In silico study of isotretinoin–collagen complexes for the design of drug–protein delivery systems
Estudio in silico de complejos isotretinoína–colágeno para el diseño de sistemas de liberación fármaco–proteína
Estudo in silico de complexos isotretinoína–colágeno para o desenho de sistemas de liberação fármaco–proteína
DOI:
https://doi.org/10.15446/rcciquifa.v55n3.127936Palabras clave:
Isotretinoin, collagen, drug delivery systems, protein-drug interactions, molecular docking, drug interactions, molecular dynamics simulation (en)isotretinoína, colágeno, sistemas de liberación de fármacos, interacciones proteína–fármaco, acoplamiento molecular, simulaciones de dinámica molecular, MM-GBSA (es)
isotretinoína, colágeno, sistemas de liberação de fármacos, interações proteína–fármaco, acoplamento molecular, simulações de dinâmica molecular, MM-GBSA (pt)
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Introduction: Isotretinoin (ITN), a vitamin A derivative commonly used for acne treatment, is limited by safety concerns related to its adverse effects. Protein-based delivery systems represent a promising strategy to improve its therapeutic profile. Collagen (COL), the most abundant mammalian protein, is an attractive biomaterial for drug delivery applications. Aim: This study investigated, using an in silico approach, the interaction between ITN and different human COL types (I, II and III) to evaluate their potential as drug-protein complexes for ITN delivery. Methods: Molecular docking between ITN and COL was performed at two pH values (4.0 and 7.0). Subsequently, 200 ns molecular dynamics (MD) simulations were carried out at each pH for every complex, and binding free energies were estimated using the MM-GBSA method. Results: Molecular docking showed that ITN exhibited the highest affinity for COL I at pH 4.0, followed by COL III, whereas the weakest interaction was observed with COL II. MD simulations suggested that the protonation state of ITN influences complex formation, supporting greater stability at pH 4.0, when ITN is in its non-ionized form. The trajectories also showed multiple potential binding sites within COL, suggesting the possibility of stoichiometries other than 1:1. MM-GBSA calculations revealed the energetic contributions stabilizing the ITN-COL complexes, with the strongest interaction for COL I at pH 4.0. Conclusions: These results suggest that COL I at pH 4.0 is a promising carrier candidate for ITN delivery, with potential applications in the development of safer anti-acne therapeutic strategies.
Introducción: La isotretinoína (ITN), un fármaco derivado de la vitamina A comúnmente utilizado para el tratamiento del acné, presenta limitaciones asociadas con problemas de seguridad relacionados con sus efectos adversos. Los sistemas de liberación basados en proteínas representan una estrategia prometedora para mejorar su perfil terapéutico. El colágeno (COL), la proteína más abundante en los mamíferos, constituye un biomaterial atractivo para aplicaciones en liberación de fármacos. Objetivo: En este estudio se investigó, mediante un enfoque in silico, la interacción entre la ITN y diferentes tipos de COL humano (I, II y III), con el fin de evaluar su potencial como complejos fármaco–proteína para la liberación de ITN. Métodos: Se realizó acoplamiento molecular entre ITN y COL a dos valores de pH, 4,0 y 7,0. Posteriormente, se llevaron a cabo simulaciones de dinámica molecular (DM) de 200 ns para cada complejo en cada condición de pH, y las energías libres de unión fueron estimadas mediante el método MM-GBSA. Resultados: El acoplamiento molecular mostró que la ITN presentó la mayor afinidad por el COL I a pH 4,0, seguida por el COL III, mientras que la interacción más débil se observó con el COL II. Las simulaciones de DM sugirieron que el estado de protonación de la ITN influye en la formación del complejo, respaldando una mayor estabilidad a pH 4,0, cuando la ITN se encuentra en su forma no ionizada. Las trayectorias también evidenciaron múltiples sitios potenciales de unión dentro del COL, lo que sugiere la posibilidad de estequiometrías distintas de 1:1. Los cálculos MM-GBSA revelaron las contribuciones energéticas que estabilizan los complejos ITN–COL, con la interacción más fuerte observada para el COL I a pH 4,0. Conclusiones: Estos resultados sugieren que el COL I a pH 4,0 es un candidato prometedor como transportador para la liberación de ITN, con potenciales aplicaciones en el desarrollo de estrategias terapéuticas antiacné más seguras.
