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.

4 © T O U C H B R I E F I N G S 2 0 1 1

Review

The Evolution of Pharmacovigilance and Safety SurveillanceThe detection, assessment, understanding, and prevention of adverse

events (AEs) of medicines, in other words pharmacovigilance, are all

an integral part of clinical research, pre- and post-approval. Good

pharmacovigilance, underpinned by the science of clinical

pharmacology, is crucial throughout the product lifecycle. A number

of recent high-profile drug withdrawals has highlighted the strengths

and weaknesses of the current systems and has provided a good

indication of how much more needs to be done. Significant harm, to

even a few patients, can have catastrophic ramifications including a

loss of trust and credibility for the pharmaceutical industry and

regulatory agencies. At a time when the industry as a whole is

undergoing tremendous pressure on productivity and revenues the

importance of effective pharmacovigilance is even more evident.

Over the past two decades, pharmacovigilance has undergone an

evolutionary shift from limited systems that mainly focused on

regulatory compliance to the richer, more sophisticated systems

currently in place today. In the 1980s systems were based on

processes owned by biopharmaceutical sponsors, with data inputs

coming in from a limited number of data sources (i.e.

consumer/healthcare professionals and via clinical trials). These

systems were built around case processing with limited focus on

intelligent information consumption and benefit:risk analysis. It was

very much a reactive, event-driven process. During the following

decade, the focus shifted to improving the flow of information.

Additionally, the drug-safety environment became progressively more

risk-averse, with regulators introducing increasingly stringent

regulatory requirements. This saw the implementation of a regulatory

framework that promoted well-structured and expeditious information

flow. Although adverse event reporting has always been mandatory

for pharmaceutical companies, on the healthcare side adverse event

reporting was, and still remains, voluntary.

From the mid-to-late 1990s, as reporting became ‘routine’, there was

a realization that there needed to be an expansion beyond the limited

data sources. The arrival of the new millennium also saw another

significant shift, with the move towards an environment that focused

on gaining a greater understanding of the data. Risk management

activities came to the fore as the pharmaceutical industry and

regulators expanded the focus of good pharmacovigilance—beyond

data collection to drug safety surveillance models that had more

inherent value (see Figure 1).

Focus on Risk Management—The Drug Safety ModelThe evolution of pharmacovigilance and safety surveillance, from

yesterday’s focus on transactional activities and information flow to

today’s awareness of the importance of risk management, led to the

introduction of the Risk Management Plan (RMP) in Europe in 2005.

The RMP is a mandatory requirement for marketing authorization

applications (MAAs) for new chemical entities, biotechnology

products, new routes of administration, new dosage forms, new

Pharmacovigilance at the Intersection of Healthcare and Life SciencesKostas Kidos

Pharmacovigilance and safety surveillance has evolved from yesterday’s focus on transactional activity andinformation 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 beyonddata collection to drug safety surveillance models that have a more inherent value. This evolution willcontinue as the trajectories of both life sciences and healthcare converge towards a common goal ofpredictive, preventive, personalized, and participatory healthcare. In this future landscape, access and insightto data from various sources will be paramount to a knowledge-based system of pharmacovigilance. Thecommon analytic foundation of healthcare and life sciences will lead toward improvements in public healthand a population-based predictive pharmacovigilance model that relies on intelligent signal detection.

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

Kidos_Oracle 25/05/2011 13:43 Page 4

indications, and new patient populations.1 The US equivalent, a Risk

Evaluation and Mitigation Strategy (REMS), is not a statutory

requirement unless the US Food and Drug Administration (FDA)

specifically requests its inclusion in certain drug applications.2

Risk management and rigorous adherence to drug safety has always

been an intrinsic part of the drug development process.

Nonetheless, the approach to risk management activities has seen

a significant change as processes become more standardized,

driven by the new regulatory requirements. The need for greater

accountability and continuous improvements in identifying and

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

define and identify the information and technologies needed to

facilitate better decision making around risk management. Thus,

the next step is to develop an understanding of the safety profile of

a medicine through access to additional, richer sources of data.

