Abstract
A core element in protecting human participants in clinical research is the assurance of a favorable risk-benefit balance, in accordance with the ethical principle of beneficence. However, the assessment of risks and benefits may sometimes be challenging. In this chapter, the authors review the concepts, ethical principles, and applicability of those terms. The assessment approaches used by regulators and research reviewers who are working in clinical practice are then discussed, together with a highlight of the new trends in risk-benefit analysis and their impact on drug development and approval. Analysis of some research situations which pose particular challenges are presented at the end of the chapter.
TopIntroduction
Assuring a favorable risk-benefit balance is crucial to human subjects' protection in clinical research and thus complies to the principle of beneficence in the Belmont report, written and published by the National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research in 1979. It is a critical requirement for all research regulations that guide each step in research. It is also the main criteria for determining the approvability of categories of participants and reviewing those categories, the degree of protection required for each category of participants, according to the type and frequency of monitoring study.
The objective of this chapter is to highlight the:
- 1.
Concepts, principles, and types of risks and benefits;
- 2.
Methodology of risk-benefit analysis;
- 3.
Applications and impact of data science technology in clinical research practice;
- 4.
Challenges to risk-benefit analysis and suggested resolutions.
TopBackground
Evaluation of the risk-benefit balance in research is a complex process, which is sometimes straightforward, but in other cases it is more challenging.
Balancing risks to patients against the total benefits to science or to future patients is one of the most difficult issues, especially in research involving vulnerable populations.
The aim of this chapter is to lead the readers through the decision-making process of risk-benefit evaluation of clinical research.
The scope: Throughout the three main sections of this paper, we discuss the main principles, approaches and challenges of risk and benefit assessment in clinical research.
TopSection I: Principles Of Risk-Benefit Assessment In Clinical Research
Risk-benefit assessment is an universal requirement for protecting human participants in clinical trials. The concept and perception of risks and benefits, their types, degree, and relative weight are complex attributes which can vary depending on the context in which the study is being conducted.
Defining Risk and Benefit
In research, risk is defined as: “a potential harm, discomfort, or inconvenience that a reasonable person in the subject's position would likely consider significant in deciding whether or not to participate in the study”. A comprehensive definition is given in the Belmont report (NCPHSBBR, 1979) as: “a possibility that harm may occur both in chance (probability) and severity (magnitude) of envision. The term 'benefit' refers to something of positive value related to health or welfare”. Accordingly, risk/benefit assessments are concerned with the probabilities and magnitudes of possible harm and benefits.
Key Terms in this Chapter
Precision Medicine: A strategy of medical treatment tailored for the medical necessities of an individual patient based on the collection of a large amount of data about that patient's (e.g., genomic study or other molecular or cellular analysis, personal, and disease data).
Omics: Is the part of biotechnology which analyzes the structure and functions of the whole makeup of a given biological function, at different levels, including the molecular gene level (“genomics”), the protein level (“proteomics”), and the metabolic level (“metabolomics”).
Equipoise: Means an equal view. It is used in research to describe an equal standing of the clinical investigator in therapeutic trials in relation to both the trial drug and an alternative treatment. This is important in order to avoid bias.
Patient-Centricity: This term defines a collaborative partnership between patients, caregivers, and pharmaceutical companies, focusing on patients' needs and preferences in the research enterprise. The term was at first created collaboratively by AstraZeneca, patients, and caregivers, and was demonstrated to be a valid approach by testing conducted on many patients' groups.
Ultraism: It is the sacrifice of self-interest for others benefit. In research, it is used when a participant is exposed to risk for the benefit to society.
Therapeutic Misconception: When the research participant in a clinical trial doesn't perceive the difference between the research intervention and the clinical care, which could impact the validity of his informed consent.
Deep Learning: Is the complex, unsupervised processing of unstructured data in order to create patterns used in decision making, patterns that are analogous to those of the human brain.