Pharmacovigilance at the intersection of healthcare and life sciences by Kostas Kidos, VP of Product Strategy, Pharmacovigilance, and Risk Management at Oracle Health Sciences

1. Review
Pharmacovigilance at the Intersection of Healthcare and Life Sciences
Kostas Kidos
Kostas Kidos is VP of Product Strategy, Pharmacovigilance, and Risk Management at Oracle. His role is to work with senior thought leaders and standards bodies in the pharmacovigilance
and risk management domain, in order to ensure that Oracle continues to develop technologies that support relevant business needs in a manner that is well integrated with the overall R&D
process. Prior to Oracle, Mr Kidos was with Merck & Co. for six years as a member of the IT senior leadership team responsible for a diverse organization setting and executing the overall
strategy on healthcare IT, pharmacovigilance, risk management, regulatory affairs, labeling, biostatistics, epidemiology, clinical content management, collaboration portals, licensing,
competitive and regulatory intelligence, and various other areas of drug development. Before that he was with Bristol-Myers Squibb for over 15 years as the Executive Director of
Pharmacovigilance Operations supporting areas such as adverse event systems, terminologies, case intake, regulatory submissions, PSURs, risk management and signaling support,
co-marketing agreements, product complaints, and training. In parallel to these positions, for 15+ years he has been leading (and with agreement by PhRMA continues to lead) the PhRMA
delegations at ICH and ISO TC215 for various safety-related topics, has served as the rapporteur/co-rapporteur for two of those topics (E2B and M5), and co-chaired with the FDA a
subcommittee on the electronic exchange of safety information.
Pharmacovigilance and safety surveillance has evolved from yesterday’s focus on transactional activity and
information flow to today’s awareness of the importance of risk management and proactive signal detection.
Risk management activities have come to the fore as the focus of good pharmacovigilance expands beyond
data collection to drug safety surveillance models that have a more inherent value. This evolution will
continue as the trajectories of both life sciences and healthcare converge towards a common goal of
predictive, preventive, personalized, and participatory healthcare. In this future landscape, access and insight
to data from various sources will be paramount to a knowledge-based system of pharmacovigilance. The
common analytic foundation of healthcare and life sciences will lead toward improvements in public health
and a population-based predictive pharmacovigilance model that relies on intelligent signal detection.
The Evolution of Pharmacovigilance and Additionally, the drug-safety environment became progressively more
Safety Surveillance risk-averse, with regulators introducing increasingly stringent
The detection, assessment, understanding, and prevention of adverse regulatory requirements. This saw the implementation of a regulatory
events (AEs) of medicines, in other words pharmacovigilance, are all framework that promoted well-structured and expeditious information
an integral part of clinical research, pre- and post-approval. Good flow. Although adverse event reporting has always been mandatory
pharmacovigilance, underpinned by the science of clinical for pharmaceutical companies, on the healthcare side adverse event
pharmacology, is crucial throughout the product lifecycle. A number reporting was, and still remains, voluntary.
of recent high-profile drug withdrawals has highlighted the strengths
and weaknesses of the current systems and has provided a good From the mid-to-late 1990s, as reporting became ‘routine’, there was
indication of how much more needs to be done. Significant harm, to a realization that there needed to be an expansion beyond the limited
even a few patients, can have catastrophic ramifications including a data sources. The arrival of the new millennium also saw another
loss of trust and credibility for the pharmaceutical industry and significant shift, with the move towards an environment that focused
regulatory agencies. At a time when the industry as a whole is on gaining a greater understanding of the data. Risk management
undergoing tremendous pressure on productivity and revenues the activities came to the fore as the pharmaceutical industry and
importance of effective pharmacovigilance is even more evident. regulators expanded the focus of good pharmacovigilance—beyond
data collection to drug safety surveillance models that had more
Over the past two decades, pharmacovigilance has undergone an inherent value (see Figure 1).
evolutionary shift from limited systems that mainly focused on
regulatory compliance to the richer, more sophisticated systems Focus on Risk Management—
currently in place today. In the 1980s systems were based on The Drug Safety Model
processes owned by biopharmaceutical sponsors, with data inputs The evolution of pharmacovigilance and safety surveillance, from
coming in from a limited number of data sources (i.e. yesterday’s focus on transactional activities and information flow to
consumer/healthcare professionals and via clinical trials). These today’s awareness of the importance of risk management, led to the
systems were built around case processing with limited focus on introduction of the Risk Management Plan (RMP) in Europe in 2005.
