Article(id=1190375271522993135, tenantId=1146029695717560320, journalId=1190317699101192196, issueId=1190375270847710190, articleNumber=1001-2494(2025)03-0209-05, orderNo=null, doi=10.11669/cpj.2025.03.001, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1712851200000, receivedDateStr=2024-04-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1761737180834, onlineDateStr=2025-10-29, pubDate=1738944000000, pubDateStr=2025-02-08, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1761737180834, onlineIssueDateStr=2025-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1761737180834, creator=13701087609, updateTime=1761737180834, updator=13701087609, issue=Issue{id=1190375270847710190, tenantId=1146029695717560320, journalId=1190317699101192196, year='2025', volume='60', issue='3', pageStart='209', pageEnd='312', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1761737180673, creator=13701087609, updateTime=1761793989024, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1190613542412890252, tenantId=1146029695717560320, journalId=1190317699101192196, issueId=1190375270847710190, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1190613542412890253, tenantId=1146029695717560320, journalId=1190317699101192196, issueId=1190375270847710190, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=209, endPage=213, ext={EN=ArticleExt(id=1190375271728514034, articleId=1190375271522993135, tenantId=1146029695717560320, journalId=1190317699101192196, language=EN, title=Application of Bio-entropy in Drug Evaluation, columnId=null, journalTitle=Chinese Pharmaceutical Journal, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The concept of entropy is originated from physics which represents a degree of disorder. Entropy increase is a natural law for extinction of things. In the field of life science, entropy could be used to measure the quality of life. Organisms utilize their inherent negative entropy mechanisms of self-organization, defense, self-healing, wear resistance, and adaptability to counteract entropy increase, and thus to cure various diseases and curb aging processes, while maintaining optimal health. As an external intervention, good drugs could trigger negative entropy mechanisms by enhancing one or more of the above machineries, thus assist the body to recover. This article explores the potential applications of biological entropy in drug discovery research, aiming to leverage its power in the future for a good understanding of drug targets, elucidation of drug mechanisms, rational design of novel drugs and rigorous evaluation of drug efficacy, especially pertinent to multi-target drugs.

, correspAuthors=Jiandong JIANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Huihui GUO, Tianle GAO, Xiaolei MA, Cai TIE, Jiandong JIANG), CN=ArticleExt(id=1190375305727541320, articleId=1190375271522993135, tenantId=1146029695717560320, journalId=1190317699101192196, language=CN, title=生物熵在药物研究中的应用, columnId=1190352408384471863, journalTitle=中国药学杂志, columnName=综述, runingTitle=null, highlight=null, articleAbstract=

熵是一个源于物理学的概念,表示物质紊乱的程度。熵增定律是揭示事物消亡的自然规律。在生物学领域,也可用熵来度量生命活动过程中的质量,一般来说,生物熵越低越好。生命体依赖其自组织、防御、自愈、抗磨损和适应等负熵能力来对抗熵增,从而逆转疾病抵抗衰老,保持健康。而药物治疗的实质是作为外部干预的力量,通过增强以上某种或几种能力,产生负熵效应,协助机体恢复至有序状态。笔者探讨了生物熵在药物研究中的应用,期望未来能够利用生物熵更正确地认识药物靶点、解释药物作用机制、设计新药以及科学地评估药物(包括中药)的疗效及安全性。

, correspAuthors=蒋建东, authorNote=null, correspAuthorsNote=
*蒋建东,男,博士,院士 研究方向:抗感染,抗癌,糖脂代谢紊乱的新药研发 Tel:(010)63017906
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郭慧慧,女,博士,副研究员 研究方向:基于肠道菌群防治疾病的新药研究;

高天乐,男,博士,副研究员 研究方向:基于天然产物的创新药研究。郭慧慧和高天乐为共同第一作者

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郭慧慧,女,博士,副研究员 研究方向:基于肠道菌群防治疾病的新药研究;

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郭慧慧,女,博士,副研究员 研究方向:基于肠道菌群防治疾病的新药研究;

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高天乐,男,博士,副研究员 研究方向:基于天然产物的创新药研究。郭慧慧和高天乐为共同第一作者

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高天乐,男,博士,副研究员 研究方向:基于天然产物的创新药研究。郭慧慧和高天乐为共同第一作者