Introdução: A isotretinoína (ITN), um derivado da vitamina A comumente utilizado no tratamento da acne, apresenta limitações associadas a questões de segurança relacionadas aos seus efeitos adversos. Os sistemas de liberação baseados em proteínas representam uma estratégia promissora para melhorar seu perfil terapêutico. O colágeno (COL), a proteína mais abundante nos mamíferos, constitui um biomaterial atrativo para aplicações em liberação de fármacos. Objetivo: Neste estudo, investigou-se, por meio de uma abordagem in silico, a interação entre a ITN e diferentes tipos de COL humano (I, II e III), com o objetivo de avaliar seu potencial como complexos fármaco–proteína para a liberação de ITN. Métodos: Realizou-se acoplamento molecular entre ITN e COL em dois valores de pH, 4,0 e 7,0. Posteriormente, foram realizadas simulações de dinâmica molecular (DM) de 200 ns para cada complexo em cada condição de pH, e as energias livres de ligação foram estimadas pelo método MM-GBSA. Resultados: O acoplamento molecular mostrou que a ITN apresentou a maior afinidade pelo COL I em pH 4,0, seguida pelo COL III, enquanto a interação mais fraca foi observada com o COL II. As simulações de DM sugeriram que o estado de protonação da ITN influencia a formação do complexo, sustentando maior estabilidade em pH 4,0, quando a ITN se encontra em sua forma não ionizada. As trajetórias também evidenciaram múltiplos sítios potenciais de ligação no COL, sugerindo a possibilidade de estequiometrias diferentes de 1:1. Os cálculos MM-GBSA revelaram as contribuições energéticas que estabilizam os complexos ITN–COL, com a interação mais forte observada para o COL I em pH 4,0. Conclusões: Estes resultados sugerem que o COL I em pH 4,0 é um candidato promissor como carreador para a liberação de ITN, com potenciais aplicações no desenvolvimento de estratégias terapêuticas antiacne mais seguras.
Referencias
1. F. Ballanger & B. Dreno. Acné. EMC - Tratado de Medicina, 14(2), 1–8 (2010). https://doi.org/10.1016/ s1636-5410(10)70507-9
2. C.S. Costa, E. Bagatin, Z. Yang, R.L. Pacheco, P. Magin, L. De Sá Urtiga-Santos, T. Pereira & R. Riera. Systemic pharmacological treatments for acne: an overview of systematic reviews. Cochrane Database of Systematic Reviews, 11, 1465–1858 (2021). https://doi.org/10.1002/14651858.cd014917
3. H.J. Castellanos-Lorduy, H.C. Pérez-Cely, E.J. Casadiego-Rincón, S.C. Henao-Riveros & C.L. Colorado. Perfil de resistencia a la tetraciclina de Cutibacterium acnes en pacientes con acné vul-gar en un centro dermatológico de Colombia. Actas Dermo-Sifiliográficas, 112(10), 873–880 (2021). https://doi.org/10.1016/j.ad.2021.05.004
4. A.S. Karadag, L.C. Parish & W.C. Lambert. Isotretinoin in retrospect. Clinics in Dermatology, 35(3), 335–339 (2017). https://doi.org/10.1016/j.clindermatol.2016.12.002
5. P. Brzezinski, K. Borowska, A. Chiriac & J. Smigielski. Adverse effects of isotretinoin: A large, retrospective review. Dermatologic Therapy, 30, e12483 (2017). https://doi.org/10.1111/dth.12483
6. R. Alfaraj, S. Hababah, E.K. Eltayb, F.Y. Alqahtani & F.S. Aleanizy. Isotretinoin self-nano-emulsifying drug delivery system: Preparation, optimization and antibacterial evaluation. Sau-di Pharmaceutical Journal, 32(6), 102063 (2024). https://doi.org/10.1016/j.jsps.2024.102063
