Big Data in Drug Safety: Making post-marketing surveillance in pharmacovigilance more efficient

Big Data in Drug Safety: Making post-marketing surveillance in pharmacovigilance more efficient
Page 1 of 16
Author: Veronika Valdova
Published April 2014, revised January 2020
Abstract
The paper makes a case for change in the way data on the safety of medicines is collected, structured,
analyzed, visualized, and shared. Post-market surveillance shall move away from active reporting of
individual case reports into national and international databases toward the collection and analysis of
anonymous structured summary data from health care providers. The objective is to enable an analysis
of total numbers of treated patients and treatment outcomes, including adverse drug reactions and off-
label drug use, to provide meaningful, population-based, statistically valid, bias-free, real-time
information on safety and efficacy of products on the market without endangering patients' privacy.
Such approach would significantly reduce privacy concerns and add value for stakeholders who are
interested in timely and accurate information on benefit:risk profile of medicinal products.
Introduction
In 1997, a group of drug safety professionals gathered in Erice, Sicily, and published a statement on
improving the information environment in drug safety. According to the report, drug safety information
should be ethically and effectively communicated in terms of both content and method. Besides, all the
evidence needed to assess and understand the risks and benefits must be openly available, and the
exchange of data and evaluations among countries must be encouraged and supported [1].
In the USA, adverse drug events (ADRs) cause 700,000 emergency department visits each year. Of these,
120,000 visits result in hospitalization. The estimated incidence rates vary from 2 to 7 adverse drug
events per 100 admissions. The calculation of a precise national incidence rate is challenging because of
differences in criteria for the detection and identification of adverse drug events and the consistency of
reporting. Research suggests that a full 40% of adverse drug events are caused by failure to perform
blood tests [2]. Anywhere from 28 to 95 percent of adverse drug events can be prevented by reducing
medication [3]. In 2011, the National Poison Data System (NPDS) received reports of 2,7 million
substance exposures, of which over 1 million were pharmaceuticals, including 521 fatal poisonings. Over
80% of exposures to pharmaceuticals were unintentional; most of them, nearly 300,000, were
therapeutic errors [4].
The current practice relies on spontaneous reporting of adverse drug events by consumers and
physicians into national systems that are then scrutinized for safety signals at national and supranational
level. The pharmacovigilance reporting system, as it is currently designed, is based on reporting of
adverse drug events into national and international databases [5, 6,7]. The largest and most important
ones are the U.S. FDA Adverse Event Reporting System (FAERS), European EudraVigilance [8], and the
WHO database VigiBase [9]. Most national pharmacovigilance reporting systems were established in the
1990s [10].
The U.S. FDA reporting system, FAERS, contains over 7 million records and reflects data to 1969. About
95% of all cases reported into FAERS come from manufacturers rather than directly from health-care
professionals [11]. The FDA publishes quarterly summary reports; however, the data comes in such a

Page 2 of 16
format that extensive processing is required to allow analysis and review [12]. Generally, post-market
surveillance focuses on the detection of new, previously undetected safety signals. The system is not
designed to detect incidence, prevalence, and trends, and support evaluation of drug efficacy and
benefit:risk profile.
Privacy considerations
Collection, processing, and analysis of pharmacovigilance data is an international affair due to the global
nature of the industry. Safety data processing requires sharing personal health information (PHI) among
multiple institutions, regulators, and private enterprises [13]. The extension of the Health Insurance
Portability and Accountability Act (HIPAA) [14] to business partners and subcontractors makes certain
pharmacovigilance activities a sensitive matter from a privacy perspective. Individual case safety reports
(ICSR) are typically deidentified for processing. However, for follow-up, the reporter has to be reachable
and the patient identifiable. Maintaining large databases of personal health information introduces a
vulnerability that increases with the number of entities involved in information sharing. Before 2014, the
highest fine for a data breach, $1.7 million, was awarded to insurer WellPoint, which left details of
612,402 patients available online [15]. In 2015, U.S. health insurer Anthem exceeded all expectations by
suffering a breach that impacted 79 million people. The company had to pay a $16 million fine imposed
by the U.S. Department of Health and Human Services (2018), in addition to another $115 million paid
out in class-action settlements [16].
The attitudes toward patient privacy vs. public health depend on culture [17]. In the U.K., handling of
personal health information changed substantially in favor of greater availability of data for research.
Program care.data emerged after a series of scandals when the National Health Service (NHS)-owned
patient data was made accessible online and sold on eBay [18,19]. As of autumn 2014, the Health &
Social Care Information Center (HSCIC) collects data from GP practices, including NHS number, date of
birth, gender, postcode, ethnicity, referrals, prescriptions, and diagnoses [20]. The HSCIC product offers
to include 'extracts containing confidential patient data', 'bespoke data linkage', 'patient status' and
'patient tracking', all that for a modest fee [21].
Gaps in signal detection
Current methods of signal detection in drug safety depend on the extrapolation of information derived
from about 5% of the total number of occurring adverse drug events that are reported in the system
[22]. Factors affecting reporting rate include media attention, litigation, nature of the adverse event,
length of time on the market, quality of manufacturer's surveillance system, the prescription status of
the product, and enforcement [23]. Performance characteristics of signal detection algorithms are
generally unknown. These algorithms can attain reasonable predictive accuracy in signaling adverse drug
events; however, not all types of events are equally detectable, and other data sources need to be used
[24]. A common technique is a combination of using pharmacovigilance databases such as FAERS and
monitoring of biomedical literature [25].
According to guidelines for small and medium businesses, qualitative signal detection methods include
'case-by-case manual review' without comparison with cumulative data [26]. Quantitative methods
utilize statistical analysis to identify drug-event pairs that occur with disproportionately high frequency,
typically using the Proportional Reporting Ratio or the Empirical Bayesian Geometric Mean [27].

