Next Article in Journal
A Quantitative Comparison of Medial and Coronal Dentate Gyrus Microdissection Strategies and a Softening-Based Workflow for Reproducible Tissue Procurement
Next Article in Special Issue
Comparative Analysis by Machine Learning of Geriatric Frailty and Alzheimer’s Disease Classification Using Independent Datasets
Previous Article in Journal
Integrating Focused Shockwave Therapy into Rehabilitation for Groin Pain Syndrome: A Prospective Study in Soccer Players
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Smart Drug-Delivery Approaches for Enhanced Management of Comorbid Conditions in Alzheimer’s Disease

by
Gabriela-Dumitrita Stanciu
1,*,
Ivona Costachescu
1,
Camelia Dascalu
1 and
Bogdan-Ionel Tamba
1,2
1
Advanced Research and Development Center for Experimental Medicine “Prof. Ostin C. Mungiu”—CEMEX, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700454 Iasi, Romania
2
Clinical Pharmacology and Algesiology, Department of Pharmacology, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700454 Iasi, Romania
*
Author to whom correspondence should be addressed.
Life 2026, 16(3), 510; https://doi.org/10.3390/life16030510
Submission received: 24 February 2026 / Revised: 13 March 2026 / Accepted: 19 March 2026 / Published: 19 March 2026

Abstract

Alzheimer’s disease (AD) remains a major unmet medical challenge due to its complex pathology, high interpatient heterogeneity and frequent association with systemic comorbidities. Conventional pharmacotherapy is limited by poor blood–brain barrier permeability, off-target effects and reduced efficacy in polymedicated elderly populations. Smart drug-delivery systems (DDS), particularly nanotechnology-based platforms, have emerged as promising strategies to enhance brain targeting, optimize controlled drug release and mitigate systemic toxicity. This review examines recent advances in intelligent DDS for AD, with a focus on nanocarriers designed to modulate amyloid aggregation, neuroinflammation, oxidative stress and cholinergic dysfunction. Special attention is given to the impact of the most common comorbid conditions on DDS pharmacokinetics, safety and clinical performance. Furthermore, the challenges associated with clinical translation, such as long-term safety, immunogenicity, manufacturing scalability and regulatory harmonization, are critically discussed. In this context, versatile controlled release platforms that integrate rational design, predictive modeling and Quality by Design-driven manufacturing are highlighted as key enablers of translational success. By bridging intelligent formulation design with scalable production and regulatory readiness, advanced controlled release systems offer a pathway toward precision and patient-centered therapies. Such platforms hold significant potential to accelerate the safe integration of smart DDS into Alzheimer’s disease management and broader neurotherapeutic applications.

1. Introduction

Alzheimer’s disease (AD) and related dementias represent one of the most pressing global health and socioeconomic challenges of the twenty-first century [1]. According to the World Health Organization, more than 55 million individuals are currently living with dementia worldwide, with prevalence expected to rise to over 152 million by 2050 due to aging populations and increased life expectancy [1,2]. This growing burden is accompanied by substantial direct healthcare costs and indirect societal impacts, including long-term caregiving, loss of productivity and profound effects on families and caregivers [2,3].
AD is clinically defined by progressive cognitive decline, memory impairment and behavioral changes, and neuropathologically characterized by extracellular beta-amyloid (Aβ) plaques, intracellular neurofibrillary tangles composed of hyperphosphorylated tau, synaptic loss, neuroinflammation and widespread neuronal dysfunction [4,5]. These pathological processes are intertwined with disturbances in neuronal communication, oxidative stress, mitochondrial dysfunction, and chronic inflammation, collectively contributing to irreversible neurodegeneration.
In addition to the complex central nervous system (CNS) pathology, AD frequently co-exists with systemic comorbid conditions that not only increase the risk for the development of AD but also modulate its progression and clinical presentation. Epidemiological evidence has consistently demonstrated associations between AD and type 2 diabetes mellitus, cardiovascular disease, depression, metabolic syndrome and chronic inflammatory disorders [6,7,8,9,10,11,12,13]. These comorbidities share overlapping mechanistic pathways, including insulin resistance, dysregulated lipid metabolism, endothelial dysfunction and chronic low-grade inflammation, which can exacerbate neurodegenerative processes and further compromise cognitive function [14,15]. Moreover, the presence of comorbidities complicates therapeutic management by altering drug absorption, distribution, metabolism and elimination, increasing the likelihood of polypharmacy, drug–drug interactions and adverse effects. Consequently, comprehensive therapeutic strategies must consider both central neurodegeneration and peripheral dysfunctions to optimize patient outcomes.
A critical barrier in the pharmacological management of AD is the restricted access of therapeutic agents to the brain imposed by the blood–brain barrier (BBB). The highly selective nature of the BBB limits the passive diffusion of most small molecules and effectively excludes large biologics, including many monoclonal antibodies and nucleic acid-based therapeutics [16]. This physiological protection contributes to the disappointing translational outcomes of many candidate drugs, which demonstrate promising results in preclinical models but fail to achieve sufficient brain concentrations in clinical settings. Conventional delivery strategies also suffer from rapid systemic clearance, off-target effects and suboptimal pharmacokinetic profiles, further limiting therapeutic efficacy [17,18].
In this context, smart drug-delivery systems have emerged as a transformative approach in neurotherapeutics. These engineered platforms, often based on nanoscale materials such as polymeric nanoparticles, lipid-based carriers, gold nanoparticles, dendrimers, and superparamagnetic iron oxide nanoparticles, are designed to enhance BBB penetration, improve drug stability, prolong systemic circulation and enable controlled or stimuli-responsive release of therapeutic agents [18,19]. Through surface functionalization with targeting ligands, receptor-mediated transport mechanisms can be exploited to facilitate selective accumulation in the CNS, while stimuli-responsive elements allow drug release to be modulated according to local pathological cues, such as pH gradients, oxidative stress or enzymatic activity [19,20].
Importantly, these systems offer opportunities to tackle the therapeutic challenges imposed by AD comorbidities. By enabling the co-delivery of multiple agents, smart nanocarriers can simultaneously address central neurodegeneration and peripheral pathologies. For example, multifunctional platforms can encapsulate neuroprotective drugs alongside antidiabetic, antihypertensive, antidepressant, or anti-inflammatory agents, providing an integrated therapeutic approach that may improve clinical outcomes while reducing systemic toxicity [20]. “Self-regulated” delivery mechanisms, capable of adjusting drug release kinetics or dosing in response to patient-specific factors such as glucose levels, further exemplify the potential for personalized intervention strategies. Despite encouraging preclinical evidence, the translation of these smart delivery systems from bench to bedside remains challenging. Critical issues include large-scale manufacturing, long-term safety, immunogenicity, regulatory approval and cost-effectiveness. Nonetheless, continued innovation in material science, stimuli-responsive designs, and integration with artificial intelligence and precision medicine holds promise for next-generation therapeutic modalities that simultaneously target central and peripheral aspects of Alzheimer’s disease.
While previous studies have focused primarily on individual nanocarrier platforms or their ability to deliver anti-Alzheimer agents, this narrative review offers a novel perspective by integrating the challenges posed by systemic comorbidities with the design and application of multifunctional smart delivery systems. It highlights co-delivery strategies, self-regulated mechanisms, and emerging AI-assisted and personalized approaches as a comprehensive framework for next-generation interventions.

2. Major Comorbid Conditions Associated with Alzheimer’s Disease

Alzheimer’s disease develops in the context of aging-related systemic vulnerability and is frequently accompanied by multiple chronic disorders that shape disease susceptibility, clinical heterogeneity and rates of progression [3]. Accumulating evidence indicates that metabolic, vascular, psychiatric, inflammatory and neurodegenerative comorbidities interact with hallmark AD pathology through overlapping biological pathways that become increasingly dysregulated with age [6,7,8,9,10,11,12,13]. Figure 1 schematically summarizes these interactions, emphasizing convergent mechanisms that link peripheral organ dysfunction to central nervous system neurodegeneration.

2.1. Metabolic Dysregulation: Type 2 Diabetes Mellitus and Metabolic Syndrome

Type 2 diabetes mellitus (T2DM) and metabolic syndrome are highly prevalent in aging populations and represent significant risk factors for cognitive decline and dementia, including AD. Multiple epidemiological studies indicate that individuals with T2DM have an elevated risk of developing AD compared with non-diabetic peers, with pooled analyses reporting hazard ratios ranging from approximately 1.5 to 3.0 [21,22].
The proposed mechanistic links between T2DM and AD are thought to begin with neuronal insulin resistance and impaired cerebral glucose metabolism [23]. Insulin plays a crucial role in brain function by modulating synaptic plasticity and supporting neurotrophic signaling. Deficits in insulin signaling have been detected in post-mortem AD brains and are associated with reduced activation of downstream pathways such as PI3K/Akt, which normally inhibit amyloidogenic processes and tau hyperphosphorylation [24].
Preclinical models reinforce this connection: rodents with diet-induced obesity or genetic models of diabetes exhibit exacerbated Aβ deposition, increased tau phosphorylation, compromised hippocampal neurogenesis, impaired long-term potentiation, deficits in learning and memory, and reduced functional performance, which may be interpreted as treatment failure or decreased therapeutic efficacy in these contexts. These observations support the concept that metabolic dysregulation can accelerate core neurodegenerative processes that characterize AD [25,26]. Beyond intrinsic pathology, diabetes and sustained hyperglycemia induce structural and functional alterations in the BBB that have direct implications for drug delivery and pharmacokinetics. Chronic high glucose levels promote oxidative stress, inflammation and upregulation of the glycation end products and receptor for AGEs (AGE–RAGE) axis, which disrupt tight junction integrity and increase BBB permeability, altering the transport of endogenous substrates (e.g., glucose, insulin) and xenobiotics across the cerebral endothelium [27,28]. These changes can result in regional BBB dysfunction, including tight junction disruption and dysregulated expression of key transporters such as P-glycoprotein, low-density lipoprotein receptor-related protein 1 (LRP1) and insulin transporters—all of which shape how drugs and pathological peptides traverse the BBB [29,30].
These pathophysiological alterations have been linked clinically and experimentally to altered drug transport dynamics. Certain antidiabetic agents such as glucagon-like peptide 1 (GLP-1) receptor agonists, sodium-glucose cotransporter 2 inhibitors (SGLT-2) and thiazolidinediones are associated with a reduced risk of dementia in individuals with T2DM [31,32]. In addition, specific antidiabetic drugs may exert neuroprotective effects beyond glucose lowering. For example, GLP-1 receptor agonists enhance insulin signaling in the brain, reduce Aβ deposition and improve synaptic plasticity in preclinical models [33]; SGLT-2 inhibitors may mitigate cerebrovascular dysfunction through improved endothelial function and reduced oxidative stress; and thiazolidinediones activate PPARγ pathways, reducing neuroinflammation and promoting neuronal survival [6,34]. Despite these promising observational findings, results from interventional trials remain mixed. Differences in patient populations, trial duration, cognitive endpoints and drug regimens likely contribute to the variability. Further well-designed, long-term randomized trials are needed to clarify whether these agents can meaningfully prevent or slow cognitive decline in T2DM.
Dyslipidemia and central adiposity, hallmark features of metabolic syndrome, further exacerbate endothelial dysfunction and microvascular injury, leading to widespread compromise of BBB integrity. The resulting BBB disruption impairs cerebral perfusion and reduces the clearance of neurotoxic proteins, including amyloid-β and hyperphosphorylated tau, which accumulate in AD. Mechanistically, dyslipidemia contributes to oxidative stress and inflammation within the cerebrovascular endothelium, while central adiposity promotes chronic low-grade systemic inflammation through the secretion of adipokines and pro-inflammatory cytokines such as tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6) [35]. Together, these processes impair cerebrovascular reactivity, disrupt nutrient and oxygen delivery to neurons and facilitate the infiltration of peripheral immune cells into the CNS. Preclinical models have demonstrated that diet-induced obesity or hyperlipidemia leads to exacerbated Aβ deposition, tau pathology and cognitive deficits, providing a mechanistic link between metabolic imbalances and neurodegeneration. Clinically, patients with metabolic syndrome show reduced cerebral perfusion and microvascular rarefaction, correlating with cognitive decline and increased risk of AD [36]. In such patients, systemic metabolic alterations can modify both the pharmacokinetics and CNS availability of neurotherapeutics, leading to either toxicity or suboptimal efficacy. Consideration of metabolic status is therefore critical when evaluating therapeutic outcomes in AD.

2.2. Cardiovascular Disease

Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality in aging populations worldwide, and growing evidence implicates atherosclerosis, the central pathogenic substrate of CVD, in the development of late-life cognitive impairment and Alzheimer’s disease [37]. Population-based cohort studies have demonstrated that elevated blood pressure during midlife confers a substantially increased risk of dementia decades later, while cumulative exposure to vascular risk factors across the lifespan correlates with accelerated cortical thinning, hippocampal atrophy and white-matter disruption on neuroimaging. These associations persist even after adjustment for traditional demographic and lifestyle variables, underscore the direct contribution of vascular pathology to neurodegenerative vulnerability [38,39].
At the level of cerebral hemodynamics, vascular aging is accompanied by progressive arterial stiffening, reduced bioavailability of endothelial nitric oxide and impaired neurovascular coupling. Hypertension and atherosclerosis amplify these alterations, compromising autoregulatory capacity and leading to sustained reductions in regional cerebral blood flow [40]. Chronic hypoperfusion preferentially affects watershed territories and metabolically active regions such as the hippocampus and posterior cingulate cortex, where it induces energetic stress, synaptic failure and increased susceptibility to amyloidogenic processing of amyloid precursor protein. In parallel, diminished perivascular drainage and transporter-mediated efflux across the BBB impair Aβ clearance, fostering parenchymal accumulation [41].
Structural and functional disruption of the BBB represents a further link connecting systemic vascular disease to AD pathology. Experimental and human neuropathological studies reveal loss of endothelial tight junction integrity, pericyte degeneration and basement membrane thickening in hypertensive states, changes that have been associated with microglial activation and perivascular inflammation. Such barrier dysfunction facilitates the entry of circulating cytokines, fibrinogen and immune cells into the brain parenchyma, amplifying local inflammatory signaling and oxidative injury to neurons and glia [40,42]. Importantly, these vascular and BBB alterations may also influence the delivery, CNS penetration and pharmacokinetics of neurotherapeutic agents. Impaired transporter activity, disrupted endothelial function and altered cerebral perfusion can reduce drug availability, modify local concentrations, and potentially diminish efficacy or increase toxicity [37,38,39,40,41,42].
Clinico-pathological investigations in aging cohorts consistently indicate that cerebrovascular lesions rarely occur in isolation from classical AD pathology. Lacunar infarcts, cerebral microbleeds, white-matter hyperintensities and arteriolosclerosis frequently coexist with amyloid plaques and neurofibrillary tangles, producing a mixed vascular–Alzheimer’s disease phenotype that dominates in advanced age [43]. This combined pathology is associated with more precipitous cognitive decline, earlier functional dependency and greater neuropsychiatric burden than either disease process alone, highlighting the synergistic rather than additive nature of vascular and neurodegenerative abnormalities [44].
Insights from preclinical models further substantiate these clinical observations. Rodents subjected to chronic hypertension or experimental hypoperfusion develop endothelial dysfunction, breakdown of BBB integrity and exaggerated neuroinflammatory responses. When superimposed on transgenic AD backgrounds, these vascular perturbations accelerate Aβ deposition, intensify tau phosphorylation, and exacerbate synaptic loss and memory impairment [45,46]. Conversely, interventions that improve cerebrovascular function including antihypertensive treatment, restoration of endothelial signaling or enhancement of cerebral perfusion attenuate neuropathological burden and partially rescue cognitive deficits, supporting a causal role for vascular dysfunction in AD pathogenesis [45,46,47].

