药学学报

Name	Function and application	Website	Reference
Synergyfinder3.0	A web-platform for interactive analysis and visualization of multi-drug combination response data	https://synergyfinder.fimm.fi	[28]
Synergyfinderplus	An interactive tool for analyzing drug combination dose-response data to facilitate better interpretation and annotation of drug combination screening datasets	https://www.synergyfinderplus.org/	[29]
SynToxProfiler	An interactive networking tool for ranking HTS drug combinations based on integrated efficacy, synergy and toxicity profiles	https://syntoxprofiler.fimm.fi	[38]
Synergy	A Python library containing widely popular synergy models, a standard tool for analyzing and visualizing drug combination and synergistic effects	https://pypi.org/project/synergy	[39]
DrugBank	A database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets	https://www.drugbank.com	[40]
DrugComb	An integrative cancer drug combination database including screening data of drug combination and prediction of the sensitivity and synergy of drug combinations by algorithms	https://DrugComb.org/	[41]
ChEMBL	ChEMBL is a manually curated, high-quality, large-scale open data database containing binding, functional and ADMET information for compounds	https://www.ebi.ac.uk/chembl/	[42]
KEGG	KEGG is a manually curated resource integrating eighteen databases. KEGG mapping tools are helpful for understanding cellular and organism-level functions from genome sequences and other molecular datasets	https://www.kegg.jp/kegg/pathway.html	[43]
Gene Ontology	A comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs)	https://geneontology.org/	[44]
STRING	An online database that can comprehensively identify gene lists and functional genomic datasets, and establish protein-protein association networks	https://string-db.org/	[45]
CDCDB	A continuously updated large-scale drug combination database, including over 40 795 drug combinations	https://icc.ise.bgu.ac.il/medical_ai/CDCDB/	[46]
NPCDR	Natural product-based drug combination and its disease-specific molecular regulation (NPCDR)	http://npcdr.idrblab.net/	[47]

Name	Function and application	Website	Reference
Synergyfinder3.0	A web-platform for interactive analysis and visualization of multi-drug combination response data	https://synergyfinder.fimm.fi	[28]
Synergyfinderplus	An interactive tool for analyzing drug combination dose-response data to facilitate better interpretation and annotation of drug combination screening datasets	https://www.synergyfinderplus.org/	[29]
SynToxProfiler	An interactive networking tool for ranking HTS drug combinations based on integrated efficacy, synergy and toxicity profiles	https://syntoxprofiler.fimm.fi	[38]
Synergy	A Python library containing widely popular synergy models, a standard tool for analyzing and visualizing drug combination and synergistic effects	https://pypi.org/project/synergy	[39]
DrugBank	A database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets	https://www.drugbank.com	[40]
DrugComb	An integrative cancer drug combination database including screening data of drug combination and prediction of the sensitivity and synergy of drug combinations by algorithms	https://DrugComb.org/	[41]
ChEMBL	ChEMBL is a manually curated, high-quality, large-scale open data database containing binding, functional and ADMET information for compounds	https://www.ebi.ac.uk/chembl/	[42]
KEGG	KEGG is a manually curated resource integrating eighteen databases. KEGG mapping tools are helpful for understanding cellular and organism-level functions from genome sequences and other molecular datasets	https://www.kegg.jp/kegg/pathway.html	[43]
Gene Ontology	A comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs)	https://geneontology.org/	[44]
STRING	An online database that can comprehensively identify gene lists and functional genomic datasets, and establish protein-protein association networks	https://string-db.org/	[45]
CDCDB	A continuously updated large-scale drug combination database, including over 40 795 drug combinations	https://icc.ise.bgu.ac.il/medical_ai/CDCDB/	[46]
NPCDR	Natural product-based drug combination and its disease-specific molecular regulation (NPCDR)	http://npcdr.idrblab.net/	[47]

Drug	Combination	Target	Efficacy evaluating model	Efficacy	Reference
Oseltamivir	Zanamivir	Neuraminidase	In vitro, in mice	Effective in the NAI-resistant influenza strains (in vitro) Not superior to zanamivir monotherapy (in mice)	[57,58]
Peramivir	Neuraminidase	In vitro, in mice	Synergistic antiviral effects	[53]
Clinical	Not superior to oseltamivir monotherapy	[59]
Baloxavir	NAIs	Cap-dependent endonuclease & neuraminidase	In vitro	Synergistic antiviral effects	[13,60]
Clinical	Not superior to NAIs alone	[61]
Oseltamivir	Cap-dependent endonuclease & neuraminidase	In mice	Improved antiviral activity	[60]
In mice, in ferret	Against baloxavir PA-resistant influenza	[13,15]
Amantadine	Oseltamivir + ribavirin	M2 ion channel, neuraminidase & RdRp	In vitro	Synergistic antiviral effects and suppressing resistance	[62-64]
Clinical	TCAD therapy had similar PKs to monotherapy	[65]
Significant decrease in viral shedding (Clinical)	[66]
Favipiravir	Peramivir	RdRp & neuraminidase	In mice	Effective in the oseltamivir-resistant influenza virus infection	[67]
Oseltamivir	Nitazoxanide	Neuraminidase & hemagglutinin	In vitro	Synergistic antiviral effects	[11,68]
In ferret	Synergistic effects only in preventing infection	[11]
Clinical	No improved effects in hospitalized patients	[69]
CPD A	Ribavirin	IMPDH	In vitro	Strongly synergistic effect with potentially acceptable safety profile	[70]
ING-1466	Oseltamivir	Hemagglutinin & neuraminidase	In mice	Increased survival	[14]
Baloxavir	Hemagglutinin & cap-dependent endonuclease	In mice	Increased survival	[14]
Pimodivir	Interferon-alpha	IAV polymerase basic protein 2 & IFN-α	In vitro	Synergistic antiviral effects	[54]
Oseltamivir	1,8-Cineol	Neuraminidase & inflammatory signaling	In mice	Strongly synergistic effect	[71]
Oseltamivir	Azithromycin	Neuraminidase & 50S rRNA	In mice	No additional benefits	[72]
Clinical	Required less oxygen support and shorter hospitalization time	[73]
Oseltamivir	Clarithromycin + naproxen	Neuraminidase, 23S rRNA & cyclooxygenase	Clinical	Faster clinical improvement	[74]

