单克隆抗体
受体-配体动力学
化学
效力
离解常数
动力学
药物发现
高通量筛选
单克隆
受体
体外
抗体
计算生物学
生物化学
生物
免疫学
物理
量子力学
作者
Eric M. Janezic,Alexander Doan,Elaine Mai,Daniel D. Bravo,Jianyong Wang,Hok Seon Kim,Christoph Spiess,Kathryn D. Bewley,Adel M. ElSohly,Wei‐Ching Liang,James T. Koerber,Pascale Richalet,Marc Vanhove,Laëtitia Comps-Agrar
摘要
Abstract Background and Purpose Monoclonal antibodies (Ab) represent the fastest growing drug class. Knowledge of the biophysical parameters ( k on , k off and K D ) that dictate Ab:receptor interaction is critical during the drug discovery process. However, with the increasing complexity of Ab formats and their targets, it became apparent that existing technologies present limitations and are not always suitable to determine these parameters. Therefore, novel affinity determination methods represent an unmet assay need. Experimental Approach We developed a pre‐equilibrium kinetic exclusion assay using recent mathematical advances to determine the k on , k off and K D of monoclonal Ab:receptor interactions on living cells. The assay is amenable to all human IgG1 and rabbit Abs. Key Results Using our novel assay, we demonstrated for several monoclonal Ab:receptor pairs that the calculated kinetic rate constants were comparable with orthogonal methods that were lower throughput or more resource consuming. We ran simulations to predict the critical conditions to improve the performance of the assays. We further showed that this method could successfully be applied to both suspension and adherent cells. Finally, we demonstrated that k on and k off , but not K D , correlate with in vitro potency for a panel of monoclonal Abs. Conclusions and Implications Our novel assay has the potential to systematically probe binding kinetics of monoclonal Abs to cells and can be incorporated in a screening cascade to identify new therapeutic candidates. Wide‐spread adoption of pre‐equilibrium assays using physiologically relevant systems will lead to a more holistic understanding of how Ab binding kinetics influence their potency.
科研通智能强力驱动
Strongly Powered by AbleSci AI