Structure Based Prediction of Asparagine Deamidation Propensity in Monoclonal Antibodies

Mechanisms Influencing the Pharmacokinetics and Disposition of Monoclonal Antibodies and Peptides

Monoclonal antibodies (mAbs) and peptides are an important class of therapeutic modalities that have brought improved health outcomes in areas with limited therapeutic optionality. Presently, there more than 90 mAb and peptide therapeutics on the United States market, with over 600 more in various clinical stages of development in a broad array of therapeutic areas, including diabetes, autoimmune disorders, oncology, neuroscience, and cardiovascular and infectious diseases. Notwithstanding this potential, there is high clinical rate of attrition, with approximately 10% reaching patients. A major contributor to the failure of the molecules is often times an incomplete or poor understanding of the pharmacokinetics (PK) and disposition profiles leading to limited or diminished efficacy. Increased and thorough characterization efforts directed at disseminating mechanisms influencing the PK and disposition of mAbs and peptides can aid in improving the design for their intended pharmacological activity, and thereby their clinical success. The PK and disposition factors for mAbs and peptides are broadly influenced by target-mediated drug disposition and nontarget-related clearance mechanisms related to the interplay between the relationship of the structure and physiochemical properties of mAbs and peptides with physiologic processes. This review focuses on nontarget-related factors influencing the disposition and PK of mAbs and peptides. Contemporary considerations around the increasing in silico approaches to identify nontarget-related molecule limitations and enhancing the druggability of mAbs and peptides, including parenteral and nonparenteral delivery strategies that are geared toward improving patient experience and compliance, are also discussed.

Deamidation and isomerization liability analysis of 131 clinical-stage antibodies

Contemporary in vivo and in vitro discovery platform technologies greatly increase the odds of identifying high-affinity monoclonal antibodies (mAbs) towards essentially any desired biologically relevant epitope. Lagging discovery throughput is the ability to select for highly developable mAbs with drug-like properties early in the process. Upstream consideration of developability metrics should reduce the frequency of failures in later development stages. As the field moves towards incorporating biophysical screening assays in parallel to discovery processes, similar approaches should also be used to ensure robust chemical stability. Optimization of chemical stability in the early stages of discovery has the potential to reduce complications in formulation development and improve the potential for successful liquid formulations. However, at present, our knowledge of the chemical stability characteristics of clinical-stage therapeutic mAbs is fragmented and lacks comprehensive comparative assessment. To address this knowledge gap, we produced 131 mAbs with amino acid sequences corresponding to the variable regions of clinical-stage mAbs, subjected these to low and high pH stresses and identified the resulting modifications at amino acid-level resolution via tryptic peptide mapping. Among this large set of mAbs, relatively high frequencies of asparagine deamidation events were observed in CDRs H2 and L1, while CDRs H3, H2 and L1 contained relatively high frequencies of instances of aspartate isomerization.

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

Monoclonal antibodies (mAbs) have become a major class of protein therapeutics that target a spectrum of diseases ranging from cancers to infectious diseases. Similar to any protein molecule, mAbs are susceptible to chemical modifications during the manufacturing process, long-term storage, and in vivo circulation that can impair their potency. One such modification is the oxidation of methionine residues. Chemical modifications that occur in the complementarity-determining regions (CDRs) of mAbs can lead to the abrogation of antigen binding and reduce the drug's potency and efficacy. Thus, it is highly desirable to identify and eliminate any chemically unstable residues in the CDRs during the therapeutic antibody discovery process. To provide increased throughput over experimental methods, we extracted features from the mAbs' sequences, structures, and dynamics, used random forests to identify important features and develop a quantitative and highly predictive in silico methionine oxidation model.