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Life
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Progress and Challenges in Emerging Therapies for Alzheimer’s Disease and...
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Progress and Challenges in Emerging Therapies for Alzheimer’s Disease and Its Comorbidities

A Special Issue of Life (ISSN 2075-1729) belonging to the section "Medical Research".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2341

Editor

Dr. Gabriela Dumitrița Stanciu

E-Mail Website
Guest Editor
Advanced Research and Development Center for Experimental Medicine “Prof. Ostin C. Mungiu”, “Grigore T. Popa” University of Medicine and Pharmacy of Iasi, 700115 Iasi, Romania
Interests: neuroscience; Alzheimer’s disease; in vivo studies; drug development; cannabinoid-based pharmaceuticals; repurposing current therapeutics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Alzheimer’s disease remains one of the most pressing public health challenges worldwide, with its incidence increasing alongside aging populations. Despite significant progress in understanding its pathophysiology, truly effective therapies remain limited. In recent years, numerous emerging therapeutic strategies—ranging from novel pharmacological agents and immunotherapies to lifestyle-based and digital interventions—have shown promise. However, translation to clinical treatment is often hampered by the frequent presence of comorbidities such as cardiovascular, metabolic and neuropsychiatric conditions, which complicate treatment responses and the design of trials.

This Special Issue aims to present cutting-edge research and critical reviews on innovative therapeutic approaches to Alzheimer’s disease and its major comorbidities. We welcome original research, systematic reviews and meta-analyses that provide mechanistic insights, clinical trial results, personalized medicine approaches and integrative strategies that consider comorbid conditions. By assembling diverse contributions, this Special Issue seeks to highlight current progress, identify remaining challenges and foster interdisciplinary dialogue to accelerate the development of effective interventions for patients affected by Alzheimer’s disease in real-world, multimorbid contexts.

Dr. Gabriela Dumitrița Stanciu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Alzheimer’s disease
  • emerging therapies/novel treatments
  • multimorbidity/comorbidities
  • translational research
  • cognitive decline
  • drug development
  • innovative approaches

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Published Papers (3 papers)

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Research

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16 pages, 1423 KB  
Article
Comparative Analysis by Machine Learning of Geriatric Frailty and Alzheimer’s Disease Classification Using Independent Datasets
by Lăcrămioara Luminița Apescaritei Apostol, Claudia Simona Ștefan, Mihai Grecu, Simona Moldovanu, Gabriela Isabela Verga, Mihaela Lungu, Gabriel Ioan Prada and Aurelia Romila
Life 2026, 16(8), 1324; https://doi.org/10.3390/life16081324 - 13 Aug 2026
Viewed by 239
Abstract
Frailty syndrome and Alzheimer’s disease are prevalent conditions in the elderly that are associated with aging, decreased quality of life, and a significant healthcare burden. Evidence for a relationship between physical frailty and neurodegenerative decline is accumulating. This study analyzed two independent datasets, [...] Read more.
Frailty syndrome and Alzheimer’s disease are prevalent conditions in the elderly that are associated with aging, decreased quality of life, and a significant healthcare burden. Evidence for a relationship between physical frailty and neurodegenerative decline is accumulating. This study analyzed two independent datasets, a frailty dataset based on gait and mobility parameters and an AD dataset with clinical, functional and lifestyle variables, in order to evaluate and compare their classification performance using machine learning. Features were optimized using dimensionality reduction techniques to keep predictors of clinical significance and hyperparameter optimized Random Forest models were built to develop the best model. Evaluation was performed with Accuracy, F1-score, Matthews Correlation Coefficient and Area Under the Curve. The results showed that the models constructed on the whole AD dataset achieved maximum predictive power with an accuracy of 0.946, which was slightly increased to an accuracy of 0.948 after the selection of significant features. Diagnostic models based on frailty were able to demonstrate an ACC predictive capacity of 0.6418, and in terms of feature selection, improvements appeared in all indicators. Regarding the features derived from Alzheimer’s disease associated with geriatric frailty, they managed to surpass the ACC frailty features of 0.741 alone, suggesting some intercalation mechanisms between neurodegeneration and physical vulnerability. These findings show that machine learning algorithms accompanied by feature selection improve clinical discrimination and prediction of frailty and neurodegenerative disorders, which offers a promising aspect for geriatric assessment. The frailty models analyzed demonstrated an ACC predictive capacity of 0.6418, even though feature selection improved all indicators. Alzheimer’s disease-derived features associated with frailty outperformed features in the frailty dataset with an ACC of 0.741, suggesting the mechanism of overlap between neurodegeneration and physical vulnerability. These results support the theory of a motor-cognitive aging continuum, indicating that algorithmic machine learning techniques coupled with feature selection mainly provide computational validation for the biological intersection of neurodegeneration and physical frailty, rather than forming an independent predictive clinical model. Using these algorithms the study highlights shared pathophysiological mechanisms, providing a significant insight into systemic geriatric deterioration. Full article
(This article belongs to the Special Issue Progress and Challenges in Emerging Therapies for Alzheimer’s Disease and Its Comorbidities)
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Review

