Advances in Biotherapeutics
Transforming drug discovery and development
Mohd Shahid, Rosalind Franklin University of Medicine and Science
Ibrahim Sajid, Department of Food Technology and Department of Pharmacognosy & Phytochemistry, School of Pharmaceutical Education & Research, Jamia Hamdard
Kristen Ahlschwede, Rosalind Franklin University of Medicine and Science
This article discusses the progress in biotherapeutics, highlighting the integration of AI and generative biology to accelerate drug discovery. It also explores the emergence of new therapeutic modalities, like CAR T-cell therapies and RNA-based treatments. Finally, the growing role of real-world data in development and regulatory processes is examined, and the need for addressing manufacturing, regulatory, and accessibility challenges for biotherapeutics through innovation and collaboration is emphasized.
According to the US Food and Drug Administration (FDA), biotherapeutics, also known as biologicals or biopharmaceuticals, are therapies derived from biological sources, which include vaccines, blood components, recombinant proteins, monoclonal antibodies, and cell-based or gene therapies. Biologicals are sourced from humans, animals, microorganisms, or are synthetically manufactured. Biotherapeutics are large molecules ranging in size from 4 to 1,000 (kDa), unlike chemically synthesised small drug molecules which are typically less than 1 kDa2. Conversely, biosimilars are biological products that are highly similar to already approved biotherapeutics, with no clinically meaningful differences. They must demonstrate structural and functional similarity, comparable pharmacokinetics/pharmacodynamics, and rigorous quality control. Although biosimilars are an effective cost-saving measure, they may be substituted for the reference product only if the biosimilar has been determined as ‘interchangeable’ product by the FDA. In addition to all of the data required elements for biosimilar submission, an interchangeable product must also demonstrate that it produces the same clinical effect in any given patient.
In the last two decades, biotherapeutics have become an important part of current healthcare, particularly for diseases with limited treatment options. Since the FDA approved recombinant human insulin (5.81 kDa) in 1982 (Humulin®), the first DNA recombinant biologic, the field has expanded rapidly, essentially in all areas related to biotechnology. Biotherapeutic drugs such as etanercept, adalimumab, rituximab, and bevacizumab have significantly improved clinical outcomes in patients with autoimmune diseases. Due to expiring patents for many biotherapeutics, the FDA has favored the development of biosimilars to mitigate revenue loss anticipated in billions. While biotherapeutics typically require 10-12 years and billions of dollars to develop, biosimilars can be developed in 7-8 years at a fraction of the cost of a biological agent.
The biotherapeutics field has recently undergone a remarkable transformation, reshaping the drug discovery and development process, changing its landscape. Biotherapeutics have introduced novel treatment modalities that offer effective therapies for diseases with limited therapeutic options and enhanced specificity and therapeutic outcomes compared to traditional small drug molecules. Here, we discuss the recent advancements in biotherapeutics/biologics, highlighting the incorporation of newer technologies, the development of novel therapeutic classes, and the recent biomanufacturing innovation.
Integration of artificial intelligence in biotherapeutics
The incorporation of artificial intelligence (AI) into biotherapeutic research has significantly accelerated the pace of drug discovery. AI algorithms are now capable of analysing vast preclinical and pharmacokinetic (PK) datasets to identify potential therapeutic targets and predict the efficacy of drug candidates. For example, Iambic TherapeuticsTM, backed by NvidiaTM, has developed the "Enchant" model, which currently offers a prediction accuracy score of 0.74 (Spearman correlation coefficient) in early-stage drug performance assessments. This high accuracy score has surpassed previous prediction models and will potentially reduce the cost and total time required for drug development by reducing late-stage failures. Specifically, when Enchant® is trained using all available preclinical data and the full dataset by Obach et. al., a well-referenced human clinical PK dataset, it surpasses previous leading prediction models, which achieved a Spearman R of only 0.58. Moreover, Enchant® demonstrates robust predictive performance across a range of human clinical pharmacokinetic (PK) properties, not just half-life, which is essential for designing optimal PK properties.