2.3. Psychiatric Comorbidities: Depression and Anxiety in Late Life

Depression and anxiety are among the most common neuropsychiatric comorbidities in Alzheimer’s disease, with prevalence estimates reaching 50% for depressive symptoms and 30% for anxiety in affected cohorts [48,49,50]. Epidemiological studies indicate that late-life anxiety and depression may precede the clinical onset of AD and act as prodromal markers or modifiers of disease progression. A 10-year longitudinal community study reported that clinically significant anxiety at baseline was associated with a nearly threefold increased risk of developing AD, even after adjusting for depression and other confounders [51]. Case–control data from the HUNT study found that depression significantly increased AD risk (OR ~4.39), whereas anxiety showed variable associations depending on dementia subtype [52]. In contrast, the Rotterdam study observed no significant link between anxiety symptoms and dementia risk, highlighting heterogeneity across populations [53]. Cohort data further indicate that individuals with AD have a substantially higher subsequent risk of developing major depression compared with age-matched controls [54].
Preclinical models provide mechanistic support for these clinical observations. APP/PSEN1 transgenic mice display anxiety-like and depression-like behaviors preceding amyloid plaque deposition, mirroring human prodromal features [55]. Similarly, in 3 × Tg-AD mice, pharmacological inhibition of phosphodiesterase-4 (PDE4) with rolipram reduced both neurodegenerative pathology and affective symptoms, implicating neuroinflammation, Aβ accumulation and tau phosphorylation in contributing to the comorbidity of AD and mood dysregulation [56].
Mechanistically, chronic activation of the hypothalamic–pituitary–adrenal (HPA) axis and prolonged glucocorticoid exposure are implicated in both mood dysregulation and AD progression, promoting hippocampal neuronal vulnerability, impairing neurogenesis and enhancing amyloidogenic processing of APP [57]. Elevated systemic inflammatory signaling, evidenced by increased circulating cytokines in depressed patients and stressed animal models, further primes central microglia toward a pro-inflammatory phenotype, exacerbating Aβ and tau pathology [57,58,59]. These mechanistic insights are consistent with findings in APP/PSEN1 and 3 × Tg-AD mice, where HPA axis dysregulation and neuroinflammation accelerate cognitive decline and neuropathological progression [55,56]. These alterations can compromise BBB integrity, modulate drug transporter activity and affect pharmacokinetics, reducing the penetration and efficacy of CNS-targeted therapies. Dysregulated neurochemical signaling associated with affective disorders may further alter receptor responsiveness and pharmacodynamic outcomes [57,58,59].
Together, clinical and preclinical evidence supports a bidirectional and mechanistically convergent relationship between depression/anxiety and AD. Early identification and management of affective symptoms may improve quality of life and modulate neuropathological progression, highlighting the importance of integrated therapeutic strategies for aging populations.

2.4. Parkinsonism and Mixed Neurodegenerative Syndromes

Parkinsonian features and mixed neurodegenerative syndromes frequently co-occur with Alzheimer’s disease, contributing to clinical heterogeneity and accelerating cognitive, motor and functional decline. Epidemiological studies indicate that a substantial subset of AD patients present parkinsonian signs, including bradykinesia, rigidity and tremor, often preceding or developing alongside cognitive deficits [60,61]. These motor features are associated with more rapid cognitive deterioration, earlier gait instability, increased neuropsychiatric symptoms and higher rates of falls compared with AD patients without Parkinsonism [62].
Biomarker studies demonstrate that misfolded α-synuclein in cerebrospinal fluid (CSF) often co-exists with phosphorylated tau and elevated Aβ levels, correlating with emergent cognitive deficits in at-risk older adults and suggesting the presence of mixed pathology even in prodromal stages of AD [62,63]. Post-mortem neuropathological analyses reveal overlapping distributions of Aβ, tau and α-synuclein in AD, dementia with Lewy bodies (DLB), and mixed AD/DLB cases, confirming that protein co-pathologies are prevalent in aging brains and contribute to clinical heterogeneity [63,64,65]. Longitudinal imaging studies indicate that Lewy body co-pathology exacerbates regional hypometabolism, synaptic dysfunction and neurodegeneration, accelerating cognitive decline compared with pure AD [66,67].
Preclinical studies provide mechanistic insight into these associations. Transgenic mouse models co-expressing Aβ, tau and α-synuclein recapitulate key features of the human mixed phenotype, including cognitive and motor deficits, synaptic loss, oxidative stress, and enhanced microglial and astrocyte activation [68,69]. These pathological processes may alter transporter activity and receptor responsiveness, potentially reducing the efficacy of CNS-targeted therapies. Pharmacological interventions targeting α-synuclein aggregation, neuroinflammation or mitochondrial dysfunction partially restore cognitive and motor outcomes, demonstrating the translational relevance of these models for testing multi-target therapies [70,71,72].
Together, these findings indicate that Parkinsonism and mixed neurodegenerative syndromes act as accelerators of AD-related neurodegeneration. Recognition of co-pathology is critical for diagnostic accuracy, prognosis and personalized therapeutic strategies, emphasizing the importance of integrated approaches targeting protein aggregation, neuroinflammation and synaptic resilience [62,63,64,65,71,72]. Translationally, preclinical models incorporating both AD and Parkinsonian pathology provide a platform for the development of multi-target therapeutics and biomarker validation, reducing reliance on multiple disease-specific models while supporting ethical and efficient preclinical research.

2.5. Chronic Systemic Inflammation and Immune Aging

Chronic systemic inflammation and age-related immune dysregulation (immunosenescence) are increasingly recognized as central contributors to Alzheimer’s disease pathogenesis. Epidemiological studies indicate that elevated peripheral inflammatory markers, including IL-1β, TNF-α, IL-6 and C-reactive protein, predict both incident AD and faster cognitive decline in older adults [73,74]. Longitudinal cohort analyses show that individuals with persistently high systemic inflammation exhibit accelerated hippocampal atrophy, reduced cortical thickness and increased amyloid deposition, supporting a mechanistic link between peripheral immune activation and central neurodegeneration [75,76].
Biomarker studies demonstrate that age-associated immune dysfunction promotes a pro-inflammatory milieu, characterized by dysregulated T cell populations, impaired regulatory networks and chronic low-grade cytokine elevation. This systemic inflammation is mirrored in the central nervous system by microglial priming and astrocytic activation, which exacerbate Aβ accumulation, tau phosphorylation and synaptic dysfunction [77,78]. Chronic cytokine elevation can downregulate hepatic and extrahepatic cytochrome P450 enzymes and drug transporters, altering drug metabolism, clearance and bioavailability. In parallel, inflammation-induced changes in BBB integrity and transporter function can reduce CNS penetration of pharmacological agents, thereby modifying drug efficacy and pharmacodynamic response [79]. Preclinical studies further support a causal role for immune aging in AD. In mouse models, chronic peripheral inflammation accelerates amyloid and tau pathology, induces microglial activation and impairs synaptic plasticity [73,74,80,81]. Conversely, interventions that reduce systemic inflammation, such as anti-cytokine therapies or senolytic agents—ameliorate cognitive deficits and reduce neurodegenerative markers, highlighting translational potential [82,83]. Age-related dysfunction in innate and adaptive immunity also synergizes with other comorbidities, including metabolic syndrome, cardiovascular disease, and neurodegenerative pathologies, amplifying neurodegeneration via convergent inflammatory pathways [35,36,47,54,74].

3. Smart Drug-Delivery Systems: Nanotechnology-Based Delivery Approaches

The therapeutic management of Alzheimer’s disease and its prevalent comorbidities remains highly challenging, largely due to limited BBB permeability, rapid systemic clearance, off-target toxicity and subtherapeutic drug concentrations at pathological sites. Smart nanotechnology-based drug delivery systems have emerged as versatile tools capable of enhancing brain targeting, improving pharmacokinetics and enabling temporally and spatially controlled interventions [84]. Such platforms are particularly well-suited to the complex and convergent pathophysiology of AD, which encompasses Aβ and tau aggregation, synaptic dysfunction, neuroinflammation, oxidative stress and mitochondrial impairment. By facilitating precise delivery of small molecules, biologics or nucleic acid therapeutics, nanocarriers can simultaneously modulate multiple pathological pathways, potentially mitigating both core AD pathology and associated comorbid processes such as metabolic dysregulation, vascular dysfunction and mood disorders [85,86].
Preclinical evidence consistently demonstrates that liposomes, polymeric nanoparticles, solid lipid nanoparticles and exosome-like carriers improve central nervous system bioavailability, attenuate neurodegenerative cascades and enhance functional outcomes in AD models [87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127]. Early translational approaches, including intranasal formulations for nose-to-brain delivery and pharmacokinetic optimization studies provide encouraging evidence for clinical applicability and safety profiling. Representative preclinical examples are summarized in Table 1, illustrating the potential of nanotechnology-based systems to address the multifactorial challenges of AD therapy.
Prevalent comorbidities in Alzheimer’s disease critically influence both the pathophysiology and the delivery of therapeutics across the blood–brain barrier. These conditions can impair BBB integrity, exacerbate oxidative stress and alter systemic pharmacokinetics, necessitating design considerations for nanocarrier systems. For instance, neuroinflammation-driven BBB disruption and increased reactive oxygen species highlight the need for multi-mechanistic nanoparticles with anti-inflammatory and antioxidant properties [96,113,120,122,128]. In patients with metabolic comorbidities (e.g., diabetes or hyperlipidemia), biomimetic coatings such as HDL-mimetic, RBC-coated or platelet–CCR2 membrane systems can enhance brain delivery while minimizing off-target effects [95,115,116,117,129,130]. Similarly, vascular comorbidities that reduce perfusion or exacerbate amyloid deposition support the application of nanocarriers exploiting receptor-mediated BBB transport (e.g., B6-SA-SeNPs, sialic acid-modified systems) [120,122,124]. Overall, these considerations demonstrate that smart nanocarrier design must integrate disease- and comorbidity-specific pathophysiological features to optimize targeting efficiency and therapeutic outcomes

4. Clinical Translation and Regulatory Challenges

Despite extensive preclinical promise of smart drug-delivery systems (DDS) in overcoming the blood–brain barrier and improving therapeutic outcomes in animal models, no nanoparticle-based therapeutics for Alzheimer’s disease have yet reached advanced clinical trials or regulatory approval. Translation remains limited due to insufficient brain targeting in humans, potential safety and toxicity concerns, complex pharmacokinetics and clearance, and difficulties in scalable manufacturing under Good Manufacturing Practices (GMP) [131,132,133,134,135]. In AD, these challenges are amplified by patient heterogeneity, widespread polypharmacy and disease-associated alterations in BBB permeability [131,136,137,138]. Consequently, biomarker-driven patient stratification and adaptive clinical trial designs are increasingly required to ensure meaningful therapeutic evaluation [137,138,139,140,141]. Bridging this translational gap will require: (i) comprehensive toxicological and pharmacokinetic characterization of nanocarrier physicochemical properties, (ii) robust Quality by Design (QbD)-based scale-up to ensure batch-to-batch consistency, (iii) early engagement with regulatory agencies to establish GMP-compliant manufacturing criteria, and (iv) ethical frameworks that balance technological innovation with patient protection [131,142,143].
The safety profile of smart DDS is of particular importance in AD due to the chronic nature of the disease and the vulnerability of elderly patients with multiple comorbidities. Nanocarriers engineered to cross the altered BBB characterized by increased permeability resulting from amyloid-induced endothelial damage may accumulate in cerebral and peripheral tissues, especially in patients with cardiovascular comorbidities, potentially leading to unintended toxicity [143,144]. Polymeric nanoparticles may persist within the brain, inducing oxidative stress or impairing neuronal function due to incomplete clearance via BBB efflux transporters, the reticuloendothelial system or AD-associated dysfunction of the glymphatic–lymphatic pathways, which exacerbates waste accumulation in aging brains, particularly in individuals with renal insufficiency [131,137,138]. Surface-modified nanocarriers, such as PEGylated liposomes, may also elicit immune responses, including complement activation or cytokine release, potentially aggravating AD-related neuroinflammation and tau pathology [137,145,146]. Importantly, chronic toxicity associated with repeated administration remains insufficiently explored, with limited long-term data on genotoxicity or organ damage, especially in patients with impaired renal function, where reduced clearance may prolong DDS exposure [131,147]. These risks are further compounded by polypharmacy; in polymedicated individuals, drug–drug interactions may compromise safety, as nanocarrier-encapsulated cholinesterase inhibitors can alter the metabolism of concomitant antihypertensive agents, particularly in patients with hepatic insufficiency affecting DDS metabolism [128,134]. Collectively, these gaps highlight the need for chronic toxicity studies in animal models that recapitulate AD with relevant comorbidities, incorporating biomarkers of neurotoxicity and systemic burden. In parallel, post-marketing pharmacovigilance remains essential to capture real-world safety outcomes, including rare hypersensitivity reactions or cumulative toxicity [139].
Scaling smart DDS from laboratory synthesis to industrial production presents significant technological challenges that directly impact clinical translation for AD therapies. Regulatory agencies, including the Food and Drug Administration (FDA), provide specific guidance for liposomal and nanoparticle formulations, covering physicochemical characterization, preclinical safety, pharmacokinetics, biodistribution, and GMP-compliant manufacturing [132,133]. Manufacturing inconsistencies, such as variations in particle size (<100 nm), surface characteristics or drug-loading efficiency, can compromise BBB penetration, release kinetics, and pharmacokinetic predictability, thereby reducing therapeutic efficacy in patients where precise dosing is critical to counteract neuroinflammation and impaired lymphatic clearance [132,134,135]. Formulation stability represents an additional challenge, as nanocarriers may aggregate or degrade during storage, adversely affecting shelf life and therapeutic integrity. This issue is particularly problematic for long-term treatment regimens in patients with renal or hepatic dysfunction, which further influences DDS clearance and metabolism [131,134,135]. Batch-to-batch variability complicates regulatory compliance and quality assurance, underscoring the importance of early process standardization using QbD principles and rigorous preclinical biodistribution and clearance studies, alongside adaptive biomarker-driven clinical monitoring [131,133,142]. Advanced quality control strategies, including in-process monitoring via Process Analytical Technology (PAT), are increasingly recognized as essential to ensure consistent nanomedicine quality [135]. Addressing these manufacturing challenges is critical for the development of scalable, cost-effective DDS aligned with the clinical demands of AD management in comorbid populations [134].
Clinical trial design for smart DDS in AD must rigorously account for patient heterogeneity, disease stage and comorbidities to achieve translational success [137,145]. AD progression is highly variable and influenced by genetic factors, such as APOE genotype, as well as common comorbid conditions (e.g., diabetes and hypertension) that can alter DDS pharmacokinetics via changes in renal clearance or hepatic metabolism [137,145]. The high prevalence of polypharmacy in elderly AD patients further increases the risk of drug–drug interactions, necessitating systematic evaluation of DDS compatibility with concomitant medications [137].
Biomarker-driven patient stratification using amyloid positron emission tomography (PET), cerebrospinal fluid tau measurements or emerging blood-based biomarkers enables targeted enrollment of early-stage patients, who are most likely to benefit from disease-modifying DDS interventions in the context of chronic neuroinflammation [142,145]. Primary endpoints should integrate validated cognitive scales (e.g., ADAS-Cog), functional outcome measures and comorbidity-specific metrics, such as cardiovascular events, with extended follow-up to distinguish sustained clinical benefit from isolated biomarker changes [145]. Unlike conventional pharmacological trials, DDS studies require adaptive designs that allow real-time monitoring of brain penetration and release kinetics through imaging or pharmacodynamic biomarkers, as well as strategies to mitigate attrition associated with cognitive decline [137,139]. Long-term efficacy assessment in comorbid cohorts remains challenging due to confounding factors, emphasizing the need for phase-specific approaches—prioritizing safety and dose optimization in Phase I/II and adequately powered efficacy assessments in later-stage trials [137,145]. Integration of real-world evidence following regulatory approval may further refine stratification strategies and ensure that DDS address the heterogeneous phenotypes encountered in clinical practice [137,145].
The successful clinical translation of DDS for AD ultimately depends on robust ethical and regulatory frameworks that promote innovation while safeguarding vulnerable patients [132,133,142]. Given the frequent impairment of decision-making capacity in AD, informed consent procedures must incorporate legally authorized representatives while preserving patients’ residual autonomy [142]. Long-term safety uncertainties necessitate transparent risk communication and continuous post-marketing surveillance, particularly in elderly individuals with renal or hepatic insufficiency [133,135,142].
Equitable access represents an additional challenge, as the high development and manufacturing costs associated with advanced DDS may limit availability in underserved regions, potentially exacerbating healthcare disparities [133,142]. Regulatory agencies such as the FDA and the European Medicines Agency (EMA) provide guidance for nanomedicines based on case-by-case evaluations of safety and efficacy; however, divergent regulatory requirements exemplified by EMA reflection papers on liposomal formulations versus FDA nanomaterial guidance continue to impede global harmonization and delay widespread clinical adoption [132,133,135]. Furthermore, AD-specific physiological barriers, including BBB dysfunction and compromised glymphatic clearance, must be explicitly addressed within regulatory assessments [137,138]. Ultimately, integrating technological innovation with ethically grounded, patient-centered regulatory strategies and continuous ethical oversight will be essential to ensure that the anticipated benefits of smart DDS outweigh their inherent risks, particularly in comorbid AD populations [133,137,142].

