低清除率药物的代谢稳定性预测模型研究进展
Advances in methodologies for predicting metabolic stability for low-clearance drugs
-
摘要: 药物代谢的稳定性测试是新药发现阶段的关键环节，实现药物的低清除率通常是药物代谢稳定性设计中的重要目标。如何准确评估低清除率药物的代谢稳定性参数，并用体外代谢数据预测人体药动学已经成为新药研发阶段的挑战。传统的肝微粒体模型和悬浮肝细胞模型的孵育时间短，低清除率药物无法产生足够的代谢转化，因此进一步模拟体内环境和延长肝细胞培养时间的新型模型逐渐发展起来。本文重点介绍了新型的低清除率药物代谢稳定性预测模型的原理和优缺点等，包括肝细胞传递式培养模型、单层贴壁肝细胞培养模型、共培养模型和微灌流模型等，同时对模型的发展趋势进行展望，以期为早期先导化合物的代谢稳定性检测提供借鉴和优化。Abstract: The metabolic stability test of drugs is a key step in drug discovery and achieving low clearance is frequently the goal in the design of drug. Increased drug metabolism stability can reduce drug dosage, enhance drug exposure and prolong drug half-life. Accurately assessing the metabolic stability parameters of low clearance drugs and predicting human pharmacokinetics has become a challenge. Traditional tools in vitro including microsomes and suspended primary hepatocytes are limited by incubation time, which is not long enough to make sufficient metabolic conversion. Determination of intrinsic clearance or metabolic pathways and mechanisms of drug are implicated. Novel models tend to further mimic the in vivo environment in order to prolong lifetime of hepatocytes and achieve sufficient metabolic turnover of drugs for monitoring. In vitro-in vivo correlation of intrinsic clearance of methodologies has evaluated to support the reliability in predicting human pharmacokinetics. Application of these methodologies greatly decreases the forthputting of experimental animals and the release of expensive clinical trials during the acquisition of pharmacokinetic parameters. In this review, we summarized the principles, advantages and disadvantages of the novel in vitro methodologies for metabolic stability dealing with low-turnover drugs, including hepatocyte relay method, plated human hepatocytes, coculture system and microfluidic devices. Future prospect is proposed for in vitro metabolic models and it provides reference and optimization in metabolic stability for early lead compounds.
-
Keywords:
- metabolic stability /
- low-clearance /
- relay method /
- coculture /
- microfluidic /
- parent elimination
-
固态物质根据其结构内部分子堆积的周期性和有序性分为晶态物质和非晶态物质。分子长程排列的周期性差异决定了药物不同的理化性质，包括溶解度、吸湿性、稳定性和生物利用度等，进而导致临床疗效差异[1]。例如，1996年利托那韦以晶型Ⅰ形式上市，但后续发现上市产品中出现溶解度低、热力学稳定的晶型Ⅱ，药物的溶出速率及生物利用度降低，严重影响了药物的临床疗效，导致该产品从市场撤出，经济损失巨大[2]。因此，药物晶型的研究及其定量分析对于晶体药物产品的开发尤为重要。目前，晶型定性定量的分析方法大致分为4类：X射线衍射法[3]、光谱法[4,5]、热分析法[6]和其他方法（如显微镜法[7]、动态蒸汽吸附法[8]）。
近年来，电子技术、计算机技术及现代仪器制造技术的飞速发展使得现代分析仪器能提供大量的多维数据，这也对实验数据的处理、解析、分类和预测提出了更高的要求。化学计量学分析可提取实验数据中关于物质体系的成分、结构等可用于定性或定量的信息，使得晶型定量水平大大提升，检测限（limit of detection，LOD）/定量限（limit of quantification，LOQ）可达痕量级别（< 1%）[9−10]。利用化学计量学解决的问题，可以满足原料药和制剂中活性药物成分（active pharmaceutical ingredient，API）的生产和质量控制过程中所涉及的监管需求。因此，深入研究晶型的定性定量分析方法，并建立适当的标准以确保原料药和制剂中的药物晶型纯度，对于药品质量的监管至关重要。
目前，已有综述对晶型定性定量分析方法[11−12]以及化学计量学应用于药物含量测定[13]等进行了详细的描述。本文侧重于晶体定性定量分析技术和化学计量学联合用于痕量晶体的定量，为制剂开发、药品质量控制、晶型专利保护等提供指导（图1）。
![]()
