uprinmatusИзвестия Юго-Западного государственного университета. Серия: Управление, вычислительная техника, информатика. Медицинское приборостроениеProceedings of the Southwest State University. Series: IT Management, Computer Science, Computer Engineering. Medical Equipment Engineering2223-1536Юго-Западный государственный университет10.21869/2223-1536-2024-14-3-36-47uprinmatus-203Research ArticleИНФОРМАЦИОННЫЕ И ИНТЕЛЛЕКТУАЛЬНЫЕ СИСТЕМЫINFORMATION AND INTELLIGENT SYSTEMSПрименение математических моделей для прогнозирования дозировки лекарств и их эффективностиApplication of mathematical models in predicting drug dosage and its efficacyhttps://orcid.org/0000-0001-8504-2418ТуфикШехине МохамадTufikShehine Mohamad

Шехине Мохамад Туфик, доцент кафедры  биологической и химической технологии

ул. К. Маркса, д. 3, г. Курск 305041

Shehine Mohamad Tufik, Associate Professor  of the Department of Biological and Chemical Technology

3 Karl Marx Str., Kursk 305041

chahine@kursksmu.nethttps://orcid.org/0000-0002-6679-2296НиколаосТзениосNikolaosTzenios

Тзениос Николаос, профессор общественного здравоохранения и медицинских исследований

Грейс-Бей

Nikolaos Tzenios, Professor of Public Health and Medical Research

Grace Bay

chahine@kursksmu.nethttps://orcid.org/0000-0003-0786-4533ЗавидовскаяК. В.ZavidovskayaK. V.

Ксения В. Завидовская, ассистент кафедры биологической и химической технологии

ул. К. Маркса, д. 3, г. Курск 305041

Ksenia Viktorovna Zavidovskaya, Аssistant  of the Department of Biological and Chemical Technology

3 Karl Marx Str., Kursk 305041

knvz@mail.ruhttps://orcid.org/0000-0003-1937-1453ЛазуринаЛ. П.LazurinaL. P.

Людмила П. Лазурина, доктор,  биологических наук, профессор, заведующий кафедрой биологической и химической технологии

ул. К. Маркса, д. 3, г. Курск 305041

Scopus ID: 55924214100

Lyudmila Petrovna Lazurina, Doctor of Science  (Biology), Professor, Head of the Department  of Biological and Chemical Technology

3 Karl Marx Str., Kursk 305041

lazurinalp@kursksmu.nethttps://orcid.org/0000-0001-8458-9083ДоценкоЮ. М.DotsenkoYu. M.

Юлия М. Доценко, ассистент кафедры биологической и химической технологии

ул. К. Маркса, д. 3, г. Курск 305041

Yulia Mikhailovna Dotsenko, Assistant of the Department of Biological and Chemical Technology

3 Karl Marx Str., Kursk 305041

yuliadoczenko@gmail.comКурский государственный медицинский университет Министерства здравоохранения Российской ФедерацииРоссияKursk State Medical University of the Ministry of Health of the Russian FederationRussian FederationУниверситет ХаризмыТёркс и КайкосCharisma UniversityTurks and Caicos Islands2024151120241433647Copyright © Туфик Ш., Николаос Т., Завидовская К.В., Лазурина Л.П., Доценко Ю.М., 20242024Туфик Ш., Николаос Т., Завидовская К.В., Лазурина Л.П., Доценко Ю.М.Tufik S., Nikolaos T., Zavidovskaya K.V., Lazurina L.P., Dotsenko Y.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://uprinmatus.elpub.ru/jour/article/view/203

Цель работы заключается в формировании всестороннего понимания того, как математические модели применяются для интерпретации сложной динамики, связанной с распределением, метаболизмом и выведением лекарств в организме человека. Использование математических моделей для прогнозирования необходимой дозы назначений лекарственных препаратов и установления его эффективности означает изменение парадигмы в области фармакологии. 

Методы, использованные в данном исследовании, были направлены на выявление и аналитический обзор статей, соответствующих целям исследования. Публикации, включенные в анализ, были подвергнуты анализу и извлечению данных, сосредоточив внимание на ключевой информации, такой как используемая методология математического моделирования, точные прогнозируемые эффекты лечения, изучаемые популяции, долгосрочные прогностические эффекты и оценка применения различных режимов дозирования препаратов.

