AI-Driven Design and Clinical Translation of Nucleotide-Peptide and Peptide-Drug Conjugates

Clinical Snapshot

Key Findings

• Effect Size: Not applicable — no quantitative effect sizes reported; findings are qualitative and descriptive

• Primary Outcome: No pre-specified primary outcome. The review narratively synthesises progress in bioconjugation chemistry, AI-assisted design, and clinical translation of NPCs and PDCs across oncology, neurodegeneration, and inflammatory disease

• Nnt Or Sensitivity: Not applicable — no NNT, sensitivity/specificity, or hazard ratio data reported; review is mechanistic and translational in focus rather than outcomes-based

• Confidence Interval: Not applicable — no statistical synthesis performed

Clinical Application

Clinical feasibility of NPCs and PDCs remains limited by challenges explicitly acknowledged in the review: enzymatic degradation, rapid systemic clearance, linker optimisation, peptide availability, scalable synthesis, and regulatory complexity. AI-assisted design tools may accelerate candidate identification but do not resolve manufacturing or clinical trial barriers No currently PBS-listed NPC or PDC agents are identified in this review's scope. The TGA would require robust Phase I–III clinical trial data before registration of any novel conjugate therapeutic. RACGP and relevant specialist colleges (RACP, COSA) have no current guidelines addressing NPCs or PDCs as a therapeutic class. Australian researchers at institutions such as the Walter and Eliza Hall Institute, Peter MacCallum Cancer Centre, and CSIRO are active in peptide therapeutics research, providing a relevant local scientific ecosystem. Clinicians should monitor emerging clinical trial registrations (ANZCTR, ClinicalTrials.gov) for PDC candidates in oncology, where the pipeline is most advanced globally (e.g., antibody-drug conjugate precedents informing PDC development) Preclinical and early-phase clinical populations with cancer, neurodegenerative diseases (e.g., Alzheimer's disease, Parkinson's disease), and inflammatory disorders. Direct clinical application is premature given the predominantly preclinical evidence base described

Abstract

While nucleotide-peptide conjugates and peptide-drug conjugates demonstrate a considerable evolution as next-generation therapeutics, the successful translation of these complex agents relies on advances in bioconjugation strategies. By anchoring nucleotides or pharmaceutical payloads to multifaceted peptide scaffolds, researchers have engineered molecular platforms with dual mechanisms of action. However, to fully realize the potential of these hybrid systems, further refinements are required to ensure both in vivo stability and retention of the desired biological activities over time. This comprehensive review examines ongoing progress toward optimizing conjugation chemistry for the development of stable and efficacious NPC and PDC candidates. Through evaluation of fundamental design principles and their clinical relevance, it aims to accelerate the journey of these multilayered biomolecular constructs from theory to practice. It details various strategies to mitigate challenges such as enzymatic degradation and rapid systemic clearance, including covalent linkages (e.g., aminoacylation and thiol oxidation) and noncovalent interactions (e.g., ligand-receptor binding). Central to this emerging field is the integration of artificial intelligence and deep learning, which are rapidly transforming the design and development of NPCs and PDCs. AI-driven tools enable predictive modeling of peptide-drug interactions, intelligent linker optimization, and in silico screening of bioactive scaffolds-thus redefining the pipeline for precision therapeutics with improved selectivity and translatability. The potential of hybrid nanostructures formed by peptide-DNA conjugates for advanced drug delivery and diagnostic applications is also discussed. Furthermore, the clinical translation of PDCs demonstrates their promise in treating conditions such as cancer, neurodegenerative diseases, and inflammatory disorders, alongside the ongoing challenges regarding peptide availability, linker optimization, and scalable synthesis. The review concludes by discussing future directions of NPCs and PDCs, highlighting the role of interdisciplinary approaches in advancing precision medicine and addressing complex therapeutic needs.

References

1. 1.Pandey, K., Dave, R., Patel, R., Verma, S., Solanki, R., Yadav, A., Gour, N., & Bhatia, D. (2026). AI-driven design and clinical translation of nucleotide-peptide and peptide-drug conjugates. ACS Biomaterials Science & Engineering. Advance online publication. https://doi.org/10.1021/acsbiomaterials.6c00187