药学学报

Targeted subunit	Mode of action	Inhibitor	Stage	Ref.
PA	Cap-dependent endonuclease inhibitors	Baloxavir Marboxil	Listed	[9]
ZX-7101	Clinical phase Ⅲ	[10]
GP681	Clinical phase Ⅲ	[11]
AL-794	Clinical phase Ⅰ	[12]
TG-1000	Clinical phase Ⅱ	[13]
R05-3A and R05-2	Preclinical	[14]
Compound 14 and 19	Preclinical	[15]
Marchantins A and E	Preclinical	[16]
PA/PB1	PA/PB1 interaction inhibitors	Compound 54	Preclinical	[17]
PB1	Nucleoside PB1 inhibitors	Favipiravir	Listed	[18]
Ribavirin	Listed	[19]
Molnupiravir	Listed	[20]
Nonnucleoside PB1 inhibitors	Compound 367	Preclinical	[21]
ASN2	Preclinical	[22]
11q	Preclinical	[23]
PB2	PB2 cap binding domain inhibitors	Pimodivir	Clinical phase Ⅲ	[24]
ZSP1273	Clinical phase Ⅲ	[25]
CC-42344	Clinical phase Ⅱ	[26]
Cap-3 and Cap-7	Preclinical	[27]
PB2-39	Preclinical	[28]
Dihydromyricetin	Preclinical	[29]

Targeted subunit

Mode of action

Inhibitor

Stage

Ref.

Cap-dependent endonuclease inhibitors

Baloxavir Marboxil

Listed

[9]

ZX-7101

Clinical phase Ⅲ

[10]

GP681

Clinical phase Ⅲ

[11]

AL-794

Clinical phase Ⅰ

[12]

TG-1000

Clinical phase Ⅱ

[13]

R05-3A and R05-2

Preclinical

[14]

Compound 14 and 19

Preclinical

[15]

Marchantins A and E

Preclinical

[16]

PA/PB1

PA/PB1 interaction inhibitors

Compound 54

Preclinical

[17]

PB1

Nucleoside PB1 inhibitors

Favipiravir

Listed

[18]

Ribavirin

Listed

[19]

Molnupiravir

Listed

[20]

Nonnucleoside PB1 inhibitors

Compound 367

Preclinical

[21]

ASN2

Preclinical

[22]

11q

Preclinical

[23]

PB2

PB2 cap binding domain inhibitors

Pimodivir

Clinical phase Ⅲ

[24]

ZSP1273

Clinical phase Ⅲ

[25]

CC-42344

Clinical phase Ⅱ

[26]

Cap-3 and Cap-7

Preclinical

[27]

PB2-39

Preclinical

[28]

Dihydromyricetin

Preclinical

[29]

Targeted subunit	Mode of action	Inhibitor	Stage	Ref.
PA	Cap-dependent endonuclease inhibitors	Baloxavir Marboxil	Listed	[9]
ZX-7101	Clinical phase Ⅲ	[10]
GP681	Clinical phase Ⅲ	[11]
AL-794	Clinical phase Ⅰ	[12]
TG-1000	Clinical phase Ⅱ	[13]
R05-3A and R05-2	Preclinical	[14]
Compound 14 and 19	Preclinical	[15]
Marchantins A and E	Preclinical	[16]
PA/PB1	PA/PB1 interaction inhibitors	Compound 54	Preclinical	[17]
PB1	Nucleoside PB1 inhibitors	Favipiravir	Listed	[18]
Ribavirin	Listed	[19]
Molnupiravir	Listed	[20]
Nonnucleoside PB1 inhibitors	Compound 367	Preclinical	[21]
ASN2	Preclinical	[22]
11q	Preclinical	[23]
PB2	PB2 cap binding domain inhibitors	Pimodivir	Clinical phase Ⅲ	[24]
ZSP1273	Clinical phase Ⅲ	[25]
CC-42344	Clinical phase Ⅱ	[26]
Cap-3 and Cap-7	Preclinical	[27]
PB2-39	Preclinical	[28]
Dihydromyricetin	Preclinical	[29]

Targeted subunit

Mode of action

Inhibitor

Stage

Ref.