Similarly, many major pharmaceutical companies are integrating AI into their drug discovery processes, accelerating novel drug development. For example, Cardiff-based AntiverseTM has partnered with Japan's NxeraTM to utilise AI in designing antibodies, aiming to transition from traditional trial-and-error methods to more drug design. This approach could not only save time and resources but also enhance the specificity of biotherapeutic agents with reduced adverse effects.
AI is transforming more than just early-stage drug discovery. By training on vast preclinical data and pharmacokinetic datasets, large language models (LLMs) are large deep learning models for AI that have been pretrained on a vast amount of data, which are gaining the ability to decode molecular interactions and protein sequences. This breakthrough in AI is paving the way for novel approaches in drug discovery and development. Across the entire biotherapeutics lifecycle, including manufacturing, clinical development, regulatory processes, and commercialisation, AI tools can be leveraged to optimise decision-making, streamline operations, and accelerate innovation. By integrating AI at every stage, biotherapeutics companies are poised to achieve unprecedented levels of efficiency and foster a new wave of breakthroughs in the industry.
Advancements in generative biology for biotherapeutic discovery
Generative biology is a groundbreaking approach that integrates AI, advanced life sciences technologies, and automation to transform the synthesis of novel biomolecules for biologics and protein therapeutics. This innovative field addresses the inherent complexities of protein drug discovery, which involve a wide range of computational and experimental techniques as well as the limited availability of relevant protein sequence-function data, vital for accurately predicting physiological outcomes of biologics.
By employing design-make-test-learn (DMTL) cycles, generative biology accelerates the identification of proteins with specific properties. The integration of AI and machine learning enhances the ability to navigate the protein sequence-function landscape, while advancements in life science technologies and automation accelerate the production and testing of complex protein therapeutics.
This convergence of multiple advanced technologies is particularly significant in the development of multi-specific drugs, which hold immense therapeutic potential. Generative biology not only accelerates the discovery process but also enhances the precision and efficacy of protein therapeutics, marking a paradigm shift in the field. Multi-specifics, a subset of proteinbased drugs capable of interacting with more than one molecular target simultaneously, represent a major leap in addressing multifactorial diseases like cancer and autoimmune disorders. These molecules offer unprecedented flexibility in drug design and have already begun to show clinical promise. For example, Zanidatamab, a humanised, bispecific, immunoglobulin (Ig) antibody which was recently developed and directed against the juxtamembrane extracellular domain (ECD4) and the dimerisation domain (ECD2) of human epidermal growth factor receptor 2 (HER2), two domains targeted by two different drugs trastuzumab (T) and pertuzumab (P), respectively, has shown promising results in patients with advanced HER2- expressing cancers.
Emergence of new therapeutic modalities
The biotherapeutics landscape is witnessing the rise of innovative therapeutic modalities that promise to address previously untreatable conditions. Notably, the development of bispecific antibodies (BsAbs) and chimeric antigen receptor (CAR) T-cell therapies has opened new avenues in cancer treatment. These novel therapeutic strategies not only enhanced the effectiveness of the treatment but also improved the safety profiles and specificity against cancers. These therapies offer enhanced precision by targeting specific antigens on cancer cells, thereby potentially reducing off-target effects and improving patient outcomes. However, high costs limit their accessibility, driven by the need for autologous treatments, specialised transport, advanced labs for viral vectors, and extended cell expansion. The advent of AI and automation could significantly reduce the processing cost and overcome these challenges.
Ribonucleic acid (RNA)-based therapies have also gained significant traction. Moderna’s and Pfizer’s success with messenger RNA (mRNA) vaccine development during the COVID-19 pandemic demonstrated the potential of RNA platforms for both vaccines and therapeutics. In general, mRNA-based vaccines function by delivering synthetic messenger RNA encoding a viral antigen, typically a surface protein, into host cells via lipid nanoparticles. Once internalised, the mRNA is translated by the host's ribosomes into the target protein, which is subsequently processed and presented via major histocompatibility complexes (MHC) to activate T cells and stimulate B cell-mediated antibody production. This process elicits both cellular and humoral immune responses, establishing immunological memory without integrating into the host genome.
