Systematic Structure-Based Virtual Screening Approach to Antibody Selection and Design of a Humanized Antibody against Multiple Addictive Opioids without Affecting Treatment Agents Naloxone and Naltrexone

基于系统结构的虚拟筛选方法,用于抗体选择和针对多种成瘾性阿片类药物的人源化抗体的设计,而不影响治疗药物纳洛酮和纳曲酮

阅读:7
作者:Chun-Hui Zhang, Kyungbo Kim, Zhenyu Jin, Fang Zheng, Chang-Guo Zhan

Abstract

Opioid drug use, especially heroin, is known as a growing national crisis in America. Heroin itself is a prodrug and is converted to the most active metabolite 6-monoacetylmorphine (6-MAM) responsible for the acute toxicity of heroin and then to a relatively less-active metabolite morphine responsible for the long-term toxicity of heroin. Monoclonal antibodies (mAbs) are recognized as a potentially promising therapeutic approach in the treatment of opioid use disorders (OUDs). Due to the intrinsic challenges of discovering an mAb against multiple ligands, here we describe a general, systematic structure-based virtual screening and design approach which has been used to identify a known anti-morphine antibody 9B1 and a humanized antibody h9B1 capable of binding to multiple addictive opioids (including 6-MAM, morphine, heroin, and hydrocodone) without significant binding with currently available OUD treatment agents naloxone, naltrexone, and buprenorphine. The humanized antibody may serve as a promising candidate for the treatment of OUDs. The experimental binding affinities reasonably correlate with the computationally predicted binding free energies. The experimental activity data strongly support the computational predictions, suggesting that the systematic structure-based virtual screening and humanization design protocol is reliable. The general, systematic structure-based virtual screening and design approach will be useful for many other antibody selection and design efforts in the future.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。