Page 3 of 16
Estimated 95% under-reporting means that only in the U.S. there would be about 15 million case reports
each year to be compared against cumulative historical records, not counting worldwide exposure and
comparison with other databases [28]. The absence of denominator in spontaneous reporting is a well-
known limitation in the assessment of safety signals, which makes it impossible to determine
epidemiological indicators. Incidence, prevalence, and rates of occurrence cannot be established from
the current drug safety surveillance systems [29].
The main shortcomings of the current drug surveillance system include low participation, the bias in
reporting toward rare, unexpected events, and uncertain causal relationship due to lack of information
[30]. Detection of safety signals from a small fraction of spontaneously reported adverse drug reactions
cannot provide a useful insight into the risk:benefit profile of medications. Information on individual
case histories shall remain a private matter between the patients and their health care providers.
Producing aggregate data at a level of logical units (e.g., hospitals) to stakeholders in the healthcare
ecosystem could be extended to the systematic assessment of treatment outcomes from the treated
population.
Consistency in causality assessment
Dominant industry databases, ARISg and Oracle Argus, require the pharmaceutical company to assess
each drug-event pair for a causal relationship between the suspect drug and the reported adverse
event. There is no good reason why the pharmaceutical industry should be required to provide an
assessment of a causal relationship between the administration of a drug and an adverse drug reaction
(ADR) for spontaneously reports originating from post-market surveillance. The reason for challenging
this practice as counterproductive in post-market surveillance is the fact that the causality flag at the
case level is inconsistently applied throughout the industry as it is subject to internal practices and
conventions. Causality assessment is a probability score that depends on the significance of the safety
event, previous association, temporality, mechanism of action, de-challenge, re-challenge, and dose-
response. Additional considerations in some algorithms (i.e., MONARCSi) include experimental data and
confounding factors [31]. Causality assessment is indispensable in clinical trials, where the sponsor must
collect all adverse events by default, the population sample is well-defined and limited, and causality
defines whether the case is reportable or not. In post-market surveillance activities, however, applying
epidemiological methods for the detection of causal relationships would provide more consistent
results, enabling comparison of products by pharmaceutical class and indication across different
producers.
Communicating information in drug safety
In 1997, a group of pharmacovigilance professionals formulated the Erice Declaration [32]. The initiative
strived to improve patient safety by addressing the need for effective communication between
stakeholders who bear collective responsibility for monitoring, evaluating, and communicating drug
safety as a public health activity [33]. The World Health Organization (WHO) and Uppsala Monitoring
Centre, in their 2002 report “The Importance of Pharmacovigilance,” [34] stressed the need for sound
and comprehensive systems that would make collaboration among multiple stakeholders possible. In
2009, just as the whole global economy started sinking into recession, the vision reappeared with some
new insights and ideas.
“Information should be ethically and effectively communicated in terms of both content and
method; drug information directed to the public in whatever form should be balanced with

Page 4 of 16
respect to risks and benefits; and all the evidence needed to assess and understand the risks and
benefits must be openly available” [35].
Detection of rates of occurrence
Neurological and psychiatric adverse events associated with mefloquine serve as a very intriguing
example of a dysfunctional surveillance system. The extent of the risks related to mefloquine remained
undetected for many years. First serious concerns came to light in 2002 at Fort Bragg, N.C., due to a
series of violent incidents. In 2004, the Armed Forces Epidemiological Board requested guidance on
optimal study design to determine comparative rates of adverse events and risk factors attributed to
suicide among deployed and recently redeployed Servicemembers [36]. The same year, the Department
of Veteran Affairs circulated a letter with a warning about suicide ideation, suicide, violent behavior
associated with mefloquine, and an indication that these effects may persist after the drug is stopped
[37].
In 2009, the Office of the Surgeon General updated its guidance on the use of mefloquine and
recommended against its use in people with a history of traumatic brain injury. The document also
specifies that any adverse effects shall be reported to the appropriate Pharmacy and Therapeutics
Committee for further review and consideration for forwarding to the FDA [38]. The guidance suggests
that not all suspected adverse drug reactions related to mefloquine use were sent to the FDA. The
Department of Defense Privacy Program defines Personal Information rather broadly as information
that can be used to distinguish or trace an individual's identity. Such disclosures are prohibited, with few
exceptions [39]. In this context, forwarding individual case safety reports on neuropsychiatric adverse
effects experienced by personnel of deployable units would be potentially risky.
In winter 2012 - 2013, Dr. Nevin presented findings relating to the neurotoxicity of mefloquine to the
FDA [40, 41]. In May 2013, Irish reporter Rita O'Reilly from RTE's Investigations Unit [42] showed a
possible link between mefloquine and suicides among Irish Defence Forces soldiers. Before the official
ban in elite Army units and inclusion of Black Box Warning on the drug label, Dr. Nevin published a
critical analysis of misuse of mefloquine in Guantanamo detainees [43]. Some media extensively
criticized the measure as prisoner abuse [44]. Nonetheless, at that time, the labeling indicated that
neuropsychiatric adverse effects are rare, and the overall benefit-risk profile of the drug remains
favorable.
Black Box Warning on the drug's label followed in July 2013. The label emphasized the need to
discontinue the drug should any neurological or psychiatric symptoms develop, and that these
symptoms may take months to resolve or become permanent [45]. In September 2013, policy
memorandum was circulated to all units under the U.S. Army Special Operations Command [46]. The
document referenced paper "Psychiatric side effects of mefloquine: Applications to forensic psychiatry"
[47], the FDA Black Box Warning, and a significant change to the drug's labeling. The circular cited
commentaries of military authors that symptoms caused by mefloquine may confound the diagnosis of
post-traumatic stress disorder (PTSD) and traumatic brain injury (TBI). USASOC commanders and
medical personnel were ordered to cease the use of mefloquine as a means of chemoprophylaxis for the
prevention of malaria, and istructed to assess the possibility and impact of mefloquine toxicity on their
populations. The mefloquine ban in elite U.S. Army units received significant media attention [48]. The
press also extensively scrutinized possible link between the drug and rampage killing of 16 Afghan
civilians by SSgt. Robert Bales [49].

