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Recombinant protein therapeutics, vaccines, and plasma products have a long record of safety. However, the use of cell culture to produce recombinant proteins is still susceptible to contamination with viruses. These contaminations cost millions of dollars to recover from, can lead to patients not receiving therapies, and are very rare, which makes learning from past events difficult. A consortium of biotech companies, together with the Massachusetts Institute of Technology, has convened to collect data on these events. This industry-wide study provides insights into the most common viral contaminants, the source of those contaminants, the cell lines affected, corrective actions, as well as the impact of such events. These results have implications for the safe and effective production of not just current products, but also emerging cell and gene therapies which have shown much therapeutic promise.The Consortium on Adventitious Agent Contamination in Biomanufacturing (CAACB) provides a comprehensive and forward-looking overview of industry’s experience with viral contamination of cell cultures used to produce recombinant proteins.

Stability studies are vital in biologics development, guiding formulation, packaging, and shelf life determination. Traditionally, predicting long-term stability based on short-term data has been challenging due to the complex behavior of biologics. However, recently have been demonstrated that by using simple kinetics and the Arrhenius equation, it is possible to achieve accurate long-term stability predictions for various quality attributes, including protein aggregates. This study focuses on effective modeling of aggregate predictions for diverse protein modalities, such as IgG1, IgG2, Bispecific IgG, Fc fusion, scFv, bivalent nanobodies, and DARPins, using a first-order kinetic model. Notably, findings highlight the significance of temperature selection in stability studies, enabling the identification of dominant degradation processes. Additionally, simplicity of the first-order kinetic model enhances reliability by reducing the number of parameters and samples required. The model’s effectiveness was further validated across various protein formats, beyond IgG, emphasizing its broad applicability and reliability. Compared to linear extrapolation, the kinetic model provided more precise and accurate stability estimates, even with limited data points. These findings highlight the benefits of using kinetic modeling with optimal temperature selection to predict protein aggregate stability and other quality attributes, aiding biologics development and shelf-life determination.

Oligonucleotides can be used to modulate gene expression via a range of processes including RNAi, target degradation by RNase H-mediated cleavage, splicing modulation, non-coding RNA inhibition, gene activation and programmed gene editing. As such, these molecules have potential therapeutic applications for myriad indications, with several oligonucleotide drugs recently gaining approval. However, despite recent technological advances, achieving efficient oligonucleotide delivery, particularly to extrahepatic tissues, remains a major translational limitation. Here, we provide an overview of oligonucleotide-based drug platforms, focusing on key approaches — including chemical modification, bioconjugation and the use of nanocarriers — which aim to address the delivery challenge.Oligonucleotide-based drugs have the potential to treat or manage a wide range of diseases. However, the widespread application of such therapies has been hampered by the difficulty in achieving efficient delivery to extrahepatic tissues. Here, Roberts et al. overview oligonucleotide-based drug platforms and assess approaches being employed to improve their delivery.

Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation. Notin, Rollins and colleagues discuss advances in computational protein design with a focus on redesign of existing proteins.

Key Points Nucleic acid aptamers, often termed chemical antibodies, are short, single-stranded DNA or RNA molecules (20–100 nucleotides in length) with defined structures that can specifically bind to a molecular target via three-dimensional structures. Similarly to the way antibodies bind to antigens, aptamers specifically recognize and bind to their cognate targets through unique three-dimensional structures. SELEX (systematic evolution of ligands by exponential enrichment) is a gold-standard methodology for generating aptamers, in which an iterative selection procedure — including binding, partitioning, recovery and re-amplification steps — is conducted. Specific sequences (that is, aptamers) can be enriched and dominate the population of library species. Aptamer-based therapeutics typically exploit one of three strategies: an aptamer can serve as an antagonist for blocking the interaction of disease-associated targets (for example, receptor–ligand interactions); an aptamer can serve as an agonist for activating the function of target receptors; or a cell-type-specific aptamer can serve as a carrier for delivering other therapeutic agents to the target cells or tissue. There are three aptamers designated for use in ophthalmology, including one drug approved by the US Food and Drug Administration (FDA) (pegaptanib (Macugen)), and two in late-stage development (ACR-1905 and E-10030). Six RNA and four DNA aptamers have undergone clinical trials for the treatment of various conditions, including macular degeneration, coagulation, oncology and inflammation. All aptamers that have entered clinical trials so far act as antagonists. Nucleic acid aptamers offer several advantages over traditional antibodies, but their clinical translation has been delayed by several factors, including insufficient potency, lack of safety data and high production costs. Here, Zhou and Rossi provide an overview of aptamer generation, focusing on recent technological advances and clinical development, as well as challenges and lessons learned. Nucleic acid aptamers, often termed 'chemical antibodies', are functionally comparable to traditional antibodies, but offer several advantages, including their relatively small physical size, flexible structure, quick chemical production, versatile chemical modification, high stability and lack of immunogenicity. In addition, many aptamers are internalized upon binding to cellular receptors, making them useful targeted delivery agents for small interfering RNAs (siRNAs), microRNAs and conventional drugs. However, several crucial factors have delayed the clinical translation of therapeutic aptamers, such as their inherent physicochemical characteristics and lack of safety data. This Review discusses these challenges, highlighting recent clinical developments and technological advances that have revived the impetus for this promising class of therapeutics.

