Evaluating the Safety Profiles of Withdrawn Medications: A Data-Driven Approach to Adverse Drug Reactions Using Statistical models

Evaluating the Safety Profiles of Withdrawn Medications: A Data-Driven Approach to Adverse Drug Reactions Using Statistical models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating the Safety Profiles of Withdrawn Medications: A Data-Driven Approach to Adverse Drug Reactions Using Statistical models Samadhan Ghubade, Sharvari Shukla This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6284906/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Adverse drug reactions (ADRs) are critical in evaluating a medicine's safety and effectiveness both during the drug development process and post-marketing surveillance. This research focuses on drugs that had serious adverse drug reactions (ADRs) and were either taken off the market or withdrawn. We try to find to identify trends in ADR reporting following the discontinuation of these medications by applying data visualization techniques to ADR reports obtained from VigiAccess and the FDA's Adverse Event Reporting System (FAERS). We find a variety of trends and contrary to the expected sharp decline in ADR reports post-withdrawal, we observe various trends—sigmoidal, exponential, and linear—which may offer new insights for comparing medication safety. Objectives This study aimed to investigate the patterns of ADR reporting associated with medications that have been withdrawn or banned due to severe adverse reactions. We plan to analyze these patterns and investigate their possible usefulness in evaluating and comparing the safety profiles of various drugs by using data visualization methods. Methods We used publicly available datasets from VigiAccess, the WHO’s global database of reported potential side effects of medicinal products, and the FDA’s Adverse Event Reporting System (FAERS) Public Dashboard. A comprehensive analysis was performed on cumulative count of ADR reports for selected medications that have been withdrawn from the market. The analysis focused on identifying various trends in ADR reporting counts of withdrawal of these medications. For visualization of these trends, we applied various curve-fitting techniques, including linear and non-linear statistical models such as sigmoidal, exponential, and linear forms. Additionally, for comparing the safety profiles of cancer drugs, a ranking was conducted using an exponential growth rate model to assess and contrast their safety dynamics. Results The withdrawn or banned drugs are expected to conclude usage, resulting in a zero cumulative count of adverse drug reactions (ADRs), a pattern expected for all banned drugs. Total 39 drugs analyzed showed various linear and nonlinear patterns, including 17 following a saturation pattern, 10 showing a linear pattern, 7 showing an exponential pattern, and 5 showing a sigmoidal pattern. Examples shown in this study found that drugs Benoxaprofen, Rosiglatazone, Temazepam, and Rofecoxib presented a strong fit with various models, with Benoxaprofen showing a saturating hyperbola model with R² value of 0.98, Rosiglatazone showing a linear model R² value of 0.96, and Temazepam indicating an excellent fit with the exponential model with R² value of 0.99. Rofecoxib followed a sigmoidal pattern with an R² value of 0.92, reflecting a strong fit with the sigmoidal model. For safety comparison of 15 drugs used in cancer treatment the drug Tamoxifen is a safer drug due to its slower ADR accumulation rate and growth rate (i.e., 0.0972), while drug Pembrolizumab has a higher exponential growth rate (i.e., 0.8277), indicating higher associated risks. Conclusion The study demonstrates the value of data visualization in uncovering diverse ADR reporting patterns for withdrawn medications. These patterns offer a novel perspective on post-market drug safety and could serve as a comparative tool for evaluating the safety profiles of various medications. Tamoxifen was found to be safer due to slower ADR accumulation, while Pembrolizumab presented greater hazards. These findings provide valuable insights into drug safety. Adverse Drug Reactions (ADRs) Model fitting withdrawn drugs Pharmacovigilance WHO Growth rate Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND All allopathic medications have the potential to have negative side effects 1 . Prior to authorizing a medication, pharma regulators assess its advantages against any potential negative side effects 2 . While evaluating the safety and effectiveness of the medications, adverse drug reactions (ADRs) are a major concern in the medication development process 3 . Data from the drug's clinical trials (CTs) is used to support its approval. But after the medication is licensed and sold, side effects that weren't seen in the initial studies may surface. This is due to the fact that CTs have time, subject count, ethnicity, age, health composition, and other restrictions 4 . It is expected of pharmaceutical corporations to continuously track the side effects that their products cause to patients 5 . Adverse drug responses (ADRs) are unpleasant occurrences that may have resulted from a medication or prescription. In the actual world, these ADRs are notified to the holders of market authorizations (MAH) and health authorities (HA) concurrently. The MAH and HAs then archive these ADRs. The WHO's "Vigibase" and the US FDA's adverse event reporting system (FAERS) are the two main databases containing these reports. Millions of these ADRs are contained in these databases. One sort of surveillance known as pharmacovigilance mostly depends on the voluntary reporting of adverse drug reactions (ADRs) by patients, pharmacists, and healthcare providers. One well-known public source for such data is The Uppsala Monitoring Centre of the World Health Organization (WHO), accessible through their website www.vigiaccess.org , which offers annual ADR report counts for various medications. Additionally, the FDA provides a helpful overview of ADRs through their Questions and Answers on the FDA's Adverse Event Reporting System (FAERS) on their website ( https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-public-dashboard ). As anticipated, adverse drug reaction (ADR) data undergo thorough and detailed analysis. This rigorous examination is essential to identify patterns, assess drug safety, and understand the potential risks associated with medications. By analyzing these data, healthcare professionals and regulatory bodies can make informed decisions, enhance patient safety, and develop strategies to mitigate adverse effects. Such analyses also contribute to improving public health policies and ensuring the efficacy and safety of pharmaceuticals on the market. The study examines pharmacovigilance and ADR reporting in developing countries, identifies challenges, and proposes improvements. It highlights the need for better reporting systems and the role of healthcare professionals in enhancing ADR documentation 6 . The study examined ADRs for cardiometabolic drugs in SSA versus the rest of the world using VigiBase data from 1992 to 2013. The study identified key ICSRs, noting higher reporting frequencies for certain drugs and ADRs in SSA, highlighting the need for improved regional pharmacovigilance systems 7 ​. Analysis of the ADR reports from Africa compared to the rest of the world using data from VigiBase, highlighting regional differences in ADR profiles and reporting patterns. The issue of adverse drug reactions (ADRs) being underreported in India, noting a number of important factors that contribute to this problem 8 . The study identifies factors such as lack of awareness among healthcare professionals, insufficient training in pharmacovigilance, and inadequate reporting systems 9 . Examines factors contributing to underreporting in India, highlights challenges, and suggests solutions to improve ADR reporting 10 . A few research projects used the Weber model. This pattern implied that the first two years following a drug's introduction to the market were when adverse event reports spontaneously occurred, with a noticeable reduction occurring shortly thereafter. Safety signal detection is another goal of ADR analysis from public databases and it is challenging to detect drug safety signals 11 . In this case, the goal is to determine as soon as possible if a particular adverse event can be considered to have been "caused" by the medication. The identification of safety signals can be aided by proportionality analysis. The format of the data in this instance is a 2x2 contingency table. Drug under study is indicated in the first row, while other medications are mixed in the second row. Cases reporting the particular adverse event of interest—such as myocardial infarction, or heart attack, to put it plainly—are included in the first column, and additional reports are listed in the second 12 . It is studied that a medicine is tentatively identified as the "cause" of an adverse event if compared to other medications, it indicates an abnormally high frequency of such kind of occurrence 13 . There are methods investigating drug-related syndromes and drug-drug interactions. Frequent events with combined medications may indicate interactions, and clustered event types suggest drug-related conditions 14 . Analysis carried out for the complexity and risks of drug-drug interactions in oral chemotherapeutic cancer treatments by studying related clinical outcomes 15 . The methods only study one or a few adverse events at a time, limiting a comprehensive evaluation of drug safety and preventing a full comparison of multiple drugs 16 . The study examines developments and potential paths in the dissemination of information about the safe use of medications, with an emphasis on the efficient distribution of safety data to diverse stakeholders within the framework of pharmacovigilance 17 . METHODS Data collection, processing and analysis This study examines the number of adverse drug reactions (ADRs) reported for medications that the manufacturer has removed from sale or that regulators have banned due to their subpar safety records. Because of this group's small size, it should be simpler to identify any patterns in the number of ADR reports. There are more than forty medications in this class. We observed at thirty-nine medications with their ADR reports out of them. The concept of withdrawal/banning is more ambiguous than it first appears. Drugs may be prohibited in some nations but not in others. Later on, it might be brought back. Only some of the several forms of a medicine that may be available may be taken off the market. Despite these challenges, it is valuable to continue exploring this area. Rather than using the published annual counts, we have analyzed cumulative counts. This is due to the fact that cumulative sums typically behave more smoothly than individual counts. Furthermore, wider trends rather than any outlier in a particular year are what we are most interested in. The data is shown with the total number of adverse occurrences on the y-axis and the year (time) on the x-axis. Regarding the pattern, it is usually expected that when a medication is banned or withdrawn, the number of yearly ADR reports for that medication will drop precipitously and stay there. Stated differently, the graph should climb first prior to withdrawal and then plateau following removal. We refer to this as a saturated shape. The nonlinear saturation curve pattern is observed for around fifty percentage of the drugs that were analyzed in general. (Ideally, the use of a drug should end immediately upon withdrawal or prohibition. ADR occurrences ought to cease immediately. However, the phenomena of stimulated reporting do occasionally occur. These reports are the result of both active searches spurred by the potential to obtain reimbursement from the drug's manufacturers and growing awareness.) Surprisingly, yet, not all medications adhere to this trend. Rather, one of the three patterns—linear, exponential, or sigmoidal—is evident. Investigating these patterns' implications is necessary. One unexpected characteristic is the frequent deviations from the anticipated saturation trend. Beyond the group of forty medications, there were more than seventeen medications with a higher ADR count. Most of them in this series adhered to the saturated pattern. Model Fitting and computation For model fitting and computation, we used SAS software, while graphical representations were created using the R package and Minitab. Saturating growth As discussed earlier, when a drug is banned, its usage is expected to cease, leading to a drop in adverse drug reactions (ADRs) to zero. This cessation of ADRs should result in a flat cumulative count over time, which is the anticipated pattern for all banned drugs, indicating that no new ADR reports are being generated post-ban. An example of a drug following this saturating curve is Benoxaprofen. Developed and marketed by Eli Lilly and Company, an American pharmaceutical firm, Benoxaprofen was used for its anti-inflammatory and analgesic properties to treat conditions like rheumatoid arthritis and osteoarthritis. The U.S. Food and Drug Administration (FDA) approved it in 1982, but it was withdrawn from the market within the same year due to serious side effects, including hepatotoxicity (liver damage), photosensitivity reactions, and renal impairment. The below given ( Fig. 1 ) displays the observed accumulation curve and an R 2 value of 0.98 indicates a decent fit with a saturating hyperbola fitted to the data. The saturating fit, which shows that the frequency of ADR reports has sharply dropped and to insignificant levels following the withdrawal, provides insight into the withdrawal's effectiveness. This identical saturation trend is present in about seventeen different medications. An inventory of these medications is included in the Table 1 . This pattern shows a consistent drop in ADR complaints after the ban, demonstrating the drug's successful withdrawal from use. Linear growth In the analysis, we identify ten drugs exhibiting a linear pattern shown in Table 1 . As discussed earlier, the primary aim is to identify drugs that deviate from the expected saturating pattern. A notable outlier is Rosiglitazone, developed and marketed by GlaxoSmithKline (GSK) for the treatment of type 2 diabetes mellitus. This drug was approved by the U.S. Food and Drug Administration (FDA) in 1999. However, in 2010, the European Medicines Agency (EMA) suspended its marketing authorization due to concerns regarding cardiovascular safety. Although these restrictions were lifted in 2013 following a re-evaluation of safety data, the usage of Rosiglitazone had already declined significantly by then. A well-fitting linear model with an R 2 value of 0.96 is shown in Fig. 2 . The yearly count of adverse drug reaction (ADR) reports appears to have steadied at about 155, according to this linear fit. A continuous volume of intake and a constant probability of adverse responses per usage are suggested by the consistent annual ADR report count. This steady rate of ADR reports aligns with the idea that while the restrictions on Rosiglitazone were lifted, its withdrawal might have been only partial, allowing its continued use in some regions. Thus, drugs like Rosiglitazone, which exhibit this linear reporting pattern, highlight a steady incidence of ADRs despite regulatory changes. Exponential growth In our next case study, we examine the drug Temazepam , which was approved by the U.S. Food and Drug Administration (FDA) in 1981. Initially marketed by Hoffmann-La Roche under the brand name Restoril, it was removed from the market in 1999 due to issues related to diversion, abuse, and a higher incidence of overdose deaths compared to other drugs in its class. The data presented in Fig. 3 provides an exponential model with an R² value of 0.99, indicating a strong fit. This model reveals a dramatic increase in the number of adverse drug reaction (ADR) reports, surpassing 2000 by 2023. This data suggests that the previous explanations do not apply in this case. Although temazepam remains available and is prescribed for short-term use due to its potential for dependence and other side effects, its consumption has increased systematically. Temazepam has also been linked to incidences of date rape and reported to be addictive. Sigmoidal curve In our analysis of the drug Rofecoxib, a nonsteroidal anti-inflammatory drug (NSAID) developed by Merck & Co., which was approved by the FDA in 1999 and withdrawn from the market in 2004 due to cardiovascular risks, we observed a notable trend. Following its withdrawal, before showing indications of leveling off, the number of adverse drug reaction (ADR) reports increased for almost eight years. This trend is represented by a sigmoidal pattern, as demonstrated by an R 2 value of 0.92 and an excellent fit displayed in Fig. 4 . The continued increase in ADR reports post-withdrawal is unexpected. We have identified six other drugs exhibiting a similar sigmoidal pattern (refer to the Table 1 ), highlighting that while this pattern resembles a saturating trend, there are distinct characteristics to be noted. Saturation is evident in this category as well, although it manifests differently compared to other patterns. The above-stated results are illustrated in table given below (Table 1 ). Table 1 Drugs prohibited based on ADR accumulation count curve form Saturation Linear Exponential Sigmoidal Belviq Dofetilide Bromhexine Rofecoxib Natalizumab Flunitrazepam Hydromorphone Aprotinin Pergolide Ketorolac Ibuprofen Lumiracoxib Phenylbutazone Ozogamicin Iproniazid Redux Tegaserod Rosiglitazone Amphetamine Roxiam Terfenadine Clobutinol Metamizole Troglitazone Destibenol Temazepam Benoxaprofen Cervidil Bromfenac Tegison Simvastatin Phenylbutazone Cisapride Eculizumab Nefazodone Meridia Trovafloxacin Efalizumab Valdecoxib DISCUSSION Method of curve fitting and medication safety comparison The ADR numbers for pharmaceuticals that are reported for drugs that are discontinued or prohibited are expected to follow the saturation model when totaled. Surprisingly, yet, some of these medications rise according to linear, exponential, or sinusoidal patterns. This might be the result of bulk reporting of ADRs that were previously unreported or of reporting incentives. Sometimes, people may continue taking a medication despite negative reports if they have a positive. It is possible to use the current curve fitting technique to currently prescribed medications and possibility of modeling using comparatively simple methods. Working with de-identified data and examining trends for important organ classes including the heart, lungs, liver, and kidneys will also be pertinent 18 . If necessary, the list of adverse events can be reduced to only those that are crucial or clinically meaningful. The primary worry is whether the assortment of forms included in the category of medications that are restricted will be sufficient to encompass the larger group. Study to identify the patterns and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review and hence comparing drug safety profiles is a useful use of pattern recognition in adverse drug reactions (ADRs) in pharmacovigilance research 19 . When evaluating drugs with comparable efficacy—i.e., those offering similar therapeutic benefits for the targeted condition—preferentially selecting the drug with a more favorable ADR profile becomes crucial. A single drug may be associated with a wide array of adverse reactions, comparisons must rely on summary measures to distill complex ADR data 20 . For example, when a drug shows a linear increase in ADR report counts over time, the slope of this line serves as a summary statistic. The average annual increase of ADR reports is seen by this slope. In theory, a steeper slope would indicate a better safety profile for medications with comparable efficacy and linear growth in ADR counts. However, interpreting this measure requires caution due to variations in drug exposure, which can significantly influence ADR counts. A drug with higher exposure will naturally accumulate more ADR reports over time. Think about two medications, Test drug and Reference drug, for example. If Reference drug sells 50 ADR reports after 100 doses sold compared to 100 ADR reports for Test drug after 1,000 doses sold, it appears that Reference drug is safer based on these apparent ADR statistics. Test drug has an ADR reporting rate of one in ten doses when exposure is taken into account, while Drug B has an ADR reporting rate of one in two doses. On this basis, Test drug is therefore relatively safer. To make such comparisons more robust, adjustments should be made for differences in drug exposure. This can be achieved by normalizing ADR counts based on annual sales or a similar proxy measure. Assuming comparable spontaneous reporting rates for the drugs in question, this method allows for a more accurate assessment of relative safety. In cases where ADR counts exhibit exponential or sigmoidal growth patterns, the growth rate parameter (r) can be utilized for comparison. Applying these methodologies to specific drug categories, such as cancer drugs, can provide insightful safety evaluations and support more informed decision-making in drug selection. practical implications Safety comparison of cancer drugs (change it to another group) Trends in the ADR counts of illegal drugs might be significant, but it's more interesting to compare prescription medications that have previously been prescribed using this method. We have examined the situation with cancer treatment drugs, a rigorous examination of their safety is paramount due to the multifaceted nature of their use and the high risk associated with their side effects. This study will involve a comparative analysis of various cancer therapies, focusing on their adverse effects, interactions with other medications, and long-term health outcomes. The research technique will assess the frequency and severity of adverse drug reactions (ADRs) by gathering and analyzing data from clinical trials, patient reports, and real-world evidence. Special attention will be given to drug interactions, particularly in polypharmacy scenarios common among cancer patients, and the impact of these interactions on overall safety. Additionally, the study will investigate long-term health consequences associated with cancer treatments, including potential secondary cancers and chronic conditions. By employing statistical models and safety assessment frameworks, this research aims to identify and mitigate risks, thereby informing more effective and personalized treatment strategies while enhancing patient safety and well-being. Therefore, our goal is to compare its ADR count to that of others. In every instance, an exponential model was fitted. Table 2 displays the outcome. Table 2 Ranking of the drugs (Cumulative ADR Observed vs. Fitted) Drug Name Model Equation R² Rank (r) Tamoxifen Exponential y = 1198.5xe 0.0972x 0.95 0.0972 Avastin Exponential y = 2215.9xe 0.1211x 0.92 0.1211 Bleomycin Exponential y = 114.55xe 0.139x 0.979 0.139 Paclitaxel Exponential y = 1526.4xe 0.1673x 0.998 0.1673 Vincristine Exponential y = 210.66xe 0.1706x 0.98 0.1706 Methotrexate Exponential y = 438.1xe 0.1853x 0.971 0.1853 Cisplatin Exponential y = 331.75xe 0.1859x 0.973 0.1859 Doxorubicin Exponential y = 922.11xe 0.208x 0.91 0.208 Imatinib Exponential y = 661.54xe 0.2427x 0.85 0.2427 Docetaxel Exponential y = 174.39xe 0.267x 0.888 0.267 Rituxima Exponential y = 243.38xe 0.2771x 0.91 0.2771 Trastuzumab Exponential y = 64.147xe 0.3025x 0.87 0.3025 Revlimid Exponential y = 67.908xe 0.5431x 0.72 0.5431 Lenalidomide Exponential y = 67.848xe 0.5432x 0.72 0.5432 Pembrolizumab Exponential y = 1.1524xe 0.8277x 0.73 0.8277 In all cases, the exponential model demonstrated a good fit for the ADR data, with satisfactory residual plots validating the model's performance. Within this framework, the exponential growth rate parameter (r) plays a crucial role in determining the shape of the ADR growth curve, reflecting the rate at which ADR counts increase over time. Specifically, a lower value of r indicates a slower rate of ADR growth, suggesting a safer drug. Importantly, this parameter is independent of the drug's usage extent, meaning that the growth rate r is solely indicative of the drug's relative safety profile, regardless of how widely the drug is used. If the usage of a drug is doubled and all counts of adverse drug reactions (ADRs) also double, the growth rate parameter r remains unchanged. This is because r specifically measures the relative rate of growth of ADR reports and is independent of the scale of the data. The doubling affects only the parameter a, which represents the initial level of ADR counts. In the case of evaluating the safety profile of sevoflurane, despite its high efficacy and low growth rate r, it appears less favorable compared to three other drugs, which demonstrate a superior safety profile based on the same growth rate measure. This unexpected finding highlights the need for further investigation by experts in the field. Understanding the nuances behind these results could provide deeper insights into the safety dynamics and help refine safety evaluations of cancer drugs and other drugs. Limitations ADRs might still be reported after a drug is withdrawn due to residual use or long-term effects. Temporary increases in ADR reporting may result from increased awareness prior to or following a drug's discontinuation. In our research, curve fitting is complicated by uncertainties, as drugs may be prohibited in some countries but not others, reintroduced later, or only certain formulations may be withdrawn. Further scope of the research Utilize machine learning and artificial intelligence to identify patterns and predict ADRs, potentially improving early detection of safety signals. Conduct longitudinal studies to track ADRs over extended periods after a drug's withdrawal, providing insights into long-term safety profiles. Develop a common decision-making probability model underlying safety of different drugs and some simulations results to improve patient’s quality of life and well-being. Declarations Acknowledgments Thanks to all reviewers and our colleagues who provided their valuable time and feedback. Authors’ contribution The conceptualization of this research was organized by Samadhan Ghubade and Dr. Sharvari Shukla, who jointly developed the initial idea and research objectives. Samadhan Ghubade done the data collection process, utilizing various open-source platforms to gather relevant datasets. Following this, authors conducted an in-depth analysis of the collected data, with active participation from both authors throughout the analytical phase to ensure robust and accurate outcomes. Samadhan Ghubade took the lead in drafting the manuscript, crafting the initial version with attention to detail and clarity. Subsequently, all authors engaged in a thorough review and editing process, contributing their insights to refine the manuscript further. The final version of the manuscript reflects the collaborative efforts of all authors, who have read and fully endorsed its content. Additionally, the authors collaborated closely to ensure that the study's findings were presented in a clear, concise, and accessible manner, enhancing the manuscript's overall impact and relevance. Funding The author (s) reported that there is no funding associated with the work featured in this article. Data Availability The corresponding author can provide the datasets used and analyzed in this study upon reasonable request. The FDA's Adverse Event Reporting System (FAERS) and Public Dashboard, as well as VigiAccess, the WHO's global database of reported possible drug side effects, were the sources of the data. Disclosure statement Ethics approval and consent to participate The FDA's Adverse Event Reporting System (FAERS) Public Dashboard and VigiAccess are two examples of open-source public datasets that were used in this investigation. There is no personally identifying information in these publicly accessible datasets. Because of this, there was no direct contact with human subjects during the investigation, therefore no specific consent was needed. Every use of the data was compliant with the terms of use and ethical standards supplied by the individual data sources. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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Prior to authorizing a medication, pharma regulators assess its advantages against any potential negative side effects\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. While evaluating the safety and effectiveness of the medications, adverse drug reactions (ADRs) are a major concern in the medication development process\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Data from the drug's clinical trials (CTs) is used to support its approval. But after the medication is licensed and sold, side effects that weren't seen in the initial studies may surface. This is due to the fact that CTs have time, subject count, ethnicity, age, health composition, and other restrictions\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. It is expected of pharmaceutical corporations to continuously track the side effects that their products cause to patients\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdverse drug responses (ADRs) are unpleasant occurrences that may have resulted from a medication or prescription. In the actual world, these ADRs are notified to the holders of market authorizations (MAH) and health authorities (HA) concurrently. The MAH and HAs then archive these ADRs. The WHO's \"Vigibase\" and the US FDA's adverse event reporting system (FAERS) are the two main databases containing these reports. Millions of these ADRs are contained in these databases.\u003c/p\u003e \u003cp\u003eOne sort of surveillance known as pharmacovigilance mostly depends on the voluntary reporting of adverse drug reactions (ADRs) by patients, pharmacists, and healthcare providers. One well-known public source for such data is The Uppsala Monitoring Centre of the World Health Organization (WHO), accessible through their website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.vigiaccess.org\u003c/span\u003e\u003cspan address=\"http://www.vigiaccess.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, which offers annual ADR report counts for various medications. Additionally, the FDA provides a helpful overview of ADRs through their Questions and Answers on the FDA's Adverse Event Reporting System (FAERS) on their website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-public-dashboard\u003c/span\u003e\u003cspan address=\"https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-public-dashboard\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs anticipated, adverse drug reaction (ADR) data undergo thorough and detailed analysis. This rigorous examination is essential to identify patterns, assess drug safety, and understand the potential risks associated with medications. By analyzing these data, healthcare professionals and regulatory bodies can make informed decisions, enhance patient safety, and develop strategies to mitigate adverse effects. Such analyses also contribute to improving public health policies and ensuring the efficacy and safety of pharmaceuticals on the market. The study examines pharmacovigilance and ADR reporting in developing countries, identifies challenges, and proposes improvements. It highlights the need for better reporting systems and the role of healthcare professionals in enhancing ADR documentation\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The study examined ADRs for cardiometabolic drugs in SSA versus the rest of the world using VigiBase data from 1992 to 2013. The study identified key ICSRs, noting higher reporting frequencies for certain drugs and ADRs in SSA, highlighting the need for improved regional pharmacovigilance systems\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e ​. Analysis of the ADR reports from Africa compared to the rest of the world using data from VigiBase, highlighting regional differences in ADR profiles and reporting patterns. The issue of adverse drug reactions (ADRs) being underreported in India, noting a number of important factors that contribute to this problem\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The study identifies factors such as lack of awareness among healthcare professionals, insufficient training in pharmacovigilance, and inadequate reporting systems\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Examines factors contributing to underreporting in India, highlights challenges, and suggests solutions to improve ADR reporting\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA few research projects used the Weber model. This pattern implied that the first two years following a drug's introduction to the market were when adverse event reports spontaneously occurred, with a noticeable reduction occurring shortly thereafter. Safety signal detection is another goal of ADR analysis from public databases and it is challenging to detect drug safety signals\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In this case, the goal is to determine as soon as possible if a particular adverse event can be considered to have been \"caused\" by the medication. The identification of safety signals can be aided by proportionality analysis. The format of the data in this instance is a 2x2 contingency table. Drug under study is indicated in the first row, while other medications are mixed in the second row. Cases reporting the particular adverse event of interest\u0026mdash;such as myocardial infarction, or heart attack, to put it plainly\u0026mdash;are included in the first column, and additional reports are listed in the second\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. It is studied that a medicine is tentatively identified as the \"cause\" of an adverse event if compared to other medications, it indicates an abnormally high frequency of such kind of occurrence\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. There are methods investigating drug-related syndromes and drug-drug interactions. Frequent events with combined medications may indicate interactions, and clustered event types suggest drug-related conditions\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Analysis carried out for the complexity and risks of drug-drug interactions in oral chemotherapeutic cancer treatments by studying related clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe methods only study one or a few adverse events at a time, limiting a comprehensive evaluation of drug safety and preventing a full comparison of multiple drugs\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The study examines developments and potential paths in the dissemination of information about the safe use of medications, with an emphasis on the efficient distribution of safety data to diverse stakeholders within the framework of pharmacovigilance\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection, processing and analysis\u003c/h2\u003e \u003cp\u003eThis study examines the number of adverse drug reactions (ADRs) reported for medications that the manufacturer has removed from sale or that regulators have banned due to their subpar safety records. Because of this group's small size, it should be simpler to identify any patterns in the number of ADR reports. There are more than forty medications in this class. We observed at thirty-nine medications with their ADR reports out of them. The concept of withdrawal/banning is more ambiguous than it first appears. Drugs may be prohibited in some nations but not in others. Later on, it might be brought back. Only some of the several forms of a medicine that may be available may be taken off the market. Despite these challenges, it is valuable to continue exploring this area.\u003c/p\u003e \u003cp\u003eRather than using the published annual counts, we have analyzed cumulative counts. This is due to the fact that cumulative sums typically behave more smoothly than individual counts.\u003c/p\u003e \u003cp\u003eFurthermore, wider trends rather than any outlier in a particular year are what we are most interested in. The data is shown with the total number of adverse occurrences on the y-axis and the year (time) on the x-axis. Regarding the pattern, it is usually expected that when a medication is banned or withdrawn, the number of yearly ADR reports for that medication will drop precipitously and stay there. Stated differently, the graph should climb first prior to withdrawal and then plateau following removal. We refer to this as a saturated shape.\u003c/p\u003e \u003cp\u003eThe nonlinear saturation curve pattern is observed for around fifty percentage of the drugs that were analyzed in general. (Ideally, the use of a drug should end immediately upon withdrawal or prohibition. ADR occurrences ought to cease immediately. However, the phenomena of stimulated reporting do occasionally occur. These reports are the result of both active searches spurred by the potential to obtain reimbursement from the drug's manufacturers and growing awareness.) Surprisingly, yet, not all medications adhere to this trend. Rather, one of the three patterns\u0026mdash;linear, exponential, or sigmoidal\u0026mdash;is evident. Investigating these patterns' implications is necessary.\u003c/p\u003e \u003cp\u003eOne unexpected characteristic is the frequent deviations from the anticipated saturation trend. Beyond the group of forty medications, there were more than seventeen medications with a higher ADR count. Most of them in this series adhered to the saturated pattern.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eModel Fitting and computation\u003c/h3\u003e\n\u003cp\u003eFor model fitting and computation, we used SAS software, while graphical representations were created using the R package and Minitab.\u003c/p\u003e\n\u003ch3\u003eSaturating growth\u003c/h3\u003e\n\u003cp\u003eAs discussed earlier, when a drug is banned, its usage is expected to cease, leading to a drop in adverse drug reactions (ADRs) to zero. This cessation of ADRs should result in a flat cumulative count over time, which is the anticipated pattern for all banned drugs, indicating that no new ADR reports are being generated post-ban.\u003c/p\u003e \u003cp\u003eAn example of a drug following this saturating curve is Benoxaprofen. Developed and marketed by Eli Lilly and Company, an American pharmaceutical firm, Benoxaprofen was used for its anti-inflammatory and analgesic properties to treat conditions like rheumatoid arthritis and osteoarthritis. The U.S. Food and Drug Administration (FDA) approved it in 1982, but it was withdrawn from the market within the same year due to serious side effects, including hepatotoxicity (liver damage), photosensitivity reactions, and renal impairment.\u003c/p\u003e \u003cp\u003eThe below given \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e displays the observed accumulation curve and an R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e value of 0.98 indicates a decent fit with a saturating hyperbola fitted to the data. The saturating fit, which shows that the frequency of ADR reports has sharply dropped and to insignificant levels following the withdrawal, provides insight into the withdrawal's effectiveness. This identical saturation trend is present in about seventeen different medications. An inventory of these medications is included in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This pattern shows a consistent drop in ADR complaints after the ban, demonstrating the drug's successful withdrawal from use.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eLinear growth\u003c/h3\u003e\n\u003cp\u003eIn the analysis, we identify ten drugs exhibiting a linear pattern shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As discussed earlier, the primary aim is to identify drugs that deviate from the expected saturating pattern. A notable outlier is Rosiglitazone, developed and marketed by GlaxoSmithKline (GSK) for the treatment of type 2 diabetes mellitus. This drug was approved by the U.S. Food and Drug Administration (FDA) in 1999. However, in 2010, the European Medicines Agency (EMA) suspended its marketing authorization due to concerns regarding cardiovascular safety. Although these restrictions were lifted in 2013 following a re-evaluation of safety data, the usage of Rosiglitazone had already declined significantly by then.\u003c/p\u003e \u003cp\u003eA well-fitting linear model with an R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e value of 0.96 is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The yearly count of adverse drug reaction (ADR) reports appears to have steadied at about 155, according to this linear fit. A continuous volume of intake and a constant probability of adverse responses per usage are suggested by the consistent annual ADR report count. This steady rate of ADR reports aligns with the idea that while the restrictions on Rosiglitazone were lifted, its withdrawal might have been only partial, allowing its continued use in some regions. Thus, drugs like Rosiglitazone, which exhibit this linear reporting pattern, highlight a steady incidence of ADRs despite regulatory changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eExponential growth\u003c/h3\u003e\n\u003cp\u003eIn our next case study, we examine the drug \u003cb\u003eTemazepam\u003c/b\u003e, which was approved by the U.S. Food and Drug Administration (FDA) in 1981. Initially marketed by Hoffmann-La Roche under the brand name Restoril, it was removed from the market in 1999 due to issues related to diversion, abuse, and a higher incidence of overdose deaths compared to other drugs in its class. The data presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides an exponential model with an R\u0026sup2; value of 0.99, indicating a strong fit. This model reveals a dramatic increase in the number of adverse drug reaction (ADR) reports, surpassing 2000 by 2023. This data suggests that the previous explanations do not apply in this case. Although temazepam remains available and is prescribed for short-term use due to its potential for dependence and other side effects, its consumption has increased systematically. Temazepam has also been linked to incidences of date rape and reported to be addictive.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSigmoidal curve\u003c/h2\u003e \u003cp\u003eIn our analysis of the drug Rofecoxib, a nonsteroidal anti-inflammatory drug (NSAID) developed by Merck \u0026amp; Co., which was approved by the FDA in 1999 and withdrawn from the market in 2004 due to cardiovascular risks, we observed a notable trend. Following its withdrawal, before showing indications of leveling off, the number of adverse drug reaction (ADR) reports increased for almost eight years. This trend is represented by a sigmoidal pattern, as demonstrated by an R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e value of 0.92 and an excellent fit displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The continued increase in ADR reports post-withdrawal is unexpected. We have identified six other drugs exhibiting a similar sigmoidal pattern (refer to the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), highlighting that while this pattern resembles a saturating trend, there are distinct characteristics to be noted. Saturation is evident in this category as well, although it manifests differently compared to other patterns.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe above-stated results are illustrated in table given below (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDrugs prohibited based on ADR accumulation count curve form\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaturation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSigmoidal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelviq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDofetilide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBromhexine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRofecoxib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatalizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlunitrazepam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHydromorphone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAprotinin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePergolide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKetorolac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIbuprofen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLumiracoxib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylbutazone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOzogamicin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIproniazid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRedux\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTegaserod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRosiglitazone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmphetamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRoxiam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerfenadine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClobutinol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetamizole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTroglitazone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDestibenol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemazepam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenoxaprofen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCervidil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBromfenac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTegison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimvastatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhenylbutazone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCisapride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEculizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNefazodone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeridia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrovafloxacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfalizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValdecoxib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMethod of curve fitting and medication safety comparison\u003c/h2\u003e \u003cp\u003eThe ADR numbers for pharmaceuticals that are reported for drugs that are discontinued or prohibited are expected to follow the saturation model when totaled. Surprisingly, yet, some of these medications rise according to linear, exponential, or sinusoidal patterns. This might be the result of bulk reporting of ADRs that were previously unreported or of reporting incentives.\u003c/p\u003e \u003cp\u003eSometimes, people may continue taking a medication despite negative reports if they have a positive.\u003c/p\u003e \u003cp\u003eIt is possible to use the current curve fitting technique to currently prescribed medications and possibility of modeling using comparatively simple methods. Working with de-identified data and examining trends for important organ classes including the heart, lungs, liver, and kidneys will also be pertinent\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. If necessary, the list of adverse events can be reduced to only those that are crucial or clinically meaningful. The primary worry is whether the assortment of forms included in the category of medications that are restricted will be sufficient to encompass the larger group.\u003c/p\u003e \u003cp\u003eStudy to identify the patterns and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review and hence comparing drug safety profiles is a useful use of pattern recognition in adverse drug reactions (ADRs) in pharmacovigilance research\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. When evaluating drugs with comparable efficacy\u0026mdash;i.e., those offering similar therapeutic benefits for the targeted condition\u0026mdash;preferentially selecting the drug with a more favorable ADR profile becomes crucial.\u003c/p\u003e \u003cp\u003eA single drug may be associated with a wide array of adverse reactions, comparisons must rely on summary measures to distill complex ADR data\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. For example, when a drug shows a linear increase in ADR report counts over time, the slope of this line serves as a summary statistic. The average annual increase of ADR reports is seen by this slope. In theory, a steeper slope would indicate a better safety profile for medications with comparable efficacy and linear growth in ADR counts. However, interpreting this measure requires caution due to variations in drug exposure, which can significantly influence ADR counts. A drug with higher exposure will naturally accumulate more ADR reports over time.\u003c/p\u003e \u003cp\u003eThink about two medications, Test drug and Reference drug, for example. If Reference drug sells 50 ADR reports after 100 doses sold compared to 100 ADR reports for Test drug after 1,000 doses sold, it appears that Reference drug is safer based on these apparent ADR statistics. Test drug has an ADR reporting rate of one in ten doses when exposure is taken into account, while Drug B has an ADR reporting rate of one in two doses. On this basis, Test drug is therefore relatively safer.\u003c/p\u003e \u003cp\u003eTo make such comparisons more robust, adjustments should be made for differences in drug exposure. This can be achieved by normalizing ADR counts based on annual sales or a similar proxy measure. Assuming comparable spontaneous reporting rates for the drugs in question, this method allows for a more accurate assessment of relative safety.\u003c/p\u003e \u003cp\u003eIn cases where ADR counts exhibit exponential or sigmoidal growth patterns, the growth rate parameter (r) can be utilized for comparison. Applying these methodologies to specific drug categories, such as cancer drugs, can provide insightful safety evaluations and support more informed decision-making in drug selection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003epractical implications\u003c/span\u003e\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSafety comparison of cancer drugs (change it to another group)\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eTrends in the ADR counts of illegal drugs might be significant, but it's more interesting to compare prescription medications that have previously been prescribed using this method.\u003c/p\u003e \u003cp\u003eWe have examined the situation with cancer treatment drugs, a rigorous examination of their safety is paramount due to the multifaceted nature of their use and the high risk associated with their side effects. This study will involve a comparative analysis of various cancer therapies, focusing on their adverse effects, interactions with other medications, and long-term health outcomes. The research technique will assess the frequency and severity of adverse drug reactions (ADRs) by gathering and analyzing data from clinical trials, patient reports, and real-world evidence. Special attention will be given to drug interactions, particularly in polypharmacy scenarios common among cancer patients, and the impact of these interactions on overall safety. Additionally, the study will investigate long-term health consequences associated with cancer treatments, including potential secondary cancers and chronic conditions. By employing statistical models and safety assessment frameworks, this research aims to identify and mitigate risks, thereby informing more effective and personalized treatment strategies while enhancing patient safety and well-being.\u003c/p\u003e \u003cp\u003eTherefore, our goal is to compare its ADR count to that of others. In every instance, an exponential model was fitted. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the outcome.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRanking of the drugs (Cumulative ADR Observed vs. Fitted)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRank (r)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTamoxifen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;1198.5xe\u003csup\u003e0.0972x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvastin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;2215.9xe\u003csup\u003e0.1211x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBleomycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;114.55xe\u003csup\u003e0.139x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaclitaxel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;1526.4xe\u003csup\u003e0.1673x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVincristine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;210.66xe\u003csup\u003e0.1706x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethotrexate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;438.1xe\u003csup\u003e0.1853x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCisplatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;331.75xe\u003csup\u003e0.1859x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoxorubicin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;922.11xe\u003csup\u003e0.208x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;661.54xe\u003csup\u003e0.2427x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocetaxel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;174.39xe\u003csup\u003e0.267x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRituxima\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;243.38xe\u003csup\u003e0.2771x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrastuzumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;64.147xe\u003csup\u003e0.3025x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevlimid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;67.908xe\u003csup\u003e0.5431x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLenalidomide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;67.848xe\u003csup\u003e0.5432x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.1524xe\u003csup\u003e0.8277x\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn all cases, the exponential model demonstrated a good fit for the ADR data, with satisfactory residual plots validating the model's performance. Within this framework, the exponential growth rate parameter (r) plays a crucial role in determining the shape of the ADR growth curve, reflecting the rate at which ADR counts increase over time. Specifically, a lower value of r indicates a slower rate of ADR growth, suggesting a safer drug. Importantly, this parameter is independent of the drug's usage extent, meaning that the growth rate r is solely indicative of the drug's relative safety profile, regardless of how widely the drug is used.\u003c/p\u003e \u003cp\u003eIf the usage of a drug is doubled and all counts of adverse drug reactions (ADRs) also double, the growth rate parameter r remains unchanged. This is because r specifically measures the relative rate of growth of ADR reports and is independent of the scale of the data. The doubling affects only the parameter a, which represents the initial level of ADR counts.\u003c/p\u003e \u003cp\u003eIn the case of evaluating the safety profile of sevoflurane, despite its high efficacy and low growth rate r, it appears less favorable compared to three other drugs, which demonstrate a superior safety profile based on the same growth rate measure. This unexpected finding highlights the need for further investigation by experts in the field. Understanding the nuances behind these results could provide deeper insights into the safety dynamics and help refine safety evaluations of cancer drugs and other drugs.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eLimitations\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eADRs might still be reported after a drug is withdrawn due to residual use or long-term effects. Temporary increases in ADR reporting may result from increased awareness prior to or following a drug's discontinuation. In our research, curve fitting is complicated by uncertainties, as drugs may be prohibited in some countries but not others, reintroduced later, or only certain formulations may be withdrawn.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eFurther scope of the research\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eUtilize machine learning and artificial intelligence to identify patterns and predict ADRs, potentially improving early detection of safety signals. Conduct longitudinal studies to track ADRs over extended periods after a drug's withdrawal, providing insights into long-term safety profiles. Develop a common decision-making probability model underlying safety of different drugs and some simulations results to improve patient\u0026rsquo;s quality of life and well-being.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to all reviewers and our colleagues who provided their valuable time and feedback.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe conceptualization of this research was organized by Samadhan Ghubade and Dr. Sharvari Shukla, who jointly developed the initial idea and research objectives. Samadhan Ghubade done the data collection process, utilizing various open-source platforms to gather relevant datasets. Following this, authors conducted an in-depth analysis of the collected data, with active participation from both authors throughout the analytical phase to ensure robust and accurate outcomes. Samadhan Ghubade took the lead in drafting the manuscript, crafting the initial version with attention to detail and clarity. Subsequently, all authors engaged in a thorough review and editing process, contributing their insights to refine the manuscript further. The final version of the manuscript reflects the collaborative efforts of all authors, who have read and fully endorsed its content. Additionally, the authors collaborated closely to ensure that the study\u0026apos;s findings were presented in a clear, concise, and accessible manner, enhancing the manuscript\u0026apos;s overall impact and relevance.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe author (s) reported that there is no funding associated with the work featured in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author can provide the datasets used and analyzed in this study upon reasonable request. The FDA\u0026apos;s Adverse Event Reporting System (FAERS) and Public Dashboard, as well as VigiAccess, the WHO\u0026apos;s global database of reported possible drug side effects, were the sources of the data.\u003c/p\u003e\n\u003cp\u003eDisclosure statement\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe FDA\u0026apos;s Adverse Event Reporting System (FAERS) Public Dashboard and VigiAccess are two examples of open-source public datasets that were used in this investigation. There is no personally identifying information in these publicly accessible datasets. Because of this, there was no direct contact with human subjects during the investigation, therefore no specific consent was needed. Every use of the data was compliant with the terms of use and ethical standards supplied by the individual data sources.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad, Aminu Saleh, and Ruchi Sharma \u0026quot;Comparative analysis of herbal and allopathic treatment systems.\u0026quot; European Journal of Molecular \u0026amp; Clinical Medicine 7.7 (2020): 2869-2876.\u003c/li\u003e\n\u003cli\u003eDarrow, Jonathan J., Jerry Avorn, and Aaron S. Kesselheim. \u0026quot;FDA approval and regulation of pharmaceuticals, 1983-2018.\u0026quot; Jama 323.2 (2020): 164-176.\u003c/li\u003e\n\u003cli\u003eKhalil, Hanan, and C. Huang. \u0026quot;Adverse drug reactions in primary care: a scoping review.\u0026quot; BMC health services research 20 (2020): 1-13.\u003c/li\u003e\n\u003cli\u003eZhang, Audrey D. \u0026quot;Assessment of clinical trials supporting US Food and Drug Administration approval of novel therapeutic agents, 1995-2017.\u0026quot; JAMA Network Open 3.4 (2020): e203284-e203284.\u003c/li\u003e\n\u003cli\u003eHalwani, Abdulrahman A. \u0026quot;Development of pharmaceutical nanomedicines: from the bench to the market.\u0026quot; Pharmaceutics 14.1 (2022): 106.\u003c/li\u003e\n\u003cli\u003eAl-Worafi, Yaser Mohammed \u0026quot;Drug safety in developing countries: Achievements and challenges.\u0026quot; (2020).\u003c/li\u003e\n\u003cli\u003eBerhe, Derbew Fikadu \u0026quot;Adverse drug reaction reports for cardiometabolic drugs from sub‐Saharan Africa: a study in VigiBase.\u0026quot; Tropical Medicine \u0026amp; International Health 20.6 (2015): 797-806.\u003c/li\u003e\n\u003cli\u003eAmpadu, Haggar H. \u0026quot;Adverse drug reaction reporting in Africa and a comparison of individual case safety report characteristics between Africa and the rest of the world: analyses of spontaneous reports in VigiBase\u0026reg;.\u0026quot; \u003cem\u003eDrug safety\u003c/em\u003e 39 (2016): 335-345.\u003c/li\u003e\n\u003cli\u003eZehravi, Mehrukh, Mudasir Maqbool, and Irfat Ara. \u0026quot;An overview about safety surveillance of adverse drug reactions and pharmacovigilance in India.\u0026quot; The Indian Journal of Nutrition and Dietetics 58.3 (2021): 408-18.\u003c/li\u003e\n\u003cli\u003eDutta, Avisek, \u0026quot;Analysis of under-reporting of adverse drug reaction: Scenario in India and neighbouring countries.\u0026quot; IP Int J Compr Adv Pharmacol 5.3 (2020): 118-124.\u003c/li\u003e\n\u003cli\u003eIbrahim, Heba, \u0026quot;Signal detection in pharmacovigilance: a review of informatics-driven approaches for the discovery of drug-drug interaction signals in different data sources.\u0026quot; Artificial intelligence in the life sciences 1 (2021): 100005.\u003c/li\u003e\n\u003cli\u003eAsmone, Ashan Senel, Yang Miang Goh, and Michelle SH Lim. \u0026quot;Prioritization of industry level interventions to improve implementation of design for safety regulations.\u0026quot; Journal of safety research 82 (2022): 352-366.\u003c/li\u003e\n\u003cli\u003eOkada, Yusuke \u0026quot;Potential triggers for thrombocytopenia and/or hemorrhage by the BNT162b2 vaccine, Pfizer-BioNTech.\u0026quot; Frontiers in Medicine 8 (2021): 751598.\u003c/li\u003e\n\u003cli\u003eMoreau, Fanny \u0026quot;Does DDI-predictor help pharmacists to detect drug-drug interactions and resolve medication issues more effectively?\u0026quot; Metabolites 11.3 (2021): 173.\u003c/li\u003e\n\u003cli\u003eSharma, Manvi \u0026quot;Clinical outcomes associated with drug\u0026ndash;drug interactions of oral chemotherapeutic agents: a comprehensive evidence-based literature review.\u0026quot; \u003cem\u003eDrugs \u0026amp; aging\u003c/em\u003e 36 (2019): 341-354.\u003c/li\u003e\n\u003cli\u003eCai, Changjing, \u0026quot;A comprehensive analysis of the efficacy and safety of COVID-19 vaccines.\u0026quot; Molecular Therapy 29.9 (2021): 2794-2805.\u003c/li\u003e\n\u003cli\u003eBahri, Priya, \u0026ldquo;Communicating for the safe use of medicines: progress and directions for the 2020s promoted by the special interest group of the international society of pharmacovigilance.\u0026quot; \u003cem\u003eDrug Safety\u003c/em\u003e 46.6 (2023): 517-532.\u003c/li\u003e\n\u003cli\u003eCar, Zlatan, \u0026ldquo;Modeling the spread of COVID‐19 infection using a multilayer perceptron.\u0026quot; Computational and mathematical methods in medicine 2020.1 (2020): 5714714.\u003c/li\u003e\n\u003cli\u003eSalas, Maribel \u0026quot;The use of artificial intelligence in pharmacovigilance: a systematic review of the literature.\u0026quot; Pharmaceutical medicine 36.5 (2022): 295-306.\u003c/li\u003e\n\u003cli\u003eBuchanan, James, and Mengchun Li. \u0026quot;Safety signaling and causal evaluation.\u0026quot; Quantitative Drug Safety and Benefit Risk Evaluation. Chapman and Hall/CRC, 2021. 45-76.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adverse Drug Reactions (ADRs), Model fitting, withdrawn drugs, Pharmacovigilance, WHO, Growth rate","lastPublishedDoi":"10.21203/rs.3.rs-6284906/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6284906/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdverse drug reactions (ADRs) are critical in evaluating a medicine's safety and effectiveness both during the drug development process and post-marketing surveillance. This research focuses on drugs that had serious adverse drug reactions (ADRs) and were either taken off the market or withdrawn. We try to find to identify trends in ADR reporting following the discontinuation of these medications by applying data visualization techniques to ADR reports obtained from VigiAccess and the FDA's Adverse Event Reporting System (FAERS). We find a variety of trends and contrary to the expected sharp decline in ADR reports post-withdrawal, we observe various trends\u0026mdash;sigmoidal, exponential, and linear\u0026mdash;which may offer new insights for comparing medication safety.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the patterns of ADR reporting associated with medications that have been withdrawn or banned due to severe adverse reactions. We plan to analyze these patterns and investigate their possible usefulness in evaluating and comparing the safety profiles of various drugs by using data visualization methods.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used publicly available datasets from VigiAccess, the WHO\u0026rsquo;s global database of reported potential side effects of medicinal products, and the FDA\u0026rsquo;s Adverse Event Reporting System (FAERS) Public Dashboard. A comprehensive analysis was performed on cumulative count of ADR reports for selected medications that have been withdrawn from the market. The analysis focused on identifying various trends in ADR reporting counts of withdrawal of these medications. For visualization of these trends, we applied various curve-fitting techniques, including linear and non-linear statistical models such as sigmoidal, exponential, and linear forms. Additionally, for comparing the safety profiles of cancer drugs, a ranking was conducted using an exponential growth rate model to assess and contrast their safety dynamics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe withdrawn or banned drugs are expected to conclude usage, resulting in a zero cumulative count of adverse drug reactions (ADRs), a pattern expected for all banned drugs. Total 39 drugs analyzed showed various linear and nonlinear patterns, including 17 following a saturation pattern, 10 showing a linear pattern, 7 showing an exponential pattern, and 5 showing a sigmoidal pattern. Examples shown in this study found that drugs Benoxaprofen, Rosiglatazone, Temazepam, and Rofecoxib presented a strong fit with various models, with Benoxaprofen showing a saturating hyperbola model with R\u0026sup2; value of 0.98, Rosiglatazone showing a linear model R\u0026sup2; value of 0.96, and Temazepam indicating an excellent fit with the exponential model with R\u0026sup2; value of 0.99. Rofecoxib followed a sigmoidal pattern with an R\u0026sup2; value of 0.92, reflecting a strong fit with the sigmoidal model. For safety comparison of 15 drugs used in cancer treatment the drug Tamoxifen is a safer drug due to its slower ADR accumulation rate and growth rate (i.e., 0.0972), while drug Pembrolizumab has a higher exponential growth rate (i.e., 0.8277), indicating higher associated risks.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study demonstrates the value of data visualization in uncovering diverse ADR reporting patterns for withdrawn medications. These patterns offer a novel perspective on post-market drug safety and could serve as a comparative tool for evaluating the safety profiles of various medications. Tamoxifen was found to be safer due to slower ADR accumulation, while Pembrolizumab presented greater hazards. These findings provide valuable insights into drug safety.\u003c/p\u003e","manuscriptTitle":"Evaluating the Safety Profiles of Withdrawn Medications: A Data-Driven Approach to Adverse Drug Reactions Using Statistical models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 09:18:53","doi":"10.21203/rs.3.rs-6284906/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f015a832-ec07-4d43-a8b7-493bc3507287","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-27T09:54:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 09:18:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6284906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6284906","identity":"rs-6284906","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}