Cap-dependent endonuclease inhibitors

Baloxavir Marboxil

Listed

[9]

ZX-7101

Clinical phase Ⅲ

[10]

GP681

Clinical phase Ⅲ

[11]

AL-794

Clinical phase Ⅰ

[12]

TG-1000

Clinical phase Ⅱ

[13]

R05-3A and R05-2

Preclinical

[14]

Compound 14 and 19

Preclinical

[15]

Marchantins A and E

Preclinical

[16]

PA/PB1

PA/PB1 interaction inhibitors

Compound 54

Preclinical

[17]

PB1

Nucleoside PB1 inhibitors

Favipiravir

Listed

[18]

Ribavirin

Listed

[19]

Molnupiravir

Listed

[20]

Nonnucleoside PB1 inhibitors

Compound 367

Preclinical

[21]

ASN2

Preclinical

[22]

11q

Preclinical

[23]

PB2

PB2 cap binding domain inhibitors

Pimodivir

Clinical phase Ⅲ

[24]

ZSP1273

Clinical phase Ⅲ

[25]

CC-42344

Clinical phase Ⅱ

[26]

Cap-3 and Cap-7

Preclinical

[27]

PB2-39

Preclinical

[28]

Dihydromyricetin

Preclinical

[29]

流感病毒RNA依赖性RNA聚合酶抑制剂筛选策略及其研究进展

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何梓健 , 袁嘉明 , 张婷婷 , 汪锦才 , 江正瑾 ^*

药学学报 | 综述 2025,60(8): 2466-2474

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药学学报 |综述 2025 , 60 (8) : 2466 -2474

流感病毒RNA依赖性RNA聚合酶抑制剂筛选策略及其研究进展

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何梓健, 袁嘉明, 张婷婷, 汪锦才, 江正瑾^*

作者信息

暨南大学药学院, 药物分析研究中心, 广东广州 511436

通讯作者:

江正瑾, Tel: 86-20-85223604, E-mail: jzjjackson@hotmail.com

作者简介:

作者贡献:

何梓健撰写了主体内容; 袁嘉明辅助撰写了部分内容并提供了部分材料; 张婷婷和汪锦才对文章进行了部分的修改; 江正瑾对整篇文章进行了构思并做了主要修改。

Screening strategy and research progress of influenza virus RNA-dependent RNA polymerase inhibitors

Zi-jian HE, Jia-ming YUAN, Ting-ting ZHANG, Jin-cai WANG, Zheng-jin JIANG^*

Affiliations

Institute of Pharmaceutical Analysis, College of Pharmacy, Jinan University, Guangzhou 511436, China

出版时间: 2025-08-12 doi: 10.16438/j.0513-4870.2025-0504

文章导航

摘要

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流感病毒引发的季节性流行持续威胁全球公共卫生安全, 其复制核心元件RNA依赖性RNA聚合酶(RdRp) 因结构高度保守与功能不可替代性, 成为抗病毒药物研发的关键靶点。本文系统解析了流感病毒RdRp的结构功能特征与抑制剂作用机制, 从分子、细胞及动物模型三个层面综述了现有RdRp抑制剂的筛选技术及新兴筛选策略。通过系统评估各技术的优势与局限, 提出未来研究方向, 为新型高效RdRp抑制剂的研发提供理论依据与技术策略。

关键词

RNA依赖性RNA聚合酶 / 抑制剂发现 / 流感病毒 / 筛选技术 / 高通量筛选

Abstract

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Seasonal influenza epidemics caused by influenza viruses continue to pose persistent threats to global public health. The core replication component RNA-dependent RNA polymerase (RdRp), distinguished by its highly conserved structure and functionally irreplaceable characteristics, has emerged as a critical target for antiviral drug development. This study systematically analyzes the structural and functional features of RdRp and the mechanisms of action of its inhibitors. It comprehensively reviews current screening technologies and emerging strategies for RdRp inhibitors across three levels: molecular, cellular, and animal model levels. By systematically evaluating the technical advantages and limitations of existing approaches, we propose future research directions to advance the development of novel high-efficiency RdRp inhibitors, thereby providing both theoretical foundations and technical frameworks for this crucial antiviral strategy.