Page 5 of 16
The Special Forces ban on mefloquine is not an example of the effectiveness of the existing reporting
system. It was the result of the efforts of Army doctors who were able to observe the effects directly.
Although neuropsychiatric adverse effects were known and listed on the drug label, the frequency of
their occurrence was either not known or not declared. Dr. Nevin based his reasoning on measuring
epidemiological parameters that the standard reporting system would never detect. Army doctors were
able to detect more realistic frequency, incidence, and prevalence, and severity of neuropsychiatric
adverse effects due to internal reporting and review system, which included data on total exposure.
Off-label prescription
Some drugs have great potential when used off-label. Off-label drug use is common and often included
in treatment guidelines. Physicians are free to prescribe any approved drug for any indication as long as
the decision is based upon emerging science and clinical evidence. According to the American Medical
Association, up to 20% of all drugs are prescribed off-label [50]. In the mid-1990s, the off-label
prescription rate increased to 40% and as high as 80% for the pediatric population [51]. Off-label
prescriptions are most prevalent among AIDS, cancer, and cardiac treatments, in the pediatric
population, and patients with terminal illnesses.
Off-label prescription is difficult to detect from medical records because prescriptions are often not
linked to specific diagnoses. Bradford used annual issues of Physician's Desk Reference (PDR) to build
yearly matches between drugs and their on-label indications in the period from 1993 to 2008. Upon
review of the rate of off-label use, he applied Detection Controlled Estimation to prescriptions from the
National Ambulatory Medical Care Survey. He found an increase from 30 to almost 40% over the
monitored period [52]. Since 1984, when the Hatch-Waxman Act was passed, generic applications can
enter the market by demonstrating bioequivalence with the branded product. The FDA grants a three-
year marketing exclusivity for new drug indications confirmed by clinical trials. Still, generic versions can
be prescribed practically at the same rate as the original version [53]. Currently, it is impossible to
detect, when an off-label prescription is beneficial for specific patient groups, and when such
intervention is harmful.
Off-label marketing
In1962, the FDA restricted the marketing of drugs to those for approved indications only. The
prohibition of off-label marketing has long been a source of controversy. The FDA is applying substantial
evidence standard before placing a new product on the market under the conditions of use
recommended in the labeling. Proponents of off-label marketing argue that this ban infringes the
companies' First Amendment right to free speech. Jacob Rogers [54] reviewed high-profile cases of off-
label promotion and main arguments of both sides. Rogers concluded that the FDA ban on off-label
promotion is a sound and constitutional public policy that passes the test for commercial speech set
forth in Central Hudson Gas & Electric Corporation v. Public Service Commission. The argument goes
that while efforts to stop misleading and false claims are legitimate, there is no public interest in
banning medical and scientific information, which is truthful [55]. Improper marketing made the
headlines several times over the last few years. For example, GlaxoSmithKline agreed to pay $3 billion
for promoting its best-selling antidepressants for unapproved uses and failing to report safety data
about a top diabetes drug. Pfizer agreed to pay $2.3 billion for illegally marketing its painkiller Bextra,
which has been withdrawn, and Abbott had to pay $1.6 billion for illegal marketing of the anti-seizure
drug Depakote. Eli Lilly settled criminal and civil charges for $1.4 billion as it illegally marketed
antipsychotic Zyprexa for use in patients particularly vulnerable to its side effects. Most recently,

Page 6 of 16
Johnson & Johnson agreed to pay $2.2 billion to settle allegations that its subsidiary Janssen
Pharmaceuticals illegally promoted antipsychotic Risperdal [56].
Fen-phen: unwarranted creativity
The concerns over the lack of regulatory control realized in the case of the drug that became known as
fen-phen. Doctors widely prescribed the FDA-approved fenfluramine (fen) in three off-label ways. First,
the drug was used in combination with phentermine (phen). Second, the use of fenfluramine was
extended beyond the brief approved periods. And third, fenfluramine was prescribed to persons who
were overweight but not obese. The FDA later determined that such usage of the drug constituted an
unacceptable risk. Conservative estimate was that 285,000 fen-phen users suffered damage to heart
valves during the brief period in which the combination was widely prescribed [57].
Bevacizumab: unappreciated success
Some off-label uses have been very successful. Bevacizumab (Avastin; Genentech), a drug approved for
intravenous treatment of solid tumors, is used for age-related macular degeneration with great success.
The intriguing question is, why is government approval not obtained to convert off-label uses of drugs to
on-label? Both bevacizumab and its major intraocular competitor, ranibizumab, are manufactured by
the same company. Bevacizumab costs $50 and ranibizumab $1950, thereby giving the manufacturer no
financial incentive to pursue FDA approval. As a result, intraocular off-label bevacizumab use may be
destined to remain off-label forever [58].
Off-label promotion precipitated far more controversy and consternation than off-label prescribing. The
enforcement activities in this area and most likely will continue doing so in the foreseeable future. The
post-market surveillance system can only detect new safety signals but cannot help with insight into
efficacy. Better quality data and its presentation ould be needed to provide evidence for approval of
common off-label uses that are beneficial or abandonment of such practice as harmful.
Observe – Orient – Decide – Act
Col. John Boyd, USAF strategist, described the so-called OODA loop, or the Boyd cycle, as a concept
necessary for sound decision-making [59]. Observation, or collection of data, shall be followed by
Orientation, that is, the analysis and synthesis of the gathered data, and Decision – and finally Action,
the execution of the decisions made. The same principle can be applied to the monitoring of safety and
efficacy of drugs after approval, under real-life conditions. Information that enters the drug safety OODA
loop has to represent the real-world as accurately as possible.
Scholarly works from not so traditional medical literature, such as Heuer's Psychology of Intelligence
Analysis [60] and Moore's Critical Thinking in Intelligence Analysis [61] or Clark's Intelligence Analysis: A
Target-Centric Approach [62] may help design effective systems for collection and processing
information, determination of its meaning, and identification and minimization of biases. The
exclusiveness of the medical profession often results in the creation of stovepipes. Just like in the
intelligence world, it isn't easy to challenge the practices adopted by the medical profession from
outside.
In medicine, not all information is equally likely to reach its audience. Negative and inconclusive studies
are much less likely to be published than studies, which confirm the tested hypothesis and support the
claim in question [63]. Publication bias has a cumulative impact on skewing the information