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生物熵在药物研究中的应用
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郭慧慧 1 , 高天乐 1 , 马晓蕾 1 , 铁偲 2 , 蒋建东 1, 3, *
中国药学杂志 | 综述 2025,60(3): 209-213
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中国药学杂志 | 综述 2025, 60(3): 209-213
生物熵在药物研究中的应用
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郭慧慧1, 高天乐1, 马晓蕾1, 铁偲2, 蒋建东1, 3, *
作者信息
  • 1 中国医学科学院北京协和医学院药物研究所, 天然药物活性物质与功能国家重点实验室, 北京 100050
  • 2 中国矿业大学(北京)化学与环境工程学院,北京 100083
  • 3 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
  • 郭慧慧,女,博士,副研究员 研究方向:基于肠道菌群防治疾病的新药研究;

    高天乐,男,博士,副研究员 研究方向:基于天然产物的创新药研究。郭慧慧和高天乐为共同第一作者

通讯作者:

*蒋建东,男,博士,院士 研究方向:抗感染,抗癌,糖脂代谢紊乱的新药研发 Tel:(010)63017906
Application of Bio-entropy in Drug Evaluation
Huihui GUO1, Tianle GAO1, Xiaolei MA1, Cai TIE2, Jiandong JIANG1, 3, *
Affiliations
  • 1 Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China
  • 2 School of Chemical and Environmental Engineering, China University of Mining and Technology, Beijing 100083, China
  • 3 Institute of Medicinal Biotechnology, Chinese Academy of Medical Science, Peking Union Medical College, Beijing 100050, China
出版时间: 2025-02-08 doi: 10.11669/cpj.2025.03.001
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熵是一个源于物理学的概念,表示物质紊乱的程度。熵增定律是揭示事物消亡的自然规律。在生物学领域,也可用熵来度量生命活动过程中的质量,一般来说,生物熵越低越好。生命体依赖其自组织、防御、自愈、抗磨损和适应等负熵能力来对抗熵增,从而逆转疾病抵抗衰老,保持健康。而药物治疗的实质是作为外部干预的力量,通过增强以上某种或几种能力,产生负熵效应,协助机体恢复至有序状态。笔者探讨了生物熵在药物研究中的应用,期望未来能够利用生物熵更正确地认识药物靶点、解释药物作用机制、设计新药以及科学地评估药物(包括中药)的疗效及安全性。

生物熵  /  高度有序  /  生理信号熵  /  中药  /  药效评价

The concept of entropy is originated from physics which represents a degree of disorder. Entropy increase is a natural law for extinction of things. In the field of life science, entropy could be used to measure the quality of life. Organisms utilize their inherent negative entropy mechanisms of self-organization, defense, self-healing, wear resistance, and adaptability to counteract entropy increase, and thus to cure various diseases and curb aging processes, while maintaining optimal health. As an external intervention, good drugs could trigger negative entropy mechanisms by enhancing one or more of the above machineries, thus assist the body to recover. This article explores the potential applications of biological entropy in drug discovery research, aiming to leverage its power in the future for a good understanding of drug targets, elucidation of drug mechanisms, rational design of novel drugs and rigorous evaluation of drug efficacy, especially pertinent to multi-target drugs.