5. Future Perspectives

Future advances in Alzheimer’s disease therapy will increasingly depend on the development of smart drug-delivery systems that move beyond disease-centric paradigms toward patient-centered and translationally viable solutions. The marked heterogeneity of AD, compounded by prevalent comorbidities necessitates delivery platforms capable of adapting drug release profiles to individual pathophysiological conditions [6,7,8,9,10,11,12,13]. In this context, versatile controlled release systems that allow modular adjustment of dose, release kinetics and targeting properties represent a critical step toward precision medicine in neurodegenerative disorders.
A key direction for the future is the design of intelligent DDS that respond dynamically to the pathological microenvironment of the AD brain. Stimuli-responsive nanocarriers that sense changes in pH, oxidative stress, enzymatic activity or blood–brain barrier integrity have the potential to enhance therapeutic efficacy while minimizing systemic exposure and cumulative toxicity [15,59,62]. Such adaptive systems are particularly relevant in AD, where BBB permeability and clearance mechanisms evolve throughout disease progression and are further altered by aging and comorbid conditions.
The translation of nanoparticle-based DDS from preclinical models to humans remains challenging. In this context, artificial intelligence provides a complementary dimension by enabling the analysis and interpretation of complex datasets, including neuroimaging, proteomics and longitudinal patient monitoring [148]. Machine learning models have proven useful for identifying early disease signatures, stratifying patients into molecular subtypes, and supporting personalized therapeutic strategies. Concurrently, AI-driven materials informatics can accelerate the optimization of nanoparticle formulations, predict BBB permeability and facilitate adaptive DDS that adjust in real time based on biomarker feedback [149,150].
Integrating AI with predictive pharmacokinetic–pharmacodynamic models enhances the rational design of DDS. Physiologically based pharmacokinetic models combined with AI frameworks allow researchers to simulate nanoparticle transport, brain accumulation and systemic clearance, incorporating patient-specific factors such as genetic background, comorbidities and disease stage [151]. This approach supports individualized dosing and treatment planning while minimizing unnecessary preclinical experiments. The concept of digital twins further enables the iterative refinement of DDS in silico, providing a platform for testing therapeutic strategies under simulated patient-specific conditions [152,153,154].
Equally important is the consideration of scalable and reproducible manufacturing strategies. Many nanotechnology-based DDS fail to progress clinically due to challenges in ensuring batch-to-batch consistency, long-term stability, and regulatory compliance [17,152]. Future platforms should be developed alongside QbD frameworks and real-time process monitoring tools, such as Process Analytical Technology, to maintain robust control over critical material and process attributes. Advanced manufacturing approaches, including microfluidics and additive manufacturing techniques, provide opportunities to produce reliable, clinically relevant formulations [155,156].
Principles derived from versatile controlled release platforms originally developed for advanced topical formulations can be adapted to central nervous system and intranasal delivery strategies. Modular architectures, predictable release kinetics and standardized characterization pipelines enable cross-application of these platforms, facilitating rapid adaptation to diverse therapeutic routes while maintaining consistent performance and safety profiles [157]. This translational flexibility is particularly valuable in neurodegenerative diseases, where alternative routes may help overcome the restrictive properties of the BBB.
Future progress in smart DDS for AD will require early and sustained engagement with regulatory and ethical frameworks. Systems should be designed with regulatory readiness in mind, supported by comprehensive characterization, predictive toxicology and transparent risk–benefit assessment. The growing role of AI in DDS design introduces additional considerations, including data standardization, algorithm transparency and oversight requirements, highlighting the importance of harmonized guidelines to balance innovation with patient safety [158].
By combining AI-assisted design, mechanistic modeling, predictive pharmacokinetics, and scalable manufacturing, versatile controlled-release platforms provide a pathway toward safe, adaptable and clinically translatable therapies for Alzheimer’s disease. Integrating nanotechnology, computational modeling and precision medicine holds the potential to deliver personalized interventions that address the complex, heterogeneous nature of this disorder.

Author Contributions

Conceptualization: G.-D.S. and B.-I.T.; methodology, formal analysis, investigation, and writing—original draft preparation: G.-D.S., I.C. and C.D.; writing—review and editing, visualization, and supervision G.-D.S. and B.-I.T.; funding acquisition: G.-D.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a project funded under Smart Growth, Digitalization and Financial Instruments Program (PoCIDIF) 2021–2027, Priority 1, Policy Objective RSO1.1, project title “Versatile controlled release system for advanced topical formulations—Intelligent platform for design, modelling, characterization and accelerated manufacturing”, acronym Smart-TOPIC, SMIS code 330783.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All relevant data are included in the article and its references.

Acknowledgments

The authors acknowledge that no administrative or technical support was received.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
ADAlzheimer’s disease
beta-amyloid
WHOWorld Health Organization 
BBBblood–brain barrier
T2DMtype 2 diabetes mellitus 
AGEsadvanced glycation end-products
RAGEreceptor for advanced glycation end-products
TNF-αtumor necrosis factor alpha 
IL-6interleukin-6 
GLP-1glucagon-like peptide 1 receptor agonists
SGLT-2sodium-glucose cotransporter 2 inhibitors
CNScentral nervous system
CVDcardiovascular disease
APP/PSEN1 transgenic miceamyloid precursor protein/presenilin-1 transgenic mice
3 × Tg micetriple-transgenic Alzheimer’s disease mice (APP Swedish/PSEN1 M146V/Tau P301L)
DLBdementia with Lewy bodies 
CSFcerebrospinal fluid
ZnOzinc oxide
siSTAT3siRNA targeting STAT3
HNSSmitochondria-targeted hybrid peptide
LRP1low-density lipoprotein receptor-related protein 1
KLVFFa self-recognition sequence derived from residues 16–20 of Aβ
ROSreactive oxygen species
SAMP8 micesenescence-accelerated prone 8 mice 
PLGA nanoparticlesnative poly(D,L-lactide-co-glycolide) nanoparticles
SAMR1 micesenescence-accelerated mouse-resistant 1 mice
rHDLreconstituted high-density lipoprotein
apoA-Iapolipoprotein A-I
TPPU1-trifluoromethoxyphenyl-3-(1-propionylpiperidin-4-yl) urea
FMV flavin mononucleotide
RFKriboflavin kinase
NACN-acetyl-L-cysteine
NPCN-propionyl-L-cysteine
NIBCN-isobutyryl-L-cysteine
NPVCN-pivaloyl-L-cysteine
GMPgood manufacturing practices 
DDSdrug-delivery systems 
EMAEuropean Medicines Agency 
PATprocess analytical technology 
QbDQuality by Design 
FDAFood and Drug Administration 