Figure 1. Common solid-state forms and physicochemical properties of drugs, and the characterization techniques as well as chemometrics for the qualitative and quantitative analysis of solid-state drugsASD:Amorphous solid dispersion;SVM: Support vector machine; ANN:Artificial neural network; HCA:Hierarchical cluster analysis; PCA:Principal component analysis;PXRD:Powder X-ray diffraction;IR:Infrared spectroscopy; NIR:Near infrared reflectance spectroscopy;DSC: Differential scanning calorimetry; PLM:Polarized light microscopy1. 晶型定性定量分析方法
目前已有多种表征技术用于晶体药物的晶型定性和定量分析。其中常用的粉末X射线衍射法（powder X-ray diffraction，PXRD）、差示扫描量热法（differential scanning calorimetry，DSC）、偏振光显微镜（polarized light microscopy，PLM）等方法主要是对药物的晶体结构进行表征分析；红外光谱（infrared spectroscopy，IR）、拉曼光谱（Raman spectroscopy，Raman）、固态核磁共振波谱（solid state nuclear magnetic resonance，ss-NMR）等技术主要通过不同晶型的晶格结构和分子的构象的差异进行表征（表1）。
Table 1. Introduction of various quantitative techniquesTechnique PXRD IR Raman ss-NMR DSC IMC DVS Sample weight/mg 200−400 5−50 2000 500−700 4−10 20−300 5−50 Probable LOD/LOQ −10% 1%−2% <1% 0.5% ~5% <5% 0.05% Internal standard Yes/No No No No No No No Calibration required No No No Yes Yes Yes Yes Destructive No No No No Yes Yes Yes Phase detection Crystalline Crystalline/
amorphousCrystalline/
amorphousCrystalline/
amorphousCrystalline/
amorphousAmorphous Amorphous Ability to differentiate surface/bulk amorphicity No No No No No Yes No Limitations Existence of preferred orientation Existence of peaks overlapping, interlacing Used as a complementary quantitative technique to IR Long data acquisition time, expensive Vulnerable to sample particle size, etc. Low specificity Vulnerable to excipient interference 1.1 PXRD
基于PXRD的晶型定量方法主要分为相对强度比（relative intensity ratio，RIR）法和Rietveld法。但RIR法受到衍射峰重叠的影响，其检测限一般为5%~10%。Rietveld法是在已有的晶体结构模型和参数的基础上，进行图谱拟合。但无定形药物的衍射图谱为衍射环，无法进行图谱拟合，因此，Rietveld法无法用于无定形药物的定量分析。
化学计量学方法中的偏最小二乘法（partial least squares，PLS）可以提供与Rietveld法相当的测定精度，且可用于无定形态物质的定量分析。Liu等[3]运用PLS对恩替卡韦两种晶型混合物的PXRD图谱进行分析，快速准确地测定混合物中各晶型含量，且检测限低至1.394%。此外，有研究表明，PLS联合PXRD可实现对多元混合物中的晶体和无定形组分的定量分析。Beyer等[14]制备了包含萘普生晶体、吲哚美辛γ晶型、α晶型和萘普生-吲哚美辛共无定形的所有可能组合的样本，通过PLS建立了每个组分的PXRD模型。结果表明，定量模型可以准确提取出各组分特征峰并测定其含量，体现了化学计量学方法强大的数据分析能力以及适用性。
1.2 光谱法
1.2.1 振动光谱
目前用于晶体定量研究的振动光谱技术主要有红外和拉曼光谱[3,15]，具有灵敏度高、测量时间短、光通量大等优点，已成为晶体定性定量分析的重要方法。
在定量分析方面，由于振动光谱的特征吸收峰数量较多，并且不同的物质可能会出现特征峰的重叠或交错，物质的定量分析往往受到较大干扰，导致准确性降低。随着化学计量学的快速发展和广泛应用，光谱数据中的有效信息可以得到最大化提取，提高了光谱技术定量分析的准确性。Antonio等[5]建立并验证了PLS联合近红外（near infrared reflectance spectroscopy，NIR）、IR和Raman测定美洛昔康晶型Ⅰ含量的方法，其中NIR检测能力最佳，其检测限和定量限分别低至0.02%和0.08%。
1.2.2 ss-NMR
ss-NMR对微小的构象变化非常敏感，不同晶型药物分子中原子所处化学环境的差异会导致ss-NMR谱图中化学位移产生变化[16]。Tinmanee等[17]通过13C ss-NMR表征，确定了加巴喷丁晶型Ⅱ和晶型Ⅲ的特征峰，并依据特征峰面积对两种晶型的含量进行了定量分析。
与其他光谱法相比，高分辨率的ss-NMR的测定通常会经历高速旋转，该测试条件可能会导致亚稳态晶型在测试过程中发生相转变。此外，该技术在实验过程中需严格控制样品的温度。因此，该技术在晶体定量方面的应用具有一定的局限性。
1.3 热分析法
1.3.1 DSC
晶态物质发生熔融或晶型转变时会产生热效应，这一过程中的热变化与晶体含量之间存在一定的线性关系，因此可应用于晶体含量的定量研究。
DSC法测试所需样品量少，操作简单，但是不同样品本身的热力学行为差异以及样品粒径、制样过程等因素均会对晶型的热行为产生的影响，导致不同样品的检测限和定量限差异较大。此外，该技术定量的准确性还受样品中晶种引起的重结晶现象以及制剂中辅料的存在的干扰。
1.3.2 等温微量热法（isothermal microcalorimetry，IMC）
IMC是利用所有的物理和化学变化过程伴随着与周围环境产生热交换的原理，对样品的热量变化进行监测的技术。与DSC相比，IMC中的样品在微量量热计内可保持等温，且IMC的灵敏度更高。IMC可以快速测定硝苯地平中无定形含量，且定量限可低至1%，明显优于传统的DSC和PXRD等定量方法[6]。
该技术也存在一定的局限性，其仅适用于在某些湿度和/或温度条件下可自发结晶的无定形固体的表征分析。此外，该技术在分析过程中还受到多种因素的影响，如有机蒸汽的比例、操作温度、湿度和压力等，对实验条件的要求较为严格[18]。
1.4 其他方法
1.4.1 显微镜法
显微镜法是观察晶型样品的有效定性分析手段，仅需少量样品便可判断药物形态、观察晶型转变过程，如透射电子显微镜（transmission electron microscopy，TEM）、偏振光显微镜（PLM）等，从而得到固态药物发生相变的信息。
