Population-specific design of de-immunized protein biotherapeutics

Robust genome and cell engineering via in vitro and in situ circularized RNAs

Circularization can improve RNA persistence, yet simple and scalable approaches to achieve this are lacking. Here we report two methods that facilitate the pursuit of circular RNAs (cRNAs): cRNAs developed via in vitro circularization using group II introns, and cRNAs developed via in-cell circularization by the ubiquitously expressed RtcB protein. We also report simple purification protocols that enable high cRNA yields (40-75%) while maintaining low immune responses. These methods and protocols facilitate a broad range of applications in stem cell engineering as well as robust genome and epigenome targeting via zinc finger proteins and CRISPR-Cas9. Notably, cRNAs bearing the encephalomyocarditis internal ribosome entry enabled robust expression and persistence compared with linear capped RNAs in cardiomyocytes and neurons, which highlights the utility of cRNAs in these non-dividing cells. We also describe genome targeting via deimmunized Cas9 delivered as cRNA and a long-range multiplexed protein engineering methodology for the combinatorial screening of deimmunized protein variants that enables compatibility between persistence of expression and immunogenicity in cRNA-delivered proteins. The cRNA toolset will aid research and the development of therapeutics.

A bispecific CD40 agonistic antibody allowing for antibody-peptide conjugate formation to enable cancer-specific peptide delivery, resulting in improved T proliferation and anti-tumor immunity in mice

Current antibody-based immunotherapy depends on tumor antigen shedding for proper T cell priming. Here we select a novel human CD40 agonistic drug candidate and generate a bispecific antibody, herein named BiA9*2_HF, that allows for rapid antibody-peptide conjugate formation. The format is designed to facilitate peptide antigen delivery to CD40 expressing cells combined with simultaneous CD40 agonistic activity. In vivo, the selected bispecific antibody BiA9*2_HF loaded with peptide cargos induces improved antigen-specific proliferation of CD8+ (10-15 fold) and CD4+ T cells (2-7 fold) over control in draining lymph nodes. In both virus-induced and neoantigen-based mouse tumor models, BiA9*2_HF demonstrates therapeutic efficacy and elevated safety profile, with complete tumor clearance, as well as measured abscopal impact on tumor growth. The BiA9*2_HF drug candidate can thus be utilized to tailor immunotherapeutics for cancer patients.

Machine learning for functional protein design

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.

Recent Advances in Pharmaceutical Design: Unleashing the Potential of Novel Therapeutics

Pharmaceutical design has made significant advancements in recent years, leading to the development of novel therapeutics with unprecedented efficacy and safety profiles. This review highlights the potential of these innovations to revolutionize healthcare and improve patient outcomes. The application of cutting-edge technologies like artificial intelligence, machine learning, and data mining in drug discovery and design has made it easier to find potential drug candidates. Combining big data and omics has led to the discovery of new therapeutic targets and personalized medicine strategies. Nanoparticles, liposomes, and microneedles are examples of advanced drug delivery systems that allow precise control over drug release, better bioavailability, and targeted delivery to specific tissues or cells. This improves the effectiveness of the treatment while reducing side effects. Stimuli-responsive materials and smart drug delivery systems enable drugs to be released on demand when specific internal or external signals are sent. Biologics and gene therapies are promising approaches in pharmaceutical design, offering high specificity and potency for treating various diseases like cancer, autoimmune disorders, and infectious diseases. Gene therapies hold tremendous potential for correcting genetic abnormalities, with recent breakthroughs demonstrating successful outcomes in inherited disorders and certain types of cancer. Advancements in nanotechnology and nanomedicine have paved the way for innovative diagnostic tools and therapeutics, such as nanoparticle-based imaging agents, targeted drug delivery systems, gene editing technologies, and regenerative medicine strategies. Finally, the review emphasizes the importance of regulatory considerations, ethical challenges, and future directions in pharmaceutical design. Regulatory agencies are adapting to the rapid advancements in the field, ensuring the safety and efficacy of novel therapeutics while fostering innovation. Ethical considerations regarding the use of emerging technologies, patient privacy, and access to advanced therapies also require careful attention.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