bio-entropy  /  highly ordered structure  /  physiological signal entropy  /  traditional Chinese medicine  /  efficacy evaluation
郭慧慧, 高天乐, 马晓蕾, 铁偲, 蒋建东. 生物熵在药物研究中的应用. 中国药学杂志, 2025 , 60 (3) : 209 -213 . DOI: 10.11669/cpj.2025.03.001
Huihui GUO, Tianle GAO, Xiaolei MA, Cai TIE, Jiandong JIANG. Application of Bio-entropy in Drug Evaluation[J]. Chinese Pharmaceutical Journal, 2025 , 60 (3) : 209 -213 . DOI: 10.11669/cpj.2025.03.001
熵 (entropy) 最初是一个物理学概念,源自热力学第二定律,用来量化能量在空间中的分布均匀程度。随着统计物理、信息论等科学理论的不断发展,熵的本质逐渐被理解为系统的内部混沌,用于衡量系统内的混乱度和无序度[1-2]。目前,熵在信息学、概率论、天体物理学、生命科学等领域都有重要应用,并在不同学科中有具体的定义[3]。在自然界的一个封闭系统中(没有外力作用),所有事物都遵循熵增定律,即朝着更加混乱、无序的状态发展,被誉为科学定律之最。而生物体作为一个复杂、高度有序运转的大系统,可能更加适合用熵来衡量生命活动过程中的质量,即为生物熵[4]。而且,薛定谔认为,生命以负熵为生。
与非生命体系不同,生命体是一个开放系统,需要与外界不停地进行物质、能量和信息的交换,执行各种复杂的生理功能,因此生命体有能力在一定条件下逆转熵增过程,从而维持其高度有序性。实际上,生命的过程就是通过不断产生负熵而维持生存的过程。具体来说,生命体主要依靠5种能力来获得负熵:自组织能力、防御能力、自愈能力、抗磨损能力以及适应能力[5] (图1)。这些能力共同作用、相互支持,使生命体能够应对各种挑战和变化,保持其有序和稳定[2]
自组织能力是指生命体能够高效地将小分子(如氨基酸、核苷、脂肪酸和葡萄糖等)自组装成复杂的大分子(如蛋白质、核酸、脂质、糖原等),并构建形成高度分化的细胞和组织,从而维持机体的高度有序运转,保障生长、发育、繁殖和新陈代谢[6]。因此,机体的自组织能力越强,其保持低熵状态、维持健康和活力的能力也就越强。
在防御能力方面,机体依靠强大的免疫系统(包括固有免疫系统、获得性的细胞和抗体介导的免疫应答、各种炎症反应、及自毁等机制)持续监控全身防御状况以抵抗外来病原体入侵,或清除不受控制的有害自体细胞,以维持机体的有序运转[7]
机体在运转的过程中,不可避免地会受到外界损伤,导致功能偏离正常状态。此时,机体的自愈能力会被激活,通过消除炎症和愈合损伤,最终恢复机体的正常功能。干细胞的组织修复功能是机体自愈能力的重要体现。自愈过程需要吸收大量的外界能量和营养物质,以产生负熵,为自组织能力提供保障[2]
抗磨损能力是指生物体具备的自我调控和纠错的机制,使机体能够自主调节功能并自动修复在日常生理活动中所遭受的磨损。这种机制具有强大的修复能力,可以修复DNA、蛋白质等分子发生的变异[8],纠正蛋白的错误折叠或空间构象的异常[9],以及消除新陈代谢过程中产生的有害化学物质[2,10]。然而,当磨损率超过机体的承受能力时,机体的有序结构就会崩塌,熵也随之增加。
适应能力是指生物体应对外界变化与环境相互动的能力,这种能力通过各种屏障系统得以维持和体现。这些屏障包括皮肤和黏膜、血脑屏障、胎盘屏障、肠-血管屏障、气-血交换屏障以及与各种屏障共存的微生物群。随着研究的深入,人们逐渐认识到屏障在确保正常物质交换和防范外部威胁中的重要性。另外,适应能力还包括启用备份系统。当某项功能受损时,生物体可启用代偿的分子机制或旁路,从而跨过受阻功能,帮助生物体继续正常运转,体现了生物基因和功能的冗余特性。例如,无氧呼吸可以作为有氧呼吸的补充;颈总动脉结扎造成脑缺血损伤时,会促进椎-基动脉系统侧支循环的建立,而不至于出现脑血循环障碍等。
