Journal of Cancer Immunology & Therapy

All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.

Perspective - Journal of Cancer Immunology & Therapy (2025) Volume 8, Issue 2

The Future of Monoclonal Antibodies: Innovations in Personalized Medicine

Wenjuan Jin *

Department of Organ Transplantation, Shandong University, China

*Corresponding Author:
Wenjuan Jin
Department of Organ Transplantation, Shandong University, China
E-mail: wen.j@qiluhospital.com

Received: 03-Apr -2025, Manuscript No. AAJCIT-25-163917; Editor assigned: 04-Apr-2025, PreQC No. AAJCIT-25-163917 (PQ); Reviewed:18-Apr-2025, QC No. AAJCIT-25-163917; Revised:23-Apr-2025, Manuscript No. AAJCIT-25-163917 (R); Published:28-Apr-2025, DOI:10.35841/aajcit-8.2.262

Citation: Jin W. The future of monoclonal antibodies: Innovations in personalized medicine. J Cancer Immunol Ther. 2025;8(2):262

Visit for more related articles at Journal of Cancer Immunology & Therapy

Introduction

Monoclonal antibodies (mAbs) have revolutionized modern medicine, offering targeted treatment for conditions ranging from cancer and autoimmune diseases to infectious diseases and neurological disorders. These laboratory-engineered molecules mimic the body’s immune response, providing precision in diagnosis and therapy. As innovations in biotechnology advance, monoclonal antibodies are becoming central to the era of personalized medicine, where treatments are tailored to individual patients based on genetic, molecular, and cellular profiles [1].

This article explores the evolution of monoclonal antibody therapies, current breakthroughs, and future directions in personalized medicine.The development of monoclonal antibodies began in 1975 when Köhler and Milstein introduced the hybridoma technique, enabling the production of identical antibodies targeting specific antigens [2].

Artificial intelligence (AI) and machine learning are accelerating monoclonal antibody discovery by predicting antigen-antibody interactions, optimizing protein engineering, and streamlining clinical trials. AI-driven platforms analyze vast biological datasets to design next-generation monoclonal antibodies with enhanced specificity and reduced immunogenicity. Early mAb therapies were derived from murine (mouse) sources, leading to immune system reactions that limited their effectiveness. Advances in genetic engineering led to humanized and fully human antibodies, reducing immunogenicity and improving therapeutic outcomes [3].

Monoclonal antibody therapies remain expensive due to complex production processes and regulatory requirements. Biosimilars, which are cost-effective alternatives to original biologics, are emerging to increase accessibility. Today, monoclonal antibodies are widely used in oncology, immunology, infectious diseases, and neurology, with ongoing advancements in bispecific antibodies, antibody-drug conjugates (ADCs), and nanobody technology enhancing their efficacy and specificity [4].

Traditional monoclonal antibodies target a single antigen, but bispecific and multispecific antibodies are engineered to bind to multiple targets simultaneously. This approach enhances treatment precision, particularly in oncology, where dual targeting can improve tumor destruction while reducing off-target effects [5].

Example: Blinatumomab, a bispecific T-cell engager (BiTE), links cancer cells to T cells, activating the immune system to destroy leukemia cells. ADCs combine monoclonal antibodies with cytotoxic drugs to deliver targeted chemotherapy directly to cancer cells while sparing healthy tissues. These innovations reduce systemic toxicity and improve therapeutic index [6].

Example: Trastuzumab emtansine (T-DM1) is an ADC used in HER2-positive breast cancer, linking trastuzumab to a potent chemotherapy agent for enhanced cancer cell destruction. Monoclonal antibodies are integral to immunotherapy, particularly immune checkpoint inhibitors, which help the immune system recognize and attack cancer cells. Personalized biomarker testing, such as PD-L1 expression and tumor mutational burden (TMB), allows for individualized treatment strategies [7].

