Bioinfo Chem
System biology and Infochemistry | Online ISSN 3071-4826
1
Citations
13.3k
Views
32
Articles
Volume 5 Number 1 2023
REVIEWS (Open Access)
Multiscale Modeling for Drug Discovery and Precision Pharmacology: Molecular Dynamics, PBPK Modeling, and Virtual Clinical Trials
Shamsuddin Sultan Khan 1*, Tufael 2
Bioinfo Chem 5 (1) 1-14 https://doi.org/10.25163/bioinformatics.5110724
Submitted: 14 April 2023 Revised: 10 June 2023 Accepted: 17 June 2023 Published: 19 June 2023
Abstract
Multiscale modeling is increasingly shaping modern drug discovery by addressing the persistent gap between molecular mechanisms and clinical outcomes. Traditional drug discovery approaches often identify therapeutic effects without fully explaining them, contributing to limited clinical translation. In this context, multiscale modeling and systems pharmacology provide a framework for integrating molecular interactions, cellular signaling, tissue dynamics, and whole-body physiology into predictive models of drug action. This review synthesizes current advances in multiscale modeling across drug discovery, including molecular dynamics simulations, differential equation–based signaling models, agent-based simulations, and physiologically based pharmacokinetic (PBPK) modeling. These approaches enable prediction of drug efficacy, toxicity, and resistance by linking molecular-scale events to system-level responses. Emerging applications, such as virtual clinical trials and patient-specific simulations, further demonstrate the potential of multiscale modeling for precision pharmacology. Despite these advances, several challenges remain. Model accuracy depends on data availability, parameter estimation, and validation strategies, including iterative predict–learn–confirm cycles. Computational complexity and the balance between mechanistic detail and practical usability continue to limit broader implementation. Overall, multiscale modeling is shifting drug discovery from empirical approaches toward a predictive and mechanistic science. By integrating systems pharmacology with computational modeling, these frameworks offer new opportunities to improve clinical translation and advance precision medicine.
Keywords: Multiscale modeling; Systems pharmacology; Drug discovery; PBPK modeling; Precision medicine
References
Ait-Oudhia, S., Ovacik, M. A., & Mager, D. E. (2017). Systems pharmacology and enhanced pharmacodynamic models for understanding antibody-based drug action and toxicity. mAbs, 9(1), 15–28. https://doi.org/10.1080/19420862.2016.1238995
Amaro, R. E., & Mulholland, A. J. (2018). Multiscale methods in drug design bridge chemical and biological complexity in the search for cures. Nature Reviews Chemistry, 2(4), 148. https://doi.org/10.1038/s41570-018-0148
Bai, J. P., Fontana, R. J., Price, N. D., & Sangar, V. (2014). Systems pharmacology modeling: An approach to improving drug safety. Biopharmaceutics & Drug Disposition, 35(1), 1–14. https://doi.org/10.1002/bdd.1871
Bhattacharya, S., Shoda, L. K., Zhang, Q., Woods, C. G., Howell, B. A., Siler, S. Q., ... & Andersen, M. E. (2012). Modeling drug- and chemical-induced hepatotoxicity with systems biology approaches. Frontiers in Physiology, 3, 462. https://doi.org/10.3389/fphys.2012.00462
Cappuccio, A., Tieri, P., & Castiglione, F. (2016). Multiscale modelling in immunology: A review. Briefings in Bioinformatics, 17(3), 408–418. https://doi.org/10.1093/bib/bbv012
Clancy, C. E., An, G., Cannon, W. R., Liu, Y., May, E. E., Ortoleva, P., ... & Eckmann, D. M. (2016). Multiscale modeling in the clinic: Drug design and development. Annals of Biomedical Engineering, 44(9), 2591–2610. https://doi.org/10.1007/s10439-016-1563-0
