理论计算在药物制剂设计中的研究进展

doi:10.11949/0438-1157.20231278

摘要/Abstract

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。

关键词: 药物制剂, 理论计算, 分子模拟, 热力学计算, 人工智能

Abstract:

Pharmaceutical formulations are not only related to the national economy and people's livelihood but also the strategic focus of national security and technological competition. Theoretical calculations play an important role in the design of pharmaceutical formulation, and modern theoretical calculation methods of pharmaceutical formulation design have been paid much attention. Theoretical calculation has important applications in the design of pharmaceutical formulations. In recent years, the development models of pharmaceutical formulations have gradually changed to the big data-driven intelligent design model with the rapid development of emerging technologies such as artificial intelligence. This article first explains the importance of theoretical calculations in the design of pharmaceutical formulations. Then, the review focuses on the current research status of theoretical calculations in pharmaceutical formulation design. Among them, various calculation methods and advantages/disadvantages were analyzed and summarized in depth such as molecular simulation, thermodynamic calculation, and artificial intelligence. On these bases, the challenges and prospects that theoretical calculations faced in designing pharmaceutical formulations were discussed. This review can provide reference and guidance for the intelligent design of pharmaceutical formulations.

Key words: pharmaceutical formulation, theoretical calculation, molecular simulation, thermodynamic calculation, artificial intelligence

中图分类号:

TQ 013.1

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/表 5

图1 纳米悬浮制剂的制备步骤和分子模拟示意图[30]

Fig.1 Schematic of steps and mechanism (molecular simulation) involved in nanosuspension formulation[30]

图2 药物载体相容性预测[35]

Fig.2 Prediction of drug-carrier compatibility[35]

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]

参考文献 70

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.

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