Drug	Combination	Target	Efficacy evaluating model	Efficacy	Reference
Oseltamivir	Zanamivir	Neuraminidase	In vitro, in mice	Effective in the NAI-resistant influenza strains (in vitro) Not superior to zanamivir monotherapy (in mice)	[57,58]
Peramivir	Neuraminidase	In vitro, in mice	Synergistic antiviral effects	[53]
Clinical	Not superior to oseltamivir monotherapy	[59]
Baloxavir	NAIs	Cap-dependent endonuclease & neuraminidase	In vitro	Synergistic antiviral effects	[13,60]
Clinical	Not superior to NAIs alone	[61]
Oseltamivir	Cap-dependent endonuclease & neuraminidase	In mice	Improved antiviral activity	[60]
In mice, in ferret	Against baloxavir PA-resistant influenza	[13,15]
Amantadine	Oseltamivir + ribavirin	M2 ion channel, neuraminidase & RdRp	In vitro	Synergistic antiviral effects and suppressing resistance	[62-64]
Clinical	TCAD therapy had similar PKs to monotherapy	[65]
Significant decrease in viral shedding (Clinical)	[66]
Favipiravir	Peramivir	RdRp & neuraminidase	In mice	Effective in the oseltamivir-resistant influenza virus infection	[67]
Oseltamivir	Nitazoxanide	Neuraminidase & hemagglutinin	In vitro	Synergistic antiviral effects	[11,68]
In ferret	Synergistic effects only in preventing infection	[11]
Clinical	No improved effects in hospitalized patients	[69]
CPD A	Ribavirin	IMPDH	In vitro	Strongly synergistic effect with potentially acceptable safety profile	[70]
ING-1466	Oseltamivir	Hemagglutinin & neuraminidase	In mice	Increased survival	[14]
Baloxavir	Hemagglutinin & cap-dependent endonuclease	In mice	Increased survival	[14]
Pimodivir	Interferon-alpha	IAV polymerase basic protein 2 & IFN-α	In vitro	Synergistic antiviral effects	[54]
Oseltamivir	1,8-Cineol	Neuraminidase & inflammatory signaling	In mice	Strongly synergistic effect	[71]
Oseltamivir	Azithromycin	Neuraminidase & 50S rRNA	In mice	No additional benefits	[72]
Clinical	Required less oxygen support and shorter hospitalization time	[73]
Oseltamivir	Clarithromycin + naproxen	Neuraminidase, 23S rRNA & cyclooxygenase	Clinical	Faster clinical improvement	[74]

抗流感病毒药物联合应用的研发及进展

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任永成 ¹ , 杨博元 ² , 王静 ¹^,^* , 岑山 ¹^,^*

药学学报 | 综述 2025,60(4): 884-894

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药学学报 | 综述 2025, 60(4): 884-894

抗流感病毒药物联合应用的研发及进展

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任永成¹, 杨博元², 王静¹^,^*, 岑山¹^,^*

作者信息

¹ 中国医学科学院、北京协和医学院医药生物技术研究所, 北京 100050

² 复旦大学上海医学院, 上海 200032

通讯作者:

岑山, Tel: 86-10-63037279, E-mail: shancen@imb.pumc.edu.cn

王静, E-mail: jingwang@imb.pumc.edu.cn

Research development and progress of anti-influenza drug combinations

Yong-cheng REN¹, Bo-yuan YANG², Jing WANG¹^,^*, Shan CEN¹^,^*

Affiliations

¹Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China

²Shanghai Medical College, Fudan University, Shanghai 200032, China

出版时间: 2025-04-12 doi: 10.16438/j.0513-4870.2024-0989

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摘要

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人流感主要由甲型流感病毒和乙型流感病毒引起, 是影响人类健康最严重的传染病之一。目前针对流感病毒的上市药物较少, 且现有药物面临耐药性等问题。抗流感药物联用的开发与研究逐渐得到人们的重视。目前, 在抗流感药物组合发现和药物效应评价等方面已经取得了一定进展。通过发现有效的联用药物可用于提高治疗效果, 延迟或减少耐药性的发生, 是实现老药新用的理想方法。本文对抗流感药物联用的评价方法和模型以及可用的数据资源和工具进行总结和介绍, 并对当前抗流感药物联用研究的最新进展进行回顾和总结, 以期为抗流感病毒药物联合应用的研发提供参考。

关键词

流感病毒 / 药物效应 / 协同作用 / 评价模型 / 药物联合治疗

Abstract

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Human influenza is mainly caused by influenza A virus and influenza B virus, and it is one of the most serious infectious diseases that pose a serious threat to human health. Currently, there are few marketed drugs for influenza viruses, and the existing drugs are faced with problems such as drug resistance. The development and research of combinations of anti-influenza drugs have gradually attracted people's attention. At present, certain progress has been made in aspects such as the discovery of anti-influenza drug combinations and the evaluation of drug effects. Discovering effective combination drugs is an ideal method for repurposing old drugs. That can be used to improve therapeutic effects and delay or reduce the occurrence of drug resistance. Herein, this article introduces the evaluation methods, models of combinations of anti-influenza drugs, as well as available data resources and tools, and summarizes the latest progress in the current research on combinations of anti-influenza drugs, with the aim of providing a reference for the research and development of the combined application of anti-influenza virus drugs.

Key words

influenza virus / drug effect / synergistic effect / evaluation model / drug combination therapy

引用本文

任永成, 杨博元, 王静, 岑山. 抗流感病毒药物联合应用的研发及进展. 药学学报, 2025 , 60 (4) : 884 -894 . DOI: 10.16438/j.0513-4870.2024-0989

Yong-cheng REN, Bo-yuan YANG, Jing WANG, Shan CEN. Research development and progress of anti-influenza drug combinations[J]. Acta Pharmaceutica Sinica, 2025 , 60 (4) : 884 -894 . DOI: 10.16438/j.0513-4870.2024-0989

正文

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流感病毒属于正黏病毒科, 是一种单股负链RNA病毒, 含有甲 (A)、乙 (B)、丙 (C)、丁 (D) 4种亚型。人流感主要由甲型流感病毒和乙型流感病毒引起。甲型流感病毒传播速度快、感染能力强、影响范围广、发病率和死亡率高, 对人类危害极大, 历史上已经引起了5次流感大流行^[1]。世界卫生组织估计, 每年约有10亿人感染流感, 其中300万至500万人症状严重 (特别是儿童、老年人和免疫功能低下者), 29万至65万人死亡^[2,3]。抗原漂移和抗原转变严重影响疫苗的保护效率, 因而抗病毒药物可能作为公众的首选治疗措施。同时, 抗病毒药物对于治疗危重病、住院患者或溢出性人畜共患感染的患者也具有显著疗效。因此, 抗流感病毒药物的研发尤为重要^[4]。