In addition to mRNA, other nucleic acid-based approaches, such as small interfering RNA (siRNA), antisense oligonucleotides (ASOs), and CRISPRbased gene editing, are making their way through clinical pipelines, further expanding the biotherapeutic toolkit. For instance, RNA interference (RNAi) offers a flexible tool for regulating gene expression by targeting mRNA posttranscriptionally. Both endogenous microRNAs (miRNAs) and synthetic siRNAs utilise RNA-induced silencing complexes to suppress specific gene expression. With ongoing innovations in delivery systems and molecular design, RNAi is emerging as a promising therapeutic platform, steadily advancing toward clinical application. For example, the FDA recently approved four siRNAbased medications, including Patisiran (Onpattro), Givosiran (Givlaari),
Lumasiran (Oxlumo), and Inclisiran (Leqvio) for different diseases.
Role of real-world data in biotherapeutic development
Real-world data (RWD) is becoming increasingly integral in biotherapeutic development, providing insights into drug performance outside controlled clinical trial settings. Real-world data is the patient’s health information typically collected from a wide range of sources, including electronic health records, disease registries, medical and insurance claims, and digital health technologies such as wearable devices. The data obtained from analysing these sources can help us generate real-world evidence (RWE) or clinical evidence about a medical intervention, therapy, or product and their potential benefits or risks. Thus, the RWD can play a key role in accelerating novel drug discovery, product formulation and clinical development, and postapproval and post-marketing studies. It can also help in promoting the use of preventive medicines and vaccines, as we have recently witnessed post-COVID-19 pandemic. Additionally, the regulatory decision-making, such as that by the FDA, can also be informed by RWD, especially when the relevant clinical trials are not available. Thus, pharmaceutical companies are now leveraging RWD to supplement regulatory submissions, develop value-based pricing models, and make informed decisions regarding lifecycle management of biotherapeutic products. When effectively used, RWD can optimise clinical trial design, accelerate development timelines, and strengthen post-market monitoring by providing deeper insights into therapeutic performance across diverse patient groups. Given the vast availability of electronic health data, the United States (US) is well-positioned to lead as a trusted and valuable source of such information for companies worldwide.
Biomanufacturing innovations and challenges
Despite the profound clinical and commercial potential of biotherapeutics, their manufacturing remains one of the key challenges. Biotherapeutics are large, complex molecules requiring specialised production systems. Traditional mammalian cell lines like CHO (Chinese Hamster Ovary) cells are widely used, but they are costly and time-consuming. Their production takes place in specialised biomanufacturing facilities that operate more like laboratories than traditional factories. These facilities rely on complex clean room environments with controlled utilities like purified water, clean steam, and advanced Heating, Ventilation, and Air Conditioning (HVAC) systems. All processes follow strict Good Manufacturing Practices (GMP) as required by the FDA, and workers must adhere to detailed Standard Operating Procedures (SOPs) to ensure product safety and regulatory compliance.
To mitigate the logistic and costrelated challenges, newer biomanufacturing techniques such as continuous bioprocessing, modular facilities, and single-use technologies are being adopted. The biopharmaceutical industry is gradually undergoing a transformative shift with the increasing utilisation of continuous biomanufacturing, replacing traditional batch methods and offering enhanced efficiency, flexibility, and sustainability with the ultimate goal to cut down the capital costs. However, the widespread adoption of continuous bioprocessing still remains challenging. For example, Quality by Design (QbD) implementation, seamless integrating upstream and downstream technologies, and effectively managing the large data flow are critical for the field's advancement. Newer biomanufacturing technologies such as the continuous viral clearance and filtration systems, continuous spin freeze-drying, and hybrid processing models are increasingly being introduced into the industry, improving the overall efficiency of the manufacturing process. Embracing standardised communication protocols such as Open Platform Communications Unified Architecture (OPC UA), and batch process control and data management systems, such as those based on International Society of Automation Standard 88 (ISA-88), will be instrumental in paving the way for the continuous biomanufacturing. These advancements in the biomanufacturing industry aim to increase flexibility, reduce waste, and lower production costs. Additionally, synthetic biology and cell-free protein synthesis (CFPS) are gaining ground as alternative platforms for faster and more controlled bioproduction. These platforms enable the production of complex biotherapeutics/ proteins without requiring the complexities of living cellular systems.