如前所述,当人体的自组织能力、防御能力、自愈能力、抗磨损能力及适应能力退化、受损或功能异常时,生命体的无序性会增加,导致机体失去低熵状态,从而引发疾病或加速衰老。熵增被认为是人类衰老过程的根源。Silva和Annamalai研究团队根据熵产速率值预测出美国人平均年龄分别为男性73.78岁和女性81.61岁,与实际人口统计数据(男性74.63岁和女性80.36岁)的平均寿命非常接近[11]
除了衰老,熵的增加还会导致机体在分子、细胞、组织、器官及系统等多个层面失去秩序,增加无序性,从而引发疾病。科学研究已经证实,当人类个体处于疾病状态时,其机体的熵值会显著升高,这体现在患病器官的熵值增加,同时也常常表现为整体生物熵值的升高[12-13]。此外,熵增的速度与疾病的严重性成正比,导致生命更快地耗竭和终结。因此,通过干预机体,产生负熵,可以有效地逆转各种病理过程。
目前,生物熵在临床中主要用于分析生理信号,通过计算生理信号熵来辅助疾病的诊断和治疗。但由于不同临床指标计算方法的差异,得出的生理信号熵变化趋势可能会有所不同。因此,在应用生物熵进行临床诊断和治疗时,必须综合考虑多种临床因素,以确保结果的准确性。
当前用于生物熵的主要统计方法有Shannon熵、条件熵、Renyi熵、样品熵、近似熵、最大熵和多尺度样本熵。每种统计方法都有自己的特点,比如Shannon熵计算公式为:Shannon Entropy H(X)=- i = 1 n p ( x i ) l o g 2 [ p ( x i ) ](n:离散数据的变量数; i:1≤in;p(xi):离散变量xi发生的概率),它在众多生物熵理论中被认为是最经典的计算方法,是其他生物熵方法的基础[14];近似熵是一种用于量化时间序列数据波动的规律性和不可预测性的非线性分析技术;样本熵作为近似熵的改进算法,可以避免自身匹配带来的偏差,目前广泛应用于生理信号包括脑电图、心电图等复杂信号的数据分析和疾病诊断[15];而样本熵没有充分考虑时间序列中可能存在的不同时间尺度,为了解决这一问题,又出现了多尺度样本熵,以便在时间尺度不确定时提供额外的观察视角[16]
然而,鉴于疾病的复杂性,单个或某些指标计算得到的生理信号熵难以用于评价整体生物熵变化,因为经典生物熵算法在计算时未能充分考虑在特定疾病条件下,混乱度和复杂性是否适合于生命机能的评判,导致实际应用中存在一定的局限性。为了克服这一局限性,未来可以借助人工智能(artificial intelligence, AI)大模型,通过对医疗大数据进行深度学习和分析,明确何种类型的混乱和复杂性在特定疾病条件下是有利于生命功能的,而何种类型是有害的。将这些信息作为变量参数引入生物熵的计算中,有望获得比现有算法更加可靠的生物熵计算模式 (图1)。
生物熵在疾病研究中的应用仍在探索的早期,主要是希望如何用生物熵这个生命的底层原理来反映疾病的状况。目前的文献报道并不多,具体的应用方法和标准尚需进一步探索和完善,其研究主要集中于应用某个特定的生理信号或检测指标计算得到的熵来辅助疾病诊断。
临床上,心血管疾病及相关风险因素的诊断中,动态血压、心电图和心血管超声等复杂且非线性的生理指标,特别适合应用熵理论进行判别与定量分析[3]。比如,糖尿病引发的心脏自主神经功能损伤,常采用动态心电图进行监测,研究发现,糖尿病患者的心电图心率变异性(heart rate variability,HRV)熵低于健康者,而且随着糖尿病严重程度的增加,HRV熵值降低更显著[3]。然而,在冠心病患者诊断过程中,当采用心磁图指标计算生物熵时,患者的相对熵值高于健康人[17];当采用HRV指标计算熵值时,患者的HRV熵值却低于健康人[18-20]。另外,对于测量脉搏计算出的样本熵,采用不同的计算方式也可得到不同的熵变化结果。当采用测试模式分解方式时,冠心病患者的脉搏样本熵小于健康者,提示病后脉搏的不规律性降低[21];而采用45°对角线分布递归定量分析方式时,冠心病患者的样本熵要高于健康者,说明冠心病(coronary artery disease,CAD)患者递归定量分析时比对照组具有更多的对角线结构[22]。这可能是由于上述结果未将“在特定疾病条件下,混乱度和复杂性是否有利于生命功能”这一变量考虑在内,导致特定生理信号熵与整体生物熵理论相悖。
生理信号熵的变化趋势可以作为量化复杂数据和疾病诊断的重要辅助方式,为疾病的预防和干预提供更有针对性的指导。目前,AI辅助诊断作为一种新的临床诊断方式,主要利用深度学习等算法对大量数据进行分析和学习以提高诊断准确率和效率,减轻医生的工作负担,降低医疗成本。但是AI是一种定性分析工具,只能提取出与疾病相关的生物信息变化,却无法对其定量分析。未来,随着科研的不断深化与技术的不断革新,我们有希望结合AI技术手段对各种生理信号熵进行修正和融合分析,形成一套整体生物熵的计算模式,进一步完善生物熵理论,促使生物熵在疾病诊断、分级和预后评估中发挥更加重要的作用。