intelligent information consumption and benefit:risk analysis. It was The RMP is a mandatory requirement for marketing authorization
very much a reactive, event-driven process. During the following applications (MAAs) for new chemical entities, biotechnology
decade, the focus shifted to improving the flow of information. products, new routes of administration, new dosage forms, new
1. Volume 9A, Rules Governing Medicinal Products in the European Union, September 2008; available from http://ec.europa.eu/health/files/eudralex/vol-9/pdf/vol9a_09-2008_en.pdf
2. Food and Drug Administration, Docket No. FDA–2008–N–0174; available from http://www.fda.gov/OHRMS/DOCKETS/98fr/FDA-2008-N-0174-N.pdf
4 © TOUCH BRIEFINGS 2011

2. Pharmacovigilance at the Intersection of Healthcare and Life Sciences
indications, and new patient populations.1 The US equivalent, a Risk Figure 1: Evolution of the Drug Safety Model
Evaluation and Mitigation Strategy (REMS), is not a statutory
requirement unless the US Food and Drug Administration (FDA)
Risk
specifically requests its inclusion in certain drug applications.2 Risk
Management
Mgmt
Signal Signal
Risk management and rigorous adherence to drug safety has always Management Management
been an intrinsic part of the drug development process.
Aggregate Increasing Aggregate
Nonetheless, the approach to risk management activities has seen Reporting Value Reporting
a significant change as processes become more standardized,
Adverse Event AE
driven by the new regulatory requirements. The need for greater Management
accountability and continuous improvements in identifying and
AE = adverse event; mgmt = management.
communicating the benefit:risk associated with a drug has led to
more-comprehensive, formalized processes that support
REMS/RMP requirements. Formalized processes also help better Several consortia and partnership initiatives are currently exploring and
define and identify the information and technologies needed to testing new methods of signal detection, data mining, and analysis of
facilitate better decision making around risk management. Thus, electronic healthcare data sources. The Observational Medical
the next step is to develop an understanding of the safety profile of Outcomes Partnership (OMOP) is a public–private partnership that
a medicine through access to additional, richer sources of data. involves multiple stakeholders, including the pharmaceutical industry,
Although the traditional data sources—safety data from clinical academic institutions, non-profit organizations, the US FDA, and other
trials and voluntary reporting of spontaneous AEs—continue to US federal agencies. OMOP is assessing the appropriate technology
provide essential information for conducting pharmacovigilance, it and data infrastructure required for systematic monitoring of
is well recognized that they have their limitations. In the digital era observational data. OMOP is also developing and testing the feasibility
the increasing availability of large collections of electronic data and performance of the analysis methods.
from the healthcare environment, in the form of electronic health
records (EHRs), insurance claims databases, outpatient data, a The FDA’s Sentinel Initiative intends to create the Sentinel System: an
variety of longitudinal observational data sources, and clinical trial electronic system that augments existing safety monitoring systems and
registries, provides alternative sources of data that could be used to expands the FDA’s capacity for evaluating safety issues related to
gain valuable insight about the behavior of the approved product FDA-approved medicines, especially in terms of information on
and to support pharmacovigilance processes. This has led to a subgroups/special populations. The Sentinel System will operate across
growing interest in how to leverage these rich data sources to different data sources—provider electronic health records, health plan
improve safety signal detection and risk management strategies. claims databases, Medicare databases, and other data sources.
Knowledge-based Pharmacovigilance Next Generation Signal Detection
The science of pharmacovigilance is a continuously evolving The execution of knowledge-based pharmacovigilance will require
discipline. As previously mentioned, the traditional data sources robust data mining and analytical tools. The long-term vision is to
that the pharmaceutical companies rely on for conducting leverage data sources to facilitate more-intelligent signal detection
pharmacovigilance has limitations. Pre-approval clinical trials cannot activities. This is where the promise of integrated analytic engines that
capture all AEs causally related to the drug, are only conducted in apply the appropriate methodologies to the appropriate dataset has
relatively small patient populations, and do not adequately assess the tremendous potential.
risks of long-term exposure to the drug. The responsibility of
post-approval reporting of spontaneous AEs lies primarily with the The incredible variety of data sources available will provide not only
drug manufacturer or marketing license holder. However, post- potential benefits but also specific challenges. In this vast
approval surveillance activities are passive processes and rely heavily environment of data it would be easy not to see the forest from the
on the voluntary participation of physicians and patient groups. trees. To ensure that the benefits of these data sources are correctly
Knowledge-based pharmacovigilance integrates active AE leveraged it will be necessary to interrogate the datasets with the
surveillance methods using rich data sources from the public and appropriate analytical methodologies.
private sectors.3 The evolutionary curve of technology invites change.
