Life Sciences White Paper Re-Imagining Drug Safety Powered by Genomics, Information Integration and Emerging Technologies
About the Authors Debashis Ghosh President Manufacturing, Life Sciences and Energy Business Group, TCS Debashis Ghosh is President - Manufacturing, Life Sciences and Energy Business Group, and also Head TCS North India Operations. He is currently responsible for the strategic, financial and business performance of the Manufacturing, Life Sciences and Energy Business Group. In his 23-year long career with TCS he has performed a variety of roles in delivery management, sales and marketing and general management. He also headed TCS' regional office in Wall Street in the US, and was responsible for many of the company's path-breaking transformation programs. Debashis is member of government-industry forums and is also in the Advisory Council of Management and Technology Institutes. He is an alumnus of Harvard Business School (where he completed Advanced Management Program) and of Indian Institute of Technology, Kharagpur (where he received Master of Technology degree in Electronics and Telecommunication). Amit Saxena Head Business Alignment of Innovations Strategy Team, Life Sciences, TCS Amit is Master of Engineering from Indian Institute of Science, Bangalore. He has been in the IT industry for more than 14 years contributing to automation and transformation strategies in various industry verticals. He has an innovative mindset and in-depth understanding of the healthcare domain. He has experience working with life sciences organizations, pharmacy chains, care providers and payers. He has anchored the patient-centric theme within the Life Sciences business unit of TCS and authored various white papers including 'Healthcare Convergence A Myth or Real- IT'. He is also a co-inventor in the three patents applied by TCS. He facilitates thought leadership activities and is responsible for business alignment of innovations initiatives. He was one of the key members to nurture the platform based business model of TCS for the clinical research and drug discovery domains. His interest areas are application of abstraction and modelling for problem solving, forecasting and strategy preparation.
About the Authors Dr. Rajgopal Srinivasan Prinicipal Scientist, Head of Bio-IT R&D Innovation Labs, Hyderabad, TCS Rajgopal Srinivasan heads the Bio-IT division of the Life Sciences R&D at Innovations Lab, Hyderabad. A graduate in Chemistry from The Indian Institute of Technology Madras, Raj also holds a Ph.D. in Chemistry from the University of Illinois at Urbana-Champaign in the USA. Following post-doctoral stints in Washington University, St. Louis and Johns Hopkins Medical School, he was a research professor at the Johns Hopkins University in the department of Biophysics. He joined TCS in 2003 as part of its Corporate R&D Centre in Hyderabad, India. An active researcher, he is the author of several publications and patents. His current research is focused on interpretation of genomic variation in collaboration with researchers at the University of California at Berkeley and San Francisco. Anil Lele Head, Pharmaceutical Analytics Services Life Sciences Business Unit, TCS Anil is Master of Pharmacy (Pharmacology) from Mumbai University. His expertise includes pharmaceutical strategic analytics, decision support consulting and pharmaceutical marketing strategy. His experience spans across 25 years in the life sciences industry in pharmacology research, medico-marketing services, brand management and marketing, strategic analytics and consulting. He has been involved in strategic analytics decision support consulting to global life sciences companies in the US and Europe which involved primary and secondary research and analytics. His work spans across several therapeutic areas, involving analysis of disease epidemiology, diagnosis/ treatment algorithms, patient flow, unmet needs, pipeline / competition, regulatory and market access scenarios for forecasting and valuation, indication prioritization, portfolio and commercial strategy and licensing evaluation.
Scientific advancements, emerging technologies and availability of large amount of information from different sources are offering newer opportunities for modernizing drug safety. In parallel, the pharmacovigilance regulatory landscape is also shifting to ensure improved safety for patients. This is the time to reimagine the drug safety sector to transform it into an advanced, proactive and patient-centric practice. In addition to internalizing regulatory direction and expectations, the drug safety sector needs to focus on three core areas genomics, information integration and emerging technologies. Each of these areas can unlock new frontiers to further improve patient safety. Developing strategic partnerships and exploring new operating models can further accelerate this transformation. This paper presents thoughts on the future of drug safety and describes a TCS 5I model of drug safety Integrate, Inquire, Identify, Improve and Inform where each focus area is carefully woven into the larger plan to ensure enhanced patient safety.
