Utility of physiologically based pharmacokinetic modeling to predict inter-antibody variability in monoclonal antibody pharmacokinetics in mice

ABSTRACTIn this investigation, we tested the hypothesis that a physiologically based pharmacokinetic (PBPK) model incorporating measured in vitro metrics of off-target binding can largely explain the inter-antibody variability in monoclonal antibody (mAb) pharmacokinetics (PK). A diverse panel of 83 mAbs was evaluated for PK in wild-type mice and subjected to 10 in vitro assays to measure major physiochemical attributes. After excluding for target-mediated elimination and immunogenicity, 56 of the remaining mAbs with an eight-fold variability in the area under the curve ([Formula: see text]: 1.74 × 106 −1.38 × 107 ng∙h/mL) and 10-fold difference in clearance (2.55–26.4 mL/day/kg) formed the training set for this investigation. Using a PBPK framework, mAb-dependent coefficients F1 and F2 modulating pinocytosis rate and convective transport, respectively, were estimated for each mAb with mostly good precision (coefficient of variation (CV%)  1. The predictive utility of the developed PBPK model was evaluated against a separate panel of 14 mAbs biased toward high clearance, among which area under the curve of PK data of 12 mAbs was predicted within 2.5-fold error, and the positive and negative predictive values for clearance prediction were 85% and 100%, respectively. MAb heparin chromatography assay output allowed a priori identification of mAb candidates with unfavorable PK

Analytical and Functional Similarity of Aflibercept Biosimilar ABP 938 with Aflibercept Reference Product

Abstract Introduction ABP 938 is being developed as a biosimilar candidate to aflibercept reference product (RP), a biologic used for certain angiogenic eye disorders. This study was designed to provide a comparative analytical assessment of the structural and functional attributes of ABP 938 and aflibercept RP sourced from the United States (US) and the European Union (EU). Methods Structural and functional characterization studies were performed using state-of-the-art analytical techniques that were appropriate to assess relevant quality attributes and capable of detecting qualitative and quantitative differences in primary structure, higher-order structure and biophysical properties, product-related substances and impurities, general properties, and biological activities. Results ABP 938 had the same amino acid sequence and exhibited similar secondary and tertiary structures, and biological activity as aflibercept RP. There were minor differences in a small number of biochemical attributes which are not expected to impact clinical performance. In addition, aflibercept RP sourced from the US and EU were analytically similar. Conclusions ABP 938 was structurally and functionally similar to aflibercept RP. Since aflibercept RP sourced from the US and EU were analytically similar, this allows for the development of a scientific bridge such that a single-source RP can be used in nonclinical and clinical studies

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

ABSTRACTBiologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration–time curve (AUC0–672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL