Overview of major international spontaneous reporting systems that can be searched via online systems.
\r\n\tThe aim of this book will be to describe the most common forms of dermatitis putting emphasis on the pathophysiology, clinical appearance and diagnostic of each disease. We also will aim to describe the therapeutic management and new therapeutic approaches of each condition that are currently being studied and are supposed to be used in the near future.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"278931ae110500350d8b64805c70f193",bookSignature:"Dr. Eleni Papakonstantinou",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7934.jpg",keywords:"Atopic eczema, Interleukin, Topical corticosteroids, Hand eczema, Blisters, Pruritus, Irritant contact dermatitis, Allergic contact dermatitis, Discoid eczema, Sebaceous glands, Inflammatory dermatitis, Facial rash",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 5th 2019",dateEndSecondStepPublish:"March 19th 2019",dateEndThirdStepPublish:"May 18th 2019",dateEndFourthStepPublish:"August 6th 2019",dateEndFifthStepPublish:"October 5th 2019",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"203520",title:"Dr.",name:"Eleni",middleName:null,surname:"Papakonstantinou",slug:"eleni-papakonstantinou",fullName:"Eleni Papakonstantinou",profilePictureURL:"https://mts.intechopen.com/storage/users/203520/images/system/203520.jpg",biography:"Dr. med. Eleni Papakonstantinou is a Doctor of Medicine graduate and board certified Dermatologist-Venereologist. She studied medicine at the Aristotle University of Thessaloniki, in Greece and she continued with her dermatology specialty in Germany (2012-2017) at the University of Magdeburg and Hannover Medical School, where she completed her dissertation in 2016 with research work on atopic dermatitis in children. During this time she gained wide experience in the whole dermatological field with special focus on the diagnosis and treatment of chronic inflammatory skin diseases and also the prevention and treatment of melanocytic and non-melanocytic skin tumors. Her research interests were beside atopic dermatitis and pruritus also the pathophysiology of blistering dermatoses. In addition to lectures at german and international congresses, she has published several articles in german and international journals and her work has been awarded with various prizes (poster prize of the German Dermatological Society for the project: 'Bullous pemphigoid and comorbidities' (DDG Leipzig 2016), 'Michael Hornstein Memorial Scholarship' (EADV Athens 2016), travel grant (EAACI Vienna 2016). Since 2017, she works as a specialist dermatologist in private practice in Dortmund, in Germany. Parallel she co-administrates an international dermatologic network, Wikiderm International and she writes a dermatology public guide for patients, as she is convinced that evidence-based knowledge has to be shared not only with colleagues but also with patients.",institutionString:"Private Practice, Dermatology and Venereology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"270941",firstName:"Sandra",lastName:"Maljavac",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/270941/images/7824_n.jpg",email:"sandra.m@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. Mauricio Barría",coverURL:"https://cdn.intechopen.com/books/images_new/6550.jpg",editedByType:"Edited by",editors:[{id:"88861",title:"Dr.",name:"R. Mauricio",surname:"Barría",slug:"r.-mauricio-barria",fullName:"R. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"63595",title:"Evolving Roles of Spontaneous Reporting Systems to Assess and Monitor Drug Safety",doi:"10.5772/intechopen.79986",slug:"evolving-roles-of-spontaneous-reporting-systems-to-assess-and-monitor-drug-safety",body:'\nPrescription of a medication is based on a balance between expected benefits, already investigated before marketing authorization, and possible risks (i.e., adverse effects), which become fully apparent only as time goes by after marketing authorization. Premarketing development, in fact, provides evidence on efficacy of drugs in ideal clinical setting of use (i.e., clinical trials); only the most frequent side effects are recognized in this step. The use of drugs in the real-world circumstances will show the actual risk-benefit profile.
\nThe World Health Organization (WHO) previously defined pharmacovigilance (PhV) as “the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other possible drug-related problems” [1], a definition that, in the recent past, was regarded as being synonymous with post-marketing surveillance for adverse drug reactions (ADRs).
\nAfter the adoption in 2012 of the new pharmacovigilance legislation (Regulation (EU) No 1235/2010 and Directive 2010/84/EU) [2, 3] approved by the European Parliament and European Council in December 2010, PhV embraces the whole risk-benefit assessment, thus dealing with multiple types of evidence emerging along the life cycle of drugs for continuous reassessment of the place in therapy of each medicine, both in clinical and in regulatory terms.
\nMany sources of data and relevant methods of analysis are used in PhV: from disproportionality analyses (DAs) in spontaneous reporting systems (SRSs) to analytical studies (cohort or case-control designs). These traditional approaches are now integrated by innovative strategies (e.g., social media mining and case-population studies) in the fourth-generation PhV [4].
\nIn this chapter, current and emerging roles of DAs in SRSs will be critically discussed, keeping in mind both regulator and researcher viewpoints. A panorama on key data sources (and their proper selection) will be described, followed by a critical appraisal of methodological issues and debated future applications, including exploitation and challenges in evidence integration (i.e., merging and combining heterogeneous sources of data into a unique indicator of risk) and patient’s reporting via social media. All these issues are based on key publications of the authors and on the latest advances published in the literature (MEDLINE, as of May 1, 2018). Finally, a call for a responsible use of these studies is offered, with a proposal (authors’ personal ideas) on a set of minimum requirements to assess the quality of DAs in terms of study conception, performing and reporting.
\nNot only notification of suspected adverse drug events is mandatory for health professionals, but also other subjects can report events to the relevant regulatory authorities. According to ICH-E2 guidelines (International Conference on Harmonization,
Clinical pharmacology knowledge is requested to design and interpret results from DMAs and to decide if further examination is needed (either within the same source of data or by other types of data) or specific bias affects the validity of the findings. Other healthcare data sources are available for PhV to corroborate results of SRS data mining, despite developed for other reasons. As a general classification, they can be pooled into two main groups: electronic medical records (EMRs) and claim databases.
\nElectronic medical records (EMRs) aim to assist physicians in daily clinical practice (including appropriate prescription) by collecting sociodemographic and clinical information (diagnoses, risk factors, treatments, and outcomes). Primary care is the most frequent setting to develop these kinds of databases such as Clinical Practice Research Datalink (CPRD; formerly General Practice Research Database—GPRD, in UK); Health Search (by the Italian College of General Practitioners); The Health Improvement Network (THIN), in UK; and Interdisciplinary Processing of Clinical Information (IPCI), in the Netherlands. The high quantity of data makes them valuable sources to address clinical pharmacology questions, including new effects of drugs (especially on primary endpoints, to confirm premarketing evidence) and assessment of appropriate drug use (closer to the main purpose of the registries).
\nClaim databases were mainly created for administrative purposes, and together with hospital databases provide valuable sources to address PhV questions: data provided (e.g., diagnoses of hospital admissions, reimbursed prescriptions of drugs and diagnostic procedures in ambulatory care) are generally used for reimbursement and other economic issues, and, as a secondary aim, they represent an important source of information for epidemiological questions (taking into account that nonreimbursed intervention is usually not recorded, information on lifestyle and actual exposure to medicines is lacking.
\nEach National Drug Agency collects its own reports in a dedicated spontaneous reporting database, and some international SRSs gather reports originating both by systematic flows from national databases and by direct submission of the reporter. Each source has specific characteristics and limitations to be considered when planning a drug safety analysis (e.g., completeness of data and options for database interrogation); however, collecting information from all these accessible sources is the mainstay in PhV.
\nTable 1 shows an overview of main international PhV databases, which cover a very large population and heterogeneous patterns of drug use and ADR reporting attitudes. Public access to SRSs is becoming a standard, as addressed in Section 8.2.
\n\n | FAERS | \nWHO—VigiBase | \nEudraVigilance | \nAustralian Database of Adverse Event Notifications (DAEN) | \nCanada Vigilance Adverse Reaction Online Database | \nJapanese Adverse Drug Event Report database (JADER) | \n
---|---|---|---|---|---|---|
Website | \n\n | \n\n | \n\n | \n\n | \n\n | \n\n | \n
Access | \nFull data access (download) since 2004a | \nWeb-based interface (VigiLize™, VigiFlow™, VigiMine, applications for full data accessc) | \nWeb-based interface (different access policies for full data accessd) | \n\n | Full data access (download) | \nFull data access (download) | \n
Timeframe | \n1969–present | \n1968–present | \n2001–present | \n1971–present | \n1965–present | \n2004–present | \n
Products covered | \nAll drugs and biologicsb | \nAll drugs and biologics, including vaccines | \nAll drugs and biologics authorized in the European Union | \nAll medicines, including vaccines, used in Australiae | \nAll drugs, biologics, vaccines, and natural health products licensed in Canadaf | \nAll drugs and biologics, including vaccines used in Japan | \n
Source of reports | \nHealthcare professionals, drug companies, patients/consumers | \nNational and regional pharmacovigilance centers (which may receive reports from patients, healthcare professionals, or drug companies) | \nNational competent authorities and marketing authorization holders (currently, no direct reporting from patients and healthcare professionals) | \nHealthcare professionals, consumers, and market authorization holders | \nHealthcare professionals, consumers, and market authorization holders | \nHealthcare professionals, consumers, and market authorization holders | \n
Current number of reports available | \n>12 million (as of April 2015), more than 1,000,000 per year (2012–2014) | \n>10 million (as of 2016) | \n>1 million received in 2013 | \nUnknown (no public statistics provided) | \nUnknown (no public statistics provided) | \n~500,000 (as of 2017) | \n
Origin of submitted reports | \nUSA and serious/unexpected reports from EU, Japan, and other extra-US countries | \nWorldwide (107 official members and 33 associate members), but majority from EU and the US | \nEU | \nAustralia | \nCanada | \nJapan | \n
Coding system for event | \nMedDRA | \nMedDRA | \nMedDRA | \nMedDRA | \nMedDRA | \nMedDRA | \n
Search strategy through “free text” in the narratives | \nNo (a Freedom of Information Act can be requested to the FDA) | \nNo | \nNo | \nNo | \nNo | \nNo | \n
Overview of major international spontaneous reporting systems that can be searched via online systems.
Different web-based tools are provided; see Böhm et al. [14]. Recently, the FDA has launched the FAERS Public Dashboard, a highly interactive web-based tool that allows to query FAERS data in a user-friendly fashion (
Devices, vaccines, and other products are not included, as they are specifically recorded in ad hoc databases: MAUDE—Manufacturer and User Facility Device Experience (ttps://
Freely available for all members in the WHO Program for International Drug Monitoring.
Specific access policies are described depending on stakeholder groups. For details, see the following link:
Medical devices are not included, as they are specifically recorded in the ad hoc Database of Adverse Event Notifications—medical devices (
Data on human blood and blood components have only been included since September 1, 2015; data on vaccines used for immunization have only been included since January 1, 2011; the majority of vaccine reports are submitted to the Canadian Adverse Events Following Immunization Surveillance System (CAEFISS).
The identification of the most appropriate source of data is a key step to properly address the research question, considering strength and limitations of the different approaches (Table 2). For instance, SRSs represent the best source of data to investigate the so-called designated medical events (DMEs), usually rare with strong drug-attributable component (e.g., Torsades de Pointes and Stevens-Johnson Syndrome) [5, 6]. Conversely, possible role of drugs in events with high background incidence (e.g., myocardial infarction) can be better investigated by healthcare databases (EMRs and claim databases) [7, 8]. No matter of the type of ADR, a typical time sequence to detect safety profile of drugs considers data mining of SRSs as the first step of the analysis, followed by investigation through healthcare databases to confirm or refuse statistically significant associations.
\n\n | Strengths | \nWeaknesses | \n
---|---|---|
Disproportionality approach | \nIt can be conducted rapidly, and it is easy to implement. It can be conducted on spontaneous reporting systems and healthcare databases. Good performance (accuracy in discriminating false from true positives) when major confounders and biases are accounted for. Highly suitable for rare events with high drug-attributable risk (e.g., TdP and DILI). | \nDoes not provide risk estimates. Loss of information due to aggregated data. Unable to handle numerous confounders. Sensitive to protopathic and indication biases. Less suitable for events with high background incidence (e.g., myocardial infarction). | \n
Traditional pharmacoepidemiological designs | \nIt provides risk estimates (cohort and case-control design). It allows controlling for confounders if matching and nesting are performed (case-control design). Robust to confounders that are stable over time (case crossover, self-controlled cohort, and self-controlled case series). Highly suitable for events with high background incidence (e.g., myocardial infarction). | \nIt needs very large dataset to have enough power to detect signals in case of rare events (cohort and case-control design). Less suitable for rare events with high drug-attributable risk (e.g., TdP and DILI). | \n
Prescription sequence symmetry analysis (PSSA) | \nRapid and easy to be performed (it only requires patient identifier, medication code, and medication dispensed date). Graphical output can be generated to help data visualization and interpretation. Highly specific and moderate sensitivity. It can control for time-constant confounders. | \nIt does not provide risk estimates (it complements disproportionality approach). Prescribing trends are affected by external factors (adjustment is required). Inappropriate identification of new use (exclusion/censoring of switchers is required). Time-variant confounders. Sensitive to inverse causality, protopathic, and indication biases. | \n
Systematic review with meta-analysis | \nIt can provide risk estimates (especially if RCT is the primary source). It does not require additional data collection. It can be conducted rapidly. It can highlight gaps in research. | \nValidity depends on the scientific rigor of the methods, quality, and type of primary source (RCT or observational studies). Meta-analysis of nonrandomized studies (observational) is currently not standardized. | \n
Overview of study designs to assess safety of medicines.
Modified from [9]. DILI: drug-induced liver injury; RCT: randomized controlled trial; TdP: torsade de pointes.
From data cleaning (a mere data managing step, see later) to statistical analyses, all steps of data management are considered tasks to address questions on ADRs. Usually, each source of data requires specific data-mining approaches (e.g., disproportion calculation for SRSs and multiple regression analysis for EMRs), but emergent strategies to better exploit the more accessible sources are now appearing in the literature (e.g., self-controlled time series and prescription sequence symmetry analysis—PSSA) [9]. In fact, data mining could virtually provide as many associations as possible between drug and effect, but without consensus among experts on the methodological steps and confirmation of pathophysiological pathways, the association can easily conduct to interpret errors.
\nTraditionally, regulatory decision-making has relied on detection of safety signals through spontaneous reports. Today, things are changing for several reasons, including increased awareness of prescribers on the importance of PhV and the emerging role of different health professionals and patients.
\nA modern model involves signal detection, signal validation (i.e., signal should represent a novel causal relationship between a drug and an event), signal prioritization (evaluation of clinical impact of the safety issue), and some other steps to drive the decision-making, also on the basis of data on how drugs are used in a population and how their utilization can be influenced. Drug consumption is also now frequently analyzed by regulators to evaluate the actual impact of risk minimization strategies in a specific settings, such as the risk of progressive multifocal leukoencephalopathy with multiple sclerosis therapies [10].
\nRegulatory agencies routinely perform analyses of SRSs to detect disproportionality signals, especially for new drugs. Although the Food and Drug Administration (FDA) and the European Medicine Agency (EMA) have different frameworks, they are promoting rigorous scientific information exchange for optimal post-approval drug safety monitoring [11]. Both agencies publicly posted the list of signals emerging from internal analyses, with the aim to promote transparency and stimulate research while avoiding alarm. Usually, many of these signals remain (fortunately) unnoticed by clinicians, and only a minority of them result in measures affecting clinical practice, such as ketoacidosis with sodium-glucose cotransporter-2 inhibitors, which in turn prompted the FDA to revise relevant labels.
\nAlso for old drugs, the importance of spontaneous reports should not be overlooked, especially because the amount of time of a drug on the market (drug age) is correlated with the number of signals detected [12]. The recent case of tiocolchicoside, restricted in recommended dose and treatment duration by the EMA, is noteworthy: after withdrawal of tetrazepam, the use of alternatives (including tiocolchicoside) and relevant spontaneous reporting increased, which made evident specific safety concerns [13].
\nIn the past, regulatory actions on a given safety issue did not support clinical practice. The case of haloperidol and the risk of torsade de pointes (TdP) is a typical example: an ECG before administration was indeed recommended in some circumstances before administering the medicine. However, it was not duly taken into account that a psychotic crisis does not usually allow appropriate ECG measurement, and this results in the inability to use injectable haloperidol in the emergency setting. The clinical consequence was a loss of this therapeutic option and its substitution with alternatives, which are not necessarily better.
\nDisproportionality analyses (DAs) are attracting considerable interest in the medical literature for several reasons:
there is increasing availability of publicly accessible SRSs and open-access tools to independently analyze international databases [14]; the various web-based resources mainly differ in terms of data transparency, possibility to customize searches and analyses (e.g., correction for confounders);
DAs are inexpensive and relatively quick and easy to perform, at least by frequentist methods such as reporting odds ratio (ROR) and proportional reporting ratio (PRR); these methods can be applied systematically to analyze a given pharmacological class or specific DMEs such as TdP [15];
they are likely to be published in a high ranking journal, especially when sophisticated analyses are presented, claiming to correct for multiple confounders [16], and a strong signal emerges. This aspect raises ethical issues: on one hand, the researcher may be more prone toward an alarming interpretation of the findings to increase the impact of the publication. On the other hand, when broadly looking at the published literature in the past 5 years, only a minority of industry-sponsored studies provided “negative findings,” that is, the lack of statistically significant DAs [17, 18].