Contents INTRODUCTION 6 CHANGING CONTEXT FOR DRUG SAFETY 6 UTILIZING GENOMICS FOR DRUG SAFETY 8 INTEGRATING INFORMATION AND INSIGHTS 9 Biological & Chemical Information 9 Clinical Information 10 Advanced Analytics 10 ADOPTING EMERGING TECHNOLOGIES 12 KEEPING PACE WITH REGULATORY EXPECTATIONS 14 Benefit Risk Assessment 14 Pharmacovigilance Legislations 14 PUTTING IT ALL TOGETHER 15 THE WAY FORWARD 16
Introduction The global healthcare environment is undergoing an era of transformation across the care delivery eco-system to provide more affordable and quality healthcare to all. Enhancements in the healthcare ecosystem are contributing to increase in life spans, while in parallel there is tremendous growth in chronic diseases which require lifelong disease management. This is leading to diverse implications in the way one approaches drug safety, which needs to be ensured both in the long term view (interfering with multitude of disease conditions and other drugs) as well as in a short term view (providing immediate relief from the disease condition and controlling the spread of disease). There is a growing focus on personalization and outcomes. Pharmaceutical organizations are leading the change to make personalized medicine a reality and increasingly moving towards demonstrating health outcomes. In few cases, health outcomes are even forming the basis for drug reimbursements, promoting risk-reward sharing, which is triggering newer models in the drug sales and marketing landscape. Drug safety is going to be a critical factor in meeting these objectives. The drug safety regulatory landscape is also shifting to ensure enhanced safety for patients. Hence, pharmaceutical organizations are constantly undergoing changes to meet the regulatory expectations on drug safety. This paper provides few crucial drug safety perspectives and also talks about how the changing context presents opportunities for transformation. The paper attempts to draw focus on next-generation drug safety models, and outlines the direction for realizing the emerging vision. Changing Context for Drug Safety Today there is an increased awareness towards health. Everyone has access to information networks and the information flows instantly across boundaries due to growing adoption of digital technologies. The key question is how to effectively leverage these for stakeholder interactions on drug safety. Adoption of technology in the healthcare ecosystem is also increasing, especially the implementation of Electronic Medical Records (EMR). EMR systems facilitate care delivery and monitoring, reduce medical errors and track health outcomes. These systems bring ease and effectiveness in sharing patient health information. The key question for drug safety is how integration with EMR can facilitate active surveillance and prevent adverse events. There is remarkable progress on how genomics is enhancing our understanding of biology and is being used to differentiate between two individuals. There is huge opportunity to apply this knowledge to devise genetic diagnostics and therapies.. The key question for drug safety is how to perform personalized safety analysis and adverse event prediction for patient-subgroups. Additionally, there are market pressures due to expiring patents, shrinking pipelines, longer drug development cycles and rising R&D costs. Lot of initiatives have been started to improve the R&D productivity. The question is can drug safety contribute towards improving R&D productivity. 6
Increasingly, the sales environment is becoming challenging with reducing face to face interactions with clinicians, stiff competition and lucrative offers for drug sales (for example demonstrating health outcomes for reimbursements). The question is - can drug safety be positioned as a market differentiator? Each industry-level macro trend is going to influence drug safety in one way or another and would hence require specific preparation to shape the future of drug safety. Scientific advancements, emerging technologies and availability of large amount of information from different sources offer newer opportunities for modernizing drug safety. There is a need to adopt a holistic outlook towards drug safety. The drug safety efforts need to be streamlined starting at the discovery stage, evaluating if the toxicity information about a compound is comprehensive and how it will translate to adverse events at the clinical level. Also, a feedback loop is required to understand how adverse events related to clinical information refines our understanding of biological processes at the cellular and molecular levels. The drug safety practice needs to play a more active role in facilitating this cycle. With the kind of information captured via electronic means resulting in accurate and instant availability one has to be prepared to extract relevant knowledge and insights. Hence it is imperative to strengthen the integration foundation. It does not only involve collecting the information at a centralized location for analysis, but also includes developing the ability to search across various disparate sources of information and integrate the findings in a single view. Drug safety organizations need to be agile and proactive to be able to adapt to changing dynamics and deliver on stakeholder's expectations. Patient safety concerns are to be understood and addressed at an early stage. In a time when personalized medicine is showing promise, patient-centricity is to be adopted as the guiding principle to improve drug safety efforts. Figure 1 highlights the direction that needs to be adopted in future for drug safety. Adopt Holistic Outlook Strengthen Integration Foundation Transform to be Agile & Proactive Embrace Patient-centric Approach Figure 1: Direction for Drug Safety 7
