Antibody engineering and its therapeutic applications

A new era of antibody discovery: an in-depth review of AI-driven approaches

Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consuming, expensive, and labor-intensive. Recent advances in artificial intelligence (AI) technologies provide complementary methods that can reduce the time and costs required for antibody design by minimizing failures and increasing the success rate of experimental tests. In this review, we scrutinize the plethora of AI-driven methodologies that have been deployed over the past 4 years for modeling antibody structures, predicting antibody-antigen interactions, optimizing antibody affinity, and generating novel antibody candidates. We also briefly address the challenges faced in integrating AI-based models with traditional antibody discovery pipelines and highlight the potential future directions in this burgeoning field.

Utilizing fab fragment-conjugated surface plasmon resonance-based biosensor for detection of Salmonella Enteritidis

Although antibodies, a key element of biorecognition, are frequently used as biosensor probes, the use of these large molecules can lead to adverse effects. Fab fragments can be reduced to allow proper antigen-binding orientation via thiol groups containing Fab sites that can directly penetrate Au sites chemically. In this study, the ability of the surface plasmon resonance (SPR) sensor to detect Salmonella was studied. Tris(2-carboxyethyl)phosphine was used as a reducing agent to obtain half antibody fragments. Sensor surface was immobilized with antibody, and bacteria suspensions were injected from low to high concentrations. Response units were changed by binding first reduced antibody fragments, then bacteria. The biosensor was able to determine the bacterial concentrations between 103 and 108 CFU/mL. Based on these results, the half antibody fragmentation method can be generalized for faster, label-free, sensitive, and selective detection of other bacteria species.

Liquid-phase separations coupled with ion mobility-mass spectrometry for next-generation biopharmaceutical analysis

INTRODUCTION

The pharmaceutical industry continues to expand its search for innovative biotherapeutics. The comprehensive characterization of such therapeutics requires many analytical techniques to fully evaluate critical quality attributes, making analysis a bottleneck in discovery and development timelines. While thorough characterization is crucial for ensuring the safety and efficacy of biotherapeutics, there is a need to further streamline analytical characterization and expedite the overall timeline from discovery to market.

AREAS COVERED

This review focuses on recent developments in liquid-phase separations coupled with ion mobility-mass spectrometry (IM-MS) for the development and characterization of biotherapeutics. We cover uses of IM-MS to improve the characterization of monoclonal antibodies, antibody-drug conjugates, host cell proteins, glycans, and nucleic acids. This discussion is based on an extensive literature search using Web of Science, Google Scholar, and SciFinder.

EXPERT OPINION

IM-MS has the potential to enhance the depth and efficiency of biotherapeutic characterization by providing additional insights into conformational changes, post-translational modifications, and impurity profiles. The rapid timescale of IM-MS positions it well to enhance the information content of existing assays through its facile integration with standard liquid-phase separation techniques that are commonly used for biopharmaceutical analysis.

Computational and artificial intelligence-based methods for antibody development

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.

Targeted Nanoparticles for the Binding of Injured Vascular Endothelium after Percutaneous Coronary Intervention

Percutaneous coronary intervention (PCI) is a common procedure for the management of coronary artery obstruction. However, it usually causes vascular wall injury leading to restenosis that limits the long-term success of the PCI endeavor. The ultimate objective of this study was to develop the targeting nanoparticles (NPs) that were destined for the injured subendothelium and attract endothelial progenitor cells (EPCs) to the damaged location for endothelium regeneration. Biodegradable poly(lactic-co-glycolic acid) (PLGA) NPs were conjugated with double targeting moieties, which are glycoprotein Ib alpha chain (GPIbα) and human single-chain antibody variable fragment (HuscFv) specific to the cluster of differentiation 34 (CD34). GPIb is a platelet receptor that interacts with the von Willebrand factor (vWF), highly deposited on the damaged subendothelial surface, while CD34 is a surface marker of EPCs. A candidate anti-CD34 HuscFv was successfully constructed using a phage display biopanning technique. The HuscFv could be purified and showed binding affinity to the CD34-positive cells. The GPIb-conjugated NPs (GPIb-NPs) could target vWF and prevent platelet adherence to vWF in vitro. Furthermore, the HuscFv-conjugated NPs (HuscFv-NPs) could capture CD34-positive cells. The bispecific NPs have high potential to locate at the damaged subendothelial surface and capture EPCs for accelerating the vessel repair.

Antibody-receptor bioengineering and its implications in designing bioelectronic devices

Antibodies play a crucial role in the defense mechanism countering pathogens or foreign antigens in eukaryotes. Its potential as an analytical and diagnostic tool has been exploited for over a century. It forms immunocomplexes with a specific antigen, which is the basis of immunoassays and aids in developing potent biosensors. Antibody-based sensors allow for the quick and accurate detection of various analytes. Though classical antibodies have prolonged been used as bioreceptors in biosensors fabrication due to their increased fragility, they have been engineered into more stable fragments with increased exposure of their antigen-binding sites in the recent era. In biosensing, the formats constructed by antibody engineering can enhance the signal since the resistance offered by a conventional antibody is much more than these fragments. Hence, signal amplification can be observed when antibody fragments are utilized as bioreceptors instead of full-length antibodies. We present the first systematic review on engineered antibodies as bioreceptors with the description of their engineering methods. The detection of various target analytes, including small molecules, macromolecules, and cells using antibody-based biosensors, has been discussed. A comparison of the classical polyclonal, monoclonal, and engineered antibodies as bioreceptors to construct highly accurate, sensitive, and specific sensors is also discussed.

A review on targeting tumor microenvironment: The main paradigm shift in the mAb-based immunotherapy of solid tumors

Monoclonal antibodies (mAbs) as biological macromolecules have been remarked the large and growing pipline of the pharmaceutical market and also the most promising tool in modern medicine for cancer therapy. These therapeutic entities, which consist of whole mAbs, armed mAbs (i.e., antibody-toxin conjugates, antibody-drug conjugates, and antibody-radionuclide conjugates), and antibody fragments, mostly target tumor cells. However, due to intrinsic heterogeneity of cancer diseases, tumor cells targeting mAb have been encountered with difficulties in their unpredictable efficacy as well as variability in remission and durable clinical benefits among cancer patients. To address these pitfalls, the area has undergone two major evolutions with the intent of minimizing anti-drug responses and addressing limitations experienced with tumor cell-targeted therapies. As a novel hallmark of cancer, the tumor microenvironment (TME) is becoming the great importance of attention to develop innovative strategies based on therapeutic mAbs. Here, we underscore innovative strategies targeting TME by mAbs which destroy tumor cells indirectly through targeting vasculature system (e.g., anti-angiogenesis), immune system modulation (i.e., stimulation, suppression, and depletion), the targeting and blocking of stroma-based growth signals (e.g., cancer-associated fibroblasts), and targeting cancer stem cells, as well as, their effector mechanisms, clinical uses, and relevant mechanisms of resistance.