S'ari等[7]通过TEM表征了热熔挤出法（hot melt extrusion，HME）制备的非洛地平无定形固体分散体（amorphous solid dispersion，ASD）中的结晶区域。该技术具有高分辨率、高放大倍数等特点，但对样品处理和真空条件要求高，且在高真空中能保持稳定。
1.4.2 化学成像技术
化学成像技术可提供物质空间分布的视觉表征图像，并可用于组分识别和定量。目前，化学成像大多基于振动光谱技术成像，如拉曼、近红外、太赫兹光谱成像等。Hisada等[19]通过拉曼成像技术分析了共无定形压片后的相分离现象，以确定片剂表面的相分布情况。
虽然该技术可提供样品中不同组分的空间分布信息，区分晶体和无定形，但当涉及高度重叠的光谱波段以及无定形和晶体之间光谱差异微小时，其定量结果的准确性将受到影响。因此，化学成像需要结合化学计量和数字图像分析，才可以准确显示样品中成分的相对分布以达到定性和定量分析的目的。
1.4.3 动态蒸汽吸附法（dynamic vapor sorption，DVS）
DVS是依据药物无定形态与晶态的吸水性差异来判断药物的状态，还可通过计算样品吸收水分的量对样品中的无定形进行定量分析。
Sheokand等[8]通过DVS定量塞来昔布中无定形的含量，样品中的无定形含量低至0.3%也能被有效检出。此外，DVS也可以与NIR等技术连用。Columbano等[20]通过配有近红外探针的动态蒸汽吸附仪监测了喷雾干燥法制备的无定形硫酸沙丁胺醇的结晶过程，观察到了样品从无定形态到晶态的转变过程。
目前用于药物晶型定性定量的技术多种多样，且在组分单一的情况下，部分技术使用单变量方法就可以达到定量痕量晶体/无定形固体药物的水平。然而，在实际的药物固体制剂开发中，大量辅料的引入会显著影响定量技术的检测能力。化学计量学方法作为一门新兴、快速发展的技术，将其与上述表征技术联用，可实现对复杂体系的晶型定性和定量研究，是研究固态药物晶型的有效辅助手段。
2. 化学计量学
近年来，化学计量学已与多种分析检测技术联合应用于晶体含量的测定，如IR、Raman、PXRD等光谱和衍射技术。化学计量学的主要优势在于可从分辨率低、噪声高、信号重叠强的不理想数据中提取到核心的数据，发现测试信号和样本参数之间的隐藏关联性。
2.1 定性研究
化学计量学中的定性分析是按照某些共同的特征对不同样本进行分类识别，从而找出不同样本之间的内在联系。常用的定性方法包括主成分分析（principal component analysis，PCA）、层次聚类分析（hierarchical cluster analysis，HCA）等。
PCA是许多机器学习算法（如PLS）常用到的预处理方法。Kapourani等[21]在运用人工神经网络定量利伐沙班ASD中药物晶体和无定形含量前，通过PCA对IR图谱进行降维处理，并评估PCA降维后是否能区分药物的晶态和无定形态。HCA的主要思路是将相似的样本聚在一起，达到分类的目的。Grisedale等[22]联合HCA、PCA及光热红外显微光谱表征，成功识别出硫酸沙丁胺醇样品中的无定形和结晶区域。
2.2 定量研究
化学计量学中的定量分析方法有主成分回归（principal component regression，PCR）、偏最小二乘法（PLS）、人工神经网络（artificial neural network，ANN）等。
2.2.1 PCR
PCR适于分析组分较复杂的样本体系，无需知道样本中具体干扰组分就可较为准确地预测待测组分的含量。PCR中的主因子可用于描述数据的变化，Hamed等[23]利用PCR定量分析洛匹那韦中晶体含量，第一主成分的得分数随着样品中结晶洛匹那韦的增加而增加，因此，第一主成分与洛匹那韦结晶度的变化相关。
2.2.2 PLS
PLS具有较强的稳健性和良好的准确性，是目前应用最广泛的多元校正方法。Rahman等[24]利用PCR和PLS联合IR、NIR定量ASD中他克莫司晶体，结果显示PLS模型对两种光谱数据分析的准确性均高于PCR，且误差较小，体现了该法简单稳健、预测精度高的优势，尤其适合样本量少、变量多的数据分析。
2.2.3 ANN
ANN是一种基于反向传播算法的生物神经网络学习机器技术，其主要优势在于它对线性和非线性关系均可建模，因此被认为是量化分析多组分光谱数据的强大工具。Barmpalexis等[25]联合衰减全反射红外光谱（attenuated total reflection Fourier transform infrared，ATR-FTIR）、Raman和ANN、PLS两种方法对共晶制剂中的共晶含量进行分析。结果显示，相比于PLS，ANN的误差较小，预测精度更高。
2.3 模型的优化
各种表征技术得到的光谱/衍射谱等数据中，包含大量的特征信息，对于样品中特定成分来说，其他成分的数据可能是冗余的，不仅会降低计算效率，还会影响建模的预测精度。因此为了提高模型的预测能力和运行效率，一般会采取数据预处理和变量筛选对模型进行优化。典型的光谱预处理方法主要有平滑、导数、标准正态变量（standard normal variate，SNV）、多元散射校正（multiplicative scatter correction，MSC），变量筛选方法有无信息变量消除法、遗传算法和竞争性自适应权重采样等。Ali等[26]为了放大卡马西平两种晶型的NIR谱图的差异，采用MSC、SNV和导数对图谱进行预处理，校正基线，改善了光谱特征，模型预测能力得到提高。
3. 化学计量学在晶体定性定量中的应用
3.1 药物多晶型测定
药物的不同晶型具有不同的理化性质，这些性质可能会影响原料药及制剂稳定性、溶出度、生物利用度等。因此，对固体状态下的药物混合物的晶型组成进行定量分析，可以避免晶型问题导致药物的无效及其他可能的安全隐患，以保证产品质量。
目前，PXRD、DSC等方法在多晶型的测定方面已得到广泛应用，同时振动光谱的无损、快速、通用的优势使其应用也逐渐增多。然而，由于药物性质的影响，谱图中晶型的特征峰和其含量之间的线性关系可能会丧失，导致定量困难。针对这一问题，可通过化学计量学方法充分提取谱图中的相关信息用于建立模型。Bhavana等[15]联合Raman、IR、NIR和PLS建立了氯硝柳胺无水物和两种一水合物晶型的快速定量分析方法，并采用MSC、导数及两者的组合的预处理算法，以消除样品制备和样品不均匀性带来的影响，随后将模型应用于球磨和湿法制粒过程中3种晶型含量的测定（图2）。表2总结了部分化学计量学方法联合定量表征技术测定药物晶体含量的研究案例。
![]()
