Article(id=1201177214285996222, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1201177206518145841, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2023-0276, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1678291200000, receivedDateStr=2023-03-09, revisedDate=1686153600000, revisedDateStr=2023-06-08, acceptedDate=null, acceptedDateStr=null, onlineDate=1764312564679, onlineDateStr=2025-11-28, pubDate=1704988800000, pubDateStr=2024-01-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1764312564679, onlineIssueDateStr=2025-11-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1764312564679, creator=13701087609, updateTime=1764312564679, updator=13701087609, issue=Issue{id=1201177206518145841, tenantId=1146029695717560320, journalId=1189982191388893191, year='2024', volume='59', issue='1', pageStart='1', pageEnd='268', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1764312562826, creator=13701087609, updateTime=1764312760268, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1201178034725417827, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1201177206518145841, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1201178034725417828, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1201177206518145841, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=84, endPage=93, ext={EN=ArticleExt(id=1201177214713815271, articleId=1201177214285996222, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=Review of evaluation and influencing factors of oral drug absorption fraction, columnId=1190335348648547107, journalTitle=Acta Pharmaceutica Sinica, columnName=Reviews, runingTitle=null, highlight=null, articleAbstract=

Fraction absorbed (Fa) is an important parameter to describe the absorption level of oral drugs, and an important basis for the development and optimization of the formulation process. Because it is easily confused with the concept of absolute bioavailability, it has not received enough attention from the industry. There are many complex factors affecting Fa. There are three time-related factors that directly affect the extent of Fa: the release time, the absorption time, and the residence time. The relationship between these three time-related factors determines the extent of Fa. Generally, we are more concerned about the apparent factors that affect the extent of Fa, including independent variables and covariates; The independent variables include administered dose, route, dosage form, etc. The covariates are divided into internal and external factors, and external factors include food factors, drug interactions, etc. Internal causes include age, sex, disease, etc. This paper analyzes and systematically combs how independent variables and covariates directly or indirectly affect the three time-related factors by affecting the body's physiology and internal environment, thus changing the complex process of Fa. Understanding this theoretical framework can better optimize the independent variables to reduce the impact of covariates on Fa. In addition, this paper also introduces the latest progress of prediction and evaluation of Fa, including the progress of complex dissolution device and the status of software prediction.

, correspAuthors=Jin YANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2024 Acta Pharmaceutica Sinica. All rights reserved., 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=Ping ZHANG, Fu-lin BI, Jin YANG), CN=ArticleExt(id=1201177216093741411, articleId=1201177214285996222, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=口服药物吸收分数的评价及影响因素综述, columnId=1190335349655180086, journalTitle=药学学报, columnName=综述, runingTitle=null, highlight=null, articleAbstract=

吸收分数(fraction absorbed, Fa) 是一个描述口服药物吸收高低的重要参数, 是制剂处方工艺开发和优化的重要依据, 由于容易与绝对生物利用度概念混淆而未得到工业界的足够重视。影响Fa的因素非常多且复杂, 直接影响Fa大小的是三个时间因素: 制剂完全释放所需时间、药物完全吸收所需时间以及胃肠道停留时间, 这三个时间因素的彼此关系决定了Fa的大小。通常大家更关心的是影响Fa大小的表观因素, 包括自变量和协变量。自变量因素如给药剂量、途径、剂型等, 协变量又分成内因和外因, 外因包括食物因素、药物相互作用等; 内因包括年龄、性别、疾病等。本文分析并系统梳理了自变量和协变量如何通过影响机体生理和内环境, 直接或间接地影响三个时间因素, 进而改变Fa这样一个复杂过程。了解该理论框架可以更好地优化自变量, 以减少协变量对Fa的影响。另外本文还介绍了预测和评估Fa的最新进展, 包括复杂溶出装置进展和软件预测的现状。

, correspAuthors=杨劲, authorNote=null, correspAuthorsNote=
*杨劲, E-mail:
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#共同第一作者.