Результаты

Результаты. Всего было проанализировано 12 публикаций, в которых использовались четыре различные методологии: модели с воздействием нескольких различных состояний; модели, учитывающие возникновение разнообразных дискретных событий; модели, основанные на воздействии информативных признаков с учетом физиологии индивидов, а также модели выживания и обобщенные линейные модели. 

Заключение

Заключение. Проведенное исследование текущего состояния математического моделирования в медицинских исследованиях с целью сравнительной эффективности предназначено для практикующих научных работников и врачей при проведении дальнейших исследований и внедрении инноваций. Несмотря на существование проблем, потенциальное влияние этих моделей направлено на преодоление разрыва между контролируемой клинической средой и реальным контекстом здравоохранения неоспоримо. Применение математических методов моделирования для прогнозирования дозировки лекарственных средств позволит повысить качество и эффективность персонифицированных медицинских назначений в ближайшие годы.

The purpose of the research is to form a comprehensive understanding of how mathematical models are used to interpret complex dynamics related to the distribution, metabolism and excretion of drugs in the human body. The use of mathematical models to predict the required dose of drug prescriptions and establish its effectiveness means a paradigm shift in the field of pharmacology.

Methods

Methods. The methodology used in this study was aimed at identifying and analytically reviewing articles that correspond to the objectives of the study. The publications included in the analysis were analyzed and data extracted, focusing on key information such as the mathematical modeling methodology used, the exact predicted treatment effects, the populations studied, long-term prognostic effects, and the assessment of the use of various drug dosing regimens.

Results

Results. In total, 12 publications were analyzed, which used four different methodologies: models with the effects of several different conditions, models that take into account the occurrence of various discrete events, models based on the effects of informative signs taking into account the physiology of individuals, as well as survival models and generalized linear models.

Conclusion

Conclusion. The conducted study of the current state of mathematical modeling in medical research for the purpose of comparative effectiveness is intended for practicing scientists and doctors in conducting further research and introducing innovations. Despite the challenges, the potential impact of these models aimed at bridging the gap between the controlled clinical environment and the real health context is undeniable. The use of mathematical modeling methods to predict the dosage of medicines will improve the quality and effectiveness of personalized medical appointments in the coming years.