Figure 2. Quantitative analysis of niclosamide polymorphs during different preparation processes using Raman, IR and NIR spectroscopy techniques combined with PLS chemometric method [15]Table 2. Chemometrics-assisted determination of API crystal contentDrug Characterization method Chemometric method Modeling area Observations Ref. Azithromycin NIR PLS 1100−2500 cm−1 Various factors (e.g., treatment method, wavelength range and moisture content) were investigated and optimized [27] LOD: 0.2%; LOQ: 0.8% Entecavir PXRD PLS 14°−34° 2θ An accurate and reliable method was developed for the quantitative determination of entecavir polymorphs in their binary mixtures [3] ATR-FTIR 400−2 000 cm−1 Furosemide Raman PLS 122−3167 cm−1 PLS can be used to quickly and accurately measure the content of various crystal forms of furosemide [28] Meloxicam IR PLS 600−4000 cm−1 Smart radial diagrams approach for selecting spectral pre-treatment techniques was presented [5] NIR 4000−12500 cm−1 Raman 1030−1650 cm−1 Niclosamide IR PLS 1380−1700 cm−1 The combination of three spectral techniques and PLS model was able to predict the polymorphic transformation of drug during the real-time processing like ball milling and wet granulation [15] NIR 4545−5000 cm−1 Raman 1050−1800 cm−1 Nimodipine ATR-FTIR MCR-ALS 1650−600 cm−1 Method is suitable for the analysis of nimodipine polymorphs and enables study of their possible thermally promoted interconversions [29] 3600−2500 cm−1 Paracetamol FT-Raman PLS 0−4000 cm−1 A fast and simple method for the quantitative analysis of form Ⅰ and form Ⅱ of paracetamol was developed [30] Piracetam PXRD PLS 8°−35° 2θ NIR combined with PLS had the highest accuracy for the drug detection [31] Raman 250−3310 cm−1 NIR 4000−10000 cm−1 3.2 晶型转变的监测
在晶型转变期间，可以用于识别和/或量化的特征峰可能会被转晶产物的特征峰掩盖，当转变为无定形时，则会出现峰的宽化和重叠。上述转变均可能会导致光谱特征和分析物含量之间的线性关系丧失，使晶型的定量变得更加困难。
PAT与化学计量学方法联用，可实时监测样品中药物晶型的动态转变，识别影响晶型变化的关键条件及参数范围[32]。为了研究无定形非诺贝特在不同温度下的晶型转变，Heinz等[33]使用原位拉曼技术和PCA模型检测了非诺贝特在−180~90 ℃温度范围内的晶型变化。结果显示，无定形非诺贝特在35 ℃转变为亚稳态晶型Ⅱ，随着温度升高，晶型Ⅱ在60 ℃转变为晶型Ⅰ，最终在80 ℃熔融（图3）。PAT联合化学计量学可实现对药物生产、储存过程中目标晶型含量的实时监测，提高对药物不同固态形式的结构性质和物理行为的分子水平的理解，以保证药物的安全性和有效性。表3总结了化学计量学辅助监测晶型转化的部分研究。
![]()
Figure 3. (A) Selected Raman spectra showing the recrystallization behavior of amorphous fenofibrate at the temperature ranging from -180 to 80 ℃; (B) Scores plots of fenofibrate from PCA analysis of its Raman spectra [33]Table 3. Chemometrics-assisted monitoring of polymorphic transformationsDrug Form transformation Characterization method Chemometric method Observations Ref. Carbamazepine Anhydrate→Dihydrate ATR-FTIR MCR-ALS The polymorphic transformation rate is delayed by HPC and HPMC [34] Cimetidine Monohydrate→Form B →Amorphous ATR-FTIR MCR-ALS The ATR-FTIR/MCR association is a promising and useful technique for monitoring solid-state phase transformations of cimetidine [35] Fenofibrate Amorphous→ Form II→Form I→liquid Raman PCA Multiple solid-state forms, including the metastable crystalline form, could be observed [33] Indomethacin Amorphous→α Raman PLS Solution-mediated transformation
The transition of polymorphism slowed down the drug dissolution rate[36] ε→ζ→η→α ATR-FTIR PCA Three new crystal forms were observed and their transitions process were monitored [37] Nitrofurantoin Monohydrate→
Amorphous→Anhydrate→MeltPXRD PCA The critical temperatures (120 ℃,140 ℃,190 ℃) were identified easily using the PCA [38] Ranitidine HCl Form I&II→Amorphous;
Amorphous→Form I&IIPXRD PLS Forms I and II could be fully amorphized within 30 min.