References

  1. Clare, L.; Jeon, Y.H. World Alzheimer Report 2025: Reimagining Life with Dementia—The Power of Rehabilitation; Alzheimer’s Disease International: London, UK, 2025; pp. 1–144. [Google Scholar]
  2. Alzheimer’s Disease International. World Alzheimer Report 2024: Global Changes in Attitudes to Dementia; Alzheimer’s Disease International: London, UK, 2024; pp. 1–176. [Google Scholar]
  3. Twiss, E.; McPherson, C.; Weaver, D.F. Global diseases deserve global solutions: Alzheimer’s disease. Neurol. Int. 2025, 17, 92. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  4. Tahami Monfared, A.A.; Tafazzoli, A.; Ye, W.; Chavan, A.; Zhang, Q. Long-term health outcomes of lecanemab in patients with early Alzheimer’s disease using simulation modeling. Neurol. Ther. 2022, 11, 863–880. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  5. Dominguez-Gortaire, J.; Ruiz, A.; Porto-Pazos, A.B.; Rodriguez-Yanez, S.; Cedron, F. Alzheimer’s Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery. Int. J. Mol. Sci. 2025, 26, 1004. [Google Scholar] [CrossRef] [Scilit]
  6. Stanciu, G.D.; Ababei, D.C.; Solcan, C.; Bild, V.; Ciobica, A.; Beschea Chiriac, S.-I.; Ciobanu, L.M.; Tamba, B.-I. Preclinical studies of canagliflozin, a sodium-glucose co-transporter 2 inhibitor, and donepezil combined therapy in Alzheimer’s disease. Pharmaceuticals 2023, 16, 1620. [Google Scholar] [CrossRef] [Scilit]
  7. Kciuk, M.; Kruczkowska, W.; Gałęziewska, J.; Wanke, K.; Kałuzińska-Kołat, Ż.; Aleksandrowicz, M.; Kontek, R. Alzheimer’s disease as type 3 diabetes: Understanding the link and implications. Int. J. Mol. Sci. 2024, 25, 11955. [Google Scholar] [CrossRef] [Scilit]
  8. Tini, G.; Scagliola, R.; Monacelli, F.; La Malfa, G.; Porto, I.; Brunelli, C.; Rosa, G.M. Alzheimer’s disease and cardiovascular disease: A particular association. Cardiol. Res. Pract. 2020, 2020, 2617970. [Google Scholar] [CrossRef] [Scilit]
  9. Untu, I.; Davidson, M.; Stanciu, G.D.; Rabinowitz, J.; Dobrin, R.P.; Vieru, D.S.; Tamba, B.I. Neurobiological and therapeutic landmarks of depression associated with Alzheimer’s disease dementia. Front. Aging Neurosci. 2025, 17, 1584607. [Google Scholar] [CrossRef] [Scilit]
  10. Sáiz-Vázquez, O.; Gracia-García, P.; Ubillos-Landa, S.; Puente-Martínez, A.; Casado-Yusta, S.; Olaya, B.; Santabárbara, J. Depression as a risk factor for Alzheimer’s disease: A systematic review of longitudinal meta-analyses. J. Clin. Med. 2021, 10, 1809. [Google Scholar] [CrossRef] [Scilit]
  11. Ezkurdia, A.; Ramírez, M.J.; Solas, M. Metabolic syndrome as a risk factor for Alzheimer’s disease: A focus on insulin resistance. Int. J. Mol. Sci. 2023, 24, 4354. [Google Scholar] [CrossRef] [Scilit]
  12. Alzarea, E.A.; Al-Kuraishy, H.M.; Al-Gareeb, A.I.; Alexiou, A.; Papadakis, M.; Beshay, O.N.; Batiha, G.E. The conceivable role of metabolic syndrome in the pathogenesis of Alzheimer’s disease: Cellular and subcellular alterations in underpinning a tale of two. Neuromol. Med. 2025, 27, 35. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  13. Wang, S.; Shi, Y.; Xin, R.; Kang, H.; Xiong, H.; Ren, J. Exploring the role of insulin resistance in bridging the metabolic syndrome and Alzheimer’s disease-a review of mechanistic studies. Front. Endocrinol. 2025, 16, 1614006. [Google Scholar] [CrossRef] [Scilit]
  14. Davidson, M.; Stanciu, G.D.; Rabinowitz, J.; Untu, I.; Dobrin, R.P.; Tamba, B.I. Exploring novel therapeutic strategies: Could psychedelic perspectives offer promising solutions for Alzheimer’s disease comorbidities? Dialogues Clin. Neurosci. 2025, 27, 1–12. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  15. Stanciu, G.D.; Luca, A.; Rusu, R.N.; Bild, V.; Beschea Chiriac, S.I.; Solcan, C.; Bild, W.; Ababei, D.C. Alzheimer’s disease pharmacotherapy in relation to cholinergic system involvement. Biomolecules 2019, 10, 40. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  16. de Koning, L.A.; Vazquez-Matias, D.A.; Beaino, W.; Vugts, D.J.; van Dongen, G.A.M.S.; van der Flier, W.M.; Ries, M.; van Vuurden, D.G.; Vijverberg, E.G.B.; van de Giessen, E. Drug delivery strategies to cross the blood-brain barrier in Alzheimer’s disease: A comprehensive review on three promising strategies. J. Prev. Alzheimer’s Dis. 2025, 12, 100204. [Google Scholar] [CrossRef] [Scilit]
  17. Rajkumar, M.; Tian, F.; Javed, B.; Prajapati, B.G.; Deepak, P.; Girigoswami, K.; Karmegam, N. Smart biosensing nanomaterials for Alzheimer’s disease: Advances in design and drug delivery strategies to overcome the blood–brain barrier. Biosensors 2026, 16, 66. [Google Scholar] [CrossRef] [Scilit]
  18. Fonseca-Santos, B.; Gremião, M.P.; Chorilli, M. Nanotechnology-based drug delivery systems for the treatment of Alzheimer’s disease. Int. J. Nanomed. 2015, 10, 4981–5003. [Google Scholar] [CrossRef] [Scilit]
  19. Bauzon, J.; Lee, G.; Cummings, J. Repurposed agents in the Alzheimer’s disease drug development pipeline. Alzheimer’s Res. Ther. 2020, 12, 98. [Google Scholar] [CrossRef] [Scilit]
  20. Tutubala, T.E.; Egunlusi, A.O.; Fisher, D.; Dube, A.; Joubert, J. Nanomedicine solutions for Alzheimer’s disease: A critical review of therapeutic nanoparticle strategies. ACS Omega 2025, 10, 53633–53657. [Google Scholar] [CrossRef] [Scilit]
  21. Geijselaers, S.L.C.; Sep, S.J.S.; Claessens, D.; Schram, M.T.; van Boxtel, M.P.J.; Henry, R.M.A.; Verhey, F.R.J.; Kroon, A.A.; Dagnelie, P.C.; Schalkwijk, C.G.; et al. The role of hyperglycemia, insulin resistance, and blood pressure in diabetes-associated differences in cognitive performance-the Maastricht study. Diabetes Care 2017, 40, 1537–1547. [Google Scholar] [CrossRef] [Scilit]
  22. Xue, M.; Xu, W.; Ou, Y.N.; Cao, X.P.; Tan, M.S.; Tan, L.; Yu, J.T. Diabetes mellitus and risks of cognitive impairment and dementia: A systematic review and meta-analysis of 144 prospective studies. Ageing Res. Rev. 2019, 55, 100944. [Google Scholar] [CrossRef] [Scilit]
  23. Chatterjee, S.; Mudher, A. Alzheimer’s disease and type 2 diabetes: A critical assessment of the shared pathological traits. Front. Neurosci. 2018, 12, 383. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  24. Pan, J.; Yao, Q.; Wang, Y.; Chang, S.; Li, C.; Wu, Y.; Shen, J.; Yang, R. The role of PI3K signaling pathway in Alzheimer’s disease. Front. Aging Neurosci. 2024, 16, 1459025. [Google Scholar] [CrossRef] [Scilit]
  25. Čater, M.; Hölter, S.M. A pathophysiological intersection of diabetes and Alzheimer’s disease. Int. J. Mol. Sci. 2022, 23, 11562. [Google Scholar] [CrossRef] [Scilit]
  26. Kale, M.B.; Bhondge, H.M.; Wankhede, N.L.; Shende, P.V.; Thanekaer, R.P.; Aglawe, M.M.; Rahangdale, S.R.; Taksande, B.G.; Pandit, S.B.; Upaganlawar, A.B.; et al. Navigating the intersection: Diabetes and Alzheimer’s intertwined relationship. Ageing Res. Rev. 2024, 100, 102415. [Google Scholar] [CrossRef] [Scilit]
  27. Ab-Hamid, N.; Omar, N.; Ismail, C.A.N.; Long, I. Diabetes and cognitive decline: Challenges and future direction. World J. Diabetes 2023, 14, 795–807. [Google Scholar] [CrossRef] [Scilit]
  28. Tabesh, M.; Sacre, J.W.; Mehta, K.; Chen, L.; Sajjadi, S.F.; Magliano, D.J.; Shaw, J.E. Associations of glycaemia-related risk factors with dementia and cognitive decline in individuals with type 2 diabetes: A systematic review and meta-analysis. Diabet. Med. 2025, 42, e70123. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  29. Rawlings, A.M.; Sharrett, A.R.; Mosley, T.H.; Ballew, S.H.; Deal, J.A.; Selvin, E. Glucose peaks and the risk of dementia and 20-year cognitive decline. Diabetes Care 2017, 40, 879–886. [Google Scholar] [CrossRef] [Scilit]
  30. Zheng, F.; Yan, L.; Yang, Z.; Zhong, B.; Xie, W. HbA1c, diabetes and cognitive decline: The English longitudinal study of ageing. Diabetologia 2018, 61, 839–848. [Google Scholar] [CrossRef] [Scilit]
  31. Cai, Z.; Zhong, J.; Zhu, G.; Zhang, J. Comparative efficacy and safety of antidiabetic agents in Alzheimer’s disease: A network meta-analysis of randomized controlled trials. J. Prev. Alzheimer’s Dis. 2025, 12, 100111. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  32. Corraliza-Gomez, M.; Vargas-Soria, M.; Garcia-Alloza, M. Effect of antidiabetic drugs in Alzheimer’s disease: A systematic review of preclinical and clinical studies. Mol. Neurodegener. 2025, 20, 112. [Google Scholar] [CrossRef] [Scilit]
  33. Fessel, J. All GLP-1 agonists should, theoretically, cure Alzheimer’s dementia but dulaglutide might be more effective than the others. J. Clin. Med. 2024, 13, 3729. [Google Scholar] [CrossRef] [Scilit]
  34. Burns, J.M.; Morris, J.K.; Vidoni, E.D.; Wilkins, H.M.; Choi, I.Y.; Lee, P.; Hunt, S.L.; Mahnken, J.D.; Brooks, W.M.; Lepping, R.J.; et al. Effects of the SGLT2 inhibitor dapagliflozin in early Alzheimer’s disease: A randomized controlled trial. Alzheimer’s Dement. 2025, 21, e70416. [Google Scholar] [CrossRef] [Scilit]
  35. Wee, J.; Sukudom, S.; Bhat, S.; Marklund, M.; Peiris, N.J.; Hoyos, C.M.; Patel, S.; Naismith, S.L.; Dwivedi, G.; Misra, A. The relationship between midlife dyslipidemia and lifetime incidence of dementia: A systematic review and meta-analysis of cohort studies. Alzheimer’s Dement. 2023, 15, e12395. [Google Scholar] [CrossRef] [Scilit]
  36. Reitz, C. Dyslipidemia and the risk of Alzheimer’s disease. Curr. Atheroscler. Rep. 2013, 15, 307. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  37. Luo, J.; Rasmussen, I.J.; Nordestgaard, B.G.; Tybjærg-Hansen, A.; Thomassen, J.Q.; Frikke-Schmidt, R. Cardiovascular diseases and risk of dementia in the general population. Eur. J. Prev. Cardiol. 2025, 1, zwaf129. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  38. Saeed, A.; Lopez, O.; Cohen, A.; Reis, S.E. Cardiovascular disease and Alzheimer’s disease: The heart-brain axis. J. Am. Heart Assoc. 2023, 12, e030780. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  39. Zhu, Y.; Li, C.; Gao, D.; Huang, X.; Zhang, Y.; Ji, M.; Zheng, F.; Xie, W. Associations of blood pressure trajectories with subsequent cognitive decline, dementia and mortality. J. Prev. Alzheimer’s Dis. 2024, 11, 1426–1434. [Google Scholar] [CrossRef] [Scilit]
  40. Wolters, F.J.; Segufa, R.A.; Darweesh, S.K.L.; Bos, D.; Ikram, M.A.; Sabayan, B.; Hofman, A.; Sedaghat, S. Coronary heart disease, heart failure, and the risk of dementia: A systematic review and meta-analysis. Alzheimer’s Dement. 2018, 14, 1493–1504. [Google Scholar] [CrossRef] [Scilit]
  41. Bhat, A.; Goel, D. Coronary heart disease, heart failure, and risk of Alzheimer’s disease: How strong is the association? Ann. Indian Acad. Neurol. 2023, 26, 852–853. [Google Scholar] [CrossRef] [Scilit]
  42. Preis, L.; Villringer, K.; Brosseron, F.; Düzel, E.; Jessen, F.; Petzold, G.C.; Ramirez, A.; Spottke, A.; Fiebach, J.B.; Peters, O. Assessing blood-brain barrier dysfunction and its association with Alzheimer’s pathology, cognitive impairment and neuroinflammation. Alzheimer’s Res. Ther. 2024, 16, 172. [Google Scholar] [CrossRef] [Scilit]
  43. Inoue, Y.; Shue, F.; Bu, G.; Kanekiyo, T. Pathophysiology and probable etiology of cerebral small vessel disease in vascular dementia and Alzheimer’s disease. Mol. Neurodegener. 2023, 18, 46. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  44. Joseph, C.R.; Melin, D.A.; Wanner, L.K.; Hartman, B.; Badelita, J.; Conser, L.C.; Kline, H.D.; Pradhan, P.M.; Love, K. Dementia from small vessel disease versus Alzheimer’s disease: Separate diseases or distinct manifestations of cerebral capillopathy due to blood–brain barrier dysfunction? a pilot study. Int. J. Mol. Sci. 2025, 26, 5040. [Google Scholar] [CrossRef] [Scilit]
  45. Shabir, O.; Moll, T.A.; Matuszyk, M.M.; Eyre, B.; Dake, M.D.; Berwick, J.; Francis, S.E. Preclinical models of disease and multimorbidity with focus upon cardiovascular disease and dementia. Mech. Ageing Dev. 2020, 192, 111361. [Google Scholar] [CrossRef] [Scilit]
  46. Shabir, O.; Berwick, J.; Francis, S.E. Neurovascular dysfunction in vascular dementia, Alzheimer’s and atherosclerosis. BMC Neurosci. 2018, 19, 62. [Google Scholar] [CrossRef] [Scilit]
  47. Li, Q.Y.; Hu, H.Y.; Zhang, G.W.; Hu, H.; Ou, Y.N.; Huang, L.Y.; Wang, A.Y.; Gao, P.Y.; Ma, L.Y.; Tan, L.; et al. Associations between cardiometabolic multimorbidity and cerebrospinal fluid biomarkers of Alzheimer’s disease pathology in cognitively intact adults: The CABLE study. Alzheimer’s Res. Ther. 2024, 16, 28. [Google Scholar] [CrossRef] [Scilit]
  48. Yang, J.; Xue, W.; Zheng, W.; Zhang, H.; Tang, C. Psychiatric symptoms and Alzheimer’s disease: Depression-anxiety comorbidity effects and their neurobiological mediating mechanisms. J. Affect. Disord. 2026, 394, 120594. [Google Scholar] [CrossRef] [Scilit]
  49. Ly, M.; Karim, H.T.; Becker, J.T.; Lopez, O.L.; Anderson, S.J.; Aizenstein, H.J.; Reynolds, C.F.; Zmuda, M.D.; Butters, M.A. Late-life depression and increased risk of dementia: A longitudinal cohort study. Transl. Psychiatry 2021, 11, 147. [Google Scholar] [CrossRef] [Scilit]
  50. Chen, P.; Guarino, P.D.; Dysken, M.W.; Pallaki, M.; Asthana, S.; Llorente, M.D.; Love, S.; Vertrees, J.E.; Schellenberg, G.D.; Sano, M. Neuropsychiatric symptoms and caregiver burden in individuals with Alzheimer’s disease: The TEAM-AD VA Cooperative Study. J. Geriatr. Psychiatry Neurol. 2018, 31, 177–185. [Google Scholar] [CrossRef] [Scilit]
  51. Gracia-García, P.; Bueno-Notivol, J.; Lipnicki, D.M.; de la Cámara, C.; Lobo, A.; Santabárbara, J. Clinically significant anxiety as a risk factor for Alzheimer’s disease: Results from a 10-year follow-up community study. Int. J. Methods Psychiatr. Res. 2023, 32, e1934. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  52. Rasmussen, H.; Rosness, T.A.; Bosnes, O.; Salvesen, Ø.; Knutli, M.; Stordal, E. Anxiety and depression as risk factors in frontotemporal dementia and Alzheimer’s disease: The HUNT study. Dement. Geriatr. Cogn. Dis. Extra 2018, 8, 414–425. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  53. de Bruijn, R.F.; Direk, N.; Mirza, S.S.; Hofman, A.; Koudstaal, P.J.; Tiemeier, H.; Ikram, M.A. Anxiety is not associated with the risk of dementia or cognitive decline: The Rotterdam Study. Am. J. Geriatr. Psychiatry 2014, 22, 1382–1390. [Google Scholar] [CrossRef] [Scilit]