математическое моделированиепрогнозированиеэффективностьлекарственные назначенияmathematical modelingforecastingefficacydrugReferencesHartmanshenn C., Scherholz M., Androulakis I.P. Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine. Journal of Pharmacokinetics and Pharmacodynamics. 2016;(43):481–504.Hartmanshenn C., Scherholz M., Androulakis I.P. Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine. Journal of Pharmacokinetics and Pharmacodynamics. 2016;(43):481–504.Marsousi N., Desmeules J.A., Rudaz S., Daali Y. Usefulness of PBPK Modeling in Incorporation of Clinical Conditions in Personalized Medicine. J. Pharm. Sci. 2017;106(9):2380–2391.Marsousi N., Desmeules J.A., Rudaz S., Daali Y. Usefulness of PBPK Modeling in Incorporation of Clinical Conditions in Personalized Medicine. J. Pharm. Sci. 2017;106(9):2380–2391.Krauss M., Tappe K., Schuppert A., Kuepfer L., Goerlitz L. Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations. PLoS One. 2015;10(10):0139423. https://doi.org/10.1371/journal.pone.0139423Krauss M., Tappe K., Schuppert A., Kuepfer L., Goerlitz L. Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations. PLoS One. 2015;10(10):0139423. https://doi.org/10.1371/journal.pone.0139423Fan C., Basharat Z., Mah K., Wei C.R. Computational approach for drug discovery against Gardnerella vaginalis in quest for safer and effective treatments for bacterial vaginosis. Sci. Rep. 2024;14(1):17437. https://doi.org/10.1038/s41598-024-68443-2Fan C., Basharat Z., Mah K., Wei C.R. Computational approach for drug discovery against Gardnerella vaginalis in quest for safer and effective treatments for bacterial vaginosis. Sci. Rep. 2024;14(1):17437. https://doi.org/10.1038/s41598-024-68443-2Djuris J., Cvijic S., Djekic L. Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration. Pharmaceuticals (Basel). 2024:17(2):177. https://doi.org/10.3390/ph17020177Djuris J., Cvijic S., Djekic L. Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration. Pharmaceuticals (Basel). 2024:17(2):177. https://doi.org/10.3390/ph17020177Wang Y., Zhu H., Madabushi R., Liu Q., Huang S.M., Zineh I. Model-informed drug development: Current US regulatory practice and future considerations. Clin. Pharmacol. Ther. 2019;105(4):899–911. https://doi.org/10.1002/cpt.1363Wang Y., Zhu H., Madabushi R., Liu Q., Huang S.M., Zineh I. Model-informed drug development: Current US regulatory practice and future considerations. Clin. Pharmacol. Ther. 2019;105(4):899–911. https://doi.org/10.1002/cpt.1363Cvijić S., Ignjatović J., Parojčić J., Ibrić S. The emerging role of physiologically-based pharmacokinetic/biopharmaceutics modeling in formulation development. Arh. Farm. 2021;71:318–335. https://doi.org/10.5937/arhfarm71-32479Cvijić S., Ignjatović J., Parojčić J., Ibrić S. The emerging role of physiologically-based pharmacokinetic/biopharmaceutics modeling in formulation development. Arh. Farm. 2021;71:318–335. https://doi.org/10.5937/arhfarm71-32479Kesisoglou F., Chung J., van Asperen J., Heimbach T. Physiologically based absorption modeling to impact biopharmaceutics and formulation strategies in drug development-industry case studies. J. Pharm. Sci. 2016;105(9):2723–2734. https://doi.org/10.1016/j.xphs.2015.11.034Kesisoglou F., Chung J., van Asperen J., Heimbach T. Physiologically based absorption modeling to impact biopharmaceutics and formulation strategies in drug development-industry case studies. J. Pharm. Sci. 2016;105(9):2723–2734. https://doi.org/10.1016/j.xphs.2015.11.034Abbiati R.A., Manca D. A modeling tool for the personalization of pharmacokinetic predictions. Comput. Chem. Eng. 2016;91:28–37. https://doi.org/10.1016/j.compchemeng.2016.03.008Abbiati R.A., Manca D. A modeling tool for the personalization of pharmacokinetic predictions. Comput. Chem. Eng. 2016;91:28–37. https://doi.org/10.1016/j.compchemeng.2016.03.008Dar K.B., Bhat A.H., Amin S., Hamid R., Anees S., Anjum S., Reshi B.A., Zargar M.A., Masood A., Ganie S.A. Modern Computational Strategies for Designing Drugs to Curb Human Diseases: A Prospect. Curr. Top. Med. Chem. 2018;18(31):2702–2719.Dar K.B., Bhat A.H., Amin S., Hamid R., Anees S., Anjum S., Reshi B.A., Zargar M.A., Masood A., Ganie S.A. Modern Computational Strategies for Designing Drugs to Curb Human Diseases: A Prospect. Curr. Top. Med. Chem. 2018;18(31):2702–2719.Singh K., Bhushan B., Singh B. Advances in Drug Discovery and Design using Computer-aided Molecular Modeling. Curr. Comput. Aided. Drug. Des. 2024;20(5):697–710. https://doi.org/10.2174/1573409920666230914123005Singh K., Bhushan B., Singh B. Advances in Drug Discovery and Design using Computer-aided Molecular Modeling. Curr. Comput. Aided. Drug. Des. 2024;20(5):697–710. https://doi.org/10.2174/1573409920666230914123005Razia I.T., Kanwal A., Riaz H.F., Malik A., Ahsan M., Khan M.S., Raza A., Sabir S., Sajid Z., Khan M.F., Tahir R.A., Sehgal S.A. Recent Trends in Computer-aided Drug Design for Anti-cancer Drug Discovery. Curr. Top. Med. Chem. 2023;23(30):2844–2862.Razia I.T., Kanwal A., Riaz H.F., Malik A., Ahsan M., Khan M.S., Raza A., Sabir S., Sajid Z., Khan M.F., Tahir R.A., Sehgal S.A. Recent Trends in Computer-aided Drug Design for Anti-cancer Drug Discovery. Curr. Top. Med. Chem. 2023;23(30):2844–2862.Hasan R., Alsaiari A.A., Fakhurji B.Z., Molla M.H.R., Asseri A.H., Sumon A.A., Park M.N., Ahammad F., Kim B. Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process. Molecules. 2022;27(13):4169.Hasan R., Alsaiari A.A., Fakhurji B.Z., Molla M.H.R., Asseri A.H., Sumon A.A., Park M.N., Ahammad F., Kim B. Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process. Molecules. 2022;27(13):4169.Atanasov A.G., Zotchev S.B., Dirsch V.M., Orhan I.E., Banach M., Rollinger J.M., Barreca D., Weckwerth W., Bauer R., Bayer E.A., et al. Natural Products in Drug Discovery: Advances and Opportunities. Nat. Rev. Drug. Discov. 2021;20:200–216.Atanasov A.G., Zotchev S.B., Dirsch V.M., Orhan I.E., Banach M., Rollinger J.M., Barreca D., Weckwerth W., Bauer R., Bayer E.A., et al. Natural Products in Drug Discovery: Advances and Opportunities. Nat. Rev. Drug. Discov. 2021;20:200–216.Atanasov A.G., Waltenberger B., Pferschy-Wenzig E.M., Linder T., Wawrosch C., Uhrin P., Temml V., Wang L., Schwaiger S., Heiss E.H., et al. Discovery and Resupply of Pharmacologically Active Plant-Derived Natural Products: A Review. Biotechnol. Adv. 2015;33:1582–1614.Atanasov A.G., Waltenberger B., Pferschy-Wenzig E.M., Linder T., Wawrosch C., Uhrin P., Temml V., Wang L., Schwaiger S., Heiss E.H., et al. Discovery and Resupply of Pharmacologically Active Plant-Derived Natural Products: A Review. Biotechnol. Adv. 2015;33:1582–1614.Li C.Q., Lei H.M., Hu Q.Y., Li G.H., Zhao P.J. Recent Advances in the Synthetic Biology of Natural Drugs. Front. Bioeng. Biotechnol. 2021;9:640. https://doi.org/10.3389/fbioe.2021Li C.Q., Lei H.M., Hu Q.Y., Li G.H., Zhao P.J. Recent Advances in the Synthetic Biology of Natural Drugs. Front. Bioeng. Biotechnol. 2021;9:640. https://doi.org/10.3389/fbioe.2021Rudrapal M., Khairnar S.J., Jadhav A.G. Drug Repurposing (DR): An Emerging Approach in Drug Discovery. In: Drug Repurposing. IntechOpen; 2020. 234 p.Rudrapal M., Khairnar S.J., Jadhav A.G. Drug Repurposing (DR): An Emerging Approach in Drug Discovery. In: Drug Repurposing. IntechOpen; 2020. 234 p.Biala G., Kedzierska E., Kruk-Slomka M., Orzelska-Gorka J., Hmaidan S., Skrok A., Kaminski J., Havrankova E., Nadaska D., Malik I. Research in the Field of Drug Design and Development. Pharmaceuticals (Basel). 2023;16(9):1283.Biala G., Kedzierska E., Kruk-Slomka M., Orzelska-Gorka J., Hmaidan S., Skrok A., Kaminski J., Havrankova E., Nadaska D., Malik I. Research in the Field of Drug Design and Development. Pharmaceuticals (Basel). 2023;16(9):1283.Gunnarsson E.B., Kim S., Choi B., Schmid J.K., Kaura K., Lenz H.J., Mumenthaler S.M., Foo J. Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework. PLoS. Comput. Biol. 2024;20(8):e1012256.Gunnarsson E.B., Kim S., Choi B., Schmid J.K., Kaura K., Lenz H.J., Mumenthaler S.M., Foo J. Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework. PLoS. Comput. Biol. 2024;20(8):e1012256.Ncube N.B., Tukulula M., Govender K.G. Leveraging computational tools to combat malaria: assessment and development of new therapeutics. J. Cheminform. 2024;16(1):50.Ncube N.B., Tukulula M., Govender K.G. Leveraging computational tools to combat malaria: assessment and development of new therapeutics. J. Cheminform. 2024;16(1):50.

The authors declare that there are no conflicts of interest present.