Exploring the Physicochemical Properties of Plant-derived Drugs through Multiscale Simulations and Data-driven Approaches
Main Article Content
Keywords
Physicochemical properties, Plant-derived drugs, Multiscale simulations, Data-driven approaches, Drug development
Abstract
Plant-derived drugs have been used for centuries in traditional medicine and continue to play a significant role in modern drug discovery. Understanding the physicochemical properties of these compounds is essential for predicting their behavior in biological systems and optimizing their therapeutic potential. This review explores the application of multiscale simulations and data-driven approaches in investigating the physicochemical properties of plant-derived drugs. The physicochemical properties discussed include solubility, permeability, partition coefficient, and stability, which are critical determinants of drug efficacy and bioavailability. Multiscale simulations, such as molecular dynamics simulations, quantum mechanical calculations, and coarse-grained modeling, provide insights into the molecular behavior and interactions of plant-derived drugs, aiding in the design and optimization of drug candidates. Data-driven approaches, including machine learning algorithms, quantitative structure-activity relationship (QSAR) modeling, and big data analytics, offer valuable tools for analyzing large datasets and predicting the physicochemical properties of plant-derived drugs. These approaches enable the identification of key molecular descriptors and patterns that influence drug behavior, facilitating the rational design and selection of drug candidates. Looking ahead, advancements in computational power, as well as the integration of experimental and computational approaches, hold promise for further enhancing our understanding of the physicochemical properties of plant-derived drugs. Continued research in this field will contribute to the discovery and development of novel, effective, and safe plant-derived therapeutics. In conclusion, the combination of multiscale simulations and data-driven approaches provides a powerful framework for exploring the physicochemical properties of plant-derived drugs. This integrated approach offers valuable insights into drug behavior, supporting the rational design and optimization of plant-derived therapeutics for various diseases and conditions.
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22. Li, X., Oduola, W., Qian, L., & Dougherty, E. (2015). Integrating multiscale modeling with drug effects for cancer treatment. Cancer Informatics, 14s5, CIN.S30797. https://doi.org/10.4137/cin.s30797
23. Li, Z., Litchfield, J., Tess, D., Carlo, A., Eng, H., Keefer, C., … & Maurer, T. (2020). A physiologically based in silico tool to assess the risk of drug-related crystalluria. Journal of Medicinal Chemistry, 63(12), 6489-6498. https://doi.org/10.1021/acs.jmedchem.9b01995
24. Lin, X., Li, X., & Lin, X. (2020). A review on applications of computational methods in drug screening and design. Molecules, 25(6), 1375. https://doi.org/10.3390/molecules25061375
25. Maganti, L., Das, S., Mascarenhas, N., & Ghoshal, N. (2011). Deciphering the structural requirements of nucleoside bisubstrate analogues for inhibition of mbta in mycobacterium tuberculosis: a fb‐qsar study and combinatorial library generation for identifying potential hits. Molecular Informatics, 30(10), 863-872. https://doi.org/10.1002/minf.201100056
26. Manoharan, P., Vijayan, R., & Ghoshal, N. (2010). Rationalizing fragment based drug discovery for bace1: insights from fb-qsar, fb-qssr, multi objective (mo-qspr) and mif studies. Journal of Computer-Aided Molecular Design, 24(10), 843-864. https://doi.org/10.1007/s10822-010-9378-9
27. Mao, B., Gozalbes, R., Barbosa, F., Migeon, J., Merrick, S., Kamm, K., … & Froloff, N. (2006). Qsar modeling of in vitro inhibition of cytochrome p450 3a4*. Journal of Chemical Information and Modeling, 46(5), 2125-2134. https://doi.org/10.1021/ci0600915
28. Meyer, H. and Müller‐Plathe, F. (2002). Formation of chain-folded structures in supercooled polymer melts examined by md simulations. Macromolecules, 35(4), 1241-1252. https://doi.org/10.1021/ma011309l
29. Moon-Koo, K. and Park, J. (2016). Identifying and prioritizing critical factors for promoting the implementation and usage of big data in healthcare. Information Development, 33(3), 257-269. https://doi.org/10.1177/0266666916652671
30. Myint, K., Wang, L., Tong, Q., & Xie, X. (2012). Molecular fingerprint-based artificial neural networks qsar for ligand biological activity predictions. Molecular Pharmaceutics, 9(10), 2912-2923. https://doi.org/10.1021/mp300237z