  54. Crump, C.; Sieh, W.; Vickrey, B.G.; Edwards, A.C.; Sundquist, J.; Sundquist, K. Risk of depression in persons with Alzheimer’s disease: A national cohort study. Alzheimer’s Dement. 2024, 16, e12584. [Google Scholar] [CrossRef] [Scilit]
  55. Martín-Sánchez, A.; Piñero, J.; Nonell, L.; Arnal, M.; Ribe, E.M.; Nevado-Holgado, A.; Lovestone, S.; Sanz, F.; Furlong, L.I.; Valverde, O. Comorbidity between Alzheimer’s disease and major depression: A behavioural and transcriptomic characterization study in mice. Alzheimer’s Res. Ther. 2021, 13, 73. [Google Scholar] [CrossRef] [Scilit]
  56. Cong, Y.F.; Liu, F.W.; Xu, L.; Song, S.S.; Shen, X.R.; Liu, D.; Hou, X.Q.; Zhang, H.T. Rolipram ameliorates memory deficits and depression-like behavior in APP/PS1/tau triple transgenic mice: Involvement of neuroinflammation and apoptosis via cAMP signaling. Int. J. Neuropsychopharmacol. 2023, 26, 585–598. [Google Scholar] [CrossRef] [Scilit]
  57. Dafsari, F.S.; Jessen, F. Depression-an underrecognized target for prevention of dementia in Alzheimer’s disease. Transl. Psychiatry 2020, 10, 160. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  58. Canet, G.; Chevallier, N.; Zussy, C.; Desrumaux, C.; Givalois, L. Central role of glucocorticoid receptors in Alzheimer’s disease and depression. Front. Neurosci. 2018, 12, 739. [Google Scholar] [CrossRef] [Scilit]
  59. Sălcudean, A.; Bodo, C.R.; Popovici, R.A.; Cozma, M.M.; Păcurar, M.; Crăciun, R.E.; Crisan, A.I.; Enatescu, V.R.; Marinescu, I.; Cimpian, D.M.; et al. Neuroinflammation-a crucial factor in the pathophysiology of depression-a comprehensive review. Biomolecules 2025, 15, 502. [Google Scholar] [CrossRef] [Scilit]
  60. Scheltens, P.; De Strooper, B.; Kivipelto, M.; Holstege, H.; Chételat, G.; Teunissen, C.E.; Cummings, J.; van der Flier, W.M. Alzheimer’s disease. Lancet 2021, 397, 1577–1590. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  61. Jack, C.R., Jr.; Andrews, J.S.; Beach, T.G.; Buracchio, T.; Dunn, B.; Graf, A.; Hansson, O.; Ho, C.; Jagust, W.; McDade, E.; et al. Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s Association Workgroup. Alzheimer’s Dement. 2024, 20, 5143–5169. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  62. Caligiore, D.; Giocondo, F.; Silvetti, M. The Neurodegenerative elderly syndrome (NES) hypothesis: Alzheimer and Parkinson are two faces of the same disease. IBRO Neurosci. Rep. 2022, 13, 330–343. [Google Scholar] [CrossRef] [Scilit]
  63. Lau, V.Z.; Awogbindin, I.O.; Frenkel, D.; Whitehead, S.N.; Tremblay, M.E. A hypothesis explaining Alzheimer’s disease, Parkinson’s disease and dementia with Lewy bodies overlap. Alzheimer’s Dement. 2025, 21, e70363. [Google Scholar] [CrossRef] [Scilit]
  64. Irwin, D.J.; Lee, V.M.; Trojanowski, J.Q. Parkinson’s disease dementia: Convergence of α-synuclein, tau and amyloid-β pathologies. Nat. Rev. Neurosci. 2013, 14, 626–636. [Google Scholar] [CrossRef] [Scilit]
  65. Arima, K.; Hirai, S.; Sunohara, N.; Aoto, K.; Izumiyama, Y.; Uéda, K.; Ikeda, K.; Kawai, M. Cellular co-localization of phosphorylated tau- and NACP/alpha-synuclein-epitopes in Lewy bodies in sporadic Parkinson’s disease and in dementia with Lewy bodies. Brain Res. 1999, 843, 53–61. [Google Scholar] [CrossRef] [Scilit]
  66. Firbank, M.J.; Watson, R.; Mak, E.; Aribisala, B.; Barber, R.; Colloby, S.J.; He, J.; Blamire, A.M.; O’Brien, J.T. Longitudinal diffusion tensor imaging in dementia with Lewy bodies and Alzheimer’s disease. Parkinsonism Relat. Disord. 2016, 24, 76–80. [Google Scholar] [CrossRef] [Scilit]
  67. Fischer, L.; Parker, D.; Maboudian, S.; Fonseca, C.; Tato-Fernández, C.; Annen, L.; Arunachalam, P.; Bacci, J.R.; Barboure, M.; Capelli, S.; et al. Longitudinal biomarker studies in human neuroimaging: Capturing biological change of Alzheimer’s pathology. Alzheimer’s Res. Ther. 2025, 18, 13. [Google Scholar] [CrossRef] [Scilit]
  68. Yamamoto, Y.; Kubota, T.; Noguchi, D.; Saido, T.C.; Ohshima, T. Co-Expression of mutant tau and α-synuclein in neurons promotes tau phosphorylation, neuronal loss, and neuroinflammation in mouse brain. Mol. Neurobiol. 2025, 62, 15832–15843. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  69. Lim, M.J.; Boschen, S.L.; Kurti, A.; Castanedes Casey, M.; Phillips, V.R.; Fryer, J.D.; Dickson, D.; Jansen-West, K.R.; Petrucelli, L.; Delenclos, M.; et al. Investigating the Pathogenic Interplay of Alpha-Synuclein, Tau, and Amyloid Beta in Lewy Body Dementia: Insights from Viral-Mediated Overexpression in Transgenic Mouse Models. Biomedicines 2023, 11, 2863. [Google Scholar] [CrossRef] [Scilit]
  70. Haque, M.E.; Akther, M.; Azam, S.; Kim, I.S.; Lin, Y.; Lee, Y.H.; Choi, D.K. Targeting α-synuclein aggregation and its role in mitochondrial dysfunction in Parkinson’s disease. Br. J. Pharmacol. 2022, 179, 23–45. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  71. González-Lizárraga, F.; Ploper, D.; Ávila, C.L.; Socías, S.B.; Dos-Santos-Pereira, M.; Machín, B.; Del-Bel, E.; Michel, P.P.; Pietrasanta, L.I.; Raisman-Vozari, R.; et al. CMT-3 targets different α-synuclein aggregates mitigating their toxic and inflammogenic effects. Sci. Rep. 2020, 10, 20258. [Google Scholar] [CrossRef] [Scilit]
  72. Fouka, M.; Mavroeidi, P.; Tsaka, G.; Xilouri, M. In search of effective treatments targeting α-synuclein toxicity in synucleinopathies: Pros and cons. Front. Cell Dev. Biol. 2020, 8, 559791. [Google Scholar] [CrossRef] [Scilit]
  73. Tudorancea, I.M.; Stanciu, G.D.; Solcan, C.; Ciorpac, M.; Szilagyi, A.; Ababei, D.C.; Gogu, R.M.; Tamba, B.I. Exploring the impact of chronic intermittent EU-GMP certified Cannabis sativa L. therapy and its relevance in a rat model of aging. J. Cannabis Res. 2025, 7, 53. [Google Scholar] [CrossRef] [Scilit]
  74. Stanciu, G.D.; Costachescu, I.; Ababei, D.C.; Szilagyi, A.; Gogu, R.M.; Craciun, V.C.; Timofte, A.D.; Caruntu, I.D.; Dobre, C.E.; Tamba, B.I. Evaluation of long-term safety profile of an EU-GMP certified Cannabis sativa L. strain in a naturally aging preclinical model. Front. Pharmacol. 2025, 16, 1716366. [Google Scholar] [CrossRef] [Scilit]
  75. Lopez-Rodriguez, A.B.; Hennessy, E.; Murray, C.L.; Nazmi, A.; Delaney, H.J.; Healy, D.; Fagan, S.G.; Rooney, M.; Stewart, E.; Lewis, A.; et al. Acute systemic inflammation exacerbates neuroinflammation in Alzheimer’s disease: IL-1β drives amplified responses in primed astrocytes and neuronal network dysfunction. Alzheimer’s Dement. 2021, 17, 1735–1755. [Google Scholar] [CrossRef] [Scilit]
  76. Chouhan, J.K.; Püntener, U.; Booth, S.G.; Teeling, J.L. Systemic Inflammation Accelerates Changes in Microglial and Synaptic Markers in an Experimental Model of Chronic Neurodegeneration. Front. Neurosci. 2022, 15, 760721. [Google Scholar] [CrossRef] [Scilit]
  77. Bivona, G.; Iemmolo, M.; Agnello, L.; Lo Sasso, B.; Gambino, C.M.; Giglio, R.V.; Scazzone, C.; Ghersi, G.; Ciaccio, M. Microglial activation and priming in Alzheimer’s disease: State of the art and future perspectives. Int. J. Mol. Sci. 2023, 24, 884. [Google Scholar] [CrossRef] [Scilit]
  78. Lee, S.I.; Yu, J.; Lee, H.; Kim, B.; Jang, M.J.; Jo, H.; Kim, N.Y.; Pak, M.E.; Kim, J.K.; Cho, S.; et al. Astrocyte priming enhances microglial Aβ clearance and is compromised by APOE4. Nat. Commun. 2025, 16, 7551. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  79. Serna, M.F.; Mosquera, M.; García-Perdomo, H.A. Inflammatory markers and their relationship with cognitive function in Alzheimer’s disease and mild cognitive impairment. systematic review and meta-analysis. Neuromol. Med. 2025, 27, 53. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  80. Stanciu, G.D.; Ababei, D.-C.; Solcan, C.; Uritu, C.-M.; Craciun, V.-C.; Pricope, C.-V.; Szilagyi, A.; Tamba, B.-I. Exploring cannabinoids with enhanced binding affinity for targeting the expanded endocannabinoid system: A promising therapeutic strategy for Alzheimer’s disease treatment. Pharmaceuticals 2024, 17, 530. [Google Scholar] [CrossRef] [Scilit]
  81. Stanciu, G.-D. Beyond conventional pharmacotherapy: Unraveling mechanisms and advancing multi-target strategies in Alzheimer’s disease. Pharmaceuticals 2025, 18, 1797. [Google Scholar] [CrossRef] [Scilit]
  82. Chen, Z.; Balachandran, Y.L.; Chong, W.P.; Chan, K.W.Y. Roles of cytokines in Alzheimer’s disease. Int. J. Mol. Sci. 2024, 25, 5803. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  83. Gonzales, M.M.; Garbarino, V.R.; Marques Zilli, E.; Petersen, R.C.; Kirkland, J.L.; Tchkonia, T.; Musi, N.; Seshadri, S.; Craft, S.; Orr, M.E. Senolytic therapy to modulate the progression of Alzheimer’s disease (SToMP-AD): A pilot clinical trial. J. Prev. Alzheimer’s Dis. 2022, 9, 22–29. [Google Scholar] [CrossRef] [Scilit]
  84. Saraiva, C.; Praça, C.; Ferreira, R.; Santos, T.; Ferreira, L.; Bernardino, L. Nanoparticle-mediated brain drug delivery: Overcoming blood-brain barrier to treat neurodegenerative diseases. J. Control. Release 2016, 235, 34–47. [Google Scholar] [CrossRef] [Scilit]
  85. Duta, C.; Dogaru, C.B.; Muscurel, C.; Stoian, I. Nanozymes: Innovative therapeutics in the battle against neurodegenerative diseases. Int. J. Mol. Sci. 2025, 26, 3522. [Google Scholar] [CrossRef] [Scilit]
  86. Martín-Rapun, R.; De Matteis, L.; Ambrosone, A.; Garcia-Embid, S.; Gutierrez, L.; de la Fuente, J.M. Targeted nanoparticles for the treatment of Alzheimer’s disease. Curr. Pharm. Des. 2017, 23, 1927–1952. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  87. He, X.; Peng, Y.; Huang, S.; Xiao, Z.; Li, G.; Zuo, Z.; Zhang, L.; Shuai, X.; Zheng, H.; Hu, X. Blood brain barrier-crossing delivery of felodipine nanodrug ameliorates anxiety-like behavior and cognitive impairment in Alzheimer’s disease. Adv. Sci. 2024, 11, e2401731. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  88. Chen, Y.; Huang, L.; Luo, Z.; Han, D.; Luo, W.; Wan, R.; Li, Y.; Ge, Y.; Lin, W.W.; Xie, Y.; et al. Pantothenate-encapsulated liposomes combined with exercise for effective inhibition of CRM1-mediated PKM2 translocation in Alzheimer’s therapy. J. Control. Release 2024, 373, 336–357. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  89. Gu, Z.; Zhao, H.; Song, Y.; Kou, Y.; Yang, W.; Li, Y.; Li, X.; Ding, L.; Sun, Z.; Lin, J.; et al. PEGylated-liposomal astaxanthin ameliorates Aβ neurotoxicity and Alzheimer-related phenotypes by scavenging formaldehyde. J. Control. Release 2024, 366, 783–797. [Google Scholar] [CrossRef] [Scilit]
  90. Al Asmari, A.K.; Ullah, Z.; Tariq, M.; Fatani, A. Preparation, characterization, and in vivo evaluation of intranasally administered liposomal formulation of donepezil. Drug Des. Devel Ther. 2016, 10, 205–215. [Google Scholar] [CrossRef] [Scilit]
  91. Saka, R.; Chella, N.; Khan, W. Development of imatinib mesylate-loaded liposomes for nose to brain delivery: In vitro and in vivo evaluation. AAPS PharmSciTech 2021, 22, 192. [Google Scholar] [CrossRef] [Scilit]
  92. Nageeb El-Helaly, S.; Abd Elbary, A.; Kassem, M.A.; El-Nabarawi, M.A. Electrosteric stealth Rivastigmine loaded liposomes for brain targeting: Preparation, characterization, ex vivo, bio-distribution and in vivo pharmacokinetic studies. Drug Deliv. 2017, 24, 692–700. [Google Scholar] [CrossRef] [Scilit]
  93. Ismail, M.F.; Elmeshad, A.N.; Salem, N.A. Potential therapeutic effect of nanobased formulation of rivastigmine on rat model of Alzheimer’s disease. Int. J. Nanomed. 2013, 8, 393–406. [Google Scholar] [CrossRef] [Scilit]
  94. Rompicherla, S.K.L.; Arumugam, K.; Bojja, S.L.; Kumar, N.; Rao, C.M. Pharmacokinetic and pharmacodynamic evaluation of nasal liposome and nanoparticle based rivastigmine formulations in acute and chronic models of Alzheimer’s disease. Naunyn Schmiedebergs Arch. Pharmacol. 2021, 394, 1737–1755. [Google Scholar] [CrossRef] [Scilit]
  95. Liu, R.; Yang, J.; Liu, L.; Lu, Z.; Shi, Z.; Ji, W.; Shen, J.; Zhang, X. An “Amyloid-β Cleaner” for the treatment of Alzheimer’s disease by normalizing microglial dysfunction. Adv. Sci. 2019, 7, 1901555. [Google Scholar] [CrossRef] [Scilit]
  96. Qian, K.; Bao, X.; Li, Y.; Wang, P.; Guo, Q.; Yang, P.; Xu, S.; Yu, F.; Meng, R.; Cheng, Y.; et al. Cholinergic neuron targeting nanosystem delivering hybrid peptide for combinatorial mitochondrial therapy in Alzheimer’s disease. ACS Nano 2022, 16, 11455–11472. [Google Scholar] [CrossRef] [Scilit]
  97. Ye, C.; Cheng, M.; Ma, L.; Zhang, T.; Sun, Z.; Yu, C.; Wang, J.; Dou, Y. Oxytocin nanogels inhibit innate inflammatory response for early intervention in Alzheimer’s disease. ACS Appl. Mater. Interfaces 2022, 14, 21822–21835. [Google Scholar] [CrossRef] [Scilit]
  98. Huang, Q.; Jiang, C.; Xia, X.; Wang, Y.; Yan, C.; Wang, X.; Lei, T.; Yang, X.; Yang, W.; Cheng, G.; et al. Pathological BBB crossing melanin-like nanoparticles as metal-ion chelators and neuroinflammation regulators against Alzheimer’s disease. Research 2023, 6, 0180. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  99. Gebril, H.M.; Aryasomayajula, A.; de Lima, M.R.N.; Uhrich, K.E.; Moghe, P.V. Nanotechnology for microglial targeting and inhibition of neuroinflammation underlying Alzheimer’s pathology. Transl. Neurodegener. 2024, 13, 2. [Google Scholar] [CrossRef] [Scilit]