Page 7 of 16
environment, especially when combined with data from spontaneous reporting systems that capture
merely 5% of occurring adverse drug events.
Information coming from post-approval surveillance suffers from the same limitations in determining
trends because the numbers of patients exposed to a specific drug, or their combination, is
unknown. Logical errors can also occur in other parts of the Boyd cycle. Flawed orientation phase will
inevitably lead to erroneous conclusions. A wide range of cognitive biases come to mind. For example, a
cognitive bias in favor of causal explanations [64] may create falsely positive links between drug effects,
desired or undesired, and their perceived impact on bodily functions. The desired direction would be to
create a methodologically sound analytical strategy that respects the need for inclusion of all relevant
data and provides the means for comprehensive real-time assessment of information to allow sound
and timely decision-making and action.
A better way of doing things
The definition of big data equals data so large that it cannot be processed on one's machine, so tools
and infrastructure are needed to do that kind of large-scale analysis. Data from a large number of
healthcare providers would certainly fulfill this definition. The information needs to be processed in a
way, which would allow the determination of patterns across the treated population. The healthcare
sector produces vast collections of textual, numerical, and imagery data from very diverse sources. This
data collected by healthcare practitioners to run their practices can be obtained, reused, and
repurposed for the determination of patterns at the population level. Data collected from a large
number of participants in a consistent manner can provide useful insight into patterns of use and
treatment outcomes.
Already existing data from healthcare providers on patient demographics, diagnoses, diagnostic and
therapeutic procedures, treatments, and outcomes, including costs and reimbursements, can be taken
and cleaned and used for analysis and turned into an interactive, customized, visual output. The
presentation of custom-designed data sets would allow clear and effective communication of factual
and meaningful information to any interested stakeholders.
The use of repurposed datasets is the main direction in the healthcare information ecosystem that is
currently pursued by health tech companies and startups. The main limitation of this broad approach to
data collection is data quality and consistency, and also its relevance. Electronic health records were
designed to support billing practices and not for the collection of data for research. Challenges specific
to pharmacovigilance data collection activities include mapping ICD-9 and ICD-10 dictionaries to Medical
Dictionary for Regulatory Activities (MedDRA), linkage of prescriptions with indications, or accurate
identification of products dispensed to patients from prescriptions to name a few.
Another way to improve insight into the performance of drugs on the market is through the
improvement of data collection methods. Data quality can be improved through automation of data
capture, linkage of existing systems into data lakes, and automation of steps that rely on manual labor.
Cumbersome data collection methods and manual case processing create bottlenecks in
pharmacovigilance operations. Scaling up traditional pharmacovigilance activities to collect a higher
share of occurring adverse drug events is, therefore, impractical due to cost constraints.
The monitoring of treatment interventions and outcomes on smaller samples enables the inclusion of
epidemiological indicators. Collecting data from logical units such as hospitals, as opposed to nationwide
efforts, allows for the calculation of rates of occurrence. Without exposure data, it does not matter how

Page 8 of 16
large a segment of the healthcare sector participates. Such information will always lack key indicators
necessary to draw any conclusions from the data collected.
Another point of contention is the need for assessment of the causal relationship between an adverse
event and the suspect drug. No assessment of causality is necessary at the case level if the evaluation of
causal associations is performed at the population level using statistical methods. The high volume of
data provides better predictive performance because of the higher probability that the sample used for
prediction will be random and free from selection bias. Evaluation of potential causal links at the
population level reduces the risk of bias due to inconsistently applied methods of causality assessment.
De-identification of patient data before modeling enables more extensive sharing of the final product
without putting patient private data at risk.
Automated collection of structured data directly from physicians can depend on databases they are
already using to run their practices. The total number of patients treated for a specific condition can be
examined to treatment outcomes, enabling statistical evaluation treatment outcomes in relation to
interventions. With real-time insight into patterns, it would be significantly easier to detect beneficial
off-label treatments to support claims, which otherwise lack supportive evidence. Data presented to
clinicians, hospital administrators, and other stakeholders in the healthcare ecosystem need to be
accessible and understandable. Visualized data is much easier to interpret even for persons who do not
have a strong background in statistics. Visual representation of information is suitable for rapid
assessment of the efficacy of novel approaches in comparison to the standard of care. The same outputs
can be used to reduce ambiguity when designing clinical trials required to obtain approval of new
indications or expansion of treated populations. At the same time, readily available data may lead to the
abandonment of emerging interventions that are not beneficial or are harmful.
Faster and more accurate evaluation of therapeutic interventions in the context of clinical care is
essential for the precise assessment of the benefit:risk profile of marketed drugs. Widespread off-label
use of medications shows that there is a mismatch between clinical need and evidence required to
support such treatment choices.
The relevance of data included in models is essential. Selective inclusion of monitored parameters and
removal of irrelevant features from models is crucial because too many features lead to higher variance
and opportunity for over-fitting without balancing the opportunity for learning better models. In
general, learning curves improve with the number of relevant features, while the inclusion of irrelevant
ones only increases confusion and noise.
Stakeholders
Patients
The most important stakeholders in the healthcare sector are – or should be – the patients. Patients
who experienced an adverse reaction to a drug may attempt legal action against the manufacturer or
the healthcare provider. The legal concepts against manufacturers include defective product design,
poorly manufactured product, or a failure to warn customers about dangers associated with a drug
adequately. Medical malpractice claims have to prove all four elements of professional negligence,
namely a professional duty owed to the patient, breach of such duty, injury caused by the breach, and
resulting damages. However, in most cases of harm caused in the course of treatment, the
K Bartlett attempted to claim compensation from Mutual Pharm Co. after having suffered Steven
Johnson Syndrome/Toxic Epidermal Necrolysis caused by NSAID sulindac. The court dismissed her claim

