| Computational tool for the early screening of monoclonal antibodies for their viscosities NJ Agrawal, B Helk, S Kumar, N Mody, HA Sathish, HS Samra, PM Buck, ... MAbs 8 (1), 43-48, 2016 | 91 | 2016 |
| Developability assessment of engineered monoclonal antibody variants with a complex self-association behavior using complementary analytical and in silico tools L Shan, N Mody, P Sormani, KL Rosenthal, MM Damschroder, ... Molecular pharmaceutics 15 (12), 5697-5710, 2018 | 56 | 2018 |
| An “Fc-silenced” IgG1 format with extended half-life designed for improved stability MJ Borrok, N Mody, X Lu, ML Kuhn, H Wu, WF Dall'Acqua, P Tsui Journal of Pharmaceutical Sciences 106 (4), 1008-1017, 2017 | 48 | 2017 |
| Utility of high throughput screening techniques to predict stability of monoclonal antibody formulations during early stage development DS Goldberg, RA Lewus, R Esfandiary, DC Farkas, N Mody, KJ Day, ... Journal of Pharmaceutical Sciences 106 (8), 1971-1977, 2017 | 45 | 2017 |
| Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics PK Lai, A Gallegos, N Mody, HA Sathish, BL Trout MAbs 14 (1), 2026208, 2022 | 32 | 2022 |
| Understanding the role of preferential exclusion of sugars and polyols from native state IgG1 monoclonal antibodies and its effect on aggregation and reversible self-association CM Sudrik, T Cloutier, N Mody, HA Sathish, BL Trout Pharmaceutical Research 36, 1-12, 2019 | 28 | 2019 |
| Molecular computations of preferential interaction coefficients of IgG1 monoclonal antibodies with sorbitol, sucrose, and trehalose and the impact of these excipients on … T Cloutier, C Sudrik, N Mody, HA Sathish, BL Trout Molecular pharmaceutics 16 (8), 3657-3664, 2019 | 23 | 2019 |
| Machine learning models of antibody–excipient preferential interactions for use in computational formulation design TK Cloutier, C Sudrik, N Mody, HA Sathish, BL Trout Molecular Pharmaceutics 17 (9), 3589-3599, 2020 | 19 | 2020 |
| Molecular computations of preferential interactions of proline, arginine. HCl, and NaCl with IgG1 antibodies and their impact on aggregation and viscosity TK Cloutier, C Sudrik, N Mody, SA Hasige, BL Trout MAbs 12 (1), 1816312, 2020 | 18 | 2020 |
| Highland games: A benchmarking exercise in predicting biophysical and drug properties of monoclonal antibodies from amino acid sequences J Coffman, B Marques, R Orozco, M Aswath, H Mohammad, ... Biotechnology and Bioengineering 117 (7), 2100-2115, 2020 | 10 | 2020 |
| Developability profiling of a panel of Fc engineered SARS-CoV-2 neutralizing antibodies A Dippel, A Gallegos, V Aleti, A Barnes, X Chen, E Christian, J Delmar, ... Mabs 15 (1), 2152526, 2023 | 5 | 2023 |
| Computational tool for the early screening of monoclonal antibodies for their viscosities. MAbs 8, 43–48 NJ Agrawal, B Helk, S Kumar, N Mody, HA Sathish, HS Samra, BL Trout | 5 | 2016 |
| Antibody formulations MN Dimitrova, N Mody US Patent 8,754,195, 2014 | 5 | 2014 |
| Identification of a new IgG mAb format with enhanced complement mediated effector function and extended half life A Digiandomenico, A Dippel, R Varkey, A Lidwell, L Zhuang, V Godfrey, ... C65. COPD: PRE-CLINICAL MODELS AND MECHANISMS, A4648-A4648, 2022 | 1 | 2022 |
| Critical reagents for ligand-binding assays: process development methodologies to enable high-quality reagents C Kittinger, J Delmar, L Hewitt, R Holcomb, C Jones, H Jones, R Kubiak, ... Bioanalysis 14 (3), 117-135, 2021 | 1 | 2021 |
| Antibody formulations MN Dimitrova, N Mody | 1 | 2014 |
| Antibody formulations MN Dimitrova, N Mody | 1 | 2012 |
| Minimizing self-association: Application of conformational and colloidal stability analyses in developing an optimal formulation N Mody, HS Samra, NJ DeJesus, SM Bishop, MN Dimitrova ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 241, 2011 | | 2011 |
