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Predicting Antibody Developability Profiles Through Early Stage Discovery Screening.

ABSTRACT: Monoclonal antibodies play an increasingly important role for the development of new drugs across multiple therapy areas. The term 'developability' encompasses the feasibility of molecules to successfully progress from discovery to development via evaluation of their physicochemical properties. These properties include the tendency for self-interaction and aggregation, thermal stability, colloidal stability, and optimization of their properties through sequence engineering. Selection of the best antibody molecule based on biological function, efficacy, safety, and developability allows for a streamlined and successful CMC phase. An efficient and practical high-throughput developability workflow (100 s-1,000 s of molecules) implemented during early antibody generation and screening is crucial to select the best lead candidates. This involves careful assessment of critical developability parameters, combined with binding affinity and biological properties evaluation using small amounts of purified material (<1 mg), as well as an efficient data management and database system. Herein, a panel of 152 various human or humanized monoclonal antibodies was analyzed in biophysical property assays. Correlations between assays for different sets of properties were established. We demonstrated in two case studies that physicochemical properties and key assay endpoints correlate with key downstream process parameters. The workflow allows the elimination of antibodies with suboptimal properties and a rank ordering of molecules for further evaluation early in the candidate selection process. This enables any further engineering for problematic sequence attributes without affecting program timelines.

SUBMITTER: Bailly M

PROVIDER: S-EPMC7153844 | biostudies-literature | 2020 Jan-Dec

REPOSITORIES: biostudies-literature

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Predicting Antibody Developability Profiles Through Early Stage Discovery Screening.

Bailly Marc M Mieczkowski Carl C Juan Veronica V Metwally Essam E Tomazela Daniela D Baker Jeanne J Uchida Makiko M Kofman Ester E Raoufi Fahimeh F Motlagh Soha S Yu Yao Y Park Jihea J Raghava Smita S Welsh John J Rauscher Michael M Raghunathan Gopalan G Hsieh Mark M Chen Yi-Ling YL Nguyen Hang Thu HT Nguyen Nhung N Cipriano Dan D Fayadat-Dilman Laurence L

mAbs 20200101 1

Monoclonal antibodies play an increasingly important role for the development of new drugs across multiple therapy areas. The term 'developability' encompasses the feasibility of molecules to successfully progress from discovery to development via evaluation of their physicochemical properties. These properties include the tendency for self-interaction and aggregation, thermal stability, colloidal stability, and optimization of their properties through sequence engineering. Selection of the best ...[more]

PMID: 32249670

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Project description:BackgroundTo assess potential roles for tumor-associated autoantibodies (TAAs) in esophageal squamous cell carcinoma (ESCC) screening: detecting early-stage malignancy, and predicting future cancer risk.MethodThirteen candidate autoantibodies identified in previous literatures were measured using multiplex serological assays in sera from cases and matched controls nested in two population-level screening cohorts in China. To evaluate the role of TAAs in detecting prevalent esophageal malignant lesions, an identification set (150 cases vs. 560 controls) and an external validation set (34 cases vs. 121 controls) were established with pre-screening sera collected ≤ 12 months prior to screening-related diagnosis. To explore the role of TAAs in predicting future ESCC risk, an exploration set (105 cases vs. 416 controls) with pre-diagnostic sera collected > 12 months before clinical diagnosis was established. Two models, the questionnaire-based model and full model additionally incorporating TAA markers, were constructed. Area under the receiver operating characteristic curve (AUC) and net reclassification improvement (NRI) were calculated to compare the performance of the two models.FindingsIn the identification set, NY-ESO-1 (OR=2·12, 95% CI=1·02-4·40) and STIP1 (OR=1·83, 95% CI=1·10-3·05) were positively associated with higher risk of esophageal malignancy. Elevated MMP-7 was associated with higher risk of malignancy in females (ORfemale=5·07, 95% CI=1·30-19·71). The estimates in validation set were consistent with these results, but were close to null in exploration set. Integration of selected TAAs improved the performance of questionnaire-based models in detecting prevalent esophageal malignancy (female: AUCfull model=0·745, 95% CI=0·675-0·814, AUCquestionnaire-based model=0·658, 95% CI=0·585-0·732, NRI=0·604, P<0·0001; male: AUCfull model=0·662, 95% CI=0·596-0·728, AUCquestionnaire-based model=0·619, 95% CI=0·548-0·690, NRI=0·357, P=0·0028). This improvement was also seen in validation set, but was not similarly effective in distinguishing long-term incident cases from healthy controls.InterpretationSerological autoantibodies against NY-ESO-1, STIP1, and MMP-7 perform well in detecting early-stage esophageal malignancy, but are less effective in predicting future ESCC risks.FundingThis work was supported by the National Science & Technology Fundamental Resources Investigation Program of China (2019FY101102), the National Natural Science Foundation of China (82073626), the National Key R&D Program of China (2016YFC0901404), the Beijing-Tianjin-Hebei Basic Research Cooperation Project (J200016), the Digestive Medical Coordinated Development Center of Beijing Hospitals Authority (XXZ0204), and the Natural Science Foundation of Beijing Municipality (7182033).

Dataset Information

Predicting Antibody Developability Profiles Through Early Stage Discovery Screening.

Publications

Predicting Antibody Developability Profiles Through Early Stage Discovery Screening.

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