目前, 美国食品药品监督管理局获批的抗流感药物分别是以金刚烷胺为代表的M2离子通道抑制剂、以奥司他韦为代表的神经氨酸酶抑制剂 (neuraminidase inhibitors, NAIs)、以阿比朵尔为代表的血凝素抑制剂和以巴洛沙韦为代表的帽依赖性核酸内切酶抑制剂。由于上市药物面临的耐药性问题以及新药开发难度, 目前抗流感药物单药上市的速度依旧难以满足人们的需求^[5]。因此, 通过药物联合使用的抗流感药物开发策略正逐渐得到人们的重视^[6]。同时, 通过药物再利用发现有效的联用候选药物也是应对未来新发突发传染病的理想方法^[7]。

目前, 药物联用在肿瘤、细菌、病毒感染等多种疾病中有着日渐广泛的研究和应用。随着网络药理学、系统药理学等学科的发展以及药物靶点活性功能、作用机制的深入研究, 许多药物表现出作用于多个靶点的机制和效果, 这表明抗流感病毒药物联用研究具有巨大的潜力。本综述对抗流感药物联用的评价方法和模型进行汇总, 并收集总结了一些抗流感药物联用研究的可用数据资源和工具, 同时对当前抗流感药物联用研究的最新进展进行介绍, 以期为抗流感病毒药物联合应用的研发提供新的思路。

1 抗流感药物联用协同性研究方法与模型的研究进展

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联合用药是一种采用两种或两种以上药物联合治疗疾病的治疗策略, 其主要目的是增加药物的疗效或减轻药物的毒副作用。抗流感药物联用希望发挥药物的协同治疗作用以提高疗效、减少疾病症状和并发症、延迟或减少耐药性的发生、减轻或降低药物毒副作用等^[8]。

药物联合作用可分为协同作用、相加作用和拮抗作用^[9]。如果基于个体效应, 抗病毒药物组合的效果优于预期, 则称该组合为协同作用; 如果组合的效果比预期的差, 则称该组合为拮抗作用。为了发现药物协同作用, 研究人员可以设计联合治疗, 靶向宿主和病原体的多个途径, 从而产生比简单的加性效应更好的协同效应。对于药物联用研究的难点之一就是如何客观评价药物联用的药效, 这也是药物联合用药成功与否的一个重要节点, 当前抗流感药物联用的研究主要通过体内和体外实验来评价药物组合与单个药物的药效的差异以及药物相互作用。

临床前研究主要包括体外研究和动物研究。有文献[10]表明在体外和实验动物中进行的临床前研究遵循相同的原则来确定协同作用。两者的主要区别在于, 在实验动物中进行的研究成本更高、耗时更长、通常使用的样本量较小, 同时检测结果的变异性更大。在体外和实验动物研究中, 样本量n = 5的药理学实验一般情况下是足够的。

在雪貂流感病毒感染模型以及小鼠流感病毒感染模型中, 目前对于药物协同作用的判断和研究主要基于药物联合是否比单药药效更强, 分别设立安慰剂组、两种单药组和药物联用组, 通过动物体温、体重、动物鼻洗液病毒分析、病毒脱落时间和病毒脱落数据的曲线下面积以及肺部病毒载量、组织病变、流感病毒致死感染存活率等指标和手段来评价药物组合在提高药效、预防感染、减少病毒脱落时间、提高药物敏感性、降低药物耐药性等药理学功能^[11-17]。

由于药物协同作用分析需要了解每个化合物的个体剂量-反应行为, 这一要求使得在资源有限或实验设计受到医学伦理约束的情况下, 药物组合评价变得困难或不切实际, 在临床试验中, 药物协同作用通常指的是观察到的药物效应大于单独使用单一药物所能达到的实际效果^[18]。

1.1 抗流感药物联用基于药效学评价模型的研究进展

如果药物不相互作用, 药物组合的潜力则需要数学和统计模型来评估和表征。目前在抗流感药物联用的体外实验中使用最多的药物效应评价模型是Loewe可加模型^[9]、Bliss独立模型^[19]和HSA最高单药模型^[20]等。

Loewe^[9]定义了等效线并在等效线的基础上提出了两药合用剂量-效应关系的表示方法, 用于定义相加作用。等效线是以两种药物剂量为坐标轴的直角坐标图形。Loewe可加模型根据药物之间相互作用的形式, 可将药物之间的相互作用分为相加作用、协同作用或拮抗作用^[21]。有研究表明, Loewe可加模型仅对作用相互排斥的药物有效, 限制了其使用^[22]。

用于药物组合的Chou-Talalay方法是基于中值效应方程 (Median-effect), 由质量作用定律原理推导出来的, Chou和Talalay提出了“组合指数”(combination index, CI) 并描述了药物相互作用, CI < 1为协同、CI = 1为相加, CI > 1为拮抗^[23], 该方法相对简单, 并开发了软件CompuSyn^[24], CI在之后得到了较为广泛的应用。Bliss独立模型是量化药物组合效应的经典方法, 它认为组合中的两种药物在单独使用时是概率独立的, Bliss独立模型法使用概率加法公式作为药物组合效应的计算方法^[19]。HSA模型将协同定义为组合的效果与最有效的单个代理之间的差异。对于HSA模型, 期望效应为最高的单药效应^[20]。