Page 9 of 16
because warnings on the drug's label were adequate [65]. Qui tam litigation has not previously been
proposed to support pharmacovigilance. Still, it is already a core method used to combat medical and
pharmaceutical fraud and abuse under the False Claims Act [66]. As a significant group of stakeholders,
patients deserve to get information about benefit:risk profile of available treatments in a form that is
accessible and usable to them, and which gives them the power to make better decisions on their
medications.
Health care providers
The information health care providers have to deal with is often conflicting and contradictory. General
treatment guidelines provide instrumental guidance, but no obligations - off-label use allows generous
space for creativity and innovation in clinical practice. Timely insight into the success rate of off-label
treatments would help verify assumptions and support clinicians in their ability to make sound decisions
based on evidence from clinical practice. Post-marketing use of drugs significantly differs from the
limited use in clinical trials. The current surveillance mechanism demonstrably cannot provide
information on efficacy, rates of occurrence of adverse drug events, and trends. Evaluation of safety and
efficacy of drugs used off-label is currently not part of post-marketing surveillance. Timely access to
visualized patterns of use in their institution would help depict the safety and efficacy of drugs and
adjust prescribing practices appropriately. Insight into their practices and outcomes would enable
physicians to make better treatment decisions and be less dependent on information provided by the
pharmaceutical industry.
Insurers
The role of insurers in the pharmacovigilance system is probably the most significant one because of the
power they can project over a large number of people through cost, accessibility of insurance plans, and
drug reimbursements. Population data could support the decision on the safety and efficacy of various
interventions. Instant access to visualized patterns and their development would allow insurers to
identify and promote the most efficacious treatments on the market and substantially improve the
efficient and safe function of the system in general.
The CEOs as a distinct class
Motivations and incentives of CEOs and management boards of publicly traded companies depend
significantly on their packages and contracts. Kaplan [67] conducted a thorough analysis of
compensation packages of CEOs and an overview of historical trends. A study of some 1,700 firms
showed that compensation was highly related to corporate performance. The U.S. pay premium
primarily reflects the performance-based pay demanded by institutional shareholders and independent
boards [68]. There is an increasingly important international managerial market for CEOs [69]. Risk
adjustment is based on estimated risk premiums stemming from the equity incentives. Quantifying risk-
aversion and wealth levels of top executives remains a crucial topic for future research in incentives and
governance [70]. The CEOs of large pharmaceutical enterprises bear an unmatched share of
responsibility for critical developments in the pharmaceutical industry, namely corporate mergers,
outsourcing, and offshoring of vital parts of the industry, including shifts in organizational culture [71].
Outsourcing has become an industry norm essential to survival. Much of the growth in outsourcing in
the past decade has taken place in Asia, particularly China and India [72]. Decisions of very few people in
the position of CEOs have a significant impact on the behavior of the entire industry.
Real-time access to visualized patterns of risk:benefit: cost profiles of drugs on the market would create
a minor revolution in predictive modeling of success or failure of drugs entering the market. If the OODA
cycle takes too long to provide accurate feedback to decision-makers, any adjustments in direction take

Page 10 of 16
much longer then if relevant, accurate, and reliable information is instantly available in an accessible
form. For corporate executives, such a measure would mean the same kind of transition as the
introduction of night vision.
Pharmaceutical professionals
The main interest of professionals in the pharmaceutical industry is to utilize their hard-gained expertise
in a steadily growing, well-paying career. Because of the high cost of investment in education and
relatively slow return, both in monetary and career progression terms, pharmaceutical professionals are
very sensitive to any activity which is likely to threaten their future career. Interests of the U.S. and EU-
based workforce conflict with the outsourcing and offshoring trend because it threatens their job
security and career development by moving the core of their profession abroad. Motivators of
corporate-sponsored professionals imported from third countries stem from the dependent relationship
on their sponsor. This dependency is in stark contrast with the relationship between a corporation and a
locally available workforce, which is generally free to enter into a contract with a different business
entity without losing legal status in the country. The confident, mobile, and economically and legally
independent professional workforce is critically important for the industry's resilience. Moving jobs
offshore and importing large numbers of people to whom different employment standards apply
effectively leads to the neutralization of stakeholders who are the most likely to maintain the industry
environment sustainable even in the absence of legally enforceable standards.
Consequences
Corporate mergers and reorganizations provide a convenient excuse for lengthy migration and manual
re-entry of safety data without the critical step, which is an analysis of the data. Lengthy data processing
extends the duration of the Boyd cycle beyond acceptable periods necessary for the detection of safety
issues. The industry cannot be expected to do the right thing for the right reasons providing the system
does not provide sufficient incentives to do so. More often than not, evidence of due diligence is enough
to keep regulators at bay. Willful blindness, a term used in criminal law which refers to the acts of a
person who intentionally fails to be informed about matters that would make the person criminally
liable, is not used in the industry in the context of failure to see a signal [74].
As apparent from the MHRA risk-based inspection process, regulators do rely on information from
insiders. Very few people who raise their concerns receive the support they would need to pursue a
claim successfully. Dr. Helen Ge, who raised concerns over the safety profile of anti-diabetic medication
pioglitazone (Actos; Takeda Pharmaceuticals), sued her employer for allegedly under-reporting side
effects, but the lawsuit was dismissed, and she was fired [75]. In April 2014, a U.S. jury ordered Takeda
Pharmaceutical Co. and Eli Lilly and Co., to pay $9 billion in punitive damages over the drug's link to
cancer. The case of high compensation for Cheryl Eckard (GSK, Cidra, Puerto Rico) is an exception rather
than a rule [76]. A safety surveillance system should never depend on insiders speaking up because of
the high cost of such a step for the individuals involved. Transparency and systemic measures enhancing
real-time feedback are essential to keep all the participants in the system honest.
In the post-9/11 world, directives on biosecurity and protection of critical infrastructure meant to
prevent disruptions in case of conflict and limit strategic dependence, became relevant because of risks
associated with outsourcing and offshoring of processing sensitive medical data. According to the
Ponemon Institute’s study, 65% of companies experience a data breach at a vendor within two years of
outsourcing data [78,79].