Utilizing Genomics for Drug Safety Today genomics, along with various tools of modern biology, allows examination of living systems at unprecedented levels of detail leading to greater understanding of biology. This understanding is further used to develop drugs that are inherently safer. There is also an increasing realization that animal models are not effective in assessing toxicity. This is increasingly evident when predicting the impact of drugs on the Central Nervous System (CNS); a rat/mouse brain is not comparable to that of a human. Therefore, in-silico modeling of humans is essential; and in order to build good models, one needs the ability to examine human functions with great detail. This creates the case to leverage new biological technologies and computational advances. Toxicity of a drug compound is the first step to study drug safety. Toxicogenomics answers how numbers and levels of proteins being produced in human cells get affected when toxic side effects occur due to drug consumption. Toxicogenomics combines toxicology and genomics along with various 'omics' approaches such as transcriptomics (study of the complete set of RNAs), metabolomics (study of the unique chemical fingerprints that specific cellular processes leave behind) and proteomics (study of proteins particularly their structures) among others. Figure 2 represents how the 'omics' based approaches are going to change the safety prediction during the R&D cycle. Animal Trials In-silico Models Genetic Correlations of ADMET, PK/PD Omics Experiments Gene expression & Proteins Animals Human Toxicity Analysis & Safety Prediction Human Trials Drug Information Biological Pathways Known Targets Human Genetic Info Adverse Event DB Figure 2: Omics based Drug Safety Prediction 8
There are a number of ways in which toxicogenomics can help to make safer drugs. For example, if one makes a reasonable assumption that drugs that produce similar profiles of gene expression levels will have similar adverse events, then it becomes possible to predict the toxic side-effects of a candidate drug molecule. Another way in which toxicogenomics can help is by identifying bio-markers that appear well before full manifestation of an adverse event. Finally, a deeper understanding of the biology that is made possible by these techniques enables accurate statistical modeling. Small genetic differences that each individual carries can affect how he or she responds to a treatment. The study of this relationship between genetic variation and response to a drug is the subject of pharmacogenomics. It also includes the use of genomic-based approaches for discovering new drugs or re-purposing old drugs for new indications. The interest in pharmacogenomics stems from the possibility that understanding the role of genetic differences in how drugs are metabolized (encompassing drug absorption, distribution, metabolism, excretion pharmacokinetics and pharmacodynamics) can help identify those patients who may be at risk from taking a particular drug. As an example, genetic variations (of a gene called SLC01B1) can hamper transport of statins to the liver, thereby causing higher levels of statin in the blood and higher incidence of myopathy. Pharmacegenomics enables identification of genetic biomarkers related to the impact of drugs, along with development of companion diagnostics to identify the right patients for the drugs. An entirely new domain is emerging by using such biomarkers for supporting health economics for treatment reimbursement related decisions. The role of drug safety is set to increase in such cases. Integrating Information and Insights Biological and Chemical Information Though there are lots of information sources containing relevant structured and unstructured information for safety analysis, there is no single platform where one can find all the available biological and chemical information. The next-generation drug safety program needs to be able to search and integrate insights from available structured information on genes, proteins, biological function, drugs, toxicity profiles, and diseases. There is an equally critical need to include relevant insights from large amounts of unstructured information (such as journal publications, articles and patents). All insights need to be seamlessly integrated with organization-specific internal knowledge on drug safety. The integrated data needs to follow a consistent vocabulary, to allow scientists to derive newer insights, create and validate safety hypotheses. Additionally, this needs to be designed in order to update itself on a continual basis. The key issue around biological and chemical information is lack of standardized ontology and consistent vocabulary. Also a different skillset and expertise is required for effective search and insight generation (as against merely integrating information which is relatively easier but not as useful). 9
Clinical Information Human trials are the primary source of clinical information but these represent limited scenarios with minimal information when compared to information available post drug launch. With the increasing adoption of IT in the healthcare delivery ecosystem, EMR data is being envisaged to bring many benefits by effectively utilizing patient data. Drug safety systems need to start integrating this data for safety analysis. Increasingly there has been a surge in adopting Electronic Data Capture (EDC) systems to increase and capture patient data electronically during clinical studies. Opportunities to integrate EDC with EMR systems for clinical insights are being explored, and this approach needs to be extended to post-marketing clinical studies. This can be achieved on a case by case basis where partnerships with care delivery networks would provide access to patient EMR data for safety analysis and risk management. The rich clinical information would not only enable scientific validations but also encourage proactive surveillance and early detection of adverse events. There is also a possibility to explore integration of clinical information available with the health insurers. Publicly funded health insurance systems are in a better position to offer avenues to pharmaceutical organizations for analyzing huge volumes of patient health data for safety, efficacy and health outcomes studies. All this clinical information can be used to examine specific drug classes and individual drugs for known and unknown adverse drug events and to generate hypotheses and signals for further investigation. There are two developmental areas for clinical information integration the first includes lack of policy and implementation frameworks for easy sharing of patient information (for instance, through formation of health information exchanges), and the second includes summarizing clinical information relevant to drug safety. Clinical symptomatology (and its linkage to treatment protocol) is under represented and interpreting physicians' notes becomes a necessity albeit the cumbersome process. EMR systems need further enhancements for detecting adverse events amidst expected health outcomes. Advanced Analytics The ability to search and combine all relevant information and insights would provide a unique opportunity to perform advanced safety analytics during the trials as well as post-marketing. This requires good algorithms to combine information from different sources with the appropriate confidence levels to draw inferences. Figure 3 represents few advanced analytics use cases. 10
Data Mining Active Surveillance Pharmacology, Pharmacokinetics & Toxicology Studies Database Spontaneously Reported ADR Database Pre-& post-marketing Clinical Studies Database Analytics Measurement of Disproportionality Clustering & Segmentation Link Analysis Deviation detection Predictive Modeling EMR Systems Database Pharmacy & Medical Health Claims Database Datasets from Internet Search Engines Pharmacogenomics Studies Database Detection, strengthening & validation of safety signals Identification of specific patient population at risk Datasets from Internet Search Engines Figure 3: Analytics Use Cases for Drug Safety 1 There is a wide spectrum of statistical analysis techniques (summarized in the Figure 4 below) that can be used for detection, strengthening or validation of drug safety signals and identification of specific patient population at risk. Statistical Analysis Techniques Measures of Disproportionality Clustering & Segmentation Link Analysis Deviation Detection Predictive Modeling Utility For safety signal detection Measures rate at which a particular adverse event co-occurs with a given drug as compared to the rate this event occurs without the drug To group patients with similar symptoms or diagnoses i.e. to a set of homogeneous subgroups, to study / determine whether there is an association of adverse drug event/s with specific subgroup/s of patients To identify associations (or links) between sets of data e.g. effect of renal impairment on the safety profile of a given drug To identify outliers or values that deviate from the norm in the dataset e.g. to identify patients with unusual symptoms, which could be related to medication, and may constitute an adverse event signal. To determine a classification criterion or rule e.g. demographic or socio-economic characteristics that discriminates between different groups of patients with and without specific drug adverse event and using the classification criteria to predict future adverse event outcome. Figure 4: Statistical Analysis Techniques and Utility for Drug Safety 11
Availability of EMR data and healthcare claims data for proactive safety surveillance will require development of innovative methods and statistical models (to supplement the techniques mentioned in Figure 4) to analyze extremely large, non-homogenous, multi-dimensional datasets. Keeping the above in context, availability of longitudinal patient data can provide interesting possibilities for analysis of adverse drug events and signal detection. Statistical methods for safety signal detection using longitudinal patient data fall in four broad categories: disproportionality methods, cohort methods, case-control based methods and surveillance approaches using techniques such as SPRT (Sequential Probability Ratio Test), 1 CUSUM (Cumulative Sum Procedure), and hidden Markov models. Extracting and analyzing adverse drug events related information from clinical notes portion of EMR data using 2 automated natural language-processing (NLP) techniques and text analytics offers another interesting possibility. Also, analyzing search activities of various population groups on search engines can provide early clues to adverse drug events and aid active safety surveillance. Safety analytics should be predictive, actionable and available in real-time. Adopting Emerging Technologies Emerging technologies are shifting architectural paradigms. Adoption of these technologies blurs the boundaries across systems and provides tremendous opportunities to leverage the power of information integration as never before. This also enables utilization of distributed resources to meet the computational needs of safety related processes. Primarily, the focus of drug safety is on pharmacovigilance (case processing activities) which operates in a transaction oriented fashion With the changing approach to drug safety due to genomics and the possibility of integrating various newer insights (generated from vast amount of continually flowing information), the entire architectural landscape of drug safety needs a relook. The guiding principles of the modern drug safety architecture would require the ability to integrate and analyze safety insights quickly; enable stakeholders to access relevant information and perform analysis, and ensure that proactive measures can be planned effectively to address patient safety. Figure 5 provides a representative, next-generation drug safety architecture with seamless flow and integration of information and insights. Such concepts need to be implemented using emerging technologies. [1] Wilson A. M., et al, Application of data mining techniques in pharmacovigilance, British Journal of Clinical Pharmacology (2003), 57:2, 127-134 Madigan D., et al, Bayesian Methods in Pharmacovigilance, BAYESIAN STATISTICS (2010), 9, 1-17 [2] LePendu P., et al, Pharmacovigilance Using Clinical Notes, Clinical Pharmacology & Therapeutics (2013), 93 (6), 547-555 12
References Disease Adverse Events (from Case Processing) Drug Events Indication Sponsors Safety Surveillance Public Databases Safety Database Statistical Models Rule based Engine Actionable Insights Risk Identification EHR/EMR Data Visualization Repository Regulatory Submission Social Media Journals Data Xform Engine NLP Engine Data Cleanup Transformation Standardization Analytics Big Data + Cloud Platform Figure 5: Next Generation Drug Safety Architecture The next-generation drug safety architecture would leverage big data technologies, high performance computing, sophisticated natural language processing, data mining capabilities and data visualization techniques. Additionally, cloud-based platforms would enable sharing across organizations. Porting drug safety systems on a cloud platform creates avenues for information sharing. It can evolve into centralized safety databases and analytics setup, integrating information from social media, literature and clinical systems (through partnerships with care delivery network) in a particular therapeutic area. This can be used by multiple pharmaceutical organizations sharing the cost of developing and maintaining such integrated safety systems and partnerships. Cloud-based platforms may also have provision to integrate proprietary information from a pharmaceutical organization and share across other partners, if needed. Many of these technological innovations are already being explored and adopted to advance the drug R&D infrastructure. Drug safety organizations need to collaborate within the enterprise to realize the benefits. 13
Keeping Pace with Regulatory Expectations The drug safety regulatory landscape is constantly undergoing changes to ensure enhanced patient safety. Understanding regulatory expectations and future direction is critical to reimagine the landscape. Benefit Risk Assessment The drug approval decisions by regulatory authorities are based on assessment of benefits and risks of a drug and whether benefits outweigh the risks. New structured approaches are being adopted to perform this assessment using quantitative as well as qualitative methods. Promoting risk management practices post the launch of the drug can help maintain positive benefit risk balance. These risk management initiatives would trigger the adoption of well-defined methodologies for drug approval decisions and can ensure that the drug benefits outweigh its risks at all times. The regulatory focus is to ensure that a thorough and transparent benefit risk assessment not only drives the drug approval decisions but enough measures are planned to keep it updated during the life cycle of the drug. Drug safety provides the information for benefit risk assessment of a drug. Post the launch of the drug, additional drug safety data is the only information that can impact the benefit risk of a drug negatively. Hence it is important that drug safety play a proactive role in ensuring a positive benefit risk profile of a drug. The focus is to identify patient sub-groups where the drugs- benefit risk profile is positive. This can be ensured by adopting risk management practices, as required. Pharmacovigilance Legislations 3 In 2007, the US enacted Food and Drug Administration Amendment Act of 2007 (FDAAA ). This empowered the FDA to ask for post-marketing studies and clinical trials from drug sponsors. It enabled the FDA to develop a comprehensive safety surveillance system. The act also empowered FDA to ask for Risk Evaluation and Mitigation 4 Strategy (REMS ) from drug sponsors. This was a significant change towards ensuring safety throughout a drug's life cycle. This enforcement ensured that the benefits of the drugs outweighed their risks at all times. 5 In the European Union (EU), a new pharmacovigilance legislation was adopted in December 2010 and amended in October 2012 which aims to promote and protect public health. The new EU Pharmacovigilance legislation focuses on Improving decision-making procedures and reducing administrative costs Strengthening procedures for collection of safety information and submission of risk management plans Greater engagement of healthcare professionals and patients Better analysis and understanding of available information [3] http://www.fda.gov/regulatoryinformation/legislation/federalfooddrugandcosmeticactfdcact/significantamendmentstothefdcact/ FoodandDrugAdministrationAmendmentsActof2007/default.htm [4] FDA, Draft Guidance for Industry: Format and Content of Proposed REMS, REMS Assessments, and Proposed REMS Modifications, Accessed at http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm184128.pdf on June 23, 2011. [5] http://www.ema.europa.eu/ema/index.jsp?curl=pages/regulation/general/general_content_000492.jsp&mid=wc0b01ac058033e8ad 14
The drug safety regulations are expected to be more stringent in the future. The scope of drug safety regulations is steadily transforming from submitting safety knowledge in taking and maintaining marketing approvals to demonstrating proactive safety actions thus minimizing risks to the patients in a clinical setting. To take the lead in such a dynamic environment, organizations need not constrain themselves to just meet the regulatory expectations but they can demonstrate initiative by going a step ahead and filling the gaps in patient safety. Putting it all together Pharmaceutical organizations need to realign safety efforts and be more patient-centric in formulating an integrated approach to drug safety. Each focus area needs to be carefully woven into the larger plan to ensure enhanced patient safety. While science would be the underlying foundation to increase our confidence in drug safety prediction, information and insights integration would make sure that the gaps in safety knowledge (due to missing information) are being filled on a continuous basis. Adoption of emerging technologies is necessary in order to build the backbone of next-generation drug safety systems. With the availability of benefit risk profiles covering various clinical scenarios and patient subgroups, personalization can be facilitated with appropriate risk management practices. The entire landscape of drug safety, as depicted in the Figure 6, needs to be envisaged as a whole and capabilities and collaborations built and aligned accordingly. Target Identification Lead Optimization Candidate Evaluation Phase I-II Trials Phase III Trials Post Launch IND NDA Integrate Biological & Chemical Information Clinical Information Inquire Identify Improve In-Silico Modeling (Quantitative, Math & Statistical Methods) Toxicogenomics Studies Clinical Assessment & Statistical Evaluations Insights from Safety Studies (Scientific & Clinical Studies) Pharmacogenomic Studies Advanced Analytics (Pharmacoepidemiology & Signal Detection) Post authorization Safety Studies Risk Prediction Risk Mgmt. Planning Risk Management Benefit Risk Assessment & Governance Inform Stakeholder Communications & Regulatory Interactions * The boundaries are indicative. Figure 6: TCS 5I Model on Drug Safety 15
The Figure 6 describes a TCS 5I Model of drug safety Integrate, Inquire, Identify, Improve and Inform. It represents different components of safety in the entire life cycle of a drug. The future drug safety function is expected to combine various perspectives using the following model: needed for drug safety by Integrate: constantly integrate information and insights needed for drug safety and be a custodian of all safety related knowledge Inquire: collaborating with R&D for capturing safety related insights and executing safety studies throughout the life cycle of the drugs Identify: perform advance analytics to predict and identify safety concerns early Improve: proactively address safety concerns via risk management practices Inform: devise safety communication strategy (including safety education to the stakeholders such as physicians, patients and care givers) and perform regulatory interactions Both inter-organizational and intra-organizational collaboration is essential for the future of the drug safety function. To provide an example of collaboration with R&D, the drug safety function needs to understand different types of in-silico models being used to predict safety to gather the confidence on safety prediction and plan future safety studies. Similarly, they need to keep a watch on maturity and acceptance levels of different safety modeling techniques in the industry and promote its adoption. The Way Forward Aligning to the perspectives described in this paper, there is a strong need to develop next-generation drug safety platforms. Thorough drug safety domain insights combined with cutting-edge technology leadership capabilities can realize this patient-centric integrated drug safety vision. Also, developing a transformation roadmap and appropriate change management models, keeping in mind the current state of technology maturity in drug safety, can enable easier adoption of newer platforms. Developing strategic partnerships and exploring new operating models by pharmaceutical organizations can further accelerate this transformation. The need of the hour is to envisage the future, set aspiring yet realistic targets and march ahead to transform into the next generation of drug safety. Prompt action by the drug safety sector has the potential to bring the competitive differentiation in addressing patient safety challenges. 16
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