Öz
Sağlık hizmetlerinin ve ilaç endüstrisinin dijital dönüşümü, farmakovijilans alanında önemli bir adım olarak kabul edilmektedir. Standart farmakovijilans yaklaşımları daha fazla zaman ve iş gücü gerektirmektedir ve büyük veri ve yapay zeka kullanımının farmakovijilans faaliyetlerinin etkinliğini artırabileceği iddia edilmektedir. Bu nedenle, bu makalede farmakovijilansın dijitalleşmesini ve yapay zeka ile veri analitiğinin rolünü ele alıyoruz. İlaç keşfinin zorlukları ve maliyetleri tartışılmakta, ilaç programlarının yüksek başarısızlık oranı ve yeni ilaçların piyasaya sürülme maliyetinin önemi vurgulanmaktadır. Ayrıca bu makale, ilaç güvenliği için gelecekteki olasılıkları vurgulamakta ve sağlık ve ilaç endüstrilerinin dijitalleşmeye odaklanarak ilerlemesi gerektiğini önermektedir.
Anahtar Kelimeler
Yapay Zeka Farmakovijilans Makine Öğrenimi İlaç Güvenliği Klinik Hata Rapor Analizi
Kaynakça
- 1. Wouters OJ, McKee M, Luyten J. Estimated research and development investment needed to bring a new medicine to market 2009-2018. JAMA. 2020;323(9):844-853.
- 2. Khan Z, Karataş Y, Rahman H. Adverse drug reactions reporting in Turkey and barriers: an urgent need for pharmacovigilance education. Ther Adv Drug Saf. 2020;11: 2042098620922483.
- 3. Ergün Y, Aykan DA. Advers ilaç reaksiyonlarına yaklaşım. Arşiv Kaynak Tarama Dergisi. 2020;29(2):97-107.
- 4. Murali K, Kaur S, Prakash A, Medhi B. Artificial intelligence in pharmacovigilance: practical utility. Indian J Pharmacol. 2019;51(6):373-376.
- 5. Luo Y, Thompson WK, Herr TM, Zeng Z, Berendsen MA, Jonnalagadda SR et al. Natural language processing for EHR-based pharmacovigilance: a structured review. Drug Saf. 2017;40(11):1075–89.
- 6. Schwartz WB, Patil RS, Szolovits P. Artificial intelligence in medicine. Where do we stand? N Engl J Med. 1987;316(11):685-8.
- 7. Avenga Team. Artificial intelligence and machine learning in pharmacovigilance: current use cases and future opportunities. Available from: https://www.avenga.com/magazine/artificial-intelligence-machine-learning-pharma/ Accessed: 18 July 2023.
- 8. Cadirci D, Elif O, Koçakoğlu Ş, Yavuz E, Alaşehirli B. Knowledge and attitudes of resident physicians about adverse drug reactions. Konuralp Medical Journal. 2020;12(3):498-502.
- 9. Pharmacy Times. Available from: https://www.pharmacytimes.com/view/artificial-intelligence-is-changing-the-face-of-pharmacovigilance Accessed: 18 July 2023
- 10. FDA. FDA Adverse Event Reporting System (FAERS) Public Dashboard. Available from: https://fis.fda.gov/sense/app/95239e26-e0be-42d9-a9609a5f7f1c25ee/sheet/7a47a261-d58b-4203-a8aa-6d3021737452/state/analysis Accessed: 19 July 2023
- 11. Bate A, Stegmann JU. Artificial intelligence and pharmacovigilance: What is happening, what could happen and what should happen?. Health Policy and Technology. 2023;12(2):100743.
- 12. Salas M, Petracek J, Yalamanchili P, Aimer O, Kasthuril D, Dhingra S et al. The use of artificial intelligence in pharmacovigilance: a systematic review of the literature. Pharmaceut Med. 2022;36(5):295- 306.
- 13. Bates DW, Levine D, Syrowatka A, Kuznetsova M, Craig KJT, Rui A, et al. The potential of artifcial intelligence to improve patient safety: a scoping review. NPJ Digit Med. 2021;4(1):54.
- 14. Life Sciences Expertise. Available from: https://www.lifesciencesexpertise.com/Download/The%20Use%20of%20AI%20in%20Pharmacovigilance.pdf Accessed: 15 July 2023
- 15. Mockute R, Desai S, Perera S, Assuncao B, Danysz K, Tetarenko N et al. Artificial intelligence within pharmacovigilance: a means to identify cognitive services and the framework for their validation. Pharmaceut Med. 2019;33(2):109-120.
- 16. Liang L, Hu J, Sun G, Hong N, Wu G, He Y et al. Artificial intelligence-based pharmacovigilance in the setting of limited resources. Drug Saf. 2022;45(5):511-519.
- 17. Soni S, Srivastava R, Bhandari A. Smart Drugs: A Review. IJIER. 2020;8(11):1-13.
- 18. Healthy Pleasures. Available from: http://healthypleasures.de/news/nootropics-smart-drugs-the-differences/ Accessed: 17 July 2023
- 19. Létinier L, Jouganous J, Benkebil M, Bel-Létoile A, Goehrs C, Singier A et al. Artificial intelligence for unstructured healthcare data: application to coding of patient reporting of adverse drug reactions. Clin Pharmacol Ther. 2021;110(2):392-400.
- 20. Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR Med Inform. 2020;8(7):e18599.
Öz
The digital transformation of healthcare and the pharmaceutical industry is considered as an important step in the field of pharmacovigilance. Standard pharmacovigilance approaches have more time and labour requirements, and it is claimed that the use of big data and artificial intelligence can improves the effectiveness of pharmacovigilance activities. Therefore, in this article we address the digitalisation of pharmacovigilance and the role of artificial intelligence and data analytics. The challenges and costs of drug discovery are discussed, highlighting the high failure rate of drug programs and the importance of the cost of bringing new drugs to market. Additionally, this article emphasizes the future possibilities for drug safety and suggests that the healthcare and pharmaceutical industries should move forward with a focus on digitalisation.
Anahtar Kelimeler
Artificial Intelligence Pharmacovigilance Machine Learning Drug Safety Clinical Error Report Analysis
Kaynakça
- 1. Wouters OJ, McKee M, Luyten J. Estimated research and development investment needed to bring a new medicine to market 2009-2018. JAMA. 2020;323(9):844-853.
- 2. Khan Z, Karataş Y, Rahman H. Adverse drug reactions reporting in Turkey and barriers: an urgent need for pharmacovigilance education. Ther Adv Drug Saf. 2020;11: 2042098620922483.
- 3. Ergün Y, Aykan DA. Advers ilaç reaksiyonlarına yaklaşım. Arşiv Kaynak Tarama Dergisi. 2020;29(2):97-107.
- 4. Murali K, Kaur S, Prakash A, Medhi B. Artificial intelligence in pharmacovigilance: practical utility. Indian J Pharmacol. 2019;51(6):373-376.
- 5. Luo Y, Thompson WK, Herr TM, Zeng Z, Berendsen MA, Jonnalagadda SR et al. Natural language processing for EHR-based pharmacovigilance: a structured review. Drug Saf. 2017;40(11):1075–89.
- 6. Schwartz WB, Patil RS, Szolovits P. Artificial intelligence in medicine. Where do we stand? N Engl J Med. 1987;316(11):685-8.
- 7. Avenga Team. Artificial intelligence and machine learning in pharmacovigilance: current use cases and future opportunities. Available from: https://www.avenga.com/magazine/artificial-intelligence-machine-learning-pharma/ Accessed: 18 July 2023.
- 8. Cadirci D, Elif O, Koçakoğlu Ş, Yavuz E, Alaşehirli B. Knowledge and attitudes of resident physicians about adverse drug reactions. Konuralp Medical Journal. 2020;12(3):498-502.
- 9. Pharmacy Times. Available from: https://www.pharmacytimes.com/view/artificial-intelligence-is-changing-the-face-of-pharmacovigilance Accessed: 18 July 2023
- 10. FDA. FDA Adverse Event Reporting System (FAERS) Public Dashboard. Available from: https://fis.fda.gov/sense/app/95239e26-e0be-42d9-a9609a5f7f1c25ee/sheet/7a47a261-d58b-4203-a8aa-6d3021737452/state/analysis Accessed: 19 July 2023
- 11. Bate A, Stegmann JU. Artificial intelligence and pharmacovigilance: What is happening, what could happen and what should happen?. Health Policy and Technology. 2023;12(2):100743.
- 12. Salas M, Petracek J, Yalamanchili P, Aimer O, Kasthuril D, Dhingra S et al. The use of artificial intelligence in pharmacovigilance: a systematic review of the literature. Pharmaceut Med. 2022;36(5):295- 306.
- 13. Bates DW, Levine D, Syrowatka A, Kuznetsova M, Craig KJT, Rui A, et al. The potential of artifcial intelligence to improve patient safety: a scoping review. NPJ Digit Med. 2021;4(1):54.
- 14. Life Sciences Expertise. Available from: https://www.lifesciencesexpertise.com/Download/The%20Use%20of%20AI%20in%20Pharmacovigilance.pdf Accessed: 15 July 2023
- 15. Mockute R, Desai S, Perera S, Assuncao B, Danysz K, Tetarenko N et al. Artificial intelligence within pharmacovigilance: a means to identify cognitive services and the framework for their validation. Pharmaceut Med. 2019;33(2):109-120.
- 16. Liang L, Hu J, Sun G, Hong N, Wu G, He Y et al. Artificial intelligence-based pharmacovigilance in the setting of limited resources. Drug Saf. 2022;45(5):511-519.
- 17. Soni S, Srivastava R, Bhandari A. Smart Drugs: A Review. IJIER. 2020;8(11):1-13.
- 18. Healthy Pleasures. Available from: http://healthypleasures.de/news/nootropics-smart-drugs-the-differences/ Accessed: 17 July 2023
- 19. Létinier L, Jouganous J, Benkebil M, Bel-Létoile A, Goehrs C, Singier A et al. Artificial intelligence for unstructured healthcare data: application to coding of patient reporting of adverse drug reactions. Clin Pharmacol Ther. 2021;110(2):392-400.
- 20. Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR Med Inform. 2020;8(7):e18599.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Sağlık Hizmetleri ve Sistemleri (Diğer) |
| Bölüm | Derleme |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Aralık 2023 |
| Kabul Tarihi | 15 Kasım 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 32 Sayı: 4 |