Chimeric antigen receptor (CAR)-T-cell therapy for solid tumors is limited due to heterogeneous target antigen expression and outgrowth of tumors lacking the antigen targeted by CAR-T cells directed against single antigens. Here, we developed a bicistronic construct to drive expression of a CAR specific for EGFRvIII, a glioblastoma-specific tumor antigen, and a bispecific T-cell engager (BiTE) against EGFR, an antigen frequently overexpressed in glioblastoma but also expressed in normal tissues. CART.BiTE cells secreted EGFR-specific BiTEs that redirect CAR-T cells and recruit untransduced bystander T cells against wild-type EGFR. EGFRvIII-specific CAR-T cells were unable to completely treat tumors with heterogenous EGFRvIII expression, leading to outgrowth of EGFRvIII-negative, EGFR-positive glioblastoma. However, CART.BiTE cells eliminated heterogenous tumors in mouse models of glioblastoma. BiTE-EGFR was locally effective but was not detected systemically after intracranial delivery of CART.BiTE cells. Unlike EGFR-specific CAR-T cells, CART.BiTE cells did not result in toxicity against human skin grafts in vivo.

Numerous monoclonal antibodies have been developed successfully for the treatment of various diseases. Nevertheless, the development of biotherapeutic antibodies is complex, expensive, and time-consuming, and to facilitate this process, careful structural analysis beyond the antibody binding site is required to develop a more efficacious antibody. In this work, we focused on protein antigens, since they induce the largest antibody changes, and provide interesting cases to compare and contrast. The structures of 15 anti-protein antibodies were analysed to compare the antigen-bound/unbound forms. Surprisingly, three different classes of binding-induced changes were identified. In class (B1), the antigen binding fragment distorted significantly, and we found changes in the loop region of the heavy chain’s constant domain; this corresponds well with expected allosteric movements. In class (B2), we found changes in the same loop region without the overall distortion. In class (B3), these changes did not present, and only local changes at the complementarity determining regions were found. Consequently, structural analysis of antibodies is crucial for therapeutic development. Careful evaluation of allosteric movements must be undertaken to develop better effector responses, especially during the transformation of these antibodies from small fragments at the discovery stage to full antibodies at the subsequent development stages.

The time from discovery to proof-of-concept trials could be reduced to 5–6 months from a traditional timeline of 10–12 months.

Remarkable progress has been made in recent decades in engineering antibodies and other protein therapeutics, including enhancements to existing functions as well as the advent of novel molecules that confer biological activities previously unknown in nature. These protein therapeutics have brought major benefits to patients across multiple areas of medicine. One major ongoing challenge is that protein therapeutics can elicit unwanted immune responses (immunogenicity) in treated patients, including the generation of anti-drug antibodies. In rare and unpredictable cases, anti-drug antibodies can seriously compromise therapeutic safety and/or efficacy. Systematic deconvolution of this immunogenicity problem is confounded by the complexity of its many contributing factors and the inherent limitations of available experimental and computational methods. Nevertheless, continued progress with the assessment and mitigation of immunogenicity risk at the preclinical stage has the potential to reduce the incidence and severity of clinical immunogenicity events. This Review focuses on identifying key unsolved anti-drug antibody-related challenges and offers some pragmatic approaches towards addressing them. Examples are drawn mainly from antibodies, given that the majority of available clinical data are from this class of protein therapeutics. Plausible and seemingly tractable solutions are in sight for some immunogenicity problems, whereas other challenges will likely require completely new approaches.Engineered protein therapeutics, including antibodies, are valuable drugs offering major health benefits, but they can elicit unwanted immune responses. This Review identifies key challenges in assessing and mitigating the risk of immunogenicity, particularly the generation of anti-drug antibodies, and suggests pragmatic steps to address them.

Ten years since the immune checkpoint inhibitor ipilimumab was approved for advanced melanoma, it is time to reflect on the lessons learned regarding modulation of the immune system to treat cancer and on novel approaches to further extend the efficacy of current and emerging immunotherapies. Here, we review the studies that led to our current understanding of the melanoma immune microenvironment in humans and the mechanistic work supporting these observations. We discuss how this information is guiding more precise analyses of the mechanisms of action of immune checkpoint blockade and novel immunotherapeutic approaches. Lastly, we review emerging evidence supporting the negative impact of melanoma metabolic adaptation on anti-tumor immunity and discuss how to counteract such mechanisms for more successful use of immunotherapy.Enormous progress has been made in the ten years since immune checkpoint blockade (ICB) was first approved for treating melanoma. Zappasodi and Huang review the current state of the art of ICB for melanoma and prospects for the future.