Loewe、Bliss、HSA这3种模型构成了后续研究的基本框架。

有研究^[25]使用CompuSyn软件用于确定药物之间的相互作用, 对于两药联合研究, 采用每种药物的EC₅₀值来确定两种化合物之间的等量比, 采用CI值分析药物相互作用, 计算每个组合的加权平均CI (CIwt), CIwt < 0.7, 定义为协同作用; 0.9 < CIwt < 1.2, 相加作用; CIwt > 1.45, 拮抗作用。研究评估了巴洛沙韦酸和其他批准的抗甲型H1N1流感pdm09和甲型H3N2亚型药物的体外组合, 结果发现巴洛沙韦和NAIs或法匹拉韦的组合对抗两种甲型流感亚型具有协同作用, 其CIwt均小于0.7, 使用人气道上皮模型 (HAE) 也证实了这些效应。有研究^[26]使用MacSynergy软件评估抗病毒作用 (结果使用“Compusyn”证实), 具有不同结合特性的M2-N31靶向抑制剂组合可以产生协同作用, 化合物M2WJ332与L1.1或DP9的组合可协同降低病毒滴度。M2WJ332与L1.1的组合显示出与两种抑制剂浓度成比例的增效作用, 相反与化合物DL7的组合导致拮抗作用, 定点M2质子通道抑制剂可与NAIs扎那米韦协同作用, 表明定点M2质子通道抑制剂可对金刚乙胺耐药的大流行性流感进行协同联合治疗。研究人员采用上述的评价模型也取得了良好的实验结果, 表明了上述模型的可用性。

目前, 随着药物联用研究的不断开展和深入, 又出现了新的药物联用药效学评价模型, 本文列举了几个具有特色的模型供抗流感药物联用研究参考。

有研究^[27]提出了一种新的参考模型, 名为零交互作用函数 (zero interaction potency, ZIP), 它通过比较单个药物和它们的组合之间的剂量-反应曲线的效价变化来捕捉药物相互作用关系, 并使用delta分数来量化与零相互作用期望的偏差, 并保持了较低的假阳性率, 已经集成到了Synergyfinderplus、Synergyfinder 3.0网站中, 便于人们使用^[28,29]。有研究^[30]在对数学模型的假设条件和数学推导进行分析的基础上, 推导并建立得到“一带一线模型”。有研究^[31]根据质量作用定律, 从药物效应的双态转换模型的平衡状态出发, 推导出了方程MuSyC, 它是一个药物组合协同作用的质量作用、状态转换模型, 旨在为评估药物组合的协同效应提供一个定量框架^[32]。尽管它通常假设组合中的单一药物具有单调的S形剂量反应, MuSyC模型也有望为最初使用标准参考模型确定的药物组合提供更一致和无偏见的解释, 它是一种参数响应面模型, 类似的还有其他响应面方法URSA^[33]、GRS^[34]、BRAID^[35]等, 有文献[36]分析相比于基于指数的分析方法, 响应面模型可以对组合实验进行更准确的基于统计的评估。但由于这些模型在使用上更为复杂, 没有简单易用的使用工具, 阻碍了其使用。

目前, 用于药物组合的现有药效学数学模型区分拮抗、加性、乘法和协同效应, 未能考虑浓度依赖性药物相互作用效应在整个剂量反应矩阵中的变化情况。有研究^[37]开发了一种双向药效学模型来捕获两种药物组合的药效。双向药效学模型通过量化上游药物功效和对下游药物药效学参数的浓度依赖性影响, 评价作用于病毒复制不同阶段的上游和下游药物之间的相互作用, 并在报道的抗埃博拉病毒和抗新冠病毒药物对联合疗效的评价中取得了更好的效果。这也给予人们对于药物联用研究考虑药物作用阶段以及时空代谢过程以启示。

随着药物联用研究的不断进展以及生物信息学研究的不断进展, 已经有一些便于药物的协同性研究和参考的数据库和网站, 一些常用的数据库见表1^{[28,29,38-47]}。

尽管科学界对生物系统中化合物组合效应的量化进行了多年的努力, 但对于协同、相加和拮抗组合^[24,48,49]的鉴定仍然没有标准化的方法, 广泛使用的方法也存在一定的局限性和问题。由于模型内在假设的存在导致偏差的产生, 限制了模型的广泛适用性, 影响了对药物协同作用的解释。Loewe可加模型依赖于精确估计的剂量-反应曲线, 使用四参数logistic模型^[48,49]后, 反应必须落在估计的E_min和E_max之间, 该策略适用于数据点数较低的高通量筛选。然而, 由于忽略了响应变量^[48,49]的变异性, Bliss独立模型估计容易出现假阳性结果。Chou-Talalay组合指数模型还依赖于中效曲线的线性, 这可能与某些疾病 (或组织类型) 或某些药物的组合无关^[50]。

药物-药物相互作用的定量模型对于合理指导复杂多因素疾病治疗中的药物组合发现和转化至关重要^[51]。药物组合的发现依赖于可靠的协同评价标准, 但对于正确的协同标准来表征组合的相互作用没有达成共识, 需要在以后的药物研发实践中进一步探索和验证。该领域的碎片化、不统一阻碍了结果分析, 影响了结果的可重复性, 延迟了潜在联合治疗的临床转化。该领域的主要障碍之一仍然是研究人员对协同作用和拮抗作用的精确量化和定义缺乏共识^[52]。这也提示科学界需要在衡量药物协同作用的模型和方法上达成共识, 并努力推动领域内建立研究分析标准, 提高药物协同研究的准确性和可重复性。值得注意的是, 一种药物组合方式在达到不同药效时的联合效果未必相同, 在特定浓度范围内会显示出最佳的协同作用, 在其他浓度范围也可能会显示出其他相互作用^[53,54]。

1.2 基于计算机技术的算法和评估模型

计算机技术已经在过去的多年中被广泛用于预测药物组合, 包括传统的机器学习方法、深度学习方法等。传统的机器学习适用于各种特征类型, 在不同规模的数据库中有较高的预测精度。长期以来, 传统的机器学习被应用于改进和优化药物发现和设计过程, 建立预测模型。深度学习方法可以学习输入属性数据 (如基因组学) 和关联输出 (如协同度评分) 之间复杂的非线性关系, 深度学习模型的精度会随着输入数据量的增加而显著提高^[55]。

不同药物组合后的药物组合数量会以指数的形式激增, 药物组合筛选是一个复杂且昂贵的过程, 目前为止只有少量的药物组合数据集发布, 当前发布的药物组合数据集主要集中于癌症研究, 但在抗流感药物联用领域中暂无类似的报道, 原因在于抗流感药物组合数据非常有限, 这限制了人工智能模型的应用。

当前比较接近的一项工作是提出了一种能够学习药物-靶点相互作用和药物-药物协同作用的深度学习框架ComboNet^[56]。该模型在协同预测准确性方面比以前的训练数据有限的方法表现更好。研究发现了两种药物组合, 瑞德西韦和利血平, 以及瑞德西韦和IQ-1S, 它们在体外表现出强大的抗SARS-CoV-2病毒协同作用。这种方法也可以推广到其他药物组合数据缺乏的领域中, 对抗流感药物联用研发具有很好的借鉴作用。