虽然生物熵在疾病诊断方面的研究已经受到关注,但在新药研究方面,如何运用熵的概念来研究新药的相关报道还较少。
首先,药物研发可以聚焦于提升5种负熵能力机制及其涵盖的潜在靶点:例如,通过激活或调节细胞能量代谢与生长因子受体来增强自组织力;针对免疫细胞受体、免疫调节因子及病原体识别分子提升抵抗力;同时利用细胞自毁机制清除有害变异细胞;优化代偿系统、屏障系统及其调节因子以促进适应力;着眼于代谢调节酶、生理激素、抗衰老蛋白、细胞保护因子、解毒酶及抗氧化系统[特别是谷胱甘肽(glutathione,GSH)、核因子红细胞来源2样2(nuclear factor erythroid 2-related factor 2,Nrf2)]来加强抗磨损力;并激活或调节血管生成因子、细胞迁移与分化调节因子以及炎症介质(尤其是TNF、IL-1β的负调节),从而激发自愈力。这些与生物负熵能力相关靶点的开发与利用,将为药物研发提供新的思路和方向。
实际上,好的药物(或疫苗)可以被视为一种外力(化学的或生物的物质),它可能是精确作用于机体负熵能力的某个靶点,激发机体自身的机能,从而对抗疾病进程,比如表皮生长因子受体(epidermal growth factor receptor, EGFR);小分子抑制剂(吉非替尼、阿法替尼等)、EGFR单抗(贝伐珠单抗、西妥昔单抗等)等抗肿瘤靶向药。但很多时候,药物可能是作用于机体负熵能力的某个或多个机制(涵盖多个靶点)发挥药效,比如二甲双胍,既可以通过与PEN2蛋白结合,激活腺苷酸活化蛋白激酶(adenosine monophosphate activated protein kinase, AMPK)信号通路,降低体内血糖水平、肝脏脂肪含量,恢复抗磨损能力[23],也可以通过增强线粒体稳态[24]、减轻炎症反应、调节免疫、自噬[25]等增强机体的自愈能力发挥降糖/脂、抗衰及抗肿瘤作用;小檗碱可通过多个靶点[如低密度脂蛋白受体(low density lipoprotein receptor, LDLR)、AMPK、胰岛素受体(insulin receptor, InsR) 、前蛋白转化酶枯草溶菌素9(proprotein convertase subtilisin/kexin type 9,PCSK9)等][26]和机制(肠道菌、能量代谢、氧化应激等)[27]调节人体能量物质的利用而改善代谢紊乱,提高机体的自组织能力、适应力和抗磨损能力;疫苗,特别是经过C-C基序趋化因子11 (C-C motif chemokine ligand 11, CCL11)等趋化因子优化的核酸疫苗,能够高效地激活人体免疫系统中的巨噬细胞、抗原呈递细胞(如树突状细胞)、T细胞及B细胞,从而全面激发并增强免疫反应[28],以上药物引起的负熵效应与疾病的进展趋势(熵增)相反。除了传统的药物治疗外,音乐治疗作为一种创新的非药物干预策略,通过听觉直接刺激大脑皮层,触发中枢多区域神经网络参与形成的愉悦与放松反应[29],并激发自愈机制,对于促进生命系统的整体平衡与和谐具有积极作用,是激发生物体内负熵机制、促进健康恢复的又一重要体现。
因此,进一步研究生物熵在药物靶点理论、药物作用机制和药效评价中的应用,将有助于更全面地了解药物治疗的本质、原理和效果,为新药设计和发现提供更科学的指导。在未来的研究中,我们还可以通过生物熵理论及量化计算,完善药物靶点理论、综合评价药效、改进药物设计、改善药物剂型,以提高新药发现的成功率和药物的质量 (图1);对于多病 (multi-morbidity),多靶点和多机制的药物更是今后关注的热点,应用生物熵理论可能尤显优势[30]
整体生物熵的概念源于耗散结构理论(dissipative structure theory)中的广义熵概念,它从整体水平上解释生命与环境中的能量、物质和信息相互作用后呈现出的熵变化情况。整体生物熵与生命活动及健康状态密切相关,也许可以用于衡量药物治疗对生命功能的整体改善,更全面地了解药物治疗的效果,为疾病的药物治疗提供更合理的评价指标。