This “uncontrolled” scenario has generated what someone coined “apophenia,” that is, the perception of meaningful patterns and causal connections among random data [19], or the so-called pharmacovigilance syndrome, that is, the incorrect use of spontaneous adverse event reports to infer that a drug causes an adverse reaction, what the incidence or prevalence of such events may be, and whether one drug has lower or higher risk than another [20]. This in turn increases the complexity in the risk-benefit assessment [21] and may generate false alarm among clinicians [22].
\nIt must be emphasized that statistical techniques, usually referred to as quantitative analyses [23], cannot be used as a standalone approach to assess a drug-related risk because no risk quantification can be offered: they should be viewed in conjunction with a qualitative analysis of individual reports, whenever feasible, and other pieces of evidence (e.g., observational studies). In other words, they cannot replace a proper clinical judgment in the individual patient.
\nIn the recent past, a debate arose on the proper use of DAs and the benefit of their publication [24, 25]. However, no actions have been taken so far. The key applications of DAs are summarized as follows:
Signal detection (including specific events or the overall safety profile). This is the main goal of DAs, especially for medicines with unpredictable pharmacokinetics-pharmacodynamics such as biologicals [26], or recently marketed drugs with still undefined safety profile. This is also justified for rare adverse events that may escape detection in premarketing clinical trials (e.g., TdP, liver injury) or in case an imbalance (not reaching statistical significance) emerged from clinical data, as happened for pioglitazone and bladder cancer [27]. The choice of comparator group is pivotal in signal detection, especially in terms of clinical implications. For instance, a novel antidiabetic drug should be compared with other antidiabetic drugs through the so-called analysis by therapeutic area (i.e., comparing the reporting of a given drug with other agents belonging to the same therapeutic class), in order to identify patients that are likely to share the common risk factors, mitigate the confounding by indication bias, and investigate the potential intraclass variations of risk [28, 29, 30, 31, 32]. As a matter of fact, a suspected risk for a drug can be interpreted by a clinical point of view only if compared to the same risk of therapeutic alternatives, especially for severe disorders (e.g., diabetes) because patient cannot be left without treatment.
Test/verify/confirm a pharmacological hypothesis. This can be illustrated by a number of examples in the recent past, including the relationship between hERG blockade and occurrence of TdP in humans [33]; the risk of diabetes by antipsychotics, which was more frequently associated with agents blocking simultaneously histamine H1 and serotonin 5-HT2C receptors [34]; the association between different receptor occupancy and antipsychotic-induced movement disorders [35], and the link between dopamine receptor agonist drugs and specific impulse control disorders [36].
Address/verify methodological issues. This aspect is receiving an increasing attention because it may strongly impact on final results. Before planning the analysis, it is important to verify all potential biases affecting the drug(s) or event(s) under investigation and prespecify strategies to handle with these confounders (see Section 7) [37, 38, 39, 40, 41, 42, 43].
Investigate the likelihood of drug-drug interactions. A few pilot initiatives proposed theoretical strategies as well as relevant automated methods to detect signals resulting from drug-drug interactions (DDIs) in PhV databases [44, 45, 46, 47, 48]. Various approaches can be used to highlight adverse drug interactions: (a) reported suspicion of interactions as noted by the reporter in a case narrative, (b) assignment of the two drugs as interacting (c) drug-drug interaction reported as adverse event, and (d) increased co-reporting for the drug pair when disproportionality is applied [49]. There is also interest in using SRSs to investigate whether a given drug-drug combination moderates the frequency of an adverse event [50, 51].
A recent systematic review highlighted that only a minority of studies aimed at confirming or supporting previous regulatory decisions on a given safety aspect [52], thus strengthening the aforementioned concept that DAs do not usually support, on their own, regulatory actions but must be integrated with other data sources.
\nApart from DAs, the value of case-by-case assessment should not be disregarded. In fact, the individual evaluation of reports performed by pharmacovigilance experts with medical background has multiple aims: (a) it may per se be used for signal detection of rare ADRs, such as in the case of DMEs by detecting potential drug-event combinations even earlier than DAs [53] and (b) it may confirm or refuse disproportionality signals, by strengthening/reducing causality assessment or by identifying duplicates by automated strategy (through the use of narratives). The key challenging aspect of case-by-case analysis is represented by causality assessment, that is, the process of differential diagnoses to prove actual causal relationship: exclusion of alternative causes, biological and temporal plausibility, evidence of dechallenge and rechallenge (usually unintentional) should be verified. The complexity of causality assessment stems from the fact that it needs to be viewed from the context of the patient treated rather than the drug product [54]. Although several approaches are available to assess causality, no single method is universally accepted and there is no gold standard [55]. The choice of the most suitable approach may also depend on the event under investigation; for instance, ALDEN is a specific algorithmic score validated for assessment of drug causality in Stevens-Johnson syndrome/toxic epidermal necrolysis [56], whereas Roussel Uclaf Causality Assessment Method (RUCAM) was implemented for drug-induced liver injury [57].
\nAs a conclusive remark, it should be recognized that most researchers are from academia, and in fact, their additional role is university teaching. In the last few years, experts of medical teaching have strengthened the importance of PhV in the core curriculum of undergraduate students of healthcare courses (i.e., medicine, pharmacy, dentistry, nursing, etc.). WHO and the most active national PhV centers are committed to better define knowledge, skills, and attitudes that students should acquire in order to have an active role in pharmacovigilance [58].
\nIntegration of heterogeneous data (literature including mass media, clinical trials, observational studies, spontaneous reporting data analysis, case reports, and preclinical data) is currently in the research domain at the preliminary level, with the degree of confidence and reliance on a given source as key unresolved issues. An attempt to achieve a risk score on the pro-arrhythmic potential of drugs was undertaken within the ARITMO project [59], where a Dempster-Shafer model was used to combine evidence from heterogeneous and independent sources using expert judgment [60]. The only published experience on data integration in pharmacovigilance comes from the (useful) interplay between SRSs and healthcare databases to increase the accuracy of signal detection [61, 62].
\nIn the following section, the issue of evidence integration for research purposes will be addressed in the context of systematic reviews, which are increasingly being used as they can make researchers and readers aware about what is known, how it is known, how evidence varies across studies, and thus about what is not already known [63].
\nIssues of data quality and inherent limitations cause remarkable impact in spontaneous reporting studies in which more sources of variability (e.g., missing data) and biases affecting the results could be identified (competition or notoriety bias). Nevertheless, so far, no specific tools or techniques have been developed to select, compare, or pool together data from DAs. This could be due to a relative paucity of this kind of analysis in the medical literature.
\nDisproportionality is used to detect “signals of disproportionate reporting” (SDRs) that, once detected, are usually investigated through other, and more precisely, study designs. It is thus rare to have additional DAs regarding the same outcome related to the same drug or drug class and that used a comparable tool for signal detection (frequentist vs. Bayesian approaches). Nevertheless, at least theoretically, techniques and statistical basis used to perform meta-analysis could also be used to analyze results from disproportionality, at least to evaluate consistency of signal across different databases. A consistent signal found in two databases could be probably prioritized in comparison with inconsistent ones. Notably, raw data cannot be pooled because of the existence of an unquantified degree of redundancy (i.e., duplicates across databases), but results can be combined to reach a single “pharmacovigilance score” [59].
\nIt is well known that results of DA cannot be considered as measures of risk: the number of cases in a spontaneous reporting database does correspond to neither the number of cases that happened under the drug nor to that of cases induced by the drug, and the number of exposed people is not measured. From this point of view, including results of disproportionality in a meta-analysis could be considered inappropriate, although identification of heterogeneity in reporting may be of interest [64]. In the absence of any clear guideline, disproportionality studies could be searched and included in (qualitative) systematic reviews, but their results must be kept separated from pooled risk estimates of (quantitative) meta-analyses [65]. A recent experience by a French team on safety of drugs acting on the nitric oxide pathway in pulmonary hypertension considers together results from a DA of VigiBase and from a meta-analysis of clinical trials and concludes that the safety profiles of riociguat and phosphodiesterase inhibitors were different, thus providing a rationale for safe prescribing [66]. This approach, as the integration of spontaneous reporting analysis in meta or teleoanalysis [67], is still a research question.
\nPreliminary findings raise the hypothesis that, provided that all technical and clinical aspects are addressed, the performance of DAs is remarkable [7] and may approach the relative risks of analytical studies, thus providing an initial indication of the likely clinical importance of an adverse event [68].
\nOnce the research question has been identified, the researcher must keep in mind the various limitations and biases affecting SRSs to reduce the likelihood of detecting spurious signals. Moreover, clinical, pharmacological, and statistical considerations are needed to select the most appropriate dataset, definition of cases, exposure, and covariables for stratification/adjustment.
