Evidence map›Paper›PMID 42187721›Full record

ReviewBiology2026

Disrupting the Undruggable: Emerging Modalities for Targeting Protein-Protein Interactions in Oncology.

Mohamed El-Tanani, Syed Arman Rabbani, Adil Farooq Wali, Yahia El-Tanani, Shrestha Sharma

Abstract readReview
In one paragraph

Review in Biology, 2026. 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

5 authors.

Mohamed El-TananiRAK College of Pharmacy, Ras Al Khaimah Medical and Health Sciences University, Ras Al Khaimah P.O. Box 11172, United Arab Emirates.
Syed Arman RabbaniRAK College of Pharmacy, Ras Al Khaimah Medical and Health Sciences University, Ras Al Khaimah P.O. Box 11172, United Arab Emirates.ORCID 0000-0002-8454-8158
Adil Farooq WaliRAK College of Pharmacy, Ras Al Khaimah Medical and Health Sciences University, Ras Al Khaimah P.O. Box 11172, United Arab Emirates.ORCID 0000-0003-1555-2820
Yahia El-TananiRoyal Cornwall Hospital Trust, NHS, Truro TR1 3LJ, UK.
Shrestha SharmaAmity Institute of Pharmacy, Amity University, Panchgaon, Gurgaon 122413, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein-protein interactions (PPIs) are critical for cellular signaling, apoptosis regulation, and immune function in the body, and dysregulation is a hallmark of cancer. The large, dynamic, and shallow nature of PPI interfaces rendered them "undruggable" by conventional small molecules in the past. Recent advances in structural biology, chemical innovation, and artificial intelligence have revolutionized the landscape of PPI-directed drug discovery. This review summarizes the mechanistic roles of PPIs in oncogenesis, critically discusses novel therapeutic interventions, such as small molecules, peptidomimetics, stapled peptides, proteolysis-targeting chimeras (PROTACs), molecular glues, and AI-based drug optimization strategies, altering the druggable proteome in oncology. Therapeutics with clinically well-validated action, including Venetoclax and AMG 510, and next-generation candidates demonstrate the translational applications of these approaches. Some of the key challenges, such as interface complexity, specificity, bioavailability, and resistance, are addressed together with countermeasures like rational design, combination therapies, enhanced delivery systems, and biomarker-based patient selection. To this end, the incorporation of multi-omics data and artificial-intelligence (AI)-driven modeling technologies is revolutionizing the personalized cancer therapeutics development space. Collectively, these advances mark a paradigm shift: PPIs, once considered inaccessible, are now at the forefront of precision oncology, offering new hope for patients with previously intractable malignancies.

Indexed as

AI drug designAMG 510cancer therapeuticsdrug resistancegraph neural networksmolecular gluesoncologypeptidomimeticsprecision oncologyPROTACsprotein–protein interactionsVenetoclax

Identifiers

PMID42187721
PMCPMC13203756

What Socratic holds

Textmetadata
LicenceCC BY
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.