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18 pages, 567 KB  
Review
Beyond Targeted Gene Panels: Whole-Exome Sequencing as a Strategic Platform for Precision Therapeutics in Alzheimer’s Disease
by Carlos Perezcano, Mariana Pérez-Coria and Ángel Ricardi-Mendoza
Life 2026, 16(9), 1410; https://doi.org/10.3390/life16091410 - 25 Aug 2026
Viewed by 165
Abstract
Alzheimer’s disease (AD) continues to be one of the greatest challenges in public health due to its multifactorial and heterogeneous nature, involving multiple physiological axes that encompass a large number of genetic, metabolic, vascular, and inflammatory interactions. In current clinical practice, medical specialties, [...] Read more.
Alzheimer’s disease (AD) continues to be one of the greatest challenges in public health due to its multifactorial and heterogeneous nature, involving multiple physiological axes that encompass a large number of genetic, metabolic, vascular, and inflammatory interactions. In current clinical practice, medical specialties, mainly neurology and psychiatry, still rely on targeted gene panels for genetic evaluation. Although these panels remain effective for certain predefined hypotheses, their restricted and predefined nature limits the detection of the broader spectrum of genetic variation that may contribute to the complex biological interactions underlying neurodegeneration. Whole-exome sequencing (WES) is, from our clinic-based perspective, one of the most comprehensive genomic approaches currently available, since it allows the analysis of the ~19,500 protein-coding regions, and depending on the library approximately 5500 additional clinically relevant genomic loci, including splice sites, untranslated regions, long non-coding RNAs (lncRNAs), pseudogenes, regulatory elements and mitochondrial DNA (mtDNA). It enables the identification of pathogenic variants and variants of uncertain significance (VUS) under the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) classification frameworks. It also expands biological interpretation to variants conventionally classified as benign, which, when interpreted collectively, may contribute to pathway-level contextualization within the hypothesis-generating theoretical framework proposed in this review without implying pathogenicity, causal inference, or immediate clinical actionability. Additionally, WES enables the identification of secondary and incidental findings that may provide clinically relevant information beyond the primary phenotype, thereby supporting preventive surveillance and clinical risk management. This review analyzes the use of WES as a strategic platform for personalized decision-making in contemporary practice given the multifactorial and heterogeneous complexity of AD. It also addresses the complexities and limitations of the ACMG/AMP recommendations for filtering and classification of variants, the lack of standardization between reports and platforms, and the need for physician training, which constitute a great challenge for the translation of data to therapeutic decision-making. While the clinical utility of whole-exome sequencing (WES) in genetic diagnosis and precision medicine is well established, this review additionally proposes a hypothesis-generating theoretical framework whereby variants conventionally classified as benign or of uncertain significance may contribute to pathway-level biological contextualization in Alzheimer’s disease. Full article
(This article belongs to the Special Issue Progress and Challenges in Emerging Therapies for Alzheimer’s Disease and Its Comorbidities)
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25 pages, 712 KB  
Review
Smart Drug-Delivery Approaches for Enhanced Management of Comorbid Conditions in Alzheimer’s Disease
by Gabriela-Dumitrita Stanciu, Ivona Costachescu, Camelia Dascalu and Bogdan-Ionel Tamba
Life 2026, 16(3), 510; https://doi.org/10.3390/life16030510 - 19 Mar 2026
Cited by 2 | Viewed by 1402
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Progress and Challenges in Emerging Therapies for Alzheimer’s Disease and Its Comorbidities)
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