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Copyright @ Taylor and Francis) , figureFileSmall=OfAKmjEDqewAU4LRXSWYYA==, figureFileBig=hEtp0ESFpD0jMrMmM8BrgA==, tableContent=null), ArticleFig(id=1201177218992005739, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201177214285996222, language=EN, label=null, caption=null, figureFileSmall=hNnT+jGTPSC8dJI5BiaqUQ==, figureFileBig=txJ5sBqgeT7l4W7ZLG5rOw==, tableContent=null), ArticleFig(id=1201177219084280434, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201177214285996222, language=CN, label=Figure 4, caption= Stratification and correlation diagram of factors affecting Fa. AUC: Area under the drug concentration-time curve; <i>C</i><sub>max</sub>: Peak concentration; pAUC: Partial area under the drug concentration-time curve , figureFileSmall=hNnT+jGTPSC8dJI5BiaqUQ==, figureFileBig=txJ5sBqgeT7l4W7ZLG5rOw==, tableContent=null), ArticleFig(id=1201177219184943735, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201177214285996222, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
Drug nameFa/%Peff/×10-4 cm·s-1Fsys/%Ref.
Furosemide40-600.0550[2]
Lisinopril350.3325[3]
Terbutaline40-500.3032[4]
Amoxicillin45-750.3070[5]
Metoprolol951.3450[6]
Amiloride80-901.6050[7]
), ArticleFig(id=1201177219310772862, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201177214285996222, language=CN, label=Table 1, caption=

Fa, Peff and Fsys values of the 6 drugs. Peff: Effective permeability coefficient in human

, figureFileSmall=null, figureFileBig=null, tableContent=
Drug nameFa/%Peff/×10-4 cm·s-1Fsys/%Ref.
Furosemide40-600.0550[2]
Lisinopril350.3325[3]
Terbutaline40-500.3032[4]
Amoxicillin45-750.3070[5]
Metoprolol951.3450[6]
Amiloride80-901.6050[7]
), ArticleFig(id=1201177219428213383, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201177214285996222, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
Drug namePeff/×10-4 cm·s-1tabs/h
Ketoprofen8.70.74
Metoprolol1.344.77
Amiloride1.604.00
Isotretinoin0.996.46
Inogatran0.03213.2
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The tabs calculated according to Equation 7. tabs: Time of drug completely absorbed

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Drug namePeff/×10-4 cm·s-1tabs/h
Ketoprofen8.70.74
Metoprolol1.344.77
Amiloride1.604.00
Isotretinoin0.996.46
Inogatran0.03213.2
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口服药物吸收分数的评价及影响因素综述
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张萍 # , 毕福林 # , 杨劲 *
药学学报 | 综述 2024,59(1): 84-93
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药学学报 | 综述 2024, 59(1): 84-93
口服药物吸收分数的评价及影响因素综述
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张萍#, 毕福林#, 杨劲*
作者信息
  • 中国药科大学药学院药理系, 江苏 南京 210009

通讯作者:

*杨劲, E-mail:
Review of evaluation and influencing factors of oral drug absorption fraction
Ping ZHANG, Fu-lin BI, Jin YANG*
Affiliations
  • Department of Pharmacology, School of Pharmaceutical Sciences, China Pharmaceutical University, Nanjing 210009, China
出版时间: 2024-01-12 doi: 10.16438/j.0513-4870.2023-0276
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吸收分数(fraction absorbed, Fa) 是一个描述口服药物吸收高低的重要参数, 是制剂处方工艺开发和优化的重要依据, 由于容易与绝对生物利用度概念混淆而未得到工业界的足够重视。影响Fa的因素非常多且复杂, 直接影响Fa大小的是三个时间因素: 制剂完全释放所需时间、药物完全吸收所需时间以及胃肠道停留时间, 这三个时间因素的彼此关系决定了Fa的大小。通常大家更关心的是影响Fa大小的表观因素, 包括自变量和协变量。自变量因素如给药剂量、途径、剂型等, 协变量又分成内因和外因, 外因包括食物因素、药物相互作用等; 内因包括年龄、性别、疾病等。本文分析并系统梳理了自变量和协变量如何通过影响机体生理和内环境, 直接或间接地影响三个时间因素, 进而改变Fa这样一个复杂过程。了解该理论框架可以更好地优化自变量, 以减少协变量对Fa的影响。另外本文还介绍了预测和评估Fa的最新进展, 包括复杂溶出装置进展和软件预测的现状。

吸收分数  /  时间因素  /  表观因素

Fraction absorbed (Fa) is an important parameter to describe the absorption level of oral drugs, and an important basis for the development and optimization of the formulation process. Because it is easily confused with the concept of absolute bioavailability, it has not received enough attention from the industry. There are many complex factors affecting Fa. There are three time-related factors that directly affect the extent of Fa: the release time, the absorption time, and the residence time. The relationship between these three time-related factors determines the extent of Fa. Generally, we are more concerned about the apparent factors that affect the extent of Fa, including independent variables and covariates; The independent variables include administered dose, route, dosage form, etc. The covariates are divided into internal and external factors, and external factors include food factors, drug interactions, etc. Internal causes include age, sex, disease, etc. This paper analyzes and systematically combs how independent variables and covariates directly or indirectly affect the three time-related factors by affecting the body's physiology and internal environment, thus changing the complex process of Fa. Understanding this theoretical framework can better optimize the independent variables to reduce the impact of covariates on Fa. In addition, this paper also introduces the latest progress of prediction and evaluation of Fa, including the progress of complex dissolution device and the status of software prediction.