Costabal, F. S., Yao, J., & Kuhl, E. (2018). Predicting the cardiac toxicity of drugs using a novel multiscale exposure-response simulator. Computer Methods in Biomechanics and Biomedical Engineering, 21(3), 232–246. https://doi.org/10.1080/10255842.2018.1439479
Diaz Ochoa, J. G., Bucher, J., Péry, A. R., Zaldivar Comenges, J. M., Niklas, J., & Mauch, K. (2013). A multi-scale modeling framework for individualized, spatiotemporal prediction of drug effects and toxicological risk. Frontiers in Pharmacology, 3, 204. https://doi.org/10.3389/fphar.2012.00204
Eissing, T., Kuepfer, L., Becker, C., Block, M., Coboeken, K., Gaub, T., ... & Lippert, J. (2011). A computational systems biology software platform for multiscale modeling and simulation. Frontiers in Physiology, 2, 4. https://doi.org/10.3389/fphys.2011.00004
Frieboes, H. B., Lowengrub, J. S., Wise, S., Zheng, X., Macklin, P., Chuang, Y. L., & Cristini, V. (2009). Computer simulation of glioma growth and morphology. NeuroImage, 37, S59–S70. https://doi.org/10.1016/j.neuroimage.2007.03.008
Hwang, W., Hwang, Y., Lee, S., & Lee, D. (2013). Rule-based multi-scale simulation for drug effect pathway analysis. BMC Medical Informatics and Decision Making, 13(Suppl 1), S4. https://doi.org/10.1186/1472-6947-13-S1-S4
Obiol-Pardo, C., Gomis-Tena, J., Sanz, F., Saiz, J., & Pastor, M. (2011). A multiscale simulation system for the prediction of drug-induced cardiotoxicity. Journal of Chemical Information and Modeling, 51(2), 483–492. https://doi.org/10.1021/ci100423z
Oduola, W. O., & Li, X. (2018). Multiscale tumor modeling with drug pharmacokinetic and pharmacodynamic profile using stochastic hybrid system. Cancer Informatics, 17, 1176935118790262. https://doi.org/10.1177/1176935118790262
Passini, E., Britton, O. J., Lu, H. R., Rohrbacher, J., Hermans, A. N., Gallacher, D. J., ... & Rodriguez, B. (2017). Human in silico drug trials demonstrate higher accuracy than animal models in predicting clinical pro-arrhythmic cardiotoxicity. Frontiers in Physiology, 8, 668. https://doi.org/10.3389/fphys.2017.00668
Su, J., Zhang, L., Zhang, W., Choi, D. S., Wen, J., Jiang, B., ... & Zhou, X. (2014). Targeting the biophysical properties of the myeloma initiating cell niches. PLoS ONE, 9(1), e85059. https://doi.org/10.1371/journal.pone.0085059
Sun, X., et al. (2012). Cytokine combination therapy prediction for bone remodeling in tissue engineering. Biomaterials, 33(33), 8265–8276. https://doi.org/10.1016/j.biomaterials.2012.07.041
Thiel, C., Cordes, H., Baier, V., Blank, L. M., & Kuepfer, L. (2017). Multiscale modeling reveals inhibitory and stimulatory effects of caffeine on acetaminophen-induced toxicity in humans. CPT: Pharmacometrics & Systems Pharmacology, 6(2), 136–146. https://doi.org/10.1002/psp4.12153
Vavourakis, V., Wijeratne, P. A., Shipley, R., Loizidou, M., Stylianopoulos, T., & Hawkes, D. J. (2017). A validated multiscale in-silico model for mechano-sensitive tumour angiogenesis and growth. PLoS Computational Biology, 13(1), e1005259. https://doi.org/10.1371/journal.pcbi.1005259
Walpole, J., Papin, J. A., & Peirce, S. M. (2013). Multiscale computational models of complex biological systems. Annual Review of Biomedical Engineering, 15, 137–154. https://doi.org/10.1146/annurev-bioeng-071811-150104
Zhao, S., & Iyengar, R. (2012). Systems pharmacology: Network analysis to identify multiscale mechanisms of drug action. Annual Review of Pharmacology and Toxicology, 52, 505–521. https://doi.org/10.1146/annurev-pharmtox-010611-134520
Recommended articles
Illuminating Biological Dark Matter: Integrating Metagenomics, Synthetic Biology, and AI to Unlock Microbial and Genomic Potential for Therapeutics and Biotechnology
Marine Microbial Natural Products as a Frontier in Drug Discovery: A Systematic Review
1
Save
Save
0
Citation
Citation
12
View
View
0
Share
Share