  100. Liu, P.; Zhang, T.; Chen, Q.; Li, C.; Chu, Y.; Guo, Q.; Zhang, Y.; Zhou, W.; Chen, H.; Zhou, Z.; et al. Biomimetic dendrimer-peptide conjugates for early multi-target therapy of Alzheimer’s disease by inflammatory microenvironment modulation. Adv. Mater. 2021, 33, e2100746. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  101. Guo, Q.; Xu, S.; Yang, P.; Wang, P.; Lu, S.; Sheng, D.; Qian, K.; Cao, J.; Lu, W.; Zhang, Q. A dual-ligand fusion peptide improves the brain-neuron targeting of nanocarriers in Alzheimer’s disease mice. J. Control. Release 2020, 320, 347–362. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  102. Yang, P.; Li, Y.; Qian, K.; Zhou, L.; Cheng, Y.; Wu, J.; Xu, M.; Wang, T.; Yang, X.; Mu, Y.; et al. Precise modulation of pericyte dysfunction by a multifunctional nanoprodrug to ameliorate Alzheimer’s disease. ACS Nano 2024, 18, 14348–14366. [Google Scholar] [CrossRef] [Scilit]
  103. Luo, Q.; Lin, Y.X.; Yang, P.P.; Wang, Y.; Qi, G.B.; Qiao, Z.Y.; Li, B.N.; Zhang, K.; Zhang, J.P.; Wang, L.; et al. A self-destructive nanosweeper that captures and clears amyloid β-peptides. Nat. Commun. 2018, 9, 1802. [Google Scholar] [CrossRef] [Scilit]
  104. Yao, L.; Gu, X.; Song, Q.; Wang, X.; Huang, M.; Hu, M.; Hou, L.; Kang, T.; Chen, J.; Chen, H.; et al. Nanoformulated alpha-mangostin ameliorates Alzheimer’s disease neuropathology by elevating LDLR expression and accelerating amyloid-beta clearance. J. Control. Release 2016, 226, 1–14. [Google Scholar] [CrossRef] [Scilit]
  105. Dai, F.; Li, X.; Lv, K.; Wang, J.; Zhao, Y. Combined core stability and degradability of nanomedicine via amorphous PDLLA-dextran bottlebrush copolymer for Alzheimer’s disease combination treatment. ACS Appl. Mater. Interfaces 2023, 15, 26385–26397. [Google Scholar] [CrossRef] [Scilit]
  106. Bhavna; Md, S.; Ali, M.; Ali, R.; Bhatnagar, A.; Baboota, S.; Ali, J. Donepezil nanosuspension intended for nose to brain targeting: In vitro and in vivo safety evaluation. Int. J. Biol. Macromol. 2014, 67, 418–425. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  107. Sunena; Singh, S.K.; Mishra, D.N. Nose to brain delivery of galantamine loaded nanoparticles: In-vivo pharmacodynamic and biochemical study in mice. Curr. Drug Deliv. 2019, 16, 51–58. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  108. Fazil, M.; Md, S.; Haque, S.; Kumar, M.; Baboota, S.; Sahni, J.K.; Ali, J. Development and evaluation of rivastigmine loaded chitosan nanoparticles for brain targeting. Eur. J. Pharm. Sci. 2012, 47, 6–15. [Google Scholar] [CrossRef] [Scilit]
  109. Anand, B.; Wu, Q.; Nakhaei-Nejad, M.; Karthivashan, G.; Dorosh, L.; Amidian, S.; Dahal, A.; Li, X.; Stepanova, M.; Wille, H.; et al. Significance of native PLGA nanoparticles in the treatment of Alzheimer’s disease pathology. Bioact. Mater. 2022, 17, 506–525. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  110. Paul, P.S.; Cho, J.Y.; Wu, Q.; Karthivashan, G.; Grabovac, E.; Wille, H.; Kulka, M.; Kar, S. Unconjugated PLGA nanoparticles attenuate temperature-dependent β-amyloid aggregation and protect neurons against toxicity: Implications for Alzheimer’s disease pathology. J. Nanobiotechnol. 2022, 20, 67. [Google Scholar] [CrossRef] [Scilit]
  111. Silva-Abreu, M.; Calpena, A.C.; Andrés-Benito, P.; Aso, E.; Romero, I.A.; Roig-Carles, D.; Gromnicova, R.; Espina, M.; Ferrer, I.; García, M.L.; et al. PPARγ agonist-loaded PLGA-PEG nanocarriers as a potential treatment for Alzheimer’s disease: In vitro and in vivo studies. Int. J. Nanomed. 2018, 13, 5577–5590. [Google Scholar] [CrossRef] [Scilit]
  112. Cabaleiro-Lago, C.; Quinlan-Pluck, F.; Lynch, I.; Lindman, S.; Minogue, A.M.; Thulin, E.; Walsh, D.M.; Dawson, K.A.; Linse, S. Inhibition of amyloid beta protein fibrillation by polymeric nanoparticles. J. Am. Chem. Soc. 2008, 130, 15437–15443. [Google Scholar] [CrossRef] [Scilit]
  113. Le Droumaguet, B.; Nicolas, J.; Brambilla, D.; Mura, S.; Maksimenko, A.; De Kimpe, L.; Salvati, E.; Zona, C.; Airoldi, C.; Canovi, M.; et al. Versatile and efficient targeting using a single nanoparticulate platform: Application to cancer and Alzheimer’s disease. ACS Nano 2012, 6, 5866–5879. [Google Scholar] [CrossRef] [Scilit]
  114. Xiong, N.; Dong, X.Y.; Zheng, J.; Liu, F.F.; Sun, Y. Design of LVFFARK and LVFFARK-functionalized nanoparticles for inhibiting amyloid β-protein fibrillation and cytotoxicity. ACS Appl. Mater. Interfaces 2015, 7, 5650–5662. [Google Scholar] [CrossRef] [Scilit]
  115. Song, Q.; Huang, M.; Yao, L.; Wang, X.; Gu, X.; Chen, J.; Chen, J.; Huang, J.; Hu, Q.; Kang, T.; et al. Lipoprotein-based nanoparticles rescue the memory loss of mice with Alzheimer’s disease by accelerating the clearance of amyloid-beta. ACS Nano 2014, 8, 2345–2359. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  116. Zhang, H.; Zhao, Y.; Yu, M.; Zhao, Z.; Liu, P.; Cheng, H.; Ji, Y.; Jin, Y.; Sun, B.; Zhou, J.; et al. Reassembly of native components with donepezil to execute dual-missions in Alzheimer’s disease therapy. J. Control. Release 2019, 296, 14–28. [Google Scholar] [CrossRef] [Scilit]
  117. Chi, M.; Liu, J.; Li, L.; Zhang, Y.; Xie, M. CeO2 in situ growth on red blood cell membranes: CQD coating and multipathway Alzheimer’s disease therapy under NIR. ACS Appl. Mater. Interfaces 2024, 16, 35898–35911. [Google Scholar] [CrossRef] [Scilit]
  118. Lin, R.R.; Jin, L.L.; Xue, Y.Y.; Zhang, Z.S.; Huang, H.F.; Chen, D.F.; Liu, Q.; Mao, Z.W.; Wu, Z.Y.; Tao, Q.Q. Hybrid membrane-coated nanoparticles for precise targeting and synergistic therapy in Alzheimer’s disease. Adv. Sci. 2024, 11, e2306675. [Google Scholar] [CrossRef] [Scilit]
  119. Zhang, M.; Chen, H.; Zhang, W.; Liu, Y.; Ding, L.; Gong, J.; Ma, R.; Zheng, S.; Zhang, Y. Biomimetic remodeling of microglial riboflavin metabolism ameliorates cognitive impairment by modulating neuroinflammation. Adv. Sci. 2023, 10, e2300180. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  120. Xiao, L.; Zhao, D.; Chan, W.H.; Choi, M.M.; Li, H.W. Inhibition of beta 1-40 amyloid fibrillation with N-acetyl-L-cysteine capped quantum dots. Biomaterials 2010, 31, 91–98. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  121. Liao, Y.H.; Chang, Y.J.; Yoshiike, Y.; Chang, Y.C.; Chen, Y.R. Negatively charged gold nanoparticles inhibit Alzheimer’s amyloid-β fibrillization, induce fibril dissociation, and mitigate neurotoxicity. Small 2012, 8, 3631–3639. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  122. Nazıroğlu, M.; Muhamad, S.; Pecze, L. Nanoparticles as potential clinical therapeutic agents in Alzheimer’s disease: Focus on selenium nanoparticles. Expert. Rev. Clin. Pharmacol. 2017, 10, 773–782. [Google Scholar] [CrossRef] [Scilit]
  123. Yin, X.; Zhou, H.; Zhang, M.; Su, M.; Wang, X.; Li, S.; Yang, Z.; Kang, Z.; Zhou, R. C3N nanodots inhibits Aβ peptides aggregation pathogenic path in Alzheimer’s disease. Nat. Commun. 2023, 14, 5718. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  124. Yin, T.; Yang, L.; Liu, Y.; Zhou, X.; Sun, J.; Liu, J. Sialic acid (SA)-modified selenium nanoparticles coated with a high blood-brain barrier permeability peptide-B6 peptide for potential use in Alzheimer’s disease. Acta Biomater. 2015, 25, 172–183. [Google Scholar] [CrossRef] [Scilit]
  125. Hou, K.; Zhao, J.; Wang, H.; Li, B.; Li, X.; Shi, X.; Wan, K.; Ai, J.; Lv, J.; Wang, D.; et al. Chiral gold nanoparticles enantioselectively rescue memory deficits in a mouse model of Alzheimer’s disease. Nat. Commun. 2020, 11, 4790. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  126. Jia, Z.; Yuan, X.; Wei, J.A.; Guo, X.; Gong, Y.; Li, J.; Zhou, H.; Zhang, L.; Liu, J. A functionalized octahedral palladium nanozyme as a radical scavenger for ameliorating Alzheimer’s disease. ACS Appl. Mater. Interfaces 2021, 13, 49602–49613. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  127. Guan, Y.; Li, M.; Dong, K.; Gao, N.; Ren, J.; Zheng, Y.; Qu, X. Ceria/POMs hybrid nanoparticles as a mimicking metallopeptidase for treatment of neurotoxicity of amyloid-β peptide. Biomaterials 2016, 98, 92–102. [Google Scholar] [CrossRef] [Scilit]
  128. Ran, Z.; Yang, L.L.; Zhou, L.Y.; Wen, T.; Wang, W.J.; Chen, L. Research trends and hotspots of nanomaterials in Alzheimer’s disease: Bibliometric analysis. Discov. Nano 2026, 21, 20. [Google Scholar] [CrossRef] [Scilit]
  129. Koga-Batko, J.; Antosz-Popiołek, K.; Nowakowska, H.; Błażejewska, M.; Kowalik, E.M.; Beszłej, J.A.; Leszek, J. Nanoparticles as an Encouraging Therapeutic Approach to Alzheimer’s Disease. Int. J. Mol. Sci. 2025, 26, 7725. [Google Scholar] [CrossRef] [Scilit]
  130. Khan, S.; Haider, M.F.; Naseem, N. Overview and applications of nanocarrier-based drug delivery for alleviating Alzheimer’s disease. J. Bio-X Res. 2025, 8, 42. [Google Scholar] [CrossRef] [Scilit]
  131. Abd El-Fattah, M.A. Challenges and opportunities of drug delivery for treatment of Alzheimer’s disease. AAPS PharmSciTech 2026, 27, 78. [Google Scholar] [CrossRef] [Scilit]
  132. European Medicines Agency. Nanotechnology-Based Medicinal Products for Human Use EU-IN Horizon Scanning Report; EMA/20989/2025/Rev. 1; European Medicines Agency: Amsterdam, The Netherlands, 2025; pp. 1–32. [Google Scholar]
  133. EMA/CHMP/79769/2006; Reflection Paper on Nanotechnology-Based Medicinal Products for Human Use. European Medicines Agency: Amsterdam, The Netherlands, 2006; pp. 1–4.
  134. Souto, E.B.; Silva, G.F.; Dias-Ferreira, J.; Zielinska, A.; Ventura, F.; Durazzo, A.; Lucarini, M.; Novellino, E.; Santini, A. Nanopharmaceutics: Part I—Clinical trials legislation and Good Manufacturing Practices (GMP) of nanotherapeutics in the EU. Pharmaceutics 2020, 12, 146. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  135. U.S. Department of Health and Human Services; Food and Drug Administration; Center for Drug Evaluation and Research (CDER); Center for Biologics Evaluation and Research (CBER). Drug Products, Including Biological Products, That Contain Nanomaterials: Guidance for Industry; FDA-2011-D-0530; Pharmaceutical Quality/CMC; Silver Spring: Washington, DC, USA, 2022; pp. 1–33.
  136. Bremer-Hoffmann, S.; Halamoda-Kenzaoui, B.; Borgos, S.E. Identification of regulatory needs for nanomedicines. J. Interdiscip. Nanomed. 2018, 3, 4–15. [Google Scholar] [CrossRef] [Scilit]
  137. Wang, K.; Yang, R.; Li, J.; Wang, H.; Wan, L.; He, J. Nanocarrier-based targeted drug delivery for Alzheimer’s disease: Addressing neuroinflammation and enhancing clinical translation. Front. Pharmacol. 2025, 16, 1591438. [Google Scholar] [CrossRef] [Scilit]
  138. Wu, D.; Chen, Q.; Chen, X.; Han, F.; Chen, Z.; Wang, Y. The blood-brain barrier: Structure, regulation, and drug delivery. Signal Transduct. Target. Ther. 2023, 8, 217. [Google Scholar] [CrossRef] [Scilit]
  139. Kariolis, M.S.; Wells, R.C.; Getz, J.A.; Kwan, W.; Mahon, C.S.; Tong, R.; Kim, D.J.; Srivastava, A.; Bedard, C.; Henne, K.R.; et al. Brain delivery of therapeutic proteins using an Fc fragment blood-brain barrier transport vehicle in mice and monkeys. Sci. Transl. Med. 2020, 12, eaay1359. [Google Scholar] [CrossRef] [Scilit]
  140. Pornnoppadol, G.; Bond, L.G.; Lucas, M.J.; Zupancic, J.M.; Kuo, Y.H.; Zhang, B.; Greineder, C.F.; Tessier, P.M. Bispecific antibody shuttles targeting CD98hc mediate efficient and long-lived brain delivery of IgGs. Cell Chem. Biol. 2024, 31, 361–372.e8. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  141. Godugu, D.; Gattu, K.; Suri, P.; Daartey, A.B.; Jadhav, K.; Rojekar, S. Nanobody therapeutics in Alzheimer’s disease: From molecular mechanisms to translational approaches. Antibodies 2026, 15, 1. [Google Scholar] [CrossRef] [Scilit]
  142. Prvulovic, D.; Hampel, H. Ethical considerations of biomarker use in neurodegenerative diseases--a case study of Alzheimer’s disease. Prog. Neurobiol. 2011, 95, 517–519. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  143. Scott, K.; Klaus, S.P. Focused ultrasound therapy for Alzheimer’s disease: Exploring the potential for targeted amyloid disaggregation. Front. Neurol. 2024, 15, 1426075. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  144. Singh, B.; Day, C.M.; Abdella, S.; Garg, S. Alzheimer’s disease current therapies, novel drug delivery systems and future directions for better disease management. J. Control. Release 2024, 367, 402–424. [Google Scholar] [CrossRef] [Scilit]
  145. Zhang, J.; Zhang, Y.; Wang, J.; Xia, Y.; Zhang, J.; Chen, L. Recent advances in Alzheimer’s disease: Mechanisms, clinical trials and new drug development strategies. Signal Transduct. Target. Ther. 2024, 9, 211. [Google Scholar] [CrossRef] [Scilit]
  146. Li, Q.; Kong, Y.; Zhong, Y.; Huang, A.; Ying, T.; Wu, Y. Half-life extension of single-domain antibody-drug conjugates by albumin binding moiety enhances antitumor efficacy. MedComm 2024, 5, e557. [Google Scholar] [CrossRef] [Scilit]
  147. Kaushik, S.; Kumari, P.; Upadhayay, A.; Singh, Y. Nanotechnology-based drug delivery systems for Alzheimer’s disease: Advances, challenges and future perspectives. Int. J. Curr. Res. 2025, 17, 34607–34612. [Google Scholar]
  148. Hassan, Y.M.; Wanas, A.; Ali, A.A.; El-Sayed, W.M. Integrating artificial intelligence with nanodiagnostics for early detection and precision management of neurodegenerative diseases. J. Nanobiotechnol. 2025, 23, 668. [Google Scholar] [CrossRef] [Scilit]
  149. He, S.; Segura Abarrategi, J.; Bediaga, H.; Arrasate, S.; González-Díaz, H. On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems. Beilstein J. Nanotechnol. 2024, 15, 535–555. [Google Scholar] [CrossRef] [Scilit]
  150. Halagali, P.; Nayak, D.; Rathnanand, M.; Tippavajhala, V.K.; Sharma, H.; Biswas, D. Synergizing sustainable green nanotechnology and AI/ML for advanced nanocarriers: A paradigm shift in the treatment of neurodegenerative diseases. In The Neurodegeneration Revolution: Emerging Therapies and Sustainable Solutions; Koduru, T.S., Osmani, R.A.M., Singh, E., Dutta, S., Eds.; Academic Press: Cambridge, MA, USA, 2025; pp. 373–397. [Google Scholar]
  151. Pérez-Ruixo, C.; Li, L.; Galpern, W.R.; Perez-Ruixo, J.J. Mechanistic population pharmacokinetic–pharmacodynamic model of the tau-targeted antibody Posdinemab in healthy participants and participants with Alzheimer’s disease. Clin. Pharmacol. Ther. 2026. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  152. Wang, D.; Florian, H.; Lynch, S.Y.; Robieson, W.; Zhuang, R.; Kusiak, C.; Ross, J.L.; Walsh, J.R.; Graff, O. Using AI-generated digital twins to boost clinical trial efficiency in Alzheimer’s disease. Alzheimer’s Dement. 2025, 11, e70181. [Google Scholar] [CrossRef] [Scilit]