Although the traditional data sources—safety data from clinical

trials and voluntary reporting of spontaneous AEs—continue to

provide essential information for conducting pharmacovigilance, it

is well recognized that they have their limitations. In the digital era

the increasing availability of large collections of electronic data

from the healthcare environment, in the form of electronic health

records (EHRs), insurance claims databases, outpatient data, a

variety of longitudinal observational data sources, and clinical trial

registries, provides alternative sources of data that could be used to

gain valuable insight about the behavior of the approved product

and to support pharmacovigilance processes. This has led to a

growing interest in how to leverage these rich data sources to

improve safety signal detection and risk management strategies.

Knowledge-based PharmacovigilanceThe science of pharmacovigilance is a continuously evolving

discipline. As previously mentioned, the traditional data sources

that the pharmaceutical companies rely on for conducting

pharmacovigilance has limitations. Pre-approval clinical trials cannot

capture all AEs causally related to the drug, are only conducted in

relatively small patient populations, and do not adequately assess the

risks of long-term exposure to the drug. The responsibility of

post-approval reporting of spontaneous AEs lies primarily with the

drug manufacturer or marketing license holder. However, post-

approval surveillance activities are passive processes and rely heavily

on the voluntary participation of physicians and patient groups.

Knowledge-based pharmacovigilance integrates active AE

surveillance methods using rich data sources from the public and

private sectors.3 The evolutionary curve of technology invites change.

The pharmaceutical industry, the healthcare sector, and regulators

have all embraced information technology (IT) as a tool to support

better processes. As digitization of healthcare data becomes routine

the pharmaceutical industry and regulators are increasingly looking at

all of those sources of information as potential sources of data (see

Figure 2). However, while there is increasing acceptance that digital

capabilities have created more opportunities to improve drug safety,

there is great variability among stakeholders about how to best

leverage healthcare data sources to improve safety and, ultimately,

improve outcomes for patients.

Several consortia and partnership initiatives are currently exploring and

testing new methods of signal detection, data mining, and analysis of

electronic healthcare data sources. The Observational Medical

Outcomes Partnership (OMOP) is a public–private partnership that

involves multiple stakeholders, including the pharmaceutical industry,

academic institutions, non-profit organizations, the US FDA, and other

US federal agencies. OMOP is assessing the appropriate technology

and data infrastructure required for systematic monitoring of

observational data. OMOP is also developing and testing the feasibility

and performance of the analysis methods.

The FDA’s Sentinel Initiative intends to create the Sentinel System: an

electronic system that augments existing safety monitoring systems and

expands the FDA’s capacity for evaluating safety issues related to

FDA-approved medicines, especially in terms of information on

subgroups/special populations. The Sentinel System will operate across

different data sources—provider electronic health records, health plan

claims databases, Medicare databases, and other data sources.

Next Generation Signal DetectionThe execution of knowledge-based pharmacovigilance will require

robust data mining and analytical tools. The long-term vision is to

leverage data sources to facilitate more-intelligent signal detection

activities. This is where the promise of integrated analytic engines that

apply the appropriate methodologies to the appropriate dataset has

tremendous potential.

The incredible variety of data sources available will provide not only

potential benefits but also specific challenges. In this vast

environment of data it would be easy not to see the forest from the

trees. To ensure that the benefits of these data sources are correctly

leveraged it will be necessary to interrogate the datasets with the

appropriate analytical methodologies.

Classical signal detection is driven by incidence counts of AEs and is

retrospective and not truly predictive. The vision is to utilize the vast

sets of medical data to proactively identify and manage emerging

safety signals. Embedding analytic engines into core processes can

help stakeholders adopt an analytical orientation and enable speed

and quality of insights for complex analyses and reporting. The use of

powerful analytical engines will enable a real-time, auditable means

for automated signal detection. This scenario will also offer real-time

analysis, documentation, and communication to provide a proactive

approach to pharmacovigilance.

Pharmacovigilance at the Intersection of Healthcare and Life Sciences

M A N A G I N G R I S K — E M B R A C I N G T H E N E W P A R A D I G M I N G L O B A L P H A R M A C O V I G I L A N C E 5

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).

Figure 1: Evolution of the Drug Safety Model

AE = adverse event; mgmt = management.

RiskMgmt

SignalManagement

AggregateReporting

Adverse EventManagement

RiskManagement

SignalManagement

AggregateReporting

IncreasingValue

AE

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Review

M A N A G I N G R I S K — E M B R A C I N G T H E N E W P A R A D I G M I N G L O B A L P H A R M A C O V I G I L A N C E6

The Future Landscape—Secure, Safe, and HarmoniousCentral to many of the conceits and concepts discussed in this paper

is the trajectory of the life sciences and healthcare sectors.

Each sector is converging on a common goal of predictive,

preventive, personalized, and participatory healthcare (see Figure 3).

So what does this mean for pharmacovigilance? The increasing

importance of healthcare data as an alternative and complementary

data source has already been discussed. The convergence of the two

sectors and the common analytic foundation of healthcare and life

sciences will lead toward improvements in public health and

population-based pharmacovigilance.

The intersection of the two fields starts with pharmacovigilance on the

life sciences side and safety at point-of-care on the healthcare side

(see Figure 3). Thus, pharmacovigilance and safety is the first and most

active step in a move toward personalized health.

The next evolutionary step in pharmacovigilance will be a future where

a collaborative environment creates and supports a continuous

knowledge-based feedback loop; data are processed with various

analytics engines; and data are presented through dashboards to

generate knowledge—this knowledge is the data that are fed back

into the continuous feedback cycle.

One of the driving forces for this change is the real need for the

pharmaceutical industry to transform from a fully integrated

pharmaceutical company business model to a networked model that

derives innovation from value-added partners. Cost pressures and

innovation shortfalls have forced this move toward a networked

Figure 2: Knowledge-based Pharmacovigilance Supported by Increase in Data Sources

Figure 3: Life Sciences and Healthcare Convergence

Benefit:Risk Management

Patient

Clinical Trial

HealthcareProfessional

Electronic Health Record

Healthcare and Life Science Consortia

Observational Medical Outcomes Partnership

Sentinel

PROTECT

InvestigatorRecruitment

PatientRecruitment

Protocol Designand Amendments

EpidemiologyStudies

New Indications

Packaging andLabeling

New Formulations

ProductDevelopment

Product LifecycleManagement

Regulator

Sponsor

New

Dat

a So

urce

s New

Use

Cas

es

Outpatient Data

Longitudinal Data

Literature andResearch

Claims Database

Partners

Targeted Therapies ‘Precision’ Healthcare

‘Evidence-based’ Healthcare

‘Managed’ Healthcare

‘Trial & Error’ Healthcare

DNA Chemistry andAdvanced Technology

Increased Regulationand Efficacy Standards

Blockbusters andMass-production

of Novel Drugs

Translational Medicine

PersonalizedHealthcare

Patient Care & Disease Management

Analytics

Pharmacovigilance& Risk Management

Safety atPoint of Care

ElectronicMedical Records

ElectronicData Capture

Paper-basedRecords

Paper-basedSystems

LIFE SCIENCES HEALTHCARE

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Pharmacovigilance at the Intersection of Healthcare and Life Sciences

M A N A G I N G R I S K — E M B R A C I N G T H E N E W P A R A D I G M I N G L O B A L P H A R M A C O V I G I L A N C E 7

environment. Leading companies are having to ask the questions:

what activities are core to the company and what are not? In order to

achieve a networked healthcare and life sciences industry model,

however, necessary transformations to the traditional clinical

development paradigm must take place. Sponsors will need to

embrace a network of partners—from hospitals and doctors to clinical

research organizations (CROs), academia, technology providers, and

manufacturing companies. Some of the practical steps that still need

to be accomplished include the need for transparent data flows across

healthcare and life sciences; defining common terminologies;

correlating standards and data sources; establishing best practices;

and, most importantly, the increasing focus of healthcare on

implementing an EHR-based infrastructure (see Table 1).

The impact of the changing model on the safety side will include

better practices and improved data collection. The pharmaceutical

industry has been outsourcing to CROs for many years, but mainly

for the purpose of clinical trial execution and rarely for safety

services. More recently, this trend has been reversed and the

industry has seen a dramatic rise in safety service offerings from

CROs. This situation has arisen, in part, because of the

rationalization of core business activities by pharmaceutical

companies. It can be argued that many data collections and data

processing activities are not core activities but are transactional.

Thus, it is a commodity that can be performed by a service company

with the right competencies better, faster, and cheaper. This

enables the pharmaceutical company to focus on the analysis and

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.

So what will the next generation pharmacovigilance model look like?

The answer(s) may come from other industries. Other industries have

successfully utilized a networked, collaborative model to stimulate

innovation and improve safety. An excellent example is the aerospace

industry, more specifically Boeing, which has rapidly shifted to

embrace a ‘network’ of partners from across the globe to develop the

Boeing 787. In the banking sector technology has been embraced to

provide real-time monitoring and analysis of transactions. The

question is whether there are use cases for utilizing similar

technologies in the life sciences and healthcare environments to

transform pharmacovigilance from today’s fragmented, inefficient,

and heterogeneous environment to a landscape that is simplified,

standardized, and less fragmented.

The same mathematical foundations that are utilized by banks to

identify fraud are being used in use cases and pilots to mine AE data

for safety signals. A significant challenge is that financial data capture

a vast amount of real-time results, whereas in healthcare and life

sciences less real-time data are captured.

Proactive, Predictive PharmacovigilancePharmacovigilance is still very observational. We see what happens

and react to analyze why the event occurs and how to mitigate the

risks. The other dimension is to be predictive. Imagine a scenario

where a global safety database points patients, healthcare

professionals, researchers, and safety professionals to populate the

environment with safety data, minimizing the need for sponsors to

collect and report the data, and to focus their resources in consuming

the data for pharmacovigilance purposes. Imagine a scenario where

ambulatory data, captured in real-time at a national or regional level,

is accessible and associated with pharmacogenomic and outpatient

data that would allow more-intelligent analysis of the pharmacological

effects supported by the patient’s compliance on its therapy. This is

pharmacovigilance for the future, based on a distributed Complex

Event Process (CEP) (see Figure 4).

Many of the basic components required to enable this future scenario

are in place or being implemented. The technology-driven

transformation of pharmacovigilance is being championed by

regulators and the pharmaceutical industry alike. In the healthcare

sector the switch from a paper system to electronic records is in

perfect alignment with the driving forces from life sciences. In an

increasingly demanding and risk-averse environment innovative

technological solutions will help to address safety concerns and

improve patient confidence and outcomes. There are many

challenges along this roadmap toward proactive, predictive

pharmacovigilance but the end goal will see greatly reduced risks and

improved responses to better protected patients. n

Table 1: Opportunities for Alignment Between Healthcare and Life Sciences

1. Requires transparent data flows across health sciences

2. Common terminologies must be defined

3. Standards must be able to correlate data sources

4. Best practices can be established

5. Healthcare must focus on implementing (EHR) infrastructure first!

Figure 4: The Next Generation Pharmacovigilance Model

Today Tomorrow

CROCRO

BPO

BPO

CRO

CROCRO

FDAEMA

PDMA

FDAEMA

PDMA

SponsorCROBPO

Sponsor

SponsorSponsor

SponsorSponsor

Sponsor

Data Sources

HealthAuthority

Patient

ClinicalTrial

HealthcareProfessional

Electronic Health Record

OutpatientData

Literature

Research

Claims DB

LongitudinalData

Partners

SafetySignals

Global DataWarehouse

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