Therapeutic Innovation & Regulatory Science

There has been an increased emphasis on the proactive and comprehensive evaluation of safety endpoints to ensure patient well-being throughout the medical product life cycle. In fact, depending on the severity of the underlying disease, it is important to plan for a comprehensive safety evaluation at the start of any development program. Statisticians should be intimately involved in this process and contribute their expertise to study design, safety data collection, analysis, reporting (including data visualization), and interpretation.

In this manuscript, we review the challenges associated with the analysis of safety endpoints and describe the safety data that are available to influence the design and analysis of premarket clinical trials.

We share our recommendations for the statistical and graphical methodologies necessary to appropriately analyze, report, and interpret safety outcomes, and we discuss the advantages and disadvantages of safety data obtained from clinical trials compared to other sources.

Clinical trials are an important source of safety data that contribute to the totality of safety information available to generate evidence for regulators, sponsors, payers, physicians, and patients. This work is a result of the efforts of the American Statistical Association Biopharmaceutical Section Safety Working Group.

Safety data are continuously evaluated throughout the life cycle of a medical product to accurately assess and characterize the risks associated with the product. The knowledge about a medical product’s safety profile continually evolves as safety data accumulate.

This paper discusses data sources and analysis considerations for safety signal detection after a medical product is approved for marketing. This manuscript is the second in a series of papers from the American Statistical Association Biopharmaceutical Section Safety Working Group.

We share our recommendations for the statistical and graphical methodologies necessary to appropriately analyze, report, and interpret safety outcomes, and we discuss the advantages and disadvantages of safety data obtained from passive postmarketing surveillance systems compared to other sources.

Signal detection has traditionally relied on spontaneous reporting databases that have been available worldwide for decades. However, current regulatory guidelines and ease of reporting have increased the size of these databases exponentially over the last few years. With such large databases, data-mining tools using disproportionality analysis and helpful graphics are often used to detect potential signals. Although the data sources have many limitations, analyses of these data have been successful at identifying safety signals postmarketing. Experience analyzing these dynamic data is useful in understanding the potential and limitations of analyses with new data sources such as social media, claims, or electronic medical records data.

Although randomized controlled clinical trials provide necessary information and serve as the basis for regulatory decision making, a significant gap exists between the evidence these trials provide and what the biomedical community needs. It is recognized that a wealth of data are routinely collected outside clinical trials. Such real-world data (RWD) are not of comparable quality, it does not have similar immunity from bias and confounding as data collected in randomized clinical trials, but it might offer additional understanding of the benefit-risk, provide new insights to different stakeholders, and aid in regulatory decision making. This can be especially true when rare but serious adverse events are considered because randomized clinical trials are often not large enough and have insufficient duration to address safety concerns fully. Also, the passage of the 21st Century Cures bill passed by Congress in 2016 means that several data sources outside traditional clinical trials will play a greater role in regulatory decision making. This manuscript is third in a series of articles from the American Statistical Association Biopharmaceutical Section Safety Working Group.

In this manuscript, authors reviewed some RWD sources and shared considerations for statistical strategies and methodologies needed to design and analyze observational safety studies and pragmatic trials.

Authors presented case studies and shared recommendations for statistical methods necessary to design and analyze safety trials using RWD.

RWD is an important source of safety data that contribute to the totality of safety information available to generate evidence for regulators, sponsors, payers, physicians, and patients. However, it is important to determine if such data are fit for purpose.

Safety evaluation is a key aspect of medical product development. It is a continual and iterative process requiring thorough thinking, and dedicated time and resources.

In this article, we discuss how safety data are transformed into evidence to establish and refine the safety profile of a medical product, and how the focus of safety evaluation, data sources, and statistical methods change throughout a medical product’s life cycle.

Some challenges and statistical strategies for medical product safety evaluation are discussed. Examples of safety issues identified in different periods, that is, premarketing and postmarketing, are discussed to illustrate how different sources are used in the safety signal identification and the iterative process of safety assessment. The examples highlighted range from commonly used pediatric vaccine given to healthy children to medical products primarily used to treat a medical condition in adults. These case studies illustrate that different products may require different approaches, and once a signal is discovered, it could impact future safety assessments.