2 药物联用抗流感病毒药物的研究进展

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目前, 抗流感药物联用研究主要是抗流感上市药物之间或与临床/临床前药物的联用, 其次是抗病毒与抗生素、抗病毒与抗炎、抗病毒与单克隆抗体、抗病毒与细胞因子、中药抗病毒等的联用, 下面将分别针对不同药物联用类型从临床前到临床研究的内容及结果进行详细的介绍, 并简要总结在表2^{[11,13-15,53,54,57-74]}。

2.1 针对同一病毒蛋白的不同作用位点的药物抗病毒联合治疗

奥司他韦和扎那米韦的联用: 奥司他韦和扎那米韦都是流感病毒NAIs的成员, 但它们与不同的NA活性位点结合。在中空纤维感染模型中, 奥司他韦和扎那米韦联合治疗非常有效地抑制了奥司他韦和扎那米韦耐药病毒株。这些发现表明, 通过明显不同的结合相互作用, 靶向相同病毒蛋白的两种药物的联合治疗是对抗耐药性出现的可行策略^[57]。研究^[58]发现, 在小鼠模型中, 扎那米韦治疗甲型H3N2, 特别是甲型H1N1 pdm09感染优于奥司他韦治疗, 与单独使用扎那米韦相比, 扎那米韦和奥司他韦联合治疗没有额外的益处。

帕拉米韦是一种新引入的具有环戊烷结构的NAI, 已在体外和体内显示出抗流感病毒的功效。其治疗流感的疗效归因于其持续的残留血浆水平^[75]。目前, 静脉注射帕拉米韦单药治疗已在日本和韩国获得许可。一项奥司他韦和帕拉米韦联用研究^[53]显示, 在小鼠实验中, 0.4 mg·kg^-1day^-1奥司他韦联合0.1 mg·kg^-1day^-1帕拉米韦的效果 (80%保护) 远远超过单独使用一种化合物的总和 (20%保护)。结果表明, 奥司他韦和帕拉米韦的组合比单独使用每种化合物的次优剂量更好地治疗小鼠流感感染。一项回顾性研究^[59]比较了奥司他韦-帕拉米韦联合治疗与奥司他韦单药治疗对甲型H7N9流感的疗效, 研究结果显示, 奥司他韦-帕拉米韦联用组与奥司他韦单药组在病毒学和临床检测指标中无明显差异, 提示在H7N9病毒感染成人中, 奥司他韦-帕拉米韦联合治疗并不优于奥司他韦单药治疗。

2.2 靶向两种或两种以上病毒蛋白的抗病毒联合治疗

以流感病毒的帽依赖核酸内切酶和神经氨酸酶为靶点的抗病毒药物的协同作用已有多项研究报道。巴洛沙韦与奥司他韦的联用: 有研究^[60]在体外和小鼠致死模型中评价了巴洛沙韦和NAI联合作用, 在体外巴洛沙韦与4种NAI的CI分别为0.49、0.52、0.58和0.59, 表明巴洛沙韦酸与多种类型的NAI具有协同作用; 在小鼠致死模型中, 与单药相比, 次优剂量的玛巴洛沙韦 (0.5 mg·kg^-1, 每日2次) 联合磷酸奥司他韦在病毒诱导的死亡率、细胞因子/趋化因子水平升高和肺部病理改变方面提供了额外的疗效。有研究^[15]发现, 在小鼠模型中联合使用巴洛沙韦和奥司他韦能有效对抗流感病毒, 同时阻碍了PA-I38X AAS (I38T, I38S, I38V) 突变的出现, 可减少抗病毒药物耐药的发生。有研究^[13]使用Bliss独立模型进行体外评价, 曲面图显示巴洛沙韦和奥司他韦的组合更有效地抑制了所有4种病毒。药物协同作用的最强区域发生在两种药物的低浓度 (0.006～1.56 nmol·L^-1巴洛沙韦) 下, 并在雪貂模型中研究巴洛沙韦和奥司他韦对抗A (H3N2)-WT或PA/I38T和A (H1N1pdm09)-WT或PA/E23K病毒生长的相互作用结果, 研究发现与具有不同作用机制的流感药物联合治疗具有协同作用, 减轻了药物敏感性降低后对于病毒的选择压力。但是在对住院的重症流感患者进行的一项III期临床试验中 (NCT03684044)^[61], 巴洛沙韦联合NAIs与单独使用NAIs相比, 没有产生更好的临床结果, 并且在临床实践中, 重症流感住院患者不应常规使用联合抗病毒药物。有研究^[62]通过生成剂量-反应曲线, 并使用药物相互作用的模型来量化药物相互作用程度, 发现根据流感病毒亚型的不同, 三重组合的协同作用比任何双重组合的协同作用大2～13倍, 表明奥司他韦、金刚烷胺和利巴韦林的三重组合在体外对多种流感病毒株显示出协同活性。金刚烷胺、利巴韦林和奥司他韦的三重组合在体外对耐药流感病毒株具有高度活性和协同作用^[63]。由金刚烷胺、奥司他韦和利巴韦林组成的三联抗病毒药物阻碍了耐药性甲型流感病毒的出现^[64]。一项临床试验^[65]研究了金刚烷胺、奥司他韦和利巴韦林联合治疗甲型流感感染的安全性和药代动力学特征, 结果发现金刚烷胺、奥司他韦和利巴韦林三联药物治疗在单剂量单药治疗期间具有与每种抗病毒药物相似的药代动力学特性, 显示出良好的安全性, 可以安全地用于免疫功能低下的患者。在2017年的一项随机、双盲、多中心的联合抗病毒药物 (奥司他韦、金刚烷胺和利巴韦林) 与单一疗法 (奥司他韦) 治疗流感的II期临床试验中 (NCT01227967), 在患有严重疾病风险的人群里, 奥司他韦、金刚烷胺和利巴韦林的组合与单独使用奥司他韦相比, 在给药第3天时病毒排出量显著减少, 具有更好的抗病毒疗效, 但在多个临床次要终点方面联合疗法没有益处, 需要更多研究来理解临床结果之间缺乏差异^[66]。