Regulatory landscape and biosimilars
Biotherapeutics have become leading revenue generators in the pharmaceutical industry. For example, Humira®, used to treat autoimmune conditions, made about US$21 billion in sales in 2022. Dupixent®, which is approved for inflammatory conditions like eczema, earned around US$9 billion the same year. Due to the short patent life of many original biotherapeutics, the FDA has established an expedited approval process for biosimilars, which have influenced the development pipeline. These initiatives aim to increase access to life-saving therapies while encouraging competition and reducing healthcare costs. Timely development and approval of biosimilars could have saved up to US$194 billion in biotherapeutics spending between 2017 and 2022. While developing a biologic typically takes 10-12 years and costs billions of dollars, biosimilars can be brought to market in 7-8 years at roughly 10 per cent–20 per cent of that cost. Unlike new biologics, biosimilars bypass early-stage clinical trials (Phases 1 and 2) since the original reference product has already established safety and efficacy. However, biosimilars must still demonstrate pharmacokinetic and clinical equivalence, usually through at least two comparative clinical studies to gain FDA approval. Establishing biosimilarity is more complex than generics due to the intricate nature of biologics. Demonstrating comparable efficacy, safety, and immunogenicity requires a multi-pronged approach involving analytical, preclinical, and clinical studies. Despite these challenges, the biosimilar market is experiencing consistent growth, with major blockbuster drugs like etanercept, adalimumab, and trastuzumab now encountering increased competition.
In summary, the advancements in biotherapeutics are reshaping the drug discovery and development landscape, offering promising solutions for a myriad of diseases. The integration of AI and generative biology has expedited the design of novel therapeutics, while the emergence of new modalities like CAR T-cell therapies and RNA-based treatments has expanded the therapeutic modalities and opportunities. Additionally, incorporating the real-world data can provide deeper insights into drug supporting more informed clinical and regulatory decision-making. Notably, innovations in biomanufacturing are beginning to address cost and scalability concerns, paving the way for broader patient access. As the biotherapeutics field continues to evolve and grow, addressing challenges in manufacturing, regulatory approval, and accessibility through sustained innovation and strategic collaboration will continue to be crucial in realising the full potential of biotherapeutics in improving global health outcomes.
References
1. US FDA. What Are “Biologics” Questions and Answers. Accessed May 28, 2025. https://www.fda.gov/about-fda/center-biologics-evaluation-and-research-cber/what-are-biologics-questions-and-answers
2. Chan JCN, Chan ATC. Biologics and biosimilars: what, why and how? ESMO Open. 2017;2(1):e000180. doi:10.1136/esmoopen-2017-000180
3. Agbogbo FK, Ecker DM, Farrand A, et al. Current perspectives on biosimilars. J Ind Microbiol Biotechnol. 2019;46(9-10):1297-1311. doi:10.1007/s10295-019-02216-z
4. Biosimilar medicines: Overview. Accessed May 28, 2025. https://www.ema.europa.eu/en/human-regulatory-overview/biosimilar-medicines-overview
5. Kang HN, Knezevic I. Regulatory evaluation of biosimilars throughout their product life-cycle. Bull World Health Organ. 2018;96(4):281-285. doi:10.2471/BLT.17.206284