Page 11 of 16
A decade ago, pharmacovigilance professionals mainly came from the ranks of physicians and
pharmacologists. With rapid development in communication safety information, different kinds of skills
will be required due to shifting from case handling to work with SQL databases, statistics, visualization,
and predictive modeling. No statistical methods or algorithms can replace the importance of medical
and scientific judgment. Still, the importance of the ability to make sense out of patterns will become
essential to the proper and timely interpretation of safety data. Abbott, in his paper Big data and
pharmacovigilance [80], described the potential of new technologies in healthcare implemented as part
of HIE and HIT, which would have the capacity to produce aggregate data from post-approval use from
all patients. Despite concerns over privacy, which would have to be addressed appropriately, such
technology would allow the evaluation of treatments in real-life conditions at the national level. The use
of big data analytical methods would enable detection of any safety signals and trends much faster than
via system of safety reporting and periodic safety update reports. A resource like that will be precious in
the determination of safety profile of individual drugs and detection of and appropriateness of off-label
use, but also for the actualization of treatment guidelines.
Information technology had changed substantially since the times when the current pharmacovigilance
systems were taking shape. What was unthinkable a decade ago, may become possible now due to the
ability to process a vast amount of data. It may be just the right time for transition to the next level in
drug safety utilizing new technologies.
Acknowledgements
Special thank you belongs to Lt.Col Roger N. Thomas, Dr. Bruce Hugman, and Dr. Brian Edwards for their
comments.

Page 12 of 16
References
[1] Hugman, B. (2006). The Erice Declaration. Drug Safety, 29(1), 91-93. doi: 10.2165/00002018-
200629010-00007
[2] Centers for Disease Control and Prevention. “Adults and Older Adult Adverse Drug Events,”
September 28, 2010.
[3] Department of Heath and Human Services, AHRQ. “Reducing and Preventing Adverse Drug Events To
Decrease Hospital Costs.” Agency for Healthcare Research and Quality - Advancing Excellence in
Healthcare, 2001.
[4] Bronstein, Alvin C, Daniel A Spyker, Luis R Cantilena, Barry H Rumack, and Richard C Dart. “Annual
Report of the American Association of Poison Control Centers National Poison Data System (NPDS): 29th
Annual Report.” Clinical Toxicology 50 (2012): 911–1164.
[5] Meyboom, Ronald HB, Antoine CG Egberts, Frank WJ Grabnau, and Yechiel A Hekster.
“Pharmacovigilance in Perspective.” Drug Safety 21, no. 6 (1999): 429–47.
[6] FDA. “Form FDA 3500A - Mandatory Reporting.” Food and Drug Administration, n.d.
[7] CIOMS. “Suspect Adverse Reaction Report Form (CIOMS Form I).” Council for International
Organizations of Medical Sciences. Accessed March 30, 2014.
[8] EudraVigilance. “EudraVigilance Data Analysis System.” EudraVigilance. Accessed March 30, 2014.
[9] Uppsala Monitoring Centre. (2020). UMC | VigiBase. Retrieved 16 January 2020, from
https://www.who-umc.org/vigibase/vigibase/
[10] “ICH Harmonized Tripartite Guideline Pharmacovigilance Planning E2E Current Step 4
Version Dated 18 November 2004.” International Conference on Harmonization of Technical
Requirements for registration of pharmaceuticals for human use, November 18, 2004.
[11] Fine, Andrew. “Introduction to Post-Marketing Drug Safety Surveillance: Pharmacovigilance in
FDA/CDER.” February 26, 2013.
[12] Food and Drug Administration. “FDA Adverse Event Reporting System (FAERS): Latest Quarterly
Data Files.” Food and Drug Administration - Protecting and Promoting Your Health. Accessed March 30,
2014.
[13] Sklamberg, Howard R. “The Challenges of Globalization.” Athens, GA: FDA/CDER/Office of
Compliance, 2013.
[14] U.S. Department of Health and Human Services Office for Civil Rights. “HIPAA Administrative
Simplification Regulation Text. 45 CFR Parts 160, 162, and 164. (Unofficial Version), as Amended through
March 26, 2013.” U.S. Department of Health and Human Services Office for Civil Rights, March 26, 2013.
[15] HHS Press Office. “WellPoint Pays HHS $1.7 Million for Leaving Information Accessible over
Internet.” HHS.gov U.S. Department of Health & Human Services, November 7, 2013.
[16] Swinhoe, D. (2020). The biggest data breach fines, penalties and settlements so far. Retrieved 16
January 2020, from https://www.csoonline.com/article/3410278/the-biggest-data-breach-fines-
penalties-and-settlements-so-far.html