7. A. Sedighidarijani, S. Golmohammadzadeh, H. Kamali, B. Khameneh, E. Khodaverdi & A. Nokhodchi. Isotretinoin-loaded topical lipid liquid crystal for the treatment of acne: In-vitro and in-vivo evaluations. AAPS PharmSciTech, 26, 117 (2025). https://doi.org/10.1208/s12249-025-03106-7
8. C. Ferraro, M. Dattilo, F. Patitucci, S. Prete, G. Scopelliti, O. Parisi & F. Puoci. Exploring protein-based carriers in drug delivery: A review. Pharmaceutics, 16(9), 1172 (2024). https://doi.org/10.3390/pharmaceutics16091172
9. M.N. Gupta, A. Alam & S.E. Hasnain. Protein promiscuity in drug discovery, drug-repurposing and antibiotic resistance. Biochimie, 175, 50–57 (2020). https://doi.org/10.1016/j.biochi.2020.05.004
10. M.W. Kirkness, K. Lehmann & N.R. Forde. Mechanics and structural stability of the collagen triple helix. Current Opinion in Chemical Biology, 53, 98–105 (2019). https://doi.org/10.1016/j.cbpa. 2019.08.001
11. K. Wang, R. Cao & H. Dong. Diversity of collagen proteins and their biomedical applications in drug delivery. Applied Sciences, 15(12), 6472 (2025). https://doi.org/10.3390/app15126472
12. V. Wesp, L. Scholz, J.M. Ziermann-Canabarro, S. Schuster & H. Stark. Constructing networks for comparison of collagen types. Journal of Integrative Bioinformatics, 21(3), 20240020 (2024). https://doi.org/10.1515/jib-2024-0020
13. L.R. Hart, C.G. Lebedenko, B.G. Goncalves, M.I. Rico, D.J. Lambo, D.S. Perez & I.A. Banerjee. Exploration of type II and III collagen binding interactions with short peptide-phenyl pyrazole conjugates via docking, molecular dynamics and laboratory experiments. Soft Materials, 21(1), 53–82 (2023). https://doi.org/10.1080/1539445x.2022.2147946
14. P. Ramakrishnan, T. Pavan-Kumar, G. Saraswathy & S. Sujatha. In silico evaluation of drugs used in treatment of oral lichen planus. Journal of Oral Pathology & Medicine, 49(9), 926–932 (2020). https://doi.org/10.1111/jop.13100
15. U. Soni, P. Singh, P. Gupta, S. Gupta, S. Singh, P. Singh & K. Mishra. Lichen planus drugs re-purposing as potential anti COVID-19 therapeutics through molecular docking and molecular dynamics simulation approach. Journal of Clinical and Translational Research, 8(2), 127–146 (2022). https://doi.org/10.18053/jctres.08.202202.003
16. B.S.F. Bravo, D.R. Azulay, R.R. Luiz, C.A. Mandarim-De-Lacerda, T. Cuzzi & M.M. Azulay. Oral isotretinoin in photoaging: objective histological evidence of efficacy and durability. Anais Bra-sileiros de Dermatologia, 90(4), 479–486 (2015). https://doi.org/10.1590/abd1806-4841.20153703
17. S. Ricard-Blum. The collagen family. Cold Spring Harbor Perspectives in Biology, 3, a004978 (2011). https://doi.org/10.1101/cshperspect.a004978
18. Y. Zhu, X. Yang & F. Sun. Structure of a triple-helix region of human collagen type I. Protein Data Bank, 2021. https://doi.org/10.2210/pdb7cwk/pdb
19. X. Yang, Y. Zhu, S. Ye & R. Zhang. Structure of a triple-helix region of human collagen type II. Protein Data Bank, 2019. https://doi.org/10.2210/pdb6jec/pdb
20. C. Hua, Y. Zhu, W. Xu, S. Ye, R. Zhang, L. Lu & S. Jiang. Characterization by high-resolution crystal structure analysis of a triple-helix region of human collagen type III with potent cell ad-hesion activity. Biochemical and Biophysical Research Communications, 508(4), 1018–1023 (2019). https://doi.org/10.1016/j.bbrc.2018.12.018