Key words

RNA-dependent RNA polymerase / inhibitor discovery / influenza virus / screening technology / high throughput screening

引用本文

何梓健, 袁嘉明, 张婷婷, 汪锦才, 江正瑾. 流感病毒RNA依赖性RNA聚合酶抑制剂筛选策略及其研究进展. 药学学报, 2025 , 60 (8) : 2466 -2474 . DOI: 10.16438/j.0513-4870.2025-0504

Zi-jian HE, Jia-ming YUAN, Ting-ting ZHANG, Jin-cai WANG, Zheng-jin JIANG. Screening strategy and research progress of influenza virus RNA-dependent RNA polymerase inhibitors[J]. Acta Pharmaceutica Sinica, 2025 , 60 (8) : 2466 -2474 . DOI: 10.16438/j.0513-4870.2025-0504

正文

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流感病毒是一种高度传染性RNA病毒, 每年引发全球季节性流行, 构成重大公共卫生威胁^[1]。疫苗作为预防流感的主流手段, 虽能有效降低感染风险, 但存在研发滞后性, 且病毒抗原漂移导致其保护效力受限^[2]。临床常用药物如金刚烷胺(因耐药性退市) 和奥司他韦(耐药株检出率持续上升) 的疗效衰减, 进一步凸显抗流感药物研发的紧迫性^[3]。在此背景下, 病毒RNA依赖性RNA聚合酶(RNA-dependent RNA polymerase, RdRp) 因其在基因组复制/转录中的关键作用及高度保守性, 成为新型抗流感病毒药物研发的理想靶点^{[4, 5]}。其中, RdRp抑制剂巴洛沙韦通过特异性抑制帽依赖性核酸内切酶活性阻断病毒mRNA合成, 已成为抗流感一线治疗药物^[6]。然而, 流感病毒持续进化迫使研究者们开发作用于RdRp不同功能域的新型抑制剂, 以应对耐药突变与潜在大流行风险^[7]。目前, 基于分子、细胞及动物模型的多水平筛选策略协同新兴技术, 显著推进了从已知化合物与天然产物中发现高效RdRp抑制剂的研发进程。本文通过系统评析RdRp抑制剂筛选技术的革新应用、核心优势与固有局限, 旨在为优化筛选路径并开发新型高效RdRp抑制剂提供理论支撑与策略框架。

1　流感病毒RdRp抑制剂的作用机制

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1.1　RdRp的结构与功能

流感病毒RdRp作为病毒复制和转录过程中的关键酶, 主要由三个亚基组成: 聚合酶酸性蛋白(polymerase acidic, PA)、聚合酶碱性蛋白1 (polymerase basic 1, PB1) 和聚合酶碱性蛋白2 (polymerase basic 2, PB2), RdRp各亚基协同作用保障病毒在宿主细胞内的高效转录和复制^[5]。三个亚基结构独特且功能各异: PA亚基具有核酸内切酶结构域和大结构域, 分别负责切割宿主帽结构RNA和参与复合物组装; PB1亚基是核心催化亚基, 负责RNA合成, 其活性中心的保守基序在合成中起关键作用; PB2亚基分为帽结合域和与PB1相互作用部分, 前者识别结合宿主帽结构RNA, 后者参与组装^[8]。

1.2　RdRp抑制剂及其作用机制

RdRp的三个亚基通过非共价相互作用结合, 形成功能性复合物, 其相互作用涉及多个疏水口袋和氢键, 为小分子药物的设计提供了潜在靶点。此外, 三个亚基参与病毒转录和翻译的多个功能活动, 通过抑制亚基活性, 或干扰和阻断亚基发挥功能的结合域, 也是RdRp抑制剂开发的重要策略。目前RdRp抑制剂的作用位点主要有以下几类: PA核酸内切酶活性位点、PB2帽结合域、PB1的RNA结合域以及亚基间蛋白-蛋白的相互作用^[8], 详见表1^[9-29]。

2　流感病毒RdRp抑制剂的筛选策略

收起

鉴于RdRp抑制剂在抗流感治疗中的重要性, 当前研究不仅聚焦于对现有抑制剂结构和设计策略的优化, 还致力于从筛选方法上实现突破, 以开发出更高效、耐药性更低的新型抑制剂。特别是, 从已知化合物和天然产物中挖掘潜在的RdRp抑制剂。为了寻找有效的RdRp抑制剂, 研究者们建立了从分子水平到细胞水平再到动物模型的综合筛选和验证体系(图1), 从而加速了新型RdRp抑制剂的发现, 也为抗流感药物的研发提供了更为可靠的实验依据。

