Investigation of Toxicity and Pharmacokinetic Profiles of Some Frequently Used Drugs in the Emergency Departments

Abstract

In this paper, pharmacokinetic profiles and toxicity values, also known as ADME parameters, for some drugs frequently used in hospital emergency services, such as Thiocolchicoside, Pheniramine maleate, Captopril, Dexketoprofen, and Paracetamol, were obtained by in silico research methods. With the study results, some critical parameters, such as intestinal-renal absorption of drugs, effects on the central nervous system, binding percentages to plasma proteins, and pure water solubility, were determined. In addition, organ and cell toxicities of each drug were determined in silico, and LD50 values were reported. According to the obtained results, the highest HIA absorption drug is dexketoprofen. Also, paracetamol has the greatest diffusion throughout the circulatory system between all drugs. Finally, the highest LD50 values are obtained for Captopril (2078 mg/kg) and Pheniramine maleate (343 mg/kg).

Keywords

• Frequently used important emergency service drugs
• in silico predictions
• ADMET
• Toxicity

Acil Servislerde Sık Kullanılan Bazı İlaçların Toksisite ve Farmakokinetik Profillerinin Araştırılması

Öz

Bu çalışmada, hastane acil servislerinde sıklıkla kullanılan Tiyokolşikosid, Feniramin maleat, Kaptopril, Deksketoprofen ve Parasetamol gibi bazı ilaçların farmakokinetik profilleri ve toksisite değerleri, diğer adıyla ADME parametreleri, in silico araştırma yöntemleri ile elde edilmiştir. Çalışma sonuçları ile ilaçların intestinal-renal emilimi, merkezi sinir sistemi üzerindeki etkileri, plazma proteinlerine bağlanma yüzdeleri ve saf suda çözünürlük gibi bazı kritik parametreler belirlenmiştir. Ayrıca her ilacın organ ve hücre toksisiteleri in silico olarak belirlenmiş ve LD50 değerleri raporlanmıştır. Elde edilen sonuçlara göre en yüksek HIA emilimli ilaç deksketoprofendir. Ayrıca parasetamol tüm ilaçlar arasında dolaşım sistemi boyunca en büyük difüzyona sahiptir. Son olarak en yüksek LD50 değerleri Kaptopril (2078 mg/kg) ve Feniramin maleat (343 mg/kg) için elde edilmiştir.

Anahtar Kelimeler

• Sık kullanılan önemli acil servis ilaçları
• in silico tahminler
• ADMET
• Toksisite

Kaynakça

1. Abdel Shaheed, C., Ferreira, G. E., Dmitritchenko, A., McLachlan, A. J., Day, R. O., Saragiotto, B., ... & Maher, C. G. (2021). The efficacy and safety of paracetamol for pain relief: an overview of systematic reviews. Medical Journal of Australia, 214(7), 324-331.
2. Aldossary, A., Campos‐Gonzalez‐Angulo, J. A., Pablo‐García, S., Leong, S. X., Rajaonson, E. M., Thiede, L., ... & Aspuru‐Guzik, A. (2024). In silico chemical experiments in the Age of AI: From quantum chemistry to machine learning and back. Advanced Materials, 2402369.
3. Alshurtan, K., Almomtin, H., Alqhtani, K. F., Alqahtani, A., Aledaili, A., Alharbi, A., ... & Aljassar, S. (2024). Breaking the Emergency Room Cycle: The Impact of Telemedicine on Emergency Department Utilization. Cureus, 16(3).
4. Banerjee, P., Eckert, A. O., Schrey, A. K., & Preissner, R. (2018). ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic acids research, 46(W1), W257-W263.
5. Ben Azouz, C., Bouraoui, H., Ghariani, N., Sriha, B., Slim, R., & Ben Salem, C. (2023). Late-Occurring Captopril-Induced Lichenoid Eruption. Journal of Pharmacy Practice, 36(6), 1311-1313.
6. Bilal, A., Tanvir, F., Ahmad, S., Mustafa, R., Fatima, G., & Shahin, F. (2024). In-silico drug discovery from phytoactive compounds against estrogen receptor beta (ERβ) inducing human mammary carcinoma. Research, 7(4), 1-17.
7. Birinci, S., Ulgu, M. M., & Gözükara, G. G. (2023). Critical Insights Based on the Ministry of Health’s 6-Year Data Analysis: An Epidemiological Study of Patient Visits Trends of Emergency Departments in Türkiye. Haydarpasa Numune Med J, 63(3), 334–339.
8. Carpenter, T. S., Kirshner, D. A., Lau, E. Y., Wong, S. E., Nilmeier, J. P., & Lightstone, F. C. (2014). A method to predict blood-brain barrier permeability of drug-like compounds using molecular dynamics simulations. Biophysical journal, 107(3), 630-641.