After being stored for 14 days under dry conditions (silica gel), amorphous samples recrystallized into form I and II[39] Theophylline Anhydrate→ Monohydrate NIR PLS Real-time monitoring of crystal form transformation during wet granulation and drying processes [40] 3.3 无定形固体分散体中药物结晶度的测定
ASD中的药物处于无定形态，在储存或溶出过程中易发生重结晶，转变为热力学更稳定的晶态，丧失ASD的高溶解度/溶出度的性质优势，影响药物体内吸收和临床疗效。研究表明，即使是微量的晶体也可能作为晶核诱导无定形发生转晶[41]。因此，痕量晶体的检测对于确保无定形态药物的安全性、稳定性和疗效至关重要。
PXRD和振动光谱是目前用于ASD结晶度监测的最为广泛的表征技术。化学计量学方法可以对PXRD整个图谱建立模型，有利于提高信噪比和检测灵敏度[30]，也可以区分振动光谱中晶体和无定形的特征吸收峰，快速测定ASD中药物结晶度。Kapourani等[21]联合ATR-FTIR和ANN对利伐沙班ASD中药物晶体和无定形进行定量分析。首先获取样品的ATR-FTIR光谱，通过PCA对谱图进行降维处理，选取3个主成分区分晶体和无定形，而后对ANN的结构进行优化，最终用于样品中晶体和无定形的定量检测（图4）。表4总结了部分化学计量学辅助测定ASD中药物结晶度的研究案例。
![]()
Figure 4. Quantitative analysis of crystal and amorphous forms in rivaroxaban ASD system using ATR-FTIR characterization and artificial neural network (ANN) method [21]Table 4. Chemometrics-assisted determination of ASD crystallinityDrug Characterization method Chemometric method Modeling area Observations Ref. Itraconazole Raman PCA 464−1 852 cm−1 The mass ratios of crystal form and amorphous form of API in drug tablets was analyzed and the content of residual crystalline API in ASD was determined [42] PLS Lopinavir NIR PLS 4100−8000 cm1 Lopinavir ASD was developed to improve dissolution, stability [41] The PLS model was validated by external samples Metoprolol Raman PLS 50−150 cm−1 A PLS model was developed and validated for drug crystal content monitoring during HME process [43] MK-A Raman PLS 490−805 cm−1 A quantitative method was developed using PLS model with ss-NMR as the reference method [44] 1653−1689 cm−1 LOD: 4.5% Piroxicam Raman MCR-ALS 1073−1661 cm−1 Quantitative analysis of piroxicam crystal formed during storage were carried out by Raman combined with MCR-ALS methods [45] Rebamipide PXRD PCA 5°−30° 2θ To investigate the effect of polymers and relative humidity on the phase transformation of amorphous rebamipide and its solid dispersion using chemometrics based on multiple datasets [46] NIR 900−1700 cm−1 Rivaroxaban ATR-FTIR ANN, PLS 800−1800 cm−1 ANN showed the superior analysis ability for the detection of drug crystal and amorphous content [21] PCR 2800−3500 cm−1 Tacrolimus PXRD PCA 4°−15.5° 2θ The selected data region showed the maximum differences in the spectra between the amorphous and crystalline tacrolimus [47] Ss-NMR PLS 6−60 (×10−6) LOQPXRD: 4%; LOQNMR: 2.5% 3.4 共晶、共无定形的表征和定量
共晶（cocrystal，CC）和共无定形（coamorphous，CM）因具有改善难溶性药物溶解度与溶出速率的优势而被广泛应用。但是不同结晶方法得到的产物中共晶纯度不同，会影响产物理化性质和体内外性能[48]。共无定形处于无定形态的药物，在外界条件（如温度、湿度）的影响下可能会发生重结晶，丧失其高溶出度的优势。因此对制备的共晶/共无定形进行表征和晶体定量分析以及稳定性考察，对于开发相应的制剂很重要。
IR、Raman等光谱技术可快速、准确地对共晶/共无定形体系进行定性和定量表征，与化学计量学方法联用还可提升光谱技术的检测灵敏度。Karimi-Jafari等[49]通过HME工艺制备布洛芬-烟酰胺共晶，联合在线拉曼检测和PLS监测共晶的形成，采用SNV、导数等方法对模型进行优化，并对机筒内不同区域共晶的形成进行评估，为HME连续生产过程中的药品质量控制提供技术支持（图5）。Costa等[50]联合PXRD、NIR、IR和PLS分析，测定片剂中奥氮平无定形和奥氮平-糖精共无定形的含量，并将模型应用于测定压片过程中施加不同压力和停留时间后的片剂中无定形成分的含量。表5为部分化学计量学联用光谱技术/衍射技术测定CC或CM中药物结晶度的案例。
![]()
Figure 5. In-line Raman spectroscopy and chemometrics for monitoring the formation of ibuprofen-nicotinamide cocrystal prepared using hot melt extrusion (HME) technique [49]Table 5. Chemometrics-assisted determination of cocrystal/coamorphousDrug Characterization method Chemometric method Modeling area Observations Ref. Caffeine:Glutaric
acid CC (1∶1)Raman PLS 40−105 cm−1 The Raman spectrum was analyzed by PLS to determine the content of CC in the model tablets [51] Carbamazepine/Ibuprofen: Nicotinamide CC (1∶1) Raman ANN 150−2700 cm−1 A quantitative method for the determination of CC content was established [25] ATR-FTIR PLS 900−1700 cm−1 ANN has a better fitting superiority than PLS 2700−4000 cm−1 Carbamazepine:Nicotinamide CC (1∶1) Raman MCR-ALS 285−1708 cm−1 The preparation processes of CC were monitored by Raman coupled with MCR-ALS [52] PLS Carbamazepine:Succinic acid CC (2∶1) Raman PLS 200−1800 cm−1 LOD: 2.00%; LOQ: 6.06% [53] Ibuprofen:Nicotinamide CC (1∶1) Raman PLS 100−3425 cm−1 In-line Raman in combination with PLS has been proved to be a promising non-invasive technique for real-time monitoring of CC formation during HME [49] Indomethacin/Furosemide:Tryptophan CM (1∶1) Ss-NMR PCA 0−200(×10-6) Amorphization was observed as reductions in PXRD reflections and was quantified based on normalized PCA scores of the ss-NMR spectra [54] Naproxen:
Indomethacin CM (1∶1)PXRD PLS 5°−35° 2θ PXRD combined with PLS enables simultaneous determination of up to four solid state fractions [19] Olanzapine:
Saccharin CM (1∶1)PXRD RIR 20.7°−21.2° 2θ A model was developed to predict the fraction of amorphous and olanzapine CM present in samples of tablets immediately after tableting [50] NIR PLS 6094−5577 cm−1 LODNIR: 0.2%; LOQNIR: 0.6% IR 1500−1620 cm−1 3.5 制剂中药物晶体的定量
在固体制剂开发和贮存过程中，粉碎、制粒、干燥、压片等制剂工艺操作及储存温湿度条件易诱导药物发生晶型转变，进而影响制剂产品的溶出度及生物利用度。因此，有必要对制剂过程和产品中的晶体组成进行定量检测。然而制剂中API晶型的定量分析易受到辅料的干扰，同时大量辅料的存在使得表征图谱中API的特征峰强度变弱，常规的分析仪器无法对制剂中API进行有效检出和定量。化学计量学与分析仪器的联用可以有效提取出表征数据中与晶型有关的关键信息，提升分析仪器的检测灵敏度。Liu等[55]对比了PXRD、NIR、ATR-FTIR和Raman在测定卡格列净片中API无水物和一水合物晶型含量的差异。结果表明，PXRD因受辅料的干扰，其定量限较高，且预测误差最大。然而，NIR、ATR-FTIR和Raman与PLS联用，可使得晶体定量限达到0.4%以下，且预测误差较小，充分展示了化学计量学方法在痕量晶体含量测定方面的适用性和灵敏性。表6为化学计量学方法应用于制剂中晶体含量测定的部分研究案例。
Table 6. Chemometrics-assisted determination of active pharmaceutical ingredients(APIs) in preparationsDrug Characterization method Chemometric method Modeling area Observations Ref. Canagliflozin
(Tablet)PXRD
NIR
ATR-FTIR
RamanPLS 2°−35° 2θ
6850−7 100 cm−1
400−4 000 cm−1
100−3 200 cm−1NIR was the best method in accuracy, repeatability and stability, followed by Raman, ATR-FTIR and PXRD methods
LODNIR: 0.01%; LOQNIR: 0.05%[55] Carbamazepine
(Tablet)NIR PCR 860−1680 nm
1245−1285 nmThe double-sided data set is more robust than the single-sided data set [56] Griseofulvin
(Tablet)Raman PLS 940−1750 cm−1 The PLS models based on the FT-Raman and low-frequency Raman data are suitable for quality control purposes
LOD: 0.58%; LOQ: 1.77%[57] Indomethacin
(Tablet)Raman PLS 1520−1730 cm−1 The method is suited for precise quantification of microanalysis of drug substances and drug products, particularly at the surface and interior of the tablet
LOD: 0.2%[58] Irbesartan
(Tablet)NIR PLS 4385−4410 cm−1 The content of trace amount of irbesartan form B in Avalide@ tablets was successfully quantified, and the accuracy was verified by ss-NMR
LOD: 1.3%; LOQ: 3.9%[59] Valsartan
(Tablet)Raman PLS 110−1100 cm−1 An analytical method was developed to simultaneously quantify crystalline and amorphous valsartan by Raman in the presence of excipients [60] 药物晶型是影响药品质量的重要因素之一。此外，从知识产权角度，具有与原晶型生物等效甚至药效更好的新晶型，可作附加专利，延长药物的专利保护期。目前关于化合物专利的申请已经扩展到覆盖化合物的所有晶型，对新化学实体的所有晶型进行分析和专利保护是新药研发的一个基本策略。因此，联用化学计量学方法提升目前分析仪器的晶型定量检测能力为药品的质量提升和药物晶型专利的保护提供强有力的技术支持。
4. 总结与展望
为保证药物的安全性和有效性，在制剂生产过程中对药物多态性进行定性/定量监测对于药物的质量控制至关重要。虽然目前已有多种定性/定量分析技术，但是现有的分析手段在实际应用中都存在一定的不足，尤其是检测灵敏度低的问题。随着统计学、分析方法、新技术的稳步发展，化学计量学应运而生，其可以最大化提取检测数据中的有效信息，建立数据与样本之间的联系，提高分析检测的灵敏度。化学计量学和仪器分析的结合，特别是振动光谱、X射线衍射技术和固态核磁共振波谱，可以实现对多组分混合样品中痕量晶体的定量分析，为提高药品质量及晶型专利保护提供技术支撑。
-
[1] Obach RS.Predicting clearance in humans from in vitro data[J].Curr Top Med Chem,2011,11(4):334-339. [2] Di L,Obach RS.Addressing the challenges of low clearance in drug research[J].AAPS J,2015,17(2):352-357. [3] Di L,Trapa P,Obach RS,et al.A novel relay method for determining low-clearance values[J].Drug Metab Dispos,2012,40(9):1860-1865. [4] Grime KH,Barton P,McGinnity DF.Application of in silico,in vitro and preclinical pharmacokinetic data for the effective and efficient prediction of human pharmacokinetics[J].Mol Pharm,2013,10(4):1191-1206. [5] Sohlenius-Sternbeck AK,Jones C,Ferguson D,et al.Practical use of the regression offset approach for the prediction of in vivo intrinsic clearance from hepatocytes[J].Xenobiotica,2012,42(9):841-853. [6] Chao P,Uss AS,Cheng KC.Use of intrinsic clearance for prediction of human hepatic clearance[J].Exp Opin Drug Metab Toxicol,2010,6(2):189-198. [7] Sun LN, Ding L, Yan ZY, et al. Determination the inhibitory potency of bencycloquidium bromide on rat liver cytochrome P450 by LC-MS/MS[J].J China Pharm Univ(中国药科大学学报),2013,44(2):134-140. [8] Lin C,Ballinger KR,Khetani SR.The application of engineered liver tissues for novel drug discovery[J].Exp Opin Drug Discov,2015,10(5):519-540. [9] Rodriguez-Antona C, Donato MT, Boobis A, et al. Cytochrome P450 expression in human hepatocytes and hepatoma cell lines:molecular mechanisms that determine lower expression in cultured cells[J].Xenobiotica,2002,32(6):505-520. [10] Jones HM,Houston JB.Substrate depletion approach for determining in vitro metabolic clearance:time dependencies in hepatocyte and microsomal incubations[J].Drug Metab Dispos,2004,32(9):973-982. [11] Di L,Atkinson K,Orozco CC,et al.In vitro-in