2.1　基于分子水平的高通量筛选

分子水平的高通量筛选作为流感病毒抑制剂研发的核心策略, 其原理建立在靶标分子动态机制的系统解析基础之上, 包括酶活性调控、受体-配体互作网络、蛋白质结合抑制效应等关键药效学指标。针对流感病毒RdRp靶点, 研究者通过构建高效检测体系, 可实现从数十万级化合物库中快速识别具有靶向抑制潜力的候选分子^{[21, 30, 31]}。在此过程中, 多种先进技术被整合形成多维评价体系, 包括荧光检测技术[如荧光偏振(fluorescence polarisation, FP)、荧光共振能量转移(fluorescence resonance energy transfer, FRET)、实时定量逆转录聚合酶链式反应(quantitative real-time reverse transcription polymerase chain reaction, qRT-PCR) 等]、酶联免疫吸附(enzyme-linked immunosorbent assay, ELISA)、表面等离子体共振(surface plasmon resonance, SPR)、分子对接和虚拟筛选等。这些方法显著提升了先导化合物发现的准确性和效率, 为开发新型抗流感药物提供了强有力的技术支撑。下文将系统阐述各技术原理及在流感病毒RdRp抑制剂筛选中的创新应用。

2.1.1　荧光法

荧光法凭借实时监测与高通量优势, 成为RdRp抑制剂筛选的核心技术。其通过动态信号捕捉机制实现精准分析: FRET直接追踪RdRp与底物结合状态的变化; FP基于分子互作引起的偏振信号差异锁定抑制剂; qRT-PCR则通过病毒RNA扩增效率的荧光衰减评估抑制效果。

FRET技术通过供体与受体分子间的偶极–偶极相互作用实现能量转移, 其效率对1~10 nm内的分子间距变化极为敏感, 可达到纳米级空间分辨率^[32]。Xing等^[33]利用RNA-FRET底物实时监测流感病毒PA内切酶的活性变化, 通过荧光信号衰减直接关联抑制剂浓度, 成功筛选出两种IC₅₀值为10~20 nmol·L^-1的苯丁酸类抑制剂。该方法无需物理分离步骤, 适用于高通量筛选, 但其信号可能因分子自聚集或距离超限(> 10 nm) 产生假阳性^{[34, 35]}。

与FRET提供空间分辨率不同, FP技术通过检测荧光分子结合靶标后旋转速度的变化(偏振度增加) 分析分子互作动态^[36]。Baughman等^[37]开发的FP竞争性结合实验以荧光素标记化合物(K_d = 0.378 μmol·L^-1) 与PA亚基N端结构域(PA_N) 为基础, 通过偏振信号筛选活性位点抑制剂, 验证了4-取代2, 4-二氧丁酸类抑制剂的结合亲和力与酶活性抑制的强相关性。尽管该方法无需洗涤步骤且操作简便, 但存在荧光标记干扰结合动力学的问题, 且分子量适用范围受限, 弱效抑制剂的信号变化易被背景噪声掩盖^{[38, 39]}。

在功能抑制层面, qRT-PCR通过病毒RNA扩增效率的荧光信号衰减定量评估RdRp活性抑制。Horio等^[40]设计的链特异性检测方法采用带标签的RT引物结合poly A序列进行反转录, 并通过qRT-PCR分析RNA合成量, 成功验证了利巴韦林三磷酸对mRNA合成的剂量依赖性抑制。尽管qRT-PCR具有高灵敏度和定量精准性, 但其易受样本污染或PCR抑制剂干扰, 且仪器和荧光探针成本较高, 需通过自动化操作和多重内参设计降低误差风险^{[41, 42]}。

FRET、FP和qRT-PCR分别从分子互作动态和功能抑制层面构建了多维筛选体系, 但均存在局限性。FRET对分子间距和探针纯度要求严苛, FP对小分子信号灵敏度不足, 而qRT-PCR则面临污染风险和高成本问题。未来需通过开发低成本探针(如纳米材料标记)、优化信号稳定性(如引入双荧光校正系统), 并结合机器学习算法提升多维度数据整合效率, 从而推动抗流感药物研发的精准化和高效化。

2.1.2　酶联免疫吸附(ELISA)

