Evidence mapPaperPMID 41428270Full record

ReviewJournal of molecular neuroscience : MN2025

Is the Era of One-Size-Fits-All Alzheimer's Treatment Officially Over?

Swati Verma, Kajal Kumari, Payal Kesharwani, Kanika Verma, Jaya Dwivedi, Sarvesh Paliwal, Swapnil Sharma

Abstract readReview
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In one paragraph

Review in Journal of molecular neuroscience : MN, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Swati VermaDepartment of Pharmacy, Banasthali Vidyapith, Banasthali, 304022, Rajasthan, India.
Kajal KumariDepartment of Pharmacy, Banasthali Vidyapith, Banasthali, 304022, Rajasthan, India.
Payal KesharwaniRam-Eesh Institute of Vocational and Technical Education, Greater Noida, Uttar Pradesh, India.
Kanika VermaDepartment of Cellular and Integrative Physiology, UNMC, Omaha, Nebraska, USA.
Jaya DwivediDepartment of Chemistry, Banasthali Vidyapith, Banasthali, Rajasthan, India.
Sarvesh PaliwalDepartment of Pharmacy, Banasthali Vidyapith, Banasthali, 304022, Rajasthan, India.
Swapnil SharmaDepartment of Pharmacy, Banasthali Vidyapith, Banasthali, 304022, Rajasthan, India. skspharmacology@gmail.com.ORCID http://orcid.org/0000-0003-2639-7096

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is prevalent in more than 55 million worldwide, a figure estimated to almost triple by 2050, highlighting the need for highly effective treatments. However, despite the large expenditure of research over several decades, over 90% of clinical trials-countless amyloid-β-targeted drugs among them-have failed, stressing the shortcomings of reductionist, one-target approaches. More and more, AD is viewed as a complex systems disorder, resulting from interlinked disruptions in proteostasis, neuroinflammation, vascular integrity, synaptic plasticity, and metabolic regulation. Such an understanding has transformed the therapeutic paradigm toward precision, multimodal treatment, integrating disease-modifying agents with biomarker-based diagnosis and patient stratification. Improved blood- and imaging-based biomarkers, new molecular targets, and drug-delivery technologies offer the hope for earlier intervention and more personalized treatment. Looking to the future, the way forward will rely on the integration of systems biology, computational modeling, and translational neuroscience into adaptive trial design able to tackle the heterogeneity of the disease. These developments combined constitute the progressive shift away from "one-size-fits-all" treatments towards a future of personalized, mechanism-based therapies in Alzheimer's disease.

Indexed as

Alzheimer DiseaseAnimalsBiomarkersHumansPrecision MedicineBiomarkers

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.