法匹拉韦, 即T-705, 可以抑制RNA依赖性RNA聚合酶 (RdRp) 合成流感病毒RNA。与利巴韦林相比, 法匹拉韦对宿主细胞本身的毒性较小, 并具有更好的抗流感活性^[76]。有研究报道^[67]在小鼠模型中, 帕拉米韦和法匹拉韦联用对奥司他韦耐药2009年甲型H1N1流感病毒具有协同抑制作用, 提高了感染小鼠肺部的抗病毒功效和小鼠生存率。

有研究^[68]在细胞水平上证明了硝唑尼特 (nitazoxanide, NTZ) 具有微摩尔级的体外抗人禽流感活性。采用Chou-Talalay组合指数法进行剂量效应分析和增效/拮抗定量分析, 结果表明NTZ与奥司他韦联合治疗对甲型流感病毒具有协同作用, CI值在0.39～0.63之间。联合使用NAIs和硝唑尼特的方案可发挥协同抗流感作用。

有研究^[11]在体外实验中通过Bliss独立模型发现, 宿主靶向药物硝唑尼特活性代谢物和奥司他韦羧酸盐在较低的药物浓度 (0.000 01～0.01 μmol·L^-1 奥司他韦羧酸盐和0.002～0.039 μmol·L^-1硝唑尼特活性代谢物) 下是协同的, 但在每种药物的较高浓度下仅是相加作用。在甲型流感病毒感染的雪貂模型中, 硝唑尼特和奥司他韦联合使用在预防感染和缩短病毒脱落时间方面比单独使用硝唑尼特或奥司他韦更有效, 但只有在预防性治疗中有效, 而不是治疗性治疗。在一项总数为67例确诊流感感染病例, 34例患者接受硝唑尼特和奥司他韦联合治疗, 33例患者单独接受奥司他韦治疗的随机盲法研究中^[69], 研究初步数据表明, 硝唑尼特的加入并不能改善流感住院患者的预后, 建议进行更大规模的研究。

有研究^[70]通过MacSynergy软件将在细胞水平测得的药物抗病毒活性进行组合效应曲面图分析, 发现小分子CPD A [一种肌苷单磷酸脱氢酶 (inosine monophosphate dehydrogenase, IMPDH) 抑制剂] 与利巴韦林 (另一种IMPDH抑制剂) 的组合具有强烈的协同抗病毒作用及可接受的安全性特征。在小鼠致死模型中, 一种口服的A型流感病毒进入抑制剂ING-1466与奥司他韦和巴洛沙韦联用时, 其保护率远超单个药物使用保护率之和, 在药效上起到协同作用^[14]。

虽然上述的抗流感药物在体外细胞水平上有较好的协同活性, 但是在体内却显示不同的结果, 这可能与药物的不同药代动力学性质有关, 需要进行更多的对照研究, 以确定临床上流感感染情况下的最佳治疗方案。目前除了一些上市抗流感药物联用有开展临床试验外, 其他的药物联用研究主要基于细胞和动物模型上开展。因此, 抗流感药物联用研究同时也需要更准确真实的抗流感药物体外协同评价模型以及更多体内研究的开展。

2.3 抗病毒药物和免疫调节剂 (免疫抑制剂、干扰素、抗炎药物等) 等药物组合

有研究报道基于ZIP参考模型计算预期响应, 给出协同作用评分 (量化为药物相互作用引起的平均超额应答), 计算了每种药物组合的最大协同面积分数-剂量反应矩阵中最具协同作用的3×3剂量窗口。结果显示, IFNα2a-匹莫迪韦对流感病毒感染具有协同作用, ZIP得分为22.1, 最具协同区域得分为42.8, 同时作者提出匹莫迪韦和IFNα靶向病毒复制的相同环节以实现协同作用^[54]。这有助于研究者更好地了解病毒与宿主的相互作用以及免疫致病机制。

2.4 直接作用流感药物与宿主靶向治疗药物、抗生素药物用于提高疗效或者改善临床获益

流感研究的一个关键挑战是设计创新的基于宿主的疗法, 能够增强抗病毒免疫力并改善流感感染相关并发症的症状。抗流感药物与宿主靶向治疗药物联用对抵抗病毒突变以及大流行的紧急运用具有光明的前景。当前有多个宿主靶向目标被研究用于药物的联合应用, 具体有细胞RNA甲基转移酶^[77], 靶向宿主激酶表皮生长因子受体和磷脂酰肌醇-3-激酶C2β^[78], 靶向宿主Raf/MEK/ERK信号通路^[79]等, 具有进一步研究的潜力。

抗病毒药物与抗氧化剂、抗炎症药物联用的研究也取得一些进展, 如靶向血小板活化和病毒复制联合对小鼠致命性流感肺炎有保护作用^[80], 抗病毒药物奥司他韦与抗氧化剂S-腺苷-甲硫氨酸^[81]、α-生育酚^[82]对治疗小鼠肺损伤具有联合作用。抗病毒药物奥司他韦与抗炎症药物1,8-桉叶酚^[71]、抗病毒药物利巴韦林与热毒宁注射液^[83]等联用, 可以调节细胞炎症反应, 发挥小鼠肺炎的保护作用。

抗病毒药物与抗生素联用主要是基于流感并发症的考虑。人类感染甲型流感病毒会导致一系列临床症状的急性呼吸道疾病, 严重时可能危及生命, 并伴有各种并发症。最常见的并发症与细菌合并感染或继发性超感染有关, 这会增强由甲型流感病毒感染引发的病理过程导致重症的产生^[84]。

有研究表明^[85], 在感染A (H1N1) pdm09病毒的小鼠中, 单次鼻内阿奇霉素治疗降低了肺部病毒载量, 缓解了感染引起的低温。阿奇霉素具有开发为治疗人类流感的有效大环内酯类药物的潜力。在小鼠流感感染模型中, 有研究发现^[72]与奥司他韦单药治疗相比, 联合疗法并未带来明显的额外临床/生理学益处。一项临床研究^[73]发现奥司他韦-阿奇霉素联合治疗在快速恢复和预防流感相关并发症方面, 特别是在高危患者中, 比单独使用奥司他韦更有效。一项前瞻性单盲研究^[74]发现克拉霉素-萘普生-奥司他韦联合治疗组比单独使用奥司他韦治疗组退热更快, 流感病毒滴度下降更快, 具有更好的疗效, 也提示应进一步考虑住院儿童流感患者联合治疗的总体获益是否大于风险。另一项比较疗效的临床^[84]研究发现, 在预防乙型流感相关并发症方面, 早期使用抗生素联合奥司他韦治疗比单独使用奥司他韦更有效, 特别是在高危流感患者中。