ELISA凭借其抗原-抗体特异性结合原理和高通量检测优势, 已成为流感病毒蛋白-蛋白相互作用(protein-protein interaction, PPI) 抑制剂筛选的核心技术。该技术通过将PB1或PB2的N端肽段生物素化固定于酶标板, 结合标记的PA或PA/PB1二聚体蛋白实现竞争性结合定量分析, 为抑制剂IC₅₀测定和构效关系研究提供高效平台^[43-45]。Wunderlich团队^[46]率先利用ELISA验证PB1衍生肽对流感A/B病毒PA的双重抑制活性(IC₅₀ < 10 μmol·L^-1), 揭示PA-PB1界面的保守性, 奠定了小分子抑制剂开发的基础。随着计算化学的融合, Tintori等^[47]通过分子对接筛选3-氰基-4, 6-二苯基吡啶类化合物, 结合ELISA验证其PA-PB1抑制活性, 并联合X射线晶体学锁定关键结合残基, 阐明抑制剂与PB1的空间互作模式, 推动理性药物设计进程。在此基础上, D'Agostino团队^[45]进一步将ELISA的应用从基础筛选拓展至深度机制解析, 通过系统比较ELISA (IC₅₀) 与细胞实验(EC₅₀) 数据, 发现甲基酯衍生物(如7a、9) 虽在ELISA中活性较弱(IC₅₀ > 140 μmol·L^-1), 但细胞抗病毒效果显著(EC₅₀ ≈ 50 μmol·L^-1), 分子动力学模拟揭示其需经细胞内酯酶水解激活的代谢转化机制, 凸显ELISA数据需结合细胞穿透性和代谢动力学综合分析。通过ELISA筛选优化的杂交化合物7c (IC₅₀ = 36 μmol·L^-1) 经分子模拟证实与PA疏水口袋的稳定结合, 标志着靶向设计的成熟。然而, 仅依靠ELISA进行筛选存在局限性, 例如某些高IC₅₀值的化合物在ELISA检测中可能未表现出显著活性, 但在细胞水平检测上却展现出良好的活性。这表明在筛选过程中, 需要结合空斑减少实验、细胞毒性测试等其他技术, 以实现对化合物的多维度综合评价。

2.1.3　表面等离子体共振(SPR)

SPR通过实时监测配体-分析物结合引起的折射率变化, 为流感病毒RdRp抑制剂筛选提供动力学和特异性数据。Watanabe团队^[48]基于PA亚基C端结构域(PA-C) 的靶向性, 利用SPR验证计算机筛选化合物PA-49与PA-C的结合(EC₅₀ = 0.53~0.62 μmol·L^-1), 初步证实靶向PA-PB1互作的可行性。Lo等^[49]则通过SPR系统性筛选165种化合物, 鉴定出PA-C结合抑制剂221/283 (IC₅₀达微摩尔水平), 并借助CM5传感器芯片精确测定优化化合物312的动力学参数, 揭示其剂量依赖性病毒抑制活性(犬肾细胞中病毒产量显著降低)。研究同时发现PA-C的17个氨基酸位点变异导致312对H5N1抑制较弱, 凸显SPR在亚型特异性分析中的独特优势。该团队进一步整合微尺度热泳验证结合亲和力, 并通过四唑盐比色法(methylthiazolyldiphenyl-tetrazolium bromide, MTT) 确认化合物100 μmol·L^-1浓度下的安全性, 建立“SPR筛选-体外验证-细胞毒性评估”多级体系, 显著提升先导化合物的转化潜力。然而, SPR无法规避细胞毒性干扰, 且低通量和高成本限制了大规模应用^[49]。