31. Oduola, W. and Li, X. (2018). Multiscale tumor modeling with drug pharmacokinetic and pharmacodynamic profile using stochastic hybrid system. Cancer Informatics, 17, 117693511879026. https://doi.org/10.1177/1176935118790262
32. Pathania, S., Ramakrishnan, S., & Bagler, G. (2015). Phytochemica: a platform to explore phytochemicals of medicinal plants. Database, 2015, bav075. https://doi.org/10.1093/database/bav075
33. Pathania, S., Ramakrishnan, S., & Bagler, G. (2015). Phytochemica: a platform to explore phytochemicals of medicinal plants. Database, 2015, bav075. https://doi.org/10.1093/database/bav075
34. Pillay, K. and Merwe, A. (2021). A big data driven decision making model: a case of the south african banking sector. South African Computer Journal, 33(2). https://doi.org/10.18489/sacj.v33i2.928
35. Pita, S., Albuquerque, M., Rodrigues, C., Castro, H., & Hopfinger, A. (2012). Receptor‐dependent 4d‐qsar analysis of peptidemimetic inhibitors of trypanosoma cruzi trypanothione reductase with receptor‐based alignment. Chemical Biology & Drug Design, 79(5), 740-748. https://doi.org/10.1111/j.1747-0285.2012.01338.x
36. Pope, B., Fitch, B., Pitman, M., Rice, J., & Reumann, M. (2011). Performance of hybrid programming models for multiscale cardiac simulations: preparing for petascale computation. Ieee Transactions on Biomedical Engineering, 58(10), 2965-2969. https://doi.org/10.1109/tbme.2011.2161580
37. Raghu, V., Ge, X., Chrysanthis, P., & Benos, P. (2017). Integrated theory-and data-driven feature selection in gene expression data analysis.. https://doi.org/10.1109/icde.2017.223
38. Rao, H., Huangfu, C., Wang, Y., Wang, X., Tang, T., Zeng, X., … & Chen, Y. (2015). Physicochemical profiles of the marketed agrochemicals and clues for agrochemical lead discovery and screening library development. Molecular Informatics, 34(5), 331-338. https://doi.org/10.1002/minf.201400143
39. Rao, H., Huangfu, C., Wang, Y., Wang, X., Tang, T., Zeng, X., … & Chen, Y. (2015). Physicochemical profiles of the marketed agrochemicals and clues for agrochemical lead discovery and screening library development. Molecular Informatics, 34(5), 331-338. https://doi.org/10.1002/minf.201400143
40. Ritchie, T., Luscombe, C., & Macdonald, S. (2009). Analysis of the calculated physicochemical properties of respiratory drugs: can we design for inhaled drugs yet?. Journal of Chemical Information and Modeling, 49(4), 1025-1032. https://doi.org/10.1021/ci800429e
41. Samy, R. and Gopalakrishnakone, P. (2007). Current status of herbal and their future perspectives. Nature Precedings. https://doi.org/10.1038/npre.2007.1176.1
42. Sharma, C., Sadek, B., Goyal, S., Sinha, S., Kamal, M., & Ojha, S. (2015). Small molecules from nature targeting g-protein coupled cannabinoid receptors: potential leads for drug discovery and development. Evidence-Based Complementary and Alternative Medicine, 2015, 1-26. https://doi.org/10.1155/2015/238482
43. Simões, R., Maltarollo, V., Oliveira, P., & Honório, K. (2018). Transfer and multi-task learning in qsar modeling: advances and challenges. Frontiers in Pharmacology, 9. https://doi.org/10.3389/fphar.2018.00074
44. Sinyavskaya, L., Renoux, C., & Durand, M. (2021). Defining the duration of the dispensation of oral anticoagulants in administrative healthcare databases. Pharmacoepidemiology and Drug Safety, 31(1), 105-109. https://doi.org/10.1002/pds.5378
45. Sun, X. and Cheng, K. (2009). Multi-scale simulation of the nano-metric cutting process. The International Journal of Advanced Manufacturing Technology, 47(9-12), 891-901. https://doi.org/10.1007/s00170-009-2125-5
46. Sun, X., Chen, S., Cheng, K., Huo, D., & Chu, W. (2006). Multiscale simulation on nanometric cutting of single crystal copper. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 220(7), 1217-1222. https://doi.org/10.1243/09544054jem540sc
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Mar 05, 2024
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Iram Saba, Hifsa Mobeen, Sumaira kanwal, Ahmad Hassan, Muhammad Ashraf, Amina Kanwal, Muhammad Ishtiaq, Muhammad Waqar. (2024). Exploring the Physicochemical Properties of Plant-derived Drugs through Multiscale Simulations and Data-driven Approaches. Journal of Population Therapeutics and Clinical Pharmacology, 31(2), 921-933. https://doi.org/10.53555/jptcp.v31i2.4948
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All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Iram Saba, Hifsa Mobeen, Sumaira kanwal, Ahmad Hassan, Muhammad Ashraf, Amina Kanwal, Muhammad Ishtiaq, Muhammad Waqar. (2024). Exploring the Physicochemical Properties of Plant-derived Drugs through Multiscale Simulations and Data-driven Approaches. Journal of Population Therapeutics and Clinical Pharmacology, 31(2), 921-933. https://doi.org/10.53555/jptcp.v31i2.4948