fraction absorbed  /  time-related factor  /  apparent factor
张萍, 毕福林, 杨劲. 口服药物吸收分数的评价及影响因素综述. 药学学报, 2024 , 59 (1) : 84 -93 . DOI: 10.16438/j.0513-4870.2023-0276
Ping ZHANG, Fu-lin BI, Jin YANG. Review of evaluation and influencing factors of oral drug absorption fraction[J]. Acta Pharmaceutica Sinica, 2024 , 59 (1) : 84 -93 . DOI: 10.16438/j.0513-4870.2023-0276
绝对生物利用度(fraction of dose entering the systemic circulation, Fsys) 是指药物到达体循环的剂量, 由口服给药与静脉给药的比值求得; 吸收分数(fraction absorbed, Fa) 是指从制剂中溶出, 然后穿过顶端细胞膜吸收进入肠细胞内的剂量分数[1]。Fsys是Fa、胃肠道代谢分数(fraction of gastrointestinal availability, Fg) 及肝脏首过代谢分数(fraction of hepatic availability, Fh) 的乘积, 如图 1所示。药物的Fa与Fsys是很容易混淆的两个概念, Fg、Fh是化合物依赖性特征, 而Fa受极其复杂的因素影响。
一个药物的Fsys、Fa、通透性有时候并不同步, 例如特布他林的Fa与阿莫西林相似, 但是特布他林的Fsys比阿莫西林低; 依那普利与阿莫西林的人体通透性数值相似, 但是依那普利的Fa远低于阿莫西林; 有的药物Fa相似, 但是人体通透性数值差别极大, 如呋塞米和特布他林(表 1[2-7])。导致药物的绝对生物利用度比较低的原因通常有两类, 一类是Fa低导致的, 另一类是首过代谢引起的。前者可以通过制剂处方的优化来改善, 而后者是化合物依赖性特征, 处方优化难以改善生物利用度。将绝对生物利用度和吸收分数进行区分对于生物药剂学而言十分重要, 尤其是据文献报道, 当Fa < 20%的时候, 药物极其容易受到各种因素的影响[8]
制剂在体内经过崩解、溶出以及跨膜转运后进入体循环。以往生物药剂学分类系统(BCS) 根据药物的溶解度和通透性的高低将药物分为四类[9], 后来有学者建议应将通透性改为可吸收性[10, 11], 例如欧盟[12]
对BCS分类系统可以进一步用无量纲参数吸收数(absorption number, An)、溶出数(dissolution number, Dn) 以及剂量数(dose number, Do) 来描述药物的吸收特征。具体计算公式如下[13]:
$ \mathrm{A}\mathrm{n}=\frac{{t}_{\mathrm{r}\mathrm{e}\mathrm{s}}}{{t}_{\mathrm{a}\mathrm{b}\mathrm{s}}}=\frac{{t}_{\mathrm{r}\mathrm{e}\mathrm{s}}}{R/{P}_{\mathrm{e}\mathrm{f}\mathrm{f}}} $
$ \mathrm{D}\mathrm{n}=\frac{{t}_{\mathrm{r}\mathrm{e}\mathrm{s}}}{{t}_{\mathrm{d}\mathrm{i}\mathrm{s}}}=\frac{{t}_{\mathrm{r}\mathrm{e}\mathrm{s}}}{{r}^{2}\rho /3D{C}_{\mathrm{s}}} $
$ \mathrm{D}\mathrm{o}=\frac{{M}_{0}/{V}_{0}}{{C}_{\mathrm{s}}} $
公式(1)~(3) 中, tres为药物在胃肠道的滞留时间, tdis为药物完全释放溶解所需时间, tabs为药物完全吸收所需时间, R为肠道半径(2 cm), Peff为有效通透性, D为扩散系数, ρ药物的密度, r为初始药物粒子半径, M0为给药剂量, V0为介质体积(默认胃的初始容积为250 mL)。