2.1.4　分子对接与计算机辅助筛选

分子对接与计算机辅助筛选技术基于流感病毒RdRp高分辨率结构(X射线晶体学/冷冻电镜), 实现了靶向RdRp不同功能域的精准药物设计^{[50, 51]}。例如, 针对PA-PB1互作界面、PB2帽结合域及PA_N活性位点, 研究者分别通过高通量对接筛选出具有3-氰基-4, 6-二苯基吡啶母核的小分子抑制剂^[47]、N-9/N-2取代的7-烷基鸟嘌呤衍生物^[52]及多酚类化合物^[53]。在此基础上, Bauman等^[54]基于改造后的晶体结构进行片段筛选, 成功鉴定出与活性位点裂隙结合的新化学支架, 为靶点空间优化提供结构依据。此外, 计算机辅助筛选通过整合药效团建模、级联对接及片段优化等多维策略显著提升效率, 例如, Chen等^[55]基于核酸内切酶活性位点构建三维药效团模型, 从45万化合物中锁定16个新型抑制剂(IC₅₀ < 50 μmol·L^-1), Xu等^[31]采用LibDock-LigandFit-GOLD级联对接策略结合细胞病变效应实验与SPR实验, 获得10个PB2结合化合物(EC₅₀ = 0.30~67.65 μmol·L^-1, K_d = 0.21~6.77 μmol·L^-1); 而Credille等^[56]通过药效团-片段联用策略优化出IC₅₀ = 14 nmol·L^-1的高活性先导化合物, 证实多技术协同的设计优势。值得注意的是, Pala等^[57]开发的耦合药效团/对接方法从含500万个分子的化合物库中筛选出2个具有双羟基-1H-吲哚-2-甲酰胺骨架的苗头化合物(IC₅₀ = 0.94/7.0 μmol·L^-1), 突显该骨架在PA抑制剂设计中的潜力。分子对接与计算机筛选展现了高效性与成本优势, 但其局限性主要体现在评分函数偏差、假阳性率高、靶标结构局限等方面^{[54, 55, 57]}。

近年来, 人工智能技术的蓬勃发展和计算机算力的持续提升, 为虚拟筛选技术突破其局限性提供了强大助力。在虚拟筛选的底层技术中, Gu等^[58]对AI驱动的对接方法进行了深入的基准测试, 系统评估了这些方法在虚拟筛选中的性能, 证实AI可提升大规模库对接效率。此外, 在蛋白质-配体相互作用建模领域, Moon等^[59]开发的PIGNet2模型通过深度学习精准预测蛋白-配体相互作用(R = 0.747), 为靶向RdRp的抑制剂筛选提供高精度工具。随着亿级化合物库与原子级结构预测的整合, AI技术正推动从苗头化合物识别到构效关系优化的全流程革新, 成为抗流感药物研发的核心驱动力。

2.2　细胞水平的筛选

尽管基于分子水平的筛选技术在早期药物发现中展现出显著优势—能够实现靶向性明确、高通量且成本可控的大规模化合物筛选, 并精准解析抑制剂与RdRp催化中心的互作机制, 但其依赖的体外酶体系仍存在局限性: 无法模拟病毒复制的真实细胞环境, 导致阳性化合物可能因跨膜效率低、代谢稳定性差或脱靶效应等问题丧失体内活性。为此, 研究者们需结合细胞水平筛选体系进一步验证化合物的功能活性, 目前主流技术包括: ①基于细胞增殖表型分析的细胞病变效应(cytopathic effect, CPE) 观察; ②量化病毒抑制效果的空斑减少实验(plaque reduction assay, PRA); ③同步评估药物活性与细胞毒性的MTT法; ④实时追踪RdRp活性的荧光素酶基因报告技术。这些方法为抑制剂开发提供了关键生物学证据。

2.2.1　表型导向的经典筛选体系

基于病毒-细胞互作模型的表型筛选技术, 如CPE、PRA及MTT法, 通过不同维度解析抗病毒活性。CPE通过病毒感染后细胞形态异常快速初筛抑制剂, 早期研究如Severson团队^[60]通过CPE结合传统筛选对10万种化合物库筛选流感病毒抑制剂, 发现部分小分子在5 µg·mL^-1浓度下即可实现50%以上的CPE抑制; Su等^[21]利用高通量筛选从120万种化合物中鉴定出靶向RdRp的抑制剂3061 (FA-2), 其IC₅₀ = 0.1 μmol·L^-1; Tang等^[61]开发的新型聚环吡啶衍生物10a通过CPE测定表现对甲型流感病的强效抑制(IC₅₀ = 0.38 nmol·L^-1), 且其与人肝微粒体的稳定性(半衰期221 min) 及选择性指数均优于已上市药物巴洛沙韦。然而, CPE易受非特异性机制干扰, 需结合体外活性验证。与CPE观察不同的是, MTT法通过线粒体酶活性量化细胞存活率, 如Bauman等^[54]对PA_N抑制剂的细胞毒性检测及Jang等^[62]对异喹啉酮类化合物的毒性评估, 但其无法区分病毒复制抑制与细胞保护作用。PRA通过空斑形成抑制直接验证病毒复制阻断, 具有高度特异性, 如D'Agostino等^[45]结合MTT与PRA发现某些在分子水平(ELISA) 显示高IC₅₀的化合物反而表现出优异细胞活性, 揭示多维验证的必要性。这些方法虽能直观反映抗病毒表型, 但CPE依赖主观形态判断, MTT缺乏分子机制解析, PRA实验周期较长且操作复杂, 均存在“黑箱”局限性^[63]。

