Senior Principal Scientist, Patient Centered Endpoints, IQVIA

Matthew Reaney

The term patient centricity has become a mantra for the pharmaceutical industry. However, life sciences companies should think about the need to move beyond abstract commitments to “patient-centricity” and rather embrace patient-centered science as a foundational approach across all stages of the product lifecycle. A crucial component of this entails actively listening to the most important stakeholder—the patient—and using scientifically rigorous patient-relevant data to advance therapeutic innovation.

Understanding the patient perspective, from the patient themselves, has become an ever-evolving and must-have in order for life sciences companies to enhance research strategies, to drive advances in drug development, and to achieve commercial success. This patient-centric initiative is driven by several trends:

Today, patients are more invested and engaged in their own health than ever before. Patients want to understand how they can improve their health and prevent disease. They crave information including access to their own health data, and many patients are willing to share their health data.

 However, while credible and accessible information is important; bad health information used in an improper way can be highly detrimental.

 Many people turn to Wikipedia for information—it was the eighth-most-visited website in 2020

 and a recent systematic review highlighted it as ranking highly in Google searches for health information and as a resource accessed frequently by the public for this aim.

 A study analyzing Wikipedia articles of 10 medical conditions, including diabetes, found statements on the webpages discordant with the latest medical research at the time in 9 of the 10 entries.

Technology has become a powerful enabler of the patient perspective through a constantly growing array of personal health technologies, such as smart phones, sensors, and other remote technologies. Just think about all the devices on the market that track everything from heart rate to stress levels. Patients have an abundance of their own personal data they can use to inform their health decisions. However, this technology is rarely being utilized to its full potential in clinical research

 and is not currently being used in routine healthcare.

Science has deepened the understanding of and ability to advance more individualized and tailored treatment options enabling patients to make shared decisions with their providers. There have been significant advancements in understanding treatment effects, identifying responders, and incorporating patient needs, perspectives, and priorities into treatment decision-making, but the tools are not always there to support it.

Regulators and payers increasingly recognize the value of applying a patient perspective in research and decision-making, although this is done inconsistently.

Challenges in the adoption of patient-centered science

To generate patient-relevant research data that can be used to support regulators, payers, healthcare professionals, and patients in making well-informed and reliable decisions, it is essential that scientific approaches are taken. Patient-centered science is evolving as an evidence-based approach for the life sciences industry, which provides a more rigorous scientific underpinning to adopting a patient perspective than a lofty declaration of patient-centricity.

However, adopting patient-centered science has its challenges for life sciences companies. Such challenges are reflected in the current communications of clinical endpoints and in engagements with patients around data:

: In the past, life sciences companies have relied on endpoints that don’t always resonate with patients. As an example, in diabetes HbA1c—a 3-month average marker of blood glucose control—is used as the traditional indicator that clinicians rely on as part of the diagnosis and clinical decision-making, but the blood glucose markers that patients rely on for their own decision-making are fundamentally different—with fluctuations and daily control key. Further, behavioral modifications needed for successful diabetes control are rarely built into the endpoint, so while we aim to understand the impact of treatment on glucose control, it is possible that some of the effect is in fact a result of behavioral changes.

: Many people don’t have the ability to understand and respond to health information. A 2015 survey suggested that 43% of English adults possess insufficient literacy skills and 61% possess insufficient literacy and numeracy skills to routinely understand health information.

 This pertains to both the collection of quality data directly from patients (which inherently assumes a level of health literacy to reliably understand and respond to the questions asked about their experiences) and the dissemination of relevant data to patients.

: Communicating clinical information is not as easy as it sounds. This is increasingly challenging with the rapid evolution of complex science, specifically in the areas of genomics and precision medicine. There are boundless opportunities for life sciences companies and health care professionals to provide clinical and scientific information to a patient in a way that is relevant, understandable, and actionable. However, the traditional approach by clinicians to lead clinical decision-making is only slowly being replaced by shared decision-making where the patient and physician partner around the decision-making.

: The role of cultural and ethnic bias has also been poorly understood, which has limited the understanding, acceptance, and adoption of therapies and lifestyle modifications among minority populations. It is only recently that the medical community and the life sciences industry has started to address the cultural barriers and health disparities that affect various, diverse populations.

So how do we all work to advance patient-centered science?