An为药物在肠道停留时间除以吸收时间(使用一室模型预估), 小于1.0时, 表示药物可能不会被完全吸收; Dn为肠道停留时间除以溶解时间, 小于1.0表示药物在通过小肠的过程中可能不会溶出全部药物, Do为给药剂量除以250 mL介质中按照pH 1.0~8.0之间最低溶解度可以溶解的药量, 大于1.0表示该剂量可能不会完全溶解在250 mL水中。以上分析表明, Fa是一个包含有三个变量(完全释放所需时间、完全吸收所需时间、药物胃肠道吸收窗内停留时间) 的多元函数(图 2)。
Fa难以直接测定, 除了没有首过效应也不与蛋白结合的药物(例如二甲双胍、核苷酸类药物等) 的Fa等于Fsys, 其他药物只能通过间接方法进行估算。Li等[14]对常用体外模型(Caco-2、Ussing Chamber等) 做过系统综述, 这里不进行详细叙述。
药物的Fa取决于药物的通透性[9, 15, 16]。其中人体单次灌注技术(in vivo single-pass perfusion technique, Loc-I-Gut) 是公认的权威直接测定人体Peff的方法[17], 通过一段有两个乳胶气囊的6通道内镜插入管插入人体小肠, 在气囊内泵入气体使得小肠内形成一个封闭的空间, 然后用固定药物浓度的灌流液持续灌注一段时间, 使用14C-PEG 4000进行校准, 依据如下公式[18]计算Fa与Peff:
$ {P}_{\mathrm{e}\mathrm{f}\mathrm{f}}=\frac{({C}_{\mathrm{i}\mathrm{n}}-{C}_{\mathrm{o}\mathrm{u}\mathrm{t}})}{{C}_{\mathrm{o}\mathrm{u}\mathrm{t}}}\frac{{Q}_{\mathrm{i}\mathrm{n}}}{2\varPi RL} $
$ \mathrm{F}\mathrm{a}=1-\frac{{C}_{\mathrm{o}\mathrm{u}\mathrm{t}}\times {\left[\mathrm{P}\mathrm{E}\mathrm{G}\right]}_{\mathrm{i}\mathrm{n}}}{{C}_{\mathrm{i}\mathrm{n}}\times {\left[\mathrm{P}\mathrm{E}\mathrm{G}\right]}_{\mathrm{o}\mathrm{u}\mathrm{t}}} $
公式(4) 和(5) 中, CinCout分别进入和流出空肠腔的药物溶液浓度, Qin为灌流速率, L为灌注肠段长度, [PEG]in和[PEG]out分别是进入和流出的14C-PEG 4000的浓度。
理想状态下, 当固定浓度的药物溶液在圆柱体肠腔内灌流过程中, 假设没有扩散过程, 即用充分搅拌模型来描述, 其吸收分数和吸收所需时间的关系如下公式[19]:
$ \mathrm{F}\mathrm{a}=1-\mathrm{e}\mathrm{x}\mathrm{p}(-\frac{2}{R}\times {P}_{\mathrm{e}\mathrm{f}\mathrm{f}}\times {t}_{\mathrm{r}\mathrm{e}\mathrm{s}}) $
将Fa为90%看作完全吸收, 则此时的tres可以认为是tabs。将公式(6) 进行变换得到如下公式:
$ {t}_{\mathrm{a}\mathrm{b}\mathrm{s}}=\mathrm{ }-\frac{\mathrm{l}\mathrm{n}\left(0.1\right)}{{P}_{\mathrm{e}\mathrm{f}\mathrm{f}}} $
将常见药物的Peff代入公式(7), 药物吸收所需时间见表 2。由于实际上药物在体内无法维持固定浓度, 并且存在扩散过程, 所以该吸收所需时间仅仅是理论值。
已有大量的文献[20-22]研究Fa与通透性的关系(图 3[20]), 该图中的插入图为Log10 (100-Fa)-Peff, 未被吸收药物量的对数与通透性Peff呈线性关系。
