Peptide-based therapeutics have become a flexible type of medicine that combines high target specificity and strong biological activity with good safety profiles. They effectively connect small-molecule drugs and biologics. Since insulin was first used in 1922, more than 100 peptide drugs have been approved around the world, and their use is growing in diabetes, cancer, rare diseases, and autoimmune disorders. However, their clinical use is still limited by rapid enzymatic degradation, short half-life, poor membrane permeability, low oral bioavailability, and formulation instability. This review presents a unified innovation framework that integrates upstream molecular engineering strategies including cyclization, peptide stapling, D- and β-amino acid substitution, PEGylation, and backbone modification with downstream advanced delivery platforms such as nanocarriers, microneedles, self-assembling hydrogels, and stimuli-responsive systems, while also addressing critical translational considerations including scalability, regulatory requirements, and manufacturing consistency. By critically linking structural modifications at the molecular level with formulation performance and clinical translation factors, and by evaluating both approved products and persistent gaps in predictive modelling and patient-centric delivery, this work offers practical insights that go beyond the scope of most existing reviews. The integrated perspective presented here highlights how these combined approaches are shifting peptide therapeutics from conventional injectable formats toward more stable, convenient, and precisely targeted solutions, paving the way for the next generation of patient-friendly peptide medicines.
Pradip Karale, Saloni Borse, Anjali Gavit et al.· Journal of Pharmaceutical In...· 0 citations
The discussion on precision oncology integrates multiomics technologies and artificial intelligence, specifically addressing biomarker discovery and personalized therapeutic strategies. In this way, clinical translation and multiomics biomarkers are reconstructed challenges such as heterogeneity, validate, algorithmic bias, regulatory complexities, and ethical issues. This review critically evaluates how advanced technologies operating in an integrated manner facilitate precision oncology by supporting fields such as genomics, transcriptomics, proteomics, metabolomics, and radiomics. To conduct the study, we performed literature search using standard databases such as PubMed, Web of Science, and Scopus, focusing on papers published between 2020 and 2025. We address the all aspects of biomarker identification and clinical applications; we employed a five-stage framework comprising multiparametric data generation, integration, biomarker discovery, rigorous validation, and regulatory implementation. To further examine this review, we have employed emerging computational approaches, including machine learning and deep learning and graph neural networks alongside regulatory frameworks and ethical, legal, and social considerations. Discussing translation barriers, we consider factors such as limited reproducibility, validation, and critical discussion particularly studies. Ultimately, a future model based on standardized, validated, and transparent learning strategies accelerates and fosters the development of clinically reliable standards. Our review provides an integrative roadmap for modern, multiomics-driven biomarker approaches in precision oncology practice.
Ujwal Havelikar, Atharva A. Shinde, Hrushikesh Mhaismale et al.· Omics· 0 citations