Protein interaction, monocyte toxicity and immunogenic properties of cerium oxide crystals with 5% or 14% gadolinium, cobalt oxide and iron oxide nanoparticles - an interdisciplinary approach

Metal oxide nanoparticles are widely used in both consumer products and medical applications, but the knowledge regarding exposure-related health effects is limited. However, it is challenging to investigate nanoparticle interaction processes with biological systems. The overall aim of this project was to improve the possibility to predict exposure-related health effects of metal oxide nanoparticles through interdisciplinary collaboration by combining workflows from the pharmaceutical industry, nanomaterial sciences, and occupational medicine. Specific aims were to investigate nanoparticle-protein interactions and possible adverse immune reactions. Four different metal oxide nanoparticles; CeO_x nanocrystals with 5% or 14% Gd, Co₃O₄, and Fe₂O₃, were characterized by dynamic light scattering and high-resolution transmission electron microscopy. Nanoparticle-binding proteins were identified and screened for HLA-binding peptides in silico. Monocyte interaction with nanoparticle-protein complexes was assessed in vitro. Herein, for the first time, immunogenic properties of nanoparticle-binding proteins have been characterized. The present study indicates that especially Co₃O₄-protein complexes can induce both 'danger signals', verified by the production of inflammatory cytokines and simultaneously bind autologous proteins, which can be presented as immunogenic epitopes by MHC class II. The clinical relevance of these findings should be further evaluated to investigate the role of metal oxide nanoparticles in the development of autoimmune disease. The general workflow identified experimental difficulties, such as nanoparticle aggregate formation and a lack of protein-free buffers suitable for particle characterization, protein analyses, as well as for cell studies. This confirms the importance of future interdisciplinary collaborations.

Artificial intelligence in early drug discovery enabling precision medicine

Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence.Areas covered: In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design.Expert opinion: Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.

Engineering therapeutic antibodies for patient safety: tackling the immunogenicity problem

Established monoclonal antibodies (mAbs) allow treatment of cancers, autoimmune diseases and other severe illnesses. Side effects either arise due to interaction with the target protein and its biology or result from of the patient's immune system reacting to the foreign protein. This immunogenic reaction against therapeutic antibodies is dependent on various factors. The presence of non-human sequences can trigger immune responses as well as chemical and post-translational modifications of the antibody. However, even fully human antibodies can induce immune response through T cell epitopes or aggregates. In this review, we briefly describe, how therapeutic antibodies can interact with the patient's immune system and summarize recent advancements in protein engineering and in silico methods to reduce immunogenicity of therapeutic monoclonal antibodies.

Report on biopharmaceutical profile of recent biotherapeutics and insilco docking studies on target bindings of known aptamer biotherapeutics

Accumulated Toxicity, disease recurrence and drug resistivity problems have been observed due to the synthetic and semisynthetic therapeutic practices, which alternatively led to focus on Bio-therapeutics production than xenobiotics. Quick plasma clearance and high potency are the reasons for trending research with huge pharma market of numerous Bio-therapeutics than ever before. Researchers proved that most of the nano and micro Bio-therapeutics have multiple beneficial therapeutic effects. We have analyzed the past, and present scenario of some notable clinically approved Bio-therapeutics to identify the future formulation needs with advanced techniques. Protein-related drugs are the foremost Bio-therapeutics such as antibodies, enzymes, and short, fragmented polypeptides show aggregation properties during storage, naked peptide moieties are resisted by the polar cell membrane, and also the antidrug antibodies were reported. Even though Nucleic acid nano-bodies are excellent target binders than proteins, they had only a few minutes of half-life. Maintaining homogeneousness upon storage of Bio-therapeutics is still a significant challenge in industrial-scale formulation. Notably, plant systems are identified as most useful cost-effective hosts to produce human enzymes than animal systems without any possible viral loads. Irrespective of numerous advancements in routes of administration and additives, subcutaneous is still a golden one to achieve better dynamics. Additionally, the interactions and effective bonds made by each class of well-known aptamer biotherapeutics which are considered as future drugs were studied.