\nAlthough the discussion on performance, accuracy, and reliability of different approaches to perform DAs was fascinating a decade ago, at present there is still no recognized gold standard methodology, and the key factor that may influence results is represented by the threshold defined for the number of cases [69, 70]. DAs in spontaneous reporting databases test whether an ADR is reported more frequently than expected; they allow identifying the so-called SDRs [23, 71]. These SDRs must be differentiated from safety signals because the existence of a SDR is not sufficient to constitute a safety signal (it does not always result in one, in fact), and a safety signal does not always imply a corresponding SDR [72].
\nAs previously described, the various SRSs differ in terms of accessibility, catchment area, drug codification, and other technical issues. For instance, two key steps must be managed when analyzing the publicly available version of FAERS: drug mapping and removal of duplicates. These aspects have been extensively covered in the previous book chapter, and the reader should refer to this publication for details [73]. The FDA is continuously working to develop a probabilistic record-linkage algorithm combining structured and unstructured data (narratives) to improve the detection rate and accordingly reduce the occurrence of false positive signals [74].
\nBefore considering a potential causal relationship for a given identified SDR, main biases that affect signal detection from spontaneous reporting must be eliminated or at least mitigated. Notably, even after accounting for major bias, clinical association cannot be inferred from SRSs, and channeling bias (selective prescription of newer drugs to patients with more severe disease [75]) is unlikely to be fully accounted by statistical adjustments. They are described later together with practical examples and relevant minimization strategies as shown in Table 3.
\nBias | \nExample | \nUnderlying reason | \nMinimization strategy | \n
---|---|---|---|
Indication bias | \nAngiotensin Converting Enzyme (ACE) inhibitors showing signal of hypoglycemia. | \nThese agents are largely used in diabetic patients. | \nSensitivity analysis including only nondiabetic patients (i.e., using antidiabetic agents). | \n
Drug competition bias | \nAnticoagulants when analyzing drug-induced bleeding. | \nAnticoagulants are expected to cause bleeding as toxic effect of their drug class. | \nAnalysis by excluding reports with anticoagulants. | \n
Event competition bias | \nExtrapyramidal syndrome (ES) when analyzing first-generation antipsychotics (FGA). | \nES is a typical ADR in FGA-treated patients. | \nAnalysis by excluding ES to detect new safety signal for FGA. | \n
Notoriety bias | \nRhabdomyolysis occurrence with statins after regulatory warnings. | \nAfter that alert, the number of events arose. | \nStudying signal before the alert. | \n
Dilution bias | \nSuicide ideation related to new antidepressant. | \nA warning issued for a whole pharmacological class has stronger impact for newer drugs because the new ADR is diluted by other ADRs for older drugs. | \nTaking into account the time of drug approval and investigate different sources of dilution (e.g., warnings, publications, etc.). | \n
Major biases in disproportionality analyses and strategies for their minimization.
Modified from [114].
Overall, we can identify: (A) indication bias when a drug is found to be associated with a given event for the sole reason that it is indicated in patients with comorbidities that increase the risk of that event; (B) competition bias also called “masking effect” when an event/drug more frequently reported for a given drug/event can “mask” identification of other possible ADRs/drugs [42, 76, 77, 78, 79, 80, 81, 82];(C) notoriety bias when media attention (e.g., regulatory warning and milestone publication) causes over-reporting of peculiar ADR for specific drugs [37, 38]; and (D) dilution bias when a whole drug class is influenced by media attention for an event, older drugs with a larger numbers of reports are less likely to generate safety signal than newer drugs (with less reports) [83].
\nThe Weber effect is an additional factor that may influence the reporting of given drugs, although it cannot be formally considered as a source of bias [84]. It was originally described as a higher reporting especially during the first 2 years after marketing approval, thus suggesting novelty per se as a risk factor for notification, although modern adverse event reporting systems seem less affected by this bias [43].
\nThe 2012 PhV legislation forced national competent authorities and marketing authorization holders to record and report cases of suspected adverse reactions reported by patients [3]. This, in turn, caused legislation remarkable increase of the total number of patient reports (+113%) after 3 years, with the Netherlands, the UK, Germany, France, and Italy accounting for 75% of all patient reports [85]. The relevance of patient reports is heterogeneous, and a recent survey on 141 countries worldwide showed that in one-fourth of them, patients were not allowed to report. Conversely, countries receiving the highest percentage of patient reports in 2014 were the USA (64%) and Canada (30%).
\nMore than 70 countries had fewer than 50 reports from patients [86]. The quality and the value of patient reports in the context of signal detection were evaluated in many published studies [87, 88, 89, 90, 91]. The value of the reports as a signal is directly dependent on the amount of clinically relevant information, in addition to the fact that an ADR report requires a thorough examination of the potential drug-event association. Most of the published studies comparing information reported by patients and healthcare professionals focused on the completeness of information [86, 92].
\nPatient reports give detailed descriptions of suspected ADRs, attribute reactions to specific medicines, and provide information useful for assessing causality. Patient reports often have richer narratives than those of healthcare professionals, including detailed information about the impact of the suspected ADR on the patient’s life [91].
\nMany studies, mainly from the UK and the Netherlands, showed that patient reports allow for the identification of new ADRs and lead to the strengthening of signal detection activities [90, 93, 94].
\nIn summary, patient’s reporting offers a different perspective in drug safety assessment and may potentially contribute in signal detection. However, it is important to further investigate its actual role in drug safety assessment; in fact, the large number of reports without clear causal relationship (recently called “precautionary report”) may alter adverse event profile by masking safety signals or, conversely, creating spurious associations [95].
\nThe relevance of patient reporting highlights the need of public access to spontaneous reporting data, and many countries now provide public access to SRSs, with the possibility to have summary presentations for reactions associated to each single drug in the database or a case listing of limited information for each single case report. Both EMA and WHO Uppsala Monitoring Centre (UMC) developed web tools to access a limited set of spontaneous reporting data in their database, EudraVigilance (
The EMA policy includes the possibility for academia or nonprofit organization to ask for a greater access to data as aggregated data outputs or line listings based on core data elements (
A different approach to transparency is followed by UMC and FDA. In VigiBase, custom search service provided by UMC is performed upon request. Any stakeholders can use the custom search services to request a limited set of data for specific studies or projects for a fee.
\nThe best level of transparency is observed for FDA data. Data for both drugs (FAERS) and vaccine (VAERS) can be obtained using web-based search tools that return structured and/or unstructured data. Moreover, the entire database is quarterly downloadable in comma-separated value (CSV) or other formats. This access needs technical skills to properly process the relational database files and any unstructured fields. However, it gives the possibility to any users to analyze FDA spontaneous reporting data even applying DAs [5]. Since June 2014, the FDA developed an innovative platform called openFDA (
An area of emerging interest for research is represented by the use of information provided by patients in social media on personal experiences when using a given drug. At present, it is under investigation whether or not (and how) social media data mining can contribute to signal detection [94, 95].
\nA recent review summarizes prevalence, frequency, and comparative value of information on adverse events of healthcare interventions from user comments and videos in social media. The study assessed over 174 social media sites, with discussion forums (71%) being the most popular. The overall prevalence of adverse event reports in social media varied from 0.2 to 8% of posts. Moreover, there was general agreement on overall concordance between adverse events mentioned in social media and those already documented in other sources (such as drug labels and published trials) [97].
\nThe web-recognizing adverse drug reaction (Web-RADR) project, leaded by EMA and funded within the innovative medicines innovation (IMI), aims to recommend policies, frameworks, tools, and methodologies in the use of social media and mobile technology to improve drug safety [98]. Specific objectives are as follows: (a) to develop the specific mobile application prototypes to support adverse drug reaction reporting and the provision of drug safety information to application users and (b) to assess the usefulness of social media data for PhV and more specifically in signal detection activities.
\nThe theoretical advantages of social media in the context of signal detection rely on potential earlier identification of rare and serious drug-related problems, in comparison with conventional SRSs, considering the opportunity to share information as fast as possible and the large number of active users in the social media. It has been reported that patient reports of suspected adverse reactions, particularly for specific reactions, can precede those of healthcare professionals [99]. One study of social media posts containing discussions of adverse drug events (“Proto-AEs”) found that there were nearly three times as many Proto-AEs found in Twitter data than reported to the FDA by consumers, with rank correlation between them at the distribution of reactions at MedDRA SOC level [100].
\nAnother important value from social media analyses comes from extracting qualitative insights into the actual discussions made by patients around a drug and an adverse event. This can be of great value for addressing issues related to the patient experience around an ADR and its impact on the quality of life [101]. Moreover, mining data from social media gives us a greater chance of capturing ADRs that a patient would not necessarily complain about to their doctor or nurse and can also help assessment of the risk perceptions of patients.