2.2.2　分子可视化的荧光素酶基因报告技术

荧光素酶基因报告技术的进步为细胞水平上的高灵敏度活性验证提供了一种新的解决方案。其通过将病毒复制关键元件(如RdRp活性) 与荧光素酶表达偶联, 可在保留表型筛选高通量优势的同时, 将病毒复制动态转化为可实时监测的荧光信号, 从而实现“表型可视化”与“机制可溯性”的融合, 为突破传统方法的瓶颈提供了新路径。例如, Zhao等^[64]开发并行表型—靶向筛选策略, 结合PR8-Gluc重组病毒表型筛选降低假阳性率。为了提高检测灵敏度, Gao团队^[30]构建的293T-Gluc稳定细胞系在感染流感病毒后, 荧光素酶信号动态范围显著(Z₀因子达0.84, CV仅5.79%), 从2 000种化合物中筛选出17种(0.85%命中率) 可减少90%~100%荧光信号的抑制剂。此外, 针对早期瞬时转染系统的批次间变异问题, Wang团队^[65]通过慢病毒递送与抗生素筛选(如杀稻瘟菌素) 构建Gluc基因稳定整合的细胞株, 采用Gluc/Bsd双报告系统实现长期稳定的高通量筛选, Z₀因子达0.793, 体现高灵敏度与高信噪比。该技术通过实时荧光信号将病毒复制动态可视化, 突破传统表型筛选的时空限制, 为解析RdRp抑制机制提供分子分辨率支持。然而, 该技术在实际应用中仍存在一些局限性, 例如Z₀因子较低、CV值较高反映重复性较差、信号随时间下降导致灵敏度受限, 以及对复杂实验条件的依赖性, 这些因素可能导致假阳性或假阴性结果的出现^[64], 需结合其他互补技术以提高准确性。

2.3　动物水平筛选

不同于分子和细胞水平的筛选, 动物模型是连接体外筛选与临床研究的关键桥梁, 为药效学、药代动力学及安全性评价提供不可或缺的临床前数据。在流感病毒研究中, 小鼠、雪貂、豚鼠等模型各具优劣。小鼠模型成本低、操作简便, 适用于药物筛选和初步疗效评估; 雪貂模型症状与人类流感相似, 适用于深入药效和传播研究; 豚鼠模型在病毒传播研究中具有独特优势^[66]。例如, Tsai等^[67]使用小鼠模型评估PB2抑制剂疗效, 通过监测存活率、体重变化和肺功能等指标, 结合EE评分系统比较不同抑制剂的疗效, 发现VX-787等化合物在低剂量(1 mg·kg^-1) 下也能提供完全保护。类似地, 新型抑制剂CD-6'SLN通过靶向血凝素(hemagglutinin, HA) 介导的病毒包膜破坏机制, 在H1N1感染小鼠模型中显著提高生存率, 且延迟给药仍保持疗效^[68]。然而, 受限于实验成本、伦理审查及通量瓶颈, 动物模型通常作为终末验证手段, 其应用多聚焦于先导化合物的关键参数确证(如治疗指数、组织分布及代谢稳定性), 而非大规模筛选^[66]。

2.4　新兴的筛选技术

高通量筛选(high throughput screening, HTS) 凭借自动化与规模化优势, 已成为流感病毒RdRp抑制剂发现的核心手段^{[21, 30]}, 但其对预建化合物库的依赖及复杂体系筛选能力的不足, 限制了天然产物等复杂样本的深度挖掘^[69]。在此背景下, 新兴技术的突破为活性分子发现提供了新路径, 比如磁珠配体垂钓技术^[70]以及at-line nanofractionation技术^{[71, 72]}等。

