Panasian system for coordination of efforts aiming the translational medicine development. Role and place of the In Silico models

   A brief-n-clear synopsis of major trends in infrastructure development along with the expert groups activities within Shanghai Cooperation Organization (SCO), 2020–2025, has been presented and discussed following by an accent on both support and control over the national public health programs and, respectively, the translational medicine related innovation projects. An additional emphasize is made on attention expressed by the SCO expert community towards the arsenal of the In Silico mathematical models taking into account their role as the tools sustainable for optimization of the new drugs preclinical trial algorithms. Thus, announced for 2025–2030 ongoing SCO reform promotes an essential possibilities spread-out for its community of experts as this deals with both financial support and scientific expertise performance in case of the international research projects proposed by scientists from member-countries working in such areas as pharmacology/pharmacy and healthcare administration. Taking this into account, a scheme of interactions between the SCO Committees, Commissions and Expert Councils is under discussion, while a special attention to juridical and financial aspects was paid. As per peculiarities of planning and conduction of these SCO activities, this issue was studied within a context of comparison to analogical potential revealing in preclinical and clinical trials on new medicines carried out according to ACS (American Chemical Society) adopted protocols. Giving an example of achievements and perspectives of the SCO criteria fitting innovation projects: the results of preclinical studies (experimental and In Silico tracks) on PMC16-nanocationites designed for tumor cells targeting with a following release of paramagnetic isotopes (²⁵Mg, ⁴³Ca, ⁶⁷Zn) which promotes the corresponding magnetic isotope effects (MIE) and, hence, the MIE-induced cytostatic (anti-tumor) consequences. In conclusion, a data supported prove makes a lot of sense in attentive studies on both SCO experience and the current trends of its applications to pharmacological research including the use of the SCO-developed programs in mathematical modeling in medicine and, in particular, in upgrade of In Silico models.

1. Yi W. Upholding the Shanghai spirit: SCO on the move [Internet]. Announce for the Tianjin summit of the Shanghai Cooperation Organization; 2025 [cited 2025 March 7]. Available from: file:///C:/Users/Sukhorukova_AA/Downloads/P020250808485309733238.pdf

2. Vasiliev AA, Spaper D, Ibragimov ZI. Ways to step up the international scientific and technological cooperation of the SCO countries. Asian Law Journal. 2020;2:92–95. (In Russ.) doi: 10.14258/ralj(2020)2.13

3. Kazemzadeh H, Mozafari M. Fullerene-based delivery systems. Drug Discovery Today. 2019;24(2):898–905. doi: 10.1016/j.drudis.2019.01.013

4. Buchachenko AL. Magnetic effects across biochemistry, molecular biology and environmental chemistry. Academic Press: New York–Boston–Toronto–London; 2024. 250 p.

5. Shiryaev O, Bukhvostov A, Kamkina O, Kuznetsov D. Streamlining drug developments through the In Silico approach to ligand-target interactions. Annals of Biostatistics and Biometric Applications. 2025;6(3):1–11. doi: 10.33552/ABBA.2025.06.000640

6. Fursov V, Bukhvostov A, Kamkina O, et al. Towards the PMC16-nanocationite preclinical trials: Mathematical modeling. Annals of Biostatistics and Biometric Applications. 2025;6(4):1–15. doi: 10.33552/ABBA.2025.06.000643

7. Kombo DC, LaMarche MJ. The logic of chemical optimization. Journal of Medicinal Chemistry. 2025;68(11):11572–11585. doi: 10.1021/acs.jmedchem.5c00445

8. Ferreira FJN, Carneiro AS. AI-driven drug discovery : A comprehensive review. ACS Omega. 2025;10(7):23889–23903. doi: 10.1021/acsomega.5c00549

9. Sun Q, Wang H, Xie J, et al. Computer-aided drug discovery for drug unsuitable targets. Chemical Reviews. 2025;125 (13):6309–6365. doi: 10.1021/acs.chemrev. 4c00969

10. Trejo-Castro AI, Martinez-Ledesma E, Martinez-Torteya A. A bibliometric review on in silico drug repurposing: Performance analysis, science mapping and text mining (2000–2023). Heliyon. 2025;11(10). doi: 10.1016/j.heliyon.2025.e42750

11. Sertkaya A, Beleche T, Jessup A, Sommers BD. Costs of Drug Development and Research and Development Intensity in the US, 2000-2018. JAMA Netw Open. 2024;7(6):e2415445. doi: 10.1001/jamanetworkopen.2024.15445

12. Al-Mohaya M, Mesut B, Kurt A, Çelik YS. In silico approaches which are used in pharmacy. J Appl Pharm Sci. 2024;14(4):239–253. doi: 10.7324/JAPS.2024.154854

13. Musuamba FT, Skottheim Rusten I, Lesage R, et al. Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility. CPT: pharmacometrics & systems pharmacology. 2021;10(8): 804–825. doi: 10.1002/psp4.12669

14. Stamatakos G. In silico medicine: multiscale mechanistic simulation, artificial intelligence and modern statistics as tools to support decisions on real clinical problems. Physica Medica. 2022;104:S5. doi: 10.1016/S1120-1797(22)03025-3

15. Chen B, Schneider LC, Röver C, et al. In silico clinical trials in drug development : a systematic review. Therapeutic Innovation & Regulatory Science. 2025. doi: 10.1007/s43441-025-00893-w ISSN 2168-4790

16. Brogi S, Ramalho TC, Kuca K, et al. Editorial: In silico Methods for Drug Design and Discovery. Front. Chem. 2020;8:612. doi: 10.3389/fchem.2020.00612

17. Spanakis M. In Silico pharmacology for evidence-based and precision medicine. Pharmaceutics. 2023;15(3):1014. doi: 10.3390/pharmaceutics15031014