\nKey challenge is represented by the identification of drugs and ADRs in the text strings through a particular type of machine learning called natural language processing (NLP). From the perspective of PhV and NLP specifically, user posts on social media contain colloquial language and also misspellings. Especially when using lexicon-based approaches, these present problems as the accuracy of direct matches decreases. Colloquial and informal language is more difficult to parse, and thus, recent research tasks have focused on developing NLP tools specifically for data from social media [102, 103]. The balance between sensitivity and specificity of these tools in identifying ADRs is a key issue because a high number of false positives could heavily impact the efficacy of signal detection activities.
\nAnother key element is the quality of the information on adverse events reported in the social media, which was analyzed only by a few works. A study where Internet narratives posted by patients were evaluated showed that the informativeness level was very incomplete and makes their assessment and use for PhV purpose difficult [104].
\nConcerning the potential of social media analyzes for earlier signal detection, contrasting data are published [105, 106].
\nSocial media data mining uses information for PhV purposes, which were not primarily shared by the patient for this purpose. This raises a number of ethical questions, especially about identification of individuals by utilizing additional information, such as the geocode location on posting, username, and other potentially personally identifiable information [107], which are still unresolved. How would patient using social media react when approached for additional information by organizations that collect PhV data? Since this is a new area, ethically sound policy guidance needs to be developed.
\nA different approach in the use of Internet data for signal detection is the use of anonymized logs of web searchers [108]. In a recent study, a web-based search query method called “query log reaction score” was developed to detect whether adverse events associated with certain drugs could be found from search engine query data. The web query methods have moderate sensitivity (80%) in detecting signals in web query data compared with reference signal detection algorithms, but many false positives were generated, and this method had low specificity [109].
\nThe continuous increasing number of spontaneous reports and the increasing quality in their systematic archiving and accessing comply scientific community to improve methods of analysis and ways to interpret them for regulatory, clinical, and research purposes.
\nA specific debated issue on the current role of data-mining procedures of SRSs regards the possibility to directly compare drugs within the same therapeutic class [110]. We are in favor of this approach and strongly encourage further research regarding the use of SRSs, under stringently defined conditions, to compare adverse event rates for drugs [111]. To this aim, all the following criteria must be fulfilled:
Same therapeutic indication(s). The effect of the underlying disease may be reduced by restricting DAs to drugs within the same therapeutic area [29, 30].
Similar market penetration and utilization. Drug consumption/prescription should be considered in order to: (i) complement DAs by highlighting possible risk differences through reporting rates (especially for vaccines and DMEs) [112]; (ii) weigh the drug risk at the population level (and assess the public health impact of ADRs); and (iii) prioritize safety signals emerging from traditional DAs [113].
Similar time on the market. This aspect should be carefully considered in the analyses to avoid the temporal or time-point bias, especially when comparing first- versus second-generation drugs. Standardization of the time on the market using the same fixed-length post-approval time-frame has been proposed [110].
Data distortions are unlikely to occur or apply in a similar manner across the drugs under investigation. Stratification (for age and sex) or adjustment should always be considered to minimize the presence of known confounders. Moreover, the existence of specific biases should be verified and accounted for.
An emerging application of SRSs, in the era of Big Data, is represented by their integration with other heterogeneous sources of healthcare data (e.g., the availability of prescription-data, hospital admission and discharge, population-based, disease-based, death registries, social media, and literature) to support proactive PhV in the risk-benefit assessment, as performed in the ARITMO projects through the Dempster-Shafer approach [59].
\nFinally, the question arises as to whether all disproportionality studies should be published in scientific journals. Supporters of scientific transparency and full release of datasets via Open Science would undoubtedly call for public availability of study results, including negative findings. A proposal was recently formulated [114].
\nThis controversy on the quality of DAs raises the concern on how best assess it and reach consensus on a “set of minimum requirements to assess the quality of DAs in terms of study conception, performing and reporting.” Provisional criteria have been recently proposed (from the experience of antidiabetic drugs) [114], but further discussion is warranted:
Clear title. Avoid the general terms such as “pharmacovigilance analysis.” Prefer the following terms: “disproportionality analysis,” “analysis of spontaneous reporting system,” and “analysis of spontaneous reports.”
Scientifically sound study conception. The scientific rationale must be clearly indicated and fall within one of these aforementioned categories (DAs are particularly suited for DMEs). Regulatory approach (i.e., identification of a potential signal during routine monitoring of spontaneous reporting systems) and commissioned analysis for regulatory purposes should not be formally eligible for publication in a journal, unless an added value emerges (e.g., the analysis is extended to the entire pharmacological class).
Transparent study design. The unit of analysis should be described. Case(s) and exposure (reference group) definition should be specifically defined. The search strategy must be stated, and a clear description behind the choice is warranted. Key confounders to be accounted for must be a priori identified. Strategies to handle these biases must be indicated, including stratified or adjusted analyses. Notoriety must be carefully assessed: a structured literature evaluation is recommended, instead of a mere check to summary of product characteristics.
Balanced discussion and conclusion. Prefer the term “disproportionality signal” and “signal of disproportionate reporting,” and avoid the terms such as “alarm signal,” “signal of risk,” “increased risk,” “association,” “incidence.” Compare the results with those emerging from similar studies (emerged from the structure literature evaluation). Limitations should be provided in a dedicated section, avoid a mere listing of known biases affecting spontaneous reporting system. Avoid the specific recommendations (decision-making approach) to support drug prescription or selection of drugs claimed to be safer.
From a technical standpoint, good signal detection practices have been published by the Innovative Medicines Initiative Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium (PROTECT) project, which have formulated 39 recommendations for those working in the PhV community [115].
\nA final issue regards the timeliness of publishing DAs when keeping with signal detection. For instance, the analysis by Elashoff et al. [16] on pancreatitis reports with incretin-based drugs, apart from methodological flaws and data misinterpretation causing unjustified alarm, was also untimely, considering that observational studies had already been carried out. Conversely, liver injury with direct-acting oral anticoagulants (DOACs) was studies because of limited predictivity of premarketing phases in detecting clinical signals of liver toxicity and previous concern with ximelagatran: the disproportionality signal raised for rivaroxaban in FAERS [116] was tested by the recent US population-based studies, which found lower hospitalization rates for liver injury with DOAC initiators than patients starting warfarin, with rivaroxaban and dabigatran associated with the highest and lowest risk [117, 118], although confounders are likely to exist [119, 120]. This case underscores the value of performing well-conducted DAs and the importance of directing subsequent analytical research to confirm or refute the drug-related hypothesis.
\nAll these unsettled issues witness the need and the importance of implementing research to finally clarify the role of DAs in clinical practice.
\nRegulators and especially clinicians are appreciating the importance and the role of DAs to monitor and assess the safety profile of marketed drugs. All “actors” dealing with SRSs must always be aware of the so-called seduction bias and self-deception bias (i.e., over-reliance on mathematical models and the subconscious confidence in expecting a given output from results), thus be reminded of inherent limitations that, at present, do not allow to assess actual risk in clinical practice, mainly because of the lack of certainty in the occurrence of adverse events and the lack of exposure data [121].
\nFrom a research perspective, there is an urgent need to raise the bar, aiming to increase the accuracy and reproducibility (in one word the quality) of this kind of study. From one side, there is a room for improvement in several aspects of the analysis of SRSs, including relevant implications and their appropriate use such as the aspect of “no findings” (i.e., findings of nondisproportional results), which has not received sufficient attention so far. Moreover, different research teams are implementing sophisticated methods to account for confounders in signal detection, so that DAs may approach relative risk. In the meantime, we propose to include disproportionality studies in (qualitative) systematic reviews keeping results separated from pooled risk estimates of (quantitative) meta-analyses [63].
\nIn conclusion, SRSs represent an invaluable source to monitor and assess the safety of medications, including drugs, vaccines, and healthcare products.
\nWe call for a responsible use and publication of DAs, which should be regulated through a consensus approach among experts; this would finally establish the use and transferability of DAs in clinical practice.
\nStability constant of the formation of metal complexes is used to measure interaction strength of reagents. From this process, metal ion and ligand interaction formed the two types of metal complexes; one is supramolecular complexes known as host-guest complexes [1] and the other is anion-containing complexes. In the solution it provides and calculates the required information about the concentration of metal complexes.
Solubility, light, absorption conductance, partitioning behavior, conductance, and chemical reactivity are the complex characteristics which are different from their components. It is determined by various numerical and graphical methods which calculate the equilibrium constants. This is based on or related to a quantity, and this is called the complex formation function.
During the displacement process at the time of metal complex formation, some ions disappear and form a bonding between metal ions and ligands. It may be considered due to displacement of a proton from a ligand species or ions or molecules causing a drop in the pH values of the solution [2]. Irving and Rossotti developed a technique for the calculation of stability constant, and it is called potentiometric technique.