Page 13 of 16
[17] Galimi, Gavin, Michael Overly, and Michelle Tidjani. “611: Privacy and Security Laws Unique to the
Healthcare Industry.” Los Angeles, CA: Association of Corporate Counsel, 2013.
[18] Brewster, Tom. “NHS Fined £200k After Computers Containing Patient Data Sold On eBay: One of
the Worst Data Breaches the ICO Has Ever Seen Leads to Hefty Fine for the NHS.” Tech Week Europe,
July 12, 2013.
[19] Solon, Olivia. “NHS Patient Data Made Publicly Available Online.” Wired.co.uk, March 3, 2014.
[20] HSCIC. “What We Will Collect from GP Records under Care.data.” Health & Social Care Information
Centre: The National Provider of Information, Data and IT Systems for Health and Social Care, 2014.
[21] Health & Social Care Information Centre. “Data Linkage and Extract Service Service Charges
2013/1.” Health & Social Care Information Centre, 2013.
[22] Hazel, Lorna, and Saad AW Shakir. “Under-Reporting of Adverse Drug Reactions - A Systematic
Review.” Drug Safety 29, no. 5 (2006): 385–96.
[23] Fine, Andrew. “Introduction to Post-Marketing Drug Safety Surveillance: Pharmacovigilance in
FDA/CDER.” February 26, 2013.
[24] Harpaz, R, W DuMouchel, P LePendu, A Bauer-Mehren, P Ryan, and NH Shah. “Performance of
Pharmacovigilance Signal-Detection Algorithms for the FDA Adverse Event Reporting System.” Clinical
Pharmacology & Therapeutics 93, no. 6 (February 11, 2013): 539–46. doi:10.1038/clpt.2013.24.
[25] Xu, Rong, and QuanQiu Wang. “Large-Scale Combining Signals from Both Biomedical Literature and
the FDA Adverse Event Reporting System (FAERS) to Improve Post-Marketing Drug Safety Signal
Detection.” BMC Bioinformatics 15, no. 17 (2014).
[26] PIPA. “PIPA Guidelines for Signal Management for Small and Medium Sized Pharmaceutical
Companies.” PIPA Guidelines for Signal Management, 2012.
[27] Shibata, Atsuko, and Manfred Hauben. “Pharmacovigilance, Signal Detection and Signal Intelligence
Overview.” Chicago, Illinois, USA, 2011.
[28] Food and Drug Administration. “FDA Adverse Event Reporting System (FAERS): Latest Quarterly
Data Files.” Food and Drug Administration - Protecting and Promoting Your Health. Accessed March 30,
2014.
[29] Lindquist, Marie. “From Signal Detection to Verification.” The Uppsala Monitoring Centre. Accessed
April 5, 2014.
[30] U.S. Food and Drug Administration. (2020). Questions and Answers on FDA's Adverse Event
Reporting System (FAERS). Retrieved 16 January 2020, from
https://www.fda.gov/drugs/surveillance/questions-and-answers-fdas-adverse-event-reporting-system-
faers
[31] Comfort, S., Dorrell, D., Meireis, S., & Fine, J. (2018). MOdified NARanjo Causality Scale for ICSRs
(MONARCSi): A Decision Support Tool for Safety Scientists. Drug Safety, 41(11), 1073-1085. doi:
10.1007/s40264-018-0690-y
[32] “The Erice Declaration - On Communicating Information Drug Safety.” Prescrire, September 27,
1997.

Page 14 of 16
[33] “Effective Communication in Pharmacovigilance - The Erice Report.” Erice, Sicily, 1997.
[34] WHO. “The Importance of Pharmacovigilance. Safety Monitoring of Medicinal Products.” WHO,
2002.
[35] Edwards, Ralph, Bruce Hugman, Marie Lindquist, and Giampaolo Velo. “Erice Statement 2009:
Communication, Medicines and Patient Safety.” Br J Clin Pharmacol 69, no. 2 (February 2010): 207–8.
[36] Ostroff, Stephen M, and Roger L Gibson. “Armed Forces Epidemiological Board (AFEB) Select
Subcommittee to Develop Mefloquine Study Options.” Department of Defense, Armed Forces
Epidemiological Board (AFEB), May 21, 2004.
[37] Hamerschlag, Arthur S. “Possible Long-Term Health Effects from the Malarial Prophylaxis
Mefloquine (Lariam).” Department of Veteran Affairs, Veteran Health Administration, June 23, 2004.
[38] Schoomaker, Eric B. “Updated Guidance on Use of Mefloquine (Lariam) for Malaria Prophylaxis.”
Department of the Army, Office of the Surgeon General, February 2, 2009.
[39] Department of Defense. “Department of Defense Directive 5400.11 - Department of Defense
Privacy Program.” Department of Defense, May 8, 2007.
[40] Nevin, Remington. “Neuropsychiatric Adverse Events Associated with Mefloquine.” November 27,
2012.
[41] Nevin, Remington. “FDA Presentation on Mefloquine Neurotoxicity.” January 26, 2013.
[42] O’Reilly, Rita. “RTE’s Investigations Unit Asks If the Anti-Malarial Drug Lariam Be Linked to Suicides
among Irish Defence Forces Soldiers.” Prime Time, May 23, 2013.
[43] Nevin, Remington. “Mass Administration of the Antimalarial Drug Meﬂoquine to Guantanamo
Detainees: A Critical Analysis.” Tropical Medicine and International Health 17, no. 10 (October 2012):
1281–88.
[44] O’Brien, Natalie. “Hicks Forced to Take High Drug Doses in Guantanamo.” The Sydney Morning
Herald, September 16, 2012.
[45] FDA. “FDA Drug Safety Communication: FDA Approves Label Changes for Antimalarial Drug
Mefloquine Hydrochloride due to Risk of Serious Psychiatric and Nerve Side Effects.” Food and Drug
Administration, July 29, 2013.
[46] USACOM. “Ceasing Use of Mefloquine in US Army Special Operations Command Units.” USACOM,
September 13, 2013.
[47] Ritchie, Elspeth Cameron, Jerald Block, and Remington Nevin. “Psychiatric Side Effects of
Mefloquine: Applications to Forensic Psychiatry.” J Am Acad Psychiatry Law 41, no. 2 (June 1, 2013):
224–35.
[48] Associated Press. “U.S. Army Doctor Tells Elite Troops to Stop Taking Anti-Malarial Drug
Mefloquine: Ban among Special Ops Forces Is the Latest Development in a Long-Running Controversy.”
Military1, September 19, 2013.
[49] Benjamin, Mark. “Robert Bales Charged: Military Works To Limit Malaria Drug In Midst Of
Afghanistan Massacre.” The Huffington Post, March 25, 2012.