笔者所在课题组首次将生物熵理论应用于肝脏组织和主动脉斑块病理结果的分析[31]。通过提取特殊染色图片的离散数据信息,研究人员计算了脂肪肝动物的Shannon熵,从而量化了肝脏组织变性和主动脉斑块的程度。该研究发现,脂肪肝状态下的生物熵明显高于健康对照组,而给予小檗碱或二甲双胍治疗后,Shannon熵值显著降低;药效与熵的相关性分析结果显示,随着药效的增加,熵值降低越显著[31]。这说明生物熵可以用于评价小檗碱/二甲双胍等多机制药物治疗脂肪肝的综合效果。通过进一步分析并利用生物熵计算初步得出印象,小檗碱与二甲双胍在治疗效果上相当,这一结论与之前对这两种药物降糖效果的临床研究结果相吻合[32]。疾病的熵增原理对抗肿瘤药物的研究也有启发。当抗肿瘤药物抑制肿瘤细胞的恶变、增殖、和转移时,肿瘤的混乱程度减小,有序性增加,趋向于稳定的低熵状态,这就相当于药物干预给体系内部输送了负熵,使体系减少无序增加有序[1]。再如,多柔比星(doxorubicin,DOX)以嵌入方式与肿瘤细胞的DNA结合,形成DOX-DNA复合物,由于该复合物比独立的DNA和DOX分子更有序,因此导致一定程度的熵减,有序度增加[1]。以上这些早期的探索提示了生物熵未来运用的可能,值得深入研究。
可是,在临床实践中,药物疗效的评价通常涉及多个检测指标的综合评估,而不是仅依赖于一个(或少数几个)指标。针对某个指标计算得到的生物熵只能代表整体生物熵的一部分。为了更准确地反映整体生物熵,需要将多个检测指标综合在一起重新计算。然而,整体生物熵作为一种新的评价体系,其整合计算的方法尚未在国际上达成明确的共识。未来,需要不断完善相关的科学研究,并由科研、临床和监管领域的专家共同探讨计算整体生物熵的可行性。例如,鉴于生存质量与生命功能和自由度的密切关系,也有考虑可将生存质量作为核心指标纳入整体生物熵的计算中。通过这种方式,更全面地评估药物治疗对生命功能的整体改善。
在整体生物熵尚未得到广泛应用的情况下,如果需要使用生物熵进行药物疗效评价,可以关注与适应证相关且难以简单定量的关键指标,进行临床生理信号熵的计算,以增加药效对比的信息维度。例如,在评价抗肿瘤药物时,总生存期和肿瘤的无进展生存期是公认的黄金标准;除此之外,还可以利用不同时间患者的计算机断层扫描 (computed tomography, CT)、 核磁共振成像(magnetic resonance imaging, MR)、正电子发射计算机断层成像(positron emission tomography, PET)等病理图像信息,计算生理信号熵的变化,以实现实时评价肿瘤的恶性程度,包括肿瘤大小的变化趋势、向周围组织侵袭的程度以及转移能力。通过纳入生理信号熵,可以构建更有参考价值的药物评价体系,提供客观可量化的指标,为医生和患者提供更准确的疗效判断。
为正确评估药物的安全性和有效性,通常需要进行大规模的临床试验和长期观察研究。在新药注册临床试验中,完善后的生物熵数值也许能成为一个数字化的综合药效评判指标,解析药物-人体相互作用的复杂体系,为临床试验结果的全息分析提供参考。
具体比如,在药物临床研究设计中,生物熵也许可以作为一个供参考的终点指标,用于判断药物在何时达到理想的治疗效果[33-34]。在临床数据收集过程中,研究者可以收集影像数据等能够计算生物熵的数据,并用适当的统计方法和模型来计算生物熵。此外,研究者还可以评估药物对生物熵的影响,并结合其他临床指标,对药物的治疗效果和安全性进行综合评估。在实践中,Chen等[35]利用熵指数监测指导人体丙泊酚联合瑞芬太尼靶控输注,结果显示,依据熵指数值进行的用药调控较常规药动学参数(Marsh程式)靶控输注各反应指标水平更佳、药物副反应更少。Sun等[36]发现,非神经系统疾病患儿接受咪达唑仑注射液镇静治疗后,其脑功能熵指数与Ramsay镇静评分具有良好的相关性。
传统中药治疗的效果评价常是在整体概念层面的(如扶正祛邪、清热解毒、活血化瘀等),①是因为中药成分多样(多糖、黄酮、皂苷、有机酸、生物碱等),含量差异也很大;②是由于各种成分的理化性质不同,口服用药时,肠道吸收率不同,生物利用度差别明显;③是作用机制复杂,有的成分能够进入循环直接作用于组织和细胞的靶(单靶点或多靶点),有的则难以吸收,留在肠道内与肠道细菌相互作用,经厌氧发酵后产生新的代谢物或转化物进入血液(如丁酸),然后直接或与其他成分协同发挥作用;④是由于好的中药注重标本兼治,而单个测定指标的结果不能体现整体疗效。由此,目前西方医学主导的侧重具体检测指标(生理、生化、病理、影像等)的药物评价方法,在面对中药这一蕴含多样成分的复杂体系时,往往显得力不从心,难以全面准确地揭示其药效机制。中药评价的标准如何才能客观地、既具体又整体地反映治疗效果,一直是个有待探索的科学问题。