To determine the stability constant, Bjerrum has used a very simple method, and that is metal salt solubility method. For the studies of a larger different variety of polycarboxylic acid-, oxime-, phenol-containing metal complexes, Martel and Calvin used the potentiometric technique for calculating the stability constant. Those ligands [3, 4] which are uncharged are also examined, and their stability constant calculations are determined by the limitations inherent in the ligand solubility method. The limitations of the metal salt solubility method and the result of solubility methods are compared with this. M-L, MLM, and (M3) L are some types of examples of metal-ligand bonding. One thing is common, and that is these entire types metal complexes all have one ligand.
The solubility method can only usefully be applied to studies of such complexes, and it is best applied for ML; in such types of system, only ML is formed. Jacqueline Gonzalez and his co-worker propose to explore the coordination chemistry of calcium complexes. Jacqueline and et al. followed this technique for evaluate the as partial model of the manganese-calcium cluster and spectrophotometric studies of metal complexes, i.e., they were carried calcium(II)-1,4-butanediamine in acetonitrile and calcium(II)-1,2-ethylendiamine, calcium(II)-1,3-propanediamine by them.
Spectrophotometric programming of HypSpec and received data allows the determination of the formation of solubility constants. The logarithmic values, log β110 = 5.25 for calcium(II)-1,3-propanediamine, log β110 = 4.072 for calcium(II)-1,4-butanediamine, and log β110 = 4.69 for calcium(II)-1,2-ethylendiamine, are obtained for the formation constants [5]. The structure of Cimetidine and histamine H2-receptor is a chelating agent. Syed Ahmad Tirmizi has examined Ni(II) cimetidine complex spectrophotometrically and found an absorption peak maximum of 622 nm with respect to different temperatures.
Syed Ahmad Tirmizi have been used to taken 1:2 ratio of metal and cimetidine compound for the formation of metal complex and this satisfied by molar ratio data. The data, 1.40–2.4 × 108, was calculated using the continuous variation method and stability constant at room temperature, and by using the mole ratio method, this value at 40°C was 1.24–2.4 × 108. In the formation of lead(II) metal complexes with 1-(aminomethyl) cyclohexene, Thanavelan et al. found the formation of their binary and ternary complexes. Glycine,
Using the stability constant method, these ternary complexes were found out, and using the parameters such as Δ log K and log X, these ternary complex data were compared with binary complex. The potentiometric technique at room temperature (25°C) was used in the investigation of some binary complex formations by Abdelatty Mohamed Radalla. These binary complexes are formed with 3D transition metal ions like Cu2+, Ni2+, Co2+, and Zn2+ and gallic acid’s importance as a ligand and 0.10 mol dm−3 of NaNO3. Such types of aliphatic dicarboxylic acids are very important biologically. Many acid-base characters and the nature of using metal complexes have been investigated and discussed time to time by researchers [7].
The above acids (gallic and aliphatic dicarboxylic acid) were taken to determine the acidity constants. For the purpose of determining the stability constant, binary and ternary complexes were carried in the aqueous medium using the experimental conditions as stated above. The potentiometric pH-metric titration curves are inferred for the binary complexes and ternary complexes at different ratios, and formation of ternary metal complex formation was in a stepwise manner that provided an easy way to calculate stability constants for the formation of metal complexes.
The values of Δ log K, percentage of relative stabilization (% R. S.), and log X were evaluated and discussed. Now it provides the outline about the various complex species for the formation of different solvents, and using the concentration distribution, these complexes were evaluated and discussed. The conductivity measurements have ascertained for the mode of ternary chelating complexes.
A study by Kathrina and Pekar suggests that pH plays an important role in the formation of metal complexes. When epigallocatechin gallate and gallic acid combine with copper(II) to form metal complexes, the pH changes its speculation. We have been able to determine its pH in frozen and fluid state with the help of multifrequency EPR spectroscopy [8]. With the help of this spectroscopy, it is able to detect that each polyphenol exhibits the formation of three different mononuclear species. If the pH ranges 4–8 for di- or polymeric complex of Cu(II), then it conjectures such metal complexes. It is only at alkaline pH values.
The line width in fluid solutions by molecular motion exhibits an incomplete average of the parameters of anisotropy spin Hamilton. If the complexes are different, then their rotational correlation times for this also vary. The analysis of the LyCEP anisotropy of the fluid solution spectra is performed using the parameters determined by the simulation of the rigid boundary spectra. Its result suggests that pH increases its value by affecting its molecular mass. It is a polyphenol ligand complex with copper, showing the coordination of an increasing number of its molecules or increasing participation of polyphenol dimers used as ligands in the copper coordination region.
The study by Vishenkova and his co-worker [8] provides the investigation of electrochemical properties of triphenylmethane dyes using a voltammetric method with constant-current potential sweep. Malachite green (MG) and basic fuchsin (BF) have been chosen as representatives of the triphenylmethane dyes [9]. The electrochemical behavior of MG and BF on the surface of a mercury film electrode depending on pH, the nature of background electrolyte, and scan rate of potential sweep has been investigated.
Using a voltammetric method with a constant-current potential sweep examines the electrical properties of triphenylmethane dye. In order to find out the solution of MG and BF, certain registration conditions have been prescribed for it, which have proved to be quite useful. The reduction peak for the currents of MG and BF has demonstrated that it increases linearly with respect to their concentration as 9.0 × 10−5–7.0 × 10−3 mol/dm3 for MG and 6.0 × 10−5–8.0 × 10−3 mol/dm3 for BF and correlation coefficients of these values are 0.9987 for MG and 0.9961 for BF [10].
5.0 × 10−5 and 2.0 × 10−5 mol/dm3 are the values used as the detection limit of MG and BF, respectively. Stability constants are a very useful technique whose size is huge. Due to its usefulness, it has acquired an umbrella right in the fields of chemistry, biology, and medicine. No science subject is untouched by this. Stability constants of metal complexes are widely used in the various areas like pharmaceuticals as well as biological processes, separation techniques, analytical processes, etc. In the presented chapter, we have tried to explain this in detail by focusing our attention on the applications and solutions of stability of metal complexes in solution.
Stability or formation or binding constant is the type of equilibrium constant used for the formation of metal complexes in the solution. Acutely, stability constant is applicable to measure the strength of interactions between the ligands and metal ions that are involved in complex formation in the solution [11]. A generally these 1-4 equations are expressed as the following ways:
Thus
K1, K2, K3, … Kn are the equilibrium constants and these are also called stepwise stability constants. The formation of the metal-ligand-n complex may also be expressed as equilibrium constants by the following steps:
The parameters K and β are related together, and these are expressed in the following example:
Now the numerator and denominator are multiplied together with the use of [metal-ligand] [metal-ligand2], and after the rearranging we get the following equation:
Now we expressed it as the following:
From the above relation, it is clear that the overall stability constant βn is equal to the product of the successive (i.e., stepwise) stability constants, K1, K2, K3,…Kn. This in other words means that the value of stability constants for a given complex is actually made up of a number of stepwise stability constants. The term stability is used without qualification to mean that the complex exists under a suitable condition and that it is possible to store the complex for an appreciable amount of time. The term stability is commonly used because coordination compounds are stable in one reagent but dissociate or dissolve in the presence of another regent. It is also possible that the term stability can be referred as an action of heat or light or compound. The stability of complex [13] is expressed qualitatively in terms of thermodynamic stability and kinetic stability.
In a chemical reaction, chemical equilibrium is a state in which the concentration of reactants and products does not change over time. Often this condition occurs when the speed of forward reaction becomes the same as the speed of reverse reaction. It is worth noting that the velocities of the forward and backward reaction are not zero at this stage but are equal.
If hydrogen and iodine are kept together in molecular proportions in a closed process vessel at high temperature (500°C), the following action begins:
In this activity, hydrogen iodide is formed by combining hydrogen and iodine, and the amount of hydrogen iodide increases with time. In contrast to this action, if the pure hydrogen iodide gas is heated to 500°C in the reaction, the compound is dissolved by reverse action, which causes hydrogen iodide to dissolve into hydrogen and iodine, and the ratio of these products increases over time. This is expressed in the following reaction:
For the formation of metal chelates, the thermodynamic technique provides a very significant information. Thermodynamics is a very useful technique in distinguishing between enthalpic effects and entropic effects. The bond strengths are totally effected by enthalpic effect, and this does not make any difference in the whole solution in order/disorder. Based on thermodynamics the chelate effect below can be best explained. The change of standard Gibbs free energy for equilibrium constant is response:
Where:
R = gas constant
T = absolute temperature
At 25°C,
ΔG = (− 5.708 kJ mol−1) · log β.
The enthalpy term creates free energy, i.e.,
For metal complexes, thermodynamic stability and kinetic stability are two interpretations of the stability constant in the solution. If reaction moves from reactants to products, it refers to a change in its energy as shown in the above equation. But for the reactivity, kinetic stability is responsible for this system, and this refers to ligand species [14].