vivo correlation for low-clearance compounds using hepatocyte relay method[J].Drug Metab Dispos,2013,41(12):2018-2023. [12] Ballard TE,Orozco CC,Obach RS.Generation of major human excretory and circulating drug metabolites using a hepatocyte relay method[J].Drug Metab Dispos,2014,42(5):899-902. [13] Yang X,Atkinson K,Di L.Novel cytochrome P450 reaction phenotyping for low-clearance compounds using the hepatocyte relay method[J].Drug Metab Dispos,2016,44(3):460-465. [14] Peng CC, Doshi U, Prakash C, et al. A novel plated hepatocyte relay assay(PHRA)for in vitro evaluation of hepatic metabolic clearance of slowly metabolized compounds[J].Drug Metab Lett,2016,10(1):3-15. [15] Bonn B,Svanberg P,Janefeldt A,et al.Determination of human hepatocyte intrinsic clearance for slowly metabolized compounds:comparison of a primary hepatocyte/stromal cell co-culture with plated primary hepatocytes and HepaRG[J].Drug Metab Dispos,2016,44(4):527-533. [16] Griffin SJ,Houston JB.Prediction of in vitro intrinsic clearance from hepatocytes:comparison of suspensions and monolayer cultures[J].Drug Metab Dispos,2005,33(1):115-120. [17] Blanchard N,Alexandre E,Abadie C,et al.Comparison of clearance predictions using primary cultures and suspensions of human hepatocytes[J].Xenobiotica,2005,35(1):1-15. [18] Smith CM,Nolan CK,Edwards MA,et al.A comprehensive evaluation of metabolic activity and intrinsic clearance in suspensions and monolayer cultures of cryopreserved primary human hepatocytes[J].J Pharm Sci,2012,101(10):3989-4002. [19] Hoffmaster KA,Turncliff RZ,LeCluyse EL,et al.P-glycoprotein expression,localization,and function in sandwich-cultured primary rat and human hepatocytes:relevance to the hepatobiliary disposition of a model opioid peptide[J].Pharm Res,2004,21(7):1294-1302. [20] Kanda K,Takahashi R,Yoshikado T,et al.Total hepatocellular disposition profiling of rosuvastatin and pitavastatin in sandwich-cultured human hepatocytes[J].Drug Metab Pharmacokinet,2018,33(3):164-172. [21] Swift B,Pfeifer ND,Brouwer KL.Sandwich-cultured hepatocytes:an in vitro model to evaluate hepatobiliary transporter-based drug interactions and hepatotoxicity[J].Drug Metab Rev,2010,42(3):446-471. [22] Brown JH,Das P,DiVito MD,et al.Nanofibrous PLGA electrospun scaffolds modified with type I collagen influence hepatocyte function and support viability in vitro[J].Acta Biomater,2018,73(4):217-227. [23] Oorts M,Keemink J,Deferm N,et al.Extra collagen overlay prolongs the differentiated phenotype in sandwich-cultured rat hepatocytes[J].J Pharmacol Toxicol Methods,2018,90(6):31-38. [24] Keemink J,Oorts M,Annaert P.Primary hepatocytes in Sandwich culture[J].Methods Mol Biol,2015,1250(7):175-188. [25] Lancett P,Williamson B,Barton P,et al.Development and characterization of a human hepatocyte low intrinsic clearance assay for use in drug discovery[J].Drug Metab Dispos,2018,46(8):1169-1178. [26] Treijtel N,Barendregt A,Freidig AP,et al.Modeling the in vitro intrinsic clearence of the slowly metabolized compound tolbutamide determined in sandwich-cultur>,2009,5(10):1159-1174. [27] Liu Y,Wei J,Lu J,et al.Micropatterned coculture of hepatocytes on electrospun fibers as a potential in vitro model for predictive drug metabolism[J].Mater Sci Eng C Mater Biol Appl,2016,63:475-484. [28] Oda H,Yoshida Y,Kawamura A,et al.Cell shape,cell-cell contact,cell-extracellular matrix contact and cell polarity are all required for the maximum induction of CYP2B1 and CYP2B2 gene expression by phenobarbital in adult rat cultured hepatocytes[J].Biochem Pharmacol,2008,75(5):1209-1217. [29] Xiao W,Perry G,Komori K,et al.New physiologically-relevant liver tissue model based on hierarchically cocultured primary rat hepatocytes with liver endothelial cells[J].Integr Biol(Camb),2015,7(11):1412-1422. [30] Kostadinova R, Boess F, Applegate D, et al. A long-term three dimensional liver co-culture system for improved prediction of clinically relevant drug-induced hepatotoxicity[J].Toxicol Appl Pharmacol,2013,268(1):1-16. [31] Khetani SR,Bhatia SN.Microscale culture of human liver cells for drug development[J].Nat Biotechnol,2008,26(1):120-126. [32] March S,Ramanan V,Trehan K,et al.Micropatterned coculture of primary human hepatocytes and supportive cells for the study of hepatotropic pathogens[J].Nat Protoc,2015,10(12):2027-2053. [33] Chan TS,Yu H,Moore A,et