6. Alexander GC, Ogasawara K, Wiegand D, Lin D, Breder CD. Clinical Development of Biologics Approved by the US Food and Drug Administration, 2003-2016. Ther Innov Regul Sci. 2019;53(6):752-758. doi:10.1177/2168479018812058
7. Busse W, Corren J, Lanier BQ, et al. Omalizumab, anti-IgE recombinant humanized monoclonal antibody, for the treatment of severe allergic asthma. Journal of Allergy and Clinical Immunology. 2001;108(2):184-190. doi:10.1067/mai.2001.117880
8. Kabir ER, Moreino SS, Sharif Siam MK. The Breakthrough of Biosimilars: A Twist in the Narrative of Biological Therapy. Biomolecules. 2019;9(9):410. doi:10.3390/biom9090410
9. Biosimilar Development, Review, and Approval. Accessed May 28, 2025. https://www.fda.gov/drugs/biosimilars/review-and-approval
10. DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: New estimates of R&D costs. J Health Econ. 2016;47:20-33. doi:10.1016/j.jhealeco.2016.01.012
11. Nvidia-backed AI firm Iambic unveils drug discovery ‘breakthrough.’ Accessed May 28, 2025. https://www.reuters.com/technology/artificial-intelligence/nvidia-backed-ai-firm-iambic-unveils-drug-discovery-breakthrough-2024-10-29/?utm_source=chatgpt.com
12. Obach RS, Lombardo F, Waters NJ. Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 670 Drug Compounds. Drug Metabolism and Disposition. 2008;36(7):1385-1405. doi:10.1124/dmd.108.020479
13. Enchant whitepaper: Breaking the data wall between lab and clinic. Accessed May 28, 2025. https://www.iambic.ai/post/enchant
14. Pharma firms buy into promise of AI shortcut. Accessed May 28, 2025. https://www.thetimes.com/business-money/technology/article/pharma-firms-buy-into-promise-of-ai-shortcut-px5lckvxp?utm_source=chatgpt.com®ion=global
15. Visan AI, Negut I. Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery. Life. 2024;14(2):233. doi:10.3390/life14020233
16. Mock M, Langmead CJ, Grandsard P, Edavettal S, Russell A. Recent advances in generative biology for biotherapeutic discovery. Trends Pharmacol Sci. 2024;45(3):255-267. doi:10.1016/j.tips.2024.01.003
17. Yang KK, Wu Z, Arnold FH. Machine-learning-guided directed evolution for protein engineering. Nat Methods. 2019;16(8):687-694. doi:10.1038/s41592-019-0496-6
18. Grisoni F, Huisman BJH, Button AL, et al. Combining generative artificial intelligence and on-chip synthesis for de novo drug design. Sci Adv. 2021;7(24). doi:10.1126/sciadv.abg3338
19. Deshaies RJ. Multispecific drugs herald a new era of biopharmaceutical innovation. Nature. 2020;580(7803):329-338. doi:10.1038/s41586-020-2168-1
20. Weisser NE, Wickman G, Abraham L, et al. Abstract 1005: The bispecific antibody zanidatamab’s (ZW25’s) unique mechanisms of action and durable anti-tumor activity in HER2-expressing cancers. Cancer Res. 2021;81(13_Supplement):1005-1005. doi:10.1158/1538-7445.AM2021-1005
21. Abdo L, Batista-Silva LR, Bonamino MH. Cost-effective strategies for CAR-T cell therapy manufacturing. Molecular Therapy Oncology. 2025;33(2):200980. doi:10.1016/j.omton.2025.200980
22. Pardi N, Hogan MJ, Porter FW, Weissman D. mRNA vaccines — a new era in vaccinology. Nat Rev Drug Discov. 2018;17(4):261-279. doi:10.1038/nrd.2017.243
23. Smith ES, Whitty E, Yoo B, Moore A, Sempere LF, Medarova Z. Clinical Applications of Short Non-Coding RNA-Based Therapies in the Era of Precision Medicine. Cancers (Basel). 2022;14(6):1588. doi:10.3390/cancers14061588
24. Yu AM, Choi YH, Tu MJ. RNA Drugs and RNA Targets for Small Molecules: Principles, Progress, and Challenges. Pharmacol Rev. 2020;72(4):862-898. doi:10.1124/pr.120.019554