9. Carta, M., Murru, L., Botta, P., Talani, G., Sechi, G., De Riu, P., ... & Biggio, G. (2006). The muscle relaxant thiocolchicoside is an antagonist of GABA receptor function in the central nervous system. Neuropharmacology, 51(4), 805-815.
10. Chunaifah, I., Venilita, R. E., Tjitda, P. J. P., Astuti, E., & Wahyuningsih, T. D. (2024). Thiophene-based N-phenyl pyrazolines: Synthesis, anticancer activity, molecular docking and ADME study. Journal of Applied Pharmaceutical Science, 14(4), 063-071.
11. Dennington, R. D., Keith, T. A., & Millam, J. M. (2008). GaussView 5.0, Gaussian. Inc., Wallingford.
12. Frisch, M. J., Trucks, G. W., Schlegel, H. B., Scuseria, G. E., Robb, M. A., Cheeseman, J. R., ... & Cioslowski, J. (2010). Gaussian 09 Revision B. 01 M., and Revision A. 02 SMP J. Gaussian, Inc., Wallingford, CT.
13. Gruber, A., Führer, F., Menz, S., Diedam, H., Göller, A. H., & Schneckener, S. (2024). Prediction of human pharmacokinetics from chemical structure: combining mechanistic modeling with machine learning. Journal of Pharmaceutical Sciences, 113(1), 55-63.
14. Jahan, S., Yang, J., Hu, J., Li, Q., & Fuller, P. J. (2024). Captopril challenge test: an underutilized test in the diagnosis of primary aldosteronism. Endocrine Connections, 13(3).
15. Kelani, K. M., Hegazy, M. A., Hassan, A. M., & Tantawy, M. A. (2022). A green TLC densitometric method for the simultaneous detection and quantification of naphazoline HCl, pheniramine maleate along with three official impurities. BMC chemistry, 16(1), 24.
16. Kuczyńska, J., Pawlak, A., & Nieradko-Iwanicka, B. (2022). The comparison of dexketoprofen and other painkilling medications (review from 2018 to 2021). Biomedicine & Pharmacotherapy, 149, 112819.
17. Lee SK, Kang Y, Chang GS, Lee IH, Park SH, Park J, 2017. Bioinformatics and Molecular Design Research Center. Yonsei University, Seoul https://preadmet. bmdrc. kr.
18. Makhdoom, H. S., Abid, A. I., Mujahid, M., Afzal, S., Sultana, K., Hussain, N., & Barkat, K. (2024). Assessment of pheniramine in alternative biological matrices by liquid chromatography tandem mass spectrometry. Forensic Science, Medicine and Pathology, 1-12.
19. Manhas, D., Dhiman, S., Kour, H., Kour, D., Sharma, K., Wazir, P., ... & Nandi, U. (2024). ADME/PK Insights of Crocetin: A Molecule Having an Unusual Chemical Structure with Druglike Features. ACS omega, 9(19), 21494-21509.
20. Noga, M., Michalska, A., & Jurowski, K. (2024). The estimation of acute oral toxicity (LD50) of G-series organophosphorus-based chemical warfare agents using quantitative and qualitative toxicology in silico methods. Archives of Toxicology, 98(6), 1809-1825.
21. Pore, S., & Roy, K. (2024). Insights into pharmacokinetic properties for exposure chemicals: predictive modelling of human plasma fraction unbound (fu) and hepatocyte intrinsic clearance (Cl int) data using machine learning. Digital Discovery, 3(9), 1852-1877.
22. Raita, Y., Goto, T., Faridi, M. K., Brown, D. F., Camargo, C. A., & Hasegawa, K. (2019). Emergency department triage prediction of clinical outcomes using machine learning models. Critical care, 23, 1-13.
23. Ramadan, Q., Fardous, R. S., Hazaymeh, R., Alshmmari, S., & Zourob, M. (2021). Pharmacokinetics‐on‐a‐chip: in vitro microphysiological models for emulating of drugs ADME. Advanced Biology, 5(9), 2100775.
24. Ruswanto R, Nofianti T, Lestari T, Septian AD, Firmansyah AP, Mardianingeum R (2024) Potential Active Compounds of Propolis as Breast Anticancer Candidates: In Silico Study. JJBS 17:153-161. https://doi.org/10.54319/jjbs/170115
25. Tuan, J., Wang, E. H., De Leon, J. R. C., Mendoza, M. J., & Varrassi, G. (2023). Management of acute cancer pain in Asia: an expert opinion on the role of tramadol/dexketoprofen fixed-dose combination. Cureus, 15(3).
26. Walter, M., Borghardt, J. M., Humbeck, L., & Skalic, M. (2024). Multi‐Task ADME/PK prediction at industrial scale: leveraging large and diverse experimentaldatasets. Molecular Informatics, e202400079.
27. Williams, J., Siramshetty, V., Nguyễn, Ð. T., Padilha, E. C., Kabir, M., Yu, K. R., ... & Shah, P. (2022). Using in vitro ADME data for lead compound selection: An emphasis on PAMPA pH 5 permeability and oral bioavailability. Bioorganic & medicinal chemistry, 56, 116588.
28. Yang, Q., Fan, L., Hao, E., Hou, X., Deng, J., Xia, Z., & Du, Z. (2024). Machine Learning Exploration of the Relationship Between Drugs and the Blood–Brain Barrier: Guiding Molecular Modification. Pharmaceutical Research, 1-13.
29. Zhang, X., Liu, T., Fan, X., & Ai, N. (2017). In silico modeling on ADME properties of natural products: Classification models for blood-brain barrier permeability, its application to traditional Chinese medicine and in vitro experimental validation. Journal of Molecular Graphics and Modelling, 75, 347-354.