al.Meeting the challenge of predicting hepatic clearance of compounds slowly metabolized by cytochrome P450 using a novel hepatocyte model,HepatoPac[J].Drug Metab Dispos,2013,41(12):2024-2032. [34] Liu YW,Li HN,Yan SL,et al.Hepatocyte cocultures with endothelial cells and fibroblasts on micropatterned fibrous mats to promote liver-specific functions and capillary formation capabilities[J].Biomacromolecules,2014,15(3):1044-1054. [35] Ukairo O,Kanchagar C,Moore A,et al.Long-term stability of primary rat hepatocytes in micropatterned cocultures[J].J Biochem Mol Toxicol,2013,27(3):204-212. [36] Shen C,Xu XM,Meng Q,et al.Studies on rifampicin and isoniazid-induced hepatotoxicity[J].J China Pharm Univ(中国药科大学学报),2005,36(3):250-230. [37] Wang WW, Khetani SR, Krzyzewski S, et al. Assessment of a micropatterned hepatocyte coculture system to generate major human excretory and circulating drug metabolites[J].Drug Metab Dispos,2010,38(10):1900-1905. [38] Ramsden D,Tweedie DJ,Chan TS,et al.Bridging in vitro and in vivo metabolism and transport of faldaprevir in human using a novel cocultured human hepatocyte system,HepatoPac[J].Drug Metab Dispos,2014,42(3):394-406. [39] Ramsden D,Tweedie DJ,St George R,et al.Generating an in vitro-in vivo correlation for metabolism and liver enrichment of a hepatitis C virus drug,faldaprevir,using a rat hepatocyte model(HepatoPac)[J].Drug Metab Dispos,2014,42(3):407-414. [40] Hutzler JM,Ring BJ,Anderson SR.Low-turnover drug molecules:a current challenge for drug metabolism scientists[J].Drug Metab Dispos,2015,43(12):1917-1928. [41] Chao P,Maguire T,Novik E,et al.Evaluation of a microfluidic based cell culture platform with primary human hepatocytes for the prediction of hepatic clearance in human[J].Biochem Pharmacol,2009,78(6):625-632. [42] Novik E,Maguire TJ,Chao P,et al.A microfluidic hepatic coculture platform for cell-based drug metabolism studies[J].Biochem Pharmacol,2010,79(7):1036-1044. [43] Hultman I, Vedin C, Abrahamsson A, et al. Use of HμREL human coculture system for prediction of intrinsic clearance and metabolite formation for slowly metabolized compounds[J].Mol Pharm,2016,13(8):2796-2807. [44] Dash A,Inman W,Hoffmaster K,et al.Liver tissue engineering in the evaluation of drug safety[J].Exp Opin Drug Metab Toxicol,2009,5(10):1159-1174. [45] Vivares A,Salle-Lefort S,Arabeyre-Fabre C,et al.Morphological behaviour and metabolic capacity of cryopreserved human primary hepatocytes cultivated in a perfused multiwell device[J].Xenobiotica,2015,45(1):29-44. [46] Darnell M,Ulvestad M,Ellis E,et al.In vitro evaluation of major in vivo drug metabolic pathways using primary human hepatocytes and HepaRG cells in suspension and a dynamic three-dimensional bioreactor system[J].J Pharmacol Exp Ther,2012,343(1):134-144. [47] Darnell M, Schreiter T, Zeilinger K, et al. Cytochrome P450-dependent metabolism in HepaRG cells cultured in a dynamic three-dimensional bioreactor[J].Drug Metab Dispos,2011,39(7):1131-1138. [48] Antherieu S,Chesne C,Li R,et al.Stable expression,activity,and inducibility of cytochromes P450 in differentiated HepaRG cells[J].Drug Metab Dispos,2010,38(3):516-525. [49] Davies B,Morris T.Physiological parameters in laboratory animals and humans[J].Pharm Res,1993,10(7):1093-1095. [50] U.S.Food and Drug Administration.Guidance for industry:Safety Testing of Drug Metabolites[EB/OL].(2016-11-22)[2018-12-17] .https://www.fda.gov/Drugs/Guidanc- eComplianceRegulatoryInfo-rmation/Guidances/ucm065014.htm. [51] European Medicines Agency. Guideline on the investigation of drug interactions[EB/OL].(2012-06-21)[2018-12-17] https://www.ema.europa.eu/en/search/search?search_api_views_fulltext=Guideline%-20on%20the%20investigation%20of% 20drug%20interactions. [52] Matthew Hutzler J,Linder CD,Melton RJ,et al.In vitro-in vivo correlation and translation to the clinical outcome for CJ-13,610,a novel inhibitor of 5-lipoxygenase[J].Drug Metab Dispos,2010,38(7):1113-1121. [53] Zientek MA,Youdim K.Reaction phenotyping:advances in the experimental strategies used to characterize the contribution of drug-metabolizing enzymes[J].Drug Metab Dispos,2015,43(1):163-181. [54] Oda S,Fukami T,Yokoi T,et al.A comprehensive review of UDP-glucuronosyltransferase and esterases for drug development[J].Drug Metab Pharmacokinet,2015,30(1):30-51.
下载:
计量
- 文章访问数: 1747
- HTML全文浏览量: 8
- PDF下载量: 2719
