Technology development to evaluate the effectiveness of viscosity reducing excipients

Landscape of Subcutaneous Administration Strategies for Monoclonal Antibodies in Oncology

In recent decades, subcutaneous (SC) administration of monoclonal antibodies (mAbs) has emerged as a promising alternative to intravenous delivery in oncology, offering comparable therapeutic efficacy while addressing patient preferences. This perspective article provides an in-depth analysis of the technological landscape surrounding SC mAb administration in oncology. It outlines various technologies under evaluation across developmental stages, spanning from preclinical investigations to the integration of established methodologies in clinical practice. Additionally, this perspective article explores emerging trends and prospective trajectories, shedding light on the evolving landscape of SC mAb administration. Furthermore, it emphasizes key checkpoints related to quality attributes essential for optimizing mAb delivery via the SC route. This review serves as a valuable resource for researchers, clinicians, and healthcare policymakers, offering insights into the advancement of SC mAb administration in oncology and its implications for patient care.

Revealing binding mechanism of β-casein to chrysin, apigenin, and luteolin and locating its binding pockets by molecular docking and molecular dynamics

Revealing the interaction mechanism of proteins with bioactive molecules and the location of their binding pockets is crucial for predicting the structure-function relationship of proteins in drug discovery and design. Despite some published papers on the interaction of β-casein with small bioactive molecules, the ambiguity of the location and constituent amino acids of β-casein binding pockets prompted us to identify them by in silico simulation of its interaction with three polyphenols, chrysin, apigenin, and luteolin. Molecular docking revealed that the primary β-casein binding pocket for chrysin consists of five nonpolar amino acids (Leu73, Phe77, Pro80, Ile89, and Pro196), three polar neutral amino acids (Ser137, Gln138, and Gln197), and two polar charged amino acids (Glu136, and Arg198). For β-casein/apigenin and β-casein/luteolin complexes, Asn83 also contributes to forming the pocket. Molecular dynamics provided more details, such as the relative contribution of determinative amino acids and the role of various forces. For example, we found that Glu210, Glu132, and Glu35 are the most destructive residues in the binding of chrysin, apigenin, and luteolin to β-casein, respectively. Also, we observed that hydrophobic forces mainly stabilize β-casein/chrysin and β-casein/apigenin, and polar solvation (including hydrogen bonds) stabilizes β-casein/luteolin, all by spontaneous processes.

Stability of Protein Pharmaceuticals: Recent Advances

There have been significant advances in the formulation and stabilization of proteins in the liquid state over the past years since our previous review. Our mechanistic understanding of protein-excipient interactions has increased, allowing one to develop formulations in a more rational fashion. The field has moved towards more complex and challenging formulations, such as high concentration formulations to allow for subcutaneous administration and co-formulation. While much of the published work has focused on mAbs, the principles appear to apply to any therapeutic protein, although mAbs clearly have some distinctive features. In this review, we first discuss chemical degradation reactions. This is followed by a section on physical instability issues. Then, more specific topics are addressed: instability induced by interactions with interfaces, predictive methods for physical stability and interplay between chemical and physical instability. The final parts are devoted to discussions how all the above impacts (co-)formulation strategies, in particular for high protein concentration solutions.'

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.

High concentration formulation developability approaches and considerations

The growing need for biologics to be administered subcutaneously and ocularly, coupled with certain indications requiring high doses, has resulted in an increase in drug substance (DS) and drug product (DP) protein concentrations. With this increase, more emphasis must be placed on identifying critical physico-chemical liabilities during drug development, including protein aggregation, precipitation, opalescence, particle formation, and high viscosity. Depending on the molecule, liabilities, and administration route, different formulation strategies can be used to overcome these challenges. However, due to the high material requirements, identifying optimal conditions can be slow, costly, and often prevent therapeutics from moving rapidly into the clinic/market. In order to accelerate and derisk development, new experimental and in-silico methods have emerged that can predict high concentration liabilities. Here, we review the challenges in developing high concentration formulations, the advances that have been made in establishing low mass and high-throughput predictive analytics, and advances in in-silico tools and algorithms aimed at identifying risks and understanding high concentration protein behavior.