中药复杂的化学组分是其发挥药效的物质基础,而生物熵理论的引入,有助于打破中药成分分析的传统局限。通过聚焦于生物5种核心负熵能力所关联的关键药理学靶点/通路,笔者提议运用前沿的现代分析技术组合(包括靶点垂钓、高效液相色谱-质谱联用技术,以及网络药理学分析等),来对中药成分进行详尽的化学解构。其核心目标在于精确甄别出能够特异性靶向作用于预设的生物负熵靶点/通路的中药成分,借此实现基于负熵机制的中药成分精细化重新分类;而且有助于揭示中药成分与生物负熵能力间复杂而精密的相互作用网络,为中药研究领域开辟全新的视角与路径,促进对中药作用机制的深入理解。
从系统论的角度来看,当中药复方中不同组分同时作用于机体不同的靶点时,有可能协同产生综合的临床治疗效应,也称药效云[37]。这种综合效应虽然起效相对较慢,但通常具有标本兼治的特点,特别适合于复杂性疾病的治疗,如各种慢性病和多病等。虽然调理过程可能较长,但其疗效持久且全面,有助于从根本上改善患者的健康状况。因此,生物熵作为综合性的生理指标,有望成为评估中药复方对生命质量(生物体整体有序状态)影响的得力工具,助力我们更精确地揭示中药复杂的多靶点、多路径作用机制,进而全面地体现出中药的整体疗效,提高患者生存质量。
综上所述,从物理学的角度看,生老病死可以被视为一个熵增的过程。机体抵抗熵增的能力应该成为药物研究的重要方向。实际上,即使没有药物,许多伤病也能自然痊愈,这主要归功于人体自身具备的几种负熵效应的能力,它们共同协作,帮助人体抵抗疾病和损伤,促进康复,维持健康状态。药物安全奏效的关键应该是增强人体的某些自愈能力,如自组织、防御、自愈等,从而帮助机体产生负熵效应;换言之,从熵的本质上来看,药物通过作用于某个或几个机制,恢复组织细胞或分子的有序性从而产生疗效(图1)。
尽管生物熵在药物研究领域的探索尚处于初级阶段,具体实例尚显匮乏,但其蕴含的巨大潜力预示着广阔的应用前景。生物熵的概念可能为深入理解药物靶点、阐释药物作用机制、设计新药以及科学评估药物(包括中药)疗效提供新的视角和启示。展望未来,生物熵有望成为衔接实验室研究与临床实践的桥梁,为药物的研发上市及临床应用提供坚实的证据基础。
  • 国家自然科学基金项目资助(82104254)
  • 国家自然科学基金项目资助(82374061)
  • 中国工程院战略研究与咨询项目资助(2023-XZ-88)
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doi: 10.11669/cpj.2025.03.001
  • 接收时间:2024-04-12
  • 首发时间:2025-10-29
  • 出版时间:2025-02-08
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  • 收稿日期:2024-04-12
基金
国家自然科学基金项目资助(82104254)
国家自然科学基金项目资助(82374061)
中国工程院战略研究与咨询项目资助(2023-XZ-88)
作者信息
    1 中国医学科学院北京协和医学院药物研究所, 天然药物活性物质与功能国家重点实验室, 北京 100050
    2 中国矿业大学(北京)化学与环境工程学院,北京 100083
    3 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

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*蒋建东,男,博士,院士 研究方向:抗感染,抗癌,糖脂代谢紊乱的新药研发 Tel:(010)63017906
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2种不同金属材料的力学参数

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占总种数比例
Percentage of
total species (%)

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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