The pharmaceutical industry, the healthcare sector, and regulators Classical signal detection is driven by incidence counts of AEs and is
have all embraced information technology (IT) as a tool to support retrospective and not truly predictive. The vision is to utilize the vast
better processes. As digitization of healthcare data becomes routine sets of medical data to proactively identify and manage emerging
the pharmaceutical industry and regulators are increasingly looking at safety signals. Embedding analytic engines into core processes can
all of those sources of information as potential sources of data (see help stakeholders adopt an analytical orientation and enable speed
Figure 2). However, while there is increasing acceptance that digital and quality of insights for complex analyses and reporting. The use of
capabilities have created more opportunities to improve drug safety, powerful analytical engines will enable a real-time, auditable means
there is great variability among stakeholders about how to best for automated signal detection. This scenario will also offer real-time
leverage healthcare data sources to improve safety and, ultimately, analysis, documentation, and communication to provide a proactive
improve outcomes for patients. approach to pharmacovigilance.
3. Food and Drug Administration Amendments Act of 2007, Pub. L. No. 110-85, § 905(a), adding Federal Food, Drug, and Cosmetic Act § 505(k), amending 21 U.S.C.A. § 355 (FDAAA).
MANAGING RISK—EMBRACING THE NEW PARADIGM IN GLOBAL PHARMACOVIGILANCE 5

3. Review
Figure 2: Knowledge-based Pharmacovigilance Supported by Increase in Data Sources
Patient Observational Medical
Outcomes Partnership
Healthcare and Life
Sentinel
Clinical Trial Science Consortia
Healthcare PROTECT
Professional
Regulator Investigator
Electronic Health Record Recruitment
New Use Cases
Product Patient
Development Recruitment
Outpatient Data
Protocol Design
New Data Sources
and Amendments
Longitudinal Data
Sponsor
Epidemiology
Studies
Literature and
Research Benefit:
Risk Management New Indications
Product Lifecycle
Claims Database Management
Packaging and
Labeling
Partners New Formulations
Figure 3: Life Sciences and Healthcare Convergence
Personalized
Healthcare
Translational Medicine
Targeted Therapies ‘Precision’ Healthcare
Patient Care & Disease Management
DNA Chemistry and
Analytics ‘Evidence-based’ Healthcare
Advanced Technology
Pharmacovigilance Safety at
Increased Regulation & Risk Management Point of Care ‘Managed’ Healthcare
and Efficacy Standards
Electronic Electronic
Blockbusters and Data Capture Medical Records ‘Trial & Error’
Mass-production
Healthcare
of Novel Drugs Paper-based Paper-based
Records Systems
LIFE SCIENCES HEALTHCARE
The Future Landscape— (see Figure 3). Thus, pharmacovigilance and safety is the first and most
Secure, Safe, and Harmonious active step in a move toward personalized health.
Central to many of the conceits and concepts discussed in this paper
is the trajectory of the life sciences and healthcare sectors. The next evolutionary step in pharmacovigilance will be a future where
Each sector is converging on a common goal of predictive, a collaborative environment creates and supports a continuous
preventive, personalized, and participatory healthcare (see Figure 3). knowledge-based feedback loop; data are processed with various
So what does this mean for pharmacovigilance? The increasing analytics engines; and data are presented through dashboards to
importance of healthcare data as an alternative and complementary generate knowledge—this knowledge is the data that are fed back
data source has already been discussed. The convergence of the two into the continuous feedback cycle.
sectors and the common analytic foundation of healthcare and life
sciences will lead toward improvements in public health and One of the driving forces for this change is the real need for the
population-based pharmacovigilance. pharmaceutical industry to transform from a fully integrated
pharmaceutical company business model to a networked model that
The intersection of the two fields starts with pharmacovigilance on the derives innovation from value-added partners. Cost pressures and
life sciences side and safety at point-of-care on the healthcare side innovation shortfalls have forced this move toward a networked
6 MANAGING RISK—EMBRACING THE NEW PARADIGM IN GLOBAL PHARMACOVIGILANCE

4. Pharmacovigilance at the Intersection of Healthcare and Life Sciences
environment. Leading companies are having to ask the questions: Table 1: Opportunities for Alignment Between
what activities are core to the company and what are not? In order to Healthcare and Life Sciences
achieve a networked healthcare and life sciences industry model,
1. Requires transparent data flows across health sciences
however, necessary transformations to the traditional clinical
2. Common terminologies must be defined
development paradigm must take place. Sponsors will need to
3. Standards must be able to correlate data sources
embrace a network of partners—from hospitals and doctors to clinical 4. Best practices can be established
research organizations (CROs), academia, technology providers, and 5. Healthcare must focus on implementing (EHR) infrastructure first!