Several strategic steps should be undertaken to advance patient-centered science:

: The traditional “push” communications approach where patients are told what to do by the provider or the life sciences company should be replaced by a new paradigm based on listening. This should be done within a scientific research framework as market research and individual case studies can be informative, but neither comprehensive nor generalizable. Fundamentally, the medical ecosystem needs to recognize that the patient is the one suffering from the condition and therefore has unique perspectives, insights, and experiences that should impact all aspects of the patient journey. It’s a learning opportunity for all parties.

Traditional clinical endpoints based on the pathology, physiology, and biology of disease need to be expanded with endpoints that relate to functional ability (sleep, mobility, etc.), and the emotional and psychological patient experience.

: In the era of patient-focused drug development there is a greater use of patient-reported outcome measures (PROMs) in clinical trials. Electronic technology has a key role to play in capturing timely and patient-relevant information to inform study endpoints. There are several areas where technology can be used to enhance patient-focused drug development: Capturing qualitative data from patients; using digital health technology tools; employing reactive technology-enabled clinical outcome assessments; and generating passive patient experience data.

Rethink the application of patient-centered science

: Patient-centered science should be incorporated and adopted across all aspects of the product lifecycle where life sciences companies make decisions about endpoints. This will include the application of Clinical Outcome Assessment (COA) and Patient Experience Data (PED) platforms that intelligently automate capturing patient experiences and information about priorities, preferences, and needs, as well as experiences with treatment, care, and outcomes, through robust and reliable electronic measures, providing real-time insights that inform clinical trial progress and demonstrate results. (See Exhibit. Example scenarios across the product lifecycle where companies may make patient-centered science investment decisions).

: Life sciences companies should aim to overcome the internal fragmentation and silos that impede the ability to think holistically about the patient experience as an integrated approach across the enterprise. This can be a challenge as it may conflict with traditional ways of working in pharmaceutical organizations. It all starts with a few small steps. One example is directly coordinating patient-centered solutions services with the CROs to accomplish more dynamic patient perspectives in clinical trial design, development, and execution.

What does good patient-centered science look like in practice? Albireo Pharma case study

The critically important role of patient-centered evidence was demonstrated in IQVIA’s work with Albireo. Patient-centered science was applied to define clinical endpoints and to secure regulatory approval of a novel drug for the treatment of a rare liver disease in children.

A global rare liver disease company focused on the development of novel bile acid modulators to treat rare pediatric cholestatic and adult liver diseases, Albireo has developed a novel bile acid modulator, odevixibat (A4250), for progressive familial intrahepatic cholestasis (PFIC). PFIC is a rare genetic disorder that causes progressive, life-threatening liver disease in children.

In many cases, PFIC leads to cirrhosis and liver failure within the first 10 years of life. The most prominent and problematic ongoing manifestation of PFIC is pruritus, or intense itching, which often results in a negative impact on the daily lives of patients and caregivers. Until last year, there was no approved drugs for PFIC. Only surgical options that include biliary diversion surgery (BDS) and liver transplantation have been available, and without them, most PFIC patients do not survive past the age of 30.

Supported by the global Phase 3 PEDFIC studies, on July 16, 2021, Albireo received European Marketing Authorization for Bylvay® (odevixibat), the first drug approved for the treatment of PFIC in patients aged 6 months or older. Four days later, on July 20, 2021, FDA followed up with a similar approval of Bylvay (odevixibat) in the U.S. for the treatment of pruritus in patients 3 months of age and older with all types of PFIC.

“Until now invasive surgery was the only approved treatment option. With the approval of Bylvay, parents may find hope in having a less invasive treatment option available,” said Emily Ventura, leader of 

 and mother to a PFIC patient. “As a community, we experience extreme challenges and diminished quality of life for children and families with PFIC. Managing the symptoms can be extremely difficult—the burden is unimaginable with our kids suffering physically, emotionally, and developmentally.”

Albireo harnessed patient-centered science from the very beginning of the development program for Bylvay, and its experience illustrates the points previously discussed.

 Through semi-structured interviews with patients and/or their parents and clinical experts Albireo was able to understand the unique experience of PFIC patients and to develop a measurement tool for the pruritus symptoms and impacts (e.g. sleep disturbance) which was presented to regulatory authorities.

: Albireo selected a truly patient-centric primary endpoint by measuring both a pruritus improvement endpoint, and a serum bile acid endpoint as primary endpoints in its pivotal trials.

: Through a well-designed, patient-friendly eDiary, patients and/or parents reported their experience of pruritus and other aspects of PFIC, twice daily throughout the trial. Albireo’s strong engagement with parents ensured that completion rates for the eDiary were high throughout treatment.

: Albireo implemented an end-to-end patient-centric strategy that began in Phase I and continues in patient support programs and real world studies.

IQVIA has been the scientific partner for Albireo throughout this journey. We look forward to seeing how this information is shared with HTA/payers, patients, and HCPs to support reimbursement decisions/formulary placement, and inform clinical care.

Achieving benefits from patient-centered science

Capturing the patient perspective can generate more relevant, valuable insights that can help drive better outcomes for life sciences companies and, at the end of the day, achieve what we should all put front and center: improved patient outcomes. Companies doing this can claim to be truly “patient centric.” Benefits of capturing the patient perspective include:

Improved understanding of the patient experience across the product lifecycle by listening to patients

Enhanced data transparency and quality by enabling patients to better understand the importance of evidence-based endpoints in clinical trials—technology-enabled endpoints can further increase reliability and validity of the data

Patient-informed clinical trial designs which maximize recruitment and retention

Harmonized clinical, commercial, and access decisions across the organization

Reduced study implementation timelines that accelerate access to new therapeutic innovations

These benefits of “patient centricity” can only be fully realized with an emphasis on good scientific principles and practices. If these data are to inform pivotal decisions in pharmaceutical/biotech companies, among regulatory and payer organizations, and in HCP-patient communications in routine clinical practice, they need to be well informed, reliable, non-misleading, and easy to interpret. As such, patient-centered science is emerging as a pivotal effort in medicine along the entire product lifecycle, redefining “patient-centricity” as the generation of patient-relevant and scientifically sound data which is both representative of, and generalizable to, the target population for the treatment under investigation. In this way it is translating the voice of the most important stakeholder—the patient—into relevant data to advance therapeutic innovation. Now it needs to become the mainstay, if not the only way in which patient centricity can be claimed.

, Vice President, EMEA Lead, Patient Centered Solutions, 

, Vice President, Patient Centered Solutions, and 

, Senior Principal Scientist, Patient Centered Endpoints; all with IQVIA

PEW Research. Most Americans Want to Share and Access More Digital Health Data. 

https://www1.pewtrusts.org/en/research-and-analysis/issue-briefs/2021/07/most-americans-want-to-share-and-access-more-digital-health-data

Tonsaker T, Bartlett G, Trpkov C. Health information on the Internet: gold mine or minefield? Can Fam Physician. 2014;60(5):407-408. 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4020634/

Cooke, R. Wikipedia is The Last Best Place on the Internet. WIRED. February 17, 2020/ 

https://www.wired.com/story/wikipedia-online-encyclopedia-best-place-internet/

Smith DA. Situating Wikipedia as a health information resource in various contexts: A scoping review. PLoS One. 2020 Feb 18;15(2):e0228786. 

https://pubmed.ncbi.nlm.nih.gov/32069322/

Hasty RT, Garbalosa RC, Barbato VA, Valdes PJ Jr, Powers DW, Hernandez E, John JS, Suciu G, Qureshi F, Popa-Radu M, San Jose S, Drexler N, Patankar R, Paz JR, King CW, Gerber HN, Valladares MG, Somji AA. Wikipedia vs peer-reviewed medical literature for information about the 10 most costly medical conditions. J Am Osteopath Assoc. 2014 May;114(5):368-73 

https://pubmed.ncbi.nlm.nih.gov/24778001/

Reaney, M., Cline, J., Wilson, J.C. et al. Generating Relevant Information from Patients in the Technology-Enhanced Era of Patient-Focused Drug Development: Opportunities and Challenges. Patient 14, 11–16 (2021). 

https://link.springer.com/article/10.1007/s40271-020-00455-2

QVIA Institute for Human Data Science. Digital Health Trends 2021. Juky 22, 2021. 

https://www.iqvia.com/insights/the-iqvia-institute/reports/digital-health-trends-2021

Rowlands G et al. A mismatch between population health literacy and the complexity of health information: an observational study. British Journal of General Practice 2015; 65 (635): e379-e386. 

Articles

Good scientific principles and practices are essential for maintaining quality data that patients can benefit from.

Harnessing Patient-Centered Science to Improve Health Outcomes and Commercial Pharma Success

Life is defined by our achievements and failures. Making goals and striving to reach them are how we categorize our day-to-day lives. This is true in our careers, our finances, our relationships, and also our health. Particularly for those living with a chronic condition, health goals must be agreed, set, and rigorously maintained. In some cases, the goals are defined by clinical parameters; for example, in diabetes, where control of blood glucose to within-recommended targets is the goal for most people. Achieving this goal helps to reduce the likelihood of micro- and macrovascular complications. In other cases, the goals are defined by our behavior; a month of no alcohol consumption among people taking blood-thinning therapy, for example. In line with the principles of behavioral and developmental psychology, it is important to reward successful outcomes where goals have been met. These rewards can be intrinsic or external. However, if we treat the outcomes in a simple binary fashion, where they are met or not, we miss important and rich information on those goals. 

	In order to better understand goal attainment, data is commonly collected using continuous interval data, so as a researcher can measure the achievements of an individual in terms of their status change (and the magnitude thereof), rather than simply whether a pre-defined goal was achieved. This measurement strategy is the cornerstone of in-vivo clinical research for the development of pharmacotherapy, and is the basis on which pharmacotherapeutic medications are developed, licenced, regulated, and made available within the healthcare system. Specifically, pivotal trials aim to answer two questions: (1) “did the experimental therapy achieve clinical benefit in patients who enrolled in the study”, and if so; (2) “is the benefit greater with the experimental therapy than with standard of care?”

	The answer to this question can, at its simplest, be achieved by the analysis of data at two timepoints; baseline (prior to initiation of therapy) and one additional timepoint, some days, weeks or months later, when the pharmacokinetics and

 pharmacodynamics of the drug indicate it should have reached peak efficacy. In reality, longer-term trials are usually carried out to ensure maintenance of an effect over time, with data collection visits occurring at regular intervals (normally one to six months apart). In Figure 1 at right, a graph shows the effect of a hypothetical experimental medication (medication A) compared to two active comparator medications (medications B and C) on physical functioning in rheumatoid arthritis. Assessment of physical functioning is made using a well-established patient-reported outcome (PRO) questionnaire, the SF-36.

	Items on this questionnaire are measured and scored using interval data, and lower scores are indicative of less impairment. In this trial, all three medications are efficacious, in that they have improved physical functioning over the three months of the study. They have done so to a “similar” (non-inferior) degree. In response to the two questions above, one must conclude based on the data presented in Figure 1, that (1) yes, there was a significant improvement in physical functioning from baseline to trial endpoint (3 months) with the experimental medication, but (2) no, the benefit was not greater than with standard of care (using traditional inferential statistical analysis).

	However, the apparent similarities in the impact of these medications on physical functioning may be a function of the simplicity of the study design; more specifically, 

the infrequent measurement of a concept which may change frequently may be masking important differences between the medications. Imagine that, in fact, the medications behaved as per Figure 2 at left, captured in the same trial using twice-weekly diary data rather than just once at the end of the trial. This shows clear differences between the medications. Although our answer to the first question (“did the experimental therapy achieve clinical benefit in patients who enrolled in the study?”) remains yes, the answer to the second question (“is the benefit greater than with standard of care?”) is now not so clear. Utilizing area-under-the-curve analyses (in addition to change-from-baseline analyses) would suggest medication A differs significantly to medication B, but not to medication C. However, the data now provides a basis for hypothesizing that these three drugs are fundamentally different. The differences are in the patient experience.

		A person who is cautious of doing too much, gets anxious about changes, but desires increased physical functioning would benefit most from medication A.

		A person who was physically active prior to the rapid development of rheumatoid arthritis will want to achieve maximal therapeutic benefit quickly so as to return as much physical functioning as possible as early as possible. For this person, medication B is the right choice. 

		A person who needs to engage in peak physical performance but is able to rest for some days afterwards would benefit from medication C (assuming that the peaks and troughs are predictable).

	It is too simplistic to suggest that more data points are more informative. Rather, the frequency of data capture must relate to the dependent variable (outcome) of interest, and be captured in a manner that reduces measurement error. For example, daily collection of “quality-of-life” data may decrease the variability in the measurement owing to participant burden and generalizations by participants when completing the scales. This would reduce the sensitivity of the assessment to identify a meaningful change from baseline within and between cohorts.

	On the other hand, researchers have shown reduced error variance by collecting episodic data in trials of overactive bladder, largely as a function of reduced recall bias and heterogeneity within participants over the course of a trial. The timing of assessment is also key; while random momentary assessment may be appropriate and beneficial for capturing “in-the-moment” fatigue in multiple sclerosis (which is known to follow a diurnal pattern), it is appropriate to standardize collection of pain and stiffness data once during a 24-hour period among rheumatoid arthritis patients. 

	Data collected on an appropriate assessment schedule gives power not only to answer the two questions regularly posed in pivotal trials, but also to explore individual responders to therapy. Figure 3 below provides population-level data from a trial of a hypothetical medication for insomnia.

	Although there is no comparator in this trial, we are able to conclude that there was a significant improvement in sleep time from baseline to trial endpoint (36 weeks) with 

the medication. Figure 4 at left shows data from four participants in the trial with very different experiences. The frequency of data capture allows us to define these as responders (for whom the medication worked) and non-responders. Participant 1, 2, and 4 could all be classified as responders, but for different reasons: 

		When participant 1 sleeps, (s)he sleeps a similar number of minutes/hours per night following initiation of medication as before medication. However, (s)he sleeps more frequently (every night) following initiation, opposed to before initiation (57% of nights).

		Participant 2 sleeps longer since initiation of medication, and has less variability in total time spent sleeping per night.

		Participant 4 has been afforded a consistent addition of one hour sleep per night since initiation of the medication.

		These individual differences in medication action and patient experience do not show on a plot of the average effect (See Figure 3).  

	The temptation in clinical research is to collect some data on a more frequent basis than others. A good example of this is treatment satisfaction, an endpoint utilized in clinical trials as a partial proxy for adherence (anticipated medication-taking behavior outside of the trial environment), but collected less frequently (in general) than clinical data. Satisfaction is a latent variable likely to be a function of expectations and experience of efficacy (positive clinical benefit) and adverse effects of medication (negative side-effects). Some combination of these will contribute to a person’s stated satisfaction with 

treatment, which can change day-to-day, in line with the perceived benefits and detriments of treatment on that day. Regular collection of all three types of data (efficacy, treatment satisfaction, side-effects) allows the researchers to overlay the observed data and understand the main contributing factors to whether high levels of satisfaction are achieved within-and between-patients. 

	As the medical community moves toward individualized and tailored therapeutics, and pharmaceutical companies are increasingly opting into risk-sharing agreements with payers, it is important to understand the patient journey with a medication, as well as the goal achievement of that medication. Collecting this data in pharmacotherapy development programs allows an understanding of the true impact of the medication in that context. This allows for more targeted prescribing, but also for appropriate expectation setting by treating physicians in clinical practice; if the patient journey does not match expectations, then adherence is likely to be poor. In addition to the use of frequent data collection to understand and prescribe medication optimally, there may be other hypotheses that can be generated from the data which can prompt future research for all stakeholders. For example:

 Is there any long-term (>1 year) clinical benefit to medication C over medication A and B (See Figure 2)? The rapid achievement of a goal and maintenance of that goal may predict future outcomes more than simply achieving the goal at three months. Key audience: regulators, payers, healthcare professionals.

 The “positive feedback loop” is a very important psychological construct in medication taking. The best predictor of positive outcomes is good response and, therefore, one could hypothesize that if patients see good outcome early (like with medication B in Figure 2), they are more likely to put up with side-effects and adhere (and, therefore, persist) better. However, what about medications A and C? How do they compare on treatment satisfaction and adherence variables? Key audience: payers, healthcare professionals, patients.

 Can a timepoint be established on Figure 2 at which an “unlikely responder” can be identified with reasonable specificity and sensitivity? Key audience: payers, healthcare professionals, patients.

	Answering these questions, and other clinically relevant questions, requires routine and regular collection of outcome data according to a schedule which allows for maximum understanding of the pharmacotherapy under investigation. In an era of “real-world evidence”, this data can support healthcare tools to enhance medication-taking behavior and clinical communication at appropriate and relevant timepoints, potentially increasing both the quality of life of patients and quality of care they receive.

, M.Sc., was Sr. Scientific Consultant, ERT, at time of research; 

, PhD, is Chief Scientific & Quality Officer, ERT

Measuring clinically relevant response requires routine and regular collection of outcomes data.

Therapeutic Success or Failure: A Journey, Not Just a Destination

	Patient-reported outcome (PRO) questionnaires can be used to collect subjective clinical and psychosocial outcome data directly from patients in clinical trials, in order to describe their perception of a disease and its treatment(s), without the interpretation of responses by a physician or others. They are the only systematic quantitative way to ask  patients directly about how they feel or function. 

	In looking at the newer classes of drugs for type 2 diabetes mellitus (T2DM), such as DPP-4 inhibitors, GLP-1 receptor analogues, novel insulins, and SGLT-2 inhibitors, PRO questionnaires have been used to

 inform endpoints in most of the pivotal programs. As with the concept of blood glucose, a PRO can contain many related but distinct concepts, including symptoms of diabetes, side-effects of treatment, health-related quality of life, psychological well-being, and treatment satisfaction. Each concept captures a different aspect of a person’s experiences, thoughts, and feelings about T2DM and/or its treatment. All of these have been measured as endpoints across the pivotal trials of the newer classes of T2DM drugs. 

	This PRO data has the potential to offer insights into the subjective experience of therapy and provide an informed perspective of the associated patient-perceived benefit-to-risk ratio of the medication. Historically, PRO data has not been held in high esteem in T2DM, partly due to the traditional glucose-centric model of care, and partly due to variable scientific credibility in their measurement and lack of clarity in their interpretation.

	However, in recent years the science of PROs has progressed considerably, with U.S. and European regulators releasing guidance clarifying their expectations of the scientific rigor that should be applied to the development and selection of PRO endpoints. A recent PRO extension to the CONSORT statement highlights that well-developed PRO instruments can provide reliable and valid data in a manner which is meaningful to clinicians and patients alike. Despite this, the only PRO data to be reflected in product labelling for any anti-hyperglycemic medication for T2DM is blood glucose (based on patient self-recording of objective data). That was, until the 2014 European approval of Trulicity (dulaglutide).

	In the efficacy and safety section of the summary of product characteristics for Trulicity, the European Medicines Agency (EMA) included the following text: “Dulaglutide significantly improved total treatment satisfaction compared to exenatide twice daily. In addition, there was significantly lower perceived frequency of hyperglycaemia and hypoglycaemia compared to exenatide twice daily.” 

	Demonstrating improvements from baseline in treatment satisfaction with Trulicity, and showing that this improvement was greater with Trulicity when compared to exenatide twice daily, allows the developer of Trulicity (Eli Lilly & Co.) to summarize with one metric the patient’s appraisal of his or her experience with treatment (i.e., both processes and outcomes), their perception of the relative balance between positive clinical outcomes and negative side-effects, and their perceived benefit of improved clinical efficacy and reduced injection frequency versus an efficacious comparator. Perhaps most importantly for translational researchers, the data is important for clinical feasibility. 

	Such PRO data, taken together with clinical efficacy and safety data, can assist HCPs in providing holistic care in T2DM that is in line with patient preferences, needs, and values per the principles of evidence-based medicine. The inclusion of PRO data in the product label demonstrates a regulatory acknowledgement of the relevance of the patient perspective as it pertains to the saturated market of antihyperglycemic medication for T2DM, and gives HCPs confidence in the scientific credibility of the PRO endpoint.

Combining PRO data with clinical feasibility data can make a difference in diabetes care.

Patient’s Voice Finally Being Heard in Diabetes Drug Research

	Type 2 diabetes mellitus (T2DM) is one of the Western world’s most common chronic conditions, with global prevalence increasing rapidly. For people with T2DM, it is important to control the level of glucose in the blood so as to maintain good health and decrease the risk (or at least delay the onset) of complications associated with the condition, including retinopathy, cardiovascular disease, nephropathy and neuropathy. It has long been acknowledged that achieving good control of blood glucose requires that healthcare professionals (HCPs) and patients work together to increase the patients’ knowledge, self-efficacy, and engagement in self-management behaviours, including taking their antihyperglycemic medication. However, adherence to diabetes medication regimens and other aspects of self-management are poor. In recent years, HCPs have recognized that the psychosocial aspects of living with diabetes must be prioritized to maximize the improvement in clinical outcomes; indeed a recent consensus statement from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) highlights the importance of providing care that is respectful of and responsive to individual patient preferences, needs, and values.

	In the past decade there have been a slew of antihyperglycemic medications complete pivotal clinical trials. The purpose of these trials is to establish the safety and efficacy of the medication and to inform marketing authorization decision-making. To this end, efficacy continues to be defined using a glucose-centric model, with primary endpoints based on blood glucose reduction and/or control. However, many newer medications offer additional benefits, including weight loss and blood pressure control. One may assume that this incremental clinical benefit, if well communicated to patients, would increase the utilization of, and adherence to, these newer therapies. However, the same therapies are oftentimes associated with negative side-effects, including gastrointenstinal and urinary complications, as well as unknown long-term safety profiles. All of this data will be utilized by marketing authorization decision-makers and reflected in product labelling if approved, but patients’ perceptions of, and experiences with medication are not routinely reflected in the same labelling. This is a problem for clinicians, prescribers and researchers interested in translational medicine; in establishing safety and efficacy in a trial and extrapolating that to non-trial environments, where we aim to maximise both clinical and psychosocial outcomes, or at least ensure that clinical outcomes are not achieved to the detriment of psychosocial ones. For this we need to gain insights into the patients’ subjective experience of therapy during the pivotal clinical trials.

	Patient-Reported Outcome (PRO) questionnaires can be used to collect subjective clinical and psychosocial outcome data directly from the patients in clinical trials in order to describe the patient’s perception of a disease and its treatment(s) without the interpretation of responses by a physician or anyone else. They are particularly relevant and important when eliciting data that can only be captured by asking patients directly about how they feel or function. In looking at the newer classes of medication for T2DM (e.g. DPP-4 inhibitors, GLP-1 receptor analogues, novel insulins, SGLT-2 inhibitors) PRO questionnaires have been used to inform endpoints in most of the pivotal programs. As with the concept of blood glucose, a PRO can contain many related but distinct concepts, including symptoms of diabetes, health-related quality of life, psychological well-being, and treatment satisfaction. Each concept captures a different aspect of an individual’s experiences, thoughts, and feelings about T2DM and/or its treatment, although there is overlap. These, as well as perceived impact of weight, have all been measured as endpoints across the pivotal trials of the newer classes of medication for T2DM.

	This PRO data has the potential to offer insights into the subjective experience of therapy and provide an informed perspective of the patients’ experience of treatment, and the associated patient-perceived benefit-to-risk ratio of the medication. Unfortunately, PRO data has not traditionally been held in high esteem in T2DM, partly owing to the traditional glucose-centric model of care, and partly owing to variable scientific credibility in their measurement and lack of clarity in their interpretation. However, in recent years the science of PROs has progressed considerably, with the US and European regulators releasing guidance documents clarifying their expectations of the scientific rigour that should be incorporated into the development and selection of PRO endpoints. A recent PRO extension to the CONSORT statement highlights that well developed PRO instruments can provide reliable and valid data in a manner which is meaningful to trialists, clinicians and patients alike. Despite this, the only PRO data to be reflected in product labelling for any antihyperglycemic mediation for T2DM is blood glucose (based on patient self-recording of objective data). That was, until the European approval of Trulicity

	In the clinical efficacy and safety section of the summary of product characteristics for Trulicity

, the European Medicines Agency (EMA) included the following text: "Dulaglutide significantly improved total treatment satisfaction compared to exenatide twice daily. In addition, there was significantly lower perceived frequency of hyperglycaemia and hypoglycaemia compared to exenatide twice daily." Demonstrating improvements from baseline in treatment satisfaction with Trulicity

, and showing that this improvement was greater with Trulicity

 when compared to exenatide twice daily allows the developer of Trulicity

, Eli Lilly, to summarize with one metric the patients’ appraisal of his or her experience with treatment (i.e., both processes and outcomes), their perception of the relative balance between positive clinical outcomes and negative side-effects of Trulicity

, and their perceived benefit of improved clinical efficacy and reduced injection frequency versus an efficacious comparator. Perhaps most importantly for translational researchers, the data is important for clinical feasibility; a patient who is satisfied with their treatment is more likely to engage and adhere, and will thus therefore derive the maximal therapeutic benefit that the medication can offer.

	Such PRO data, taken together with clinical efficacy and safety data, can assist HCPs in providing holistic care in T2DM that is in line with patient preferences, needs and values per the principles of evidence-based medicine. The inclusion of PRO data in the product label demonstrates a regulatory acknowledgement of the relevance of the patient perspective as it pertains to the saturated market of antihyperglycemic medication for T2DM, and gives HCPs confidence in the scientific credibility of the PRO endpoint.

	To my mind therefore this is a huge step on the path to which one day, I hope, will yield an even more patient-centric model in which the patients’ perspective becomes a key endpoints in pivotal trials of atihyperglycemic medication for T2DM; perhaps even as part of a composite endpoint; improvement of clinical outcomes in the absence of psychosocial detriment.

Type 2 diabetes mellitus is one of the Western world’s most common chronic conditions, with global prevalence increasing rapidly.

The Patient’s Voice is Finally Being Heard in Diabetes Drug Research

 (dulaglutide), approved by the EMA in November 2014 for the treatment of Type 2 diabetes in adults, is the first diabetes drug to have a Patient Reported Outcome (PRO) (other than self-monitored blood glucose profiles) included in it’s summary of product characteristics ("label"). The following text has been included under the clinical efficacy and safety section: "Dulaglutide significantly improved total treatment satisfaction compared to exenatide twice daily. In addition, there was significantly lower perceived frequency of hyperglycaemia and hypoglycaemia compared to exenatide twice daily."

	This claim is important for multiple reasons. Not only does it exemplify EMA’s stance on the use of PROs in new medical product development, but also because the developer–Eli Lilly–has garnered important insight into patients’ perspectives of dulaglutide. Demonstrating improvements from baseline in treatment satisfaction with dulaglutide, and showing that this improvement was greater with dulaglutide when compared to exenatide twice daily allows Eli Lilly to summarize with one metric both the relative balance (from the users’ perspective) between positive clinical outcomes and negative side-effects of dulaglutide, and the patient perceived benefit of improved clinical efficacy and reduced injection frequency versus an efficacious comparator. It also loosely allows them to hypothesize patients’ adherence to dulaglutide in clinical practice. As indicated in a 

 by Eli Lilly when the data was published in 2013, "These results, coupled with dulaglutide's positive clinical data, suggest that…(dulaglutide) may be an attractive treatment option for patients with Type 2 diabetes."

	The data is derived from a Phase III trial comparing dulaglutide to exenatide twice daily (open-label) and placebo (double-blind) when added to maximally tolerated doses of metformin and pioglitazone

. The treatment satisfaction data was presented at the American Diabetes Association meeting in 2013.

	Since Type 2 diabetes is a chronic self-management condition, patients are asked to engage with many aspects of self care, including the routine taking of medication to control their blood glucose. Adherence rates for medication in Type 2 diabetes is poor, despite patients' acknowledgement about the importance of their medication (and the clinical and economic detriments of non-adherent behavior).

	A patient who is satisfied with their treatment is more likely to engage and will therefore derive the maximal therapeutic benefit that medication can offer. Treatment satisfaction is therefore a relevant endpoint for clinical research in Type 2 diabetes, and perhaps more importantly, a key point for discussion between provider and patient in evidence-based prescribing. The inclusion of treatment satisfaction data in the label for dulaglutide shows that the EMA is acknowledging the relevance of the patient perspective as it pertains to the saturated market of Type 2 diabetes treatment.

	Additional editorials/commentaries related to this topic include; selecting PRO measures in diabetes research

, and the importance of understanding patient preferences in diabetes research

http://www.ncbi.nlm.nih.gov/pubmed/24879836

http://www.abstractsonline.com/Plan/ViewAbstract.aspx?mID=3217&sKey=e68ac573-fe45-4c2f-9485-6270854fc10b&cKey=9164e616-5bf1-4f0c-83bf-d82c6601db84&mKey=89918d6d-3018-4ea9-9d4f-711f98a7ae5d

http://spectrum.diabetesjournals.org/content/27/4/229.short

http://www.hoajonline.com/jdrcm/2050-0866/3/7

http://onlinelibrary.wiley.com/doi/10.1111/1753-0407.12187/epdf

https://www.ert.com/clinical/resources/webinars