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Targeting peptide-HLA complexes for precision immunotherapy.

Jie Bi¹, Lingxiao Jiang¹, Jiaqi Zhao¹

¹Laboratory of Precision Medicine and Biopharmaceuticals, School of Pharmacy, Zhejiang University, Hangzhou, China.

Trends in Pharmacological Sciences

|January 13, 2026

Summary

This summary is machine-generated.

Precision immunotherapy targets specific peptide-human leukocyte antigen (pHLA) complexes for selective cell elimination. Advances in AI and synthetic biology accelerate the development of novel pHLA therapeutics for various diseases.

Keywords:

T-cell engager TCR-T TCR-mimic antibody de novo-designed protein peptide–HLA complex soluble TCR

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Area of Science:

Immunology
Biotechnology
Computational Biology

Background:

Precision immunotherapy aims to eliminate abnormal cells using the immune system.
Targeting peptide-human leukocyte antigen (pHLA) complexes offers specific intervention due to minimal expression in healthy tissues.
Designing highly specific pHLA binders with low cross-reactivity remains a significant challenge.

Purpose of the Study:

To review current approaches and emerging technologies in pHLA-targeted immunotherapies.
To discuss mechanisms, preclinical, and clinical data for pHLA-based therapeutics.
To highlight the role of AI and computational design in advancing pHLA-targeted treatments.

Main Methods:

T-cell receptor (TCR) mimic antibodies (TCRm) and de novo pHLA binder design.
Development of T cell engagers and adoptive cell therapies.
Application of artificial intelligence (AI), immunopeptidomics, and computational protein design.

Main Results:

Synthetic approaches like TCRm and de novo binders show promising specificity and efficacy.
AI-driven methods and computational design accelerate the discovery of pHLA therapeutics.
Emerging technologies are enabling pan-allelic development of pHLA-targeted therapies.

Conclusions:

pHLA-targeted immunotherapies represent a promising frontier in treating cancer, autoimmune, and infectious diseases.
Continued advances in synthetic biology and AI are crucial for realizing the full potential of these therapies.
The field is rapidly evolving, with significant preclinical and clinical data emerging.