Design and engineering of tumor-targeted, dual-acting cytotoxic nanoparticles

The possibility to conjugate tumor-targeted cytotoxic nanoparticles and conventional antitumoral drugs in single pharmacological entities would open a wide spectrum of opportunities in nanomedical oncology. This principle has been explored here by using CXCR4-targeted self-assembling protein nanoparticles based on two potent microbial toxins, the exotoxin A from Pseudomonas aeruginosa and the diphtheria toxin from Corynebacterium diphtheriae, to which oligo-floxuridine and monomethyl auristatin E respectively have been chemically coupled. The resulting multifunctional hybrid nanoconjugates, with a hydrodynamic size of around 50 nm, are stable and internalize target cells with a biological impact. Although the chemical conjugation minimizes the cytotoxic activity of the protein partner in the complexes, the concept of drug combination proposed here is fully feasible and highly promising when considering multiple drug treatments aimed to higher effectiveness or when facing the therapy of cancers with acquired resistance to classical drugs.

Improved prediction of HLA antigen presentation hotspots: Applications for immunogenicity risk assessment of therapeutic proteins

Immunogenicity risk assessment is a critical element in protein drug development. Currently, the risk assessment is most often performed using MHC-associated peptide proteomics (MAPPs) and/or T-cell activation assays. However, this is a highly costly procedure that encompasses limited sensitivity imposed by sample sizes, the MHC repertoire of the tested donor cohort and the experimental procedures applied. Recent work has suggested that these techniques could be complemented by accurate, high-throughput and cost-effective prediction of in silico models. However, this work covered a very limited set of therapeutic proteins and eluted ligand (EL) data. Here, we resolved these limitations by showcasing, in a broader setting, the versatility of in silico models for assessment of protein drug immunogenicity. A method for prediction of MHC class II antigen presentation was developed on the hereto largest available mass spectrometry (MS) HLA-DR EL data set. Using independent test sets, the performance of the method for prediction of HLA-DR antigen presentation hotspots was benchmarked. In particular, the method was showcased on a set of protein sequences including four therapeutic proteins and demonstrated to accurately predict the experimental MS hotspot regions at a significantly lower false-positive rate compared with other methods. This gain in performance was particularly pronounced when compared to the NetMHCIIpan-3.2 method trained on binding affinity data. These results suggest that in silico methods trained on MS HLA EL data can effectively and accurately be used to complement MAPPs assays for the risk assessment of protein drugs.

Mitigation of T-cell dependent immunogenicity by reengineering factor VIIa analogue

Vatreptacog alfa (VA), a recombinant activated human factor VII (rFVIIa) variant with 3 amino acid substitutions, was developed to provide increased procoagulant activity in hemophilia patients with inhibitors to factor VIII or factor IX. In phase 3 clinical trials, changes introduced during the bioengineering of VA resulted in the development of undesired anti-drug antibodies in some patients, leading to the termination of a potentially promising therapeutic protein product. Here, we use preclinical biomarkers associated with clinical immunogenicity to validate our deimmunization strategy applied to this bioengineered rFVIIa analog. The reengineered rFVIIa analog variants retained increased intrinsic thrombin generation activity but did not elicit T-cell responses in peripheral blood mononuclear cells isolated from 50 HLA typed subjects representing the human population. Our algorithm, rational immunogenicity determination, offers a broadly applicable deimmunizing strategy for bioengineered proteins.

Design, creation and in vitro testing of a reduced immunogenicity humanized anti-CD25 monoclonal antibody that retains functional activity

Humanized and fully human sequence-derived therapeutic antibodies retain the capacity to induce anti-drug antibodies. Daclizumab (humanized version of the murine anti-Tac antibody; E.HAT) was selected for a proof of concept application of engineering approaches to reduce potential immunogenicity due to its demonstrated immunogenicity in the clinic. Reduced immunogenicity variants of E.HAT were created by identifying and modifying a CD4+ T cell epitope region in the VH region. Variant epitope region peptides were selected for their reduced capacity to induce CD4+ T cell proliferative responses in vitro. Variant antibody molecules were created, and CD25 affinity and potency were similar to the unmodified parent antibody. Fab fragments from the variant antibodies induced a lower frequency and magnitude of responses in human peripheral blood mononuclear cells proliferation tests. By the empirical selection of two amino acid mutations, fully functional humanized E.HAT antibodies with reduced potential to induce immune responses in vitro were created.

