Pub Date : 2024-09-05DOI: 10.2174/0127724328311400240823062829
Hara Prasad Mishra, Rachna Gupta
Predictions are made by artificial intelligence, especially through machine learning, which uses algorithms and past knowledge. Notably, there has been an increase in interest in using artificial intelligence, particularly generative AI, in the pharmacovigilance of pharmaceuticals under development, as well as those already in the market. This review was conducted to understand how generative AI can play an important role in pharmacovigilance and improving drug safety monitoring. Data from previously published articles and news items were reviewed in order to obtain information. We used PubMed and Google Scholar as our search engines, and keywords (pharmacovigilance, artificial intelligence, machine learning, drug safety, and patient safety) were used. In toto, we reviewed 109 articles published till 31 January 2024, and the obtained information was interpreted, compiled, evaluated, and conclusions were reached. Generative AI has transformative potential in pharmacovigilance, showcasing benefits, such as enhanced adverse event detection, data-driven risk prediction, and optimized drug development. By making it easier to process and analyze big datasets, generative artificial intelligence has applications across a variety of disease states. Machine learning and automation in this field can streamline pharmacovigilance procedures and provide a more efficient way to assess safety-related data. Nevertheless, more investigation is required to determine how this optimization affects the caliber of safety analyses. In the near future, the increased utilization of artificial intelligence is anticipated, especially in predicting side effects and Adverse Drug Reactions (ADRs).
{"title":"Leveraging Generative AI for Drug Safety and Pharmacovigilance.","authors":"Hara Prasad Mishra, Rachna Gupta","doi":"10.2174/0127724328311400240823062829","DOIUrl":"https://doi.org/10.2174/0127724328311400240823062829","url":null,"abstract":"<p><p>Predictions are made by artificial intelligence, especially through machine learning, which uses algorithms and past knowledge. Notably, there has been an increase in interest in using artificial intelligence, particularly generative AI, in the pharmacovigilance of pharmaceuticals under development, as well as those already in the market. This review was conducted to understand how generative AI can play an important role in pharmacovigilance and improving drug safety monitoring. Data from previously published articles and news items were reviewed in order to obtain information. We used PubMed and Google Scholar as our search engines, and keywords (pharmacovigilance, artificial intelligence, machine learning, drug safety, and patient safety) were used. In toto, we reviewed 109 articles published till 31 January 2024, and the obtained information was interpreted, compiled, evaluated, and conclusions were reached. Generative AI has transformative potential in pharmacovigilance, showcasing benefits, such as enhanced adverse event detection, data-driven risk prediction, and optimized drug development. By making it easier to process and analyze big datasets, generative artificial intelligence has applications across a variety of disease states. Machine learning and automation in this field can streamline pharmacovigilance procedures and provide a more efficient way to assess safety-related data. Nevertheless, more investigation is required to determine how this optimization affects the caliber of safety analyses. In the near future, the increased utilization of artificial intelligence is anticipated, especially in predicting side effects and Adverse Drug Reactions (ADRs).</p>","PeriodicalId":29871,"journal":{"name":"Current Reviews in Clinical and Experimental Pharmacology","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-24DOI: 10.2174/0127724328313815240723044625
Tina Kianfar, Raquibul Hasan, Yaser Azizi, Fatemeh Ramezani
In this study, a meta-analysis was conducted to investigate the therapeutic effect of Dapagliflozin (DAPA) on animals suffering from myocardial ischemia reperfusion compared to the group that did not receive treatment. According to the inclusion and exclusion criteria two researchers performed the primary and secondary screening based on the title abstract and full text. After data extraction, meta-analysis was performed using STATA software. Standardized mean differences were used to analyze the results of the reported studies. Subgroup analysis and quality control of articles were also conducted A total of 21 separate experiments showed that DAPA increased mean fractional shortening (%FS) and ejection fraction (%EF) compared to the untreated animals. A significant reduction in the weight and size of the infarcted area and significant increases in dp/dt+ , dp/dt- , left ventricular end-systolic internal dimensions (LVIDs), left ventricular end-diastolic internal dimensions (LVIDd), Volume systole and Volume diastole were observed in treated animals. DAPA has the potential to become a candidate for the treatment of post-ischemic heart damage, pending animal and human studies to validate this.
根据纳入和排除标准,两名研究人员根据标题摘要和全文进行了初筛和复筛。提取数据后,使用 STATA 软件进行荟萃分析。使用标准化平均差来分析报告的研究结果。共有 21 项单独的实验表明,与未经处理的动物相比,DAPA 增加了平均骨折缩短率(%FS)和射血分数(%EF)。在接受治疗的动物身上,可以观察到梗死区的重量和大小明显减少,dp/dt+、dp/dt-、左心室收缩末期内径(LVIDs)、左心室舒张末期内径(LVIDd)、收缩容积和舒张容积明显增加。
{"title":"The Effect of Dapagliflozin on Heart Function in Animal Models of\u0000Cardiac Ischemia, A Systematic Review and Meta-analysis","authors":"Tina Kianfar, Raquibul Hasan, Yaser Azizi, Fatemeh Ramezani","doi":"10.2174/0127724328313815240723044625","DOIUrl":"https://doi.org/10.2174/0127724328313815240723044625","url":null,"abstract":"\u0000\u0000In this study, a meta-analysis was conducted to investigate the therapeutic\u0000effect of Dapagliflozin (DAPA) on animals suffering from myocardial ischemia reperfusion compared to the group that did not receive treatment.\u0000\u0000\u0000\u0000According to the inclusion and exclusion criteria two researchers performed the primary\u0000and secondary screening based on the title abstract and full text. After data extraction, meta-analysis\u0000was performed using STATA software. Standardized mean differences were used to analyze the results of the reported studies. Subgroup analysis and quality control of articles were also conducted\u0000\u0000\u0000\u0000A total of 21 separate experiments showed that DAPA increased mean fractional shortening (%FS) and ejection fraction (%EF) compared to the untreated animals. A significant reduction\u0000in the weight and size of the infarcted area and significant increases in dp/dt+\u0000, dp/dt-\u0000, left ventricular\u0000end-systolic internal dimensions (LVIDs), left ventricular end-diastolic internal dimensions\u0000(LVIDd), Volume systole and Volume diastole were observed in treated animals.\u0000\u0000\u0000\u0000DAPA has the potential to become a candidate for the treatment of post-ischemic heart\u0000damage, pending animal and human studies to validate this.\u0000","PeriodicalId":29871,"journal":{"name":"Current Reviews in Clinical and Experimental Pharmacology","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}