Page 15 of 16
[50] National Task Force on CME Provider/Industry Collaboration. “On-Label and off-Label Usage of
Prescription Medicines and Devices, and the Relationship to CME.” AMA-ASSN. Accessed March 30,
2014.
[51] Tabarrok, Alexander T. “Assessing the FDA via the Anomaly of Off-Label Drug Prescribing.” The
Independent Review V, no. 1 (Summer 2000): 25–53.
[52] Bradford, W. David, John L Turner, and Jonathan W Williams. “Oﬀ-Label Use of Pharmaceuticals:
Trends and Drivers,” April 22, 2013.
[53] Bradford, W. David, John L Turner, and Jonathan W Williams. “Oﬀ-Label Use of Pharmaceuticals:
Trends and Drivers,” April 22, 2013.
[54] Rogers, Jacob. “Freedom of Speech and the FDA’s Regulation of Off-Label Drug Uses.” The George
Washington Law Review 76 (2008): 1429–43.
[55] Osborn, J. “Feds Have Beaten Pharma into Submission over off-Label Drug Use, but at What Cost?”
Forbes, Pharma & Healthcare, November 7, 2013.
[56] Osborn, J. “Can I Tell You the Truth? A Comparative Perspective on Regulating off-Label Scientific
and Medical Information.” Yale Journal of Public Policy, Law and Ethics 10, no. 2 (March 3, 2013).
[57] Rogers, Jacob. “Freedom of Speech and the FDA’s Regulation of Off-Label Drug Uses.” The George
Washington Law Review 76 (2008): 1429–43.
[58] Stewart, MW. “Off-Label Drug Use: The Bevacizumab Story.” Mayo-Clinic Proceedings 88, no. 3
(March 2013): 305. doi:10.1016/j.mayocp.2012.12.007.
[59] Richards, Chet. “Boyd’s OODA Loop (It’s Not What You Think),” 2012.
[60] Heuer, Richards J. Psychology of Intelligence Analysis. Center for the Study of Intelligence, Central
Intelligence Agency, 1999.
[61] Moore, David T. Critical Thinking and Intelligence Analysis. Washington, DC: National Defense
Intelligence College, 2007.
[62] Clark, R. (2016). Intelligence Analysis: A Target-Centric Approach (5th ed.). CQ Press.
[63] Goldacre, Ben. “What Doctors Don’t Know about the Drugs They Prescribe.” presented at the TED
Talks, June 2012.
[64] Heuer, Richards J. Psychology of Intelligence Analysis. Center for the Study of Intelligence, Central
Intelligence Agency, 1999.
[65] Bartlett v. Mutual Pharm. Co., (SC 2013).
[66] Abbott, Ryan. “Big Data and Pharmacovigilance: Using Health Information Exchanges to
Revolutionize Drug Safety.” Iowa Law Review 99, no. 1 (2013): 225–92.
[67] Kaplan, Steven N. “The Real Story behind Executive Pay. The Myth of Crony Capitalism.” Foreign
Affairs 92, no. No 3 (June 2013): 20–26.
[68] Fernandes, Nuno, Miguel A Ferreira, Pedro Matos, and Kevin J Murphy. “Are U.S. CEOs Paid More?
New International Evidence.” Review of Financial Studies 26, no. 2 (2013): 323–67.

Page 16 of 16
[69] Fernandes, Nuno, Miguel A Ferreira, Pedro Matos, and Kevin J Murphy. The Pay Divide: Why Are
U.S. Top Executives Paid More? Finance Working Paper. European Corporate Governance Institute
(ECGI), August 17, 2009.
[70] Conyon, Martin J, John E Core, and Wayne R Guay. “Are US CEOs Paid More Than UK CEOs?
Inferences From Risk-Adjusted Pay (CRI 2009-003).” Cornell University ILR School, 2009.
[71] Van Vugt, Florian. “Examining the Effects of a Global Crisis on CEO Compensation: Fortune 100
Evidence.” University of Twente, 2013.
[72] Langer, Eric S. “Pharmaceutical Outsourcing Trends: The Next Decade.” BioPharm International, no.
Outsourcing Special Issue (March 2012).
[73] Nguyen-Van-Tam, Dominic. “Pharmacovigilance Inspectorate Consultative Committee Meeting
Minutes, Point 7: Potential Changes for the next Round of Risk-Based Inspections.” MHRA, August 5,
2012.
[74] United States of America, et al., ex rel. Helen GE, M.D., Plaintiffs and Relator v. Takeda
Pharmaceuticals Company Limited and Takeda Pharmaceuticals North America, Inc, (United States
District Court of Massachusetts 2013).
[75] “Actos Cancer Link Nets Eli Lilly, Takeda $9B Fine.” CBC News, April 8, 2014.
[76] Pelley, Scott. “Glaxo Whistle-Blower Lawsuit: Bad Medicine.” 60 Minutes. CBSNews, December 29,
2010.
[77] National Association of Criminal Defense Lawyers. “Willful Blindness.” Criminal Defense Issues,
2013.
[78] The White House. “PPD-21: Critical Infrastructure Security and Resilience.” The White House,
December 2, 2013.
[79] European Commission. “Commission Staff Working Document on a New Approach to the European
Program for Critical Infrastructure Protection Making European Critical Infrastructure More Secure.”
European Commission, Brussels, August 28, 2013.
[80] Abbott, Ryan. “Big Data and Pharmacovigilance: Using Health Information Exchanges to
Revolutionize Drug Safety.” Iowa Law Review 99, no. 1 (2013): 225–92.

Big Data in Drug Safety: Making post-marketing surveillance in pharmacovigilance more efficient

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Big Data in Drug Safety: Making post-marketing surveillance in pharmacovigilance more efficient

Similar to Big Data in Drug Safety: Making post-marketing surveillance in pharmacovigilance more efficient (20)

More from Arete-Zoe, LLC

More from Arete-Zoe, LLC (20)

Recently uploaded

Recently uploaded (20)

Big Data in Drug Safety: Making post-marketing surveillance in pharmacovigilance more efficient