21. S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, et al. PubChem 2023 update. Nucleic Acids Research, 51(D1), D1373-D1380 (2023). https://doi.org/10.1093/nar/gkac956
22. M.D. Hanwell, D.E. Curtis, D.C. Lonie, T. Vandermeersch, E. Zurek & G.R. Hutchison. Avoga-dro: an advanced semantic chemical editor, visualization, and analysis platform. Journal of Cheminformatics, 4, 17 (2012). https://doi.org/10.1186/1758-2946-4-17
23. O. Trott & A.J. Olson. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461 (2010). https://doi.org/10.1002/jcc.21334
24. J. Eberhardt, D. Santos-Martins, A.F. Tillack & S. Forli. AutoDock Vina 1.2.0: New docking methods, expanded force field, and Python bindings. Journal of Chemical Information and Model-ing, 61(8), 3891–3898 (2021). https://doi.org/10.1021/acs.jcim.1c00203
25. E.F. Pettersen, T.D. Goddard, C.C. Huang, G.S. Couch, D.M. Greenblatt, E.C. Meng & T.E. Ferrin. UCSF Chimera—A visualization system for exploratory research and analysis. Journal of Com-putational Chemistry, 25(13), 1605–1612 (2004). https://doi.org/10.1002/jcc.20084
26. G. Corso, H. Stärk, B. Jing, R. Barzilay & T. Jaakkola. DiffDock: Diffusion steps, twists, and turns for molecular docking. arXiv; 2023. https://doi.org/10.48550/arXiv.2210.01776
27. P. Szpak. Fish bone chemistry and ultrastructure: implications for taphonomy and stable iso-tope analysis. Journal of Archaeological Science, 38(12), 3358–3372 (2011). https://doi.org/10.1016/ j.jas.2011.07.022
28. M. Van Der Rest & R. Garrone. Collagen family of proteins. The FASEB Journal, 5(13), 2814–2823 (1991). https://doi.org/10.1096/fasebj.5.13.1916105
29. S. Genheden & U. Ryde. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10(5), 449–461 (2015). https://doi.org/10.1517/ 17460441.2015.1032936
30. H. Sun, Y. Li, S. Tian, L. Xu & T. Hou. Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simu-lation protocols using PDBbind data set. Physical Chemistry Chemical Physics, 16(31), 16719–16729 (2014). https://doi.org/10.1039/c4cp01388c
31. S. Makeneni, D.F. Thieker & R.J. Woods. Applying pose clustering and MD simulations to elimi-nate false positives in molecular docking. Journal of Chemical Information and Modeling, 58(3), 605–614 (2018). https://doi.org/10.1021/acs.jcim.7b00588
32. H. Sahrai, R. Kian, A. Shamkhali & R. Kheradmand. Evaluation of solvent effect on the effective interactions of isotretinoin and tretinoin: isomeric forms of vitamin A. Heliyon, 10(3), e25174 (2024). https://doi.org/10.1016/j.heliyon.2024.e25174
33. N. Homeyer & H. Gohlke. Free energy calculations by the molecular mechanics Pois-son−Boltzmann surface area method. Molecular Informatics, 31(2), 114–122 (2012). https://doi.org/10.1002/ minf.201100135
34. M. Feig, A. Onufriev, M.S. Lee, W. Im, D.A. Case & C.L. Brooks. Performance comparison of generalized Born and Poisson methods in the calculation of electrostatic solvation energies for protein structures. Journal of Computational Chemistry, 25(2), 265–284 (2004). https://doi.org/10.1002/ jcc.10378
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