抗流感病毒药物与抗生素的组合可以防止流感疾病发展为继发性细菌感染, 具有治疗重症、减少并发症的潜力, 但也应当平衡抗生素的益处与抗生素耐药性的风险^[86]。

2.5 其他

除了上述的研究报道以外, 目前还有针对抗病毒药物与单克隆抗体、中药联用等方面的研究。采用病毒聚合酶抑制剂法匹拉韦与病毒血凝素受体结合位点两种单克隆抗体联合治疗的三联治疗可清除裸鼠体内的流感病毒, 展现出抗病毒药物与抗病毒单抗的联用潜力^[87]。在给药方式的联用上也有一定的进展, 一项研究^[88]通过气道给予相对少量的3种抗流感抗体的单一组合可以实现多种相关流感类型 (H1N1、H3N2和B) 的有效治疗。与全身递送相比, 气道递送是一种更有效的抗流感抗体给药方式, 可行且具有成本效益, 可广泛用于联合疗法。

中药联用的抗病毒疗法也具有很大的研究价值, 研究^[89]发现鱼腥草总黄酮及多糖通过多模式调节肺和肠道稳态, 协同缓解了H1N1诱导的小鼠病毒性肺炎, 为治疗病毒性肺炎提供了新的治疗策略。

研究^[90]发现, 母乳中的天然成分3'-唾液酸乳糖和骨桥蛋白具有最佳的抗病毒能力, IC₅₀值分别为33.46和1.65 μmol·L^-1。两者组合通过协同作用有效地减少炎症风暴并表现出抗流感病毒的作用。这也提示研究者可以扩大抗流感药物联用的药物发现来源。

以上的综述提示在抗流感药物联用的研究中应注意: ① 抗病毒作用机制会影响联合治疗的有效性, 在开发联合疗法时应考虑不同作用机制之间的相互作用^[91]。② 需要广泛使用抗病毒治疗时, 需要多个药物的组合, 以减轻流感治疗过程中的耐药性出现^[92]。③ 在确定最佳治疗策略时, 还需要考虑抗病毒药物的药物剂量、ADMET、成本或成本效益比以及药物的毒副作用^[93]。④ 在药物联合治疗中考虑标本兼治, 改善重症流感患者的护理标准和预后。

3 总结与展望

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由于流感病毒具有多种亚型, 使其难以完全预防和控制^[94], 因此抗流感药物联用为抵抗流感提供了很好的选择。在抗流感药物联用的研究中, 研究人员可以设计联合疗法靶向宿主和病原体的多种途径, 从而产生比简单累加效应更大的益处。除了提高治疗效果外, 还可以使药物以较低的个体化剂量使用, 从而提高患者的耐受性并降低药物毒性^[6]。同时, 也应当注意避免盲目追求协同效应, 超过一定限度, 追求协同作用可能会以牺牲疗效为代价^[95]。

高通量筛选技术使新药组合候选物的发现更加系统和快速。通过高通量筛选, 成千上万的药物组合可以在临床前模型系统中进行多次剂量测试, 以确定具有协同作用的药物组合^[96]。尽管在体外实验中具有良好协同作用的药物组合在体内也可能没有协同作用, 但不断发现在体外具有良好协同作用的药物组合对于补充抗流感药物, 延长抗流感药物使用寿命, 增加药物的选择, 加快药物研发速度, 延缓耐药性仍然具有重要的意义。

虽然药物联用领域以及抗流感药物联用方向已经取得了很多的进展, 但仍然存在一些关键性问题亟待解决: ① 药物组合的发现依赖于可靠的协同评价标准, 但对于正确的协同标准来表征组合的相互作用没有达成共识^[51], 同时, 也需要从定量药理学、系统生物学、统计学多个学科角度研发新的更准确、客观动态的, 符合药代动力学的体内和体外模型。② 当前人工智能在药物研发显示上出极大的前景。当前药物联用的研究数据库主要集中于肿瘤相关疾病的治疗研究, 以及细菌、真菌等微生物领域的研究^[97], 关于病毒学相关学科的研究内容比较匮乏, 缺乏相应数据库的建立。当前数据库的数量和质量仍然欠缺, 需要科研人员披露优质的抗病毒药物联用数据集以训练具有更好性能的人工智能模型。③ 在抗流感药物联用应注重扩展化合物来源以及多种类型和功能药物的联用, 关注新的抗病毒治疗策略-探索有效的联合疗法, 改善现有抗病毒疗效。④ 在抗流感药物联用发现过程中, 有必要进行更多的功能研究来阐明药物相互作用的作用机制, 这对于将患者分层以进行更有针对性、个性化治疗至关重要, 也可以为未来精准医疗提供指导。抗流感药物联用研究涉及从体外研究到实验动物研究以及最终的临床研究中, 这是一个整体性、全链条的药理学问题, 还需要科研工作者们付出更多的努力。

基金

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国家自然科学基金资助项目(81971950)
中国医学科学院医学与健康科技创新工程项目(2021-I2M-1-038)

参考文献

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doi: 10.16438/j.0513-4870.2024-0989

接收时间：2024-10-12
首发时间：2025-10-29
出版时间：2025-04-12

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收稿日期：2024-10-12
修回日期：2024-11-29

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国家自然科学基金资助项目(81971950)

中国医学科学院医学与健康科技创新工程项目(2021-I2M-1-038)

作者信息

¹ 中国医学科学院、北京协和医学院医药生物技术研究所, 北京 100050

² 复旦大学上海医学院, 上海 200032

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岑山, Tel: 86-10-63037279, E-mail: shancen@imb.pumc.edu.cn

王静, E-mail: jingwang@imb.pumc.edu.cn

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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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RefWorks
TxT