25. Traber GM, Yu AM. RNAi-Based Therapeutics and Novel RNA Bioengineering Technologies. J Pharmacol Exp Ther. 2023;384(1):133-154. doi:10.1124/jpet.122.001234
26. The use of real-world data in drug development. Accessed May 28, 2025. https://phrma.org/blog/the-use-of-real-world-data-in-drug-development#:~:text=This%20involves%20evaluating%20the%20effectiveness,and%20after%20it’s%20been%20approved
27. Top Trends Shaping Pharma in 2024: AI, Gene Editing, Biosimilars and Real-World Data. Accessed May 28, 2025. https://www.technologynetworks.com/drug-discovery/articles/top-trends-shaping-pharma-in-2024-ai-gene-editing-biosimilars-and-real-world-data-386833
28. Drobnjakovic M, Hart R, Kulvatunyou B (Serm), Ivezic N, Srinivasan V. Current challenges and recent advances on the path towards continuous biomanufacturing. Biotechnol Prog. 2023;39(6). doi:10.1002/btpr.3378
29. Yu LX, Amidon G, Khan MA, et al. Understanding Pharmaceutical Quality by Design. AAPS J. 2014;16(4):771-783. doi:10.1208/s12248-014-9598-3
30. Rathore AS, Winkle H. Quality by design for biopharmaceuticals. Nat Biotechnol. 2009;27(1):26-34. doi:10.1038/nbt0109-26
31. Lu Y, Morris K, Frechette S. Current Standards Landscape for Smart Manufacturing Systems.; 2016. doi:10.6028/NIST.IR.8107
32. Hong SH. “Cell-Free Synthetic Biology”: Synthetic Biology Meets Cell-Free Protein Synthesis. Methods Protoc. 2019;2(4):80. doi:10.3390/mps2040080
33. Top Pharma Companies & Drugs in 2022. Accessed May 28, 2025. https://www.pharmacompass.com/radio-compass-blog/top-pharma-companies-and-drugs-in-2022-pfizer-breaks-us-100-billion-barrier-abbvie-s-humira-retains-second-spot?utm_source=chatgpt.com
34. Chen HH, Yemeke T, Ozawa S. Reduction of biologic pricing following biosimilar introduction: Analysis across 57 countries and regions, 2012–19. PLoS One. 2024;19(6):e0304851. doi:10.1371/journal.pone.0304851
Author Bio
Dr. M. Shahid is a cardiovascular and metabolic pharmacologist and Associate Professor at Rosalind Franklin University of Medicine and Science. He earned his Ph.D. from the University of Delhi and completed a postdoctoral fellowship at Massachusetts General Hospital, Harvard Medical School. His NIH- and AHA-funded research focuses on immune cell roles in cardiometabolic diseases. Dr. Shahid has received multiple honors, including the Faculty Excellence Award at Chicago State University, the Eleanor & Miles Shore Fellowship from Harvard, and the Coulson Award for Innovation at RFU. He has delivered invited talks at MGH, Rush, Cambridge, and other international institutions.
Mr. Ibrahim Sajid is currently pursuing a Bachelor of Technology degree in Food Science and Technology at the Department of Food Technology, School of Pharmaceutical Education & Research, Jamia Hamdard, New Delhi. He is expected to graduate in July 2025. His research interests include the safety evaluation and pharmacological investigation of herbal and food products. He plans to pursue graduate studies in Food Science and Technology in the United States.
Kristen Ahlschwede earned her Doctor of Philosophy degree in Biopharmaceutics and Pharmacokinetics from the College of Pharmacy and Pharmaceutical Sciences at Florida A&M University. Following the completion of her Ph.D., she pursued postdoctoral training at the U.S. Food and Drug Administration’s National Center for Toxicological Research (NCTR). Dr. Ahlschwede is currently an Associate Professor in the College of Pharmacy at Rosalind Franklin University of Medicine and Science. Her research focuses on drug delivery, nanotechnology, pharmacokinetics, and antibody conjugation.
