 2019.

RESULTS

Recent developments in strategies of targeted cancer therapy were reported. Specifically, on the two types of targeted therapy; first, immune-based therapy such as the use of immune checkpoint inhibitors (ICIs), immune cytokines, tumor-targeted superantigens (TTS) and ligand targeted therapeutics (LTTs). The second strategy deals with enzyme/small molecules-based therapies, such as the use of a proteolysis targeting chimera (PROTAC), antibody-drug conjugates (ADC) and antibody-directed enzyme prodrug therapy (ADEPT). The precise targeting of the drug to the gene or protein under attack was also investigated, in other words, how precision medicine can be used to tailor treatments.

CONCLUSION

The conventional therapeutic paradigm for cancer and other diseases has focused on a single type of intervention for all patients. However, a large literature in oncology supports the therapeutic benefits of a precision medicine approach to therapy as well as combination therapies.

Perceptions of precision medicine among diverse dementia caregivers and professional providers

Introduction

Underrepresented groups experience health disparities and a history of exploitation by researchers and the health-care system that may contribute to distrust of new treatments and technologies. This study aims to understand how diverse family caregivers and health-care professionals view the benefits and risks of precision medicine as well as cultural dimensions to consider when developing and implementing precision medicine interventions in dementia care.

Methods

Eight focus group sessions and one individual interview were conducted over a 6-month period. Fifty-four focus group participants included African-American, American Indian, rural Caucasian, Latino, and West African caregivers and health professionals. The majority of participants were female (73%) and were of Hispanic/Latino ethnicity (68%). About a third of participants identified their race as white. Participants were presented with four hypothetical scenarios related to precision medicine diagnostic and treatment approaches in dementia care: (1) genetic testing for dementia risk, (2) health-care informatics to determine individualized medication dosages based on health and family history, (3) a smartphone application providing dementia caregiving tips, and (4) remote activity monitoring technology in the home. Focus groups' responses were coded using thematic analysis.

Results

Participants indicated skepticism regarding the use of precision medicine in their communities. Concerns included cost of precision medicine and insurance coverage; lack of alignment with cultural norms; fraught relationships between communities, health professionals, and researchers; data ownership and privacy; and the trade-off between knowing risk and treatment benefit.

Discussion

Establishing relationships with underserved communities is crucial to advancing precision medicine in dementia care. Appropriate engagement with diverse racial, ethnic, and geographic communities may require significant investment but is necessary to deliver precision medicine effectively.

Blood-based biomarkers for Down syndrome and Alzheimer's disease: A systematic review

Down syndrome (DS) occurs due to triplication of chromosome 21. Individuals with DS face an elevated risk for development of Alzheimer's disease (AD) due to increased amyloid beta (Aβ) resulting from the over-expression of the amyloid precursor protein found on chromosome 21. Diagnosis of AD among individuals with DS poses particular challenges resulting in an increased focus on alternative diagnostic methods such as blood-based biomarkers. The aim of this review was to evaluate the current state of the literature of blood-based biomarkers found in individuals with DS and particularly among those also diagnosed with AD or in prodromal stages (mild cognitive impairment [MCI]). A systematic review was conducted utilizing a comprehensive search strategy. Twenty-four references were identified, of those, 22 fulfilled inclusion criteria were selected for further analysis with restriction to only plasma-based biomarkers. Studies found Aβ to be consistently higher among individuals with DS; however, the link between Aβ peptides (Aβ1-42 and Aβ1-40) and AD among DS was inconsistent. Inflammatory-based proteins were more reliably found to be elevated leading to preliminary work focused on an algorithmic approach with predominantly inflammatory-based proteins to detect AD and MCI as well as predict risk of incidence among DS. Separate work has also shown remarkable diagnostic accuracy with the use of a single protein (NfL) as compared to combined proteomic profiles. This review serves to outline the current state of the literature and highlights the potential plasma-based biomarkers for use in detecting AD and MCI among this at-risk population.