  153. Gkintoni, E.; Halkiopoulos, C. Digital Twin Cognition: AI-Biomarker Integration in Biomimetic Neuropsychology. Biomimetics 2025, 10, 640. [Google Scholar] [CrossRef] [Scilit]
  154. Ren, Y.; Pieper, A.A.; Cheng, F. Utilization of precision medicine digital twins for drug discovery in Alzheimer’s disease. Neurotherapeutics 2025, 22, e00553. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  155. Li, Y.; Li, D.; Zhao, P.; Nandakumar, K.; Wang, L.; Song, Y. Microfluidics-based systems in diagnosis of Alzheimer’s disease and biomimetic modeling. Micromachines 2020, 11, 787. [Google Scholar] [CrossRef] [Scilit]
  156. Ferrari, I.; Limiti, E.; Giannitelli, S.M.; Trombetta, M.; Rainer, A.; D’Amelio, M.; La Barbera, L.; Gori, M. Microfluidic-based technologies for crossing the blood–brain barrier against Alzheimer’s disease: Novel strategies and challenges. Int. J. Mol. Sci. 2025, 26, 9478. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  157. Wu, X.; Zang, R.; Qiu, Y.; Zhang, Y.; Peng, J.; Cheng, Z.; Wei, S.; Liu, M.; Diao, Y. Intranasal drug delivery technology in the treatment of central nervous system diseases: Challenges, advances, and future research directions. Pharmaceutics 2025, 17, 775. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  158. Bae, H.; Ji, H.; Konstantinov, K.; Sluyter, R.; Ariga, K.; Kim, Y.H.; Kim, J.H. Artificial intelligence-driven nanoarchitectonics for smart targeted drug delivery. Adv. Mater. 2025, 37, e10239. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Figure 1. Shared pathophysiological mechanisms linking major comorbidities to Alzheimer’s disease. Age-associated comorbid conditions, including metabolic dysregulation (type 2 diabetes and metabolic syndrome), cardiovascular disease, affective disorders (depression and anxiety), chronic systemic inflammation and parkinsonian syndromes interact with Alzheimer’s disease through overlapping pathophysiological mechanisms. Metabolic disorders contribute to AD pathogenesis primarily through insulin signaling impairment, which disrupts neuronal glucose metabolism and promotes amyloidogenic processing of amyloid precursor protein. Chronic hyperglycemia and insulin resistance further exacerbate oxidative stress and neuroinflammatory responses, facilitating tau hyperphosphorylation and synaptic dysfunction. Vascular dysfunction, endothelial damage and impaired cerebral perfusion can compromise blood–brain barrier integrity and reduce the clearance of amyloid-β peptides. These alterations promote neuroinflammation and accelerate neurodegenerative cascades. Similarly, affective disorders such as depression and anxiety are linked to dysregulation of the HPA axis and chronic elevation of glucocorticoids, which can impair hippocampal neurogenesis, increase oxidative stress and amplify inflammatory signaling pathways involved in AD progression. Parkinsonian syndromes and other neurodegenerative conditions share several pathogenic mechanisms with AD, including mitochondrial dysfunction, impaired proteostasis, defective autophagy and abnormal protein aggregation. These converging mechanisms facilitate the accumulation of misfolded proteins such as Aβ and tau, ultimately contributing to neuronal dysfunction and progressive neurodegeneration. Together, these interconnected pathways highlight the biological links between systemic comorbidities and AD pathology, supporting the concept that Alzheimer’s disease should be considered a multifactorial disorder influenced by both central and peripheral pathological processes. AD, Alzheimer’s disease; HPA axis dysregulation, hypothalamic–pituitary–adrenal; IL-1β, interleukin-1 beta; TNF-a, tumor necrosis factor alpha; IL-6, interleukin-6; Aβ, amyloid-beta; BDNF, brain-derived neurotrophic factor.
Figure 1. Shared pathophysiological mechanisms linking major comorbidities to Alzheimer’s disease. Age-associated comorbid conditions, including metabolic dysregulation (type 2 diabetes and metabolic syndrome), cardiovascular disease, affective disorders (depression and anxiety), chronic systemic inflammation and parkinsonian syndromes interact with Alzheimer’s disease through overlapping pathophysiological mechanisms. Metabolic disorders contribute to AD pathogenesis primarily through insulin signaling impairment, which disrupts neuronal glucose metabolism and promotes amyloidogenic processing of amyloid precursor protein. Chronic hyperglycemia and insulin resistance further exacerbate oxidative stress and neuroinflammatory responses, facilitating tau hyperphosphorylation and synaptic dysfunction. Vascular dysfunction, endothelial damage and impaired cerebral perfusion can compromise blood–brain barrier integrity and reduce the clearance of amyloid-β peptides. These alterations promote neuroinflammation and accelerate neurodegenerative cascades. Similarly, affective disorders such as depression and anxiety are linked to dysregulation of the HPA axis and chronic elevation of glucocorticoids, which can impair hippocampal neurogenesis, increase oxidative stress and amplify inflammatory signaling pathways involved in AD progression. Parkinsonian syndromes and other neurodegenerative conditions share several pathogenic mechanisms with AD, including mitochondrial dysfunction, impaired proteostasis, defective autophagy and abnormal protein aggregation. These converging mechanisms facilitate the accumulation of misfolded proteins such as Aβ and tau, ultimately contributing to neuronal dysfunction and progressive neurodegeneration. Together, these interconnected pathways highlight the biological links between systemic comorbidities and AD pathology, supporting the concept that Alzheimer’s disease should be considered a multifactorial disorder influenced by both central and peripheral pathological processes. AD, Alzheimer’s disease; HPA axis dysregulation, hypothalamic–pituitary–adrenal; IL-1β, interleukin-1 beta; TNF-a, tumor necrosis factor alpha; IL-6, interleukin-6; Aβ, amyloid-beta; BDNF, brain-derived neurotrophic factor.
Life 16 00510 g001
Table 1. Nanotechnology-based drug delivery systems in Alzheimer’s disease: Representative preclinical evidence.
Table 1. Nanotechnology-based drug delivery systems in Alzheimer’s disease: Representative preclinical evidence.
PlatformTargeting Strategy/MechanismMain OutcomesTherapeutic Strategy/AdvantagesLimitationsPayload/Study Type
Liposomes
Felodipine-modified liposomesphospholipid bilayers vesiclesmodulated endoplasmic reticulum stress, inhibited NLRP3 inflammasome activation, reduced Aβ aggregation and promoted mitophagy, collectively attenuating neuronal apoptosis, improved cognition
-
Aβ plaques
-
high biocompatibility; ability to encapsulate lipophilic drugs; improved drug stability and brain delivery; potential multi-target modulation of AD pathology
possible instability and rapid clearance; limited targeting specificity without ligand functionalization; scalability and long-term safety require further investigationfelodipine (felodipine@LND), 5 × FAD transgenic mice [87]
Transferrin-modified liposomesreceptor-mediated BBB targetingBBB penetration, decreased neuroinflammation and neuronal apoptosis, enhanced cognitive performance
-
neuroinflammation, neuronal apoptosis, BBB dysfunction
-
efficient receptor-mediated BBB targeting; enhanced brain drug delivery; good biocompatibility
possible off-target uptake in peripheral tissues; stability and large-scale manufacturing challengespantothenate (Pan@TRF@Liposome NPs), APPs/PS1 mice [88]
ATX-loaded PEGylation of liposomesenhanced solubility, BBB permeabilitydecreased brain endogenous formaldehyde levels, attenuated oxidative stress, reduced Aβ oligomerization and plaque formation, and improved spatial learning and memory
-
oxidative stress, Aβ aggregation
-
improved solubility and stability of hydrophobic drugs; prolonged circulation time; enhanced BBB penetration
potential PEG-related immune responses; limited active targeting without specific ligands; complexity of large-scale productionastaxanthin antioxidant (PEG–ATX@NPs), APPs/PS1 mice [89]
PEG/donepezil liposomessustained release and passive BBB penetrationimproved brain and plasma bioavailability
-
cholinergic dysfunction
-
sustained drug release; improved pharmacokinetic profile; enhanced brain delivery of donepezil
lack of active targeting; potential PEG-related immune responses; possible drug leakage or stability issuesdonepezil, Wistar rats [90] rivastigmine; AlCl3-induced AD rats [93]
Imatinib mesylate loaded liposomessustained release (up to 96 h); intranasal nose-to-brain delivery enhancing BBB bypassprolonged drug release; cytotoxic effects up to 25 μg/mL; improved brain penetration and residence time 
-
Aβ plaques and neuroinflammation
-
direct nose-to-brain transport bypassing BBB; prolonged drug release; reduced systemic exposure; improved brain bioavailability
variable intranasal absorption; potential mucosal irritation; limited dosing capacity and long-term safety dataimatinib mesylate; in vitro (N2a cells); in vivo (Sprague Dawley rats) [91]
Lecithin and Tween® 80/rivastigmine LiposomesPEG-DSPE steric hindrance + DDAB electrostatic stabilization; intranasal nose-to-brain deliveryincreased rivastigmine’s bioavailability and delayed its release, stable formulation, no tissue toxicity
-
cholinergic dysfunction
-
direct nose-to-brain transport bypassing BBB; improved drug stability and bioavailability; controlled release; reduced systemic exposure
variability of intranasal absorption; potential mucosal irritation; limited drug loading and dosing constraintsrivastigmine; in vivo (rabbits), ex vivo (sheep nasal mucosa) [92]
Soya lecithin/rivastigmine liposomesintranasal nose-to-brain; liposomal encapsulationreduced clearance, improved memory in Morris’s water maze and passive avoidance, strong PK-PD correlation with AChE inhibition
-
cholinergic dysfunction
-
direct BBB bypass; enhanced brain bioavailability; protection of drug from rapid metabolism; sustained release
variability in nasal absorption; limited dosing capacity; potential mucosal irritationrivastigmine; acute scopolamine and chronic colchicine-induced AD rats [94]
Polymeric nanoparticles
Zwitterionic poly(carboxybetaine) (PCB)-based nanoparticle (MCPZFS NP)BBB penetration; microglia targeting; Aβ recruitment; multi-mechanistic modulation (anti-inflammatory, pro-phagocytic)reduced proinflammatory cytokines; enhanced Aβ clearance; improved cognition; attenuated Aβ burden; good safety profile
-
Aβ plaques and neuroinflammation
-
enhanced Aβ clearance; anti-inflammatory effects; good biocompatibility and safety profile
complex nanoparticle design and synthesis; limited long-term safety data; translational and large-scale manufacturing challengesfingolimod + siSTAT3 + ZnO,
in vitro (microglia); in vivo (APPs/PS1 mice) [95]
FGL-modified PEG–PTMC(Cit) nanoparticles [FGL-NP(Cit)/HNSS]BBB and cholinergic neuron targeting; acid-responsive charge switching for lysosomal escape; mitochondrial targeting via SS31 moietyrestored mitochondrial function; reduced Aβ and tau pathology; improved cognition; enhanced antioxidant capacity
-
mitochondrial dysfunction, Aβ and tau pathology, oxidative stress
-
enhanced neuronal and mitochondrial targeting; improved intracellular delivery and lysosomal escape
complex design and synthesis; potential challenges in large-scale production; limited long-term safety and clinical translation datahybrid peptide HNSS (SS31 + S14G-Humanin), 3 × Tg-AD mice [96]
Oxytocin (OT)-loaded angiopep-2-modified chitosan nanogels (AOC NGs)LRP1-mediated BBB targeting; microglia modulationprevented cognitive impairment and delayed hippocampal atrophy
-
neuroinflammation and neurodegeneration
-
enhanced brain delivery; anti-inflammatory and neuroprotective effects; good biocompatibility of chitosan nanogels
possible receptor saturation; limited long-term safety data; stability and large-scale manufacturing challengesoxytocin, APP/PS1 mice [97]
Multifunctional melanin-like metal ion chelators and neuroinflammation regulators (named PDA@K)Aβ-binding via KLVFF motif; metal ion chelation; ROS scavengingreduced Aβ aggregation; decreased oxidative stress
-
Aβ aggregation, oxidative stress
-
potential inhibition of Aβ aggregation
limited in vivo and long-term safety data; unclear pharmacokinetics and BBB transport; translational challengesmelanin-like polydopamine core, in vitro (bEnd.3, BV2, and PC-12 cell lines); in vivo (FAD transgenic mice) [98]
Sugar-based amphiphilic nanoparticlesmicroglial scavenger receptor targetingreduced neuroinflammation and Aβ burden
-
neuroinflammation and Aβ clearance
-
specific microglial targeting; enhanced clearance of Aβ; potential anti-inflammatory effects; biocompatible
unclear BBB penetration and pharmacokinetics; challenges in large-scale productionanti-inflammatory agents, BV2 mouse microglia cell line and SH-SY5Y human neuroblastoma cell line [99]
A reactive oxygen species (ROS)-responsive dendrimer-peptide conjugate (APBP)ROS-triggered release; microglial targetingreduced ROS level, decreased Aβ burden, alleviated glial cell activation
-
neuroinflammation, ROS, and Aβ pathology
-
multifunctional modulation of oxidative stress and Aβ; good biocompatibility
complex synthesis; limited in vivo and long-term safety data; BBB penetration needs further characterizationpeptide therapeutics, APP/PS1 mice [100]
Dual-ligand fusion peptide modified nanoparticlesenhanced BBB penetration; neuron-targeted deliveryimproved cognitive function; reduced pathological markers
-
AD pathological markers
-
enhanced BBB penetration; neuron-specific targeting; multi-mechanistic modulation of AD pathology