Stable and unstable are thermodynamic terms, while labile and inert are kinetic terms. As a rule of thumb, those complexes which react completely within about 1 minute at 25°C are considered labile, and those complexes which take longer time than this to react are considered inert. [Ni(CN)4]2− is thermodynamically stable but kinetically inert because it rapidly exchanges ligands.
The metal complexes [Co(NH3)6]3+ and such types of other complexes are kinetically inert, but these are thermodynamically unstable. We may expect the complex to decompose in the presence of acid immediately because the complex is thermodynamically unstable. The rate is of the order of 1025 for the decomposition in acidic solution. Hence, it is thermodynamically unstable. However, nothing happens to the complex when it is kept in acidic solution for several days. While considering the stability of a complex, always the condition must be specified. Under what condition, the complex which is stable or unstable must be specified such as acidic and also basic condition, temperature, reactant, etc.
A complex may be stable with respect to a particular condition but with respect to another. In brief, a stable complex need not be inert and similarly, and an unstable complex need not be labile. It is the measure of extent of formation or transformation of complex under a given set of conditions at equilibrium [15].
Thermodynamic stability has an important role in determining the bond strength between metal ligands. Some complexes are stable, but as soon as they are introduced into aqueous solution, it is seen that these complexes have an effect on stability and fall apart. For an example, we take the [Co (SCN)4]2+ complex. The ion bond of this complex is very weak and breaks down quickly to form other compounds. But when [Fe(CN)6]3− is dissolved in water, it does not test Fe3+ by any sensitive reagent, which shows that this complex is more stable in aqueous solution. So it is indicated that thermodynamic stability deals with metal-ligand bond energy, stability constant, and other thermodynamic parameters.
This example also suggests that thermodynamic stability refers to the stability and instability of complexes. The measurement of the extent to which one type of species is converted to another species can be determined by thermodynamic stability until equilibrium is achieved. For example, tetracyanonickelate is a thermodynamically stable and kinetic labile complex. But the example of hexa-amine cobalt(III) cation is just the opposite:
Thermodynamics is used to express the difference between stability and inertia. For the stable complex, large positive free energies have been obtained from ΔG0 reaction. The ΔH0, standard enthalpy change for this reaction, is related to the equilibrium constant, βn, by the well thermodynamic equation:
For similar complexes of various ions of the same charge of a particular transition series and particular ligand, ΔS0 values would not differ substantially, and hence a change in ΔH0 value would be related to change in βn values. So the order of values of ΔH0 is also the order of the βn value.
Kinetic stability is referred to the rate of reaction between the metal ions and ligand proceeds at equilibrium or used for the formation of metal complexes. To take a decision for kinetic stability of any complexes, time is a factor which plays an important role for this. It deals between the rate of reaction and what is the mechanism of this metal complex reaction.
As we discuss above in thermodynamic stability, kinetic stability is referred for the complexes at which complex is inert or labile. The term “inert” was used by Tube for the thermally stable complex and for reactive complexes the term ‘labile’ used [16]. The naturally occurring chlorophyll is the example of polydentate ligand. This complex is extremely inert due to exchange of Mg2+ ion in the aqueous media.
The nature of central atom of metal complexes, dimension, its degree of oxidation, electronic structure of these complexes, and so many other properties of complexes are affected by the stability constant. Some of the following factors described are as follows.
In the coordination chemistry, metal complexes are formed by the interaction between metal ions and ligands. For these type of compounds, metal ions are the coordination center, and the ligand or complexing agents are oriented surrounding it. These metal ions mostly are the transition elements. For the determination of stability constant, some important characteristics of these metal complexes may be as given below.
Ligands are oriented around the central metal ions in the metal complexes. The sizes of these metal ions determine the number of ligand species that will be attached or ordinated (dative covalent) in the bond formation. If the sizes of these metal ions are increased, the stability of coordination compound defiantly decreased. Zn(II) metal ions are the central atoms in their complexes, and due to their lower size (0.74A°) as compared to Cd(II) size (0.97A°), metal ions are formed more stable.
Hence, Al3+ ion has the greatest nuclear charge, but its size is the smallest, and the ion N3− has the smallest nuclear charge, and its size is the largest [17]. Inert atoms like neon do not participate in the formation of the covalent or ionic compound, and these atoms are not included in isoelectronic series; hence, it is not easy to measure the radius of this type of atoms.
The properties of stability depend on the size of the metal ion used in the complexes and the total charge thereon. If the size of these metal ions is small and the total charge is high, then their complexes will be more stable. That is, their ratio will depend on the charge/radius. This can be demonstrated through the following reaction:
An ionic charge is the electric charge of an ion which is formed by the gain (negative charge) or loss (positive charge) of one or more electrons from an atom or group of atoms. If we talk about the stability of the coordination compounds, we find that the total charge of their central metal ions affects their stability, so when we change their charge, their stability in a range of constant can be determined by propagating of error [18]. If the charge of the central metal ion is high and the size is small, the stability of the compound is high:
In general, the most stable coordination bonds can cause smaller and highly charged rations to form more stable coordination compounds.
When an electron pair attracts a central ion toward itself, a strong stability complex is formed, and this is due to electron donation from ligand → metal ion. This donation process is increasing the bond stability of metal complexes exerted the polarizing effect on certain metal ions. Li+, Na+, Mg2+, Ca2+, Al3+, etc. are such type of metal cation which is not able to attract so strongly from a highly electronegative containing stable complexes, and these atoms are O, N, F, Au, Hg, Ag, Pd, Pt, and Pb. Such type of ligands that contains P, S, As, Br and I atom are formed stable complex because these accepts electron from M → π-bonding. Hg2+, Pb2+, Cd2+, and Bi3+ metal ions are also electronegative ions which form insoluble salts of metal sulfide which are insoluble in aqueous medium.
Volatile ligands may be lost at higher temperature. This is exemplified by the loss of water by hydrates and ammonia:
The transformation of certain coordination compounds from one to another is shown as follows:
A ligand is an ion or small molecule that binds to a metal atom (in chemistry) or to a biomolecule (in biochemistry) to form a complex, such as the iron-cyanide coordination complex Prussian blue or the iron-containing blood-protein hemoglobin. The ligands are arranged in spectrochemical series which are based on the order of their field strength. It is not possible to form the entire series by studying complexes with a single metal ion; the series has been developed by overlapping different sequences obtained from spectroscopic studies [19]. The order of common ligands according to their increasing ligand field strength is
The above spectrochemical series help us to for determination of strength of ligands. The left last ligand is as weaker ligand. These weaker ligand cannot forcible binding the 3d electron and resultant outer octahedral complexes formed. It is as-
Increasing the oxidation number the value of Δ increased.
Δ increases from top to bottom.
However, when we consider the metal ion, the following two useful trends are observed:
Δ increases with increasing oxidation number.
Δ increases down a group. For the determination of stability constant, the nature of the ligand plays an important role.
The following factors described the nature of ligands.
The size and charge are two factors that affect the production of metal complexes. The less charges and small sizes of ligands are more favorable for less stable bond formation with metal and ligand. But if this condition just opposite the product of metal and ligand will be a more stable compound. So, less nuclear charge and more size= less stable complex whereas if more nuclear charge and small in size= less stable complex. We take fluoride as an example because due to their smaller size than other halide and their highest electro negativity than the other halides formed more stable complexes. So, fluoride ion complexes are more stable than the other halides:
As compared to S2− ion, O22− ions formed more stable complexes.
It is suggested by Calvin and Wilson that the metal complexes will be more stable if the basic character or strength of ligands is higher. It means that the donating power of ligands to central metal ions is high [20].
It means that the donating power of ligands to central metal ions is high. In the case of complex formation of aliphatic diamines and aromatic diamines, the stable complex is formed by aliphatic diamines, while an unstable coordination complex is formed with aromatic diamines. So, from the above discussion, we find that the stability will be grater if the e-donation power is greater.
Thus it is clear that greater basic power of electron-donating species will form always a stable complex. NH3, CN−, and F− behaved as ligands and formed stable complexes; on the other hand, these are more basic in nature.
We know that if the concentration of coordination group is higher, these coordination compounds will exist in the water as solution. It is noted that greater coordinating tendency show the water molecules than the coordinating group which is originally present. SCN− (thiocynate) ions are present in higher concentration; with the Co2+ metal ion, it formed a blue-colored complex which is stable in state, but on dilution of water medium, a pink color is generated in place of blue, or blue color complex is destroyed by [Co(H2O)6]2+, and now if we added further SCN−, the pink color will not appear:
Now it is clear that H2O and SCN− are in competition for the formation of Co(II) metal-containing complex compound. In the case of tetra-amine cupric sulfate metal complex, ammonia acts as a donor atom or ligand. If the concentration of NH3 is lower in the reaction, copper hydroxide is formed but at higher concentration formed tetra-amine cupric sulfate as in the following reaction:
For a metal ion, chelating ligand is enhanced and affinity it and this is known as chelate effect and compared it with non-chelating and monodentate ligand or the multidentate ligand is acts as chelating agent. Ethylenediamine is a simple chelating agent (Figure 1).
