Cook Rebecca L, Martelly William, Agu Chidozie V, Gushgari Lydia R, Moreno Salvador, Kesiraju Sailaja, Mohan Mukilan, Takulapalli Bharath
bioRxiv. 2025 Feb 7:2025.01.11.632576. doi: 10.1101/2025.01.11.632576.
Drug discovery continues to face a staggering 90% failure rate, with many setbacks occurring during late-stage clinical trials. To address this challenge, there is an increasing focus on developing and evaluating new technologies to enhance the "design" and "test" phases of antibody-based drugs (e.g., monoclonal antibodies, bispecifics, CAR-T therapies, ADCs) and biologics during early preclinical development, with the goal of identifying lead molecules with a higher likelihood of clinical success. Artificial intelligence (AI) is becoming an indispensable tool in this domain, both for improving molecules identified through traditional approaches and for the de novo design of novel therapeutics. However, critical bottlenecks persist in the "build" and "test" phases of AI-designed antibodies and protein binders, impeding early preclinical evaluation. While AI models can rapidly generate thousands to millions of putative drug designs, technological and cost limitations mean that only a few dozen candidates are typically produced and tested. Drug developers often face a tradeoff between ultra-high-throughput wet lab methods that provide binary yes/no binding data and biophysical methods that offer detailed characterization of a limited number of drug-target pairs. To address these bottlenecks, we previously reported the development of the Sensor-integrated Proteome On Chip (SPOC®) platform, which enables the production and capture-purification of 1,000 - 2,400 folded proteins directly onto a surface plasmon resonance (SPR) biosensor chip for measuring kinetic binding rates with picomolar affinity resolution. In this study, we extend the SPOC technology to the expression of single-chain antibodies (sc-antibodies), specifically scFv and VHH constructs. We demonstrate that these constructs are capture-purified at high levels on SPR biosensors and retain functionality as shown by the binding specificity to their respective target antigens, with affinities comparable to those reported in the literature. SPOC outputs comprehensive kinetic data including quantitative binding (R ), on-rate ( ), off-rate ( ), affinity ( ), and half-life ( ), for each of thousands of on-chip sc-antibodies. Additionally, we present a case study showcasing single amino acid mutational scan of the complementarity-determining regions (CDRs) of a HER2 VHH (nanobody) paratope. Using 92 unique mutated variants from four different amino acid substitutions, we pinpoint critical residues within the paratope that could further enhance binding affinity. This study serves as a demonstration of a novel high-throughput approach for biophysical screening of hundreds to thousands of single chain antibody sequences in a single assay, generating high affinity resolution kinetic data to support antibody discovery and AI-enabled pipelines.
药物研发的失败率仍然高达90%,令人震惊,许多挫折发生在后期临床试验阶段。为应对这一挑战,人们越来越关注开发和评估新技术,以在临床前早期开发阶段加强基于抗体的药物(如单克隆抗体、双特异性抗体、嵌合抗原受体T细胞疗法、抗体药物偶联物)和生物制品的“设计”和“测试”阶段,目标是识别出更有可能取得临床成功的先导分子。人工智能(AI)正在成为这一领域不可或缺的工具,既用于改进通过传统方法识别的分子,也用于全新设计新型疗法。然而,在人工智能设计的抗体和蛋白质结合剂的“构建”和“测试”阶段,关键瓶颈依然存在,阻碍了临床前的早期评估。虽然人工智能模型可以快速生成数千到数百万个假定的药物设计,但技术和成本限制意味着通常只能生产和测试几十种候选药物。药物研发人员常常面临权衡:超高通量的湿实验室方法能提供二元的结合与否的数据,而生物物理方法能对有限数量的药物-靶点对进行详细表征。为解决这些瓶颈,我们之前报道了传感器集成蛋白质组芯片(SPOC®)平台的开发,该平台能够将1000 - 2400种折叠蛋白直接生产并捕获纯化到表面等离子体共振(SPR)生物传感器芯片上,用于以皮摩尔亲和力分辨率测量动力学结合速率。在本研究中,我们将SPOC技术扩展到单链抗体(sc-抗体)的表达,特别是scFv和VHH构建体。我们证明这些构建体在SPR生物传感器上能高水平地捕获纯化,并保留其功能,表现为对各自靶抗原的结合特异性,其亲和力与文献报道的相当。SPOC为数千种芯片上的sc-抗体中的每一种输出全面的动力学数据,包括定量结合(R )、结合速率( )、解离速率( )、亲和力( )和半衰期( )。此外,我们展示了一个案例研究,对HER2 VHH(纳米抗体)互补决定区(CDR)的抗原结合位点进行单氨基酸突变扫描。使用来自四种不同氨基酸替换的92种独特突变变体,我们确定了抗原结合位点内可进一步提高结合亲和力的关键残基。这项研究展示了一种新颖的高通量方法,用于在一次测定中对数百到数千个单链抗体序列进行生物物理筛选,生成高亲和力分辨率的动力学数据,以支持抗体发现和基于人工智能的流程。