Monitoring Drug-Induced Dental Caries: Insights from FAERS Pharmacovigilance Data

Monitoring Drug-Induced Dental Caries: Insights from FAERS Pharmacovigilance Data | 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 Monitoring Drug-Induced Dental Caries: Insights from FAERS Pharmacovigilance Data Wei Gu, Guang Yang, XueFeng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6318442/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 Objective Dental caries has emerged as a significant public health concern, impacting quality of life. Conducting a comprehensive investigation into this association is crucial, particularly considering the potential for drug-induced dental caries. Methods This study aimed to identify and analyze risk-related signals of drug-induced dental caries while enhancing drug safety assessment. Statistical algorithms, including Proportional Reporting Ratio (PPR), Reporting Odds Ratio (ROR), and Empirical Bayes Geometric Mean (EBGM), were employed to analyze data obtained from the FDA's Adverse Event Reporting System (FAERS) spanning from Q1 2004 to Q3 2024, in order to detect potential adverse reaction signals. Medications associated with dental caries were categorized into different treatment classes. Results A case analysis of 17,484 reported adverse drug events (ADE) related to dental caries revealed a higher incidence among women aged ≥ 68. The number of dental caries reports has steadily increased since the establishment of the FAERS database, peaking in 2010, primarily in the United States. Disproportionate analysis of drugs identified the top five medications associated with adverse events in dental caries. Conclusions This study provides real-world data regarding the identification of drugs that potentially induce dental caries, offering a comprehensive approach to explore drug safety concerning dental caries. Our findings support the development of pharmacovigilance strategies targeting dental caries and contribute to optimizing medication management in clinical practice. post-marketing surveillance drug-induced dental caries signal detection pharmacovigilance Time-to-onset Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Dental caries, a prevalent and impactful disease, involves the chronic and progressive destruction of tooth hard tissue under the influence of various factors, predominantly bacteria [ 1 ] . Global statistics from the 2017 World Burden of Disease report indicate that approximately 2.44 billion people worldwide suffer from dental caries, highlighting its increasing socioeconomic burden [ 2 ] . In the pre-caries stage, bacteria on the tooth surface form a protective film primarily composed of cariogenic bacteria, notably Streptococcus mutans [ 3 ] . While early caries can lead to severe pulp and apical lesions, the occurrence and development of dental caries are reversible processes, and timely and effective treatment measures can prevent further deterioration, promoting quiescence or even reversal of the condition. To reduce caries incidence, emphasis should be placed on early prevention and treatment, with secondary prevention methods such as early detection, diagnosis, and intervention effectively reducing irreversible tooth damage, psychological stress, and economic burden associated with early caries [ 4 , 5 ] . The multifactorial theory of caries explains the mechanism of its occurrence and development, with bacterial, dietary, host, and temporal factors all playing crucial roles, each being indispensable [ 6 ] . Drug effects also contribute significantly to caries formation. Research has shown that certain medications may contribute to the development of dental caries. For instance, studies conducted at Luz Hospital in Lisbon, Portugal, investigated the relationship between anti-asthma medications and dental caries in children aged 3 to 15 years, revealing a higher incidence among children treated with these drugs [ 7 ] . Additionally, the impact of drugs on the oral microbiota may contribute to dental caries development, as oral bacteria and fungi, particularly Candida species like Candida albicans and Candida dubliniensis, play a role in dental caries formation [ 8 ] . Metabolites of drugs, especially with long-term use, may also influence oral health by altering the composition or function of the oral microbiota, potentially contributing to dental caries [ 9 ] . Furthermore, drugs can affect the development of dental caries by modifying interactions within the oral microbiota, where bacterial interactions in plaque influence the pathogenic properties of Streptococcus mutans and subsequent dental caries formation [ 10 ] . Changes in the oral environment due to drug use may also impact dental caries occurrence, with medications potentially causing dry mouth, a known risk factor for dental caries. Dry mouth reduces saliva secretion, which naturally provides anti-dental caries effects [ 11 ] . This study, based on real-world data, aims to raise awareness of drug-related dental caries and provide insights to enhance management strategies. The findings have important implications for clinical decision-making, patient education, and future research directions in this field. Methods Data sources This retrospective pharmacovigilance study utilized data from the FDA Adverse Events Reporting System (FAERS) database, covering the period from the first quarter of 2004 to the third quarter of 2024 [ 12 – 14 ] . FAERS is a global voluntary reporting system that collects information on adverse drug events (ADE)and safety data for approved drugs and therapeutic biologics. The database codes adverse reactions(ADR) using the Dictionary of Medical Practices for Regulatory Activities (MedDRA) Preferred Terms (PTs). Reports are voluntarily provided by health care professionals, pharmaceutical companies, consumers, and others [ 15 , 16 ] . The publicly accessible FAERS database, updated quarterly, stores data in ASCII code or XML format. It consists of several sub-files, including Demographic Record (DEMO), Adverse Event Record (REAC), Drug Record (DRUG), Outcome Record (OUTC), Report Source Record (RPSR), Therapy Record (THER), and Indication Record (INDI). Since patient data in FAERS is anonymized and the database is openly accessible, informed consent and ethical approval were not required. Data Processing In this study, adverse reaction signals were defined as reported information that may indicate a causal relationship between a drug and an adverse event. Specifically focusing on "dental caries" as the adverse reaction, ADE reports associated with dental caries were obtained, excluding non-drug-induced reports, uncertain information, duplications, and drug overdoses. The number of primary suspicion and concomitant drug-induced dental caries ADR reports was determined. Basic patient information from the ADR reports, including gender, age, reporting year, reporting country, medication status, and safety warning signals, was collected. Pharmacovigilance Disproportionality Analysis Disproportionality analysis methods, including Proportional Reporting Ratio (PPR), Reporting Odds Ratio (ROR), and Empirical Bayes Geometric Mean (EBGM), were employed to identify potential adverse event signals. R software and Microsoft Excel 2021 were used for data processing and statistical analysis. These techniques are designed to confirm results and minimize the chance of false positives in detecting safety signals. Table 1 includes detailed formulas and criteria for these three algorithms. In this research, positive signals satisfied both ROR and PPR criteria, indicating a potential association between the drug and the event. Table 1 Calculation formulas and judgment criteria of three risk signal analysis methods. Algorithms Equation Criteria ROR ROR = ad/bc 95%CI > 1, a ≥ 3 95%CI = eln(ROR)±1.96༈1/a + 1/b + 1/c + 1/d༉0.5 PPR PRR = a(c + d)/c༈a + b༉ PRR ≥ 2, χ 2 ≥ 4, a ≥ 3 χ 2 = [(ad-bc)2] ༈a + b + c + d༉/༻༈a + b༉༈c + d༉༈a + c༉༈b + d༉༽ EBGM EBGM = \sqrt{\frac{(a + 0.5) \times (d + 0.5)}{(b + 0.5) \times (c + 0.5)}} χ 2 ≥ 4, a ≥ 3 Note: a represents the number of target ADE reports for the target drug; b represents the number of other ADE reports of the target drug; c represents the number of target ADE reports for non-target drugs; d represents the number of other ADE reports for non-target drugs. Time-to-Onset Time-to-onset (TTO) refers to the duration between drug use and the appearance of hematologic toxicity, representing the percentage of adverse outcomes caused by different drugs over time. The difference in onset time is calculated by subtracting the start date of drug use (START_DT) from the onset date of the adverse event (EVENT_DT). Reports with date discrepancies (START_DT later than EVENT_DT), inaccurate time input, or incomplete data are excluded from analysis. Serious outcomes include life-threatening events, hospitalizations, disabilities, or deaths. The proportion of serious outcomes is determined by dividing the number of reports with serious outcomes by the total number of reports. In our study, we utilized the Weibull shape parameter (WSP) test, median, and quartile to compare the time-to-onset of drug-induced dental caries. The WSP test was employed in time-to-onset statistical analysis to assess the volatility of adverse event incidence. The shape of the Weibull distribution is defined by two factors: scale (α) and shape (β). We obtained the median time-to-onset and WSP of the signal following drug use to predict the risk of these adverse events increasing or decreasing over time. Results Descriptive Analysis of Study Subjects From the first quarter of 2004 to the third quarter of 2024, a total of 17,484 ADEs related to dental caries were reported in the FAERS database. The United States accounted for the highest number of reported drug-induced dental caries adverse reactions, representing 73.72% of cases (Fig. 1 A). Since the initiation of data collection in FAERS, reports of drug-induced dental caries have generally shown an increasing trend, peaking in 2010 (Fig. 1 B). Excluding unknowns, the analysis revealed that patients aged 68 years or older accounted for the largest proportion of cases, representing 15.99% of the total (Fig. 1 C). Furthermore, patients with a weight range of 59.02–72.56 kg accounted for the largest proportion, comprising 9.0191% of cases (Fig. 1 D). In terms of gender, females represented a higher percentage than males, constituting 63% of cases (Fig. 1 E). Adverse reaction outcomes showed that 'other serious (important medical events)' comprised 57.89%, and 'hospitalization' accounted for 42.12% (Fig. 1 F). Data Mining Results of Dental Caries-Inducing Drugs The utilization of the ROR, PPR, and EBGM methods identified the top 30 drugs associated with dental caries-induced adverse reactions (Fig. 2 ). Among these, the five most frequently reported drugs were alendronate sodium (frequency 991), denosumab (frequency 556), zoledronic acid (frequency 421), buprenorphine/naloxone (frequency 400), and oxycontin (frequency 208). Age Ranking Analysis: Buprenorphine/naloxone was found to induce the highest number of drug-induced dental caries cases among individuals under 43 years of age. Alendronate sodium caused the highest number of drug-induced dental caries cases in individuals aged 43 to 55 and those aged 65 and older. In the 55 to 68-year-old age group, denosumab was associated with the highest number of drug-induced dental caries cases. Figure 3 displays the respective ROR, PPR, and EBGM scores for each age group, with alendronate sodium consistently obtaining the highest score. Gender Ranking Analysis: Among women, the three drugs most commonly associated with drug-induced dental caries were alendronate sodium, denosumab, and zoledronic acid. For men, the top three drugs linked to drug-induced dental caries were zoledronic acid, buprenorphine/naloxone, and oxycontin. Figure 4 provides further details on the ROR, PPR, and EBGM scores for both genders, indicating the highest aredia scores obtained by each drug. Time-to-Onset Analysis To assess the potential risk pattern over time, we calculated the median TTO and WSP for drugs associated with drug-induced dental caries. The analysis encompassed the top 30 drugs, with data relating to TTO unavailable for drugs ranked 20–30. WSP analysis revealed that five drugs had a 95% confidence interval (CI) upper limit less than 1 for all shape parameters, indicating early failure. One drug displayed a β value close to or equal to 1, suggesting random failure. Furthermore, seven drugs exhibited a β value greater than 1, with their respective 95% CI also exceeding 1, indicating wear failure. Table 2 provides the TTO and WSP analysis results for the top 20 drugs associated with drug-induced dental caries. Table 2 TTO of the top 20 drugs associated with drug-induced dental caries. Drugs Weibull distribution Failure type Cases TTO (days) Scale parameter Shape parameter n Min–max α 95% CI β 95% CI alendronate sodium 991 3-5082 524.38 468.48-580.29 0.89 0.83–0.95 Early failure denosumab 556 3-2213 344.49 214.85-474.13 0.79 0.61–0.97 Early failure zoledronic acid 421 1-2721 548.37 472.27-624.47 1.14 1.01–1.28 Wear Failure buprenorphine/naloxone 400 1-6223 1225.93 897.33-1554.54 0.85 0.70–0.99 Early failure oxycontin 208 NA NA NA NA NA NA nicotine 201 16-2557 699.12 -244.36-1642.59 0.58 0.23–0.93 Early failure fentanyl 166 61-2922 1092.65 778.02-1407.29 1.28 0.92–1.65 Wear Failure aredia 162 640–2013 1507.08 454.35- 2559.81 2.09 -0.39- 4.51 Wear Failure buprenorphine 157 28-4545 1245.43 880.75- 1610.12 1.06 0.82–1.31 Wear failure Tenofovir disoproxil fumarate 94 NA NA NA NA NA NA sodium oxybate 84 1-312 62.56 -86.02- 211.14 0.50 0.04–0.96 Early Failure advair diskus 80 1-1136 256.32 -147.70- 660.35 0.66 0.16–1.16 Random failure boniva 64 36-1037 443.69 -465.69- 1353.07 0.71 -0.11- 1.54 Wear failure truvada 56 NA NA NA NA NA NA ondansetron 37 NA NA NA NA NA NA atripla 34 NA NA NA NA NA NA risedronate sodium 33 4-2090 625.97 393.26-858.68 1.09 0.76–1.41 Wear failure fluticasone propionate/salmeterol xinafoate 29 NA NA NA NA NA NA burosumab-twza 28 73-1044 458.68 104.85-812.51 1.10 0.40–1.80 Wear failure asfotase alfa 18 NA NA NA NA NA NA Discussions This study conducted a comprehensive analysis of drug-induced dental caries using data from the FAERS database since its establishment in the first quarter of 2004. To the best of our knowledge, this is the first study to explore drug-induced dental caries in the FAERS database and validate it with actual data. Given that the understanding of the mechanism underlying drug-induced dental caries is still limited, our findings offer important data and a theoretical foundation for minimizing drug-induced dental caries and guiding appropriate drug use in clinical settings. Cariogenic bacteria are typically acidogenic and acid-resistant, forming a protective film on the tooth surface that promotes demineralization and remineralization of tooth hard tissue, thereby contributing to the occurrence and development of early dental caries. Clinical manifestations of early dental caries include chalky lesions and dull discoloration on the tooth surface [ 17 – 19 ] . As the fifth most common human disease, dental caries has a high incidence, widespread prevalence, low treatment rate, and high retreatment rate, significantly affecting oral health worldwide [ 20 ] . Therefore, preventive measures targeting the occurrence of dental caries are vital. The top five drugs identified in our study as having the highest cariogenic potential were alendronate sodium, denosumab, zoledronic acid, buprenorphine/naloxone, and oxycontin. Alendronate sodium, commonly used to treat osteoporosis, has been associated with oral health problems, particularly tooth decay. One study suggested that alendronate sodium does not significantly enhance tooth resistance to acids, potentially increasing the risk of dental caries by affecting tooth mineralization processes [ 21 ] . Additionally, alendronate sodium treatment has been linked to macroscopic and microscopic features of drug-related osteonecrosis of the jaw following tooth extraction, further raising the risk of tooth decay [ 22 ] . Denosumab, known to cause osteonecrosis of the jaw, is a severe oral complication that typically occurs in patients receiving antiresorptive medications, especially after dental procedures [ 23 ] . Although the mechanism of osteonecrosis of the jaw remains unclear, denosumab may increase the risk of oral infection and dental caries by interfering with bone remodeling and healing processes [ 24 ] . Zoledronic acid has been found to inhibit tooth eruption and formation in rats, leading to dental abnormalities [ 25 ] . Its use during tooth development can alter tooth structure, increasing the risk of dental caries. Moreover, zoledronic acid may indirectly contribute to dental caries development by affecting the health of periodontal tissues, leading to reduced vascularization and increased necrotic areas [ 26 ] . Misuse of oxycontin can cause dry mouth, reducing saliva secretion, which plays a crucial role in neutralizing oral acidity and inhibiting bacterial growth. Decreased saliva can lead to tooth demineralization, thereby increasing the risk of dental caries [ 11 ] . Furthermore, abuse of oxycontin may result in changes in dietary habits, such as increased sugar intake, which serves as the main food source for oral bacteria. These bacteria metabolize sugar to produce acids that lead to tooth demineralization and dental caries formation [ 27 ] . These findings align with our study results. Age and gender also influence the prevalence of dental caries. In our study, women exhibited a higher incidence of drug-induced tooth decay compared to men, with the highest number of cases observed among individuals aged 68 years and older. Research indicates that the prevalence of dental caries increases with age, possibly due to factors such as tooth wear, changes in oral hygiene habits, and decreased saliva production [ 28 ] . Additionally, gender differences play a role in the prevalence of dental caries, with women generally having a higher incidence than men. This gender difference could be attributed to physiological changes experienced by women at different stages of life, such as puberty, pregnancy, and menopause [ 29 ] . These findings are consistent with our study results. Assessing the timing of adverse drug reactions is crucial for prevention. The Weibull parameter has been used to predict the onset time of ADEs, providing valuable information for clinical pharmacological management. Our study revealed that wear failure was the primary category associated with drug-induced dental caries, suggesting an increasing incidence of ADEs over time. Therefore, long-term medication should consider the risk of dental caries, as adverse event frequency tends to rise with prolonged treatment duration. While our analysis benefits from utilizing FAERS databases and data mining techniques, there are limitations inherent to all pharmacovigilance databases. Firstly, the FAERS database relies on self-reporting, leading to potential data loss (e.g., missing gender and age information) and reporting biases (e.g., selective reporting, incomplete reports, errors, delayed reports, and unverified data). It is challenging to account for confounding factors such as dosage, treatment duration, comorbidities, drug combinations, and other influences on hematotoxicity occurrence simultaneously. Secondly, the FAERS database only includes instances of adverse events and does not provide incidence rates due to a lack of denominator representing overall drug exposure. Thirdly, this study lacks experimental evidence, and further clinical studies and trials are necessary to validate our results. Lastly, as FAERS-based causality analysis does not include drug mechanism studies, causation cannot be proven, nor can risk quantification be provided. The assessment primarily focuses on signal strength, described as statistical correlation [ 30 ] . Conclusions Utilizing the extensive population data, wide geographic coverage, and public accessibility of FAERS, our study contributes valuable insights into drug-induced dental caries. It enables the rapid identification of drugs that may potentially cause dental caries and lays a robust foundation for future investigations into drug-related dental caries data. The findings of this study offer a real-world perspective and inform the development of drug safety strategies to address medication-related injuries. However, it is important to note that as a pharmacovigilance analysis based on FAERS data, this study only establishes a potential association between the drug and ADEs. Further comprehensive investigations are necessary to establish causal relationships and determine the true impact of these drugs on dental caries. Declarations Acknowledgements Not applicable Funding This study is supported by the S&T Program of Chengde (202006A161). Availability of data and materials FAERS database (https://www.fda.gov). Authors' contributions WXF contributed to the literature searched, designed the study and check the manuscript; YG contributed to the acquisition of data; GW reviewed the data and drafted the manuscript. All authors have read and approved the final manuscript. Ethics approval and consent to participate Not applicable. 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J Dent, 2024: 105122. https://doi.org/10.1016/j.jdent.2024.105122 PMID: 38871071. Trombley TM, Agarwal SC, Beauchesne PD, Goodson C, Candilio F, Coppa A, Rubini M. Making sense of medieval mouths: Investigating sex differences of dental pathological lesions in a late medieval Italian community [J]. Am J Phys Anthropol, 2019, 169(2): 253–269. https://doi.org/10.1002/ajpa.23821 PMID: 30924143. Abdel-Wahab N, Shah M, Suarez-Almazor ME. Adverse Events Associated with Immune Checkpoint Blockade in Patients with Cancer: A Systematic Review of Case Reports [J]. PLoS ONE. 2016;11(7):e0160221. https://doi.org/10.1371/journal.pone.0160221 PMID: 27472273. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6318442","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451735501,"identity":"4c3cdfcd-d6c3-4a04-830b-1c7bd9e94718","order_by":0,"name":"Wei Gu","email":"","orcid":"","institution":"Affiliated Hospital of Chengde Medical College","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Gu","suffix":""},{"id":451735502,"identity":"2346f700-7a16-4e15-8ab3-02a5292eea71","order_by":1,"name":"Guang Yang","email":"","orcid":"","institution":"Affiliated Hospital of Chengde Medical College","correspondingAuthor":false,"prefix":"","firstName":"Guang","middleName":"","lastName":"Yang","suffix":""},{"id":451735503,"identity":"ce1d1738-1ba9-486a-aa8c-53f1a263284e","order_by":2,"name":"XueFeng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYJCCDxCKsfFBQoUNUToYZ0DpZoMHZ9JI0sLAJvmw7RBh9QY3kg82fNxRm9g/I7mtIoHtAAN/e3cCXi2SM9ISG2eeOZ4440Zi240EnjsMEmfObsCrhV8ix/wxb9uxxAawFolnDAYSufi1sEnkf2z+C9QyH6ilIMHgMGEtQFsYmxnbahI3ALUwJCQQoUWy55lhY2/bAeONZx42SyQcSOMh6BeD48kPG3621cnOO57+8OPPfzZy/O29+LVAwWHHBoEEMIuHGOUgUGfPwH+AWMWjYBSMglEw0gAAlx9V/ayFcLcAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Chengde Medical College","correspondingAuthor":true,"prefix":"","firstName":"XueFeng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-03-27 08:23:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6318442/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6318442/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82308508,"identity":"470b43f6-0949-457f-8119-3a874f5b71e0","added_by":"auto","created_at":"2025-05-09 01:35:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2371169,"visible":true,"origin":"","legend":"\u003cp\u003eBasic situation of drug-induced dental caries adverse reaction reports from 2004 to 2024: A: Year of drug-induced dental caries adverse reactions from 2004 to 2024. B: Bubble chart of drug-induced dental caries adverse reactions. C: Age proportion of drug-induced dental caries adverse reactions reported. D: Weight proportion of drug-induced dental caries adverse reactions reported. E: Gender proportion of drug-induced dental caries adverse reactions reported. F: Proportion of outcomes reported by drug-induced dental caries.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6318442/v1/3f0de7fbec53f3bdc54dc2e4.jpg"},{"id":82307656,"identity":"4259d16e-02db-4869-bc0f-b783f7403188","added_by":"auto","created_at":"2025-05-09 01:27:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1191917,"visible":true,"origin":"","legend":"\u003cp\u003eTop 30 risk signals for the number of reported ADRs related to drug-induced dental caries.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6318442/v1/5e3401803116a52c89f8c69f.jpg"},{"id":82308510,"identity":"a6079bea-f19d-4485-9b7c-9e3281f734d0","added_by":"auto","created_at":"2025-05-09 01:35:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3112487,"visible":true,"origin":"","legend":"\u003cp\u003eRisk signals by age in the number of ADRs reported for drug-induced dental caries.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6318442/v1/99fa1f57f1201fa41afb2409.jpg"},{"id":82307667,"identity":"86bfff70-f539-447b-a650-ea549f090781","added_by":"auto","created_at":"2025-05-09 01:27:11","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4811523,"visible":true,"origin":"","legend":"\u003cp\u003eRisk signals for the number of reported ADRs related to drug-induced dental caries in order of sex.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6318442/v1/70674f23ac3dd3707fce21e8.jpg"},{"id":100547877,"identity":"08d2cdde-2291-4088-8648-9bff34c67bd3","added_by":"auto","created_at":"2026-01-19 08:16:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10445652,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6318442/v1/c1fd8494-07f2-4f1b-9db9-d24c5f9e7e6b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Monitoring Drug-Induced Dental Caries: Insights from FAERS Pharmacovigilance Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDental caries, a prevalent and impactful disease, involves the chronic and progressive destruction of tooth hard tissue under the influence of various factors, predominantly bacteria\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Global statistics from the 2017 World Burden of Disease report indicate that approximately 2.44\u0026nbsp;billion people worldwide suffer from dental caries, highlighting its increasing socioeconomic burden\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In the pre-caries stage, bacteria on the tooth surface form a protective film primarily composed of cariogenic bacteria, notably Streptococcus mutans\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. While early caries can lead to severe pulp and apical lesions, the occurrence and development of dental caries are reversible processes, and timely and effective treatment measures can prevent further deterioration, promoting quiescence or even reversal of the condition. To reduce caries incidence, emphasis should be placed on early prevention and treatment, with secondary prevention methods such as early detection, diagnosis, and intervention effectively reducing irreversible tooth damage, psychological stress, and economic burden associated with early caries\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The multifactorial theory of caries explains the mechanism of its occurrence and development, with bacterial, dietary, host, and temporal factors all playing crucial roles, each being indispensable\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Drug effects also contribute significantly to caries formation.\u003c/p\u003e \u003cp\u003eResearch has shown that certain medications may contribute to the development of dental caries. For instance, studies conducted at Luz Hospital in Lisbon, Portugal, investigated the relationship between anti-asthma medications and dental caries in children aged 3 to 15 years, revealing a higher incidence among children treated with these drugs\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Additionally, the impact of drugs on the oral microbiota may contribute to dental caries development, as oral bacteria and fungi, particularly Candida species like Candida albicans and Candida dubliniensis, play a role in dental caries formation\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Metabolites of drugs, especially with long-term use, may also influence oral health by altering the composition or function of the oral microbiota, potentially contributing to dental caries\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Furthermore, drugs can affect the development of dental caries by modifying interactions within the oral microbiota, where bacterial interactions in plaque influence the pathogenic properties of Streptococcus mutans and subsequent dental caries formation\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Changes in the oral environment due to drug use may also impact dental caries occurrence, with medications potentially causing dry mouth, a known risk factor for dental caries. Dry mouth reduces saliva secretion, which naturally provides anti-dental caries effects\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. This study, based on real-world data, aims to raise awareness of drug-related dental caries and provide insights to enhance management strategies. The findings have important implications for clinical decision-making, patient education, and future research directions in this field.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eThis retrospective pharmacovigilance study utilized data from the FDA Adverse Events Reporting System (FAERS) database, covering the period from the first quarter of 2004 to the third quarter of 2024\u003csup\u003e[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. FAERS is a global voluntary reporting system that collects information on adverse drug events (ADE)and safety data for approved drugs and therapeutic biologics. The database codes adverse reactions(ADR) using the Dictionary of Medical Practices for Regulatory Activities (MedDRA) Preferred Terms (PTs). Reports are voluntarily provided by health care professionals, pharmaceutical companies, consumers, and others\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The publicly accessible FAERS database, updated quarterly, stores data in ASCII code or XML format. It consists of several sub-files, including Demographic Record (DEMO), Adverse Event Record (REAC), Drug Record (DRUG), Outcome Record (OUTC), Report Source Record (RPSR), Therapy Record (THER), and Indication Record (INDI). Since patient data in FAERS is anonymized and the database is openly accessible, informed consent and ethical approval were not required.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Processing\u003c/h3\u003e\n\u003cp\u003eIn this study, adverse reaction signals were defined as reported information that may indicate a causal relationship between a drug and an adverse event. Specifically focusing on \"dental caries\" as the adverse reaction, ADE reports associated with dental caries were obtained, excluding non-drug-induced reports, uncertain information, duplications, and drug overdoses. The number of primary suspicion and concomitant drug-induced dental caries ADR reports was determined. Basic patient information from the ADR reports, including gender, age, reporting year, reporting country, medication status, and safety warning signals, was collected.\u003c/p\u003e\n\u003ch3\u003ePharmacovigilance Disproportionality Analysis\u003c/h3\u003e\n\u003cp\u003eDisproportionality analysis methods, including Proportional Reporting Ratio (PPR), Reporting Odds Ratio (ROR), and Empirical Bayes Geometric Mean (EBGM), were employed to identify potential adverse event signals. R software and Microsoft Excel 2021 were used for data processing and statistical analysis. These techniques are designed to confirm results and minimize the chance of false positives in detecting safety signals. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e includes detailed formulas and criteria for these three algorithms. In this research, positive signals satisfied both ROR and PPR criteria, indicating a potential association between the drug and the event.\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\u003eCalculation formulas and judgment criteria of three risk signal analysis methods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlgorithms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eROR\u0026thinsp;=\u0026thinsp;ad/bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u0026thinsp;\u0026gt;\u0026thinsp;1, a\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95%CI\u0026thinsp;=\u0026thinsp;eln(ROR)\u0026plusmn;1.96༈1/a\u0026thinsp;+\u0026thinsp;1/b\u0026thinsp;+\u0026thinsp;1/c\u0026thinsp;+\u0026thinsp;1/d༉0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRR\u0026thinsp;=\u0026thinsp;a(c\u0026thinsp;+\u0026thinsp;d)/c༈a\u0026thinsp;+\u0026thinsp;b༉\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePRR\u0026thinsp;\u0026ge;\u0026thinsp;2, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;4, a\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e= [(ad-bc)2] ༈a\u0026thinsp;+\u0026thinsp;b\u0026thinsp;+\u0026thinsp;c\u0026thinsp;+\u0026thinsp;d༉/༻༈a\u0026thinsp;+\u0026thinsp;b༉༈c\u0026thinsp;+\u0026thinsp;d༉༈a\u0026thinsp;+\u0026thinsp;c༉༈b\u0026thinsp;+\u0026thinsp;d༉༽\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEBGM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEBGM = \\sqrt{\\frac{(a\u0026thinsp;+\u0026thinsp;0.5) \\times (d\u0026thinsp;+\u0026thinsp;0.5)}{(b\u0026thinsp;+\u0026thinsp;0.5) \\times (c\u0026thinsp;+\u0026thinsp;0.5)}}\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;4, a\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: a represents the number of target ADE reports for the target drug; b represents the number of other ADE reports of the target drug; c represents the number of target ADE reports for non-target drugs; d represents the number of other ADE reports for non-target drugs.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eTime-to-Onset\u003c/h3\u003e\n\u003cp\u003eTime-to-onset (TTO) refers to the duration between drug use and the appearance of hematologic toxicity, representing the percentage of adverse outcomes caused by different drugs over time. The difference in onset time is calculated by subtracting the start date of drug use (START_DT) from the onset date of the adverse event (EVENT_DT). Reports with date discrepancies (START_DT later than EVENT_DT), inaccurate time input, or incomplete data are excluded from analysis. Serious outcomes include life-threatening events, hospitalizations, disabilities, or deaths. The proportion of serious outcomes is determined by dividing the number of reports with serious outcomes by the total number of reports. In our study, we utilized the Weibull shape parameter (WSP) test, median, and quartile to compare the time-to-onset of drug-induced dental caries. The WSP test was employed in time-to-onset statistical analysis to assess the volatility of adverse event incidence. The shape of the Weibull distribution is defined by two factors: scale (α) and shape (β). We obtained the median time-to-onset and WSP of the signal following drug use to predict the risk of these adverse events increasing or decreasing over time.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Analysis of Study Subjects\u003c/h2\u003e \u003cp\u003eFrom the first quarter of 2004 to the third quarter of 2024, a total of 17,484 ADEs related to dental caries were reported in the FAERS database. The United States accounted for the highest number of reported drug-induced dental caries adverse reactions, representing 73.72% of cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Since the initiation of data collection in FAERS, reports of drug-induced dental caries have generally shown an increasing trend, peaking in 2010 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Excluding unknowns, the analysis revealed that patients aged 68 years or older accounted for the largest proportion of cases, representing 15.99% of the total (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Furthermore, patients with a weight range of 59.02\u0026ndash;72.56 kg accounted for the largest proportion, comprising 9.0191% of cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In terms of gender, females represented a higher percentage than males, constituting 63% of cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Adverse reaction outcomes showed that 'other serious (important medical events)' comprised 57.89%, and 'hospitalization' accounted for 42.12% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Mining Results of Dental Caries-Inducing Drugs\u003c/h3\u003e\n\u003cp\u003eThe utilization of the ROR, PPR, and EBGM methods identified the top 30 drugs associated with dental caries-induced adverse reactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among these, the five most frequently reported drugs were alendronate sodium (frequency 991), denosumab (frequency 556), zoledronic acid (frequency 421), buprenorphine/naloxone (frequency 400), and oxycontin (frequency 208).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAge Ranking Analysis: Buprenorphine/naloxone was found to induce the highest number of drug-induced dental caries cases among individuals under 43 years of age. Alendronate sodium caused the highest number of drug-induced dental caries cases in individuals aged 43 to 55 and those aged 65 and older. In the 55 to 68-year-old age group, denosumab was associated with the highest number of drug-induced dental caries cases. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the respective ROR, PPR, and EBGM scores for each age group, with alendronate sodium consistently obtaining the highest score.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGender Ranking Analysis: Among women, the three drugs most commonly associated with drug-induced dental caries were alendronate sodium, denosumab, and zoledronic acid. For men, the top three drugs linked to drug-induced dental caries were zoledronic acid, buprenorphine/naloxone, and oxycontin. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides further details on the ROR, PPR, and EBGM scores for both genders, indicating the highest aredia scores obtained by each drug.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eTime-to-Onset Analysis\u003c/h3\u003e\n\u003cp\u003eTo assess the potential risk pattern over time, we calculated the median TTO and WSP for drugs associated with drug-induced dental caries. The analysis encompassed the top 30 drugs, with data relating to TTO unavailable for drugs ranked 20\u0026ndash;30. WSP analysis revealed that five drugs had a 95% confidence interval (CI) upper limit less than 1 for all shape parameters, indicating early failure. One drug displayed a β value close to or equal to 1, suggesting random failure. Furthermore, seven drugs exhibited a β value greater than 1, with their respective 95% CI also exceeding 1, indicating wear failure. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides the TTO and WSP analysis results for the top 20 drugs associated with drug-induced dental caries.\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\u003eTTO of the top 20 drugs associated with drug-induced dental caries.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDrugs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003eWeibull distribution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFailure type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTO (days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eScale parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eShape parameter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u0026ndash;max\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealendronate sodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3-5082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e524.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e468.48-580.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.83\u0026ndash;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEarly failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edenosumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3-2213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e344.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e214.85-474.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.61\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEarly failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ezoledronic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-2721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e548.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e472.27-624.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u0026ndash;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWear Failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebuprenorphine/naloxone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-6223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1225.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e897.33-1554.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.70\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEarly failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoxycontin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enicotine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16-2557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e699.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-244.36-1642.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.23\u0026ndash;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEarly failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efentanyl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61-2922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1092.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e778.02-1407.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u0026ndash;1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWear Failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003earedia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e640\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1507.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e454.35- 2559.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.39- 4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWear Failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebuprenorphine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28-4545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1245.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e880.75- 1610.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.82\u0026ndash;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWear failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTenofovir disoproxil fumarate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esodium oxybate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-86.02- 211.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEarly Failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadvair diskus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-1136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e256.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-147.70- 660.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRandom failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eboniva\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36-1037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e443.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-465.69- 1353.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.11- 1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWear failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etruvada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eondansetron\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eatripla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erisedronate sodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4-2090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e625.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e393.26-858.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWear failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efluticasone propionate/salmeterol xinafoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eburosumab-twza\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73-1044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e458.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e104.85-812.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.40\u0026ndash;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWear failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003easfotase alfa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eThis study conducted a comprehensive analysis of drug-induced dental caries using data from the FAERS database since its establishment in the first quarter of 2004. To the best of our knowledge, this is the first study to explore drug-induced dental caries in the FAERS database and validate it with actual data. Given that the understanding of the mechanism underlying drug-induced dental caries is still limited, our findings offer important data and a theoretical foundation for minimizing drug-induced dental caries and guiding appropriate drug use in clinical settings.\u003c/p\u003e \u003cp\u003eCariogenic bacteria are typically acidogenic and acid-resistant, forming a protective film on the tooth surface that promotes demineralization and remineralization of tooth hard tissue, thereby contributing to the occurrence and development of early dental caries. Clinical manifestations of early dental caries include chalky lesions and dull discoloration on the tooth surface\u003csup\u003e[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. As the fifth most common human disease, dental caries has a high incidence, widespread prevalence, low treatment rate, and high retreatment rate, significantly affecting oral health worldwide\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Therefore, preventive measures targeting the occurrence of dental caries are vital.\u003c/p\u003e \u003cp\u003eThe top five drugs identified in our study as having the highest cariogenic potential were alendronate sodium, denosumab, zoledronic acid, buprenorphine/naloxone, and oxycontin. Alendronate sodium, commonly used to treat osteoporosis, has been associated with oral health problems, particularly tooth decay. One study suggested that alendronate sodium does not significantly enhance tooth resistance to acids, potentially increasing the risk of dental caries by affecting tooth mineralization processes\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Additionally, alendronate sodium treatment has been linked to macroscopic and microscopic features of drug-related osteonecrosis of the jaw following tooth extraction, further raising the risk of tooth decay\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Denosumab, known to cause osteonecrosis of the jaw, is a severe oral complication that typically occurs in patients receiving antiresorptive medications, especially after dental procedures\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Although the mechanism of osteonecrosis of the jaw remains unclear, denosumab may increase the risk of oral infection and dental caries by interfering with bone remodeling and healing processes\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Zoledronic acid has been found to inhibit tooth eruption and formation in rats, leading to dental abnormalities\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Its use during tooth development can alter tooth structure, increasing the risk of dental caries. Moreover, zoledronic acid may indirectly contribute to dental caries development by affecting the health of periodontal tissues, leading to reduced vascularization and increased necrotic areas\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Misuse of oxycontin can cause dry mouth, reducing saliva secretion, which plays a crucial role in neutralizing oral acidity and inhibiting bacterial growth. Decreased saliva can lead to tooth demineralization, thereby increasing the risk of dental caries\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Furthermore, abuse of oxycontin may result in changes in dietary habits, such as increased sugar intake, which serves as the main food source for oral bacteria. These bacteria metabolize sugar to produce acids that lead to tooth demineralization and dental caries formation\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. These findings align with our study results.\u003c/p\u003e \u003cp\u003eAge and gender also influence the prevalence of dental caries. In our study, women exhibited a higher incidence of drug-induced tooth decay compared to men, with the highest number of cases observed among individuals aged 68 years and older. Research indicates that the prevalence of dental caries increases with age, possibly due to factors such as tooth wear, changes in oral hygiene habits, and decreased saliva production\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Additionally, gender differences play a role in the prevalence of dental caries, with women generally having a higher incidence than men. This gender difference could be attributed to physiological changes experienced by women at different stages of life, such as puberty, pregnancy, and menopause\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. These findings are consistent with our study results.\u003c/p\u003e \u003cp\u003eAssessing the timing of adverse drug reactions is crucial for prevention. The Weibull parameter has been used to predict the onset time of ADEs, providing valuable information for clinical pharmacological management. Our study revealed that wear failure was the primary category associated with drug-induced dental caries, suggesting an increasing incidence of ADEs over time. Therefore, long-term medication should consider the risk of dental caries, as adverse event frequency tends to rise with prolonged treatment duration.\u003c/p\u003e \u003cp\u003eWhile our analysis benefits from utilizing FAERS databases and data mining techniques, there are limitations inherent to all pharmacovigilance databases. Firstly, the FAERS database relies on self-reporting, leading to potential data loss (e.g., missing gender and age information) and reporting biases (e.g., selective reporting, incomplete reports, errors, delayed reports, and unverified data). It is challenging to account for confounding factors such as dosage, treatment duration, comorbidities, drug combinations, and other influences on hematotoxicity occurrence simultaneously. Secondly, the FAERS database only includes instances of adverse events and does not provide incidence rates due to a lack of denominator representing overall drug exposure. Thirdly, this study lacks experimental evidence, and further clinical studies and trials are necessary to validate our results. Lastly, as FAERS-based causality analysis does not include drug mechanism studies, causation cannot be proven, nor can risk quantification be provided. The assessment primarily focuses on signal strength, described as statistical correlation\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eUtilizing the extensive population data, wide geographic coverage, and public accessibility of FAERS, our study contributes valuable insights into drug-induced dental caries. It enables the rapid identification of drugs that may potentially cause dental caries and lays a robust foundation for future investigations into drug-related dental caries data. The findings of this study offer a real-world perspective and inform the development of drug safety strategies to address medication-related injuries. However, it is important to note that as a pharmacovigilance analysis based on FAERS data, this study only establishes a potential association between the drug and ADEs. Further comprehensive investigations are necessary to establish causal relationships and determine the true impact of these drugs on dental caries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is supported by the S\u0026amp;T Program of Chengde (202006A161).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFAERS database (https://www.fda.gov).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWXF contributed to the literature searched, designed the study and check the manuscript; YG contributed to the acquisition of data; GW reviewed the data and drafted the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOgwo C, Brown G, Warren J, Caplan D, Levy S. Dental caries incidence and associated factors in young adults [J]. 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PLoS ONE. 2016;11(7):e0160221.\u003c/span\u003e \u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0160221\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0160221\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PMID: 27472273.\u003c/span\u003e\u003c/li\u003e\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":"post-marketing surveillance, drug-induced dental caries, signal detection, pharmacovigilance, Time-to-onset","lastPublishedDoi":"10.21203/rs.3.rs-6318442/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6318442/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eDental caries has emerged as a significant public health concern, impacting quality of life. Conducting a comprehensive investigation into this association is crucial, particularly considering the potential for drug-induced dental caries.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study aimed to identify and analyze risk-related signals of drug-induced dental caries while enhancing drug safety assessment. Statistical algorithms, including Proportional Reporting Ratio (PPR), Reporting Odds Ratio (ROR), and Empirical Bayes Geometric Mean (EBGM), were employed to analyze data obtained from the FDA's Adverse Event Reporting System (FAERS) spanning from Q1 2004 to Q3 2024, in order to detect potential adverse reaction signals. Medications associated with dental caries were categorized into different treatment classes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA case analysis of 17,484 reported adverse drug events (ADE) related to dental caries revealed a higher incidence among women aged\u0026thinsp;\u0026ge;\u0026thinsp;68. The number of dental caries reports has steadily increased since the establishment of the FAERS database, peaking in 2010, primarily in the United States. Disproportionate analysis of drugs identified the top five medications associated with adverse events in dental caries.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study provides real-world data regarding the identification of drugs that potentially induce dental caries, offering a comprehensive approach to explore drug safety concerning dental caries. Our findings support the development of pharmacovigilance strategies targeting dental caries and contribute to optimizing medication management in clinical practice.\u003c/p\u003e","manuscriptTitle":"Monitoring Drug-Induced Dental Caries: Insights from FAERS Pharmacovigilance Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 01:27:05","doi":"10.21203/rs.3.rs-6318442/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":"3c6736ac-234b-4b4e-aeba-033cb2dade10","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-16T21:38:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 01:27:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6318442","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6318442","identity":"rs-6318442","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}