在FDA发布的BCS分类指南中将药物按照Fa < 50%、50%~89%以及≥ 90%的吸收范围分类[23], 当人体Fa ≥ 90%时, 药物则被认为具有高通透性[24]。根据图 3, 低通透性和高通透性之间的临界通透性值范围在0.5~1.5×10-4 cm·s-1。例如, 美托洛尔的Fa为95%[25], 在BCS分类指南中被定为高通透性标记物。当通透性数值高于美托洛尔通透性数值(Peff值为1.34×10-4 cm·s-1) 的药物, 即测试药物/美托洛尔的通透性值比大于1.0, 将被归为高通透性药物。根据图 3, Peff值大于1.5~4×10-4 cm·s-1或约1 cm·h-1的药物则具有良好的吸收, 预期Fa将大于95%。
虽然可以直接通过Loc-I-Gut技术测定药物在人体的通透性值, 但是Loc-I-Gut不能直接评估制剂的Fa, 且实验操作繁琐, 价格昂贵, 涉及伦理学问题, 其应用受到很大限制。
通常情况下, 当有足够的时间使得药物能完全释放, 则药物的Fa会增加, 但是这个关系并不绝对, 例如当给予相同的日剂量时, 盐酸二甲双胍缓释制剂的生物利用度只有速释制剂的79%[26]; 相同日剂量下, 缓释普萘洛尔的生物利用度也只有速释制剂的57%。虽然缓释制剂充分利用了胃肠道停留时间, 但是释放的药物不能被完全吸收, 部分药物是无效释放。而当给予大鼠同样剂量的麦冬多糖时, 控释制剂比速释制剂的药时曲线下面积(area under the concentration-time curve, AUC) 增加163%, Fa增加160%左右; 而对于不同释放速度的控释制剂, 其达峰时间(peak time, tmax) 不同, AUC和Fa均不同; 当释放速度更加低时, 其AUC和Fa与速释制剂没有明显差别[27]
胃肠道的迁移速度影响着药物制剂在胃肠道的停留时间, 并对药物的Fa产生显著影响。对于膜通透性有限、易在肠道内降解或溶出成为体内吸收限速步骤的药物, 肠道转运时间对其吸收有显著影响。大量文献报道, 利用射线扫描技术、氢气呼吸试验、插管技术以及γ-闪烁扫描技术等测量小肠转运时间[28]。根据报道, 食物对胃排空的影响十分显著, 溶液剂、微丸及片剂、胶囊剂受食物影响情况不同, 高脂早餐后, 溶液剂的胃中停留时间约为1 h, 微丸剂大约1~5 h, 片剂、胶囊在胃中甚至可以保留9 h; 小肠迁移速度受食物影响较小, 各剂型平均停留时间为3~4 h[29]。药物粒子大小也会影响停留时间, 大粒子比小粒子在胃内停留时间长[30]
药物在胃肠道的停留时间受很多因素影响, 包括协变量食物、合并用药以及自变量辅料的影响。腹泻缩短胃肠道停留时间[31], 促胃动力药(如甲氧氯普胺、多潘立酮) 以及阿片类药物都能改变药物在胃肠道的停留时间, 从而导致药物的吸收改变, Greiff等[32]对此作过具体叙述。一些渗透活性辅料(如甘露醇、山梨醇、PEG 400等) 导致肠壁膜两侧渗透压发生改变, 从而导致渗透性腹泻, 加速小肠转运, 缩短药物的胃肠道停留时间, 降低药物的Fa和生物利用度(如西咪替丁、雷尼替丁等)[33]
目前可以通过制剂技术来延长药物在胃肠道内的停留时间, 增加制剂的密度、浮力、黏附能力或者改变几何形状是常用的技术[34]
增加浮力的制剂包括地尔硫䓬漂浮片[35]、维拉帕米漂浮颗粒[36]等; 当制剂密度达到2.6~2.8 g·mL-1, 制剂可以在胃中长时间停留[37]。但是这种技术的缺点是无法应用于大部分药物以及技术很难达到预期的浮力与密度标准。
可以在液体制剂中添加聚合物、凝胶等增加制剂的黏度和黏附作用, 例如将液体布地奈德与增稠剂(如枫糖浆或三氯蔗糖) 混合可增加在食管黏膜的黏附性, 获得更长的滞留时间, 从而增加药物的局部吸收[38, 39]。此技术的缺点是无法精准定位附着部位。
以色列Intec Pharmaceuticals Ltd.公司研发出一种名为Accordion Pill Carbidopa/Levodopa (AP-CDLD) 的治疗帕金森病的新型制剂, 该制剂采用了一种折叠成手风琴状、可生物降解的高分子材料, 内含控释药物层和速释药物层, 该高分子材料可以在胃中延展并保留长达12 h[40]