complex design and synthesis; limited long-term safety data; challenges in large-scale productionneuroprotective agents; in vitro (HT22 cells), in vivo (Aβ-induced mice model) [101]
multifunctional nanoprodrugs (curcumin–hybrid peptide conjugates)pericyte-targeted delivery; improved BBB penetrationreduced Aβ pathology; improved behavioral performance
-
Aβ pathology and neurodegeneration
-
enhanced BBB penetration; potential neuroprotective effects
complex synthesis; limited in vivo safety and pharmacokinetic data; translational scalability challengescurcumin conjugates; APP/PS1 mice [102]
Self-destructive nanosweepersAβ capture and degradation; enhanced phagocytosispromoted Aβ clearance; reversed behavioral deficits
-
Aβ pathology and neurodegeneration
-
active Aβ clearance; stimulates microglial phagocytosis; biocompatible
complex design and synthesis; limited in vivo safety and BBB penetration data; scalability challengesmultifunctional peptide–polymer systems; in vitro (N2a cells), in vivo (APP/PS1 mice) [103]
Nanoparticles encapsulating alpha-mangostinanti-amyloid and antioxidant mechanismsreduced Aβ aggregation; improved cognition
-
Aβ aggregation and ROS
-
enhanced brain delivery; antioxidant and anti-amyloid effects; improved bioavailability
limited BBB penetration data; in vivo safety and pharmacokinetics not fully characterized; scalability challengesalpha-mangostin; in vitro (BV-2 cells), in vivo (SAMP8 and SAMR1 mice) [104]
Amorphous PDLLA–dextran bottlebrush copolymersimproved solubility and sustained brain deliveryameliorated cognitive deficits and oxidative stress
-
ROS and cognitive deficits
-
enhanced solubility and brain delivery; sustained release; biocompatible
limited BBB penetration and pharmacokinetic data; in vivo safety not fully characterized; scalability challengeshydrophilic antioxidants; in vitro (SH-SY5Y cell), in vivo (SAMP8 mice) [105]
Chitosan/donepezil polymericintranasal olfactory delivery for direct nose-to-brain transportimproving donepezil/galantamine/rivastigmine pharmacokinetic characteristics and bioavailability
-
cholinergic dysfunction
-
improved brain bioavailability; enhanced pharmacokinetic profile; biocompatible
variability in nasal absorption; limited dosing capacity; potential mucosal irritationdonepezil; Sprague-Dawley rats [106] galantamine; scopolamine-induced amnesia in Swiss albino mice [107] rivastigmine, Wistar rats [108]
Native poly(D,L-lactide-co-glycolide)-PLGAdirect interaction with hydrophobic domain of Aβ1–42suppressed spontaneous aggregation of 10 μM Aβ1–42 at 25–50 μM PLGA; induced fibril disassembly; reduced tau phosphorylation and ERK1/2 & GSK-3β activation; increased neuronal viability; in 5 × FAD mice: attenuated memory deficits (novel object recognition), reduced cortical Aβ & plaque load; no observable toxicity
-
Aβ aggregation, tau pathology
-
biocompatible and biodegradable; inhibits Aβ aggregation; good in vivo safety profile
limited BBB penetration characterization; pharmacokinetics and long-term safety need further study; scalability challengesPLGA; in vitro (mouse cortical neurons; iPSC-derived AD neurons); in vivo (5 × FADmice) [109,110,111]
Poly(N-isopropylacrylamide-co-N-tert-butylacrylamide) nanoparticlesbind monomeric and oligomeric Aβ; prolong nucleation lag phase; retard fibrillation kineticsdelayed Aβ fibril formation by extending nucleation phase; inhibited aggregation progression
-
Aβ aggregation
-
inhibits Aβ aggregation; potential disease-modifying effect; biocompatible
limited BBB penetration and pharmacokinetic data; in vivo safety not fully characterized; scalability challengesnative polymeric NPs; in vitro Aβ aggregation assay [112]
PEGylated poly(alkyl cyanoacrylate) nanoparticleshigh-affinity binding to Aβ peptidesinhibited Aβ aggregation; decreased Aβ-induced cytotoxicity in neuronal cells
-
Aβ aggregation
-
inhibits aggregation; neuroprotective; PEGylation improves stability and circulation
limited BBB penetration and pharmacokinetic data; in vivo safety not fully characterized; scalability challengesfunctionalized with curcumin derivatives or anti-Aβ1–42 antibodies; in vitro neuronal cell models [113]
Iminodiacetic acid-conjugated nanoparticles (IDA-NP)direct inhibition of Aβ42 fibrillationreduced metal-induced Aβ aggregation; protected neurons from Aβ cytotoxicity
-
Aβ aggregation
-
direct inhibition of Aβ fibrillation; potential disease-modifying effect
limited BBB penetration and pharmacokinetic data; in vivo safety not fully characterized; scalability challengesiminodiacetic acid, PC12 cell line [114]
Biomimetic nanoparticles
apolipoprotein E3-reconstituted high-density lipoprotein (ApoE3-rHDL)biomimetic HDL structure; enhanced BBB crossing; high-affinity binding to Aβ monomers and oligomers; promotion of microglial uptake and lysosomal degradationreduced Aβ deposition, attenuated microgliosis, ameliorated neurologic changes and rescued memory deficits
-
Aβ aggregation and neuroinflammation
-
promotes Aβ clearance via microglia; multi-mechanistic neuroprotection; biocompatible
complex synthesis; long-term safety and pharmacokinetics need characterization; scalability challengesApoE3-functionalized rHDL; in vivo (SAMP8 mice)
[115]
Donepezil-loaded ApoA-I rHDL nanoparticlesAβ clearance via HDL-mimetic binding + AChE inhibitionsimultaneous Aβ reduction and cholinesterase inhibition; improved therapeutic efficacy
-
Aβ aggregation and cholinergic dysfunction
-
dual-target therapeutic effect; enhanced BBB penetration; biocompatible
complex synthesis; long-term safety and pharmacokinetics need characterization; scalability challengesdonepezil + ApoA-I rHDL; in vitro (human brain endothelial hCMEC/D3 cells, human SH-SY5Y neuroblastoma cells and murine microglia BV-2 cells) and in vivo (Aβ-induced mouse and rat models) [116]
Cerium oxide nanocrystals in situ on red blood cell membranes (CQD–Ce–RBC)biomimetic RBC coating for prolonged circulation and biocompatibilityreduced ROS; inhibited Aβ1–42 aggregation; improved cognition; reduced neuroinflammation, TNF-α, IL-1β, IL-6
-
ROS, Aβ aggregation and neuroinflammation
-
prolonged circulation and biocompatibility; multi-mechanistic disease-modifying effect; improved brain delivery
complex synthesis; BBB penetration not fully characterized; scalability challengescerium oxide (CeO2) nanocrystals + nitrogen-doped carbon quantum dots (CQDs) embedded in red blood cell (RBC) membrane; in vitro (SH-SY5Y neuronal cells), in vivo (APP/PS1 mice) [117]
Hybrid platelet–CCR2 membrane-coated liposomes (TR@CPLs)enhance BBB penetration and target neuroinflammatory lesionsimproved cell viability; significant cognitive improvement; reduced amyloid plaque deposition, glial infiltration and neuroinflammation; no observable systemic toxicity
-
neuroinflammation and Aβ pathology
-
enhanced BBB penetration; neuroprotection; biocompatible; reduced systemic toxicity
complex synthesis; scalability and long-term safety need evaluation; pharmacokinetics in vivo not fully characterizedrapamycin (autophagy enhancer) + TPPU (soluble epoxide hydrolase inhibitor); in vitro (HEK293T cells), in vivo (5xFAD mice) [118]
Biomimetic microglial nanoparticles (MNPs@FMN)improve BBB penetration and microglial-targeted delivery; FMN-mediated inhibition of riboflavin kinase (RFK) via regulation of KMT2Bameliorated cognitive deficits, restored synaptic plasticity, reduced hippocampal expression of RFK and pro-inflammatory markers
-
neuroinflammation and synaptic dysfunction
-
enhanced BBB penetration; biomimetic design improves biocompatibility
complex synthesis; long-term safety and pharmacokinetics not fully characterized; scalability challengesflavin mononucleotide (FMV); in vitro (microglial BV2 cell), in vivo (5 × FAD mice) [119]
- Inorganic nanoparticles
N-acetyl-L-cysteine capped quantum dots (NAC-QDs)inhibition of Aβ fibrillationstrong inhibition of amyloid fibrillation, suppression of fibril growth and elongation
-
Aβ aggregation/fibrillation
-
potent inhibition of Aβ aggregation; controlled fibril growth
limited BBB penetration data; in vivo safety not fully characterized; potential toxicity of quantum dots; scalability challengeswater-dispersed quantum dots capped with N-acetyl-L-cysteine; in vitro Aβ fibrillation [120]
Gold nanoparticles (AuNPs)inhibit fibrillization, redirect aggregation toward fragmented fibrils and spherical oligomersinhibited Aβ fibrillization and reduced neurotoxicity in neuronal cells
-
Aβ aggregation
-
potent Aβ fibrillation inhibition; neuroprotective; facile surface functionalization
limited BBB penetration and in vivo pharmacokinetic data; long-term safety not fully characterized; scalability challengesbare and carboxyl-conjugated nanoparticles; neuroblastoma cell [121]
Cu2S quantum dots (QDs) functionalized with four cysteine derivatives: N-acetyl-L-cysteine (NAC), N-propionyl-L-cysteine (NPC), N-isobutyryl-L-cysteine (NIBC), and N-pivaloyl-L-cysteine (NPVC)40 misfolding and fibrillationsuppression of Aβ40 aggregation
-
direct inhibition of Aβ40 aggregation
-
potent inhibition of Aβ misfolding and fibrillation; potential imaging functionality
limited BBB penetration and in vivo pharmacokinetic data; potential quantum dot toxicity; long-term safety not fully characterized; scalability challengesN-acetyl-L-cysteine, N-propionyl-L-cysteine, N-isobutyryl-L-cysteine, N-pivaloyl-L-cysteine; PC-12 cells [122]
Ultra-small C3N nanodotsinhibition of Aβ42 peptide aggregationalleviated aggregation-induced cytotoxicity, increasing cell viability; exhibited improved cognitive function
-
direct inhibition of Aβ42 aggregation
-
potent Aβ aggregation inhibition; reduces cytotoxicity; biocompatible
limited BBB penetration and pharmacokinetic data; long-term in vivo safety not fully characterized; scalability challengesin vitro (primary mouse neurons) and in vivo (APP/PS1 mice) [123]
Sialic acid-modified selenium nanoparticles conjugated with B6 peptide B6-SA-SeNPs)receptor-mediated endogenous BBB transport systemsenhanced BBB permeability, inhibited Aβ aggregation and protected neuronal cells from Aβ-induced apoptosis
-
BBB transport
-
enhanced BBB penetration; inhibits Aβ aggregation; neuroprotective; biocompatible
limited in vivo pharmacokinetic and long-term safety data; scalability challengesB6-SA-SeNPs, a synthetic selenoprotein analogue; PC12 and bEnd.3 cells, in vitro BBB Transwell [124]
Chiral L- and D-glutathione-stabilized gold nanoparticlesinhibition activity against Aβ aggregations; BBB permeabilityinhibited Aβ42 aggregation and crossed the BBB
-
BBB transport and inhibition of Aβ aggregation
-
dual effect: inhibits Aβ aggregation and crosses BBB; biocompatible
limited long-term safety and pharmacokinetic data; scalability challengesL- and D-glutathione; APP/PS1 mice [125]
Octahedral palladium nanoparticles (Pd NPs) functionalized with polyethylene glycol and borneol (Pd@PEG@Bor)BBB permeabilityreduced intracellular ROS levels, protected mitochondrial integrity, and decreased neuroinflammation, reduced Aβ plaque deposition and improved cognitive function
-
BBB penetration
-
enhanced BBB penetration; neuroprotection; PEGylation improves stability and circulation
limited long-term safety and pharmacokinetic data; potential metal nanoparticle toxicity; scalability challengesoctahedral palladium; in vitro (SH-SY5Y cells) and in vivo (3 × Tg mice) [126]
Ceria/polyoxometalate hybrid nanoparticles (CeONP@POMD)both proteolytic and superoxide dismutase activitiesdegraded Aβ monomers and fibrils, inhibited Aβ-induced cytotoxicity, and reduced intracellular ROS; good biocompatibility
-
Aβ aggregation and oxidative stress
-
multi-mechanistic activity; reduces cytotoxicity; good biocompatibility; potential disease-modifying effect
limited BBB penetration and in vivo pharmacokinetic data; long-term safety and scalability not fully characterizedin vitro (PC12 cells and BV2 cells) and in vivo (S4880202 mice) [127]
BBB, blood–brain barrier; ATX-loaded PEGylation of liposomes encapsulating astaxanthin, polyethylene glycol-modified liposomal nanoparticles; NPs, nanoparticles; Aβ, beta-amyloid; ZnO, zinc oxide; siSTAT3, siRNA targeting STAT3; HNSS, mitochondria-targeted hybrid peptide; LRP1, low-density lipoprotein receptor–related protein 1; KLVFF, a self-recognition sequence derived from residues 16–20 of Aβ; ROS, reactive oxygen species; SAMP8 mice, senescence-accelerated prone 8 mice; SAMR1 mice, senescence-accelerated mouse-resistant 1 mice; AlCl3, aluminum chloride; PK-PD, pharmacokinetic and pharmacodynamic; PLGA, native poly(D,L-lactide-co-glycolide) nanoparticles; rHDL, reconstituted high-density lipoprotein; apoA-I, apolipoprotein A-I; TPPU, 1-trifluoromethoxyphenyl-3-(1-propionylpiperidin-4-yl) urea; FMV, flavin mononucleotide; RFK, riboflavin kinase; NAC, N-acetyl-L-cysteine; NPC, N-propionyl-L-cysteine; NIBC, N-isobutyryl-L-cysteine; NPVC, N-pivaloyl-L-cysteine.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Stanciu, G.-D.; Costachescu, I.; Dascalu, C.; Tamba, B.-I. Smart Drug-Delivery Approaches for Enhanced Management of Comorbid Conditions in Alzheimer’s Disease. Life 2026, 16, 510. https://doi.org/10.3390/life16030510

AMA Style

Stanciu G-D, Costachescu I, Dascalu C, Tamba B-I. Smart Drug-Delivery Approaches for Enhanced Management of Comorbid Conditions in Alzheimer’s Disease. Life. 2026; 16(3):510. https://doi.org/10.3390/life16030510

Chicago/Turabian Style

Stanciu, Gabriela-Dumitrita, Ivona Costachescu, Camelia Dascalu, and Bogdan-Ionel Tamba. 2026. "Smart Drug-Delivery Approaches for Enhanced Management of Comorbid Conditions in Alzheimer’s Disease" Life 16, no. 3: 510. https://doi.org/10.3390/life16030510

APA Style

Stanciu, G.-D., Costachescu, I., Dascalu, C., & Tamba, B.-I. (2026). Smart Drug-Delivery Approaches for Enhanced Management of Comorbid Conditions in Alzheimer’s Disease. Life, 16(3), 510. https://doi.org/10.3390/life16030510

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop