化工学报 ›› 2024, Vol. 75 ›› Issue (4): 1429-1438.DOI: 10.11949/0438-1157.20231278
张政1(), 汪妩琼1, 张雅静1, 王康军1(), 吉远辉2()
收稿日期:
2023-12-04
修回日期:
2024-02-13
出版日期:
2024-04-25
发布日期:
2024-06-06
通讯作者:
王康军,吉远辉
作者简介:
张政(1990—),男,博士,讲师,zhengzhang@syuct.edu.cn
基金资助:
Zheng ZHANG1(), Wuqiong WANG1, Yajing ZHANG1, Kangjun WANG1(), Yuanhui JI2()
Received:
2023-12-04
Revised:
2024-02-13
Online:
2024-04-25
Published:
2024-06-06
Contact:
Kangjun WANG, Yuanhui JI
摘要:
药物制剂不仅关系国计民生,而且也是国家安全和科技竞争的战略重点。理论计算在药物制剂设计过程中发挥了重要作用,有关药物制剂设计中的现代理论计算方法一直备受关注。近年来,随着人工智能等新兴技术的飞速发展,药物制剂的研发模式也逐渐向大数据驱动的智能设计模式转变。首先,阐述了理论计算在药物制剂设计中的重要性。然后,重点探讨了药物制剂设计中理论计算的研究现状,分别对分子模拟、热力学计算和人工智能等方法进行了归纳和总结,深入分析了各类方法的优缺点。在此基础上,讨论了理论计算在药物制剂设计中面临的挑战,并对其未来发展方向提出了展望,有望为药物制剂的智能设计提供参考与指导。
中图分类号:
张政, 汪妩琼, 张雅静, 王康军, 吉远辉. 理论计算在药物制剂设计中的研究进展[J]. 化工学报, 2024, 75(4): 1429-1438.
Zheng ZHANG, Wuqiong WANG, Yajing ZHANG, Kangjun WANG, Yuanhui JI. Research progress in theoretical calculation of pharmaceutical formulation design[J]. CIESC Journal, 2024, 75(4): 1429-1438.
图3 (a) 三种吲哚美辛(IND)制剂在缓冲溶液(pH 6.5)中的溶解动力学以及相应的相关性和预测(实线);(b) 三种IND制剂中IND溶解的表面反应速率常数(ks)和扩散速率常数(kd)[51]
Fig.3 (a) Dissolution kinetics of IND from three formulations in buffered solution (pH 6.5), and corresponding correlation and prediction (full line); (b) ks and kd of IND dissolution from three formulations[51]
图4 纳米制剂颗粒表面改性和颗粒尺寸相关的膜/纳米颗粒相互作用相图[62]
Fig.4 Phase diagram illustrating the nature of membrane/ nanoparticle interaction as a function of particle surface treatment and particle size[62]
图5 同时多目标优化过程的示意图(包括用于建模的人工神经网络和用于优化的连续遗传算法)[64]
Fig.5 Schematic representation of the simultaneous multi-objective optimization processes including artificial neural networks for modeling and continuous genetic algorithms for optimization[64]
1 | Haggag Y A, Abd Elrahman A A, Ulber R, et al. Fucoidan in pharmaceutical formulations: a comprehensive review for smart drug delivery systems[J]. Marine Drugs, 2023, 21(2): 112. |
2 | Jeevanandam J, Chan Y S, Danquah M K. Nano-formulations of drugs: recent developments, impact and challenges[J]. Biochimie, 2016, 128: 99-112. |
3 | 尹健, 张治国, 吉远辉, 等. 医药化工领域研究现状和发展态势[J]. 中国科学基金, 2023, 37(1): 120-125. |
Yin J, Zhang Z G, Ji Y H, et al. Research status and development trends in the field of pharmaceutical chemical engineering[J]. Chinese Science Foundation, 2023, 37(1): 120-125. | |
4 | Takayama K, Fujikawa M, Obata Y, et al. Neural network based optimization of drug formulations[J]. Advanced Drug Delivery Reviews, 2003, 55(9): 1217-1231. |
5 | Zhang Y J, Williams III R O, Tucker H O. Formulation strategies in immunotherapeutic pharmaceutical products[J]. World Journal of Clinical Oncology, 2020, 11(5): 275-282. |
6 | 谷友刚.国内制药企业高端制剂发展概况及挑战[J]. 中国药业, 2021, 30(9): 4-7. |
Gu Y G. Development and challenges of high-end pharmaceutical preparations in domestic pharmaeutical enterprises[J]. China Pharm., 2021, 30(9): 4-7. | |
7 | Yoshida T, Kojima H. Oral drug delivery systems applied to launched products: value for the patients and industrial considerations[J]. Molecular Pharmaceutics, 2023, 20(11): 5312-5331. |
8 | Hashim L E, Sabri A H, Mohamad M A, et al. Circumventing the gastrointestinal barrier for oral delivery of therapeutic proteins and peptides (PPTS): current trends and future trajectories[J]. Current Drug Delivery, 2024, 21(2): 211-235. |
9 | Shepherd S J, Issadore D, Mitchell M J. Microfluidic formulation of nanoparticles for biomedical applications[J]. Biomaterials, 2021, 274: 120826. |
10 | Tudu M, Samanta A. Natural polysaccharides: chemical properties and application in pharmaceutical formulations[J]. European Polymer Journal, 2023, 184: 111801. |
11 | Halwani A A. Development of pharmaceutical nanomedicines: from the bench to the market[J]. Pharmaceutics, 2022, 14(1): 106. |
12 | Ashwini T, Narayan R, Shenoy P A, et al. Computational modeling for the design and development of nano based drug delivery systems[J]. Journal of Molecular Liquids, 2022, 368: 120596. |
13 | Wang N N, Zhang Y S, Wang W, et al. How can machine learning and multiscale modeling benefit ocular drug development?[J]. Advanced Drug Delivery Reviews, 2023, 196: 114772. |
14 | Gao H S, Su Y, Wang W, et al. Integrated computer-aided formulation design: a case study of andrographolide/cyclodextrin ternary formulation[J]. Asian Journal of Pharmaceutical Sciences, 2021, 16(4): 494-507. |
15 | Babu N J, Nangia A. Solubility advantage of amorphous drugs and pharmaceutical cocrystals[J]. Crystal Growth Design, 2011, 11(7): 2662-2679. |
16 | Han K, Chen S, Chen W H, et al. Synergistic gene and drug tumor therapy using a chimeric peptide[J]. Biomaterials, 2013, 34(19): 4680-4689. |
17 | Haag R, Kratz F. Polymer therapeutics: concepts and applications[J]. Angewandte Chemie International Edition, 2006, 45(8): 1198-1215. |
18 | Lin X, Hu Y, Liu L, et al. Physical stability of amorphous solid dispersions: a physicochemical perspective with thermodynamic, kinetic and environmental aspects[J]. Pharmaceutical Research, 2018, 35(6): 125. |
19 | Fischlschweiger M, Enders S. Thermodynamic principles for the design of polymers for drug formulations[J]. Annual Review of Chemical and Biomolecular Engineering, 2019, 10: 311-335. |
20 | Ji Y H, Hao D L, Luebbert C, et al. Insights into influence mechanism of polymeric excipients on dissolution of drug formulations: a molecular interaction-based theoretical model analysis and prediction[J]. AIChE Journal, 2021, 67(11): e17372. |
21 | Zhang H F, Zhou L Z. Study on regenerative processing performance of chlorinated polyethylene based on wireless network and artificial intelligence technology[J]. Computational Intelligence and Neuroscience, 2022, 2022: 3811320. |
22 | Ge K, Ji Y H, Lu X H. A novel interfacial thermodynamic model for predicting solubility of nanoparticles coated by stabilizers[J]. Chinese Journal of Chemical Engineering, 2021, 31: 103-112. |
23 | Ashton M D, Hardy J G. Progress in active ingredient formulations[J]. Johnson Matthey Technology Review, 2019, 63(3): 211-225. |
24 | Wang S, Di J W, Wang D, et al. State-of-the-art review of artificial neural networks to predict, characterize and optimize pharmaceutical formulation[J]. Pharmaceutics, 2022, 14(1): 183. |
25 | Wu J T, Lv J, Zhao L, et al. Exploring the role of microbial proteins in controlling environmental pollutants based on molecular simulation[J]. Science of the Total Environment, 2023, 905: 167028. |
26 | Cui Y. Using molecular simulations to probe pharmaceutical materials[J]. Journal of Pharmaceutical Sciences, 2011, 100(6): 2000-2019. |
27 | Costache A D, Sheihet L, Zaveri K, et al. Polymer-drug interactions in tyrosine-derived triblock copolymer nanospheres: a computational modeling approach[J]. Molecular Pharmaceutics, 2009, 6(5): 1620-1627. |
28 | Gupta J, Nunes C, Vyas S, et al. Prediction of solubility parameters and miscibility of pharmaceutical compounds by molecular dynamics simulations[J]. The Journal of Physical Chemistry. B, 2011, 115(9): 2014-2023. |
29 | Huynh L, Grant J, Leroux J C, et al. Predicting the solubility of the anti-cancer agent docetaxel in small molecule excipients using computational methods[J]. Pharmaceutical Research, 2008, 25(1): 147-157. |
30 | Mehta C H, Narayan R, Aithal G, et al. Molecular simulation driven experiment for formulation of fixed dose combination of darunavir and ritonavir as anti-HIV nanosuspension[J]. Journal of Molecular Liquids, 2019, 293: 111469. |
31 | Cheng Y, Ji Y H. Mitochondria-targeting nanomedicine self-assembled from GSH-responsive paclitaxel-ss-berberine conjugate for synergetic cancer treatment with enhanced cytotoxicity[J]. Journal of Controlled Release, 2020, 318: 38-49. |
32 | Cheng Y, Ji Y H, Tong J W. Triple stimuli-responsive supramolecular nanoassembly with mitochondrial targetability for chemophotothermal therapy[J]. Journal of Controlled Release, 2020, 327: 35-49. |
33 | Luebbert C, Stoyanov E. Tailored ASD destabilization-balancing shelf life stability and dissolution performance with hydroxypropyl cellulose[J]. International Journal of Pharmaceutics: X, 2023, 5: 100187. |
34 | Latere Dwan'Isa J P, Rouxhet L, Préat V, et al. Prediction of drug solubility in amphiphilic di-block copolymer micelles: the role of polymer-drug compatibility[J]. Pharmazie, 2007, 62(7): 499-504. |
35 | Sun Y J, Wu H Y, Dong W, et al. Molecular simulation approach to the rational design of self-assembled nanoparticles for enhanced peroral delivery of doxorubicin[J]. Carbohydrate Polymers, 2019, 218: 279-288. |
36 | Nsairat H, Khater D, Sayed U, et al. Liposomes: structure, composition, types, and clinical applications[J]. Heliyon, 2022, 8(5): e09394. |
37 | Wang S L, Chen Y Y, Guo J C, et al. Liposomes for tumor targeted therapy: a review[J]. International Journal of Molecular Sciences, 2023, 24(3): 2643. |
38 | Cern A, Barenholz Y, Tropsha A, et al. Computer-aided design of liposomal drugs: in silico prediction and experimental validation of drug candidates for liposomal remote loading[J]. Journal of Controlled Release, 2014, 173: 125-131. |
39 | George A, Shah P A, Shrivastav P S. Natural biodegradable polymers based nano-formulations for drug delivery: a review[J]. International Journal of Pharmaceutics, 2019, 561: 244-264. |
40 | Bannigan P, Bao Z Q, Hickman R J, et al. Machine learning models to accelerate the design of polymeric long-acting injectables[J]. Nature Communications, 2023, 14: 35. |
41 | Meunier M, Goupil A, Lienard P. Predicting drug loading in PLA-PEG nanoparticles[J]. International Journal of Pharmaceutics, 2017, 526(1/2): 157-166. |
42 | Tian F L, Yue T T, Li Y, et al. Computer simulation studies on the interactions between nanoparticles and cell membrane[J]. Science China Chemistry, 2014, 57(12): 1662-1671. |
43 | Lee S H, Bajracharya R, Min J Y, et al. Strategic approaches for colon targeted drug delivery: an overview of recent advancements[J]. Pharmaceutics, 2020, 12(1): 68. |