manufacturing companies. Some of the practical steps that still need
to be accomplished include the need for transparent data flows across
Figure 4: The Next Generation Pharmacovigilance Model
healthcare and life sciences; defining common terminologies;
correlating standards and data sources; establishing best practices;
and, most importantly, the increasing focus of healthcare on Data Sources
implementing an EHR-based infrastructure (see Table 1). Health
Authority
Patient
The impact of the changing model on the safety side will include FDA FDA
EMA Clinical EMA
better practices and improved data collection. The pharmaceutical Trial
PDMA PDMA
industry has been outsourcing to CROs for many years, but mainly
Healthcare
Sponsor
for the purpose of clinical trial execution and rarely for safety Professional Safety
Sponsor
Signals
services. More recently, this trend has been reversed and the Sponsor Electronic Health
Sponsor Record
industry has seen a dramatic rise in safety service offerings from Sponsor
Outpatient
CROs. This situation has arisen, in part, because of the Sponsor Data
rationalization of core business activities by pharmaceutical CRO
CRO CRO Literature
Sponsor
Global Data CRO
companies. It can be argued that many data collections and data BPO Warehouse
CRO CRO Research BPO
processing activities are not core activities but are transactional. BPO
Claims DB
Thus, it is a commodity that can be performed by a service company
Today Longitudinal Tomorrow
with the right competencies better, faster, and cheaper. This Data
enables the pharmaceutical company to focus on the analysis and Partners
examination of the data; the complex computations that give
meaning to the data; and to provide valuable insights that help to
improve the lifecycle management of the product. risks. The other dimension is to be predictive. Imagine a scenario
where a global safety database points patients, healthcare
So what will the next generation pharmacovigilance model look like? professionals, researchers, and safety professionals to populate the
The answer(s) may come from other industries. Other industries have environment with safety data, minimizing the need for sponsors to
successfully utilized a networked, collaborative model to stimulate collect and report the data, and to focus their resources in consuming
innovation and improve safety. An excellent example is the aerospace the data for pharmacovigilance purposes. Imagine a scenario where
industry, more specifically Boeing, which has rapidly shifted to ambulatory data, captured in real-time at a national or regional level,
embrace a ‘network’ of partners from across the globe to develop the is accessible and associated with pharmacogenomic and outpatient
Boeing 787. In the banking sector technology has been embraced to data that would allow more-intelligent analysis of the pharmacological
provide real-time monitoring and analysis of transactions. The effects supported by the patient’s compliance on its therapy. This is
question is whether there are use cases for utilizing similar pharmacovigilance for the future, based on a distributed Complex
technologies in the life sciences and healthcare environments to Event Process (CEP) (see Figure 4).
transform pharmacovigilance from today’s fragmented, inefficient,
and heterogeneous environment to a landscape that is simplified, Many of the basic components required to enable this future scenario
standardized, and less fragmented. are in place or being implemented. The technology-driven
transformation of pharmacovigilance is being championed by
The same mathematical foundations that are utilized by banks to regulators and the pharmaceutical industry alike. In the healthcare
identify fraud are being used in use cases and pilots to mine AE data sector the switch from a paper system to electronic records is in
for safety signals. A significant challenge is that financial data capture perfect alignment with the driving forces from life sciences. In an
a vast amount of real-time results, whereas in healthcare and life increasingly demanding and risk-averse environment innovative
sciences less real-time data are captured. technological solutions will help to address safety concerns and
improve patient confidence and outcomes. There are many
Proactive, Predictive Pharmacovigilance challenges along this roadmap toward proactive, predictive
Pharmacovigilance is still very observational. We see what happens pharmacovigilance but the end goal will see greatly reduced risks and
and react to analyze why the event occurs and how to mitigate the improved responses to better protected patients. n
MANAGING RISK—EMBRACING THE NEW PARADIGM IN GLOBAL PHARMACOVIGILANCE 7