Clinical Trials in Practice
From Foundations to AI Agents
Preface
Every approved medicine began as an uncertain hypothesis tested in willing volunteers. The clinical trial is where that hypothesis meets reality—where a molecule that looked promising in a laboratory confronts the complexity of human biology, where statistical models become individual patients, and where years of preclinical work either translate into therapeutic value or quietly fail.
The stakes are substantial. Trials that are well-designed and well-executed can demonstrate that a treatment extends life or reduces suffering; trials that are poorly designed can obscure a real benefit, miss a serious harm, or consume years and hundreds of millions of dollars without yielding interpretable results. Estimates of the capitalized cost to bring a drug to market are commonly in the billions of dollars, reflecting not only the cost of programs that succeed, but the accumulated investment in the majority that do not—compounded by long timelines and the cost of capital tied up along the way (DiMasi, Grabowski, and Hansen 2016).
Trials have also grown more complex. The era of large, simple trials in common diseases is giving way to precision medicine and rare disease development, where patient populations are smaller, eligibility criteria more restrictive, and recruitment correspondingly harder. Protocols now routinely include biomarker-driven stratification, multiple treatment arms, and global multi-regional execution. In response, the statistical methods of clinical trials are evolving: adaptive designs that allow sample size re-estimation, treatment arm selection, or early stopping based on accumulating data; Bayesian approaches that formally incorporate prior information and yield posterior probabilities rather than p-values; and platform trials that evaluate multiple therapies within a single master protocol. These methods offer efficiency gains but demand more sophisticated planning, simulation, and regulatory engagement—topics this book addresses in depth.
This book treats clinical trials as both a scientific method and a regulated operational system. It covers the foundations—history, ethics, regulations—and then moves through the phases of drug development, with particular depth on Phase III confirmatory trials.
A distinctive feature is the way the book connects methodology, operations, and technology. AI and automation appear where they matter—at the interfaces between systems of record (EDC/CTMS/eTMF/RTSM), governed workflows, and regulated decision-making. The regulatory and ethics chapters provide the constraints (GCP, Part 11, oversight, bias and governance). The Phase III “deep dive” chapters provide the operating reality (protocol complexity, recruitment, data quality, monitoring, safety, documentation). A dedicated technology-and-innovation chapter then ties these threads together: how modern trials run on layered IT infrastructure, how “agentic” systems are built from retrieval + tools + control logic, and what it would take for automation to be defensible under inspection (audit trails, validation, access control, and human accountability).
Throughout, the book takes a realistic view: AI can reduce friction and surface risks earlier, but it requires validation, governance, and oversight to be defensible in an inspection. The goal is to help readers understand not only what is changing, but how to evaluate whether a given tool or workflow is appropriate for regulated clinical research.
Who This Book Is For
The book is written for clinical development professionals—including medical directors and study managers—who seek a unified view of design, operations, and the technology shifts reshaping their field. It is equally valuable for site staff and investigators wanting to understand sponsor-side quality expectations, as well as pharmaceutical professionals, graduate students, and organizational decision-makers evaluating the implementation of AI and automation within regulated environments.
This is not a “how to pass the FDA exam” manual, nor a programming handbook. The goal is practical fluency: enough conceptual and operational grounding to evaluate designs, read protocols and SAPs critically, anticipate execution risks, and understand what “good” looks like under GCP.
Organization
The book is organized into four parts.
Part I: Foundations covers the historical evolution of clinical research, a lifecycle roadmap for how trials fit into drug development, the international regulatory framework, and the ethical principles that govern research involving human subjects.
Part II: Clinical Trial Phases surveys the drug development lifecycle: the economics of development, and the objectives, designs, and decision points of Phase I (first-in-human), Phase II (proof of concept), Phase III (confirmatory), and Phase IV (post-marketing) studies.
Part III: Phase III Deep Dive is the core of the book. It provides detailed treatment of confirmatory trial methodology and execution: statistical methods, study design, protocol development, randomization, endpoint selection, site selection and qualification, patient recruitment, logistics and supply chain, data management, monitoring, safety reporting, and essential documentation. The concluding chapter in this part focuses on technology and innovation—how modern clinical operations run on layered digital systems, and how automation and AI change workflows while raising questions about auditability and accountability.
Part IV: Trial Completion and Beyond addresses study closeout, the clinical study report, and regulatory submissions—the final steps that determine whether years of clinical work translate into an approved medicine.