Abstract:A model-based framework is presented to predict monoclonal antibody (mAb) pharmacokinetics (PK) in humans based on
in vitro
measures of antibody physiochemical properties. A physiologically based pharmacokinetic (PBPK) model is used to explore the predictive potential of 14
in vitro
assays designed to measure various antibody physiochemical properties, including nonspecific cell-surface interactions, FcRn binding, thermal stability, hydrophobicity, and self-associa… Show more
“…Figure 11 shows a comparison of human clearance data from several studies. 14 , 16 , 37 , 38 Clear discrepancies are observed in most comparisons, even for mAbs showing fast clearance, which are precisely the ones that need to be identified during early screening using the in vitro assessments and in silico descriptors discussed above. Figure 11.…”
Section: Resultsmentioning
confidence: 99%
“…High-throughput in vitro assays designed to be surrogates for known physiological mechanisms offer huge promise for de-risking at the screening stage 8 , 14 , 41 , 42 and as depletion reagents 35 during discovery and optimization as well. While quantitative agreement between different polyspecificity assays cannot be expected due to reagent and assay complexity, we note that multiple assays can identify the outliers in other assays, especially within the same cluster (Supplementary Figure S3).…”
With the growing significance of antibodies as a therapeutic class, identifying developability risks early during development is of paramount importance. Several high-throughput in vitro assays and in silico approaches have been proposed to de-risk antibodies during early stages of the discovery process. In this review, we have compiled and collectively analyzed published experimental assessments and computational metrics for clinical antibodies. We show that flags assigned based on in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than their in silico counterparts. Additionally, we assessed the performance of published models for developability predictions on molecules not used during model training. We find that generalization to data outside of those used for training remains a challenge for models. Finally, we highlight the challenges of reproducibility in computed metrics arising from differences in homology modeling, in vitro assessments relying on complex reagents, as well as curation of experimental data often used to assess the utility of high-throughput approaches. We end with a recommendation to enable assay reproducibility by inclusion of controls with disclosed sequences, as well as sharing of structural models to enable the critical assessment and improvement of in silico predictions.
“…Figure 11 shows a comparison of human clearance data from several studies. 14 , 16 , 37 , 38 Clear discrepancies are observed in most comparisons, even for mAbs showing fast clearance, which are precisely the ones that need to be identified during early screening using the in vitro assessments and in silico descriptors discussed above. Figure 11.…”
Section: Resultsmentioning
confidence: 99%
“…High-throughput in vitro assays designed to be surrogates for known physiological mechanisms offer huge promise for de-risking at the screening stage 8 , 14 , 41 , 42 and as depletion reagents 35 during discovery and optimization as well. While quantitative agreement between different polyspecificity assays cannot be expected due to reagent and assay complexity, we note that multiple assays can identify the outliers in other assays, especially within the same cluster (Supplementary Figure S3).…”
With the growing significance of antibodies as a therapeutic class, identifying developability risks early during development is of paramount importance. Several high-throughput in vitro assays and in silico approaches have been proposed to de-risk antibodies during early stages of the discovery process. In this review, we have compiled and collectively analyzed published experimental assessments and computational metrics for clinical antibodies. We show that flags assigned based on in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than their in silico counterparts. Additionally, we assessed the performance of published models for developability predictions on molecules not used during model training. We find that generalization to data outside of those used for training remains a challenge for models. Finally, we highlight the challenges of reproducibility in computed metrics arising from differences in homology modeling, in vitro assessments relying on complex reagents, as well as curation of experimental data often used to assess the utility of high-throughput approaches. We end with a recommendation to enable assay reproducibility by inclusion of controls with disclosed sequences, as well as sharing of structural models to enable the critical assessment and improvement of in silico predictions.
“…Additionally, readouts from the current assay can directly yield analyte-specific, single-cell internalization rates that can be incorporated into computational models. For example, cellular measures can provide PK modeling efforts with NSE rates and/or rate constants for each biologic of interest rather than inferring this parameter with biophysical techniques or from general measures of fluid-phase endocytosis (44, 45). Furthermore, specific cell types can be used to tailor the internalization kinetic measurements to the tissue being modeled, such as primary human endothelial cells or interstitial resident immune cells.…”
Past studies have demonstrated higher clearance for monoclonal antibodies possessing increased rates of non-specific endocytosis. However, this metric is oftentimes evaluated indirectly using biophysical techniques or cell surface binding studies that may not provide insight into the specific rates of cellular turnover. Furthermore, few examples evaluating non-specific endocytosis have been reported for a therapeutic antibody that reached clinical assessment. In the current report, we evaluated a therapeutic human immunoglobulin G2 monoclonal antibody targeted against the interleukin-4 receptor alpha chain (IL-4Rα) that exhibited elevated target independent clearance in previous Phase 1 and 2 studies. We confirmed high non-specific clearance of the anti-IL-4Rα antibody as compared to a reference antibody during pharmacokinetic assessments in wild type mice where target-mediated disposition was absent. We then developed a cell-based method capable of measuring cellular protein endocytosis and demonstrated the anti-IL-4Rα antibody exhibited marked non-specific uptake relative to the reference compound. Antibody homology modeling identified the anti-IL-4Rα antibody possessed positive charge patches whose removal via targeted mutations substantially reduced its non-specific endocytosis. We then expanded the scope of the study by evaluating a panel of consisting of both preclinical and clinical monoclonal antibodies and demonstrate those with the highest rates of non-specific uptakein vitroexhibit elevated target independent clearance, low subcutaneous bioavailability, or both. Our results support the observation that high non-specific endocytosis is a negative attribute in monoclonal antibody development and demonstrate the utility of a generic cell-based screen as a quantitative tool to measure non-specific endocytosis of protein therapeutics at the single-cell level.Highlights- Developed a novel, reproducible cellular assay to directly quantify non-specific endocytosis of therapeutic proteins.- A previous clinical candidate monoclonal antibody with rapid target-independent clearance in mice and humans possessed extensive non-specific endocytosis that was due to exposed positive charge features.- Demonstration of distinct rates of endocytosis into mammalian cells for disparate monoclonal antibodies, even those with common specificity for targets or isoelectric points.- Cell-based assay to quantify the potential impact of non-specific endocytosis on target-independent clearance and/or subcutaneous bioavailability of monoclonal antibodies.
“…To enable even higher throughput and the use of low antibody concentrations, two nanoparticle-based assays have been reported, affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) 54–57 and charge-stabilized self-interaction nanoparticle spectroscopy (CS-SINS). 50 AC-SINS is most commonly performed in a solution mimicking physiological conditions (pH 7.4, phosphate-buffered saline), 3 and its measurements have most commonly been linked to pharmacokinetic properties, 33 , 58 although it has also been used for formulation applications. 59 CS-SINS is performed in a common formulation condition (pH 6, 10 mM histidine) and has been reported to identify antibodies with low viscosity and opalescence in concentrated antibody formulations.…”
Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present
in silico, in vitro
, and
in vivo
methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.
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