44 | Li W, Tang J, Lee D, et al. Clinical translation of long-acting drug delivery formulations[J]. Nature Reviews Materials, 2022, 7: 406-420. |
45 | Yaman S, Ramachandramoorthy H, Iyer P, et al. Targeted chemotherapy via HER2-based chimeric antigen receptor (CAR) engineered T-cell membrane coated polymeric nanoparticles[J]. Bioactive Materials, 2024, 34: 422-435. |
46 | Zeng H H, Yan G Q, Zheng R S, et al. Cancer cell membrane-biomimetic nanoparticles based on gelatin and mitoxantrone for synergetic chemo-photothermal therapy of metastatic breast cance [J]. ACS Biomaterials Science & Engineering, 2024, 10(2): 875-889. |
47 | Fullstone G, Wood J, Holcombe M, et al. Modelling the transport of nanoparticles under blood flow using an agent-based approach[J]. Scientific Reports, 2015, 5: 10649. |
48 | Kumar R, Thakur A K, Kulabhi A, et al. Solubility prediction of lornoxicam in different pure solvents using semi-empirical correlations and thermodynamic models[J]. International Journal of Thermodynamics, 2023, 26(1): 12-16. |
49 | Kalepu S, Manthina M, Padavala V. Oral lipid-based drug delivery systems—an overview[J]. Acta Pharmaceutica Sinica B, 2013, 3(6): 361-372. |
50 | Ji Y H, Lemberg M, Prudic A, et al. Modeling and analysis of dissolution of paracetamol/Eudragit® formulations[J]. Chemical Engineering Research and Design, 2017, 121: 22-31. |
51 | Ji Y H, Leśniak A, Prudic A, et al. Drug release kinetics and mechanism from PLGA formulations[J]. AIChE Journal, 2016, 62: 4055-4065. |
52 | Paus R, Prudic A, Ji Y H. Influence of excipients on solubility and dissolution of pharmaceuticals[J]. International Journal of Pharmaceutics, 2015, 485(1/2): 277-287. |
53 | Prudic A, Ji Y H, Luebbert C, et al. Influence of humidity on the phase behavior of API/polymer formulations[J]. European Journal of Pharmaceutics and Biopharmaceutics, 2015, 94: 352-362 |
54 | Prudic A, Lesniak A K, Ji Y H, et al. Thermodynamic phase behaviour of indomethacin/PLGA formulations[J]. European Journal of Pharmaceutics and Biopharmaceutics, 2015, 93: 88-94. |
55 | Prudic A, Kleetz T, Korf M, et al. Influence of copolymer composition on the phase behavior of solid dispersions[J]. Molecular Pharmaceutics, 2014, 11(11): 4189-4198. |
56 | Prudic A, Ji Y H, Sadowski G. Thermodynamic phase behavior of API/polymer solid dispersions[J]. Molecular Pharmaceutics, 2014, 11(7): 2294-2304. |
57 | Paus R, Hart E, Ji Y H. A novel approach for predicting the dissolution profiles of pharmaceutical tablets[J]. European Journal of Pharmaceutics and Biopharmaceutics, 2015, 96: 53-64. |
58 | Ji Y H, Paus R, Prudic A, et al. A novel approach for analyzing the dissolution mechanism of solid dispersions[J]. Pharmaceutical Research, 2015, 32(8): 2559-2578. |
59 | Sadatmousavi P, Chen P. Self/co-assembling peptide, EAR8-II, as a potential carrier for a hydrophobic anticancer drug pirarubicin (THP): characterization and in-vitro delivery[J]. International Journal of Molecular Sciences, 2013, 14(12): 23315-23329. |
60 | Chanphai P, Tajmir-Riahi H A. DNA binding efficacy with functionalized folic acid-PAMAM nanoparticles[J]. Chemico-Biological Interactions, 2018, 290: 52-56. |
61 | Zhao W W, Cui B, Peng H X, et al. Novel method to investigate the interaction force between etoposide and APTES-functionalized Fe3O4@nSiO2@mSiO2 nanocarrier for drug loading and release processes[J]. The Journal of Physical Chemistry C, 2015, 119(8): 4379-4386. |
62 | Ginzburg V V, Balijepalli S. Modeling the thermodynamics of the interaction of nanoparticles with cell membranes[J]. Nano Letters, 2007, 7(12): 3716-3722. |
63 | Li X L. Size and shape effects on receptor-mediated endocytosis of nanoparticles[J]. Journal of Applied Physics, 2012, 111(2): 024702. |
64 | Li Y Q, Abbaspour M R, Grootendorst P V, et al. Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology[J]. European Journal of Pharmaceutics and Biopharmaceutics, 2015, 94: 170-179. |
65 | Metwally A A, Hathout R M. Computer-assisted drug formulation design: novel approach in drug delivery[J]. Molecular Pharmaceutics, 2015, 12(8): 2800-2810. |
66 | Amasya G, Aksu B, Badilli U, et al. QbD guided early pharmaceutical development study: production of lipid nanoparticles by high pressure homogenization for skin cancer treatment[J]. International Journal of Pharmaceutics, 2019, 563: 110-121. |
67 | Paleyes A, Urma R G, Lawrence N D. Challenges in deploying machine learning: a survey of case studies[J]. ACM Computing Surveys, 55(6): 114. |
68 | Fu X, Zhong L M, Cao Y B, et al. Quantitative analysis of excipient dominated drug formulations by Raman spectroscopy combined with deep learning[J]. Analytical Methods: Advancing Methods and Applications, 2021, 13(1): 64-68. |
69 | Koletti A E, Tsarouchi E, Kapourani A, et al. Gelatin nanoparticles for NSAID systemic administration: quality by design and artificial neural networks implementation[J]. International Journal of Pharmaceutics, 2020, 578: 119118. |
70 | Zhao Q Q, Ye Z, Su Y, et al. Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques[J]. Acta Pharmaceutica Sinica B, 2019, 9(6): 1241-1252. |
[1] | 周康, 王建新, 于海, 魏朝良, 范丰奇, 车昕昊, 张磊. 基于分子动力学模拟的矿物基础油泡沫破裂性能研究[J]. 化工学报, 2024, 75(4): 1668-1678. |
[2] | 刘东飞, 张帆, 刘铮, 卢滇楠. 机器学习势及其在分子模拟中的应用综述[J]. 化工学报, 2024, 75(4): 1241-1255. |
[3] | 司友明, 郑凌峰, 陈鹏忠, 樊江莉, 彭孝军. 新型锑氧簇光刻胶的性能与机理研究[J]. 化工学报, 2024, 75(4): 1705-1717. |
[4] | 刘梦绮, 王凯, 骆广生. 基于人工智能的微分散基础研究[J]. 化工学报, 2024, 75(4): 1096-1104. |
[5] | 吴凡, 彭旭东, 江锦波, 孟祥铠, 梁杨杨. 分子动力学模拟预测天然气密度和黏度的可行性研究[J]. 化工学报, 2024, 75(2): 450-462. |
[6] | 宋明昊, 赵霏, 刘淑晴, 李国选, 杨声, 雷志刚. 离子液体脱除模拟油中挥发酚的多尺度模拟与研究[J]. 化工学报, 2023, 74(9): 3654-3664. |
[7] | 胡建波, 刘洪超, 胡齐, 黄美英, 宋先雨, 赵双良. 有机笼跨细胞膜易位行为的分子动力学模拟研究[J]. 化工学报, 2023, 74(9): 3756-3765. |
[8] | 赵佳佳, 田世祥, 李鹏, 谢洪高. SiO2-H2O纳米流体强化煤尘润湿性的微观机理研究[J]. 化工学报, 2023, 74(9): 3931-3945. |
[9] | 汪林正, 陆俞冰, 张睿智, 罗永浩. 基于分子动力学模拟的VOCs热氧化特性分析[J]. 化工学报, 2023, 74(8): 3242-3255. |
[10] | 陈吉, 洪泽, 雷昭, 凌强, 赵志刚, 彭陈辉, 崔平. 基于分子动力学的焦炭溶损反应及其机理研究[J]. 化工学报, 2023, 74(7): 2935-2946. |
[11] | 董明, 徐进良, 刘广林. 超临界水非均质特性分子动力学研究[J]. 化工学报, 2023, 74(7): 2836-2847. |
[12] | 刘远超, 蒋旭浩, 邵钶, 徐一帆, 钟建斌, 李耑. 几何尺寸及缺陷对石墨炔纳米带热输运特性的影响[J]. 化工学报, 2023, 74(6): 2708-2716. |
[13] | 顾浩, 张福建, 刘珍, 周文轩, 张鹏, 张忠强. 力电耦合作用下多孔石墨烯膜时间维度的脱盐性能及机理研究[J]. 化工学报, 2023, 74(5): 2067-2074. |
[14] | 李辰鑫, 潘艳秋, 何流, 牛亚宾, 俞路. 基于碳微晶结构的炭膜模型及其气体分离模拟[J]. 化工学报, 2023, 74(5): 2057-2066. |
[15] | 唐政, 郑涛, 刘晗, 张睿, 刘植昌, 刘海燕, 徐春明, 孟祥海. 双金属卤化物络合萃取分离直馏石脑油中的芳烃[J]. 化工学报, 2023, 74(12): 4926-4933. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||