上述三个时间因素之间的相互关系会直接影响Fa的大小, 但是在临床实践中并不能直接获取这三个因素的信息, 从而难以预测和估算Fa的高低。而一些能直接收集到的表观信息和因素, 例如剂型、剂量、年龄、处方、食物、合并用药等因素也会影响Fa的大小, 不过这些因素是通过对一些生理过程和内环境的影响, 例如胃pH、胃肠道内容物的黏度、消化液(包括胆汁)的组成、胃肠道血流量等, 进而影响药物完全释放时间、药物消化道吸收窗停留时间、药物完全吸收时间, 而最终影响Fa的大小, 有必要对上述的复杂关系和过程进行系统梳理。
疾病及食物会改变人体内脏血流速度, 当胃肠道血流量下降时, 血液运走药物的能力降低, 无法维持漏槽状态, 因此药物的吸收降低。研究表明, 绒毛血供的改变可能会影响某些药物的肠道吸收[41]。例如在大鼠中, 安替比林和水杨酸(主要由血浆蛋白结合转运) 在非血管灌注肠中的吸收率降低60%, 两种药物的吸收都受到血流限制[42]
胃肠道黏膜内存在各种消化酶和肠道菌群产生的酶, 能代谢某些药物, 药物在胃肠道滞留时间越长, 这种代谢反应就越容易发生, 影响药物的吸收。Fagerholm[43]作了关于胃肠道代谢如何影响药物的吸收以及如何预测这种影响的系统综述。在这里只讨论CYP3A4酶、食物(果汁、酚类及多酚类物质)、药物等可以抑制CYP3A4酶, 降低药物的代谢, 使得吸收增加。典型代表是葡萄柚汁可以抑制CYP3A4酶, 降低非洛地平的体前代谢, 其AUC、血药峰浓度(peak concentration, Cmax) 分别增加267%和345%[44]; 葡萄柚汁还抑制有机阴离子转运肽1A2转运蛋白, 当给予300 mL葡萄柚汁后, 非索非那定的AUC0-8 h下降52%, 提前2 h服用葡萄柚汁则下降38%, 提前4 h服用则对AUC无影响[45, 46]
Abuhelwa等[47]对食物和胃肠道pH值变化如何影响药物的吸收作了详细研究, 食物、胃肠道或全身性疾病(如恶性贫血等)、年龄、昼夜节律和共同给药等均会影响pH值, 这些变化可能会影响药物的释放、药物的溶解度、药物的稳定性以及肠道通透性等, 导致药物的吸收发生改变。例如健康受试者同时服用200 mg剂量的伊曲康唑(一种水溶性差的弱碱性药物, pKa = 3.7) 和抗酸剂混悬液, 伊曲康唑的口服吸收率和程度显著降低, AUC和Cmax分别降低66%和70%[48]。胃pH升高与酮康唑[49, 50]、伊曲康唑[51]、双嘧达莫[50]、桂利嗪[52]等药物的口服吸收减少有关。
食物中的脂肪类物质会促进胆汁分泌, 胆汁具有表面活性, 能增加难溶性药物的溶解度, 通过影响药物从制剂中的溶出释放过程来改变Fa。例如, 餐后胆汁分泌增加, 通过增加卡马西平[53]及苯妥英[54]的溶解度增加吸收。在大鼠中, 悬浮液中环孢素A的生物利用度为5.4%, 而当大鼠胆汁插管后, 其生物利用度降至1.6%[55]。早期研究表明, 胆汁可促进脂溶性维生素的吸收, 比如维生素A[56]、维生素K[57, 58]、维生素D[59]等。
在某些疾病状态下, 机体消化液成分、消化道壁膜组成会发生改变, 药物在胃肠道的通透性增大导致Fa增加。克罗恩疾病可能会破坏细胞间的紧密性, 导致某些药物的细胞旁路通透性增加, 如蔗糖和乳果糖[60]; 溃疡性结肠炎可能会改变胃肠道膜的组成, 从而影响药物的胃肠道通透性, 患者结肠和直肠黏膜中BCRP、MRP2和P-gp的表达水平显著降低[61], 磺胺吡啶(MRP2和BCRP底物, 用于治疗肠炎)在此疾病状态下的肠道吸收可能会增加, 生物利用度也增加[62], 不良反应也将增加。
迁移速度影响胃肠道停留时间, 年龄、处方工艺、食物合并用药以及疾病等都能改变迁移速度。阿托品能减慢胃排空速率与肠内容物的运行速率, 从而增加某些药物的吸收; 甲氧氯普胺等促胃动力药能促进胃排空且增加肠运行速率, 减少在消化道内的滞留时间, 从而减少药物的吸收, 例如甲氧氯普胺可以增强美西律的吸收速率, 而阿托品则降低了吸收速率[63]
相比三个时间因素, 临床中能直接观测到表观因素对Fa的影响, 并对这些表观因素进一步分类, 将与剂量、制剂相关的因素归为自变量, 其他为协变量, 协变量又分为内因(年龄、疾病) 和外因(食物、合并用药)。这些表观因素对机体生理过程的影响进而影响Fa, 直接表现为药代参数(例如AUC、Cmaxtmax等) 的改变。
食物通过影响药物的溶解度、释放度、胃肠pH、减缓胃排空、影响消化液组成、促进胆汁分泌、增加淋巴运输以及改变药物的通透性等改变Fa。食物与药物相互作用可能导致以下四种结局之一: 吸收延迟、吸收减少、吸收增加或者吸收不受影响。关于食物影响已经研究透彻[64, 65], 不作细致讨论。
合并用药通过改变药物的溶解、通透性、改变胃肠迁移速度(如胃动力药类)、肠血流量以及降低药物在胃肠代谢影响Fa。关于合并用药中产生的药物相互作用有非常多的研究和报道[66-68], 例如碳酸氢钠加速胃排空并增加对乙酰氨基酚的吸收[69], 还能改变氟伐他汀与膜磷脂的相互作用[70]。甲氧氯普胺能降低西咪替丁的吸收[71]; 当地高辛与甲氧氯普胺联合给药后, 地高辛血药浓度显著降低(0.72~0.46 μg·L-1)。很早就报道过同时服用铁剂会显著降低口服四环素的血清水平[72]。乙醇在日常中经常使用, 乙醇可能增加亲脂性药物的溶解度, 增加药物的吸收, 例如在给予50%乙醇水溶液后, 安定的Cmax几乎翻倍[73]; 苯巴比妥可减少灰黄霉素的吸收[74]
某些疾病会改变胃肠道内容物的迁移速度、消化道壁膜的组成等, 导致Fa改变。吉非替尼的主要不良反应包括腹泻, 临床试验结果表明, 腹泻患者AUC值仅为未腹泻患者AUC的46%[75], 主要原因是胃排空与胃肠道迁移速度加快, 导致药物在胃肠道中的停留时间缩短, 部分药物来不及从制剂中释放就被排出机体。甲状腺功能异常会改变肠道运动的自主节律性[76], 导致药物在胃肠道的停留时间改变, 药物的吸收也改变。在甲状腺功能减退的儿童中观察到对维生素B2的吸收增加, 而甲状腺功能亢进的儿童维生素B2的吸收降低[77]
老年人的结肠迁移速度较慢, 同时胃内pH会升高[78], 这些因素可能会影响药物的Fa值。老年人胃内pH的升高会导致双嘧达莫的吸收受损[79]; 在年龄较大的受试者(≥65岁) 中, 非索非那定的血浆峰值水平比年轻受试者中(< 65岁) 的血浆峰值高99%[80]
不仅药物本身的吸收是剂量依赖性的, 食物对药物的影响也具有剂量依赖性。许多药物低剂量表现出线性动力学特征, Fa比较高; 随着剂量增加, 药物吸收饱和, AUC不能随之增加, 呈现出非线性动力学特征。在研究12.5、25、50 mg·kg-1给药剂量后, 由于吸收饱和, γ-氨基丁酸的AUC与剂量不成比例增加[81, 82]。阿比特龙的水溶性极低, 在早期开展的250~1 000 mg食物影响研究探索阶段发现, 低剂量下, 吸收呈线性动力学特征, 高剂量在空腹时发生吸收饱和, 且食物对阿比特龙AUC的影响具有显著的剂量依赖性[83]
制剂及其工艺通过改变pH和迁移速度影响制剂释放过程和胃肠道停留时间, 进而改变Fa。活性药物成分(API) 颗粒大小[84]、API理化性质[85]、片剂大小、肠溶衣等会影响药物在胃肠道的溶解释放过程, 从而影响药物的吸收情况[86]
制剂中辅料(山梨醇[87]、甘露醇[33]、PEG 400[33]等) 通过改变药物的溶解度、减少代谢或外排、改变通透性以及改变胃肠转运时间等来影响药物的吸收。少量的表面活性剂(如十二烷基硫酸钠) 或山梨醇可能改变肠膜稳定性, 这将导致药物的通透性改变, 如仅4 mg十二烷基硫酸钠就会使阿仑膦酸盐的生物利用度增加500%~600%[88], 7 mg山梨醇会降低利培酮的Cmax, 60 mg时会降低AUC[87]
直接因素和表观因素以及机体中间变化过程之间关系如图 4所示。这些因素之间并不独立, 是相互影响的。比如多种因素导致胃内pH升高, 这将会降低水溶性差的弱碱性药物的溶解度, 然而当药物具有一定亲脂性时, 这种影响可能会被胆汁介导的溶解增加所抵消。
评估药物的Fa涉及到多个因素和生理过程, 且受到各种协变量因素的影响, 目前尚无公认的、高效的、准确的一揽子解决方案, 一直是工业界希望解决的难点和痛点。解决问题的思路主要从两个方向进行努力: 一种是用更复杂的体外溶出和吸收装置模拟体内过程, 另一种是利用软件获取各种生理参数, 结合药物的物理化学特性、药代特性, 更好地虚拟模拟体内过程。
利用复杂的体外装置尽量准确地模拟体内过程, 同时考虑三个直接因素: 利用生物相关溶出介质模拟人体内释放情况, 利用流通池或往复筒等方法设定体内特定生理环境下的停留时间, 利用平行人工膜体外渗透技术(parallel artificial membrane permeability assay, PAMPA) 等模拟人体内的吸收情况。目前文献报道较多的是TNO TIM-1系统和LOGAN PERMETRO系统。
TNO TIM-1系统模拟了人体胃和小肠内腔内发生的体内动态生理过程, 包括生理量的酶变化情况和胆汁盐的分泌情况, 阿斯利康公司利用该设备对难溶性药物AZD8055[89]进行了吸收预测; TNO TIM-1系统还可研究药物(如对乙酰氨基酚[90]) 的口服吸收以及食物影响等。
LOGAN最新的PERMETRO系统是集成了溶出和渗透功能的全自动测试系统, 可同时测试药物的溶出和渗透结果, 可以评估特定条件下Fa的大小。渗透部分使用的膜是PermeaPad, 是一种具有三明治式层状结构的人工纤维素-磷脂仿生膜, 用于测试药物的表观渗透系数, 使用PermeaPad测得的数据与文献报道中成熟体外测定方法(Caco-2或PAMPA) 之间存在良好的相关性。
目前有多个商用软件可以应用于Fa的预测, 包括GastroPlusTM、SimCYP®、GI Sim等。其中应用比较广泛的是GastroPlusTM, 该软件基于高级房室吸收与转运模型开发, 并经历了多次改进。其基本原理是分段研究药物在生物相关溶出介质下的释放、药物的跨膜通透能力、制剂的生物黏附能力等, 然后结合人体生理参数, 预测人体内药物的释放、吸收等。利用GastroPlusTM可以预测API的溶解度、粒度和粒度分布等不同理化因素对口服药物吸收的影响[91-93]。在GastroPlusTM中, 可以使用药物的物理化学参数, 结合临床前体内药代数据来预测药物吸收[94]
对于制剂开发研究人员来说, 当进入临床阶段后才发现制剂的生物利用度易受协变量外因(如食物) 和协变量内因(如pH等) 的影响时, 再进行处方优化会极大地影响项目进度和成本。提前预判这些潜在风险是一个亟待解决的科学问题。Fa与自变量以及协变量因素之间关系错综复杂, 本文综述了影响药物Fa的各种因素, 可以通过判断释放和吸收环的限速步骤, 进而优化处方工艺或给药方案, 以降低协变量内外因对药物体内暴露水平的影响, 充分发挥药品固有疗效, 控制药品的临床表现。本文对这些因素进行系统梳理, 以期对口服制剂的处方优化提供一定借鉴和参考。
作者贡献: 张萍与毕福林负责本文的文献收集及主要内容撰写; 杨劲负责本文的选题、框架设计、指导、修改以及内容把关。
利益冲突: 作者声明无利益冲突。
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doi: 10.16438/j.0513-4870.2023-0276
  • 接收时间:2023-03-09
  • 首发时间:2025-11-28
  • 出版时间:2024-01-12
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  • 收稿日期:2023-03-09
  • 修回日期:2023-06-08
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    中国药科大学药学院药理系, 江苏 南京 210009

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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
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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