Example: Pembrolizumab (Keytruda) is an anti-PD-1 monoclonal antibody used in cancers with high PD-L1 expression, demonstrating remarkable success in non-small cell lung cancer. CRISPR and other gene-editing technologies are transforming monoclonal antibody development. Researchers can precisely engineer antibody sequences for improved efficacy, reduced immunogenicity, and enhanced half-life [8].

Example: CRISPR-based gene editing is being explored to create fully human antibodies optimized for individualized immune responses. Nanobodies are single-domain antibody fragments derived from camelid antibodies, offering unique advantages such as smaller size, deeper tissue penetration, and enhanced stability. These properties make them ideal for targeted imaging and therapy, particularly in neurology and infectious diseases [9].

Monoclonal antibodies have become essential in combating infectious diseases, including COVID-19, Ebola, and respiratory syncytial virus (RSV). Advances in rapid antibody discovery and manufacturing enable quicker responses to emerging pathogens. Example: Casirivimab and Imdevimab, monoclonal antibodies for COVID-19, neutralized SARS-CoV-2, reducing hospitalization risk in high-risk patients [10].

Conclusion

Monoclonal antibodies have transformed modern medicine, offering targeted and personalized treatments across various diseases. Innovations such as bispecific antibodies, ADCs, nanobody technology, and AI-driven discovery are shaping the future of antibody therapies. However, challenges such as high costs and therapeutic resistance remain. Continued advancements in biotechnology, gene editing, and biosimilar development will further integrate monoclonal antibodies into personalized medicine, enhancing patient outcomes and accessibility.

References

  1. Vitale I, Sistigu A, Manic G, et al., Mutational and antigenic landscape in tumor progression and cancer immunotherapy. Trends Cell Biol. 2019;29(5):396-416.

    2. Indexed at, Google Scholar, Cross Ref

  2. Walk EE, Yohe SL, Beckman A, et al., The cancer immunotherapy biomarker testing landscape. Arch Path Lab. 2020;144(6):706-24.

    3. Indexed at, Google Scholar, Cross Ref

  3. Kennedy LB, Salama AK. A review of cancer immunotherapy toxicity. CA Cancer J Clin 2020;70(2):86-104.

    4. Indexed at, Google Scholar, Cross Ref

  4. Vesely MD, Schreiber RD. Cancer immunoediting: antigens, mechanisms, and implications to cancer immunotherapy. Ann N Y Acad Sci. 2013;1284(1):1-5.

    5. Indexed at, Google Scholar, Cross Ref

  5. Morrissey KM, Yuraszeck TM, Li CC, et al., Immunotherapy and novel combinations in oncology: current landscape, challenges, and opportunities. Clin Transl Sci. 2016;9(2):89.

    6. Indexed at, Google Scholar, Cross Ref

  6. Prieto J, Melero I, Sangro B. Immunological landscape and immunotherapy of hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2015;12(12):681-700.

    7. Indexed at, Google Scholar, Cross Ref

  7. Kotch C, Barrett D, Teachey DT. Tocilizumab for the treatment of chimeric antigen receptor T cell-induced cytokine release syndrome. Expert Rev Clin Immunol. 2019;15(8):813-22.

    8. Indexed at, Google Scholar, Cross Ref

  8. Khasraw M, Reardon DA, Weller M, et al., PD-1 Inhibitors: Do they have a Future in the Treatment of Glioblastoma?. Clin Cancer Res. 2020;26(20):5287-96.

    9. Indexed at, Google Scholar, Cross Ref

  9. Kelderman S, Schumacher TN, Haanen JB. Acquired and intrinsic resistance in cancer immunotherapy. Mol Oncol. 2014;8(6):1132-9.

    10. Indexed at, Google Scholar, Cross Ref

  10. Carpenter EL, Mossé YP. Targeting ALK in neuroblastoma—preclinical and clinical advancements. Nat Rev Clin Oncol. 2012;9(7):391-9.

    11. Indexed at, Google Scholar, Cross Ref

Get the App