Name	Function and application	Website	Reference
Synergyfinder3.0	A web-platform for interactive analysis and visualization of multi-drug combination response data	https://synergyfinder.fimm.fi	[28]
Synergyfinderplus	An interactive tool for analyzing drug combination dose-response data to facilitate better interpretation and annotation of drug combination screening datasets	https://www.synergyfinderplus.org/	[29]
SynToxProfiler	An interactive networking tool for ranking HTS drug combinations based on integrated efficacy, synergy and toxicity profiles	https://syntoxprofiler.fimm.fi	[38]
Synergy	A Python library containing widely popular synergy models, a standard tool for analyzing and visualizing drug combination and synergistic effects	https://pypi.org/project/synergy	[39]
DrugBank	A database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets	https://www.drugbank.com	[40]
DrugComb	An integrative cancer drug combination database including screening data of drug combination and prediction of the sensitivity and synergy of drug combinations by algorithms	https://DrugComb.org/	[41]
ChEMBL	ChEMBL is a manually curated, high-quality, large-scale open data database containing binding, functional and ADMET information for compounds	https://www.ebi.ac.uk/chembl/	[42]
KEGG	KEGG is a manually curated resource integrating eighteen databases. KEGG mapping tools are helpful for understanding cellular and organism-level functions from genome sequences and other molecular datasets	https://www.kegg.jp/kegg/pathway.html	[43]
Gene Ontology	A comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs)	https://geneontology.org/	[44]
STRING	An online database that can comprehensively identify gene lists and functional genomic datasets, and establish protein-protein association networks	https://string-db.org/	[45]
CDCDB	A continuously updated large-scale drug combination database, including over 40 795 drug combinations	https://icc.ise.bgu.ac.il/medical_ai/CDCDB/	[46]
NPCDR	Natural product-based drug combination and its disease-specific molecular regulation (NPCDR)	http://npcdr.idrblab.net/	[47]

Name

Function and application

Website

Reference

Synergyfinder3.0

A web-platform for interactive analysis and visualization of multi-drug
combination response data

https://synergyfinder.fimm.fi

[28]

Synergyfinderplus

An interactive tool for analyzing drug combination dose-response data to
facilitate better interpretation and annotation of drug combination screening datasets

https://www.synergyfinderplus.org/

[29]

SynToxProfiler

An interactive networking tool for ranking HTS drug combinations based on integrated efficacy, synergy and toxicity profiles

https://syntoxprofiler.fimm.fi

[38]

Synergy

A Python library containing widely popular synergy models, a standard tool for analyzing and visualizing drug combination and synergistic effects

https://pypi.org/project/synergy

[39]

DrugBank

A database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets

https://www.drugbank.com

[40]

DrugComb

An integrative cancer drug combination database including screening data of drug combination and prediction of the sensitivity and synergy of drug
combinations by algorithms

https://DrugComb.org/

[41]

ChEMBL

ChEMBL is a manually curated, high-quality, large-scale open data database containing binding, functional and ADMET information for compounds

https://www.ebi.ac.uk/chembl/

[42]

KEGG

KEGG is a manually curated resource integrating eighteen databases. KEGG mapping tools are helpful for understanding cellular and organism-level functions from genome sequences and other molecular datasets

https://www.kegg.jp/kegg/pathway.html

[43]

Gene Ontology

A comprehensive resource concerning the functions of genes and gene
products (proteins and noncoding RNAs)

https://geneontology.org/

[44]

STRING

An online database that can comprehensively identify gene lists and functional genomic datasets, and establish protein-protein association networks

https://string-db.org/

[45]

CDCDB

A continuously updated large-scale drug combination database, including over 40 795 drug combinations

https://icc.ise.bgu.ac.il/medical_ai/CDCDB/

[46]

NPCDR

Natural product-based drug combination and its disease-specific molecular regulation (NPCDR)

http://npcdr.idrblab.net/

[47]

Drug	Combination	Target	Efficacy evaluating model	Efficacy	Reference
Oseltamivir	Zanamivir	Neuraminidase	In vitro, in mice	Effective in the NAI-resistant influenza strains (in vitro) Not superior to zanamivir monotherapy (in mice)	[57,58]
Peramivir	Neuraminidase	In vitro, in mice	Synergistic antiviral effects	[53]
Clinical	Not superior to oseltamivir monotherapy	[59]
Baloxavir	NAIs	Cap-dependent endonuclease & neuraminidase	In vitro	Synergistic antiviral effects	[13,60]
Clinical	Not superior to NAIs alone	[61]
Oseltamivir	Cap-dependent endonuclease & neuraminidase	In mice	Improved antiviral activity	[60]
In mice, in ferret	Against baloxavir PA-resistant influenza	[13,15]
Amantadine	Oseltamivir + ribavirin	M2 ion channel, neuraminidase & RdRp	In vitro	Synergistic antiviral effects and suppressing resistance	[62-64]
Clinical	TCAD therapy had similar PKs to monotherapy	[65]
Significant decrease in viral shedding (Clinical)	[66]
Favipiravir	Peramivir	RdRp & neuraminidase	In mice	Effective in the oseltamivir-resistant influenza virus infection	[67]
Oseltamivir	Nitazoxanide	Neuraminidase & hemagglutinin	In vitro	Synergistic antiviral effects	[11,68]
In ferret	Synergistic effects only in preventing infection	[11]
Clinical	No improved effects in hospitalized patients	[69]
CPD A	Ribavirin	IMPDH	In vitro	Strongly synergistic effect with potentially acceptable safety profile	[70]
ING-1466	Oseltamivir	Hemagglutinin & neuraminidase	In mice	Increased survival	[14]
Baloxavir	Hemagglutinin & cap-dependent endonuclease	In mice	Increased survival	[14]
Pimodivir	Interferon-alpha	IAV polymerase basic protein 2 & IFN-α	In vitro	Synergistic antiviral effects	[54]
Oseltamivir	1,8-Cineol	Neuraminidase & inflammatory signaling	In mice	Strongly synergistic effect	[71]
Oseltamivir	Azithromycin	Neuraminidase & 50S rRNA	In mice	No additional benefits	[72]
Clinical	Required less oxygen support and shorter hospitalization time	[73]
Oseltamivir	Clarithromycin + naproxen	Neuraminidase, 23S rRNA & cyclooxygenase	Clinical	Faster clinical improvement	[74]

Drug

Combination

Target

Efficacy evaluating model

Efficacy

Reference

Oseltamivir

Zanamivir

Neuraminidase

In vitro, in mice

Effective in the NAI-resistant influenza strains (in vitro)
Not superior to zanamivir monotherapy (in mice)

[57,58]