Nonclinical immunogenicity risk assessment of therapeutic proteins

Therapeutic protein drugs have significantly improved the management of many severe and chronic diseases. However, their development and optimal clinical application are complicated by the induction of unwanted immune responses. Therapeutic protein-induced antidrug antibodies can alter drug pharmacokinetics and pharmacodynamics leading to impaired efficacy and occasionally serious safety issues. There has been a growing interest over the past decade in developing methods to assess the risk of unwanted immunogenicity during preclinical drug development, with the aim to mitigate the risk during the molecular design phase, clinical development and when products reach the market. Here, we discuss approaches to therapeutic protein immunogenicity risk assessment, with attention to assays and in vivo models used to mitigate this risk.

Structural and Dynamic Properties of Allergen and Non-Allergen Forms of Tropomyosin

To what extent do structural and biophysical features of food allergen proteins distinguish them from other proteins in our diet? Invertebrate tropomyosins (Tpms) as a class are considered "pan-allergens," inducing food allergy to shellfish and respiratory allergy to dust mites. Vertebrate Tpms are not known to elicit allergy or cross-reactivity, despite their high structural similarity and sequence identity to invertebrate homologs. We expect allergens are sufficiently stable against gastrointestinal proteases to survive for immune sensitization in the intestines, and that proteolytic stability will correlate with thermodynamic stability. Thermal denaturation of shrimp Tpm shows that it is more stable than non-allergen vertebrate Tpm. Shrimp Tpm is also more resistant to digestion. Molecular dynamics uncover local dynamics that select epitopes and global differences in flexibility between shrimp and pig Tpm that discriminate allergens from non-allergens. Molecular determinants of allergenicity depend not only on sequence but on contributions of protein structure and dynamics.

Anti-Drug Antibodies: Emerging Approaches to Predict, Reduce or Reverse Biotherapeutic Immunogenicity

The development of anti-drug antibodies (ADAs) following administration of biotherapeutics to patients is a vexing problem that is attracting increasing attention from pharmaceutical and biotechnology companies. This serious clinical problem is also spawning creative research into novel approaches to predict, avoid, and in some cases even reverse such deleterious immune responses. CD4⁺ T cells are essential players in the development of most ADAs, while memory B-cell and long-lived plasma cells amplify and maintain these responses. This review summarizes methods to predict and experimentally identify T-cell and B-cell epitopes in therapeutic proteins, with a particular focus on blood coagulation factor VIII (FVIII), whose immunogenicity is clinically significant and is the subject of intensive current research. Methods to phenotype ADA responses in humans are described, including T-cell stimulation assays, and both established and novel approaches to determine the titers, epitopes and isotypes of the ADAs themselves. Although rational protein engineering can reduce the immunogenicity of many biotherapeutics, complementary, novel approaches to induce specific tolerance, especially during initial exposures, are expected to play significant roles in future efforts to reduce or reverse these unwanted immune responses.

FoldX as Protein Engineering Tool: Better Than Random Based Approaches?

Improving protein stability is an important goal for basic research as well as for clinical and industrial applications but no commonly accepted and widely used strategy for efficient engineering is known. Beside random approaches like error prone PCR or physical techniques to stabilize proteins, e.g. by immobilization, in silico approaches are gaining more attention to apply target-oriented mutagenesis. In this review different algorithms for the prediction of beneficial mutation sites to enhance protein stability are summarized and the advantages and disadvantages of FoldX are highlighted. The question whether the prediction of mutation sites by the algorithm FoldX is more accurate than random based approaches is addressed.