Structure of ethylenediamine.
Due to the bidentate nature of ethylenediamine, it forms two bonds with metal ion or central atom. Water forms a complex with Ni(II) metal ion, but due to its monodentate nature, it is not a chelating ligand (Figures 2 and 3).
Structure of chelating configuration of ethylenediamine ligand.
Structure of chelate with three ethylenediamine ligands.
The dentate cheater of ligand provides bonding strength to the metal ion or central atom, and as the number of dentate increased, the tightness also increased. This phenomenon is known as chelating effect, whereas the formation of metal complexes with these chelating ligands is called chelation:
or
Some factors are of much importance for chelation as follows.
The sizes of the chelating ring are increased as well as the stability of metal complex decreased. According to Schwarzenbach, connecting bridges form the chelating rings. The elongated ring predominates when long bridges connect to the ligand to form a long ring. It is usually observed that an increased a chelate ring size leads to a decrease in complex stability.
He interpreted this statement. The entropy of complex will be change if the size of chelating ring is increased, i.e., second donor atom is allowed by the chelating ring. As the size of chelating ring increased, the stability should be increased with entropy effect. Four-membered ring compounds are unstable, whereas five-membered are more stable. So the chelating ring increased its size and the stability of the formed metal complexes.
The number of chelating rings also decides the stability of complexes. Non-chelating metal compounds are less stable than chelating compounds. These numbers increase the thermodynamic volume, and this is also known as an entropy term. In recent years ligands capable of occupying as many as six coordination positions on a single metal ion have been described. The studies on the formation constants of coordination compounds with these ligands have been reported. The numbers of ligand or chelating agents are affecting the stability of metal complexes so as these numbers go up and down, the stability will also vary with it.
For the Ni(II) complexes with ethylenediamine as chelating agent, its log K1 value is 7.9 and if chelating agents are trine and penten, then the log K1 values are 7.9 and 19.3, respectively. If the metal ion change Zn is used in place of Ni (II), then the values of log K1 for ethylenediamine, trine, and penten are 6.0, 12.1, and 16.2, respectively. The log βMY values of metal ions are given in Table 1.
Metal ion | log βMY (25°C, I = 0.1 M) |
---|---|
Ca2+ | 11.2 |
Cu2+ | 19.8 |
Fe3+ | 24.9 |
Metal ion vs. log βMY values.
Ni(NH3)62+ is an octahedral metal complex, and at 25 °C its log β6 value is 8.3, but Ni(ethylenediamine)32+ complex is also octahedral in geometry, with 18.4 as the value of log β6. The calculated stability value of Ni(ethylenediamine)32+ 1010 times is more stable because three rings are formed as chelating rings by ethylenediamine as compared to no such ring is formed. Ethylenediaminetetraacetate (EDTA) is a hexadentate ligand that usually formed stable metal complexes due to its chelating power.
A special effect in molecules is when the atoms occupy space. This is called steric effect. Energy is needed to bring these atoms closer to each other. These electrons run away from near atoms. There can be many ways of generating it. We know the repulsion between valence electrons as the steric effect which increases the energy of the current system [21]. Favorable or unfavorable any response is created.
For example, if the static effect is greater than that of a product in a metal complex formation process, then the static increase would favor this reaction. But if the case is opposite, the skepticism will be toward retardation.
This effect will mainly depend on the conformational states, and the minimum steric interaction theory can also be considered. The effect of secondary steric is seen on receptor binding produced by an alternative such as:
Reduced access to a critical group.
Stick barrier.
Electronic resonance substitution bond by repulsion.
Population of a conformer changes due to active shielding effect.
The macrocyclic effect is exactly like the image of the chelate effect. It means the principle of both is the same. But the macrocyclic effect suggests cyclic deformation of the ligand. Macrocyclic ligands are more tainted than chelating agents. Rather, their compounds are more stable due to their cyclically constrained constriction. It requires some entropy in the body to react with the metal ion. For example, for a tetradentate cyclic ligand, we can use heme-B which forms a metal complex using Fe+2 ions in biological systems (Figure 4).
Structure of hemoglobin is the biological complex compound which contains Fe(II) metal ion.
The n-dentate chelating agents play an important role for the formation of more stable metal complexes as compared to n-unidentate ligands. But the n-dentate macrocyclic ligand gives more stable environment in the metal complexes as compared to open-chain ligands. This change is very favorable for entropy (ΔS) and enthalpy (ΔH) change.
There are so many parameters to determination of formation constants or stability constant in solution for all types of chelating agents. These numerous parameters or techniques are refractive index, conductance, temperature, distribution coefficients, refractive index, nuclear magnetic resonance volume changes, and optical activity.
Solubility products are helpful and used for the insoluble salt that metal ions formed and complexes which are also formed by metal ions and are more soluble. The formation constant is observed in presence of donor atoms by measuring increased solubility.
To determine the solubility constant, it involves the distribution of the ligands or any complex species; metal ions are present in two immiscible solvents like water and carbon tetrachloride, benzene, etc.
In this method metal ions or ligands are present in solution and on exchanger. A solid polymers containing with positive and negative ions are ion exchange resins. These are insoluble in nature. This technique is helpful to determine the metal ions in resin phase, liquid phase, or even in radioactive metal. This method is also helpful to determine the polarizing effect of metal ions on the stability of ligands like Cu(II) and Zn(II) with amino acid complex formation.
At the equilibrium free metal and ions are present in the solution, and using the different electrometric techniques as described determines its stability constant.
This method is based upon the titration method or follows its principle. A stranded acid-base solution used as titrate and which is titrated, it may be strong base or strong acid follows as potentiometrically. The concentration of solution using 103− M does not decomposed during the reaction process, and this method is useful for protonated and nonprotonated ligands.
This is the graphic method used to determine the stability constant in producing metal complex formation by plotting a polarograph between the absences of substances and the presence of substances. During the complex formation, the presence of metal ions produced a shift in the half-wave potential in the solution.
If a complex is relatively slow to form and also decomposes at measurable rate, it is possible, in favorable situations, to determine the equilibrium constant.
This involves the study of the equilibrium constant of slow complex formation reactions. The use of tracer technique is extremely useful for determining the concentrations of dissociation products of the coordination compound.
This method is based on the study of the effect of an equilibrium concentration of some ions on the function at a definite organ of a living organism. The equilibrium concentration of the ion studied may be determined by the action of this organ in systems with complex formation.
The solution of 25 ml is adopted by preparing at the 1.0 × 10−5 M ligand or 1.0 × 10−5 M concentration and 1.0 × 10−5 M for the metal ion:
The solutions containing the metal ions were considered both at a pH sufficiently high to give almost complete complexation and at a pH value selected in order to obtain an equilibrium system of ligand and complexes.
In order to avoid modification of the spectral behavior of the ligand due to pH variations, it has been verified that the range of pH considered in all cases does not affect absorbance values. Use the collected pH values adopted for the determinations as well as selected wavelengths. The ionic strengths calculated from the composition of solutions allowed activity coefficient corrections. Absorbance values were determined at wavelengths in the range 430–700 nm, every 2 nm.
For a successive metal complex formation, use this method. If ligand is protonate and the produced complex has maximum number of donate atoms of ligands, a selective light is absorbed by this complex, while for determination of stability constant, it is just known about the composition of formed species.
Bjerrum (1941) used the method stepwise addition of the ligands to coordination sphere for the formation of complex. So, complex metal–ligand-n forms as the following steps [22]. The equilibrium constants, K1, K2, K3, … Kn are called stepwise stability constants. The formation of the complex metal-ligandn may also be expressed by the following steps and equilibrium constants.
Where:
M = central metal cation
L = monodentate ligand
N = maximum coordination number for the metal ion M for the ligand
If a complex ion is slow to reach equilibrium, it is often possible to apply the method of isotopic dilution to determine the equilibrium concentration of one or more of the species. Most often radioactive isotopes are used.
This method was extensively used by Werner and others to study metal complexes. In the case of a series of complexes of Co(III) and Pt(IV), Werner assigned the correct formulae on the basis of their molar conductance values measured in freshly prepared dilute solutions. In some cases, the conductance of the solution increased with time due to a chemical change, e.g.,
It is concluded that the information presented is very important to determine the stability constant of the ligand metal complexes. Some methods like spectrophotometric method, Bjerrum’s method, distribution method, ion exchange method, electrometric techniques, and potentiometric method have a huge contribution in quantitative analysis by easily finding the stability constants of metal complexes in aqueous solutions.
All the authors thank the Library of University of Delhi for reference books, journals, etc. which helped us a lot in reviewing the chapter.
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