Many challenges still remain in this area despite advances in methodologies, infrastructure, public awareness, international harmonization, and regulatory enforcement. Innovations in safety assessment methodologies are pressing in order to make the medical product development process more efficient and effective, and the assessment of medical product marketing approval more streamlined and structured. Health care payers, providers, and patients may have different perspectives when weighing in on clinical, financial and personal needs when therapies are being evaluated.

“Complete Extrapolation” of efficacy from adult or other pediatric data, to the pediatric population, is an important scientific tool that reduces the need for pediatric efficacy trials. Dose finding and safety studies in pediatrics are still needed. “No Extrapolation” requires 2 pediatric efficacy trials. “Partial Extrapolation” eliminates the need to conduct 2 pediatric efficacy trials; 1 efficacy or exposure/response study may be sufficient. We examined pediatric extrapolation from 2009 to 2014 evaluating any changes in extrapolation assumptions and the causes for these changes since a prior analysis published in 2011.

We reviewed all 157 products with 388 pediatric studies submitted to the FDA from 2009 through 2014. We assessed whether efficacy was extrapolated from adult or other pediatric data and categorized extrapolation as Complete, Partial, or No, and identified the reasons for the changes.

Partial extrapolation decreased, whereas use of No and Complete extrapolation noticeably increased. Complete, Partial, or No extrapolations changed from 14%, 68%, and 18% in the 2011 study to 34%, 29%, and 37% respectively in the current study. The changes were mostly due to a better understanding of pediatric pathophysiology, why trials have failed, and improved endpoints.

Evolving science and data obtained from clinical trials increases the certainty of extrapolation assumptions and drives decisions to utilize extrapolation. Lessons learned from the conduct of these trials are critical to improving evidence-based medicine. Extrapolation of Efficacy is a powerful scientific tool that streamlines pediatric product development. Increased knowledge and evolving science inform utilization of this tool.

To identify the elements necessary for successful collaboration between patient groups and academic and industry sponsors of clinical trials, in order to develop recommendations for best practices for effective patient group engagement.

In-depth interviews, informed by a previously reported survey, were conducted to identify the fundamentals of successful patient group engagement. Thirty-two respondents from 3 sectors participated: patient groups, academic researchers, and industry. The findings were presented to a multistakeholder group of experts in January 2015. The expert group came to consensus on a set of actionable recommendations for best practices for patient groups and research sponsors.

Interview respondents acknowledged that not all patient groups are created equal in terms of what they can contribute to a clinical trial. The most important elements for effective patient group engagement include establishing meaningful partnerships, demonstrating mutual benefits, and collaborating as partners from the planning stage forward. Although there is a growing appreciation by sponsors about the benefits of patient group engagement, there remains some resistance and some uncertainty about how best to engage. Barriers include mismatched expectations and a perception that patient groups lack scientific sophistication and that “wishful thinking” may cloud their recommendations.

Patient groups are developing diverse skillsets and acquiring assets to leverage in order to become collaborators with industry and academia on clinical trials. Growing numbers of research sponsors across the clinical trials enterprise are recognizing the benefits of continuous and meaningful patient group engagement, but there are still mindsets to change, and stakeholders need further guidance on operationalizing a new model of clinical trial conduct.

While patient groups, regulators, and sponsors are increasingly considering engaging with patients in the design and conduct of clinical development programs, sponsors are often reluctant to go beyond pilot programs because of uncertainty in the return on investment. We developed an approach to estimate the financial value of patient engagement.

Expected net present value (ENPV) is a common technique that integrates the key business drivers of cost, time, revenue, and risk into a summary metric for project strategy and portfolio decisions. We assessed the impact of patient engagement on ENPV for a typical oncology development program entering phase 2 or phase 3.

For a pre–phase 2 project, the cumulative impact of a patient engagement activity that avoids one protocol amendment and improves enrollment, adherence, and retention is an increase in net present value (NPV) of $62MM ($65MM for pre–phase 3) and an increase in ENPV of $35MM ($75MM for pre–phase 3). Compared with an investment of $100,000 in patient engagement, the NPV and ENPV increases can exceed 500-fold the investment. This ENPV increase is the equivalent of accelerating a pre–phase 2 product launch by 2½ years (1½ years for pre–phase 3).

Risk-adjusted financial models can assess the impact of patient engagement. A combination of empirical data and subjective parameter estimates shows that engagement activities with the potential to avoid protocol amendments and/or improve enrollment, adherence, and retention may add considerable financial value. This approach can help sponsors assess patient engagement investment decisions.

Only recently, regulations on the names of medicines were developed. Regulations are mainly focused on avoiding the approval of medicine names that may be confusing to others. Furthermore, legal requirements do not include testing for human factors, such as potential users’ preferences.

To develop a set of new brand names of medicines, to determine subjects’ preferred names, and to evaluate if the linguistic features of these names were related to subjects’ preferences.

Forty-six new names linguistically equivalent to the Portuguese brand names of medicines were developed. A panel of 13 postgraduates on linguistic studies were purposively enrolled. Participants were required to select and categorize the 6 most preferred names.

From the 29 selected names: 62.1% ended in consonants, 65.5% contained at least one syllable of the CVC type, and 62.1% presented final stress. Considering these 3 linguistic features, there were statistically significant differences between the preferred and underpreferred names: χ² = 4.572, P = .032; χ² = 5.599, P = .018; and χ² = 4.572; P = .032, respectively.

Some linguistic features of the evaluated names were related to subjects’ preferences. Tests on subjects’ preferences about the names of medicines may provide additional safety features addressed by the present regulations.

Global health care manufacturer Novo Nordisk commissioned research regarding awareness of drug safety department activities and potential to increase patient feedback. Objectives were to examine patients’ knowledge of pharmaceutical manufacturers’ responsibilities and efforts regarding drug safety, their perceptions and experiences related to these efforts, and how these factors influence their thoughts and behaviors. Data were collected before and after respondents read a description of a drug safety department and its practices.

We conducted quantitative survey research across 608 health care consumers receiving treatment for diabetes in the United States, Germany, United Kingdom, and Italy. This research validated initial, exploratory qualitative research (across 40 comparable consumers from the same countries) which served to guide design of the larger study.

Before reading a drug safety department description, 55% of respondents were unaware these departments collect safety information on products and patients. After reading the description, 34% reported the department does more than they expected to ensure drug safety, and 56% reported “more confidence” in the industry as a whole. Further, 66% reported themselves more likely to report an adverse event or product complaint, and 60% reported that they were more likely to contact a drug safety department with questions. The most preferred communication methods were websites/online forums (39%), email (27%), and telephone (25%).

Learning about drug safety departments elevates consumers’ confidence in manufacturers’ safety efforts and establishes potential for patients to engage in increased self-monitoring and reporting. Study results reveal potentially actionable insights for the industry across patient and physician programs and communications.

Proprietary names are often used when prescribing drug products in the United States. The purpose of this study is to describe prescribers’ use of proprietary names for generic products, branded-generic names, on prescription orders and to identify prescribing practice trends to inform the development and evaluation of new proprietary names.

To identify Abbreviated New Drug Application (ANDA) with branded-generic names approved between January 2003 and December 2012, we utilized the database provided by the FDA Office of Communications, Drugs@FDA. A national outpatient retail prescription database, IMS’s Vector One: National (VONA) was used to identify prescribing trends by examining data for branded-generic names identified in Drugs@FDA as they were written on prescriptions for years 2003 to 2012, the last year of data collection for VONA. IMS Health, IMS National Sales Perspectives (IMS NSP) was used to retrieve the date that product sales were first reported (launch date).

Our search of Drugs@FDA identified 65 distinct branded-generic names approved between January 2003 and December 2012. Data show that most of these products with branded-generic names are written on prescriptions and sold to pharmacies within a year of FDA approval. In some cases, the use of branded-generic names persists for up to 9 years after drug approval.

This descriptive study confirmed that branded-generic names are used in prescribing. Thus, evaluation of orthographic and phonetic similarities between proposed proprietary names and branded-generic names is necessary when formulating and evaluating new proprietary names.