磁珠配体垂钓技术通过靶标固定化磁珠(如流感病毒RdRp或其他亚基) 实现特异性配体捕获, 结合液相色谱-质谱联用技术快速鉴定结构。例如, Luo等^[70]将神经氨酸酶(neuraminidase, NA) 固定化磁珠用于蛇莓粗提物中NA抑制剂的快速筛选, 从中发现24种潜在NA抑制剂, 其中银椴苷对PR8敏感株和H274Y耐药株的NA均具有显著抑制活性(IC₅₀ = 197.9/125.4 μmol·L^-1), 并在犬肾细胞模型中有效抑制两种毒株的复制; 本课题组采用相似的策略开发了基于亲和固定以及共价固定的PA-MB体系, 已成功应用于多个中药复杂体系中的PA抑制剂的快速筛选(待发表)。该技术通过超顺磁性实现快速分离, 避免了传统HTS对低丰度成分的漏检问题, 但其存在较强的非特异性吸附问题^[73]。

At-line nanofractionation技术则通过高分辨液相色谱分离与自动化流分收集, 实现生物活性检测与结构解析的并行化研究。其核心流程包括: 色谱分离-柱后分流(质谱检测/微流分收集)-高通量活性检测-高分辨活性轮廓谱图构建^[71]。本课题组将其拓展至流感领域, 从金银花中成功筛选出20多个微摩尔级别的针对奥司他韦敏感/耐药的神经氨酸酶抑制剂^[74]; 并通过与FRET技术联用, 在艾叶、诃子等中药中发现多个纳摩尔到微摩尔级的PA_N抑制剂(待发表)。该技术无需预分离即可定位活性成分, 尤其适用于中药复方等复杂体系的靶向挖掘, 但其分辨率与灵敏度的不足可能影响共流出成分/低丰度活性分子的精准检测^[75]。

3　总结与展望

收起

流感病毒RdRp作为病毒复制的核心元件, 因其结构保守性与功能不可替代性, 已成为抗流感药物研发的关键靶点。本文系统梳理了当前针对流感病毒RdRp抑制剂的筛选策略与研究进展, 主要取得以下结论: 首先, 分子水平的筛选虽具有高靶向性和高通量优势, 但体外实验模型无法反映真实细胞环境对化合物代谢或跨膜效率的影响; 其次, 细胞水平虽能模拟生理环境以及宿主-病毒相互作用, 但存在脱靶效应及内源性代谢物干扰, 且存在通路交叉干扰问题; 再者, 基于动物模型的体内评价体系虽能系统解析抑制剂的药代动力学/药效学特征, 但受制于模型构建流程冗长及检测通量低等瓶颈。值得注意的是, 具有高通量、高灵敏优势的新兴技术如磁珠配体垂钓技术、at-line nanofractionation等, 显著提升了复杂体系中RdRp抑制剂的发现效率。

展望未来, RdRp筛选策略的优化将聚焦以下三点: ①强化多技术协同, 通过整合传统生化检测技术(如荧光技术) 与前沿筛选技术(如类器官感染模型、人工智能驱动的虚拟筛选与预测技术等) 相结合, 形成从分子水平、细胞水平再到动物水平的综合评价平台, 提升筛选的准确率; ②加强RdRp与宿主因子或其他功能靶点的互作机制研究, 探索新型药物联用策略, 从而有效降低耐药风险和不良反应; ③深化生物信息学与人工智能的交叉融合, 通过深度学习预测变构位点、分子模拟解析结合机制, 结合AI药物性质预测平台实现虚拟筛选到先导化合物优化的智能迭代, 缩短实验验证周期, 形成高靶向性、低脱靶的抑制剂设计范式。通过多学科交叉创新与多层次技术融合, 有望突破现有筛选体系在效率及耐药方面的瓶颈, 推动兼具广谱性、低耐药风险的新一代RdRp抑制剂研发进程, 为应对流感病毒进化及潜在大流行提供核心策略支撑。

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2025年第60卷第8期

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doi: 10.16438/j.0513-4870.2025-0504

接收时间：2025-04-30
首发时间：2026-09-20
出版时间：2025-08-12

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收稿日期：2025-04-30
修回日期：2025-06-21

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暨南大学药学院, 药物分析研究中心, 广东广州 511436

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江正瑾, Tel: 86-20-85223604, E-mail: jzjjackson@hotmail.com

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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